diff --git a/AULA_01_Material_de_estudo.ipynb b/AULA_01_Material_de_estudo.ipynb
new file mode 100644
index 0000000..3b9472a
--- /dev/null
+++ b/AULA_01_Material_de_estudo.ipynb
@@ -0,0 +1,6486 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPzpvIuvoHtIMHzgvV+G4Ts",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Lyanan85/In-GameSeries/blob/master/AULA_01_Material_de_estudo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "g8Y5s_9Cm_rh"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas=pd.read_csv(\"https://raw.githubusercontent.com/alura-cursos/data-science-analise-exploratoria/main/Aula_0/ml-latest-small/ratings.csv\")\n",
+        "notas"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "5WJka4cQnEpx",
+        "outputId": "471a1312-be3f-4195-d56d-d649b55f44e8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
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+          "data": {
+            "text/plain": [
+              "        userId  movieId  rating   timestamp\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
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+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "\n",
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+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('notas')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+        "notas.shape\n"
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+        },
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+      "outputs": [
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+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(100836, 4)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.columns=[\"usuarioid\", \"filmeid\", \"nota\", \"momento\"]\n",
+        "notas.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "7tECTDSuqwDe",
+        "outputId": "edcdb2e4-0853-4c4f-cc62-a1677468f886"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   usuarioid  filmeid  nota    momento\n",
+              "0          1        1   4.0  964982703\n",
+              "1          1        3   4.0  964981247\n",
+              "2          1        6   4.0  964982224\n",
+              "3          1       47   5.0  964983815\n",
+              "4          1       50   5.0  964982931"
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+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "\n",
+              "    <script>\n",
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+              "        document.querySelector('#df-5ca56bb1-db18-41b3-9924-877034edf5b9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-5ca56bb1-db18-41b3-9924-877034edf5b9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        element.appendChild(docLink);\n",
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+              "  </div>\n",
+              "\n",
+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
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+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
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+              "    width: 32px;\n",
+              "  }\n",
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+              "  .colab-df-quickchart:hover {\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-602f63b6-8810-469d-b12f-ca25d6f20a69 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "notas"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas[\"nota\"].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t8AcnhRUrvgL",
+        "outputId": "9f88f183-aace-4fe8-e972-1f61219986da"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([4. , 5. , 3. , 2. , 1. , 4.5, 3.5, 2.5, 0.5, 1.5])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas[\"nota\"].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "-gY5Rw_AsFFp",
+        "outputId": "fd317956-4d5d-440a-efef-536f4ff7449f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "nota\n",
+              "4.0    26818\n",
+              "3.0    20047\n",
+              "5.0    13211\n",
+              "3.5    13136\n",
+              "4.5     8551\n",
+              "2.0     7551\n",
+              "2.5     5550\n",
+              "1.0     2811\n",
+              "1.5     1791\n",
+              "0.5     1370\n",
+              "Name: count, dtype: int64"
+            ],
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+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "    .dataframe tbody tr th {\n",
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+              "\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nota</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4.0</th>\n",
+              "      <td>26818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3.0</th>\n",
+              "      <td>20047</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5.0</th>\n",
+              "      <td>13211</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3.5</th>\n",
+              "      <td>13136</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4.5</th>\n",
+              "      <td>8551</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2.0</th>\n",
+              "      <td>7551</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2.5</th>\n",
+              "      <td>5550</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1.0</th>\n",
+              "      <td>2811</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1.5</th>\n",
+              "      <td>1791</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0.5</th>\n",
+              "      <td>1370</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas[\"nota\"].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KG5w8TxJuna0",
+        "outputId": "09d7df64-e45b-4dfa-be00-c47d435bacaf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3.501556983616962"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas[\"nota\"].plot()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "wgSHb92DvG_q",
+        "outputId": "fede4e4f-f42c-454e-e976-4a85b58dbe29"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas[\"nota\"].plot(kind='hist')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3VcI7Lp2xMxL",
+        "outputId": "800ed4db-c6a9-40cc-e63a-e2d78c4b9a66"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: ylabel='Frequency'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mediana=notas[\"nota\"].median()\n",
+        "media=notas[\"nota\"].mean()\n",
+        "print(f\"mediana é {mediana}\")\n",
+        "print(f\"media é {media}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6kqdiIF_yCzk",
+        "outputId": "25d9352e-78f6-4e20-9bdd-a5ca9163d67c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "mediana é 3.5\n",
+            "media é 3.501556983616962\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas[\"nota\"].describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "id": "Uo_Sn0WK0xPN",
+        "outputId": "c7cfde62-d8e1-43a9-8842-6b1b7809d717"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    100836.000000\n",
+              "mean          3.501557\n",
+              "std           1.042529\n",
+              "min           0.500000\n",
+              "25%           3.000000\n",
+              "50%           3.500000\n",
+              "75%           4.000000\n",
+              "max           5.000000\n",
+              "Name: nota, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>nota</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>100836.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>3.501557</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.042529</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>3.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>4.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>5.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.boxplot(notas[\"nota\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sJu0kNUe2Efd",
+        "outputId": "acb048db-dfba-4709-f753-16bb3aaee622"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
+            "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: ylabel='nota'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "filmes = pd.read_csv(\"https://raw.githubusercontent.com/alura-cursos/data-science-analise-exploratoria/main/Aula_0/ml-latest-small/movies.csv\")\n",
+        "filmes.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 223
+        },
+        "id": "ivt7uMiW2xNy",
+        "outputId": "005264bd-306f-42f0-9c01-0df1298a749d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   movieId                               title  \\\n",
+              "0        1                    Toy Story (1995)   \n",
+              "1        2                      Jumanji (1995)   \n",
+              "2        3             Grumpier Old Men (1995)   \n",
+              "3        4            Waiting to Exhale (1995)   \n",
+              "4        5  Father of the Bride Part II (1995)   \n",
+              "\n",
+              "                                        genres  \n",
+              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
+              "1                   Adventure|Children|Fantasy  \n",
+              "2                               Comedy|Romance  \n",
+              "3                         Comedy|Drama|Romance  \n",
+              "4                                       Comedy  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9eddc8c5-d7e6-4e1a-9d77-98d85d0266f4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>movieId</th>\n",
+              "      <th>title</th>\n",
+              "      <th>genres</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Toy Story (1995)</td>\n",
+              "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Jumanji (1995)</td>\n",
+              "      <td>Adventure|Children|Fantasy</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Grumpier Old Men (1995)</td>\n",
+              "      <td>Comedy|Romance</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Waiting to Exhale (1995)</td>\n",
+              "      <td>Comedy|Drama|Romance</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5</td>\n",
+              "      <td>Father of the Bride Part II (1995)</td>\n",
+              "      <td>Comedy</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9eddc8c5-d7e6-4e1a-9d77-98d85d0266f4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9eddc8c5-d7e6-4e1a-9d77-98d85d0266f4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9eddc8c5-d7e6-4e1a-9d77-98d85d0266f4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d180685f-6903-449e-9203-4a1fe8030123\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d180685f-6903-449e-9203-4a1fe8030123')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d180685f-6903-449e-9203-4a1fe8030123 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "filmes",
+              "summary": "{\n  \"name\": \"filmes\",\n  \"rows\": 9742,\n  \"fields\": [\n    {\n      \"column\": \"movieId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52160,\n        \"min\": 1,\n        \"max\": 193609,\n        \"num_unique_values\": 9742,\n        \"samples\": [\n          45635,\n          1373,\n          7325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9737,\n        \"samples\": [\n          \"Teenage Mutant Ninja Turtles (2014)\",\n          \"America's Sweethearts (2001)\",\n          \"Cast Away (2000)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 951,\n        \"samples\": [\n          \"Crime|Mystery|Romance|Thriller\",\n          \"Action|Adventure|Comedy|Western\",\n          \"Crime|Drama|Musical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "0hPnWpW5GZtP",
+        "outputId": "bef6e2d1-5521-417c-86d4-29a3ccd4df30"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   usuarioid  filmeid  nota    momento\n",
+              "0          1        1   4.0  964982703\n",
+              "1          1        3   4.0  964981247\n",
+              "2          1        6   4.0  964982224\n",
+              "3          1       47   5.0  964983815\n",
+              "4          1       50   5.0  964982931"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cec0de85-b486-4a3d-8a17-ba2a4e9ace0e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>usuarioid</th>\n",
+              "      <th>filmeid</th>\n",
+              "      <th>nota</th>\n",
+              "      <th>momento</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964982703</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964981247</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964982224</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>47</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>964983815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>50</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>964982931</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cec0de85-b486-4a3d-8a17-ba2a4e9ace0e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cec0de85-b486-4a3d-8a17-ba2a4e9ace0e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cec0de85-b486-4a3d-8a17-ba2a4e9ace0e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-e0aefd09-0c7d-42e7-840e-75a14b4d4d46\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0aefd09-0c7d-42e7-840e-75a14b4d4d46')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-e0aefd09-0c7d-42e7-840e-75a14b4d4d46 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "notas"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.columns=[\"usuarioid\", \"filmeid\",\"nota\",\"momento\"]"
+      ],
+      "metadata": {
+        "id": "_E_Jf7-wGmvl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "RZwITPP-HDg1",
+        "outputId": "b1d234ad-9c01-4806-cf8e-2de742c7f083"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   usuarioid  filmeid  nota    momento\n",
+              "0          1        1   4.0  964982703\n",
+              "1          1        3   4.0  964981247\n",
+              "2          1        6   4.0  964982224\n",
+              "3          1       47   5.0  964983815\n",
+              "4          1       50   5.0  964982931"
+            ],
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+              "    .colab-df-container {\n",
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+              "      background-color: #E8F0FE;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-751f05f3-bf13-47e6-acf8-7a132191f483');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-850b5d2a-fc1f-4026-a0bc-535fa169877a button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "notas"
+            }
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "qn85Cg29ovJg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "VSwearfXovVa"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.query(\"filmeid==1\")[\"nota\"].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iYPwiumxItxy",
+        "outputId": "e566bae1-8a20-49cd-d877-0b2f5577634d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3.9209302325581397"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd"
+      ],
+      "metadata": {
+        "id": "zZCgvozYI3aQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas=pd.read_csv(\"https://raw.githubusercontent.com/alura-cursos/data-science-analise-exploratoria/main/Aula_0/ml-latest-small/ratings.csv\")\n",
+        "notas"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "C4GgaOYIQACj",
+        "outputId": "d603bded-0925-49df-9637-1a0a4deb66fa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        userId  movieId  rating   timestamp\n",
+              "0            1        1     4.0   964982703\n",
+              "1            1        3     4.0   964981247\n",
+              "2            1        6     4.0   964982224\n",
+              "3            1       47     5.0   964983815\n",
+              "4            1       50     5.0   964982931\n",
+              "...        ...      ...     ...         ...\n",
+              "100831     610   166534     4.0  1493848402\n",
+              "100832     610   168248     5.0  1493850091\n",
+              "100833     610   168250     5.0  1494273047\n",
+              "100834     610   168252     5.0  1493846352\n",
+              "100835     610   170875     3.0  1493846415\n",
+              "\n",
+              "[100836 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
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+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>userId</th>\n",
+              "      <th>movieId</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>timestamp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
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+              "      <td>1</td>\n",
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+              "      <td>964982703</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964981247</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964982224</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>47</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>964983815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>50</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>964982931</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100831</th>\n",
+              "      <td>610</td>\n",
+              "      <td>166534</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1493848402</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100832</th>\n",
+              "      <td>610</td>\n",
+              "      <td>168248</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1493850091</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100833</th>\n",
+              "      <td>610</td>\n",
+              "      <td>168250</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1494273047</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100834</th>\n",
+              "      <td>610</td>\n",
+              "      <td>168252</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1493846352</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100835</th>\n",
+              "      <td>610</td>\n",
+              "      <td>170875</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1493846415</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100836 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c26617f2-7e3a-44b4-8004-dde0c045f7bb')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c26617f2-7e3a-44b4-8004-dde0c045f7bb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c26617f2-7e3a-44b4-8004-dde0c045f7bb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-38772cb9-cb96-4e69-a5cf-e41428ec8b70\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38772cb9-cb96-4e69-a5cf-e41428ec8b70')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-38772cb9-cb96-4e69-a5cf-e41428ec8b70 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_53be4524-7ce8-40e7-a17c-5f7400806c87\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('notas')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_53be4524-7ce8-40e7-a17c-5f7400806c87 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('notas');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "notas"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Uc-DR3ewP8fL",
+        "outputId": "0488840a-3600-4fab-9541-302f833dbfa2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   userId  movieId  rating  timestamp\n",
+              "0       1        1     4.0  964982703\n",
+              "1       1        3     4.0  964981247\n",
+              "2       1        6     4.0  964982224\n",
+              "3       1       47     5.0  964983815\n",
+              "4       1       50     5.0  964982931"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ec9364a2-221d-42fb-9edd-f36fc557a7c1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>userId</th>\n",
+              "      <th>movieId</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>timestamp</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964982703</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964981247</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>964982224</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>47</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>964983815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>50</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>964982931</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec9364a2-221d-42fb-9edd-f36fc557a7c1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ec9364a2-221d-42fb-9edd-f36fc557a7c1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ec9364a2-221d-42fb-9edd-f36fc557a7c1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-d9485404-fc84-44d1-974f-c6da49c1677d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9485404-fc84-44d1-974f-c6da49c1677d')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d9485404-fc84-44d1-974f-c6da49c1677d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "notas"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.columns=[\"usuarioid\", \"filmeid\",\"nota\",\"momento\"]"
+      ],
+      "metadata": {
+        "id": "-yUun2yAQF30"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.query(\"filmeid==1\")[\"nota\"].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9lXINC9CQjJa",
+        "outputId": "a9dad945-fa59-43ed-dab6-e8a48af1174f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3.9209302325581397"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.query(\"filmeid==2\")[\"nota\"].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2Bnu-72GQlru",
+        "outputId": "efd43368-beb7-4bb0-9962-bf027ced7fb9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3.4318181818181817"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notas.groupby(\"filmeid\")[\"nota\"].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "hljPQR7IQpsW",
+        "outputId": "4db3d7d8-0b3c-4edf-f8d8-40f5c1ddf0a6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "filmeid\n",
+              "1         3.920930\n",
+              "2         3.431818\n",
+              "3         3.259615\n",
+              "4         2.357143\n",
+              "5         3.071429\n",
+              "            ...   \n",
+              "193581    4.000000\n",
+              "193583    3.500000\n",
+              "193585    3.500000\n",
+              "193587    3.500000\n",
+              "193609    4.000000\n",
+              "Name: nota, Length: 9724, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>nota</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>filmeid</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.920930</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.431818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3.259615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.357143</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>3.071429</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193581</th>\n",
+              "      <td>4.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193583</th>\n",
+              "      <td>3.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193585</th>\n",
+              "      <td>3.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193587</th>\n",
+              "      <td>3.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>193609</th>\n",
+              "      <td>4.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>9724 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "medias_por_filme=notas.groupby(\"filmeid\")[\"nota\"].mean()\n",
+        "medias_por_filme.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "id": "C1pO2SmrQzKm",
+        "outputId": "86cb9390-18b2-4a2a-af24-eb171f95557c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "filmeid\n",
+              "1    3.920930\n",
+              "2    3.431818\n",
+              "3    3.259615\n",
+              "4    2.357143\n",
+              "5    3.071429\n",
+              "Name: nota, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>nota</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>filmeid</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3.920930</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.431818</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3.259615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.357143</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>3.071429</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "medias_por_filme.plot(kind=\"hist\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SDJ0ufNiSLdX",
+        "outputId": "e264c3da-f5f5-4e5e-fd6c-3be11d7adacc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: ylabel='Frequency'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.boxplot(medias_por_filme)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IE6V2AolSwpI",
+        "outputId": "15ce8ba9-4471-402f-e113-3121d8d105a8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/seaborn/categorical.py:640: FutureWarning: SeriesGroupBy.grouper is deprecated and will be removed in a future version of pandas.\n",
+            "  positions = grouped.grouper.result_index.to_numpy(dtype=float)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: ylabel='nota'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "medias_por_filme.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_zJFsbZBWGmD",
+        "outputId": "ad2db714-d676-49ba-b886-9272294b5be3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    9724.000000\n",
+              "mean        3.262448\n",
+              "std         0.869874\n",
+              "min         0.500000\n",
+              "25%         2.800000\n",
+              "50%         3.416667\n",
+              "75%         3.911765\n",
+              "max         5.000000\n",
+              "Name: nota, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>nota</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>9724.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>3.262448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.869874</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2.800000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.416667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>3.911765</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>5.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "sns.displot(medias_por_filme, kde=True)\n",
+        "plt.title(\"Histograma das medias dos filmes\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 546
+        },
+        "id": "c0tyOhOZXLpq",
+        "outputId": "86dfef90-98a6-44f4-c8a4-ce117d7b7ffa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Histograma das medias dos filmes')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb = pd.read_csv(\"https://raw.githubusercontent.com/alura-cursos/data-science-analise-exploratoria/main/Aula_0/tmdb_5000_movies.csv\")\n",
+        "tmdb.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 729
+        },
+        "id": "LtlJG9czbqlO",
+        "outputId": "cd1afbcb-cc37-4002-fab0-b30c7bc51fec"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      budget                                             genres  \\\n",
+              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
+              "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
+              "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
+              "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
+              "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
+              "\n",
+              "                                       homepage      id  \\\n",
+              "0                   http://www.avatarmovie.com/   19995   \n",
+              "1  http://disney.go.com/disneypictures/pirates/     285   \n",
+              "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
+              "3            http://www.thedarkknightrises.com/   49026   \n",
+              "4          http://movies.disney.com/john-carter   49529   \n",
+              "\n",
+              "                                            keywords original_language  \\\n",
+              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
+              "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
+              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
+              "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
+              "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
+              "\n",
+              "                             original_title  \\\n",
+              "0                                    Avatar   \n",
+              "1  Pirates of the Caribbean: At World's End   \n",
+              "2                                   Spectre   \n",
+              "3                     The Dark Knight Rises   \n",
+              "4                               John Carter   \n",
+              "\n",
+              "                                            overview  popularity  \\\n",
+              "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
+              "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
+              "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
+              "3  Following the death of District Attorney Harve...  112.312950   \n",
+              "4  John Carter is a war-weary, former military ca...   43.926995   \n",
+              "\n",
+              "                                production_companies  \\\n",
+              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
+              "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
+              "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
+              "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
+              "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   \n",
+              "\n",
+              "                                production_countries release_date     revenue  \\\n",
+              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
+              "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
+              "2  [{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...   2015-10-26   880674609   \n",
+              "3  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-07-16  1084939099   \n",
+              "4  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-03-07   284139100   \n",
+              "\n",
+              "   runtime                                   spoken_languages    status  \\\n",
+              "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
+              "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "2    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
+              "3    165.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "4    132.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "\n",
+              "                                          tagline  \\\n",
+              "0                     Enter the World of Pandora.   \n",
+              "1  At the end of the world, the adventure begins.   \n",
+              "2                           A Plan No One Escapes   \n",
+              "3                                 The Legend Ends   \n",
+              "4            Lost in our world, found in another.   \n",
+              "\n",
+              "                                      title  vote_average  vote_count  \n",
+              "0                                    Avatar           7.2       11800  \n",
+              "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
+              "2                                   Spectre           6.3        4466  \n",
+              "3                     The Dark Knight Rises           7.6        9106  \n",
+              "4                               John Carter           6.1        2124  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fb899730-6288-42a8-a325-b3217309635e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>budget</th>\n",
+              "      <th>genres</th>\n",
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+              "      <th>revenue</th>\n",
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+              "      <th>spoken_languages</th>\n",
+              "      <th>status</th>\n",
+              "      <th>tagline</th>\n",
+              "      <th>title</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>237000000</td>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
+              "      <td>http://www.avatarmovie.com/</td>\n",
+              "      <td>19995</td>\n",
+              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
+              "      <td>150.437577</td>\n",
+              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
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+              "      <td>2009-12-10</td>\n",
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+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>Enter the World of Pandora.</td>\n",
+              "      <td>Avatar</td>\n",
+              "      <td>7.2</td>\n",
+              "      <td>11800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>300000000</td>\n",
+              "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
+              "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
+              "      <td>285</td>\n",
+              "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>Pirates of the Caribbean: At World's End</td>\n",
+              "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
+              "      <td>139.082615</td>\n",
+              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
+              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
+              "      <td>2007-05-19</td>\n",
+              "      <td>961000000</td>\n",
+              "      <td>169.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>At the end of the world, the adventure begins.</td>\n",
+              "      <td>Pirates of the Caribbean: At World's End</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>4500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>245000000</td>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
+              "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
+              "      <td>206647</td>\n",
+              "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>Spectre</td>\n",
+              "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
+              "      <td>107.376788</td>\n",
+              "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
+              "      <td>[{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...</td>\n",
+              "      <td>2015-10-26</td>\n",
+              "      <td>880674609</td>\n",
+              "      <td>148.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>A Plan No One Escapes</td>\n",
+              "      <td>Spectre</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>4466</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>250000000</td>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
+              "      <td>http://www.thedarkknightrises.com/</td>\n",
+              "      <td>49026</td>\n",
+              "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>The Dark Knight Rises</td>\n",
+              "      <td>Following the death of District Attorney Harve...</td>\n",
+              "      <td>112.312950</td>\n",
+              "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
+              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
+              "      <td>2012-07-16</td>\n",
+              "      <td>1084939099</td>\n",
+              "      <td>165.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>The Legend Ends</td>\n",
+              "      <td>The Dark Knight Rises</td>\n",
+              "      <td>7.6</td>\n",
+              "      <td>9106</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>260000000</td>\n",
+              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
+              "      <td>http://movies.disney.com/john-carter</td>\n",
+              "      <td>49529</td>\n",
+              "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
+              "      <td>en</td>\n",
+              "      <td>John Carter</td>\n",
+              "      <td>John Carter is a war-weary, former military ca...</td>\n",
+              "      <td>43.926995</td>\n",
+              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
+              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
+              "      <td>2012-03-07</td>\n",
+              "      <td>284139100</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
+              "      <td>Released</td>\n",
+              "      <td>Lost in our world, found in another.</td>\n",
+              "      <td>John Carter</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>2124</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb899730-6288-42a8-a325-b3217309635e')\"\n",
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+              "      fill: #174EA6;\n",
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+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "        document.querySelector('#df-fb899730-6288-42a8-a325-b3217309635e button.colab-df-convert');\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fb899730-6288-42a8-a325-b3217309635e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "<div id=\"df-30043651-6269-4b6d-ac8a-0dfdff1d28a2\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
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+              "  .colab-df-quickchart {\n",
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+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "    height: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    fill: var(--disabled-fill-color);\n",
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+              "  .colab-df-spinner {\n",
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+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
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+              "    60% {\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
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+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
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+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
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+              "</div>\n",
+              "\n",
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+            ],
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+              "type": "dataframe",
+              "variable_name": "tmdb",
+              "summary": "{\n  \"name\": \"tmdb\",\n  \"rows\": 4803,\n  \"fields\": [\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40722391,\n        \"min\": 0,\n        \"max\": 380000000,\n        \"num_unique_values\": 436,\n        \"samples\": [\n          439000,\n          68000000,\n          700000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genres\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1175,\n        \"samples\": [\n          \"[{\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}]\",\n          \"[{\\\"id\\\": 28, \\\"name\\\": \\\"Action\\\"}, {\\\"id\\\": 35, \\\"name\\\": \\\"Comedy\\\"}, {\\\"id\\\": 80, \\\"name\\\": \\\"Crime\\\"}, {\\\"id\\\": 18, \\\"name\\\": \\\"Drama\\\"}]\",\n          \"[{\\\"id\\\": 12, \\\"name\\\": \\\"Adventure\\\"}, {\\\"id\\\": 16, \\\"name\\\": \\\"Animation\\\"}, {\\\"id\\\": 10751, \\\"name\\\": \\\"Family\\\"}, {\\\"id\\\": 14, \\\"name\\\": \\\"Fantasy\\\"}, {\\\"id\\\": 878, \\\"name\\\": \\\"Science Fiction\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"homepage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1691,\n        \"samples\": [\n          \"https://www.warnerbros.com/running-scared\",\n          \"http://www.51birchstreet.com/index.php\",\n          \"http://movies2.foxjapan.com/glee/\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 88694,\n        \"min\": 5,\n        \"max\": 459488,\n        \"num_unique_values\": 4803,\n        \"samples\": [\n          8427,\n          13006,\n          18041\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4222,\n        \"samples\": [\n          \"[{\\\"id\\\": 782, \\\"name\\\": \\\"assassin\\\"}, {\\\"id\\\": 1872, \\\"name\\\": \\\"loss of father\\\"}, {\\\"id\\\": 2908, \\\"name\\\": \\\"secret society\\\"}, {\\\"id\\\": 3045, \\\"name\\\": \\\"mission of murder\\\"}, {\\\"id\\\": 9748, \\\"name\\\": \\\"revenge\\\"}]\",\n          \"[{\\\"id\\\": 2987, \\\"name\\\": \\\"gang war\\\"}, {\\\"id\\\": 4942, \\\"name\\\": \\\"victim of murder\\\"}, {\\\"id\\\": 5332, \\\"name\\\": \\\"greed\\\"}, {\\\"id\\\": 6062, \\\"name\\\": \\\"hostility\\\"}, {\\\"id\\\": 156212, \\\"name\\\": \\\"spaghetti western\\\"}]\",\n          \"[{\\\"id\\\": 703, \\\"name\\\": \\\"detective\\\"}, {\\\"id\\\": 1299, \\\"name\\\": \\\"monster\\\"}, {\\\"id\\\": 6101, \\\"name\\\": \\\"engine\\\"}, {\\\"id\\\": 10988, \\\"name\\\": \\\"based on tv series\\\"}, {\\\"id\\\": 15162, \\\"name\\\": \\\"dog\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"xx\",\n          \"ta\",\n          \"es\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4801,\n        \"samples\": [\n          \"I Spy\",\n          \"Love Letters\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"overview\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"When the Switchblade, the most sophisticated prototype stealth fighter created yet, is stolen from the U.S. government, one of the United States' top spies, Alex Scott, is called to action. What he doesn't expect is to get teamed up with a cocky civilian, World Class Boxing Champion Kelly Robinson, on a dangerous top secret espionage mission. Their assignment: using equal parts skill and humor, catch Arnold Gundars, one of the world's most successful arms dealers.\",\n          \"When \\\"street smart\\\" rapper Christopher \\\"C-Note\\\" Hawkins (Big Boi) applies for a membership to all-white Carolina Pines Country Club, the establishment's proprietors are hardly ready to oblige him.\",\n          \"As their first year of high school looms ahead, best friends Julie, Hannah, Yancy and Farrah have one last summer sleepover. Little do they know they're about to embark on the adventure of a lifetime. Desperate to shed their nerdy status, they take part in a night-long scavenger hunt that pits them against their popular archrivals. Everything under the sun goes on -- from taking Yancy's father's car to sneaking into nightclubs!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.816649749537806,\n        \"min\": 0.0,\n        \"max\": 875.581305,\n        \"num_unique_values\": 4802,\n        \"samples\": [\n          13.267631,\n          0.010909,\n          5.842299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_companies\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3697,\n        \"samples\": [\n          \"[{\\\"name\\\": \\\"Paramount Pictures\\\", \\\"id\\\": 4}, {\\\"name\\\": \\\"Cherry Alley Productions\\\", \\\"id\\\": 2232}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}, {\\\"name\\\": \\\"Dune Entertainment\\\", \\\"id\\\": 444}, {\\\"name\\\": \\\"Regency Enterprises\\\", \\\"id\\\": 508}, {\\\"name\\\": \\\"Guy Walks into a Bar Productions\\\", \\\"id\\\": 2645}, {\\\"name\\\": \\\"Deep River Productions\\\", \\\"id\\\": 2646}, {\\\"name\\\": \\\"Friendly Films (II)\\\", \\\"id\\\": 81136}]\",\n          \"[{\\\"name\\\": \\\"Twentieth Century Fox Film Corporation\\\", \\\"id\\\": 306}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"production_countries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 469,\n        \"samples\": [\n          \"[{\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"FR\\\", \\\"name\\\": \\\"France\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}, {\\\"iso_3166_1\\\": \\\"CA\\\", \\\"name\\\": \\\"Canada\\\"}, {\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}]\",\n          \"[{\\\"iso_3166_1\\\": \\\"DE\\\", \\\"name\\\": \\\"Germany\\\"}, {\\\"iso_3166_1\\\": \\\"ES\\\", \\\"name\\\": \\\"Spain\\\"}, {\\\"iso_3166_1\\\": \\\"GB\\\", \\\"name\\\": \\\"United Kingdom\\\"}, {\\\"iso_3166_1\\\": \\\"US\\\", \\\"name\\\": \\\"United States of America\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"release_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3280,\n        \"samples\": [\n          \"1966-10-16\",\n          \"1987-07-31\",\n          \"1993-09-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 162857100,\n        \"min\": 0,\n        \"max\": 2787965087,\n        \"num_unique_values\": 3297,\n        \"samples\": [\n          11833696,\n          10462500,\n          17807569\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.611934588844207,\n        \"min\": 0.0,\n        \"max\": 338.0,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          74.0,\n          85.0,\n          170.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spoken_languages\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 544,\n        \"samples\": [\n          \"[{\\\"iso_639_1\\\": \\\"es\\\", \\\"name\\\": \\\"Espa\\\\u00f1ol\\\"}, {\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"fr\\\", \\\"name\\\": \\\"Fran\\\\u00e7ais\\\"}, {\\\"iso_639_1\\\": \\\"hu\\\", \\\"name\\\": \\\"Magyar\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"en\\\", \\\"name\\\": \\\"English\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"pt\\\", \\\"name\\\": \\\"Portugu\\\\u00eas\\\"}]\",\n          \"[{\\\"iso_639_1\\\": \\\"de\\\", \\\"name\\\": \\\"Deutsch\\\"}, {\\\"iso_639_1\\\": \\\"it\\\", \\\"name\\\": \\\"Italiano\\\"}, {\\\"iso_639_1\\\": \\\"la\\\", \\\"name\\\": \\\"Latin\\\"}, {\\\"iso_639_1\\\": \\\"pl\\\", \\\"name\\\": \\\"Polski\\\"}]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Released\",\n          \"Post Production\",\n          \"Rumored\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3944,\n        \"samples\": [\n          \"When you're 17, every day is war.\",\n          \"An Unspeakable Horror. A Creative Genius. Captured For Eternity.\",\n          \"May the schwartz be with you\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4800,\n        \"samples\": [\n          \"I Spy\",\n          \"Who's Your Caddy?\",\n          \"Sleepover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1946121628478925,\n        \"min\": 0.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          5.1,\n          7.2,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1234,\n        \"min\": 0,\n        \"max\": 13752,\n        \"num_unique_values\": 1609,\n        \"samples\": [\n          7604,\n          3428,\n          225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.displot(tmdb[\"revenue\"])\n",
+        "plt.title(\"distribuição da receita dos filmes\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "mjtAcR8ZmteL",
+        "outputId": "f79aff2f-ef5c-4091-ad10-d4fd6dd18b8a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# prompt: gráfico de distribuição do orçamento dos filmes(budget)\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "sns.displot(tmdb[\"budget\"])\n",
+        "plt.title(\"distribuição do orçamento dos filmes\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "ERRo_H3EpMMG",
+        "outputId": "e5fa0a56-57c9-4336-a161-6af4b40a0660"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jp9mvQtNqiSZ",
+        "outputId": "50342fe4-ec73-4e0b-cb0b-857f23472c22"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 4803 entries, 0 to 4802\n",
+            "Data columns (total 20 columns):\n",
+            " #   Column                Non-Null Count  Dtype  \n",
+            "---  ------                --------------  -----  \n",
+            " 0   budget                4803 non-null   int64  \n",
+            " 1   genres                4803 non-null   object \n",
+            " 2   homepage              1712 non-null   object \n",
+            " 3   id                    4803 non-null   int64  \n",
+            " 4   keywords              4803 non-null   object \n",
+            " 5   original_language     4803 non-null   object \n",
+            " 6   original_title        4803 non-null   object \n",
+            " 7   overview              4800 non-null   object \n",
+            " 8   popularity            4803 non-null   float64\n",
+            " 9   production_companies  4803 non-null   object \n",
+            " 10  production_countries  4803 non-null   object \n",
+            " 11  release_date          4802 non-null   object \n",
+            " 12  revenue               4803 non-null   int64  \n",
+            " 13  runtime               4801 non-null   float64\n",
+            " 14  spoken_languages      4803 non-null   object \n",
+            " 15  status                4803 non-null   object \n",
+            " 16  tagline               3959 non-null   object \n",
+            " 17  title                 4803 non-null   object \n",
+            " 18  vote_average          4803 non-null   float64\n",
+            " 19  vote_count            4803 non-null   int64  \n",
+            "dtypes: float64(3), int64(4), object(13)\n",
+            "memory usage: 750.6+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "id": "CVZlt-S3rBYx",
+        "outputId": "841c0475-4f61-4d3e-e712-ad933d7ffc03"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             budget             id   popularity       revenue      runtime  \\\n",
+              "count  4.803000e+03    4803.000000  4803.000000  4.803000e+03  4801.000000   \n",
+              "mean   2.904504e+07   57165.484281    21.492301  8.226064e+07   106.875859   \n",
+              "std    4.072239e+07   88694.614033    31.816650  1.628571e+08    22.611935   \n",
+              "min    0.000000e+00       5.000000     0.000000  0.000000e+00     0.000000   \n",
+              "25%    7.900000e+05    9014.500000     4.668070  0.000000e+00    94.000000   \n",
+              "50%    1.500000e+07   14629.000000    12.921594  1.917000e+07   103.000000   \n",
+              "75%    4.000000e+07   58610.500000    28.313505  9.291719e+07   118.000000   \n",
+              "max    3.800000e+08  459488.000000   875.581305  2.787965e+09   338.000000   \n",
+              "\n",
+              "       vote_average    vote_count  \n",
+              "count   4803.000000   4803.000000  \n",
+              "mean       6.092172    690.217989  \n",
+              "std        1.194612   1234.585891  \n",
+              "min        0.000000      0.000000  \n",
+              "25%        5.600000     54.000000  \n",
+              "50%        6.200000    235.000000  \n",
+              "75%        6.800000    737.000000  \n",
+              "max       10.000000  13752.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a3086cfb-2049-4d59-863b-5774d7b46652\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>budget</th>\n",
+              "      <th>id</th>\n",
+              "      <th>popularity</th>\n",
+              "      <th>revenue</th>\n",
+              "      <th>runtime</th>\n",
+              "      <th>vote_average</th>\n",
+              "      <th>vote_count</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>4.803000e+03</td>\n",
+              "      <td>4803.000000</td>\n",
+              "      <td>4803.000000</td>\n",
+              "      <td>4.803000e+03</td>\n",
+              "      <td>4801.000000</td>\n",
+              "      <td>4803.000000</td>\n",
+              "      <td>4803.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2.904504e+07</td>\n",
+              "      <td>57165.484281</td>\n",
+              "      <td>21.492301</td>\n",
+              "      <td>8.226064e+07</td>\n",
+              "      <td>106.875859</td>\n",
+              "      <td>6.092172</td>\n",
+              "      <td>690.217989</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4.072239e+07</td>\n",
+              "      <td>88694.614033</td>\n",
+              "      <td>31.816650</td>\n",
+              "      <td>1.628571e+08</td>\n",
+              "      <td>22.611935</td>\n",
+              "      <td>1.194612</td>\n",
+              "      <td>1234.585891</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000e+00</td>\n",
+              "      <td>5.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.900000e+05</td>\n",
+              "      <td>9014.500000</td>\n",
+              "      <td>4.668070</td>\n",
+              "      <td>0.000000e+00</td>\n",
+              "      <td>94.000000</td>\n",
+              "      <td>5.600000</td>\n",
+              "      <td>54.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.500000e+07</td>\n",
+              "      <td>14629.000000</td>\n",
+              "      <td>12.921594</td>\n",
+              "      <td>1.917000e+07</td>\n",
+              "      <td>103.000000</td>\n",
+              "      <td>6.200000</td>\n",
+              "      <td>235.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>4.000000e+07</td>\n",
+              "      <td>58610.500000</td>\n",
+              "      <td>28.313505</td>\n",
+              "      <td>9.291719e+07</td>\n",
+              "      <td>118.000000</td>\n",
+              "      <td>6.800000</td>\n",
+              "      <td>737.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3.800000e+08</td>\n",
+              "      <td>459488.000000</td>\n",
+              "      <td>875.581305</td>\n",
+              "      <td>2.787965e+09</td>\n",
+              "      <td>338.000000</td>\n",
+              "      <td>10.000000</td>\n",
+              "      <td>13752.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a3086cfb-2049-4d59-863b-5774d7b46652')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a3086cfb-2049-4d59-863b-5774d7b46652 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a3086cfb-2049-4d59-863b-5774d7b46652');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f37d5d1d-f213-4477-890b-b55f6757b693\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f37d5d1d-f213-4477-890b-b55f6757b693')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f37d5d1d-f213-4477-890b-b55f6757b693 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"tmdb\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"budget\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 129161253.05847104,\n        \"min\": 0.0,\n        \"max\": 380000000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29045039.87528628,\n          15000000.0,\n          4803.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154035.43639610297,\n        \"min\": 5.0,\n        \"max\": 459488.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          57165.484280657925,\n          14629.0,\n          4803.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"popularity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1676.09470184157,\n        \"min\": 0.0,\n        \"max\": 4803.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21.492300588174057,\n          12.921594,\n          4803.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 969427230.5834863,\n        \"min\": 0.0,\n        \"max\": 2787965087.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4803.0,\n          82260638.65167603,\n          92917187.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"runtime\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1660.9907007543372,\n        \"min\": 0.0,\n        \"max\": 4801.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          106.87585919600083,\n          103.0,\n          4801.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_average\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1696.3073516335521,\n        \"min\": 0.0,\n        \"max\": 4803.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.092171559442016,\n          6.2,\n          4803.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vote_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4735.515903869439,\n        \"min\": 0.0,\n        \"max\": 13752.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          690.2179887570269,\n          235.0,\n          4803.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb.query(\"revenue<500\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "emj0Om6mrDQ8",
+        "outputId": "a2aabfd3-0b23-4bc3-8b74-2264748ec84d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         budget                                             genres  \\\n",
+              "83     27000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
+              "135   150000000  [{\"id\": 18, \"name\": \"Drama\"}, {\"id\": 27, \"name...   \n",
+              "265           0  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 14, \"nam...   \n",
+              "309    84000000  [{\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 35, \"na...   \n",
+              "376    90000000  [{\"id\": 878, \"name\": \"Science Fiction\"}, {\"id\"...   \n",
+              "...         ...                                                ...   \n",
+              "4797          0  [{\"id\": 10769, \"name\": \"Foreign\"}, {\"id\": 53, ...   \n",
+              "4799       9000  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 10749, \"...   \n",
+              "4800          0  [{\"id\": 35, \"name\": \"Comedy\"}, {\"id\": 18, \"nam...   \n",
+              "4801          0                                                 []   \n",
+              "4802          0                [{\"id\": 99, \"name\": \"Documentary\"}]   \n",
+              "\n",
+              "                                               homepage      id  \\\n",
+              "83                                                  NaN   79698   \n",
+              "135                     http://www.thewolfmanmovie.com/    7978   \n",
+              "265                                                 NaN   10588   \n",
+              "309                                                 NaN   10214   \n",
+              "376                                                 NaN   10357   \n",
+              "...                                                 ...     ...   \n",
+              "4797                                                NaN   67238   \n",
+              "4799                                                NaN   72766   \n",
+              "4800  http://www.hallmarkchannel.com/signedsealeddel...  231617   \n",
+              "4801                        http://shanghaicalling.com/  126186   \n",
+              "4802                                                NaN   25975   \n",
+              "\n",
+              "                                               keywords original_language  \\\n",
+              "83                                                   []                en   \n",
+              "135   [{\"id\": 494, \"name\": \"father son relationship\"...                en   \n",
+              "265   [{\"id\": 977, \"name\": \"cat\"}, {\"id\": 1155, \"nam...                en   \n",
+              "309   [{\"id\": 1009, \"name\": \"baby\"}, {\"id\": 2546, \"n...                en   \n",
+              "376   [{\"id\": 1552, \"name\": \"subway\"}, {\"id\": 2859, ...                en   \n",
+              "...                                                 ...               ...   \n",
+              "4797                                                 []                en   \n",
+              "4799                                                 []                en   \n",
+              "4800  [{\"id\": 248, \"name\": \"date\"}, {\"id\": 699, \"nam...                en   \n",
+              "4801                                                 []                en   \n",
+              "4802  [{\"id\": 1523, \"name\": \"obsession\"}, {\"id\": 224...                en   \n",
+              "\n",
+              "                 original_title  \\\n",
+              "83                   The Lovers   \n",
+              "135                 The Wolfman   \n",
+              "265          The Cat in the Hat   \n",
+              "309             Son of the Mask   \n",
+              "376                     Volcano   \n",
+              "...                         ...   \n",
+              "4797                     Cavite   \n",
+              "4799                  Newlyweds   \n",
+              "4800  Signed, Sealed, Delivered   \n",
+              "4801           Shanghai Calling   \n",
+              "4802          My Date with Drew   \n",
+              "\n",
+              "                                               overview  popularity  \\\n",
+              "83    The Lovers is an epic romance time travel adve...    2.418535   \n",
+              "135   Lawrence Talbot, an American man on a visit to...   21.214571   \n",
+              "265   Conrad and Sally Walden are home alone with th...   18.251129   \n",
+              "309   Tim Avery, an aspiring cartoonist, finds himse...   17.815595   \n",
+              "376   An earthquake shatters a peaceful Los Angeles ...   19.836124   \n",
+              "...                                                 ...         ...   \n",
+              "4797  Adam, a security guard, travels from Californi...    0.022173   \n",
+              "4799  A newlywed couple's honeymoon is upended by th...    0.642552   \n",
+              "4800  \"Signed, Sealed, Delivered\" introduces a dedic...    1.444476   \n",
+              "4801  When ambitious New York attorney Sam is sent t...    0.857008   \n",
+              "4802  Ever since the second grade when he first saw ...    1.929883   \n",
+              "\n",
+              "                                   production_companies  \\\n",
+              "83    [{\"name\": \"Corsan\", \"id\": 7299}, {\"name\": \"Bli...   \n",
+              "135   [{\"name\": \"Universal Pictures\", \"id\": 33}, {\"n...   \n",
+              "265   [{\"name\": \"Imagine Entertainment\", \"id\": 23}, ...   \n",
+              "309   [{\"name\": \"New Line Cinema\", \"id\": 12}, {\"name...   \n",
+              "376   [{\"name\": \"Twentieth Century Fox Film Corporat...   \n",
+              "...                                                 ...   \n",
+              "4797                                                 []   \n",
+              "4799                                                 []   \n",
+              "4800  [{\"name\": \"Front Street Pictures\", \"id\": 3958}...   \n",
+              "4801                                                 []   \n",
+              "4802  [{\"name\": \"rusty bear entertainment\", \"id\": 87...   \n",
+              "\n",
+              "                                   production_countries release_date  revenue  \\\n",
+              "83    [{\"iso_3166_1\": \"AU\", \"name\": \"Australia\"}, {\"...   2015-02-13        0   \n",
+              "135   [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2010-02-11        0   \n",
+              "265   [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2003-11-21        0   \n",
+              "309   [{\"iso_3166_1\": \"DE\", \"name\": \"Germany\"}, {\"is...   2005-02-18        0   \n",
+              "376   [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   1997-04-25        0   \n",
+              "...                                                 ...          ...      ...   \n",
+              "4797                                                 []   2005-03-12        0   \n",
+              "4799                                                 []   2011-12-26        0   \n",
+              "4800  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2013-10-13        0   \n",
+              "4801  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-05-03        0   \n",
+              "4802  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2005-08-05        0   \n",
+              "\n",
+              "      runtime                                   spoken_languages    status  \\\n",
+              "83      109.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "135     102.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "265      82.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "309      94.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "376     104.0  [{\"iso_639_1\": \"de\", \"name\": \"Deutsch\"}, {\"iso...  Released   \n",
+              "...       ...                                                ...       ...   \n",
+              "4797     80.0                                                 []  Released   \n",
+              "4799     85.0                                                 []  Released   \n",
+              "4800    120.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "4801     98.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "4802     90.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
+              "\n",
+              "                                                tagline  \\\n",
+              "83                            Love is longer than life.   \n",
+              "135      When the moon is full the legend comes to life   \n",
+              "265                            Don't mess with the hat.   \n",
+              "309                                         Who's next?   \n",
+              "376                                  The coast is toast   \n",
+              "...                                                 ...   \n",
+              "4797                                                NaN   \n",
+              "4799  A newlywed couple's honeymoon is upended by th...   \n",
+              "4800                                                NaN   \n",
+              "4801                           A New Yorker in Shanghai   \n",
+              "4802                                                NaN   \n",
+              "\n",
+              "                          title  vote_average  vote_count  \n",
+              "83                   The Lovers           4.8          34  \n",
+              "135                 The Wolfman           5.5         549  \n",
+              "265          The Cat in the Hat           4.9         366  \n",
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+              "      <td>[{\"id\": 977, \"name\": \"cat\"}, {\"id\": 1155, \"nam...</td>\n",
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+              "      <td>Conrad and Sally Walden are home alone with th...</td>\n",
+              "      <td>18.251129</td>\n",
+              "      <td>[{\"name\": \"Imagine Entertainment\", \"id\": 23}, ...</td>\n",
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+              "      <td>[{\"id\": 1009, \"name\": \"baby\"}, {\"id\": 2546, \"n...</td>\n",
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+              "      <td>Tim Avery, an aspiring cartoonist, finds himse...</td>\n",
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+              "      <td>A newlywed couple's honeymoon is upended by th...</td>\n",
+              "      <td>Newlyweds</td>\n",
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+              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
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+              "      <td>A New Yorker in Shanghai</td>\n",
+              "      <td>Shanghai Calling</td>\n",
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+              "      <td>7</td>\n",
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+              "      <td>[{\"name\": \"rusty bear entertainment\", \"id\": 87...</td>\n",
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+              "      <td>NaN</td>\n",
+              "      <td>My Date with Drew</td>\n",
+              "      <td>6.3</td>\n",
+              "      <td>16</td>\n",
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+              "      fill: #174EA6;\n",
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+              "      margin-bottom: 4px;\n",
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+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-94253f57-e289-4452-ae47-898c98df7186');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "  [theme=dark] .colab-df-quickchart {\n",
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+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
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+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
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+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d412b5b2-57fd-4448-9143-ee736c70da2e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "repr_error": "0"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "com_faturamento= tmdb.query(\"revenue > 0\")\n",
+        "sns.displot(com_faturamento[\"revenue\"])\n",
+        "plt.title(\"distruibução dos filmes com faturamento\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "O_pxTLsVrd65",
+        "outputId": "f4bd622f-3974-4351-d4db-92258a2940b9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb[\"original_language\"].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fcNUFr49r1Ut",
+        "outputId": "786b73f9-b791-410b-e423-75908763202c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['en', 'ja', 'fr', 'zh', 'es', 'de', 'hi', 'ru', 'ko', 'te', 'cn',\n",
+              "       'it', 'nl', 'ta', 'sv', 'th', 'da', 'xx', 'hu', 'cs', 'pt', 'is',\n",
+              "       'tr', 'nb', 'af', 'pl', 'he', 'ar', 'vi', 'ky', 'id', 'ro', 'fa',\n",
+              "       'no', 'sl', 'ps', 'el'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb[\"original_language\"].value_counts()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "XWcfRAQNvNdK",
+        "outputId": "61e7ca1a-d3f2-427c-a0ee-f1f74bf6fada"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "original_language\n",
+              "en    4505\n",
+              "fr      70\n",
+              "es      32\n",
+              "zh      27\n",
+              "de      27\n",
+              "hi      19\n",
+              "ja      16\n",
+              "it      14\n",
+              "cn      12\n",
+              "ru      11\n",
+              "ko      11\n",
+              "pt       9\n",
+              "da       7\n",
+              "sv       5\n",
+              "nl       4\n",
+              "fa       4\n",
+              "th       3\n",
+              "he       3\n",
+              "ta       2\n",
+              "cs       2\n",
+              "ro       2\n",
+              "id       2\n",
+              "ar       2\n",
+              "vi       1\n",
+              "sl       1\n",
+              "ps       1\n",
+              "no       1\n",
+              "ky       1\n",
+              "hu       1\n",
+              "pl       1\n",
+              "af       1\n",
+              "nb       1\n",
+              "tr       1\n",
+              "is       1\n",
+              "xx       1\n",
+              "te       1\n",
+              "el       1\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>original_language</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>en</th>\n",
+              "      <td>4505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fr</th>\n",
+              "      <td>70</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>es</th>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>zh</th>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>de</th>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hi</th>\n",
+              "      <td>19</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ja</th>\n",
+              "      <td>16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>it</th>\n",
+              "      <td>14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cn</th>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ru</th>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ko</th>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pt</th>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>da</th>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sv</th>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nl</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fa</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>th</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>he</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ta</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cs</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ro</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ar</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>vi</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sl</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ps</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>no</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ky</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hu</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pl</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>af</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nb</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>tr</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>is</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>xx</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>te</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>el</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# lingua => categorica... sem ordem...\n",
+        "\n",
+        "# budget (orçamento => quantitativa continua)"
+      ],
+      "metadata": {
+        "id": "lpOAdaZeveuD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# nota de movielens => 0.5, 1 , 1.5, ... , 5=> não tem 2.5\n",
+        "# quantidade de votos => 1,2,3,4,5... não existe 2.5"
+      ],
+      "metadata": {
+        "id": "IwCnrOQQvv02"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "mais_de_10_votos=tmdb.query(\"vote_count>10\")\n",
+        "sns.displot(mais_de_10_votos[\"vote_average\"],kde=True)\n",
+        "plt.title(\"distribuição das medias das notas dos filmes com mais de 10 votos\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "psr6YwHLx3Ey",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "outputId": "d7d68005-ee0e-4757-c062-d05abeb5c469"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tmdb[\"original_language\"].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Li6uaixG9QON",
+        "outputId": "a49c5a6b-cbdf-400a-ce8e-a320afdd5810"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "original_language\n",
+              "en    4505\n",
+              "fr      70\n",
+              "es      32\n",
+              "zh      27\n",
+              "de      27\n",
+              "hi      19\n",
+              "ja      16\n",
+              "it      14\n",
+              "cn      12\n",
+              "ru      11\n",
+              "ko      11\n",
+              "pt       9\n",
+              "da       7\n",
+              "sv       5\n",
+              "nl       4\n",
+              "fa       4\n",
+              "th       3\n",
+              "he       3\n",
+              "ta       2\n",
+              "cs       2\n",
+              "ro       2\n",
+              "id       2\n",
+              "ar       2\n",
+              "vi       1\n",
+              "sl       1\n",
+              "ps       1\n",
+              "no       1\n",
+              "ky       1\n",
+              "hu       1\n",
+              "pl       1\n",
+              "af       1\n",
+              "nb       1\n",
+              "tr       1\n",
+              "is       1\n",
+              "xx       1\n",
+              "te       1\n",
+              "el       1\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>original_language</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>en</th>\n",
+              "      <td>4505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fr</th>\n",
+              "      <td>70</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>es</th>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>zh</th>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>de</th>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hi</th>\n",
+              "      <td>19</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ja</th>\n",
+              "      <td>16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>it</th>\n",
+              "      <td>14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cn</th>\n",
+              "      <td>12</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ru</th>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ko</th>\n",
+              "      <td>11</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pt</th>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>da</th>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sv</th>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nl</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fa</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>th</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>he</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ta</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cs</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ro</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>id</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ar</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>vi</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sl</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ps</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>no</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ky</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>hu</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pl</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>af</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>nb</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>tr</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>is</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>xx</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>te</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>el</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " contagem_de_lingua=tmdb[\"original_language\"].value_counts() .to_frame().reset_index()\n",
+        " contagem_de_lingua.columns=[\"original_language\",\"total\"]\n",
+        " contagem_de_lingua.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "yYFOjrjsDoCk",
+        "outputId": "50829812-5d91-48d9-cba7-00288ce6720c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  original_language  total\n",
+              "0                en   4505\n",
+              "1                fr     70\n",
+              "2                es     32\n",
+              "3                zh     27\n",
+              "4                de     27"
+            ],
+            "text/html": [
+              "\n",
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+              "      <td>4505</td>\n",
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+              "      <th>1</th>\n",
+              "      <td>fr</td>\n",
+              "      <td>70</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>es</td>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>zh</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>de</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
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+              "    </button>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ffd8b6de-4981-47b7-9847-4ced9001dd64 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ffd8b6de-4981-47b7-9847-4ced9001dd64');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4ff60fd3-f1a9-41d8-ba2c-af88e8fe01a4\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ff60fd3-f1a9-41d8-ba2c-af88e8fe01a4')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4ff60fd3-f1a9-41d8-ba2c-af88e8fe01a4 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "contagem_de_lingua",
+              "summary": "{\n  \"name\": \"contagem_de_lingua\",\n  \"rows\": 37,\n  \"fields\": [\n    {\n      \"column\": \"original_language\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"he\",\n          \"sv\",\n          \"de\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 739,\n        \"min\": 1,\n        \"max\": 4505,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          4505,\n          70,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.barplot(data=contagem_de_lingua, x=\"original_language\", y=\"total\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 467
+        },
+        "id": "vuWfGy38I7Qs",
+        "outputId": "c424c591-36a0-4f4d-f166-3670d9687a66"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='original_language', ylabel='total'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.countplot(data=tmdb, x=\"original_language\")\n",
+        "plt.title(\"contagem de filmes por lingua\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "tDgNj09SJ1KJ",
+        "outputId": "192661f0-da5e-4b58-af18-65ead3f8b494"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# grafico de pizza do pandas\n",
+        "\n",
+        "contagem_de_lingua.plot(kind=\"pie\",y=\"total\",labels=contagem_de_lingua[\"original_language\"])\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 840
+        },
+        "id": "ZE3Sb2vDOujX",
+        "outputId": "0132fa62-45d3-4cde-f49b-8eacb522e430"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: ylabel='total'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "total_por_lingua=tmdb[\"original_language\"].value_counts()\n",
+        "total_geral= total_por_lingua.sum()\n",
+        "total_ingles= total_por_lingua.loc[\"en\"]\n",
+        "total_do_resto= total_geral - total_ingles\n",
+        "print(f\"a contagem de todas as línguas é de: {total_geral}\")\n",
+        "print(f\"a linguagem que lidera a contagem de filmes por lingua é ingles com a contagem de: {total_ingles} \")\n",
+        "print(f\"como o ingles tem a maioria dos pontos somatatorios da contagem, não levando ele em consideração a contagem rstante fica de: {total_do_resto} \")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qrLD4ZYaTTaq",
+        "outputId": "6e9f3512-0dde-4c42-8743-53c09e3f092b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "a contagem de todas as línguas é de: 4803\n",
+            "a linguagem que lidera a contagem de filmes por lingua é ingles com a contagem de: 4505 \n",
+            "como o ingles tem a maioria dos pontos somatatorios da contagem, não levando ele em consideração a contagem rstante fica de: 298 \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f\"tranformando isso em uma tabela teremos:\\n\")\n",
+        "\n",
+        "dados = {\n",
+        "    \"lingua\":[ \"ingles\", \"outros\"],\n",
+        "    \"total\": [total_ingles, total_do_resto]\n",
+        "}\n",
+        "dados = pd.DataFrame(dados)\n",
+        "dados\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 160
+        },
+        "id": "qRGjqbskYyFx",
+        "outputId": "59fc6303-04c2-43ed-f94a-f3426e5248d3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tranformando isso em uma tabela teremos:\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   lingua  total\n",
+              "0  ingles   4505\n",
+              "1  outros    298"
+            ],
+            "text/html": [
+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>lingua</th>\n",
+              "      <th>total</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ingles</td>\n",
+              "      <td>4505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>outros</td>\n",
+              "      <td>298</td>\n",
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+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "        document.querySelector('#df-21f98e4e-daa5-4742-bc75-d3bd81c4cf56 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-21f98e4e-daa5-4742-bc75-d3bd81c4cf56');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "  .colab-df-quickchart {\n",
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+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "    fill: var(--disabled-fill-color);\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
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+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_f2eced99-8dda-4c92-8df9-2163b5f2b237\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
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+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dados')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_f2eced99-8dda-4c92-8df9-2163b5f2b237 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('dados');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "dados",
+              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"lingua\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"outros\",\n          \"ingles\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2974,\n        \"min\": 298,\n        \"max\": 4505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          298,\n          4505\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.barplot(data=dados, x=\"lingua\", y=\"total\")\n",
+        "plt.title(\"como já mencionado ingles é a lingua predominante\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "YkOrlAJEfhSL",
+        "outputId": "0c87dc55-590d-4c1b-e37a-35d473debd0a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "ZTwMtJ_FkxkZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "z-IlkkspkP1h"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
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diff --git "a/Gen 1/Japanese/150 - \343\203\237\343\203\245\343\202\246\343\203\204\343\203\274 - A4BB.pk1" "b/Gen 1/Japanese/150 - \343\203\237\343\203\245\343\202\246\343\203\204\343\203\274 - A4BB.pk1"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/211 \342\230\205 - QWILFISH - 691600000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/211 \342\230\205 - QWILFISH - 691600000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/214 \342\230\205 - HERACROSS - 910800000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/214 \342\230\205 - HERACROSS - 910800000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/223 \342\230\205 - REMORAID - 2B5600000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/223 \342\230\205 - REMORAID - 2B5600000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/231 \342\230\205 - PHANPY - 246600000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/231 \342\230\205 - PHANPY - 246600000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/235 \342\230\205 - SMEARGLE - DDAA00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/235 \342\230\205 - SMEARGLE - DDAA00000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/261 \342\230\205 - POOCHYENA - E8E700000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/261 \342\230\205 - POOCHYENA - E8E700000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/263 \342\230\205 - ZIGZAGOON - DC5D00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/263 \342\230\205 - ZIGZAGOON - DC5D00000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/278 \342\230\205 - WINGULL - A69800000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/278 \342\230\205 - WINGULL - A69800000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/293 \342\230\205 - WHISMUR - A5DA00000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/293 \342\230\205 - WHISMUR - A5DA00000004.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/296 \342\230\205 - MAKUHITA - D4C900000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/296 \342\230\205 - MAKUHITA - D4C900000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/302 \342\230\205 - SABLEYE - 182A00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/302 \342\230\205 - SABLEYE - 182A00000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/303 \342\230\205 - MAWILE - D97B00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/303 \342\230\205 - MAWILE - D97B00000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/311 \342\230\205 - PLUSLE - 8C8B00000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/311 \342\230\205 - PLUSLE - 8C8B00000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/312 \342\230\205 - MINUN - 8FBF00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/312 \342\230\205 - MINUN - 8FBF00000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/316 \342\230\205 - GULPIN - 8E6A00000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/316 \342\230\205 - GULPIN - 8E6A00000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/333 \342\230\205 - SWABLU - 057200000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/333 \342\230\205 - SWABLU - 057200000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/340 \342\230\205 - WHISCASH - 528800000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/340 \342\230\205 - WHISCASH - 528800000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/359 \342\230\205 - ABSOL - D52100000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/359 \342\230\205 - ABSOL - D52100000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/366 \342\230\205 - CLAMPERL - 331500000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/366 \342\230\205 - CLAMPERL - 331500000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/369 \342\230\205 - RELICANTH - BA4100000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/369 \342\230\205 - RELICANTH - BA4100000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/370 \342\230\205 - LUVDISC - 6D8800000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/370 \342\230\205 - LUVDISC - 6D8800000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/396 \342\230\205 - STARLY - 9BD000000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/396 \342\230\205 - STARLY - 9BD000000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/412 \342\230\205 - BURMY - 5B8F00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/412 \342\230\205 - BURMY - 5B8F00000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/415 \342\230\205 - COMBEE - C61000000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/415 \342\230\205 - COMBEE - C61000000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/417 \342\230\205 - PACHIRISU - 779100000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/417 \342\230\205 - PACHIRISU - 779100000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/422-01 \342\230\205 - SHELLOS - 9CC400000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/422-01 \342\230\205 - SHELLOS - 9CC400000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/425 \342\230\205 - DRIFLOON - 445F0000009D.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/425 \342\230\205 - DRIFLOON - 445F0000009D.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/431 \342\230\205 - GLAMEOW - 6A0800000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/431 \342\230\205 - GLAMEOW - 6A0800000004.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/434 \342\230\205 - MOUFOUETTE - 7C3B00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/434 \342\230\205 - MOUFOUETTE - 7C3B00000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/438 \342\230\205 - BONSLY - 603B00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/438 \342\230\205 - BONSLY - 603B00000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/439 \342\230\205 - MIME JR. - BBBF00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/439 \342\230\205 - MIME JR. - BBBF00000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/441 \342\230\205 - CHATOT - 355C00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/441 \342\230\205 - CHATOT - 355C00000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/442 \342\230\205 - SPIRITOMB - AAED00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/442 \342\230\205 - SPIRITOMB - AAED00000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/443 \342\230\205 - GIBLE - F1E600000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/443 \342\230\205 - GIBLE - F1E600000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/446 \342\230\205 - MUNCHLAX - 59E800000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/446 \342\230\205 - MUNCHLAX - 59E800000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/449 \342\230\205 - HIPPOPOTAS - AAF000000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/449 \342\230\205 - HIPPOPOTAS - AAF000000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/451 \342\230\205 - SKORUPI - 9E9B00000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/451 \342\230\205 - SKORUPI - 9E9B00000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/453 \342\230\205 - CROAGUNK - A40C00000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/453 \342\230\205 - CROAGUNK - A40C00000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/455 \342\230\205 - CARNIVINE - 0A2A00000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/455 \342\230\205 - CARNIVINE - 0A2A00000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/456 \342\230\205 - FINNEON - ED7600000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/456 \342\230\205 - FINNEON - ED7600000004.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/458 \342\230\205 - MANTYKE - 628C00000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th March 19 (ver 2)/458 \342\230\205 - MANTYKE - 628C00000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/011 \342\230\205 - METAPOD - 0D8500000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/011 \342\230\205 - METAPOD - 0D8500000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/015 \342\230\205 - BEEDRILL - DBB300000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/015 \342\230\205 - BEEDRILL - DBB300000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/019 \342\230\205 - RATTATA - 623E00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/019 \342\230\205 - RATTATA - 623E00000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/020 \342\230\205 - RATICATE - 2E9700000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/020 \342\230\205 - RATICATE - 2E9700000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/021 \342\230\205 - SPEAROW - 480100000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/021 \342\230\205 - SPEAROW - 480100000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/027 \342\230\205 - SANDSHREW - 4D0700000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/027 \342\230\205 - SANDSHREW - 4D0700000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/039 \342\230\205 - JIGGLYPUFF - 511700000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/039 \342\230\205 - JIGGLYPUFF - 511700000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/041 \342\230\205 - ZUBAT - 0DCF00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/041 \342\230\205 - ZUBAT - 0DCF00000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/043 \342\230\205 - ODDISH - 49F90000009A.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/043 \342\230\205 - ODDISH - 49F90000009A.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/043 \342\230\205 - ODDISH - 49F90000009A.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/046 \342\230\205 - PARAS - 497500000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/046 \342\230\205 - PARAS - 497500000003.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/057 \342\230\205 - PRIMEAPE - E6A300000000.pk4" and /dev/null differ
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/060 \342\230\205 - POLIWAG - 84DE00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/060 \342\230\205 - POLIWAG - 84DE00000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/066 \342\230\205 - MACHOP - B3DE00000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/066 \342\230\205 - MACHOP - B3DE00000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/074 \342\230\205 - GEODUDE - 052800000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/074 \342\230\205 - GEODUDE - 052800000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/077 \342\230\205 - PONYTA - 0F850000009A.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/077 \342\230\205 - PONYTA - 0F850000009A.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/079 \342\230\205 - SLOWPOKE - E7A300000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/079 \342\230\205 - SLOWPOKE - E7A300000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/083 \342\230\205 - FARFETCH'D - 8B3100000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/083 \342\230\205 - FARFETCH'D - 8B3100000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/087 \342\230\205 - DEWGONG - 18840000009B.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/087 \342\230\205 - DEWGONG - 18840000009B.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/088 \342\230\205 - GRIMER - D06200000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/088 \342\230\205 - GRIMER - D06200000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/092 \342\230\205 - GASTLY - D66F0000009C.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/092 \342\230\205 - GASTLY - D66F0000009C.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/095 \342\230\205 - ONIX - 616300000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/095 \342\230\205 - ONIX - 616300000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/095 \342\230\205 - ONIX - A57B0000009B.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/095 \342\230\205 - ONIX - A57B0000009B.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/098 \342\230\205 - KRABBY - 638B00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/098 \342\230\205 - KRABBY - 638B00000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/102 \342\230\205 - EXEGGCUTE - 41C200000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/102 \342\230\205 - EXEGGCUTE - 41C200000003.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/102 \342\230\205 - EXEGGCUTE - 41C200000003.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/108 \342\230\205 - LICKITUNG - 3A4C00000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/108 \342\230\205 - LICKITUNG - 3A4C00000004.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/109 \342\230\205 - KOFFING - 689E00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/109 \342\230\205 - KOFFING - 689E00000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/110 \342\230\205 - WEEZING - 268F00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/110 \342\230\205 - WEEZING - 268F00000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/111 \342\230\205 - RHYHORN - C75D0000009A.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/111 \342\230\205 - RHYHORN - C75D0000009A.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/112 \342\230\205 - RHYDON - D7950000009C.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/112 \342\230\205 - RHYDON - D7950000009C.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/112 \342\230\205 - RHYDON - D7950000009C.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/114 \342\230\205 - TANGELA - 7B9100000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/114 \342\230\205 - TANGELA - 7B9100000004.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/118 \342\230\205 - GOLDEEN - 854200000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/118 \342\230\205 - GOLDEEN - 854200000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/119 \342\230\205 - SEAKING - 1B2E00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/119 \342\230\205 - SEAKING - 1B2E00000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/122 \342\230\205 - MR. MIME - CDE200000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/122 \342\230\205 - MR. MIME - CDE200000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/126 \342\230\205 - MAGMAR - 995200000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/126 \342\230\205 - MAGMAR - 995200000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/129 \342\230\205 - MAGIKARP - 114700000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/129 \342\230\205 - MAGIKARP - 114700000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/147 \342\230\205 - DRATINI - 6AB200000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/147 \342\230\205 - DRATINI - 6AB200000002.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/147 \342\230\205 - DRATINI - 6AB200000002.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/161 \342\230\205 - SENTRET - 055E00000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/161 \342\230\205 - SENTRET - 055E00000005.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/161 \342\230\205 - SENTRET - 055E00000005.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/162 \342\230\205 - FURRET - 03D500000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/162 \342\230\205 - FURRET - 03D500000007.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/162 \342\230\205 - FURRET - 03D500000007.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/163 \342\230\205 - HOOTHOOT - 8C7600000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/163 \342\230\205 - HOOTHOOT - 8C7600000003.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/163 \342\230\205 - HOOTHOOT - 8D3A0000009F.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/164 \342\230\205 - NOCTOWL - 3EA200000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/164 \342\230\205 - NOCTOWL - 3EA200000000.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/164 \342\230\205 - NOCTOWL - 3EA200000000.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/166 \342\230\205 - LEDIAN - E7E500000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/166 \342\230\205 - LEDIAN - E7E500000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/167 \342\230\205 - SPINARAK - 260300000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/167 \342\230\205 - SPINARAK - 260300000004.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/168 \342\230\205 - ARIADOS - 8B3500000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/168 \342\230\205 - ARIADOS - 8B3500000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/172 \342\230\205 - PICHU - 0C7900000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/172 \342\230\205 - PICHU - 0C7900000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/179 \342\230\205 - MAREEP - A35600000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/179 \342\230\205 - MAREEP - A35600000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/180 \342\230\205 - FLAAFFY - 685E00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/180 \342\230\205 - FLAAFFY - 685E00000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/183 \342\230\205 - MARILL - 6A210000009B.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/183 \342\230\205 - MARILL - 6A210000009B.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/183 \342\230\205 - MARILL - 6A210000009B.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/184 \342\230\205 - AZUMARILL - 23A600000098.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/184 \342\230\205 - AZUMARILL - 23A600000098.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/184 \342\230\205 - AZUMARILL - 23A600000098.pk4" and /dev/null differ
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/226 \342\230\205 - MANTINE - B96600000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/226 \342\230\205 - MANTINE - B96600000002.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/266 \342\230\205 - SILCOON - DBBF0000009A.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/266 \342\230\205 - SILCOON - DBBF0000009A.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/279 \342\230\205 - PELIPPER - F82F00000098.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/279 \342\230\205 - PELIPPER - F82F00000098.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/280 \342\230\205 - RALTS - 0F7000000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/280 \342\230\205 - RALTS - 0F7000000005.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/299 \342\230\205 - NOSEPASS - 83620000009F.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/299 \342\230\205 - NOSEPASS - 83620000009F.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/307 \342\230\205 - MEDITITE - 56550000009C.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/307 \342\230\205 - MEDITITE - 56550000009C.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/308 \342\230\205 - MEDICHAM - FD8000000099.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/308 \342\230\205 - MEDICHAM - FD8000000099.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/315 \342\230\205 - ROSELIA - 37610000009B.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/315 \342\230\205 - ROSELIA - 37610000009B.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/315 \342\230\205 - ROSELIA - ACA200000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/315 \342\230\205 - ROSELIA - ACA200000003.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/427 \342\230\205 - BUNEARY - 4DB40000009D.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/433 \342\230\205 - CHINGLING - D6C90000009F.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/433 \342\230\205 - CHINGLING - D6C90000009F.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/8th May 18/433 \342\230\205 - CHINGLING - D6C90000009F.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/014 \342\230\205 - KAKUNA - 5B2E00000006.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/022 \342\230\205 - FEAROW - B7D500000000.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/023 \342\230\205 - EKANS - B19F00000004.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/024 \342\230\205 - ARBOK - 66BF00000006.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/035 \342\230\205 - CLEFAIRY - D5A700000003.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/044 \342\230\205 - GLOOM - 7AB700000002.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/048 \342\230\205 - VENONAT - 1AA300000003.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/050 \342\230\205 - DIGLETT - F8AA00000007.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/055 \342\230\205 - GOLDUCK - 88FF00000001.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/061 \342\230\205 - POLIWHIRL - A40A00000000.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/070 \342\230\205 - WEEPINBELL - FB7400000005.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/080 \342\230\205 - SLOWBRO - 618300000002.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/085 \342\230\205 - DODRIO - AE9600000004.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/089 \342\230\205 - MUK - E37500000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/089 \342\230\205 - MUK - E37500000001.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/097 \342\230\205 - HYPNO - C21E00000005.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/099 \342\230\205 - KINGLER - 040F00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/099 \342\230\205 - KINGLER - 040F00000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/104 \342\230\205 - CUBONE - 8F3200000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/104 \342\230\205 - CUBONE - 8F3200000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/105 \342\230\205 - MAROWAK - C06800000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/105 \342\230\205 - MAROWAK - C06800000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/123 \342\230\205 - SCYTHER - A39700000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/123 \342\230\205 - SCYTHER - A39700000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/125 \342\230\205 - ELECTABUZZ - FD1300000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/125 \342\230\205 - ELECTABUZZ - FD1300000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/127 \342\230\205 - PINSIR - D73700000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/127 \342\230\205 - PINSIR - D73700000000.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/130 \342\230\205 - GYARADOS - 97D500000005.pk4" and /dev/null differ
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/191 \342\230\205 - SUNKERN - E70100000000.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/191 \342\230\205 - SUNKERN - E70100000000.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/213 \342\230\205 - SHUCKLE - FED200000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/213 \342\230\205 - SHUCKLE - FED200000005.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/229 \342\230\205 - HOUNDOOM - 499A00000004.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/234 \342\230\205 - STANTLER - 23E900000002.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/265 \342\230\205 - WURMPLE - AC4800000005.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/265 \342\230\205 - WURMPLE - AC4800000005.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/265 \342\230\205 - WURMPLE - AC4800000005.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/267 \342\230\205 - BEAUTIFLY - 83A100000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/267 \342\230\205 - BEAUTIFLY - 83A100000006.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/269 \342\230\205 - DUSTOX - 0A2400000004.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/270 \342\230\205 - LOTAD - 413600000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/270 \342\230\205 - LOTAD - 413600000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/271 \342\230\205 - LOMBRE - E36E00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/271 \342\230\205 - LOMBRE - E36E00000007.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/271 \342\230\205 - LOMBRE - E36E00000007.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/273 \342\230\205 - SEEDOT - 330D00000004.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/273 \342\230\205 - SEEDOT - 330D00000004.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/273 \342\230\205 - SEEDOT - 330D00000004.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/274 \342\230\205 - NUZLEAF - 8DD500000001.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/276 \342\230\205 - TAILLOW - 97A000000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/276 \342\230\205 - TAILLOW - 97A000000001.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/284 \342\230\205 - MASQUERAIN - 83F300000006.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/285 \342\230\205 - SHROOMISH - 271600000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/285 \342\230\205 - SHROOMISH - 271600000002.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/285 \342\230\205 - SHROOMISH - 271600000002.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/286 \342\230\205 - BRELOOM - C62200000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/286 \342\230\205 - BRELOOM - C62200000002.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/288 \342\230\205 - VIGOROTH - 0E2900000005.pk4" and /dev/null differ
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/299 \342\230\205 - NOSEPASS - 6D3F00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/299 \342\230\205 - NOSEPASS - 6D3F00000006.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/324 \342\230\205 - TORKOAL - 4D5800000003.pk4" and /dev/null differ
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/329 \342\230\205 - VIBRAVA - 843C00000002.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/331 \342\230\205 - CACNEA - 598C00000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/331 \342\230\205 - CACNEA - 598C00000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/335 \342\230\205 - ZANGOOSE - C3CC00000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/335 \342\230\205 - ZANGOOSE - C3CC00000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/336 \342\230\205 - SEVIPER - 974000000001.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/336 \342\230\205 - SEVIPER - 974000000001.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/339 \342\230\205 - BARBOACH - C9FF00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/339 \342\230\205 - BARBOACH - C9FF00000003.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/339 \342\230\205 - BARBOACH - C9FF00000003.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/341 \342\230\205 - CORPHISH - 240C00000002.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/341 \342\230\205 - CORPHISH - 240C00000002.pk4"
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Binary files "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/341 \342\230\205 - CORPHISH - 240C00000002.pk4" and /dev/null differ
diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/353 \342\230\205 - SHUPPET - 6ADC00000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/353 \342\230\205 - SHUPPET - 6ADC00000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/354 \342\230\205 - BANETTE - D54100000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/354 \342\230\205 - BANETTE - D54100000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/356 \342\230\205 - DUSCLOPS - 760B00000007.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/356 \342\230\205 - DUSCLOPS - 760B00000007.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/363 \342\230\205 - SPHEAL - 2F7700000006.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/363 \342\230\205 - SPHEAL - 2F7700000006.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/364 \342\230\205 - SEALEO - 627E00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/364 \342\230\205 - SEALEO - 627E00000003.pk4"
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diff --git "a/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/371 \342\230\205 - BAGON - 7B5E00000003.pk4" "b/Gen 4/DPPt & HGSS/English/Cute Charm/9th March 19/371 \342\230\205 - BAGON - 7B5E00000003.pk4"
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diff --git "a/Gen 4/Pok\303\251walker/Volcano Path/074 - GEODUDE - 99E8A90000A1.pk4" "b/Gen 4/Pok\303\251walker/Volcano Path/074 - GEODUDE - 99E8A90000A1.pk4"
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diff --git "a/Gen 4/Pok\303\251walker/Warm Beach/116 - HORSEA - FD16A9000003.pk4" "b/Gen 4/Pok\303\251walker/Warm Beach/116 - HORSEA - FD16A9000003.pk4"
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diff --git "a/Gen 5/BW & B2W2/Hidden Grotto/Funfest/196 - \343\202\250\343\203\274\343\203\225\343\202\243 - D1F59C41A162.pk5" "b/Gen 5/BW & B2W2/Hidden Grotto/Funfest/196 - \343\202\250\343\203\274\343\203\225\343\202\243 - D1F59C41A162.pk5"
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diff --git "a/Gen 5/BW & B2W2/Hidden Grotto/Funfest/372 - \343\202\263\343\203\242\343\203\253\343\203\274 - E064DAF1B43B.pk5" "b/Gen 5/BW & B2W2/Hidden Grotto/Funfest/372 - \343\202\263\343\203\242\343\203\253\343\203\274 - E064DAF1B43B.pk5"
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diff --git a/INTEGRANDO_PANDAS_SQLITE.ipynb b/INTEGRANDO_PANDAS_SQLITE.ipynb
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+++ b/INTEGRANDO_PANDAS_SQLITE.ipynb
@@ -0,0 +1,3356 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Lyanan85/In-GameSeries/blob/venv/INTEGRANDO_PANDAS_SQLITE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FSrFckpJZxWP"
+      },
+      "source": [
+        "## Integrando sqlite e pandas\n",
+        "#### material de estudo\n",
+        "-insigths basicos\n",
+        "-conceitos\n",
+        "-gráficos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "M27LYw0xZxWQ"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import sqlite3"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H7-JGKkIZxWR"
+      },
+      "source": [
+        "## A variavel conn recebe o comando que conecta com o banco de dados\n",
+        "\n",
+        "### a variavel cursor recebe  o cursor, que é utilizado para navegar no banco de dados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FYABjnaOZxWR"
+      },
+      "outputs": [],
+      "source": [
+        "conn=sqlite3.connect('Material_de_estudo.db')\n",
+        "cursor=conn.cursor()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JPEbIKwSZxWR"
+      },
+      "source": [
+        "## Verificando a tabela principal com os dados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Nsewyr6JZxWR",
+        "outputId": "e0b46ed6-8028-4dcf-b4aa-6641b7f6184c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>StateAbbr</th>\n",
+              "      <th>StateDesc</th>\n",
+              "      <th>CityName</th>\n",
+              "      <th>GeographicLevel</th>\n",
+              "      <th>DataSource</th>\n",
+              "      <th>Category</th>\n",
+              "      <th>UniqueID</th>\n",
+              "      <th>Measure</th>\n",
+              "      <th>Data_Value_Unit</th>\n",
+              "      <th>...</th>\n",
+              "      <th>High_Confidence_Limit</th>\n",
+              "      <th>Data_Value_Footnote_Symbol</th>\n",
+              "      <th>Data_Value_Footnote</th>\n",
+              "      <th>PopulationCount</th>\n",
+              "      <th>Geolocation</th>\n",
+              "      <th>CategoryID</th>\n",
+              "      <th>MeasureId</th>\n",
+              "      <th>CityFIPS</th>\n",
+              "      <th>TractFIPS</th>\n",
+              "      <th>Short_Question_Text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>IN</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>Fishers</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>1823278</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>24.7</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>76794</td>\n",
+              "      <td>POINT (-85.969310702 39.9587635263)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>1823278.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Milpitas</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0647766</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.5</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>66790</td>\n",
+              "      <td>POINT (-121.892083025 37.433869763)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>647766.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CT</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>Stamford</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0973000</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.2</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>122643</td>\n",
+              "      <td>POINT (-73.5589363142 41.1049145013)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>973000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Elk Grove</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0622020</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>153015</td>\n",
+              "      <td>POINT (-121.384137762 38.4159992006)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>622020.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>FL</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Palm Bay</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>1254000</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>78.4</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>103190</td>\n",
+              "      <td>POINT (-80.6666722846 27.9838255249)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>1254000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810098</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Clovis</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Unhealthy Behaviors</td>\n",
+              "      <td>0614218-06019005911</td>\n",
+              "      <td>Binge drinking among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>3346</td>\n",
+              "      <td>POINT (-119.650325584 36.7945965568)</td>\n",
+              "      <td>UNHBEH</td>\n",
+              "      <td>BINGE</td>\n",
+              "      <td>614218.0</td>\n",
+              "      <td>6.019006e+09</td>\n",
+              "      <td>Binge Drinking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810099</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Fremont</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Unhealthy Behaviors</td>\n",
+              "      <td>0626000-06001442400</td>\n",
+              "      <td>Current smoking among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>14.3</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>6253</td>\n",
+              "      <td>POINT (-121.973779294 37.5313807021)</td>\n",
+              "      <td>UNHBEH</td>\n",
+              "      <td>CSMOKING</td>\n",
+              "      <td>626000.0</td>\n",
+              "      <td>6.001442e+09</td>\n",
+              "      <td>Current Smoking</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810100</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>HI</td>\n",
+              "      <td>Hawaii</td>\n",
+              "      <td>Honolulu</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>15003009400</td>\n",
+              "      <td>Coronary heart disease among adults aged &gt;=18 ...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7.1</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>5155</td>\n",
+              "      <td>POINT (-158.038203503 21.4942478343)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>CHD</td>\n",
+              "      <td>15003.0</td>\n",
+              "      <td>1.500301e+10</td>\n",
+              "      <td>Coronary Heart Disease</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810101</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Los Angeles</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0644000-06037211410</td>\n",
+              "      <td>Mental health not good for &gt;=14 days among adu...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>3095</td>\n",
+              "      <td>POINT (-118.307315461 34.0726142562)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>MHLTH</td>\n",
+              "      <td>644000.0</td>\n",
+              "      <td>6.037211e+09</td>\n",
+              "      <td>Mental Health</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810102</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>TX</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>Dallas</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>4819000-48085031719</td>\n",
+              "      <td>All teeth lost among adults aged &gt;=65 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td></td>\n",
+              "      <td></td>\n",
+              "      <td>1733</td>\n",
+              "      <td>POINT (-96.7748765537 32.9943502109)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>TEETHLOST</td>\n",
+              "      <td>4819000.0</td>\n",
+              "      <td>4.808503e+10</td>\n",
+              "      <td>Teeth Loss</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>810103 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        Year StateAbbr    StateDesc     CityName GeographicLevel DataSource  \\\n",
+              "0       2015        IN      Indiana      Fishers            City      BRFSS   \n",
+              "1       2015        CA   California     Milpitas            City      BRFSS   \n",
+              "2       2015        CT  Connecticut     Stamford            City      BRFSS   \n",
+              "3       2015        CA   California    Elk Grove            City      BRFSS   \n",
+              "4       2015        FL      Florida     Palm Bay            City      BRFSS   \n",
+              "...      ...       ...          ...          ...             ...        ...   \n",
+              "810098  2016        CA   California       Clovis    Census Tract      BRFSS   \n",
+              "810099  2016        CA   California      Fremont    Census Tract      BRFSS   \n",
+              "810100  2016        HI       Hawaii     Honolulu    Census Tract      BRFSS   \n",
+              "810101  2016        CA   California  Los Angeles    Census Tract      BRFSS   \n",
+              "810102  2016        TX        Texas       Dallas    Census Tract      BRFSS   \n",
+              "\n",
+              "                   Category             UniqueID  \\\n",
+              "0           Health Outcomes              1823278   \n",
+              "1           Health Outcomes              0647766   \n",
+              "2           Health Outcomes              0973000   \n",
+              "3           Health Outcomes              0622020   \n",
+              "4                Prevention              1254000   \n",
+              "...                     ...                  ...   \n",
+              "810098  Unhealthy Behaviors  0614218-06019005911   \n",
+              "810099  Unhealthy Behaviors  0626000-06001442400   \n",
+              "810100      Health Outcomes          15003009400   \n",
+              "810101      Health Outcomes  0644000-06037211410   \n",
+              "810102      Health Outcomes  4819000-48085031719   \n",
+              "\n",
+              "                                                  Measure Data_Value_Unit  \\\n",
+              "0        High blood pressure among adults aged >=18 Years               %   \n",
+              "1        High blood pressure among adults aged >=18 Years               %   \n",
+              "2        High blood pressure among adults aged >=18 Years               %   \n",
+              "3        High blood pressure among adults aged >=18 Years               %   \n",
+              "4       Cholesterol screening among adults aged >=18 Y...               %   \n",
+              "...                                                   ...             ...   \n",
+              "810098        Binge drinking among adults aged >=18 Years               %   \n",
+              "810099       Current smoking among adults aged >=18 Years               %   \n",
+              "810100  Coronary heart disease among adults aged >=18 ...               %   \n",
+              "810101  Mental health not good for >=14 days among adu...               %   \n",
+              "810102        All teeth lost among adults aged >=65 Years               %   \n",
+              "\n",
+              "        ... High_Confidence_Limit Data_Value_Footnote_Symbol  \\\n",
+              "0       ...                  24.7                              \n",
+              "1       ...                  22.5                              \n",
+              "2       ...                  26.2                              \n",
+              "3       ...                  27.8                              \n",
+              "4       ...                  78.4                              \n",
+              "...     ...                   ...                        ...   \n",
+              "810098  ...                  19.0                              \n",
+              "810099  ...                  14.3                              \n",
+              "810100  ...                   7.1                              \n",
+              "810101  ...                  12.0                              \n",
+              "810102  ...                  11.8                              \n",
+              "\n",
+              "        Data_Value_Footnote  PopulationCount  \\\n",
+              "0                                      76794   \n",
+              "1                                      66790   \n",
+              "2                                     122643   \n",
+              "3                                     153015   \n",
+              "4                                     103190   \n",
+              "...                     ...              ...   \n",
+              "810098                                  3346   \n",
+              "810099                                  6253   \n",
+              "810100                                  5155   \n",
+              "810101                                  3095   \n",
+              "810102                                  1733   \n",
+              "\n",
+              "                                 Geolocation CategoryID   MeasureId  \\\n",
+              "0        POINT (-85.969310702 39.9587635263)    HLTHOUT      BPHIGH   \n",
+              "1        POINT (-121.892083025 37.433869763)    HLTHOUT      BPHIGH   \n",
+              "2       POINT (-73.5589363142 41.1049145013)    HLTHOUT      BPHIGH   \n",
+              "3       POINT (-121.384137762 38.4159992006)    HLTHOUT      BPHIGH   \n",
+              "4       POINT (-80.6666722846 27.9838255249)    PREVENT  CHOLSCREEN   \n",
+              "...                                      ...        ...         ...   \n",
+              "810098  POINT (-119.650325584 36.7945965568)     UNHBEH       BINGE   \n",
+              "810099  POINT (-121.973779294 37.5313807021)     UNHBEH    CSMOKING   \n",
+              "810100  POINT (-158.038203503 21.4942478343)    HLTHOUT         CHD   \n",
+              "810101  POINT (-118.307315461 34.0726142562)    HLTHOUT       MHLTH   \n",
+              "810102  POINT (-96.7748765537 32.9943502109)    HLTHOUT   TEETHLOST   \n",
+              "\n",
+              "         CityFIPS     TractFIPS     Short_Question_Text  \n",
+              "0       1823278.0           NaN     High Blood Pressure  \n",
+              "1        647766.0           NaN     High Blood Pressure  \n",
+              "2        973000.0           NaN     High Blood Pressure  \n",
+              "3        622020.0           NaN     High Blood Pressure  \n",
+              "4       1254000.0           NaN   Cholesterol Screening  \n",
+              "...           ...           ...                     ...  \n",
+              "810098   614218.0  6.019006e+09          Binge Drinking  \n",
+              "810099   626000.0  6.001442e+09         Current Smoking  \n",
+              "810100    15003.0  1.500301e+10  Coronary Heart Disease  \n",
+              "810101   644000.0  6.037211e+09           Mental Health  \n",
+              "810102  4819000.0  4.808503e+10              Teeth Loss  \n",
+              "\n",
+              "[810103 rows x 24 columns]"
+            ]
+          },
+          "execution_count": 134,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "\n",
+        "tabelaprincipal=pd.read_sql_query(\"\"\"SELECT* FROM Material_de_estudo\"\"\", conn)\n",
+        "tabelaprincipal"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AeM08lViZxWR"
+      },
+      "source": [
+        "## Utilizamos o meto sql_query para realizar uma consulta no banco de dados\n",
+        "### finalizamos a consulta com o conn\n",
+        "\n",
+        "\n",
+        "##### pesquisando quantos e quais anos essa pesquisa se refere"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "64BDTJE6ZxWS"
+      },
+      "source": [
+        "## Contando a quantidade de pesquisar por ano\n",
+        "o select year seleciona a coluna ano, COUNT(*) Conta a tabela inteira, após receber um apelido é usado a cláusula where in pra filtra dois resultados, depois o resultado é agrupado com a cláusula group by"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y8oSA_3wZxWS",
+        "outputId": "beb89e09-8f72-4d5a-e027-42064e83a3d4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Contagem_Entrevistados</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>116024</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>694079</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Year  Contagem_Entrevistados\n",
+              "0  2015                  116024\n",
+              "1  2016                  694079"
+            ]
+          },
+          "execution_count": 111,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "contagem_casos=pd.read_sql_query(\"SELECT Year, COUNT(*) AS Contagem_Entrevistados FROM Material_de_estudo where Year in (2015,2016) GROUP BY Year\", conn)\n",
+        "contagem_casos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Representando a quantidade de pesquisas por ano através de um gráfico"
+      ],
+      "metadata": {
+        "id": "3UxUWqn1Zz9W"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KU4W5GmIZxWS",
+        "outputId": "1ea8d9b7-0462-4564-dcf0-ec362935b715"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 900x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "Gráfico_casos_ano=contagem_casos\n",
+        "contagem_casos.set_index('Year', inplace=True)\n",
+        "Gráfico_casos_ano.plot(kind=\"barh\", color=\"Skyblue\",figsize=(9,4))\n",
+        "plt.title(\"Comparando os casos de 2015 e 2016\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7HJSMs8gZxWS"
+      },
+      "source": [
+        "## Conferindo de forma decrescente os Estados referentes a esta pesquisa"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f7ce9DqVZxWS",
+        "outputId": "a2e5c58f-fa92-4611-cfd2-5b8553970e10"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>StateDesc</th>\n",
+              "      <th>Contagem_estados_por_casos</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>California</td>\n",
+              "      <td>156778</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Texas</td>\n",
+              "      <td>89422</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>New York</td>\n",
+              "      <td>70133</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Florida</td>\n",
+              "      <td>39025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Illinois</td>\n",
+              "      <td>37458</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>29678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Ohio</td>\n",
+              "      <td>24111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>23305</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Michigan</td>\n",
+              "      <td>21672</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Colorado</td>\n",
+              "      <td>20549</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Pennsylvania</td>\n",
+              "      <td>18392</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Washington</td>\n",
+              "      <td>15448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Tennessee</td>\n",
+              "      <td>15299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>15281</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>14630</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Massachusetts</td>\n",
+              "      <td>14346</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Oklahoma</td>\n",
+              "      <td>13532</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Georgia</td>\n",
+              "      <td>13251</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Missouri</td>\n",
+              "      <td>13028</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Wisconsin</td>\n",
+              "      <td>11849</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>Louisiana</td>\n",
+              "      <td>10875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>Nevada</td>\n",
+              "      <td>10639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>10593</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>10402</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>Minnesota</td>\n",
+              "      <td>9123</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>New Jersey</td>\n",
+              "      <td>9013</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>Kansas</td>\n",
+              "      <td>8913</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Kentucky</td>\n",
+              "      <td>7527</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>Utah</td>\n",
+              "      <td>7431</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>Hawaii</td>\n",
+              "      <td>6721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>6603</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>New Mexico</td>\n",
+              "      <td>6273</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>Nebraska</td>\n",
+              "      <td>6216</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>33</th>\n",
+              "      <td>Iowa</td>\n",
+              "      <td>5889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34</th>\n",
+              "      <td>Maryland</td>\n",
+              "      <td>5655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35</th>\n",
+              "      <td>South Carolin</td>\n",
+              "      <td>5131</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>36</th>\n",
+              "      <td>District of C</td>\n",
+              "      <td>5065</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>37</th>\n",
+              "      <td>Arkansas</td>\n",
+              "      <td>3679</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>38</th>\n",
+              "      <td>Rhode Island</td>\n",
+              "      <td>2993</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>39</th>\n",
+              "      <td>Mississippi</td>\n",
+              "      <td>2229</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>40</th>\n",
+              "      <td>Idaho</td>\n",
+              "      <td>2070</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>41</th>\n",
+              "      <td>South Dakota</td>\n",
+              "      <td>1760</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>Alaska</td>\n",
+              "      <td>1596</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>New Hampshire</td>\n",
+              "      <td>1428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>Montana</td>\n",
+              "      <td>1340</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>North Dakota</td>\n",
+              "      <td>756</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>West Virginia</td>\n",
+              "      <td>728</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>Delaware</td>\n",
+              "      <td>728</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>Maine</td>\n",
+              "      <td>616</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>Wyoming</td>\n",
+              "      <td>504</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>Vermont</td>\n",
+              "      <td>364</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>United States</td>\n",
+              "      <td>56</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        StateDesc  Contagem_estados_por_casos\n",
+              "0      California                      156778\n",
+              "1           Texas                       89422\n",
+              "2        New York                       70133\n",
+              "3         Florida                       39025\n",
+              "4        Illinois                       37458\n",
+              "5         Arizona                       29678\n",
+              "6            Ohio                       24111\n",
+              "7   North Carolin                       23305\n",
+              "8        Michigan                       21672\n",
+              "9        Colorado                       20549\n",
+              "10   Pennsylvania                       18392\n",
+              "11     Washington                       15448\n",
+              "12      Tennessee                       15299\n",
+              "13        Indiana                       15281\n",
+              "14       Virginia                       14630\n",
+              "15  Massachusetts                       14346\n",
+              "16       Oklahoma                       13532\n",
+              "17        Georgia                       13251\n",
+              "18       Missouri                       13028\n",
+              "19      Wisconsin                       11849\n",
+              "20      Louisiana                       10875\n",
+              "21         Nevada                       10639\n",
+              "22        Alabama                       10593\n",
+              "23         Oregon                       10402\n",
+              "24      Minnesota                        9123\n",
+              "25     New Jersey                        9013\n",
+              "26         Kansas                        8913\n",
+              "27       Kentucky                        7527\n",
+              "28           Utah                        7431\n",
+              "29         Hawaii                        6721\n",
+              "30    Connecticut                        6603\n",
+              "31     New Mexico                        6273\n",
+              "32       Nebraska                        6216\n",
+              "33           Iowa                        5889\n",
+              "34       Maryland                        5655\n",
+              "35  South Carolin                        5131\n",
+              "36  District of C                        5065\n",
+              "37       Arkansas                        3679\n",
+              "38   Rhode Island                        2993\n",
+              "39    Mississippi                        2229\n",
+              "40          Idaho                        2070\n",
+              "41   South Dakota                        1760\n",
+              "42         Alaska                        1596\n",
+              "43  New Hampshire                        1428\n",
+              "44        Montana                        1340\n",
+              "45   North Dakota                         756\n",
+              "46  West Virginia                         728\n",
+              "47       Delaware                         728\n",
+              "48          Maine                         616\n",
+              "49        Wyoming                         504\n",
+              "50        Vermont                         364\n",
+              "51  United States                          56"
+            ]
+          },
+          "execution_count": 113,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#GROUP BY StateDesc: Agrupa os dados por cada valor único da coluna \"StateDesc\". Isso permite contar as ocorrências de cada estado.\n",
+        "\n",
+        "contagem_estados=pd.read_sql_query(\"SELECT StateDesc, COUNT(*) as Contagem_estados_por_casos FROM Material_de_estudo GROUP BY StateDesc ORDER BY    Contagem_estados_por_casos DESC\",conn)\n",
+        "contagem_estados"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_vQK5Cb1ZxWS"
+      },
+      "source": [
+        "## Agrupando os 20 Estados mais relevantes para esta pesquisa usando a cláusula limit"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HBlXJlswZxWS",
+        "outputId": "b6a062cc-bc74-4a70-a721-26ded7c280ac"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>StateDesc</th>\n",
+              "      <th>Contagem_estados_por_casos</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>California</td>\n",
+              "      <td>156778</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Texas</td>\n",
+              "      <td>89422</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>New York</td>\n",
+              "      <td>70133</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Florida</td>\n",
+              "      <td>39025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Illinois</td>\n",
+              "      <td>37458</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>29678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Ohio</td>\n",
+              "      <td>24111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>23305</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Michigan</td>\n",
+              "      <td>21672</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Colorado</td>\n",
+              "      <td>20549</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Pennsylvania</td>\n",
+              "      <td>18392</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Washington</td>\n",
+              "      <td>15448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Tennessee</td>\n",
+              "      <td>15299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>15281</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>14630</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Massachusetts</td>\n",
+              "      <td>14346</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Oklahoma</td>\n",
+              "      <td>13532</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Georgia</td>\n",
+              "      <td>13251</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Missouri</td>\n",
+              "      <td>13028</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Wisconsin</td>\n",
+              "      <td>11849</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "        StateDesc  Contagem_estados_por_casos\n",
+              "0      California                      156778\n",
+              "1           Texas                       89422\n",
+              "2        New York                       70133\n",
+              "3         Florida                       39025\n",
+              "4        Illinois                       37458\n",
+              "5         Arizona                       29678\n",
+              "6            Ohio                       24111\n",
+              "7   North Carolin                       23305\n",
+              "8        Michigan                       21672\n",
+              "9        Colorado                       20549\n",
+              "10   Pennsylvania                       18392\n",
+              "11     Washington                       15448\n",
+              "12      Tennessee                       15299\n",
+              "13        Indiana                       15281\n",
+              "14       Virginia                       14630\n",
+              "15  Massachusetts                       14346\n",
+              "16       Oklahoma                       13532\n",
+              "17        Georgia                       13251\n",
+              "18       Missouri                       13028\n",
+              "19      Wisconsin                       11849"
+            ]
+          },
+          "execution_count": 114,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "Estados_mais_relevantes=pd.read_sql_query(\"SELECT StateDesc, COUNT(*) as Contagem_estados_por_casos FROM Material_de_estudo GROUP BY StateDesc ORDER BY    Contagem_estados_por_casos DESC limit 20\",conn)\n",
+        "Estados_mais_relevantes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r27s5QPyZxWT"
+      },
+      "source": [
+        "## Criando um gráfico para os 20 estados mais relevantes para esta pesquisa\n",
+        "#### o metodo set_index define o index(0,1,2) nos estados na coluna State desc da consulta cima\n",
+        "e o método implace=True faz com que os dados sejam, sobescritos na propia planilha"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UlFQSjPzZxWT",
+        "outputId": "3c64162b-7197-4681-8b89-65c3c6456784"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "Estados_mais_relevantes.set_index('StateDesc', inplace=True)\n",
+        "Grafico_relevancia=Estados_mais_relevantes.plot(kind='barh', color='purple', figsize=(10,4))\n",
+        "plt.title(\"Estados mais relevantes para esta pesquisa\")\n",
+        "plt.tight_layout()\n",
+        "plt.xlabel(\"Número de Casos\")\n",
+        "plt.ylabel(\"Estados\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "32S7eaDbZxWT"
+      },
+      "source": [
+        "## Descobrindo os casos mais frequentes nessa pesquisa"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jWwUL7t0ZxWT",
+        "outputId": "7f077d5f-8340-4d90-e464-cf01bb357d2a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Measure</th>\n",
+              "      <th>contagem_casos_frequentes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Visits to doctor for routine checkup within th...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Visits to dentist or dental clinic among adult...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Taking medicine for high blood pressure contro...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Stroke among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Sleeping less than 7 hours among adults aged &gt;...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Physical health not good for &gt;=14 days among a...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Obesity among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>No leisure-time physical activity among adults...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Mental health not good for &gt;=14 days among adu...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>High cholesterol among adults aged &gt;=18 Years ...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Diagnosed diabetes among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Current smoking among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Current asthma among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>Coronary heart disease among adults aged &gt;=18 ...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Chronic kidney disease among adults aged &gt;=18 ...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>Cancer (excluding skin cancer) among adults ag...</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Binge drinking among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>Arthritis among adults aged &gt;=18 Years</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>Current lack of health insurance among adults ...</td>\n",
+              "      <td>28973</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>Papanicolaou smear use among adult women aged ...</td>\n",
+              "      <td>28912</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>Fecal occult blood test, sigmoidoscopy, or col...</td>\n",
+              "      <td>28847</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>Mammography use among women aged 50–74 Years</td>\n",
+              "      <td>28727</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>All teeth lost among adults aged &gt;=65 Years</td>\n",
+              "      <td>28601</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>Older adult men aged &gt;=65 Years who are up to ...</td>\n",
+              "      <td>28464</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Older adult women aged &gt;=65 Years who are up t...</td>\n",
+              "      <td>28453</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                              Measure  \\\n",
+              "0   Visits to doctor for routine checkup within th...   \n",
+              "1   Visits to dentist or dental clinic among adult...   \n",
+              "2   Taking medicine for high blood pressure contro...   \n",
+              "3                 Stroke among adults aged >=18 Years   \n",
+              "4   Sleeping less than 7 hours among adults aged >...   \n",
+              "5   Physical health not good for >=14 days among a...   \n",
+              "6                Obesity among adults aged >=18 Years   \n",
+              "7   No leisure-time physical activity among adults...   \n",
+              "8   Mental health not good for >=14 days among adu...   \n",
+              "9   High cholesterol among adults aged >=18 Years ...   \n",
+              "10   High blood pressure among adults aged >=18 Years   \n",
+              "11    Diagnosed diabetes among adults aged >=18 Years   \n",
+              "12       Current smoking among adults aged >=18 Years   \n",
+              "13        Current asthma among adults aged >=18 Years   \n",
+              "14  Coronary heart disease among adults aged >=18 ...   \n",
+              "15  Chronic obstructive pulmonary disease among ad...   \n",
+              "16  Chronic kidney disease among adults aged >=18 ...   \n",
+              "17  Cholesterol screening among adults aged >=18 Y...   \n",
+              "18  Cancer (excluding skin cancer) among adults ag...   \n",
+              "19        Binge drinking among adults aged >=18 Years   \n",
+              "20             Arthritis among adults aged >=18 Years   \n",
+              "21  Current lack of health insurance among adults ...   \n",
+              "22  Papanicolaou smear use among adult women aged ...   \n",
+              "23  Fecal occult blood test, sigmoidoscopy, or col...   \n",
+              "24       Mammography use among women aged 50–74 Years   \n",
+              "25        All teeth lost among adults aged >=65 Years   \n",
+              "26  Older adult men aged >=65 Years who are up to ...   \n",
+              "27  Older adult women aged >=65 Years who are up t...   \n",
+              "\n",
+              "    contagem_casos_frequentes  \n",
+              "0                       29006  \n",
+              "1                       29006  \n",
+              "2                       29006  \n",
+              "3                       29006  \n",
+              "4                       29006  \n",
+              "5                       29006  \n",
+              "6                       29006  \n",
+              "7                       29006  \n",
+              "8                       29006  \n",
+              "9                       29006  \n",
+              "10                      29006  \n",
+              "11                      29006  \n",
+              "12                      29006  \n",
+              "13                      29006  \n",
+              "14                      29006  \n",
+              "15                      29006  \n",
+              "16                      29006  \n",
+              "17                      29006  \n",
+              "18                      29006  \n",
+              "19                      29006  \n",
+              "20                      29006  \n",
+              "21                      28973  \n",
+              "22                      28912  \n",
+              "23                      28847  \n",
+              "24                      28727  \n",
+              "25                      28601  \n",
+              "26                      28464  \n",
+              "27                      28453  "
+            ]
+          },
+          "execution_count": 116,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "casos_frequentes=pd.read_sql_query(\"SELECT Measure, count(*) as contagem_casos_frequentes FROM Material_de_estudo GROUP BY Measure ORDER BY contagem_casos_frequentes DESC\", conn)\n",
+        "casos_frequentes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I-XzD8_qZxWT"
+      },
+      "source": [
+        "## comparar as tendências temporais da taxa de pressão alta e triagem de colesterol ao longo dos anos.\n",
+        "\"High blood pressure among adults aged >=18 Years \" e \"Cholesterol screening among adults aged >=18 Years  na pesquisa"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hN4SpjV0ZxWT",
+        "outputId": "cb5d04fb-2dc0-4d8d-c5fe-bf8328f5c3b0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>StateAbbr</th>\n",
+              "      <th>StateDesc</th>\n",
+              "      <th>CityName</th>\n",
+              "      <th>GeographicLevel</th>\n",
+              "      <th>DataSource</th>\n",
+              "      <th>Category</th>\n",
+              "      <th>UniqueID</th>\n",
+              "      <th>Measure</th>\n",
+              "      <th>Data_Value_Unit</th>\n",
+              "      <th>...</th>\n",
+              "      <th>High_Confidence_Limit</th>\n",
+              "      <th>Data_Value_Footnote_Symbol</th>\n",
+              "      <th>Data_Value_Footnote</th>\n",
+              "      <th>PopulationCount</th>\n",
+              "      <th>Geolocation</th>\n",
+              "      <th>CategoryID</th>\n",
+              "      <th>MeasureId</th>\n",
+              "      <th>CityFIPS</th>\n",
+              "      <th>TractFIPS</th>\n",
+              "      <th>Short_Question_Text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>0 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [Year, StateAbbr, StateDesc, CityName, GeographicLevel, DataSource, Category, UniqueID, Measure, Data_Value_Unit, DataValueTypeID, Data_Value_Type, Data_Value, Low_Confidence_Limit, High_Confidence_Limit, Data_Value_Footnote_Symbol, Data_Value_Footnote, PopulationCount, Geolocation, CategoryID, MeasureId, CityFIPS, TractFIPS, Short_Question_Text]\n",
+              "Index: []\n",
+              "\n",
+              "[0 rows x 24 columns]"
+            ]
+          },
+          "execution_count": 117,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "filtro=pd.read_sql_query(\"SELECT * FROM Material_de_estudo where Measure='High blood pressure among adults aged >=18 Years' and 'Cholesterol screening among adults aged >=18 Years'\", conn)\n",
+        "filtro"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YIX6nGdWZxWT"
+      },
+      "source": [
+        "## Como podemos verificar, a tabela esta vazia, isso se da pelos motivos de que existem espaços vazios nessa pesquisa, assim sendo necessario um pequeno tratamento de dados\n",
+        "\n",
+        "essa linha de codigo mostra para nós que existem espaços vazios"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B6YoGGnVZxWT",
+        "outputId": "e8e2153e-1fe4-4c19-ee9d-6de990b82709"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "                                              Measure\n",
+            "0    High blood pressure among adults aged >=18 Years\n",
+            "1   Cholesterol screening among adults aged >=18 Y...\n",
+            "2              Arthritis among adults aged >=18 Years\n",
+            "3                 Stroke among adults aged >=18 Years\n",
+            "4                Obesity among adults aged >=18 Years\n",
+            "5        Mammography use among women aged 50–74 Years\n",
+            "6         All teeth lost among adults aged >=65 Years\n",
+            "7     Diagnosed diabetes among adults aged >=18 Years\n",
+            "8         Binge drinking among adults aged >=18 Years\n",
+            "9   Cancer (excluding skin cancer) among adults ag...\n",
+            "10  Mental health not good for >=14 days among adu...\n",
+            "11  Chronic obstructive pulmonary disease among ad...\n",
+            "12       Current smoking among adults aged >=18 Years\n",
+            "13  Coronary heart disease among adults aged >=18 ...\n",
+            "14  Papanicolaou smear use among adult women aged ...\n",
+            "15        Current asthma among adults aged >=18 Years\n",
+            "16  Sleeping less than 7 hours among adults aged >...\n",
+            "17  Visits to dentist or dental clinic among adult...\n",
+            "18  Chronic kidney disease among adults aged >=18 ...\n",
+            "19  Current lack of health insurance among adults ...\n",
+            "20  Taking medicine for high blood pressure contro...\n",
+            "21  High cholesterol among adults aged >=18 Years ...\n",
+            "22  Visits to doctor for routine checkup within th...\n",
+            "23  Older adult women aged >=65 Years who are up t...\n",
+            "24  Physical health not good for >=14 days among a...\n",
+            "25  Fecal occult blood test, sigmoidoscopy, or col...\n",
+            "26  Older adult men aged >=65 Years who are up to ...\n",
+            "27  No leisure-time physical activity among adults...\n"
+          ]
+        }
+      ],
+      "source": [
+        "unique_measures = pd.read_sql_query(\"SELECT DISTINCT Measure FROM Material_de_estudo\", conn)\n",
+        "print(unique_measures)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pl8POpt9ZxWT"
+      },
+      "source": [
+        "## Tratamento de dados simples\n",
+        "#### A cláusula trim remove espaços vazios, metodo banco de dados sqlite\n",
+        "#### Se fosse um arquivo csv se usario o metodo str.strip do pandas"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rw9rlCf5ZxWU"
+      },
+      "source": [
+        "\n",
+        "## o trim remove os espaços em branco na coluna measure\n",
+        "## CASE WHEN: É como dizer \"Se o caso for que...\".\n",
+        "\n",
+        " ##### o then da o valor de 1 para cada valor especificado no filtro, e o comando case when termina com a cláusula end\n",
+        " ##### após a filtragem o SUM conta todos os valores que receberam 1  \n",
+        " #####\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jF6nJpipZxWU",
+        "outputId": "493862f9-b8ed-4850-9864-f56540e0cfb8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Contagem_Pressao_Alta</th>\n",
+              "      <th>Contagem_Colesterol_Alto</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>29006</td>\n",
+              "      <td>29006</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Year  Contagem_Pressao_Alta  Contagem_Colesterol_Alto\n",
+              "0  2015                  29006                     29006"
+            ]
+          },
+          "execution_count": 119,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "filtro2015_2016 = pd.read_sql_query(\"\"\"\n",
+        "    SELECT Year,\n",
+        "           SUM(CASE WHEN TRIM(Measure) = 'High blood pressure among adults aged >=18 Years' THEN 1 ELSE 0 END) AS Contagem_Pressao_Alta,\n",
+        "           SUM(CASE WHEN TRIM(Measure) = 'Cholesterol screening among adults aged >=18 Years' THEN 1 ELSE 0 END) AS Contagem_Colesterol_Alto\n",
+        "    FROM Material_de_estudo\n",
+        "    WHERE TRIM(Measure) IN ('High blood pressure among adults aged >=18 Years', 'Cholesterol screening among adults aged >=18 Years')\n",
+        "    AND Year IN ('2015', '2016')  -- Filtra apenas os anos 2015 e 2016\n",
+        "    GROUP BY Year\n",
+        "\"\"\", conn)\n",
+        "filtro2015_2016"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dvYv0EI_ZxWU"
+      },
+      "source": [
+        "## como podemos ver não temos dados filtrados nesta pesquisa para o ano de  2016, somente para o ano de 2015"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6F5vC-ojZxWU"
+      },
+      "source": [
+        "## Descobrindo Quantos casos de colesterol e pressão alta tivemos nessa pesquisa no ano de 2015"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "y8nVj_ewZxWU",
+        "outputId": "aa6dbe28-a504-40e1-ebef-00fbd972914e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>Data_Value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>72.657522</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>30.491621</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Ano                                             Medida  Data_Value\n",
+              "0  2015  Cholesterol screening among adults aged >=18 Y...   72.657522\n",
+              "1  2015   High blood pressure among adults aged >=18 Years   30.491621"
+            ]
+          },
+          "execution_count": 120,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "DADOS2015 = pd.read_sql_query(\"\"\"\n",
+        "    SELECT\n",
+        "        Year AS Ano,\n",
+        "        Measure AS Medida,\n",
+        "        AVG(Data_Value) AS Data_Value\n",
+        "    FROM Material_de_Estudo\n",
+        "    WHERE TRIM(Measure) in ('High blood pressure among adults aged >=18 Years', 'Cholesterol screening among adults aged >=18 Years') and Year=2015\n",
+        "    GROUP BY Year, Measure\n",
+        "\"\"\", conn)\n",
+        "DADOS2015"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yGIHn_bmZxWU"
+      },
+      "source": [
+        "## Criando gráfico para representar os Casos de Pressão alta e Colesterol alto"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HsL3S4ibZxWU",
+        "outputId": "f80cb7f4-a920-45ea-9610-8c69c4b2d8e7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "sns.barplot(data=DADOS2015, x=\"Ano\", y=\"Data_Value\", hue=\"Medida\")\n",
+        "plt.title(\"Média de Casos em 2015 por Tipo de Medida\")  # Adicionando um título\n",
+        "plt.legend(title='Tipo de Caso')  # Corrigido para 'title' com t minúsculo\n",
+        "plt.tight_layout()\n",
+        "plt.xlabel(\"Ano\")\n",
+        "plt.ylabel(\"Média de Casos\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bEwTVVuSZxWU"
+      },
+      "source": [
+        "## Descobrindo a média de frequencia de TODOS os casos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LxfI2GLsZxWU",
+        "outputId": "688ae010-8cad-45a3-f5aa-0a3b7280d7bb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>Data_Value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Papanicolaou smear use among adult women aged ...</td>\n",
+              "      <td>82.999305</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Mammography use among women aged 50–74 Years</td>\n",
+              "      <td>79.100245</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>72.657522</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Taking medicine for high blood pressure contro...</td>\n",
+              "      <td>71.966826</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Visits to doctor for routine checkup within th...</td>\n",
+              "      <td>69.384538</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Fecal occult blood test, sigmoidoscopy, or col...</td>\n",
+              "      <td>61.861370</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Visits to dentist or dental clinic among adult...</td>\n",
+              "      <td>61.303903</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Sleeping less than 7 hours among adults aged &gt;...</td>\n",
+              "      <td>36.673082</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>High cholesterol among adults aged &gt;=18 Years ...</td>\n",
+              "      <td>34.530764</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Older adult men aged &gt;=65 Years who are up to ...</td>\n",
+              "      <td>31.744107</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>30.491621</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Obesity among adults aged &gt;=18 Years</td>\n",
+              "      <td>30.067875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Older adult women aged &gt;=65 Years who are up t...</td>\n",
+              "      <td>29.894556</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>No leisure-time physical activity among adults...</td>\n",
+              "      <td>25.417875</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Arthritis among adults aged &gt;=18 Years</td>\n",
+              "      <td>22.348820</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Current smoking among adults aged &gt;=18 Years</td>\n",
+              "      <td>18.070966</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Binge drinking among adults aged &gt;=18 Years</td>\n",
+              "      <td>17.636353</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Current lack of health insurance among adults ...</td>\n",
+              "      <td>16.204335</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>All teeth lost among adults aged &gt;=65 Years</td>\n",
+              "      <td>16.052394</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Mental health not good for &gt;=14 days among adu...</td>\n",
+              "      <td>13.097115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Physical health not good for &gt;=14 days among a...</td>\n",
+              "      <td>12.746976</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Diagnosed diabetes among adults aged &gt;=18 Years</td>\n",
+              "      <td>10.791957</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Current asthma among adults aged &gt;=18 Years</td>\n",
+              "      <td>9.684510</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Chronic obstructive pulmonary disease among ad...</td>\n",
+              "      <td>6.314533</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Coronary heart disease among adults aged &gt;=18 ...</td>\n",
+              "      <td>5.810815</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Cancer (excluding skin cancer) among adults ag...</td>\n",
+              "      <td>5.508897</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Stroke among adults aged &gt;=18 Years</td>\n",
+              "      <td>3.226765</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Chronic kidney disease among adults aged &gt;=18 ...</td>\n",
+              "      <td>2.969704</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     Ano                                             Medida  Data_Value\n",
+              "0   2016  Papanicolaou smear use among adult women aged ...   82.999305\n",
+              "1   2016       Mammography use among women aged 50–74 Years   79.100245\n",
+              "2   2015  Cholesterol screening among adults aged >=18 Y...   72.657522\n",
+              "3   2015  Taking medicine for high blood pressure contro...   71.966826\n",
+              "4   2016  Visits to doctor for routine checkup within th...   69.384538\n",
+              "5   2016  Fecal occult blood test, sigmoidoscopy, or col...   61.861370\n",
+              "6   2016  Visits to dentist or dental clinic among adult...   61.303903\n",
+              "7   2016  Sleeping less than 7 hours among adults aged >...   36.673082\n",
+              "8   2015  High cholesterol among adults aged >=18 Years ...   34.530764\n",
+              "9   2016  Older adult men aged >=65 Years who are up to ...   31.744107\n",
+              "10  2015   High blood pressure among adults aged >=18 Years   30.491621\n",
+              "11  2016               Obesity among adults aged >=18 Years   30.067875\n",
+              "12  2016  Older adult women aged >=65 Years who are up t...   29.894556\n",
+              "13  2016  No leisure-time physical activity among adults...   25.417875\n",
+              "14  2016             Arthritis among adults aged >=18 Years   22.348820\n",
+              "15  2016       Current smoking among adults aged >=18 Years   18.070966\n",
+              "16  2016        Binge drinking among adults aged >=18 Years   17.636353\n",
+              "17  2016  Current lack of health insurance among adults ...   16.204335\n",
+              "18  2016        All teeth lost among adults aged >=65 Years   16.052394\n",
+              "19  2016  Mental health not good for >=14 days among adu...   13.097115\n",
+              "20  2016  Physical health not good for >=14 days among a...   12.746976\n",
+              "21  2016    Diagnosed diabetes among adults aged >=18 Years   10.791957\n",
+              "22  2016        Current asthma among adults aged >=18 Years    9.684510\n",
+              "23  2016  Chronic obstructive pulmonary disease among ad...    6.314533\n",
+              "24  2016  Coronary heart disease among adults aged >=18 ...    5.810815\n",
+              "25  2016  Cancer (excluding skin cancer) among adults ag...    5.508897\n",
+              "26  2016                Stroke among adults aged >=18 Years    3.226765\n",
+              "27  2016  Chronic kidney disease among adults aged >=18 ...    2.969704"
+            ]
+          },
+          "execution_count": 122,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "DADOS2015 = pd.read_sql_query(\"\"\"\n",
+        "    SELECT\n",
+        "        Year AS Ano,\n",
+        "        Measure AS Medida,\n",
+        "        AVG(Data_Value) AS Data_Value\n",
+        "    FROM Material_de_Estudo\n",
+        "\n",
+        "    GROUP BY Year, Measure ORDER BY Data_Value DESC\n",
+        "\"\"\", conn)\n",
+        "DADOS2015"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1LNEqFthZxWU"
+      },
+      "source": [
+        "## Descobrindo a media de casos de colesterol e pressão alte entre o estados americanos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AIQm6NhuZxWU",
+        "outputId": "a41e0ef5-bd4d-4f62-f504-74e76190585c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>Datas_Value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>West Virginia</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>80.575000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>District of C</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>77.517778</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Virginia</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>76.357774</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>76.143308</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>United States</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>76.100000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>North Dakota</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>24.888889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100</th>\n",
+              "      <td>Minnesota</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>24.416358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>101</th>\n",
+              "      <td>Colorado</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>24.364185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>102</th>\n",
+              "      <td>Utah</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>22.767729</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>103</th>\n",
+              "      <td>Vermont</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>20.146154</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>104 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    DescricaoEstado                                             Medida  \\\n",
+              "0     West Virginia  Cholesterol screening among adults aged >=18 Y...   \n",
+              "1     District of C  Cholesterol screening among adults aged >=18 Y...   \n",
+              "2          Virginia  Cholesterol screening among adults aged >=18 Y...   \n",
+              "3     North Carolin  Cholesterol screening among adults aged >=18 Y...   \n",
+              "4     United States  Cholesterol screening among adults aged >=18 Y...   \n",
+              "..              ...                                                ...   \n",
+              "99     North Dakota   High blood pressure among adults aged >=18 Years   \n",
+              "100       Minnesota   High blood pressure among adults aged >=18 Years   \n",
+              "101        Colorado   High blood pressure among adults aged >=18 Years   \n",
+              "102            Utah   High blood pressure among adults aged >=18 Years   \n",
+              "103         Vermont   High blood pressure among adults aged >=18 Years   \n",
+              "\n",
+              "     Datas_Value  \n",
+              "0      80.575000  \n",
+              "1      77.517778  \n",
+              "2      76.357774  \n",
+              "3      76.143308  \n",
+              "4      76.100000  \n",
+              "..           ...  \n",
+              "99     24.888889  \n",
+              "100    24.416358  \n",
+              "101    24.364185  \n",
+              "102    22.767729  \n",
+              "103    20.146154  \n",
+              "\n",
+              "[104 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 123,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "estadosfrequentes=pd.read_sql_query(\"\"\"\n",
+        "        SELECT\n",
+        "            StateDesc AS DescricaoEstado,\n",
+        "            Measure AS Medida,\n",
+        "            AVG(Data_Value) AS Datas_Value\n",
+        "        FROM Material_de_estudo\n",
+        "        WHERE TRIM(Measure) IN ('High blood pressure among adults aged >=18 Years', 'Cholesterol screening among adults aged >=18 Years') AND Year=2015\n",
+        "        GROUP BY StateDesc, Measure\n",
+        "        ORDER BY Datas_Value DESC\"\"\", conn)\n",
+        "estadosfrequentes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_0iAHTZiZxWV"
+      },
+      "source": [
+        "## usando o metodo pivot table\n",
+        "### ele ciora uma tabelça dinamica, onde voce atribui as seguintes informações (index, columns  e values) a sua tabela criada"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hNz0jJYwZxWV",
+        "outputId": "b20bab93-d223-440b-ae14-932a9fe090a0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Medida</th>\n",
+              "      <th>Cholesterol screening among adults aged &gt;=18 Years</th>\n",
+              "      <th>High blood pressure among adults aged &gt;=18 Years</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Alabama</th>\n",
+              "      <td>75.808683</td>\n",
+              "      <td>40.003922</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Alaska</th>\n",
+              "      <td>68.815789</td>\n",
+              "      <td>27.101754</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>73.059961</td>\n",
+              "      <td>27.579199</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arkansas</th>\n",
+              "      <td>71.525000</td>\n",
+              "      <td>35.117969</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>73.074238</td>\n",
+              "      <td>26.870608</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Colorado</th>\n",
+              "      <td>72.616433</td>\n",
+              "      <td>24.364185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Connecticut</th>\n",
+              "      <td>73.125847</td>\n",
+              "      <td>30.847034</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Delaware</th>\n",
+              "      <td>72.238462</td>\n",
+              "      <td>37.488462</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>District of C</th>\n",
+              "      <td>77.517778</td>\n",
+              "      <td>30.148889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Florida</th>\n",
+              "      <td>75.967083</td>\n",
+              "      <td>32.176267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Georgia</th>\n",
+              "      <td>74.396137</td>\n",
+              "      <td>36.707296</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hawaii</th>\n",
+              "      <td>71.085169</td>\n",
+              "      <td>31.267797</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Idaho</th>\n",
+              "      <td>70.779452</td>\n",
+              "      <td>28.161644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Illinois</th>\n",
+              "      <td>72.875776</td>\n",
+              "      <td>31.612491</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Indiana</th>\n",
+              "      <td>70.032463</td>\n",
+              "      <td>32.998321</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Iowa</th>\n",
+              "      <td>69.960396</td>\n",
+              "      <td>29.044554</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kansas</th>\n",
+              "      <td>70.904839</td>\n",
+              "      <td>31.245484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kentucky</th>\n",
+              "      <td>73.641729</td>\n",
+              "      <td>37.934211</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Louisiana</th>\n",
+              "      <td>72.681627</td>\n",
+              "      <td>39.635696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Maine</th>\n",
+              "      <td>75.736364</td>\n",
+              "      <td>27.372727</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Maryland</th>\n",
+              "      <td>75.148259</td>\n",
+              "      <td>38.774129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Massachusetts</th>\n",
+              "      <td>73.170392</td>\n",
+              "      <td>28.443725</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Michigan</th>\n",
+              "      <td>73.591534</td>\n",
+              "      <td>37.941799</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Minnesota</th>\n",
+              "      <td>70.839815</td>\n",
+              "      <td>24.416358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Mississippi</th>\n",
+              "      <td>70.582278</td>\n",
+              "      <td>43.716456</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Missouri</th>\n",
+              "      <td>71.426411</td>\n",
+              "      <td>33.241986</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Montana</th>\n",
+              "      <td>70.014894</td>\n",
+              "      <td>26.459574</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nebraska</th>\n",
+              "      <td>69.578440</td>\n",
+              "      <td>27.745413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nevada</th>\n",
+              "      <td>71.756640</td>\n",
+              "      <td>26.907317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Hampshire</th>\n",
+              "      <td>75.780392</td>\n",
+              "      <td>28.768627</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Jersey</th>\n",
+              "      <td>70.618323</td>\n",
+              "      <td>33.687888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Mexico</th>\n",
+              "      <td>71.780841</td>\n",
+              "      <td>26.786449</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New York</th>\n",
+              "      <td>75.043266</td>\n",
+              "      <td>29.447621</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>North Carolin</th>\n",
+              "      <td>76.143308</td>\n",
+              "      <td>31.425253</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>North Dakota</th>\n",
+              "      <td>67.381481</td>\n",
+              "      <td>24.888889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ohio</th>\n",
+              "      <td>70.497020</td>\n",
+              "      <td>36.936234</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oklahoma</th>\n",
+              "      <td>70.859619</td>\n",
+              "      <td>33.824524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>70.809375</td>\n",
+              "      <td>25.358807</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pennsylvania</th>\n",
+              "      <td>71.062481</td>\n",
+              "      <td>33.353333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rhode Island</th>\n",
+              "      <td>75.939623</td>\n",
+              "      <td>31.631132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Carolin</th>\n",
+              "      <td>72.799435</td>\n",
+              "      <td>32.155932</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Dakota</th>\n",
+              "      <td>70.896667</td>\n",
+              "      <td>27.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tennessee</th>\n",
+              "      <td>74.411663</td>\n",
+              "      <td>37.579732</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Texas</th>\n",
+              "      <td>69.596661</td>\n",
+              "      <td>29.855810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>United States</th>\n",
+              "      <td>76.100000</td>\n",
+              "      <td>30.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Utah</th>\n",
+              "      <td>63.116335</td>\n",
+              "      <td>22.767729</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Vermont</th>\n",
+              "      <td>65.730769</td>\n",
+              "      <td>20.146154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Virginia</th>\n",
+              "      <td>76.357774</td>\n",
+              "      <td>33.164299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Washington</th>\n",
+              "      <td>71.336723</td>\n",
+              "      <td>27.596987</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>West Virginia</th>\n",
+              "      <td>80.575000</td>\n",
+              "      <td>40.033333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wisconsin</th>\n",
+              "      <td>71.048792</td>\n",
+              "      <td>31.075362</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wyoming</th>\n",
+              "      <td>72.844444</td>\n",
+              "      <td>29.344444</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Medida           Cholesterol screening among adults aged >=18 Years  \\\n",
+              "DescricaoEstado                                                       \n",
+              "Alabama                                                  75.808683    \n",
+              "Alaska                                                   68.815789    \n",
+              "Arizona                                                  73.059961    \n",
+              "Arkansas                                                 71.525000    \n",
+              "California                                               73.074238    \n",
+              "Colorado                                                 72.616433    \n",
+              "Connecticut                                              73.125847    \n",
+              "Delaware                                                 72.238462    \n",
+              "District of C                                            77.517778    \n",
+              "Florida                                                  75.967083    \n",
+              "Georgia                                                  74.396137    \n",
+              "Hawaii                                                   71.085169    \n",
+              "Idaho                                                    70.779452    \n",
+              "Illinois                                                 72.875776    \n",
+              "Indiana                                                  70.032463    \n",
+              "Iowa                                                     69.960396    \n",
+              "Kansas                                                   70.904839    \n",
+              "Kentucky                                                 73.641729    \n",
+              "Louisiana                                                72.681627    \n",
+              "Maine                                                    75.736364    \n",
+              "Maryland                                                 75.148259    \n",
+              "Massachusetts                                            73.170392    \n",
+              "Michigan                                                 73.591534    \n",
+              "Minnesota                                                70.839815    \n",
+              "Mississippi                                              70.582278    \n",
+              "Missouri                                                 71.426411    \n",
+              "Montana                                                  70.014894    \n",
+              "Nebraska                                                 69.578440    \n",
+              "Nevada                                                   71.756640    \n",
+              "New Hampshire                                            75.780392    \n",
+              "New Jersey                                               70.618323    \n",
+              "New Mexico                                               71.780841    \n",
+              "New York                                                 75.043266    \n",
+              "North Carolin                                            76.143308    \n",
+              "North Dakota                                             67.381481    \n",
+              "Ohio                                                     70.497020    \n",
+              "Oklahoma                                                 70.859619    \n",
+              "Oregon                                                   70.809375    \n",
+              "Pennsylvania                                             71.062481    \n",
+              "Rhode Island                                             75.939623    \n",
+              "South Carolin                                            72.799435    \n",
+              "South Dakota                                             70.896667    \n",
+              "Tennessee                                                74.411663    \n",
+              "Texas                                                    69.596661    \n",
+              "United States                                            76.100000    \n",
+              "Utah                                                     63.116335    \n",
+              "Vermont                                                  65.730769    \n",
+              "Virginia                                                 76.357774    \n",
+              "Washington                                               71.336723    \n",
+              "West Virginia                                            80.575000    \n",
+              "Wisconsin                                                71.048792    \n",
+              "Wyoming                                                  72.844444    \n",
+              "\n",
+              "Medida           High blood pressure among adults aged >=18 Years  \n",
+              "DescricaoEstado                                                    \n",
+              "Alabama                                                 40.003922  \n",
+              "Alaska                                                  27.101754  \n",
+              "Arizona                                                 27.579199  \n",
+              "Arkansas                                                35.117969  \n",
+              "California                                              26.870608  \n",
+              "Colorado                                                24.364185  \n",
+              "Connecticut                                             30.847034  \n",
+              "Delaware                                                37.488462  \n",
+              "District of C                                           30.148889  \n",
+              "Florida                                                 32.176267  \n",
+              "Georgia                                                 36.707296  \n",
+              "Hawaii                                                  31.267797  \n",
+              "Idaho                                                   28.161644  \n",
+              "Illinois                                                31.612491  \n",
+              "Indiana                                                 32.998321  \n",
+              "Iowa                                                    29.044554  \n",
+              "Kansas                                                  31.245484  \n",
+              "Kentucky                                                37.934211  \n",
+              "Louisiana                                               39.635696  \n",
+              "Maine                                                   27.372727  \n",
+              "Maryland                                                38.774129  \n",
+              "Massachusetts                                           28.443725  \n",
+              "Michigan                                                37.941799  \n",
+              "Minnesota                                               24.416358  \n",
+              "Mississippi                                             43.716456  \n",
+              "Missouri                                                33.241986  \n",
+              "Montana                                                 26.459574  \n",
+              "Nebraska                                                27.745413  \n",
+              "Nevada                                                  26.907317  \n",
+              "New Hampshire                                           28.768627  \n",
+              "New Jersey                                              33.687888  \n",
+              "New Mexico                                              26.786449  \n",
+              "New York                                                29.447621  \n",
+              "North Carolin                                           31.425253  \n",
+              "North Dakota                                            24.888889  \n",
+              "Ohio                                                    36.936234  \n",
+              "Oklahoma                                                33.824524  \n",
+              "Oregon                                                  25.358807  \n",
+              "Pennsylvania                                            33.353333  \n",
+              "Rhode Island                                            31.631132  \n",
+              "South Carolin                                           32.155932  \n",
+              "South Dakota                                            27.333333  \n",
+              "Tennessee                                               37.579732  \n",
+              "Texas                                                   29.855810  \n",
+              "United States                                           30.650000  \n",
+              "Utah                                                    22.767729  \n",
+              "Vermont                                                 20.146154  \n",
+              "Virginia                                                33.164299  \n",
+              "Washington                                              27.596987  \n",
+              "West Virginia                                           40.033333  \n",
+              "Wisconsin                                               31.075362  \n",
+              "Wyoming                                                 29.344444  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "tabela_pivot = estadosfrequentes.pivot_table(\n",
+        "    index='DescricaoEstado',\n",
+        "    columns='Medida',\n",
+        "    values='Datas_Value')\n",
+        "display(tabela_pivot)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e-x2rPXqZxWV",
+        "outputId": "2d699ac5-5750-4991-97fd-7e8d63741849"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Medida</th>\n",
+              "      <th>Cholesterol screening among adults aged &gt;=18 Years</th>\n",
+              "      <th>High blood pressure among adults aged &gt;=18 Years</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Alabama</th>\n",
+              "      <td>75.808683</td>\n",
+              "      <td>40.003922</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Alaska</th>\n",
+              "      <td>68.815789</td>\n",
+              "      <td>27.101754</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>73.059961</td>\n",
+              "      <td>27.579199</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arkansas</th>\n",
+              "      <td>71.525000</td>\n",
+              "      <td>35.117969</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>73.074238</td>\n",
+              "      <td>26.870608</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Colorado</th>\n",
+              "      <td>72.616433</td>\n",
+              "      <td>24.364185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Connecticut</th>\n",
+              "      <td>73.125847</td>\n",
+              "      <td>30.847034</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Delaware</th>\n",
+              "      <td>72.238462</td>\n",
+              "      <td>37.488462</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>District of C</th>\n",
+              "      <td>77.517778</td>\n",
+              "      <td>30.148889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Florida</th>\n",
+              "      <td>75.967083</td>\n",
+              "      <td>32.176267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Georgia</th>\n",
+              "      <td>74.396137</td>\n",
+              "      <td>36.707296</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hawaii</th>\n",
+              "      <td>71.085169</td>\n",
+              "      <td>31.267797</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Idaho</th>\n",
+              "      <td>70.779452</td>\n",
+              "      <td>28.161644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Illinois</th>\n",
+              "      <td>72.875776</td>\n",
+              "      <td>31.612491</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Indiana</th>\n",
+              "      <td>70.032463</td>\n",
+              "      <td>32.998321</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Iowa</th>\n",
+              "      <td>69.960396</td>\n",
+              "      <td>29.044554</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kansas</th>\n",
+              "      <td>70.904839</td>\n",
+              "      <td>31.245484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kentucky</th>\n",
+              "      <td>73.641729</td>\n",
+              "      <td>37.934211</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Louisiana</th>\n",
+              "      <td>72.681627</td>\n",
+              "      <td>39.635696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Maine</th>\n",
+              "      <td>75.736364</td>\n",
+              "      <td>27.372727</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Maryland</th>\n",
+              "      <td>75.148259</td>\n",
+              "      <td>38.774129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Massachusetts</th>\n",
+              "      <td>73.170392</td>\n",
+              "      <td>28.443725</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Michigan</th>\n",
+              "      <td>73.591534</td>\n",
+              "      <td>37.941799</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Minnesota</th>\n",
+              "      <td>70.839815</td>\n",
+              "      <td>24.416358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Mississippi</th>\n",
+              "      <td>70.582278</td>\n",
+              "      <td>43.716456</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Missouri</th>\n",
+              "      <td>71.426411</td>\n",
+              "      <td>33.241986</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Montana</th>\n",
+              "      <td>70.014894</td>\n",
+              "      <td>26.459574</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nebraska</th>\n",
+              "      <td>69.578440</td>\n",
+              "      <td>27.745413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nevada</th>\n",
+              "      <td>71.756640</td>\n",
+              "      <td>26.907317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Hampshire</th>\n",
+              "      <td>75.780392</td>\n",
+              "      <td>28.768627</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Jersey</th>\n",
+              "      <td>70.618323</td>\n",
+              "      <td>33.687888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Mexico</th>\n",
+              "      <td>71.780841</td>\n",
+              "      <td>26.786449</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New York</th>\n",
+              "      <td>75.043266</td>\n",
+              "      <td>29.447621</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>North Carolin</th>\n",
+              "      <td>76.143308</td>\n",
+              "      <td>31.425253</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>North Dakota</th>\n",
+              "      <td>67.381481</td>\n",
+              "      <td>24.888889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ohio</th>\n",
+              "      <td>70.497020</td>\n",
+              "      <td>36.936234</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oklahoma</th>\n",
+              "      <td>70.859619</td>\n",
+              "      <td>33.824524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>70.809375</td>\n",
+              "      <td>25.358807</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pennsylvania</th>\n",
+              "      <td>71.062481</td>\n",
+              "      <td>33.353333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rhode Island</th>\n",
+              "      <td>75.939623</td>\n",
+              "      <td>31.631132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Carolin</th>\n",
+              "      <td>72.799435</td>\n",
+              "      <td>32.155932</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Dakota</th>\n",
+              "      <td>70.896667</td>\n",
+              "      <td>27.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tennessee</th>\n",
+              "      <td>74.411663</td>\n",
+              "      <td>37.579732</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Texas</th>\n",
+              "      <td>69.596661</td>\n",
+              "      <td>29.855810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>United States</th>\n",
+              "      <td>76.100000</td>\n",
+              "      <td>30.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Utah</th>\n",
+              "      <td>63.116335</td>\n",
+              "      <td>22.767729</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Vermont</th>\n",
+              "      <td>65.730769</td>\n",
+              "      <td>20.146154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Virginia</th>\n",
+              "      <td>76.357774</td>\n",
+              "      <td>33.164299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Washington</th>\n",
+              "      <td>71.336723</td>\n",
+              "      <td>27.596987</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>West Virginia</th>\n",
+              "      <td>80.575000</td>\n",
+              "      <td>40.033333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wisconsin</th>\n",
+              "      <td>71.048792</td>\n",
+              "      <td>31.075362</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wyoming</th>\n",
+              "      <td>72.844444</td>\n",
+              "      <td>29.344444</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Medida           Cholesterol screening among adults aged >=18 Years  \\\n",
+              "DescricaoEstado                                                       \n",
+              "Alabama                                                  75.808683    \n",
+              "Alaska                                                   68.815789    \n",
+              "Arizona                                                  73.059961    \n",
+              "Arkansas                                                 71.525000    \n",
+              "California                                               73.074238    \n",
+              "Colorado                                                 72.616433    \n",
+              "Connecticut                                              73.125847    \n",
+              "Delaware                                                 72.238462    \n",
+              "District of C                                            77.517778    \n",
+              "Florida                                                  75.967083    \n",
+              "Georgia                                                  74.396137    \n",
+              "Hawaii                                                   71.085169    \n",
+              "Idaho                                                    70.779452    \n",
+              "Illinois                                                 72.875776    \n",
+              "Indiana                                                  70.032463    \n",
+              "Iowa                                                     69.960396    \n",
+              "Kansas                                                   70.904839    \n",
+              "Kentucky                                                 73.641729    \n",
+              "Louisiana                                                72.681627    \n",
+              "Maine                                                    75.736364    \n",
+              "Maryland                                                 75.148259    \n",
+              "Massachusetts                                            73.170392    \n",
+              "Michigan                                                 73.591534    \n",
+              "Minnesota                                                70.839815    \n",
+              "Mississippi                                              70.582278    \n",
+              "Missouri                                                 71.426411    \n",
+              "Montana                                                  70.014894    \n",
+              "Nebraska                                                 69.578440    \n",
+              "Nevada                                                   71.756640    \n",
+              "New Hampshire                                            75.780392    \n",
+              "New Jersey                                               70.618323    \n",
+              "New Mexico                                               71.780841    \n",
+              "New York                                                 75.043266    \n",
+              "North Carolin                                            76.143308    \n",
+              "North Dakota                                             67.381481    \n",
+              "Ohio                                                     70.497020    \n",
+              "Oklahoma                                                 70.859619    \n",
+              "Oregon                                                   70.809375    \n",
+              "Pennsylvania                                             71.062481    \n",
+              "Rhode Island                                             75.939623    \n",
+              "South Carolin                                            72.799435    \n",
+              "South Dakota                                             70.896667    \n",
+              "Tennessee                                                74.411663    \n",
+              "Texas                                                    69.596661    \n",
+              "United States                                            76.100000    \n",
+              "Utah                                                     63.116335    \n",
+              "Vermont                                                  65.730769    \n",
+              "Virginia                                                 76.357774    \n",
+              "Washington                                               71.336723    \n",
+              "West Virginia                                            80.575000    \n",
+              "Wisconsin                                                71.048792    \n",
+              "Wyoming                                                  72.844444    \n",
+              "\n",
+              "Medida           High blood pressure among adults aged >=18 Years  \n",
+              "DescricaoEstado                                                    \n",
+              "Alabama                                                 40.003922  \n",
+              "Alaska                                                  27.101754  \n",
+              "Arizona                                                 27.579199  \n",
+              "Arkansas                                                35.117969  \n",
+              "California                                              26.870608  \n",
+              "Colorado                                                24.364185  \n",
+              "Connecticut                                             30.847034  \n",
+              "Delaware                                                37.488462  \n",
+              "District of C                                           30.148889  \n",
+              "Florida                                                 32.176267  \n",
+              "Georgia                                                 36.707296  \n",
+              "Hawaii                                                  31.267797  \n",
+              "Idaho                                                   28.161644  \n",
+              "Illinois                                                31.612491  \n",
+              "Indiana                                                 32.998321  \n",
+              "Iowa                                                    29.044554  \n",
+              "Kansas                                                  31.245484  \n",
+              "Kentucky                                                37.934211  \n",
+              "Louisiana                                               39.635696  \n",
+              "Maine                                                   27.372727  \n",
+              "Maryland                                                38.774129  \n",
+              "Massachusetts                                           28.443725  \n",
+              "Michigan                                                37.941799  \n",
+              "Minnesota                                               24.416358  \n",
+              "Mississippi                                             43.716456  \n",
+              "Missouri                                                33.241986  \n",
+              "Montana                                                 26.459574  \n",
+              "Nebraska                                                27.745413  \n",
+              "Nevada                                                  26.907317  \n",
+              "New Hampshire                                           28.768627  \n",
+              "New Jersey                                              33.687888  \n",
+              "New Mexico                                              26.786449  \n",
+              "New York                                                29.447621  \n",
+              "North Carolin                                           31.425253  \n",
+              "North Dakota                                            24.888889  \n",
+              "Ohio                                                    36.936234  \n",
+              "Oklahoma                                                33.824524  \n",
+              "Oregon                                                  25.358807  \n",
+              "Pennsylvania                                            33.353333  \n",
+              "Rhode Island                                            31.631132  \n",
+              "South Carolin                                           32.155932  \n",
+              "South Dakota                                            27.333333  \n",
+              "Tennessee                                               37.579732  \n",
+              "Texas                                                   29.855810  \n",
+              "United States                                           30.650000  \n",
+              "Utah                                                    22.767729  \n",
+              "Vermont                                                 20.146154  \n",
+              "Virginia                                                33.164299  \n",
+              "Washington                                              27.596987  \n",
+              "West Virginia                                           40.033333  \n",
+              "Wisconsin                                               31.075362  \n",
+              "Wyoming                                                 29.344444  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "tabela_pivot = estadosfrequentes.pivot_table(\n",
+        "    index='DescricaoEstado',\n",
+        "    columns='Medida',\n",
+        "    values='Datas_Value')\n",
+        "display(tabela_pivot)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-vWPOmtOZxWV"
+      },
+      "source": [
+        "## Grafico para representar o estados com a maior taxa de pressão alta e triagem de colesterol"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SVfdeLmpZxWV",
+        "outputId": "889a61ea-42e5-4d7c-b198-d2e3251f3d83"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 800x1000 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(8,10))\n",
+        "GraficoPorEstado=tabela_pivot.plot(kind='bar', color=[\"Purple\", \"Black\"],figsize=(12,8))\n",
+        "plt.title(\"Taxa de pressão alta e triagem de colesterol por estados \")\n",
+        "plt.xlabel(\"Estados\")\n",
+        "plt.ylabel(\"Medias de taxas %\")\n",
+        "plt.xticks(rotation=450)\n",
+        "plt.legend(title=\"Medida\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uWv_Y8vmZxWV"
+      },
+      "source": [
+        "## Gráfico de mapa de calor\n",
+        " annot=True' exibe os valores dentro das células.\n",
+        " 'cmap=\"coolwarm\"' usa uma paleta de cores que vai do azul ao vermelho.\n",
+        " 'linewidths=.5' define a espessura das linhas de separação entre as células."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bg6OwRbWZxWV",
+        "outputId": "458233a1-4d02-4c22-ebaf-c993252cbb7d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1800x1000 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(18,10))\n",
+        "sns.heatmap(tabela_pivot, annot=True, cmap=\"coolwarm\", linewidths=.5, cbar_kws={'label':'Valores médios'})\n",
+        "plt.title(\"Gráfico mapa de calor: Taxa de pressão alta e triagem de colesterol por estados\")\n",
+        "plt.xticks(rotation=360)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qqRwZriMZxWV"
+      },
+      "source": [
+        "# Correlação entre pressão alta e triagem de colesterol\n",
+        "## O gráfico de correlação, ou a matriz de correlação, é uma ferramenta que mede o grau de relação linear entre duas variáveis. Os valores dessa correlação variam entre -1 e 1:\n",
+        "### O comando .corr() é uma função do pandas usada para calcular a correlação entre as colunas de um DataFrame.\n",
+        "Quando usamos esse comando, ele nos dá uma matriz de correlação, que mostra o quão relacionadas estão as variáveis numéricas entre si."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "t7Vpi_7LZxWW",
+        "outputId": "d10e5081-7a0c-4c5b-ae6b-4dca28748ade"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Correlação entre os casos de pressão alta e colesterol alto\n",
+            "Medida                                              Cholesterol screening among adults aged >=18 Years  \\\n",
+            "Medida                                                                                                   \n",
+            "Cholesterol screening among adults aged >=18 Years                                           1.000000    \n",
+            "High blood pressure among adults aged >=18 Years                                             0.455418    \n",
+            "\n",
+            "Medida                                              High blood pressure among adults aged >=18 Years  \n",
+            "Medida                                                                                                \n",
+            "Cholesterol screening among adults aged >=18 Years                                          0.455418  \n",
+            "High blood pressure among adults aged >=18 Years                                            1.000000  \n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1300x1000 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Calcula a correlação entre os casos de pressão alta e colesterol alto\n",
+        "correlacao = tabela_pivot.corr()\n",
+        "\n",
+        "# Exibe a correlação\n",
+        "print(\"Correlação entre os casos de pressão alta e colesterol alto\")\n",
+        "print(correlacao)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NAhKazanZxWW"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Cria o heatmap da correlação\n",
+        "plt.figure(figsize=(13, 10))\n",
+        "sns.heatmap(correlacao, annot=True, cmap=\"Blues\", linewidths=0.5, fmt=\".2f\", vmin=-1, vmax=1, cbar_kws={'shrink': 0.8})\n",
+        "plt.title(\"Correlação entre Pressão Alta e Triagem de Colesterol\", fontsize=16)\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.yticks(rotation=0)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6BBNXcanZxWW"
+      },
+      "source": [
+        "## como podemos ver, existe uma correlação entre os dados, mas é uma correlação fraca"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FkZR2-SmZxWW"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.6"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index d1a4707..a850d79 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,3 @@
-# Project Pokémon In-Game Series
 
-### What is this?
-The collective effort of Project Pokémon to preserve and provide in-game Pokémon data.
+MATERIAIS DE ESTUDO
 
-### How do I use this?
-Drag the .PKX/.GP1/.CK3/.XK3/.PB7 file into the appropriate save that is opened by PKHeX.
-
-### Help, I need description to the files. I don't understand what I'm looking at.
-In that case, please browse our gallery here: https://projectpokemon.org/home/files/category/85-in-game-series/<br>
-It is pretty much filled with relevant information regarding the respective files.
-
-### Disclaimer:
-This is still a work-in-progress, and likely not as complete as our on-site gallery.<br>
-While the files ARE legal, as usual, use at your own risk!
diff --git a/projeot1.ipynb b/projeot1.ipynb
new file mode 100644
index 0000000..ee75874
--- /dev/null
+++ b/projeot1.ipynb
@@ -0,0 +1,3398 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Lyanan85/In-GameSeries/blob/venv/projeot1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XzEYOhU7uN0Z"
+      },
+      "source": [
+        "# Analise de casos de saúde em estados americanos entre 2015 e 2016\n",
+        "## analise com insights\n",
+        " material de estudo"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EHZ_RzWZuN0b"
+      },
+      "source": [
+        "## Importando bibliotecas importantes e a tabela original"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jUnc-HD7uN0b",
+        "outputId": "84e0d9d2-b05d-4530-eedc-b6fcb6a9d27f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Year</th>\n",
+              "      <th>StateAbbr</th>\n",
+              "      <th>StateDesc</th>\n",
+              "      <th>CityName</th>\n",
+              "      <th>GeographicLevel</th>\n",
+              "      <th>DataSource</th>\n",
+              "      <th>Category</th>\n",
+              "      <th>UniqueID</th>\n",
+              "      <th>Measure</th>\n",
+              "      <th>Data_Value_Unit</th>\n",
+              "      <th>...</th>\n",
+              "      <th>High_Confidence_Limit</th>\n",
+              "      <th>Data_Value_Footnote_Symbol</th>\n",
+              "      <th>Data_Value_Footnote</th>\n",
+              "      <th>PopulationCount</th>\n",
+              "      <th>Geolocation</th>\n",
+              "      <th>CategoryID</th>\n",
+              "      <th>MeasureId</th>\n",
+              "      <th>CityFIPS</th>\n",
+              "      <th>TractFIPS</th>\n",
+              "      <th>Short_Question_Text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>IN</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>Fishers</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>1823278</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>24.7</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>76794</td>\n",
+              "      <td>POINT (-85.969310702 39.9587635263)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>1823278.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Milpitas</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0647766</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>66790</td>\n",
+              "      <td>POINT (-121.892083025 37.433869763)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>647766.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CT</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>Stamford</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0973000</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.2</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>122643</td>\n",
+              "      <td>POINT (-73.5589363142 41.1049145013)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>973000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Elk Grove</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0622020</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>153015</td>\n",
+              "      <td>POINT (-121.384137762 38.4159992006)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>622020.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>FL</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Palm Bay</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>1254000</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>78.4</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>103190</td>\n",
+              "      <td>POINT (-80.6666722846 27.9838255249)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>1254000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Year StateAbbr    StateDesc   CityName GeographicLevel DataSource  \\\n",
+              "0  2015        IN      Indiana    Fishers            City      BRFSS   \n",
+              "1  2015        CA   California   Milpitas            City      BRFSS   \n",
+              "2  2015        CT  Connecticut   Stamford            City      BRFSS   \n",
+              "3  2015        CA   California  Elk Grove            City      BRFSS   \n",
+              "4  2015        FL      Florida   Palm Bay            City      BRFSS   \n",
+              "\n",
+              "          Category UniqueID  \\\n",
+              "0  Health Outcomes  1823278   \n",
+              "1  Health Outcomes  0647766   \n",
+              "2  Health Outcomes  0973000   \n",
+              "3  Health Outcomes  0622020   \n",
+              "4       Prevention  1254000   \n",
+              "\n",
+              "                                             Measure Data_Value_Unit  ...  \\\n",
+              "0   High blood pressure among adults aged >=18 Years               %  ...   \n",
+              "1   High blood pressure among adults aged >=18 Years               %  ...   \n",
+              "2   High blood pressure among adults aged >=18 Years               %  ...   \n",
+              "3   High blood pressure among adults aged >=18 Years               %  ...   \n",
+              "4  Cholesterol screening among adults aged >=18 Y...               %  ...   \n",
+              "\n",
+              "  High_Confidence_Limit Data_Value_Footnote_Symbol  Data_Value_Footnote  \\\n",
+              "0                  24.7                        NaN                  NaN   \n",
+              "1                  22.5                        NaN                  NaN   \n",
+              "2                  26.2                        NaN                  NaN   \n",
+              "3                  27.8                        NaN                  NaN   \n",
+              "4                  78.4                        NaN                  NaN   \n",
+              "\n",
+              "   PopulationCount                           Geolocation CategoryID  \\\n",
+              "0            76794   POINT (-85.969310702 39.9587635263)    HLTHOUT   \n",
+              "1            66790   POINT (-121.892083025 37.433869763)    HLTHOUT   \n",
+              "2           122643  POINT (-73.5589363142 41.1049145013)    HLTHOUT   \n",
+              "3           153015  POINT (-121.384137762 38.4159992006)    HLTHOUT   \n",
+              "4           103190  POINT (-80.6666722846 27.9838255249)    PREVENT   \n",
+              "\n",
+              "    MeasureId   CityFIPS TractFIPS    Short_Question_Text  \n",
+              "0      BPHIGH  1823278.0       NaN    High Blood Pressure  \n",
+              "1      BPHIGH   647766.0       NaN    High Blood Pressure  \n",
+              "2      BPHIGH   973000.0       NaN    High Blood Pressure  \n",
+              "3      BPHIGH   622020.0       NaN    High Blood Pressure  \n",
+              "4  CHOLSCREEN  1254000.0       NaN  Cholesterol Screening  \n",
+              "\n",
+              "[5 rows x 24 columns]"
+            ]
+          },
+          "execution_count": 232,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "df1=pd.read_csv(\"500_Cities__Local_Data_for_Better_Health__2018_release.csv\")\n",
+        "df1.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tkiuuyJAuN0c"
+      },
+      "source": [
+        "## Renomeando tabela\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HDGNDU2euN0c",
+        "outputId": "d8a597a7-27d1-40ad-c850-3c7f54ca1652"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>SiglaEstado</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>NomeCidade</th>\n",
+              "      <th>NivelGeografico</th>\n",
+              "      <th>FonteDados</th>\n",
+              "      <th>Categoria</th>\n",
+              "      <th>IDUnico</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>UnidadeValorDados</th>\n",
+              "      <th>...</th>\n",
+              "      <th>LimiteSuperiorConfianca</th>\n",
+              "      <th>SimboloNotaRodapeDados</th>\n",
+              "      <th>NotaRodapeDados</th>\n",
+              "      <th>ContagemPopulacional</th>\n",
+              "      <th>Geolocalizacao</th>\n",
+              "      <th>IDCategoria</th>\n",
+              "      <th>IDMedida</th>\n",
+              "      <th>FIPSCidade</th>\n",
+              "      <th>FIPSTrato</th>\n",
+              "      <th>TextoCurtoPergunta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>IN</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>Fishers</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>1823278</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>24.7</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>76794</td>\n",
+              "      <td>POINT (-85.969310702 39.9587635263)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>1823278.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Milpitas</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0647766</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>66790</td>\n",
+              "      <td>POINT (-121.892083025 37.433869763)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>647766.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CT</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>Stamford</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0973000</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.2</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>122643</td>\n",
+              "      <td>POINT (-73.5589363142 41.1049145013)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>973000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Elk Grove</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0622020</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>153015</td>\n",
+              "      <td>POINT (-121.384137762 38.4159992006)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>622020.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>FL</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Palm Bay</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>1254000</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>78.4</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>103190</td>\n",
+              "      <td>POINT (-80.6666722846 27.9838255249)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>1254000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Ano SiglaEstado DescricaoEstado NomeCidade NivelGeografico FonteDados  \\\n",
+              "0  2015          IN         Indiana    Fishers            City      BRFSS   \n",
+              "1  2015          CA      California   Milpitas            City      BRFSS   \n",
+              "2  2015          CT     Connecticut   Stamford            City      BRFSS   \n",
+              "3  2015          CA      California  Elk Grove            City      BRFSS   \n",
+              "4  2015          FL         Florida   Palm Bay            City      BRFSS   \n",
+              "\n",
+              "         Categoria  IDUnico  \\\n",
+              "0  Health Outcomes  1823278   \n",
+              "1  Health Outcomes  0647766   \n",
+              "2  Health Outcomes  0973000   \n",
+              "3  Health Outcomes  0622020   \n",
+              "4       Prevention  1254000   \n",
+              "\n",
+              "                                              Medida UnidadeValorDados  ...  \\\n",
+              "0   High blood pressure among adults aged >=18 Years                 %  ...   \n",
+              "1   High blood pressure among adults aged >=18 Years                 %  ...   \n",
+              "2   High blood pressure among adults aged >=18 Years                 %  ...   \n",
+              "3   High blood pressure among adults aged >=18 Years                 %  ...   \n",
+              "4  Cholesterol screening among adults aged >=18 Y...                 %  ...   \n",
+              "\n",
+              "  LimiteSuperiorConfianca SimboloNotaRodapeDados  NotaRodapeDados  \\\n",
+              "0                    24.7                    NaN              NaN   \n",
+              "1                    22.5                    NaN              NaN   \n",
+              "2                    26.2                    NaN              NaN   \n",
+              "3                    27.8                    NaN              NaN   \n",
+              "4                    78.4                    NaN              NaN   \n",
+              "\n",
+              "   ContagemPopulacional                        Geolocalizacao IDCategoria  \\\n",
+              "0                 76794   POINT (-85.969310702 39.9587635263)     HLTHOUT   \n",
+              "1                 66790   POINT (-121.892083025 37.433869763)     HLTHOUT   \n",
+              "2                122643  POINT (-73.5589363142 41.1049145013)     HLTHOUT   \n",
+              "3                153015  POINT (-121.384137762 38.4159992006)     HLTHOUT   \n",
+              "4                103190  POINT (-80.6666722846 27.9838255249)     PREVENT   \n",
+              "\n",
+              "     IDMedida  FIPSCidade FIPSTrato     TextoCurtoPergunta  \n",
+              "0      BPHIGH   1823278.0       NaN    High Blood Pressure  \n",
+              "1      BPHIGH    647766.0       NaN    High Blood Pressure  \n",
+              "2      BPHIGH    973000.0       NaN    High Blood Pressure  \n",
+              "3      BPHIGH    622020.0       NaN    High Blood Pressure  \n",
+              "4  CHOLSCREEN   1254000.0       NaN  Cholesterol Screening  \n",
+              "\n",
+              "[5 rows x 24 columns]"
+            ]
+          },
+          "execution_count": 233,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Dicionário de tradução de colunas\n",
+        "colunas_traduzidas = {\n",
+        "    \"Year\": \"Ano\",\n",
+        "    \"StateAbbr\": \"SiglaEstado\",\n",
+        "    \"StateDesc\": \"DescricaoEstado\",\n",
+        "    \"CityName\": \"NomeCidade\",\n",
+        "    \"GeographicLevel\": \"NivelGeografico\",\n",
+        "    \"DataSource\": \"FonteDados\",\n",
+        "    \"Category\": \"Categoria\",\n",
+        "    \"UniqueID\": \"IDUnico\",\n",
+        "    \"Measure\": \"Medida\",\n",
+        "    \"Data_Value_Unit\": \"UnidadeValorDados\",\n",
+        "    \"High_Confidence_Limit\": \"LimiteSuperiorConfianca\",\n",
+        "    \"Data_Value_Footnote_Symbol\": \"SimboloNotaRodapeDados\",\n",
+        "    \"Data_Value_Footnote\": \"NotaRodapeDados\",\n",
+        "    \"PopulationCount\": \"ContagemPopulacional\",\n",
+        "    \"Geolocation\": \"Geolocalizacao\",\n",
+        "    \"CategoryID\": \"IDCategoria\",\n",
+        "    \"MeasureId\": \"IDMedida\",\n",
+        "    \"CityFIPS\": \"FIPSCidade\",\n",
+        "    \"TractFIPS\": \"FIPSTrato\",\n",
+        "    \"Short_Question_Text\": \"TextoCurtoPergunta\"\n",
+        "}\n",
+        "\n",
+        "# Aplicar a tradução das colunas\n",
+        "df = df1.rename(columns=colunas_traduzidas)\n",
+        "\n",
+        "# Exibir o DataFrame com os nomes das colunas traduzidos\n",
+        "df.head()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BECIWf_nuN0c"
+      },
+      "source": [
+        "## Verificando quais anos a pesquisa se refere:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q2cMIW1DuN0d",
+        "outputId": "970d2fde-fc85-4f89-94f5-25ccebb39bb7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Ano\n",
+              "2016    694079\n",
+              "2015    116024\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 234,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#valores da coluna ano\n",
+        "\n",
+        "df[\"Ano\"].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "baPqdPeDuN0d"
+      },
+      "source": [
+        "## Conferindo de forma decrescente os valores unicos referente a esta pesquisa\n",
+        "coluna DescricaoEstado"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Qg6Sw-ikuN0d",
+        "outputId": "5e310854-c41e-4743-9bbf-81119f31bf4b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "DescricaoEstado\n",
+              "California       156778\n",
+              "Texas             89422\n",
+              "New York          70133\n",
+              "Florida           39025\n",
+              "Illinois          37458\n",
+              "Arizona           29678\n",
+              "Ohio              24111\n",
+              "North Carolin     23305\n",
+              "Michigan          21672\n",
+              "Colorado          20549\n",
+              "Pennsylvania      18392\n",
+              "Washington        15448\n",
+              "Tennessee         15299\n",
+              "Indiana           15281\n",
+              "Virginia          14630\n",
+              "Massachusetts     14346\n",
+              "Oklahoma          13532\n",
+              "Georgia           13251\n",
+              "Missouri          13028\n",
+              "Wisconsin         11849\n",
+              "Louisiana         10875\n",
+              "Nevada            10639\n",
+              "Alabama           10593\n",
+              "Oregon            10402\n",
+              "Minnesota          9123\n",
+              "New Jersey         9013\n",
+              "Kansas             8913\n",
+              "Kentucky           7527\n",
+              "Utah               7431\n",
+              "Hawaii             6721\n",
+              "Connecticut        6603\n",
+              "New Mexico         6273\n",
+              "Nebraska           6216\n",
+              "Iowa               5889\n",
+              "Maryland           5655\n",
+              "South Carolin      5131\n",
+              "District of C      5065\n",
+              "Arkansas           3679\n",
+              "Rhode Island       2993\n",
+              "Mississippi        2229\n",
+              "Idaho              2070\n",
+              "South Dakota       1760\n",
+              "Alaska             1596\n",
+              "New Hampshire      1428\n",
+              "Montana            1340\n",
+              "North Dakota        756\n",
+              "Delaware            728\n",
+              "West Virginia       728\n",
+              "Maine               616\n",
+              "Wyoming             504\n",
+              "Vermont             364\n",
+              "United States        56\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 235,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#contagem de estados\n",
+        "dfestados=df[\"DescricaoEstado\"].value_counts()\n",
+        "dfestados"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Io7NUbKnuN0d"
+      },
+      "source": [
+        "## Agrupando os 20 estados mais relevantes nesta pesquisa"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "36T2p-DtuN0d",
+        "outputId": "0f78b561-dd2e-43a9-be90-cc4228dc10a1"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "DescricaoEstado\n",
+              "California       156778\n",
+              "Texas             89422\n",
+              "New York          70133\n",
+              "Florida           39025\n",
+              "Illinois          37458\n",
+              "Arizona           29678\n",
+              "Ohio              24111\n",
+              "North Carolin     23305\n",
+              "Michigan          21672\n",
+              "Colorado          20549\n",
+              "Pennsylvania      18392\n",
+              "Washington        15448\n",
+              "Tennessee         15299\n",
+              "Indiana           15281\n",
+              "Virginia          14630\n",
+              "Massachusetts     14346\n",
+              "Oklahoma          13532\n",
+              "Georgia           13251\n",
+              "Missouri          13028\n",
+              "Wisconsin         11849\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 236,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "df_estados=dfestados.head(20)\n",
+        "df_estados\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PoxNoHlDuN0d"
+      },
+      "source": [
+        "## Gráfico referente aos paises com mais destaque nesta pesquisa\n",
+        "#### usando a variavel agrupada anteriormente juntamente a biblioteca matplotlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gHSJKAjJuN0e",
+        "outputId": "b33bd74e-ac49-49d0-ac77-90c9dc219ad4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 900x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "df_estados.plot(kind=\"barh\", color=\"Skyblue\", figsize=(9,4))\n",
+        "plt.title(\"Estados com mais individuos nesta pesquisa\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ZGRo4WHuN0e"
+      },
+      "source": [
+        "# Conceito da criação de duas tabelas em uma célula de codigo\n",
+        "grafico incompleto, essa parte do codigo é apenas para o conceito"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wTIvCMLNuN0e"
+      },
+      "source": [
+        "### Criando mais duas tabelas, casos gerais de 2015 e 2016\n",
+        "#### Filtrando os casos gerais de 2015 e 2016 e mantendo as colunas \"Ano\", \"Medida\" e \"DescricaoEstado\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qwu0RlrDuN0e"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "casosgerais = df.loc[df[\"Ano\"].isin([2015, 2016]), [\"Ano\", \"DescricaoEstado\", \"Medida\"]]\n",
+        "casosgerais2015=casosgerais.loc[casosgerais[\"Ano\"]==2015]\n",
+        "casosgerais2016=casosgerais.loc[casosgerais[\"Ano\"]==2016]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mqWLVwQxuN0e"
+      },
+      "source": [
+        "### Verificando as tabelas filtradas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ba-sCM1yuN0e",
+        "outputId": "b5d5a724-c7b4-4ff2-cde0-0b786823bebd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>Medida</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>California</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>California</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809684</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>District of C</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809712</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Taking medicine for high blood pressure contro...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809895</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810021</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810033</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>High cholesterol among adults aged &gt;=18 Years ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>116024 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Ano DescricaoEstado  \\\n",
+              "0       2015         Indiana   \n",
+              "1       2015      California   \n",
+              "2       2015     Connecticut   \n",
+              "3       2015      California   \n",
+              "4       2015         Florida   \n",
+              "...      ...             ...   \n",
+              "809684  2015   District of C   \n",
+              "809712  2015   North Carolin   \n",
+              "809895  2015   North Carolin   \n",
+              "810021  2015   North Carolin   \n",
+              "810033  2015   North Carolin   \n",
+              "\n",
+              "                                                   Medida  \n",
+              "0        High blood pressure among adults aged >=18 Years  \n",
+              "1        High blood pressure among adults aged >=18 Years  \n",
+              "2        High blood pressure among adults aged >=18 Years  \n",
+              "3        High blood pressure among adults aged >=18 Years  \n",
+              "4       Cholesterol screening among adults aged >=18 Y...  \n",
+              "...                                                   ...  \n",
+              "809684   High blood pressure among adults aged >=18 Years  \n",
+              "809712  Taking medicine for high blood pressure contro...  \n",
+              "809895  Cholesterol screening among adults aged >=18 Y...  \n",
+              "810021   High blood pressure among adults aged >=18 Years  \n",
+              "810033  High cholesterol among adults aged >=18 Years ...  \n",
+              "\n",
+              "[116024 rows x 3 columns]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(casosgerais2015)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nyHnI1XYuN0f",
+        "outputId": "e2a2e64a-7a28-4aaf-b574-085526dea0df"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>Medida</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Illinois</td>\n",
+              "      <td>Arthritis among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>Arthritis among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Stroke among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>Obesity among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>Mammography use among women aged 50–74 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810098</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Binge drinking among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810099</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Current smoking among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810100</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Hawaii</td>\n",
+              "      <td>Coronary heart disease among adults aged &gt;=18 ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810101</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Mental health not good for &gt;=14 days among adu...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810102</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>All teeth lost among adults aged &gt;=65 Years</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>694079 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Ano DescricaoEstado  \\\n",
+              "6       2016        Illinois   \n",
+              "8       2016         Alabama   \n",
+              "9       2016         Florida   \n",
+              "10      2016         Indiana   \n",
+              "13      2016           Texas   \n",
+              "...      ...             ...   \n",
+              "810098  2016      California   \n",
+              "810099  2016      California   \n",
+              "810100  2016          Hawaii   \n",
+              "810101  2016      California   \n",
+              "810102  2016           Texas   \n",
+              "\n",
+              "                                                   Medida  \n",
+              "6                  Arthritis among adults aged >=18 Years  \n",
+              "8                  Arthritis among adults aged >=18 Years  \n",
+              "9                     Stroke among adults aged >=18 Years  \n",
+              "10                   Obesity among adults aged >=18 Years  \n",
+              "13           Mammography use among women aged 50–74 Years  \n",
+              "...                                                   ...  \n",
+              "810098        Binge drinking among adults aged >=18 Years  \n",
+              "810099       Current smoking among adults aged >=18 Years  \n",
+              "810100  Coronary heart disease among adults aged >=18 ...  \n",
+              "810101  Mental health not good for >=14 days among adu...  \n",
+              "810102        All teeth lost among adults aged >=65 Years  \n",
+              "\n",
+              "[694079 rows x 3 columns]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(casosgerais2016)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kY7YcEqKuN0f",
+        "outputId": "c71aab9f-bfad-4734-e9a6-70110a9cb7dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>Medida</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>California</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>California</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810098</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Binge drinking among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810099</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Current smoking among adults aged &gt;=18 Years</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810100</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Hawaii</td>\n",
+              "      <td>Coronary heart disease among adults aged &gt;=18 ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810101</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Mental health not good for &gt;=14 days among adu...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810102</th>\n",
+              "      <td>2016</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>All teeth lost among adults aged &gt;=65 Years</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>810103 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Ano DescricaoEstado  \\\n",
+              "0       2015         Indiana   \n",
+              "1       2015      California   \n",
+              "2       2015     Connecticut   \n",
+              "3       2015      California   \n",
+              "4       2015         Florida   \n",
+              "...      ...             ...   \n",
+              "810098  2016      California   \n",
+              "810099  2016      California   \n",
+              "810100  2016          Hawaii   \n",
+              "810101  2016      California   \n",
+              "810102  2016           Texas   \n",
+              "\n",
+              "                                                   Medida  \n",
+              "0        High blood pressure among adults aged >=18 Years  \n",
+              "1        High blood pressure among adults aged >=18 Years  \n",
+              "2        High blood pressure among adults aged >=18 Years  \n",
+              "3        High blood pressure among adults aged >=18 Years  \n",
+              "4       Cholesterol screening among adults aged >=18 Y...  \n",
+              "...                                                   ...  \n",
+              "810098        Binge drinking among adults aged >=18 Years  \n",
+              "810099       Current smoking among adults aged >=18 Years  \n",
+              "810100  Coronary heart disease among adults aged >=18 ...  \n",
+              "810101  Mental health not good for >=14 days among adu...  \n",
+              "810102        All teeth lost among adults aged >=65 Years  \n",
+              "\n",
+              "[810103 rows x 3 columns]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "display(casosgerais)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hNb1cD9fuN0g"
+      },
+      "source": [
+        "## Criando novas variáveis\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dVYQ0h3ruN0g"
+      },
+      "outputs": [],
+      "source": [
+        "CASOSGERAIS2015=casosgerais2015\n",
+        "CASOSGERAIS2016=casosgerais2016\n",
+        "CASOSGERAIS=casosgerais\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EYGJ39i4uN0g"
+      },
+      "source": [
+        "## CONCEITO de gráfico lado a lado\n",
+        "Alternando para 2,1 deixa o grafico em cima e embaixo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NUfTFM_5uN0g",
+        "outputId": "25a0610d-2210-4878-8b99-e0520396b7a5"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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T//znP+e8zK8iq1atUmlpqZKTkx2OmySHS9XOzuv06dM6ceKEbrrpJv3666/av3+/pMod27JjkJSU5DD++OOPS5L556Ts+6xevVpFRUWV/j4ZGRkqLi7Wo48+6jA+cuTIcrErV67UTTfdpIYNG+rEiRPmKyoqSiUlJdq4caNDfGxsrJo0aeIw5ubmZl5iWlpaqpMnT6q4uFhdunS54CVhfn5+2rx5s44ePVrp72cYhv7973/rzjvvlGEYDnlHR0crNze33HYffPBBhzNQIyIiZBhGucvyIiIidOTIkXJPwb2QZcuWafHixXr88cfVunVrc/xCPwsr83OwIosXL1ZKSoruuecePfDAA/rPf/6jYcOGacWKFfryyy+rtCYAXMmoaalpz4WalpqWmrZmatqyW3ydPn1aGRkZeuihh/TQQw9p3bp1MgxD06dPv+g1UfvRtAWqic1mU0xMjHx9ffXuu+/Kzc3NnCsrBCq6j07ZpSwVXYb1R82aNVNUVFS5V2BgoCQpNDRUSUlJeu211+Tv76/o6GjNmzevUvfQkn7/n09ycrJ53yN/f381adJEOTk5Dmv8+OOPDv/TKnPNNdc4vP/xxx8rHHd3d9df/vIXc76qef/vf/+TJIcnGv/RuXKQpLZt2+rEiRPKy8tzGD/7Hm3S70Wap6enwyUxZeOnTp06b47nW1OSQkJCKhw/e93Dhw+bBXr9+vXVpEkT3XLLLZJ03n00YMAAde/eXQ8//LACAwM1cOBArVix4oLF7v/+9z+5uroqLCzsvHF79+7V3XffLV9fX/n4+KhJkya6//77HfKqzLH98ccf5erqqlatWjmsHxQUJD8/P/MY3nLLLYqNjdXkyZPl7++vu+66S0uWLLng/anKPv/H9Rs1aqSGDRs6jH377bdKS0tTkyZNHF5RUVGSfr+c7WyhoaEVbnPp0qXq0KGDPD091bhxYzVp0kRr1qy54J/p6dOna8+ePQoJCVHXrl01adKkC/4l6vjx48rJydGiRYvK5T1kyJAK876YP4+lpaWV/hki/X7ZZHx8vKKjo/X88887zF3oZ2Flfg5WVtlfkNatW1dtawLAlYCalpr2fKhpqWmpaWumpi37TPfu3R3yb968uW688UZt2rTpotdE7UfTFqgGubm5uuOOO5STk6O0tDQFBwc7zDdt2lTS778h/6Njx46pUaNGFf6Wripmzpypr7/+Wk899ZR+++03PfbYY7r22mv1008/XfCzI0eO1PPPP6/77rtPK1as0CeffKL09HQ1btz4on6rfbnzrm5n/+XkfGPS778RruqalVm3pKREt99+u9asWaNx48Zp1apVSk9PV2pqqiSd97h4eXlp48aNWrdunR544AF9/fXXGjBggG6//fZyD1a4WDk5Obrlllv01VdfacqUKfrwww+Vnp6uF198sVxelT22f3zgxB+5uLjo3XffVWZmphITE82HFISHh1fbw6dKS0t1++23V3j2T3p6umJjYx3iKyrI3nrrLT300EO66qqrtHjxYqWlpSk9PV233XbbBf87uu+++/T999/rlVdeUXBwsGbMmKFrr71WH3/88XlzlqT777//nHl3797d4TNV/fN4IV999ZX++te/ql27dnr33XcdHswgXfhn4R9/dl6KsmL35MmT1bYmAPzZUdM6N+/qRk17YdS0v6OmdeSMmrbsM2W/vDpbQEBApX+xgj8XHkQGXKL8/HzdeeedOnjwoNatW1fhb3L/7//+T02aNNG2bdvKzV3oJu5V0b59e7Vv314TJ07Upk2b1L17dy1cuFDPPfecpHMXEu+++67i4uIcniCbn59f7qm4LVq00Lffflvu8wcOHCgXVzb+l7/8xRwvLCzUoUOHzN/0VjbvPyp72MSePXvKrVVRDn+0f/9++fv7y9vbu8LPWsHu3bt18OBBLV26VA8++KA5np6eXqnPu7q6qmfPnurZs6dmzZqlF154QU8//bQ+/fTTc+6zq666SqWlpfrmm2/O+Wfzs88+0y+//KL33nvP4aEVhw4dqjD+fMe2RYsWKi0t1bfffqu2bduan8nKylJOTo55DMt069ZN3bp10/PPP69ly5Zp8ODBeueddxwuATxb2ee/++47h7MIfvnll3LFz1VXXaUzZ86cc99Uxrvvvqu//OUveu+99xz+W0tJSanU55s2bapHH31Ujz76qLKzs9W5c2c9//zzuuOOOyqMb9KkiRo0aKCSkpJLyvtS/e9//1Pv3r0VEBCgjz76SPXr1y8XU/bnadu2bQ6Xoh49elQ//fST+SCZ6lB2NscfL/UDAFSMmvb/o6atftS01LTUtOfXvn171a1bVz///HO5uaNHj1LTXqE40xa4BCUlJRowYIAyMzO1cuVKRUZGnjM2NjZWq1ev1pEjR8yxjIwMHTx4UPfee2+15GO328vdp6d9+/ZydXV1uHTD29u7XNEq/f4byT/+9vGVV14p9xvsPn366Msvv9SWLVvMsePHj+vtt992iIuKipK7u7tefvllh3UXL16s3NxcxcTEXFTef9S5c2eFhoZq9uzZ5b5P2faaNm2qTp06aenSpQ4xe/bs0SeffKI+ffqcc30rKPst8dn7zzAMzZkz54KfregMw7IC43z7tV+/fnJ1ddWUKVPK/Ra9LI+K8iosLNT8+fMd4itzbMuOwR+fYjtr1ixJMv+cnDp1qtyfz8p8n549e6pOnTpasGCBw/jcuXPLxd53333KzMzU2rVry83l5ORU6j5YFe2bzZs3KzMz87yfKykpKXfJVkBAgIKDg8/7/dzc3BQbG6t///vf2rNnT7n548ePXzDnS2Wz2dSrVy+5urpq7dq15ywqr732WrVp00aLFi1y+LmyYMECubi4qH///he9bbvdXm7/GIZh/sU4Ojr6otcEgCsNNS01bU2jpqWmpaY9vwYNGqhPnz7atGmTeS9lSdq3b582bdqk22+//eK/EGo9zrQFLsHjjz+uDz74QHfeeadOnjypt956y2G+7F5IkvTUU09p5cqV6tGjh0aNGqUzZ85oxowZat++vXmPngs5ePBguW1Iv19Ccfvtt2v9+vVKTEzUvffeq6uvvlrFxcV68803zf8BlgkPD9e6des0a9YsBQcHKzQ0VBEREerbt6/efPNN+fr6KiwsTJmZmVq3bp0aN27ssL2xY8fqzTffVO/evTVq1Ch5e3tr0aJFatGihb7++mszrkmTJpowYYImT56s3r17669//asOHDig+fPn6/rrrzf3T2Xz/iNXV1ctWLBAd955pzp16qQhQ4aoadOm2r9/v/bu3WsWKTNmzNAdd9yhyMhIxcfH67ffftMrr7wiX19fTZo0qVL73lnatGmjq666Sk888YR+/vln+fj46N///nelLo+ZMmWKNm7cqJiYGLVo0ULZ2dmaP3++mjVrphtvvPGcn2vVqpWefvppPfvss7rpppt0zz33yMPDQ1u3blVwcLCmTp2qG264QQ0bNlRcXJwee+wxubi46M033yxXgFbm2Hbs2FFxcXFatGiReYnali1btHTpUvXr1898KMnSpUs1f/583X333brqqqt0+vRp/fOf/5SPj895/6ISGBioUaNGaebMmfrrX/+q3r1766uvvtLHH38sf39/hzMHnnzySX3wwQfq27evHnroIYWHhysvL0+7d+/Wu+++qx9++KHcfeD+qG/fvnrvvfd09913KyYmRocOHdLChQsVFhZ23kveTp8+rWbNmql///7q2LGj6tevr3Xr1mnr1q0OZwpVZNq0afr0008VERGhYcOGKSwsTCdPntSOHTu0bt26Gr9FQO/evfX9999r7Nix+vzzz/X555+bc2U/n8rMmDFDf/3rX9WrVy8NHDhQe/bs0dy5c/Xwww87nJUiyWy87t27V5L05ptvmmtPnDhRkrRjxw4NGjRIgwYNUqtWrfTbb7/p/fff1xdffKHhw4erc+fONfrdAeDPgJqWmramUdNS01LTnr+mlaQXXnhBGRkZuu222/TYY49Jkl5++WU1atRITz31VM18aVibAaDKbrnlFkPSOV9/tGfPHqNXr15GvXr1DD8/P2Pw4MGGzWar1LbOt51bbrnFMAzD+P77742hQ4caV111leHp6Wk0atTI6NGjh7Fu3TqHtfbv32/cfPPNhpeXlyHJiIuLMwzDME6dOmUMGTLE8Pf3N+rXr29ER0cb+/fvN1q0aGHGlPn666+NW265xfD09DT+7//+z3j22WeNxYsXG5KMQ4cOOcTOnTvXaNOmjVG3bl0jMDDQGDFihHHq1ClzvrJ5n8vnn39u3H777UaDBg0Mb29vo0OHDsYrr7ziELNu3Tqje/fuhpeXl+Hj42PceeedxjfffOMQk5KSYkgyjh8/7jAeFxdneHt7l9vuLbfcYlx77bUXzE+SkZCQ4DB26NAhQ5IxY8YMh/FPP/3UkGSsXLnSHPvmm2+MqKgoo379+oa/v78xbNgw46uvvjIkGUuWLCmXf5mMjAzjrrvuMoKDgw13d3cjODjYGDRokHHw4MEL5mwYhvH6668b1113neHh4WE0bNjQuOWWW4z09HRz/osvvjC6detmeHl5GcHBwcbYsWONtWvXGpKMTz/91DCMyh/boqIiY/LkyUZoaKhRt25dIyQkxJgwYYKRn59vxuzYscMYNGiQ0bx5c8PDw8MICAgw+vbta2zbtu2C36W4uNh45plnjKCgIMPLy8u47bbbjH379hmNGzc2HnnkEYfY06dPGxMmTDBatWpluLu7G/7+/sYNN9xg/OMf/zAKCwsNwzj38TMMwygtLTVeeOEFo0WLFoaHh4dx3XXXGatXrzbi4uKMFi1aOMRKMlJSUgzDMIyCggLjySefNDp27Gj+We7YsaMxf/78C34/wzCMrKwsIyEhwQgJCTHq1q1rBAUFGT179jQWLVpkxlT058swDGPJkiWGJGPr1q0O4+f6b+KPKvPz6Wzvv/++0alTJ8PDw8No1qyZMXHiRHPfVnbdMt9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+            "text/plain": [
+              "<Figure size 1400x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "#fazendo um grafico lado a lado\n",
+        "fig, (ax1, ax2)=plt.subplots(1,2, figsize=(14,5))\n",
+        "CASOSGERAIS2015.plot(kind=\"hist\", color=\"Skyblue\",ax=ax1)\n",
+        "ax1.set_title(\"20 Estados com mais casos gerais em 2015\")\n",
+        "ax1.set_xlabel(\"Ano\")\n",
+        "\n",
+        "\n",
+        "CASOSGERAIS2016.plot(kind=\"hist\", color=\"purple\",ax=ax2)\n",
+        "ax2.set_title(\"20 Estados com mais casos gerais em 2016\")\n",
+        "ax2.set_xlabel(\"Ano\")\n",
+        "\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U6KvyQpYuN0g"
+      },
+      "source": [
+        "## Verificando a quantidade de casos por ano nos Estados Americanos"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fFhVK7A-uN0g",
+        "outputId": "9e83203b-0ed6-44d5-e456-385ce5ea31dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Ano\n",
+              "2016    694079\n",
+              "2015    116024\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 244,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#contagem de valores na variavel estadoscasos\n",
+        "\n",
+        "ESTADOSCASOS=df[\"Ano\"].value_counts()\n",
+        "ESTADOSCASOS"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Kmwr7s8iuN0g"
+      },
+      "source": [
+        "## comparando casos de 2015 e 2016 através de um gráfico"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zsw3nAFguN0g",
+        "outputId": "4a56e64e-c991-4421-bdd7-1d50300292e9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 900x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "#COMPARANDO OS CASOS DE 2015 E 2016\n",
+        "ESTADOSCASOS.plot(kind=\"barh\", color=\"Skyblue\", figsize=(9,4))\n",
+        "plt.title(\"Comparação de casos entre os anos de 2015 e 2016\")\n",
+        "plt.tight_layout()\n",
+        "plt.xticks(rotation=360)\n",
+        "plt.show()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ghQWx7iguN0h"
+      },
+      "source": [
+        "## comparar as tendências temporais da taxa de pressão alta e triagem de colesterol ao longo dos anos.\n",
+        "#### (\"High blood pressure among adults aged >=18 Years \",\"Cholesterol screening among adults aged >=18 Years ) na pesquisa"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gQwesdY1uN0h"
+      },
+      "source": [
+        "## Verificando quais são os motivos dos casos e selecionando os dois primeiros para uma análise breve\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jXnqGnazuN0h",
+        "outputId": "ff8a817b-dbaa-4e3a-aa7d-fb27a449f8d6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Medida\n",
+              "High blood pressure among adults aged >=18 Years                                                                                                                                                  29006\n",
+              "Cholesterol screening among adults aged >=18 Years                                                                                                                                                29006\n",
+              "Arthritis among adults aged >=18 Years                                                                                                                                                            29006\n",
+              "Stroke among adults aged >=18 Years                                                                                                                                                               29006\n",
+              "Obesity among adults aged >=18 Years                                                                                                                                                              29006\n",
+              "Diagnosed diabetes among adults aged >=18 Years                                                                                                                                                   29006\n",
+              "Cancer (excluding skin cancer) among adults aged >=18 Years                                                                                                                                       29006\n",
+              "Binge drinking among adults aged >=18 Years                                                                                                                                                       29006\n",
+              "Mental health not good for >=14 days among adults aged >=18 Years                                                                                                                                 29006\n",
+              "Chronic obstructive pulmonary disease among adults aged >=18 Years                                                                                                                                29006\n",
+              "Coronary heart disease among adults aged >=18 Years                                                                                                                                               29006\n",
+              "Current smoking among adults aged >=18 Years                                                                                                                                                      29006\n",
+              "No leisure-time physical activity among adults aged >=18 Years                                                                                                                                    29006\n",
+              "Physical health not good for >=14 days among adults aged >=18 Years                                                                                                                               29006\n",
+              "Current asthma among adults aged >=18 Years                                                                                                                                                       29006\n",
+              "Sleeping less than 7 hours among adults aged >=18 Years                                                                                                                                           29006\n",
+              "Chronic kidney disease among adults aged >=18 Years                                                                                                                                               29006\n",
+              "Visits to dentist or dental clinic among adults aged >=18 Years                                                                                                                                   29006\n",
+              "High cholesterol among adults aged >=18 Years who have been screened in the past 5 Years                                                                                                          29006\n",
+              "Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressure                                                                                             29006\n",
+              "Visits to doctor for routine checkup within the past Year among adults aged >=18 Years                                                                                                            29006\n",
+              "Current lack of health insurance among adults aged 18–64 Years                                                                                                                                    28973\n",
+              "Papanicolaou smear use among adult women aged 21–65 Years                                                                                                                                         28912\n",
+              "Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50–75 Years                                                                                                              28847\n",
+              "Mammography use among women aged 50–74 Years                                                                                                                                                      28727\n",
+              "All teeth lost among adults aged >=65 Years                                                                                                                                                       28601\n",
+              "Older adult men aged >=65 Years who are up to date on a core set of clinical preventive services: Flu shot past Year, PPV shot ever, Colorectal cancer screening                                  28464\n",
+              "Older adult women aged >=65 Years who are up to date on a core set of clinical preventive services: Flu shot past Year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 Years    28453\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 246,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "contagem=df[\"Medida\"].value_counts()\n",
+        "contagem.head(50)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5zo12NQUuN0h"
+      },
+      "source": [
+        "Essa parte do codigo é responsavel por filtrar na coluna medida os motivos selecionados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "63FVaHOLuN0h"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "\n",
+        "dffiltro=df[df[\"Medida\"].isin([\"High blood pressure among adults aged >=18 Years \",\"Cholesterol screening among adults aged >=18 Years \"])]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tLU-j4mwuN0h"
+      },
+      "source": [
+        "## Criando uma nova planilha e criando uma nova colunas contendo os valores para a pesquisa\n",
+        "\n",
+        "Porém como da para ver, os dados estão vazios"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tL57yBbluN0h",
+        "outputId": "8d7d69ce-8795-4715-fb38-5a6d249b8c0f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>Data_Value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [Ano, Medida, Data_Value]\n",
+              "Index: []"
+            ]
+          },
+          "execution_count": 248,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "\n",
+        "df_grouped = dffiltro.groupby(['Ano', 'Medida'])['Data_Value'].mean().reset_index()\n",
+        "df_grouped"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x_GEZs3TuN0i"
+      },
+      "source": [
+        "## Analisando os motivos\n",
+        "como essa linha de codigo conseguimos ver que existem espaços vazios, e por isso está dando como vazio"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LBxtjNLuuN0i",
+        "outputId": "822266cb-19e3-4130-9271-7a47d66c747e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['High blood pressure among adults aged >=18 Years'\n",
+            " 'Cholesterol screening among adults aged >=18 Years'\n",
+            " 'Arthritis among adults aged >=18 Years'\n",
+            " 'Stroke among adults aged >=18 Years'\n",
+            " 'Obesity among adults aged >=18 Years'\n",
+            " 'Mammography use among women aged 50–74 Years'\n",
+            " 'All teeth lost among adults aged >=65 Years'\n",
+            " 'Diagnosed diabetes among adults aged >=18 Years'\n",
+            " 'Binge drinking among adults aged >=18 Years'\n",
+            " 'Cancer (excluding skin cancer) among adults aged >=18 Years'\n",
+            " 'Mental health not good for >=14 days among adults aged >=18 Years'\n",
+            " 'Chronic obstructive pulmonary disease among adults aged >=18 Years'\n",
+            " 'Current smoking among adults aged >=18 Years'\n",
+            " 'Coronary heart disease among adults aged >=18 Years'\n",
+            " 'Papanicolaou smear use among adult women aged 21–65 Years'\n",
+            " 'Current asthma among adults aged >=18 Years'\n",
+            " 'Sleeping less than 7 hours among adults aged >=18 Years'\n",
+            " 'Visits to dentist or dental clinic among adults aged >=18 Years'\n",
+            " 'Chronic kidney disease among adults aged >=18 Years'\n",
+            " 'Current lack of health insurance among adults aged 18–64 Years'\n",
+            " 'Taking medicine for high blood pressure control among adults aged >=18 Years with high blood pressure'\n",
+            " 'High cholesterol among adults aged >=18 Years who have been screened in the past 5 Years'\n",
+            " 'Visits to doctor for routine checkup within the past Year among adults aged >=18 Years'\n",
+            " 'Older adult women aged >=65 Years who are up to date on a core set of clinical preventive services: Flu shot past Year, PPV shot ever, Colorectal cancer screening, and Mammogram past 2 Years'\n",
+            " 'Physical health not good for >=14 days among adults aged >=18 Years'\n",
+            " 'Fecal occult blood test, sigmoidoscopy, or colonoscopy among adults aged 50–75 Years'\n",
+            " 'Older adult men aged >=65 Years who are up to date on a core set of clinical preventive services: Flu shot past Year, PPV shot ever, Colorectal cancer screening'\n",
+            " 'No leisure-time physical activity among adults aged >=18 Years']\n"
+          ]
+        }
+      ],
+      "source": [
+        "#escrever o nome dos valores unicos de df\n",
+        "#se reapara que tem valores em branco\n",
+        "print(df[\"Medida\"].unique())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fFNkUwXWuN0i"
+      },
+      "source": [
+        "# Tipo simples de tratamento de dados\n",
+        "## O Metodo str.strip remove os espaços vazios de uma string\n",
+        "O Metodo isin da um true para cada item no filtro, assim realizando a filtragem"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hyn55RgAuN0i",
+        "outputId": "a6169e16-4016-456c-d133-0715dcefac1f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>SiglaEstado</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>NomeCidade</th>\n",
+              "      <th>NivelGeografico</th>\n",
+              "      <th>FonteDados</th>\n",
+              "      <th>Categoria</th>\n",
+              "      <th>IDUnico</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>UnidadeValorDados</th>\n",
+              "      <th>...</th>\n",
+              "      <th>LimiteSuperiorConfianca</th>\n",
+              "      <th>SimboloNotaRodapeDados</th>\n",
+              "      <th>NotaRodapeDados</th>\n",
+              "      <th>ContagemPopulacional</th>\n",
+              "      <th>Geolocalizacao</th>\n",
+              "      <th>IDCategoria</th>\n",
+              "      <th>IDMedida</th>\n",
+              "      <th>FIPSCidade</th>\n",
+              "      <th>FIPSTrato</th>\n",
+              "      <th>TextoCurtoPergunta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>IN</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>Fishers</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>1823278</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>24.7</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>76794</td>\n",
+              "      <td>POINT (-85.969310702 39.9587635263)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>1823278.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Milpitas</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0647766</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>22.5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>66790</td>\n",
+              "      <td>POINT (-121.892083025 37.433869763)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>647766.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CT</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>Stamford</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0973000</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>26.2</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>122643</td>\n",
+              "      <td>POINT (-73.5589363142 41.1049145013)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>973000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>CA</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Elk Grove</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>0622020</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>153015</td>\n",
+              "      <td>POINT (-121.384137762 38.4159992006)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>622020.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>FL</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Palm Bay</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>1254000</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>78.4</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>103190</td>\n",
+              "      <td>POINT (-80.6666722846 27.9838255249)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>1254000.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809132</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>NC</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Raleigh</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>3755000-37183052503</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>82.5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6282</td>\n",
+              "      <td>POINT (-78.7096824329 35.8314468148)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>3755000.0</td>\n",
+              "      <td>3.718305e+10</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809661</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>NC</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Charlotte</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>3712000-37119001916</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>79.5</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3725</td>\n",
+              "      <td>POINT (-80.7050702391 35.2028378945)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>3712000.0</td>\n",
+              "      <td>3.711900e+10</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809684</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>DC</td>\n",
+              "      <td>District of C</td>\n",
+              "      <td>Washington</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>1150000-11001008904</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>43.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3309</td>\n",
+              "      <td>POINT (-76.9748483102 38.9009420937)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>1150000.0</td>\n",
+              "      <td>1.100101e+10</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>809895</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>NC</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Wilmington</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Prevention</td>\n",
+              "      <td>3774440-37129011903</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>56.8</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8514</td>\n",
+              "      <td>POINT (-77.8629612169 34.2296328312)</td>\n",
+              "      <td>PREVENT</td>\n",
+              "      <td>CHOLSCREEN</td>\n",
+              "      <td>3774440.0</td>\n",
+              "      <td>3.712901e+10</td>\n",
+              "      <td>Cholesterol Screening</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>810021</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>NC</td>\n",
+              "      <td>North Carolin</td>\n",
+              "      <td>Wilmington</td>\n",
+              "      <td>Census Tract</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>3774440-37129010100</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>41.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>2512</td>\n",
+              "      <td>POINT (-77.9361663816 34.2420805592)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>3774440.0</td>\n",
+              "      <td>3.712901e+10</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>58012 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Ano SiglaEstado DescricaoEstado  NomeCidade NivelGeografico  \\\n",
+              "0       2015          IN         Indiana     Fishers            City   \n",
+              "1       2015          CA      California    Milpitas            City   \n",
+              "2       2015          CT     Connecticut    Stamford            City   \n",
+              "3       2015          CA      California   Elk Grove            City   \n",
+              "4       2015          FL         Florida    Palm Bay            City   \n",
+              "...      ...         ...             ...         ...             ...   \n",
+              "809132  2015          NC   North Carolin     Raleigh    Census Tract   \n",
+              "809661  2015          NC   North Carolin   Charlotte    Census Tract   \n",
+              "809684  2015          DC   District of C  Washington    Census Tract   \n",
+              "809895  2015          NC   North Carolin  Wilmington    Census Tract   \n",
+              "810021  2015          NC   North Carolin  Wilmington    Census Tract   \n",
+              "\n",
+              "       FonteDados        Categoria              IDUnico  \\\n",
+              "0           BRFSS  Health Outcomes              1823278   \n",
+              "1           BRFSS  Health Outcomes              0647766   \n",
+              "2           BRFSS  Health Outcomes              0973000   \n",
+              "3           BRFSS  Health Outcomes              0622020   \n",
+              "4           BRFSS       Prevention              1254000   \n",
+              "...           ...              ...                  ...   \n",
+              "809132      BRFSS       Prevention  3755000-37183052503   \n",
+              "809661      BRFSS       Prevention  3712000-37119001916   \n",
+              "809684      BRFSS  Health Outcomes  1150000-11001008904   \n",
+              "809895      BRFSS       Prevention  3774440-37129011903   \n",
+              "810021      BRFSS  Health Outcomes  3774440-37129010100   \n",
+              "\n",
+              "                                                   Medida UnidadeValorDados  \\\n",
+              "0        High blood pressure among adults aged >=18 Years                 %   \n",
+              "1        High blood pressure among adults aged >=18 Years                 %   \n",
+              "2        High blood pressure among adults aged >=18 Years                 %   \n",
+              "3        High blood pressure among adults aged >=18 Years                 %   \n",
+              "4       Cholesterol screening among adults aged >=18 Y...                 %   \n",
+              "...                                                   ...               ...   \n",
+              "809132  Cholesterol screening among adults aged >=18 Y...                 %   \n",
+              "809661  Cholesterol screening among adults aged >=18 Y...                 %   \n",
+              "809684   High blood pressure among adults aged >=18 Years                 %   \n",
+              "809895  Cholesterol screening among adults aged >=18 Y...                 %   \n",
+              "810021   High blood pressure among adults aged >=18 Years                 %   \n",
+              "\n",
+              "        ... LimiteSuperiorConfianca SimboloNotaRodapeDados  NotaRodapeDados  \\\n",
+              "0       ...                    24.7                    NaN              NaN   \n",
+              "1       ...                    22.5                    NaN              NaN   \n",
+              "2       ...                    26.2                    NaN              NaN   \n",
+              "3       ...                    27.8                    NaN              NaN   \n",
+              "4       ...                    78.4                    NaN              NaN   \n",
+              "...     ...                     ...                    ...              ...   \n",
+              "809132  ...                    82.5                    NaN              NaN   \n",
+              "809661  ...                    79.5                    NaN              NaN   \n",
+              "809684  ...                    43.0                    NaN              NaN   \n",
+              "809895  ...                    56.8                    NaN              NaN   \n",
+              "810021  ...                    41.0                    NaN              NaN   \n",
+              "\n",
+              "        ContagemPopulacional                        Geolocalizacao  \\\n",
+              "0                      76794   POINT (-85.969310702 39.9587635263)   \n",
+              "1                      66790   POINT (-121.892083025 37.433869763)   \n",
+              "2                     122643  POINT (-73.5589363142 41.1049145013)   \n",
+              "3                     153015  POINT (-121.384137762 38.4159992006)   \n",
+              "4                     103190  POINT (-80.6666722846 27.9838255249)   \n",
+              "...                      ...                                   ...   \n",
+              "809132                  6282  POINT (-78.7096824329 35.8314468148)   \n",
+              "809661                  3725  POINT (-80.7050702391 35.2028378945)   \n",
+              "809684                  3309  POINT (-76.9748483102 38.9009420937)   \n",
+              "809895                  8514  POINT (-77.8629612169 34.2296328312)   \n",
+              "810021                  2512  POINT (-77.9361663816 34.2420805592)   \n",
+              "\n",
+              "       IDCategoria    IDMedida  FIPSCidade     FIPSTrato  \\\n",
+              "0          HLTHOUT      BPHIGH   1823278.0           NaN   \n",
+              "1          HLTHOUT      BPHIGH    647766.0           NaN   \n",
+              "2          HLTHOUT      BPHIGH    973000.0           NaN   \n",
+              "3          HLTHOUT      BPHIGH    622020.0           NaN   \n",
+              "4          PREVENT  CHOLSCREEN   1254000.0           NaN   \n",
+              "...            ...         ...         ...           ...   \n",
+              "809132     PREVENT  CHOLSCREEN   3755000.0  3.718305e+10   \n",
+              "809661     PREVENT  CHOLSCREEN   3712000.0  3.711900e+10   \n",
+              "809684     HLTHOUT      BPHIGH   1150000.0  1.100101e+10   \n",
+              "809895     PREVENT  CHOLSCREEN   3774440.0  3.712901e+10   \n",
+              "810021     HLTHOUT      BPHIGH   3774440.0  3.712901e+10   \n",
+              "\n",
+              "           TextoCurtoPergunta  \n",
+              "0         High Blood Pressure  \n",
+              "1         High Blood Pressure  \n",
+              "2         High Blood Pressure  \n",
+              "3         High Blood Pressure  \n",
+              "4       Cholesterol Screening  \n",
+              "...                       ...  \n",
+              "809132  Cholesterol Screening  \n",
+              "809661  Cholesterol Screening  \n",
+              "809684    High Blood Pressure  \n",
+              "809895  Cholesterol Screening  \n",
+              "810021    High Blood Pressure  \n",
+              "\n",
+              "[58012 rows x 24 columns]"
+            ]
+          },
+          "execution_count": 251,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#o metodo strip reemove espaços em branco\n",
+        "#e o isin da um true para cada linha que conter o especificado\n",
+        "#tipo simples de tratamento de dados\n",
+        "dffiltro = df[df[\"Medida\"].str.strip().isin([\n",
+        "    \"High blood pressure among adults aged >=18 Years\",\n",
+        "    \"Cholesterol screening among adults aged >=18 Years\"\n",
+        "])]\n",
+        "dffiltro"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JPsXpjXsuN0i"
+      },
+      "source": [
+        "## Verificando os dados filtrados para 2015"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uAC8XMfkuN0i",
+        "outputId": "8c918ead-a705-4206-a593-523204c6872a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Medida\n",
+              "High blood pressure among adults aged >=18 Years      29006\n",
+              "Cholesterol screening among adults aged >=18 Years    29006\n",
+              "Name: count, dtype: int64"
+            ]
+          },
+          "execution_count": 71,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "\n",
+        "dffiltro[dffiltro[\"Ano\"] == 2015][\"Medida\"].value_counts()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "61O1mVWMuN0i"
+      },
+      "source": [
+        "## Verificando os dados filtrados para 2016\n",
+        "como da para ver, não temos casos em 2016"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FmD98nSKuN0i",
+        "outputId": "268e6a82-23e1-4282-f131-910ac0e2bdd9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Series([], Name: count, dtype: int64)"
+            ]
+          },
+          "execution_count": 72,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "\n",
+        "dffiltro[dffiltro[\"Ano\"] == 2016][\"Medida\"].value_counts()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eICJO84euN0j",
+        "outputId": "93476f03-3d9f-40ea-bf8a-99318502a92d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Ano\n",
+            "2015    58012\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(dffiltro[\"Ano\"].value_counts())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0eIkMVYPuN0j",
+        "outputId": "706a29e3-fce5-40f0-eae4-29f9c734d7cf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Empty DataFrame\n",
+            "Columns: [Ano, SiglaEstado, DescricaoEstado, NomeCidade, NivelGeografico, FonteDados, Categoria, IDUnico, Medida, UnidadeValorDados, DataValueTypeID, Data_Value_Type, Data_Value, Low_Confidence_Limit, LimiteSuperiorConfianca, SimboloNotaRodapeDados, NotaRodapeDados, ContagemPopulacional, Geolocalizacao, IDCategoria, IDMedida, FIPSCidade, FIPSTrato, TextoCurtoPergunta]\n",
+            "Index: []\n",
+            "\n",
+            "[0 rows x 24 columns]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(df[(df[\"Ano\"] == 2016) & (df[\"Medida\"].isin([\n",
+        "    \"High blood pressure among adults aged >=18 Years\",\n",
+        "    \"Cholesterol screening among adults aged >=18 Years\"\n",
+        "]))])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xGMLDtX9uN0j"
+      },
+      "source": [
+        "verificando os 50 primeiros para vermos se encontramos um caso de 2015 (basta alternar o numero 50 para um maior para vizualizar mais dados)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Vrf4Jw4UuN0j",
+        "outputId": "ade129a0-659e-4676-ae6e-2c82ffe422af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>SiglaEstado</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>NomeCidade</th>\n",
+              "      <th>NivelGeografico</th>\n",
+              "      <th>FonteDados</th>\n",
+              "      <th>Categoria</th>\n",
+              "      <th>IDUnico</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>UnidadeValorDados</th>\n",
+              "      <th>...</th>\n",
+              "      <th>LimiteSuperiorConfianca</th>\n",
+              "      <th>SimboloNotaRodapeDados</th>\n",
+              "      <th>NotaRodapeDados</th>\n",
+              "      <th>ContagemPopulacional</th>\n",
+              "      <th>Geolocalizacao</th>\n",
+              "      <th>IDCategoria</th>\n",
+              "      <th>IDMedida</th>\n",
+              "      <th>FIPSCidade</th>\n",
+              "      <th>FIPSTrato</th>\n",
+              "      <th>TextoCurtoPergunta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>0 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [Ano, SiglaEstado, DescricaoEstado, NomeCidade, NivelGeografico, FonteDados, Categoria, IDUnico, Medida, UnidadeValorDados, DataValueTypeID, Data_Value_Type, Data_Value, Low_Confidence_Limit, LimiteSuperiorConfianca, SimboloNotaRodapeDados, NotaRodapeDados, ContagemPopulacional, Geolocalizacao, IDCategoria, IDMedida, FIPSCidade, FIPSTrato, TextoCurtoPergunta]\n",
+              "Index: []\n",
+              "\n",
+              "[0 rows x 24 columns]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "dffiltro_2016 = df.loc[(df[\"Ano\"] == 2016) & (df[\"Medida\"].isin([\n",
+        "    \"High blood pressure among adults aged >=18 Years\",\n",
+        "    \"Cholesterol screening among adults aged >=18 Years\"\n",
+        "]))]\n",
+        "display(dffiltro.head(0))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F_bGS1LjuN0j"
+      },
+      "source": [
+        "## Recriando a tabela anterior após ao tratamento de dados mas agora com valores\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AfpX_STauN0j",
+        "outputId": "064d62c7-6dc1-4fed-cc4e-82602c2dba8a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>Data_Value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>Cholesterol screening among adults aged &gt;=18 Y...</td>\n",
+              "      <td>72.657522</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>30.491621</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Ano                                             Medida  Data_Value\n",
+              "0  2015  Cholesterol screening among adults aged >=18 Y...   72.657522\n",
+              "1  2015   High blood pressure among adults aged >=18 Years   30.491621"
+            ]
+          },
+          "execution_count": 76,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#recriando a tabela anterior após ao tratamento de dados\n",
+        "DF_grouped=dffiltro.groupby([\"Ano\", \"Medida\"])[\"Data_Value\"].mean().reset_index()\n",
+        "DF_grouped"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GISZobiKuN0j"
+      },
+      "source": [
+        "Agora com essa tabela é possivel criar uma grafico, em cima, usando o seaborn e o matplotlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GZmWVi-FuN0j",
+        "outputId": "7d97fdea-d7d4-4047-c3fb-c460231032be"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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vLle4cGFDkrFq1SqL499//33DxcXFOHz4sMX2oUOHGjly5DBiYmIMwzCM119/3XB3dzfu3LmTZiwVKlTIcIhyakP0v/76a0OS8dtvv5m3tWzZ0nBwcLAYxnvmzBnDzc3NqFWrVrrnSFauXDmjQ4cO5udvvvlmqtN9MjO8PLXvztTiT01kZKQhyerpGKGhoYa7u7tx5coV87bevXsbOXLkSLW8t7e30b59+1T3ZTTEOjw83Bg3bpyxbNkyY86cOcZzzz1nSDKGDBmSYZzWvocy41G1ffz48YYkI0+ePEaVKlWMr776ypg2bZrh4+Nj5M6d2zhz5kyKYxo2bGgEBQVluk0AMo/h5QCs1rZtW/3777/66aefdP36df30009pDi1fsmSJPDw81KBBA/3zzz/mR3BwsFxdXbVu3TpJUmRkpBISEtS3b1+LnpX7e1fTcm+v9I0bN/TPP/+oevXqMgzD3JuQlp9//lnVqlVTlSpVzNu8vb3NQ7uTrVmzRlevXtWLL75o0ZYcOXKoatWq5rY8rFdffdWqcve2+cqVK4qNjdVzzz2n3bt3m7evWrVKklIMX+zbt6/Fc8MwtHTpUoWHh8swDIv2hYWFKTY21qJeSerYsaPF8OyqVavKMAx16dLFolzVqlV16tQp3blzJ8227Ny5UxcuXFCvXr0sFsiLiIhIcWu5JUuWKCgoSKVKlbKIs169epKU7utw7do1SbJ6iOrPP/8sX19fvfjii+Zt9vb26tevn+Li4hQVFZXqcZm5np6enjp9+rR27NhhVUz3+u6775SUlKS2bdtanMPX11eBgYEproWjo6M6d+6cJW1My8O8Pslu3Lghb29veXt7q3jx4nrzzTcVEhKi77//3qJcQECAwsLCUpz/ueeeU+7cuS3OHxoaqsTERP3222+S7l73GzduWAwVv5+np6cOHDigI0eOpFnm3tEot27d0j///KNq1apJkvk1TkxM1C+//KKWLVuqaNGi5vL58+fXSy+9pI0bN5rfm2n5448/tG/fPovXKfm7aPXq1ekem557v0fSij8tyUPQc+fOneF5Ro8ercjISI0dO9Zi9EV6i2I6OTlZPUXofsuXL9eQIUPUokULdenSRVFRUQoLC9P48eN1+vTpdI+19j1krUfZ9uQFO00mk9auXauXXnpJr776qpYtW6YrV65o6tSpKY5JbicA22N4OQCreXt7KzQ0VAsXLtTNmzeVmJioNm3apFr2yJEjio2NVb58+VLdf+HCBUky3981MDAwxbms+YMuJiZGw4cP1/Lly1PMFY+NjU332JMnT6Y6tLNkyZIWz5P/8E5OHu7n7u6eYZwZyZkzpwoVKmRV2Z9++kmjRo3S3r17Lebs3fujxcmTJ2VnZ6eAgACLY4sXL27x/OLFi7p69apmzZqlWbNmpXq+5Ncqmb+/v8Xz5OT43qHgyduTkpIUGxtrMTT4Xmm9/vb29hZJinT3dTh48KB5eHVGcd4r+TW6fv16mmXujyswMFB2dpa/TQcFBVnEfb/MXM833nhDkZGRqlKliooXL66GDRvqpZdeUo0aNTKM78iRIzIMI8V1S3b/nPWCBQum+CP/QduYXkwP+vokc3Jy0o8//ijp7g8FAQEBqX4u7n9fJ5//jz/+yPD8r732mhYvXqzGjRurYMGCatiwodq2batGjRqZy7733ntq0aKFSpQoobJly6pRo0Z65ZVXzEOzJeny5csaOXKkFi1alKJtyd89Fy9e1M2bN1N8p0h3r3NSUpJOnTqlMmXKpHlNvvzyS7m4uKho0aI6evSo+ToVKVJEX331VapTRqxhTfwZMQwj3f3ffPON3n77bXXt2jXFj4rOzs5pTiG6detWqtN8HoTJZNKAAQO0evVqrV+/Pt1h29a+h6zxqNuefEx4eLhcXV3N26tVq6aAgIBU1wAwDCPdaQQAsg5JN4BMeemll9S9e3edO3dOjRs3TvM2RklJScqXL5+++uqrVPen9UdNZiQmJqpBgwa6fPmy3njjDZUqVUouLi76+++/FRERkWW32kqu54svvpCvr2+K/TlzPvxXqaOjY4rkJzUbNmxQ8+bNVatWLU2bNk358+eXvb295s6da16AKzOS2/byyy+rU6dOqZa5N9GQpBw5cqRaLq3tGf1hbq2kpCSVK1dO48ePT3X//Un/vUqVKiVJ2rdvX5bEkpbMXM+goCBFR0frp59+0qpVq7R06VJNmzZNw4cPN9/mKb3zmEwmrVy5MtXrfu8f3ZKyLIHJKKYHfX2S5ciRw7zAU3pSa09SUpIaNGigIUOGpHpM8i2j8uXLp71792r16tVauXKlVq5cqblz56pjx47mReRq1aqlY8eO6YcfftAvv/yizz77TBMmTNCMGTPUrVs3SXdH/mzevFmDBw/WM888I1dXVyUlJalRo0ZZ9t1jGIa+/vpr3bhxQ6VLl06x/8KFC4qLi0vxelvjYeJP/hHt/h8677VmzRp17NhRTZs21YwZM1Lsz58/vxITE3XhwgWLH2cTEhJ06dIlFShQINNtSkvye+/y5cvplrP2PZSR7Gh78jE+Pj4p9uXLly/V1+rKlSsWc80B2A5JN4BMef7559WzZ09t3bpV33zzTZrlihUrpsjISNWoUSPdP/gLFy4s6W4Pw709mxcvXkz3DzrpbgJ1+PBhzZ8/Xx07djRvT2/Y6P3nTm34aHR0dIq2SHf/cLEmIbClpUuXysnJSatXr7a4N/rcuXMtyhUuXFhJSUk6fvy4RW9ock9ZMm9vb7m5uSkxMTFb2nbv63/vSILbt2/r+PHjqlChgnlbsWLF9Pvvv6t+/fqZ7p0pUaKESpYsqR9++EGTJk3KMEkpXLiw/vjjDyUlJVn8GHLo0CGLuO+X2evp4uKidu3aqV27dkpISFCrVq30wQcfaNiwYXJyckqzncWKFZNhGAoICHjg+w8/aBvT8jCvT1YoVqyY4uLirLruDg4OCg8PV3h4uJKSkvTaa69p5syZeuedd8yjQby8vNS5c2d17txZcXFxqlWrlkaMGKFu3brpypUrWrt2rUaOHGmxUOD93yfe3t7KlStXiu8U6e51trOzS/fHiKioKJ0+fVrvvfeeeQRCsitXrqhHjx5atmxZmr23ab0O1safluQfsY4fP57q/m3btun5559X5cqVtXjx4lR/mExe2Gvnzp1q0qSJefvOnTuVlJSUYuGvh/HXX39JyvjH3sy8h9KSXW0PDg6WdHeRu/udOXPG/Jrd6/7vWAC2w5xuAJni6uqq6dOna8SIEQoPD0+zXNu2bZWYmKj3338/xb47d+7o6tWrku7etsTe3l6ffvqpRY+oNavEJvfw3XucYRgWt/5JT5MmTbR161Zt377dvO3ixYspeufDwsLk7u6u0aNH6/bt2ynquXjxolXnywo5cuSQyWRSYmKieduJEydSrAydPN912rRpFts//fTTFPW1bt1aS5cu1f79+1Ocz9Ztq1y5sry9vTVjxgyL4Zbz5s0zv0eStW3bVn///bdmz56dop5///03w9sDjRw5UpcuXVK3bt1SnWf+yy+/6KeffpJ0971x7tw5ix+W7ty5o08//VSurq6qXbt2qufIzPW8/9ZMDg4OKl26tAzDML/PXFxcJCnFtWjVqpVy5MihkSNHphhJYBiGVbd9etA2puVhX5+H1bZtW23ZsiXVec5Xr141v+b3Xxs7Ozvz6IPk6Rr3l3F1dVXx4sXN+1P77pFSfm/lyJFDDRs21A8//GBxq7fz589r4cKFqlmzZrrTU5KHlg8ePFht2rSxeHTv3l2BgYFpjiaS/m/e+f3vH2vjT0vBggXl5+ennTt3pth38OBBNW3aVEWKFNFPP/2U5o+u9erVk5eXl6ZPn26xffr06cqVK9cDDZu/fPmyxXejdPcHvLFjx8rBwUF169ZN93hr30Npyc62lyxZUhUqVNAPP/xgMU/7l19+0alTp8yr7ieLjY3VsWPH0l0BH0DWoacbQKalNWz2XrVr11bPnj01ZswY7d27Vw0bNpS9vb2OHDmiJUuWaNKkSWrTpo28vb01aNAgjRkzRs2aNVOTJk20Z88erVy5MsNhb6VKlVKxYsU0aNAg/f3333J3d9fSpUsz7CFPNmTIEH3xxRdq1KiRXn/9dfMtw5J7AJO5u7tr+vTpeuWVV1SpUiW1b99e3t7eiomJ0YoVK1SjRg1NmTLFqnM+rKZNm2r8+PFq1KiRXnrpJV24cEFTp05V8eLFLWIODg5W69atNXHiRF26dMl8y7DDhw9LsuwBGzt2rNatW6eqVauqe/fuKl26tC5fvqzdu3crMjIywyGZD8Pe3l6jRo1Sz549Va9ePbVr107Hjx/X3LlzU8zpfuWVV7R48WL16tVL69atU40aNZSYmKhDhw5p8eLF5vs2p6Vdu3bat2+fPvjgA+3Zs0cvvviiChcurEuXLmnVqlVau3ateYh+jx49NHPmTEVERGjXrl0qUqSIvv32W23atEkTJ05Md0E2a69nw4YN5evrqxo1asjHx0cHDx7UlClT1LRpU3P9yb1Xb731ltq3by97e3uFh4erWLFiGjVqlIYNG6YTJ06oZcuWcnNz0/Hjx/X999+rR48eGjRoULrX/mHamJqHfX0e1uDBg7V8+XI1a9ZMERERCg4O1o0bN7Rv3z59++23OnHihPLmzatu3brp8uXLqlevngoVKqSTJ0/q008/1TPPPGPuTS5durTq1Kmj4OBgeXl5aefOnfr222/Vp08fSXe/E2rVqqUPP/xQt2/fVsGCBfXLL7+k2vM7atQorVmzRjVr1tRrr72mnDlzaubMmYqPj9eHH36YZnvi4+O1dOlSNWjQQE5OTqmWad68uSZNmpRimHIyZ2dnlS5dWt98841KlCghLy8vlS1bVmXLlrU6/rS0aNFC33//vcW84OvXryssLExXrlzR4MGDLe43Ld3tSQ4JCTHH9v7776t379564YUXFBYWpg0bNujLL7/UBx98IC8vL/NxsbGx5h8MN23aJEmaMmWKPD095enpaX5dli9frlGjRqlNmzYKCAjQ5cuXtXDhQu3fv1+jR49OdXrQvax9D6Umu9suSRMmTFCDBg1Us2ZN9ezZU7GxsRo/frxKlCiRYl55ZGSkDMNQixYt0r0mALLIo1wqHcCT595bhqXn/luGJZs1a5YRHBxsODs7G25ubka5cuWMIUOGWNy+JDEx0Rg5cqSRP39+w9nZ2ahTp46xf/9+o3DhwhneMuzPP/80QkNDDVdXVyNv3rxG9+7djd9//92QZMydOzfD9v3xxx9G7dq1DScnJ6NgwYLG+++/b8yZMyfV27KsW7fOCAsLMzw8PAwnJyejWLFiRkREhLFz584Mz5MsrVuGubi4pFo+tVuGzZkzxwgMDDQcHR2NUqVKGXPnzjXeffdd4/6v9Bs3bhi9e/c2vLy8DFdXV6Nly5ZGdHS0IckYO3asRdnz588bvXv3Nvz8/Ax7e3vD19fXqF+/vjFr1iyL9ksylixZYnFsWu+R5JguXryY4XWZNm2aERAQYDg6OhqVK1c2fvvtN6N27doWtwwzjLu3tBo3bpxRpkwZw9HR0cidO7cRHBxsjBw50oiNjc3wPIZhGGvXrjVatGhh5MuXz8iZM6fh7e1thIeHGz/88EOKa9K5c2cjb968hoODg1GuXLlU31NK5ZZM1lzPmTNnGrVq1TLy5MljODo6GsWKFTMGDx6coh3vv/++UbBgQcPOzi7Fe2fp0qVGzZo1DRcXF8PFxcUoVaqU0bt3byM6Otpcpnbt2mnemuxh2piah3l90vsc3Cut7xrDuHt7v2HDhhnFixc3HBwcjLx58xrVq1c3Pv74Y/Nt7r799lujYcOGRr58+QwHBwfD39/f6Nmzp3H27FlzPaNGjTKqVKlieHp6Gs7OzkapUqWMDz74wOJWeadPnzaef/55w9PT0/Dw8DBeeOEF48yZM6leq927dxthYWGGq6urkStXLqNu3brG5s2b023n0qVLDUnGnDlz0iyzfv16Q5IxadIkwzBS/77YvHmzERwcbDg4OFjElpn4U7N7925DksUt4o4fP25ISvNx7/d5slmzZhklS5Y0HBwcjGLFihkTJkywuH1kRvXe296dO3ca4eHhRsGCBQ0HBwfD1dXVqFmzprF48eIM25PMmvdQarK77cnWrFljVKtWzXBycjK8vLyMV155xeK9naxdu3ZGzZo1rb4uAB6OyTCyaIUbAMBjb+/evapYsaK+/PLLFLdGA4DMqF+/vgoUKKAvvvgiu0NBJpw7d04BAQFatGgRPd3AI8KcbgB4SqV2r9eJEyfKzs5OtWrVyoaIADxNRo8erW+++SbTt5dD9po4caLKlStHwg08QvR0A8BTauTIkdq1a5fq1q2rnDlzmm+PlDyXFwAAALZH0g0AT6k1a9Zo5MiR+vPPPxUXFyd/f3+98soreuutt7Lk3uIAAADIGEk3AAAAAAA2wpxuAAAAAABshKQbAAAAAAAbeeon9SUlJenMmTNyc3OTyWTK7nAAAAAAAE8BwzB0/fp1FShQQHZ2afdnP/VJ95kzZ+Tn55fdYQAAAAAAnkKnTp1SoUKF0tz/1Cfdbm5uku5eCHd392yOBgAAAADwNLh27Zr8/PzMOWdanvqkO3lIubu7O0k3AAAAACBLZTSNmYXUAAAAAACwEZJuAAAAAABshKQbAAAAAAAbeerndAMAgCdDUlKSEhISsjsMAAAkSfb29sqRI8dD10PSDQAAsl1CQoKOHz+upKSk7A4FAAAzT09P+fr6ZrhYWnpIugEAQLYyDENnz55Vjhw55OfnJzs7Zr8BALKXYRi6efOmLly4IEnKnz//A9dF0g0AALLVnTt3dPPmTRUoUEC5cuXK7nAAAJAkOTs7S5IuXLigfPnyPfBQc35KBgAA2SoxMVGS5ODgkM2RAABgKfnH4Nu3bz9wHSTdAADgsfAw8+UAALCFrPi/iaQbAAAAeIqsX79eJpNJV69elSTNmzdPnp6e6R4zYsQIPfPMMzaPDfgvIukGAAAAHqGIiAiZTCb16tUrxb7evXvLZDIpIiIiy87Xrl07HT58OMvqA5A5JN0AAADAI+bn56dFixbp33//NW+7deuWFi5cKH9//yw9l7Ozs/Lly5eldQKwHkk3AAAA8IhVqlRJfn5++u6778zbvvvuO/n7+6tixYrmbUlJSRozZowCAgLk7OysChUq6Ntvv7Wo6+eff1aJEiXk7OysunXr6sSJExb7UxtePnbsWPn4+MjNzU1du3bVrVu3LPbv2LFDDRo0UN68eeXh4aHatWtr9+7dWdN44D+GpBsAAADIBl26dNHcuXPNzz///HN17tzZosyYMWO0YMECzZgxQwcOHNCAAQP08ssvKyoqSpJ06tQptWrVSuHh4dq7d6+6deumoUOHpnvexYsXa8SIERo9erR27typ/Pnza9q0aRZlrl+/rk6dOmnjxo3aunWrAgMD1aRJE12/fj2LWg/8d3CfbgAAACAbvPzyyxo2bJhOnjwpSdq0aZMWLVqk9evXS5Li4+M1evRoRUZGKiQkRJJUtGhRbdy4UTNnzlTt2rU1ffp0FStWTJ988okkqWTJktq3b5/GjRuX5nknTpyorl27qmvXrpKkUaNGKTIy0qK3u169ehbHzJo1S56enoqKilKzZs2y7BoA/wUk3QAAAEA28Pb2VtOmTTVv3jwZhqGmTZsqb9685v1Hjx7VzZs31aBBA4vjEhISzEPQDx48qKpVq1rsT07Q03Lw4MEUi7iFhIRo3bp15ufnz5/X22+/rfXr1+vChQtKTEzUzZs3FRMT80BtBf7LSLqR5YIHL8juEAAAD2jXRx2zOwTgP6VLly7q06ePJGnq1KkW++Li4iRJK1asUMGCBS32OTo62jSuTp066dKlS5o0aZIKFy4sR0dHhYSEKCEhwabnBZ5GJN0AAABANmnUqJESEhJkMpkUFhZmsa906dJydHRUTEyMateunerxQUFBWr58ucW2rVu3pnvOoKAgbdu2TR07/t+PbPcfs2nTJk2bNk1NmjSRdHfu+D///GN1uwD8H5JuAAAAIJvkyJFDBw8eNP/7Xm5ubho0aJAGDBigpKQk1axZU7Gxsdq0aZPc3d3VqVMn9erVS5988okGDx6sbt26adeuXZo3b16653z99dcVERGhypUrq0aNGvrqq6904MABFS1a1FwmMDBQX3zxhSpXrqxr165p8ODBcnZ2zvL2A/8FrF4OAAAAZCN3d3e5u7unuu/999/XO++8ozFjxigoKEiNGjXSihUrFBAQIEny9/fX0qVLtWzZMlWoUEEzZszQ6NGj0z1fu3bt9M4772jIkCEKDg7WyZMn9eqrr1qUmTNnjq5cuaJKlSrplVdeUb9+/bjXN/CATIZhGNkdhC1du3ZNHh4eio2NTfPLDFmLOd0A8OTKjjndt27d0vHjxxUQECAnJ6dHfn4AANKS3v9R1uaa9HQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCPZmnQXKVJEJpMpxaN3796S7q4U17t3b+XJk0eurq5q3bq1zp8/n50hAwAAAABgtWxNunfs2KGzZ8+aH2vWrJEkvfDCC5KkAQMG6Mcff9SSJUsUFRWlM2fOqFWrVtkZMgAAAAAAVsuZnSf39va2eD527FgVK1ZMtWvXVmxsrObMmaOFCxeqXr16kqS5c+cqKChIW7duVbVq1bIjZAAAAAAArPbYzOlOSEjQl19+qS5dushkMmnXrl26ffu2QkNDzWVKlSolf39/bdmyJc164uPjde3aNYsHAAAAAADZ4bFJupctW6arV68qIiJCknTu3Dk5ODjI09PTopyPj4/OnTuXZj1jxoyRh4eH+eHn52fDqAEAANJnMpm0bNmyh6qjTp066t+/f5bE86itX79eJpNJV69eze5QslxWvLawXpEiRTRx4kSryz9t77158+alyI3wZHhsku45c+aocePGKlCgwEPVM2zYMMXGxpofp06dyqIIAQAALJ07d059+/ZV0aJF5ejoKD8/P4WHh2vt2rXZHVqanrZEJDudPXtWjRs3zu4wYCWS1ofz3XffqWHDhsqTJ49MJpP27t2bosy5c+f0yiuvyNfXVy4uLqpUqZKWLl2aan2GYSg0NFRhYWEp9k2bNk2enp46ffp0VjcjW2TrnO5kJ0+eVGRkpL777jvzNl9fXyUkJOjq1asWH47z58/L19c3zbocHR3l6Ohoy3ABAMAjEDx4wSM9366POmaq/IkTJ1SjRg15enrqo48+Urly5XT79m2tXr1avXv31qFDh2wU6ePBMAwlJiYqZ87H4s9JSY8+pvT+JgWyQ0xMjPz9/W1S940bN1SzZk21bdtW3bt3T7VMx44ddfXqVS1fvlx58+bVwoUL1bZtW+3cuVMVK1a0KGsymTR37lyVK1dOM2fOVM+ePSVJx48f15AhQzR9+nQVKlQoy9tx+/Zt2dvbZ3m96Xkserrnzp2rfPnyqWnTpuZtwcHBsre3t/ilODo6WjExMQoJCcmOMAEAAMxee+01mUwmbd++Xa1bt1aJEiVUpkwZDRw4UFu3brUo+88//+j5559Xrly5FBgYqOXLl1vsj4qKUpUqVeTo6Kj8+fNr6NChunPnTprnjo+P16BBg1SwYEG5uLioatWqWr9+vXn/yZMnFR4erty5c8vFxUVlypTRzz//rBMnTqhu3bqSpNy5c8tkMpmn9iUlJWnMmDEKCAiQs7OzKlSooG+//dZcZ3IP+cqVKxUcHCxHR0dt3LhR8fHx6tevn/LlyycnJyfVrFlTO3bssPo6GoahESNGyN/fX46OjipQoID69etn0dY33nhDfn5+cnR0VPHixTVnzpx0Y8qoLZK0f/9+NW7cWK6urvLx8dErr7yif/75x7y/Tp066tevn4YMGSIvLy/5+vpqxIgRFnXcO7z8xIkTMplM+u6771S3bl3lypVLFSpUSLEW0ezZs+Xn56dcuXLp+eef1/jx4zPsfX3jjTdUokQJ5cqVS0WLFtU777yj27dvm/ePGDFCzzzzjD7//HP5+/vL1dVVr732mhITE/Xhhx/K19dX+fLl0wcffGBRb0xMjFq0aCFXV1e5u7urbdu2FrfnTa73iy++UJEiReTh4aH27dvr+vXr5jLXr19Xhw4d5OLiovz582vChAkZToc4duyYWrRoIR8fH7m6uurZZ59VZGSkRZkLFy4oPDxczs7OCggI0FdffWWxP/l639vbevXqVZlMJovPQrL169erc+fOio2NNd+mOPn1nDZtmgIDA+Xk5CQfHx+1adMmzdgvXbqkF198UQULFlSuXLlUrlw5ff311xZlrLkmGX2Gpbs98/7+/ub3yqVLl9KMK1nt2rVVrVo1TZ8+XVeuXMmwfGa88sorGj58uMWaW/fbvHmz+vbtqypVqqho0aJ6++235enpqV27dqVa3s/PT5MmTdKgQYN0/PhxGYahrl27qmHDhnrllVcy/JyuWrVKNWvWlKenp/LkyaNmzZrp2LFj5v3J75NvvvlGtWvXlpOTk7766qs0vyNtJduT7qSkJM2dO1edOnWy+FXSw8NDXbt21cCBA7Vu3Trt2rVLnTt3VkhICCuXAwCAbHX58mWtWrVKvXv3louLS4r99ydRI0eOVNu2bfXHH3+oSZMm6tChgy5fvixJ+vvvv9WkSRM9++yz+v333zV9+nTNmTNHo0aNSvP8ffr00ZYtW7Ro0SL98ccfeuGFF9SoUSMdOXJEktS7d2/Fx8frt99+0759+zRu3Di5urrKz8/PPNQzOjpaZ8+e1aRJkyTdXRdnwYIFmjFjhg4cOKABAwbo5ZdfVlRUlMW5hw4dqrFjx+rgwYMqX768hgwZoqVLl2r+/PnavXu3ihcvrrCwMHP7MrJ06VJNmDBBM2fO1JEjR7Rs2TKVK1fOvL9jx476+uuvNXnyZB08eFAzZ86Uq6trujFl1JarV6+qXr16qlixonbu3KlVq1bp/Pnzatu2rUW98+fPl4uLi7Zt26YPP/xQ7733nvkWt2l56623NGjQIO3du1clSpTQiy++aP4BZdOmTerVq5def/117d27Vw0aNEiRCKfGzc1N8+bN059//qlJkyZp9uzZmjBhgkWZY8eOaeXKlVq1apW+/vprzZkzR02bNtXp06cVFRWlcePG6e2339a2bdsk3f0bvEWLFrp8+bKioqK0Zs0a/fXXX2rXrl2KepctW6affvpJP/30k6KiojR27Fjz/oEDB2rTpk1avny51qxZow0bNmj37t3pticuLk5NmjTR2rVrtWfPHjVq1Ejh4eGKiYkxl4mIiNCpU6e0bt06ffvtt5o2bZouXLiQ4bVKS/Xq1TVx4kS5u7ubb1c8aNAg7dy5U/369dN7772n6OhorVq1SrVq1Uqznlu3bik4OFgrVqzQ/v371aNHD73yyivavn17pq5JRp/hbdu2qWvXrurTp4/27t2runXrpvudkOy3335T8+bNNXnyZOXPn19t27bVihUrlJiYmKLshg0b5Orqmu7j/h87MlK9enV98803unz5spKSkrRo0SLdunVLderUSfOYTp06qX79+urSpYumTJmi/fv3a+bMmVZ9Tm/cuKGBAwdq586dWrt2rezs7PT8888rKSnJ4hxDhw7V66+/roMHDyosLCzN70hbyfbxQJGRkYqJiVGXLl1S7JswYYLs7OzUunVrxcfHKywsTNOmTcuGKAEAAP7P0aNHZRiGSpUqZVX5iIgIvfjii5Kk0aNHa/Lkydq+fbsaNWqkadOmyc/PT1OmTJHJZFKpUqV05swZvfHGGxo+fLjs7Cz7SGJiYjR37lzFxMSY18IZNGiQVq1apblz52r06NGKiYlR69atzclr0aJFzcd7eXlJkvLly2f+cSA+Pl6jR49WZGSkeURh0aJFtXHjRs2cOVO1a9c2H//ee++pQYMGku7+wTt9+nTNmzfPPLd59uzZWrNmjebMmaPBgwdneG1iYmLk6+ur0NBQ2dvby9/fX1WqVJEkHT58WIsXL9aaNWvMvWv3tiW1mKxpy5QpU1SxYkWNHj3aXMfnn38uPz8/HT58WCVKlJAklS9fXu+++64kKTAwUFOmTNHatWvN50rNoEGDzKM3R44cqTJlyujo0aMqVaqUPv30UzVu3FiDBg2SJJUoUUKbN2/WTz/9lO41evvtt83/LlKkiAYNGqRFixZpyJAh5u1JSUn6/PPP5ebmptKlS6tu3bqKjo7Wzz//LDs7O5UsWVLjxo3TunXrVLVqVa1du1b79u3T8ePHzQsPL1iwQGXKlNGOHTv07LPPmuudN2+e3NzcJN3t7Vy7dq0++OADXb9+XfPnz9fChQtVv359SXdHsGa0RlOFChVUoUIF8/P3339f33//vZYvX64+ffro8OHDWrlypbZv326OY86cOQoKCkq33vQ4ODjIw8NDJpPJYlpATEyMXFxc1KxZM7m5ualw4cIphkHfq2DBgubXT5L69u2r1atXa/HixapSpYpV18Saz/CkSZPUqFEj82uc/F5ZtWpVuu308/PTm2++qTfffFPbt2/XggULFBERoZw5c6pDhw6KiIhQ2bJlJUmVK1dOdV72vXx8fNLdf7/FixerXbt2ypMnj3LmzKlcuXLp+++/V/HixdM9btasWSpTpox+++03LV26VN7e3ho1alSGn9PWrVtb1PP555/L29tbf/75p7mdktS/f3+1atXK/Dy970hbyPaku2HDhjIMI9V9Tk5Omjp1qqZOnfqIowIAAEhbWn+7pKV8+fLmf7u4uMjd3d3ca3fw4EGFhITIZDKZy9SoUUNxcXE6ffp0ivmZ+/btU2JiojkxTBYfH688efJIkvr166dXX31Vv/zyi0JDQ9W6dWuLGO539OhR3bx5M0UymZCQkCIBqVy5svnfx44d0+3bt1WjRg3zNnt7e1WpUkUHDx5M95oke+GFFzRx4kQVLVpUjRo1UpMmTRQeHq6cOXNq7969ypEjh0XSn5p7Y7KmLb///rvWrVuXas/WsWPHLJLue+XPnz/D3tZ7j8mfP7+ku0OlS5UqpejoaD3//PMW5atUqZJh0v3NN99o8uTJOnbsmOLi4nTnzh25u7tblClSpIg5MZbuJks5cuSw+NHGx8fH4n3n5+dncaef0qVLy9PTUwcPHjQnu/fXe+81+Ouvv3T79m3zjyTS3dGqJUuWTLc9cXFxGjFihFasWKGzZ8/qzp07+vfff8093QcPHlTOnDkVHBxsPqZUqVI2WQStQYMGKly4sPn916hRI/NUkNQkJiZq9OjRWrx4sf7++28lJCQoPj7eXN6aa2LNZ/jgwYMp3ishISEZJt33qlKliqpUqaLx48dr2LBhGj9+vCIjI82JtrOzc4bJcGa98847unr1qiIjI5U3b14tW7ZMbdu21YYNGyxGsNwvX7586tmzp5YtW6aWLVtKsu5zeuTIEQ0fPlzbtm3TP//8Y+7hjomJsUi67/2OkDL/Hfmwsj3pBgAAeNIEBgbKZDJZvVja/Yv2mEymFMMfrRUXF6ccOXJo165dypEjh8W+5D9Ou3XrprCwMK1YsUK//PKLxowZo08++UR9+/ZNs05JWrFihQoWLGix7/4FalMbTv8w/Pz8FB0drcjISK1Zs0avvfaaPvroI0VFRcnZ2dmqOu6NyZq2xMXFKTw8XOPGjUtRV3KiLD3Y63bvMck/pDzoay1JW7ZsUYcOHTRy5EiFhYXJw8NDixYt0ieffJLmeZPPnRXvu6x87yYbNGiQ1qxZo48//ljFixeXs7Oz2rRpo4SEBKvrSP4x4d4fwO6d524tNzc37d69W+vXr9cvv/yi4cOHa8SIEdqxY0eqSf5HH32kSZMmaeLEiSpXrpxcXFzUv3//TMVuzWc4K0RHR+uLL77Ql19+qdjYWHXv3l1du3Y179+wYUOGq+/PnDlTHTp0sOp8x44dMw8PL1OmjKS7oxo2bNigqVOnasaMGekenzNnTovpxtZ8TsPDw1W4cGHNnj1bBQoUUFJSksqWLZvi9bj/eyuz35EPi6QbAAAgk7y8vBQWFqapU6eqX79+Kf6gu//uK+kJCgrS0qVLZRiGOUnbtGmT3NzcUl25t2LFikpMTNSFCxf03HPPpVmvn5+fevXqpV69emnYsGGaPXu2+vbtKwcHB0mymONZunRpOTo6KiYmJsNe5XsVK1ZMDg4O2rRpkwoXLizpbuKzY8eOTN1X3NnZWeHh4QoPD1fv3r1VqlQp7du3T+XKlVNSUpKioqLSXbzpXta0Jfk2RkWKFHmkq6+XLFkyxSJzGS06t3nzZhUuXFhvvfWWedvJkycfOpagoCCdOnVKp06dMvd2//nnn7p69apKly5tVR1FixaVvb29duzYYR6RERsbq8OHD6c7L3rTpk2KiIgw9+TGxcXpxIkT5v2lSpXSnTt3tGvXLnOPe3R0tMVt7ry9vSXdvW1b8giGjIZKOzg4pDq3OWfOnAoNDVVoaKjeffddeXp66tdff7UYjnxv7C1atNDLL78s6e4PKocPHzZfM2uuiTWf4aCgIPP8+2T3L9CYmn/++UeLFi3SF198oV27dqlBgwYaO3asWrZsKScnJ4uyWT28/ObNm5KUYkpMjhw5HuiHmow+p5cuXVJ0dLRmz55tvo4bN260uv60viNtgaQbAADgAUydOlU1atRQlSpV9N5776l8+fK6c+eO1qxZo+nTp1s9vPq1117TxIkT1bdvX/Xp00fR0dF69913NXDgwBR/vEp353Z26NBBHTt21CeffKKKFSvq4sWLWrt2rcqXL6+mTZuqf//+aty4sUqUKKErV65o3bp15vmwhQsXlslk0k8//aQmTZrI2dlZbm5uGjRokAYMGKCkpCTVrFlTsbGx2rRpk9zd3dWpU6dUY3dxcdGrr76qwYMHy8vLS/7+/vrwww918+ZNix619MybN0+JiYmqWrWqcuXKpS+//FLOzs4qXLiw8uTJo06dOqlLly6aPHmyKlSooJMnT+rChQspFj1LZk1bevfurdmzZ+vFF180r05+9OhRLVq0SJ999lmK3ses0rdvX9WqVUvjx49XeHi4fv31V61cudJiasH9AgMDFRMTo0WLFunZZ5/VihUr9P333z90LKGhoSpXrpw6dOigiRMn6s6dO3rttddUu3btFENx0+Lm5qZOnTqZX/98+fLp3XfflZ2dXYZt+u677xQeHi6TyaR33nnHIikrWbKkGjVqpJ49e2r69OnKmTOn+vfvbzHywdnZWdWqVdPYsWMVEBCgCxcuWMx9T02RIkUUFxentWvXqkKFCsqVK5d+/fVX/fXXX6pVq5Zy586tn3/+WUlJSWkOkQ8MDNS3336rzZs3K3fu3Bo/frzOnz9vTrqtuSbWfIb79eunGjVq6OOPP1aLFi20evVqq4aWV61aVU5OTurUqZOWLVtmMXLjfpkdXn758mXFxMTozJkzku7+ECLdvXWer6+vSpUqpeLFi6tnz576+OOPlSdPHi1btkxr1qzJcApFajL6nObOnVt58uTRrFmzlD9/fsXExGjo0KFW1Z3ed6QtZPvq5QAAAE+iokWLavfu3apbt67+97//qWzZsmrQoIHWrl2r6dOnW11PwYIF9fPPP2v79u2qUKGCevXqpa5du6abQMydO1cdO3bU//73P5UsWVItW7a06FlLTExU7969FRQUpEaNGqlEiRLmxWgLFiyokSNHaujQofLx8VGfPn0k3V3M6p133tGYMWPMx61YsUIBAQHpxj927Fi1bt1ar7zyiipVqqSjR49q9erVyp07t1Xt9/T01OzZs1WjRg2VL19ekZGR+vHHH81zW6dPn642bdrotddeU6lSpdS9e3fduHEj3TozakuBAgW0adMmJSYmqmHDhipXrpz69+8vT0/PVH/oyCo1atTQjBkzNH78eFWoUEGrVq3SgAEDUvRA3qt58+YaMGCA+vTpo2eeeUabN2/WO++889CxmEwm/fDDD8qdO7dq1aql0NBQFS1aVN98802m6hk/frxCQkLUrFkzhYaGqkaNGgoKCkq3TePHj1fu3LlVvXp1hYeHKywsTJUqVbIok7z4WO3atdWqVSv16NFD+fLlsyjz+eef686dOwoODlb//v0zXN27evXq6tWrl9q1aydvb299+OGH8vT01Hfffad69eopKChIM2bM0Ndff20eHn2/t99+W5UqVVJYWJjq1KkjX19f8xzkzFyTjD7D1apV0+zZszVp0iRVqFBBv/zyS4Y/Kkh3p1UcOHBAQ4YMSTfhfhDLly9XxYoVzQsFtm/fXhUrVjQPG7e3t9fPP/8sb29vhYeHq3z58lqwYIHmz5+vJk2aZPp8GX1O7ezstGjRIu3atUtly5bVgAED9NFHH1lVd3rfkbZgMjK7EsgT5tq1a/Lw8FBsbGyKBSdgG8GDF2R3CACAB7Tro46P/Jy3bt3S8ePHFRAQkO4f6sDTqHv37jp06JA2bNiQ3aFkiRs3bqhgwYL65JNPrB7t8LTjmjzZ0vs/ytpck+HlAAAAwCPy8ccfq0GDBnJxcdHKlSs1f/78J/qWuHv27NGhQ4dUpUoVxcbG6r333pMktWjRIpsjyz5cE9yPpBsAAAB4RLZv364PP/xQ169fV9GiRTV58mR169Ytu8N6KB9//LGio6Pl4OCg4OBgbdiwQXnz5s3usLIV1wT3IukGAAAAHpHFixdndwhZqmLFitq1a1d2h/FY4ZrgfiykBgAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAA2NG/ePHl6embqmIiICLVs2TJTx4wYMULPPPNMltf7IOrUqaP+/fvb/Dx48plMJi1btszq8g/yeXqcWfO5xZOPpBsAAOABpJXArl+/XiaTSVevXpUktWvXTocPH360wQH/ESStD2fWrFmqU6eO3N3dLb637nX48GG1aNFCefPmlbu7u2rWrKl169alWl98fLzKlCmjHj16pNg3ZMgQBQQE6Pr161ndjMdezuwOAAAAIDUx75V7pOfzH77PJvU6OzvL2dnZJnX/lyQkJMjBwSG7w7DwOMaEp09MTIz8/f1tUvfNmzfVqFEjNWrUSMOGDUu1TLNmzRQYGKhff/1Vzs7Omjhxopo1a6Zjx47J19fXoqyjo6MWLFigkJAQtW7dWmFhYZKkrVu3asKECYqMjJSbm1uWtsEwDCUmJipnzsc3taWnGwAAwIZSGw47atQo5cuXT25uburWrZuGDh2aam/dxx9/rPz58ytPnjzq3bu3bt++neH5Zs6cKT8/P+XKlUtt27ZVbGxsmmXj4+PVr18/5cuXT05OTqpZs6Z27NhhUSYqKkpVqlSRo6Oj8ufPr6FDh+rOnTvm/Tdu3FDHjh3l6uqq/Pnz65NPPskwxuTeyfRiTR5J8MEHH6hAgQIqWbKkJOnUqVNq27atPD095eXlpRYtWujEiRPm49avX68qVarIxcVFnp6eqlGjhk6ePClJ+v3331W3bl25ubnJ3d1dwcHB2rlzp0VM95o4caKKFCny0DHdLzExUV27dlVAQICcnZ1VsmRJTZo0yaJM8rlGjx4tHx8feXp66r333tOdO3c0ePBgeXl5qVChQpo7d67Fcfv27VO9evXk7OysPHnyqEePHoqLi0tRb3rvrbNnz6pp06ZydnZWQECAFi5cqCJFimjixIlptmnHjh1q0KCB8ubNKw8PD9WuXVu7d++2KHPkyBHVqlVLTk5OKl26tNasWWOx//5RIpK0d+9emUymVK/nvHnzNHLkSP3+++8ymUwymUyaN2+eDMPQiBEj5O/vL0dHRxUoUED9+vVLM/Zjx46pRYsW8vHxkaurq5599llFRkZalLHmmly9elXdunWTt7e33N3dVa9ePf3+++8W9YwdO1Y+Pj5yc3NT165ddevWrTTjShYQEKDQ0FB98cUXunnzZoblM6N///4aOnSoqlWrlur+f/75R0eOHNHQoUNVvnx5BQYGauzYsbp586b279+f6jHBwcF666231LVrV129elW3bt1S586d1bdvX9WuXVsbN27Uc889J2dnZ/n5+alfv366ceOG+fgvvvhClStXlpubm3x9ffXSSy/pwoUL5v3J75OVK1cqODhYjo6O2rhxY7qf7+xG0g0AAPAIffXVV/rggw80btw47dq1S/7+/po+fXqKcuvWrdOxY8e0bt06zZ8/X/PmzdO8efPSrfvo0aNavHixfvzxR61atUp79uzRa6+9lmb5IUOGaOnSpZo/f752796t4sWLKywsTJcvX5Yk/f3332rSpImeffZZ/f7775o+fbrmzJmjUaNGmesYPHiwoqKi9MMPP+iXX37R+vXrUyRbDxrr2rVrFR0drTVr1uinn37S7du3FRYWJjc3N23YsEGbNm2Sq6urGjVqpISEBN25c0ctW7ZU7dq19ccff2jLli3q0aOHTCaTJKlDhw4qVKiQduzYoV27dmno0KGyt7fPMNaHiSk1SUlJKlSokJYsWaI///xTw4cP15tvvqnFixdblPv111915swZ/fbbbxo/frzeffddNWvWTLlz59a2bdvUq1cv9ezZU6dPn5Z09weQsLAw5c6dWzt27NCSJUsUGRmpPn36WNSb0XurY8eOOnPmjNavX6+lS5dq1qxZFklPaq5fv65OnTpp48aN2rp1qwIDA9WkSRPzUOKkpCS1atVKDg4O2rZtm2bMmKE33ngjU9f+fu3atdP//vc/lSlTRmfPntXZs2fVrl07LV26VBMmTNDMmTN15MgRLVu2TOXKpT1yJi4uTk2aNNHatWu1Z88eNWrUSOHh4YqJicnUNXnhhRd04cIFrVy5Urt27VKlSpVUv3598+dp8eLFGjFihEaPHq2dO3cqf/78mjZtWobt/PPPP1WlShW9/fbb8vHxUZcuXRQVFSXDMFKU/eqrr+Tq6pruY8OGDdZeYuXJk0clS5bUggULdOPGDd25c0czZ85Uvnz5FBwcnOZxb731lnx9fdWvXz+9/fbbMplMGj16tI4dO6ZGjRqpdevW+uOPP/TNN99o48aNFu/R27dv6/3339fvv/+uZcuW6cSJE4qIiEhxjqFDh2rs2LE6ePCgypcvnyWfb1t5fPvgAQAAHnM//fSTXF1dLbYlJiame8ynn36qrl27qnPnzpKk4cOH65dffrHojZSk3Llza8qUKcqRI4dKlSqlpk2bau3aterevXuadd+6dUsLFixQwYIFzedq2rSpPvnkkxTDQG/cuKHp06dr3rx5aty4sSRp9uzZWrNmjebMmaPBgwdr2rRp8vPz05QpU2QymVSqVCmdOXNGb7zxhoYPH66bN29qzpw5+vLLL1W/fn1J0vz581WoUKEMr501sbq4uOizzz4zD+H+8ssvlZSUpM8++8ycSM+dO1eenp5av369KleurNjYWDVr1kzFihWTJAUFBZnPGRMTo8GDB6tUqVKSpMDAwAzjvF9mY2rYsGGKOuzt7TVy5Ejz84CAAG3ZskWLFy9W27Ztzdu9vLw0efJk2dnZqWTJkvrwww918+ZNvfnmm5KkYcOGaezYsdq4caPat2+vhQsXmq+ri4uLJGnKlCkKDw/XuHHj5OPjIyn999ahQ4cUGRmpHTt2qHLlypKkzz77LMNrVa9ePYvns2bNkqenp6KiotSsWTNFRkbq0KFDWr16tQoUKCBJGj16tPm99yCcnZ3l6uqqnDlzWry/Y2Ji5Ovrq9DQUNnb28vf319VqlRJs54KFSqoQoUK5ufvv/++vv/+ey1fvlx9+vSx6pps3LhR27dv14ULF+To6Cjp7kiVZcuW6dtvv1WPHj00ceJEde3aVV27dpV0d8RLZGRkhr3dJUuW1OjRo/XBBx9o/fr1WrBggcLDw5U3b1517NhRnTp1UkBAgCSpefPmqlq1arr1JX/mrGEymRQZGamWLVvKzc1NdnZ2ypcvn1atWqXcuXOneVzOnDm1YMECBQcHKykpSZs2bZKTk5PGjBmjDh06mBdaDAwM1OTJk1W7dm1Nnz5dTk5O6tKli7meokWLavLkyXr22WcVFxdn8X373nvvqUGDBubnWfH5thV6ugEAAB5Q3bp1tXfvXovHZ599lu4x0dHRKRKA1BKCMmXKKEeOHObn+fPnz7C30d/f3+IP6pCQECUlJSk6OjpF2WPHjun27duqUaOGeZu9vb2qVKmigwcPSpIOHjyokJAQczIpSTVq1FBcXJxOnz6tY8eOKSEhweKPfC8vL/Ow64eNtVy5chZzpn///XcdPXpUbm5u5l47Ly8v3bp1S8eOHZOXl5ciIiIUFham8PBwTZo0SWfPnjUfP3DgQHXr1k2hoaEaO3asjh07lmGc98tsTGmZOnWqgoOD5e3tLVdXV82aNcuiZ1W6+x6ws/u/P9d9fHwsemxz5MihPHnymN8XBw8eVIUKFcwJt3T39br/uqb33oqOjlbOnDlVqVIl8/7ixYunm2BJ0vnz59W9e3cFBgbKw8ND7u7uiouLM7fp4MGD8vPzMyfc0t3X3BZeeOEF/fvvvypatKi6d++u77//3mJKxP3i4uI0aNAgBQUFydPTU66urjp48KA5dmuuye+//664uDjlyZPHolf5+PHj5vfBwYMHUyTEmbkGJpNJdevW1dy5c3X69GmFhIRo5MiRGjBggLmMm5ubihcvnu4jM2tMGIah3r17K1++fNqwYYO2b9+uli1bKjw83OKzlZrSpUurdevWatCggfnHit9//13z5s2zuEZhYWFKSkrS8ePHJUm7du1SeHi4/P395ebmptq1a0tSis9Hcp3JsuLzbSv0dAMAADwgFxcXFS9e3GJb8lDfh3X/sEiTyaSkpKQsqftJcW/yKN1NjoKDg/XVV1+lKOvt7S3pbi9zv379tGrVKn3zzTd6++23tWbNGlWrVk0jRozQSy+9pBUrVmjlypV69913tWjRIj3//POys7NLMVw3tTn0DxLT/RYtWqRBgwbpk08+UUhIiNzc3PTRRx9p27ZtFuVSew9kxfvCFu+tTp066dKlS5o0aZIKFy4sR0dHhYSEpDnEPjXJPzDc+zpYs47B/fz8/BQdHa3IyEitWbNGr732mj766CNFRUWlOtx40KBBWrNmjT7++GNzUtqmTZtMxR4XF6f8+fNr/fr1KfZl5S3Odu/erQULFujrr7+WyWQyJ5rJvvrqK/Xs2TPdOlauXKnnnnvOqvP9+uuv+umnn3TlyhW5u7tLkqZNm6Y1a9Zo/vz5Gjp0aLrH58yZ02KBs7i4OPXs2TPVOfb+/v7mKRJhYWH66quv5O3trZiYGIWFhaV4Pe7/LKb3+c5uJN0AAACPUMmSJbVjxw517NjRvO3+xcseVExMjM6cOWPuTdy6dat5aPL9ihUrJgcHB23atEmFCxeWdDfB2bFjh3noZ1BQkJYuXSrDMMy93Zs2bZKbm5sKFSokLy8v2dvba9u2bebVla9cuaLDhw+be6eyItZklSpV0jfffKN8+fKZE4DUVKxYURUrVtSwYcMUEhKihQsXmheKKlGihEqUKKEBAwboxRdf1Ny5c/X888/L29tb586ds2jr3r17021DZmK616ZNm1S9enWLOexZ0SsXFBSkefPm6caNG+aEZNOmTRle13uVLFlSd+7c0Z49e8xzdo8ePaorV66ke9ymTZs0bdo0NWnSRNLdxeX++ecfi9hOnTqls2fPKn/+/JLuvub3Sv6R4uzZs+Ze5IxeAwcHh1SndDg7Oys8PFzh4eHq3bu3SpUqpX379ln0Vt8be0REhDk5i4uLs1i4zZprUqlSJZ07d045c+a0WHzvXkFBQdq2bZvFZ//+a5Ca06dP68svv9QXX3yhY8eOKTw8XHPmzFGjRo1SrNid1cPLkxduu3fERfLzB/mhplKlSvrzzz9T/FiZbN++fbp06ZLGjh0rPz8/ScrUYmhpfb6zG8PLAQAAHqG+fftqzpw5mj9/vo4cOaJRo0bpjz/+sBjC/aCcnJzUqVMn/f7779qwYYP69euntm3bppjPLd3tJXr11Vc1ePBgrVq1Sn/++ae6d++umzdvmuecvvbaazp16pT69u2rQ4cO6YcfftC7776rgQMHys7OTq6ururatasGDx6sX3/9Vfv371dERESKP9AfNtZkHTp0UN68edWiRQtt2LBBx48f1/r169WvXz+dPn1ax48f17Bhw7RlyxadPHlSv/zyi44cOaKgoCD9+++/6tOnj9avX6+TJ09q06ZN2rFjh3nOd506dXTx4kV9+OGHOnbsmKZOnaqVK1dm2I6MYkpNYGCgdu7cqdWrV+vw4cN65513suSHlw4dOpiv6/79+7Vu3Tr17dtXr7zyink+d0ZKlSql0NBQ9ejRQ9u3b9eePXvUo0cPOTs7p/seDQwM1BdffKGDBw9q27Zt6tChg8Uw5tDQUJUoUcLiNX/rrbcs6ihevLj8/Pw0YsQIHTlyRCtWrMhwNfwiRYro+PHj2rt3r/755x/Fx8dr3rx5mjNnjvbv36+//vpLX375pZydnc0/LqUW+3fffae9e/fq999/10svvWSRUFpzTUJDQxUSEqKWLVvql19+0YkTJ7R582a99dZb5qTx9ddf1+eff665c+fq8OHDevfdd3XgwIH0XxBJhQsX1vfff6/evXvr7NmzWrJkiZo1a5bqLbIyO7z83Llz2rt3r44ePSrpbtK7d+9e8+JvISEhyp07t/l1O3z4sAYPHqzjx4+radOmGcZ+vzfeeEObN29Wnz59tHfvXh05ckQ//PCDeSE1f39/OTg46NNPP9Vff/2l5cuX6/3338+w3ow+39mNpBsAAOAR6tChg4YNG6ZBgwapUqVKOn78uCIiIuTk5PTQdRcvXlytWrVSkyZN1LBhQ5UvXz7d1ZHHjh2r1q1b65VXXlGlSpV09OhRrV692tzLWLBgQf3888/avn27KlSooF69eqlr1656++23zXV89NFHeu655xQeHq7Q0FDVrFkz3VWNHzRWScqVK5d+++03+fv7q1WrVgoKCjLfdsnd3V25cuXSoUOH1Lp1a5UoUUI9evRQ79691bNnT+XIkUOXLl1Sx44dVaJECbVt21aNGzc2L2gWFBSkadOmaerUqapQoYK2b9+uQYMGZdiOjGJKTc+ePdWqVSu1a9dOVatW1aVLl9JdZd5auXLl0urVq3X58mU9++yzatOmjerXr68pU6Zkqp4FCxbIx8dHtWrV0vPPP6/u3bvLzc0t3ffonDlzdOXKFVWqVEmvvPKK+VZ0yezs7PT999/r33//VZUqVdStWzd98MEHFnXY29vr66+/1qFDh1S+fHmNGzfOYqX81LRu3VqNGjVS3bp15e3tra+//lqenp6aPXu2atSoofLlyysyMlI//vij8uTJk2od48ePV+7cuVW9enWFh4crLCwsRY94RtfEZDLp559/Vq1atdS5c2eVKFFC7du318mTJ80/eLRr107vvPOOhgwZouDgYJ08eVKvvvpquu2TpAMHDmjbtm167bXXMpxbn1kzZsxQxYoVzQs01qpVSxUrVtTy5cslSXnz5tWqVasUFxenevXqqXLlytq4caN++OEHi8XnrFW+fHlFRUXp8OHDeu6551SxYkUNHz7cPOLF29tb8+bN05IlS1S6dGmNHTtWH3/8cYb1ZvT5zm4mI7W15p8i165dk4eHh2JjY60e8oOHEzx4QXaHAAB4QLs+6phxoSx269YtHT9+XAEBAVmSeD6JGjRoIF9fX33xxRfZHcojMWLECC1btsyq4dvIfqdPn5afn58iIyPNq9T/13FN/jvS+z/K2lyTOd0AAACP0M2bNzVjxgyFhYUpR44c+vrrr80LPgGPg19//VVxcXEqV66czp49qyFDhqhIkSKqVatWdoeWbbgmeBgk3QAAAI9Q8jDUDz74QLdu3VLJkiW1dOlShYaGZndogKS7C+q9+eab+uuvv+Tm5qbq1avrq6++SnXl7/8KrgkeBsPLkeUYXg4ATy6GlwMA8H+yYng5C6kBAAAAAGAjJN0AAAAAANgISTcAAHgsPOUz3gAAT6Cs+L+JpBsAAGSrHDlySJISEhKyORIAACzdvHlTkh5q0TxWLwcAANkqZ86cypUrly5evCh7e3vZ2dEnAADIXoZh6ObNm7pw4YI8PT3NPxA/CJJuAACQrUwmk/Lnz6/jx4/r5MmT2R0OAABmnp6e8vX1fag6SLoBAEC2c3BwUGBgIEPMAQCPDXt7+4fq4U5G0g0AAB4LdnZ23KcbAPDUYdIUAAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADaS7Un333//rZdffll58uSRs7OzypUrp507d5r3G4ah4cOHK3/+/HJ2dlZoaKiOHDmSjREDAAAAAGCdbE26r1y5oho1asje3l4rV67Un3/+qU8++US5c+c2l/nwww81efJkzZgxQ9u2bZOLi4vCwsJ069atbIwcAAAAAICMZet9useNGyc/Pz/NnTvXvC0gIMD8b8MwNHHiRL399ttq0aKFJGnBggXy8fHRsmXL1L59+0ceMwAAAAAA1srWnu7ly5ercuXKeuGFF5QvXz5VrFhRs2fPNu8/fvy4zp07p9DQUPM2Dw8PVa1aVVu2bEm1zvj4eF27ds3iAQAAAABAdsjWpPuvv/7S9OnTFRgYqNWrV+vVV19Vv379NH/+fEnSuXPnJEk+Pj4Wx/n4+Jj33W/MmDHy8PAwP/z8/GzbCAAAAAAA0pCtSXdSUpIqVaqk0aNHq2LFiurRo4e6d++uGTNmPHCdw4YNU2xsrPlx6tSpLIwYAAAAAADrZWvSnT9/fpUuXdpiW1BQkGJiYiRJvr6+kqTz589blDl//rx53/0cHR3l7u5u8QAAAAAAIDtka9Jdo0YNRUdHW2w7fPiwChcuLOnuomq+vr5au3atef+1a9e0bds2hYSEPNJYAQAAAADIrGxdvXzAgAGqXr26Ro8erbZt22r79u2aNWuWZs2aJUkymUzq37+/Ro0apcDAQAUEBOidd95RgQIF1LJly+wMHQAAAACADGVr0v3ss8/q+++/17Bhw/Tee+8pICBAEydOVIcOHcxlhgwZohs3bqhHjx66evWqatasqVWrVsnJySkbIwcAAAAAIGMmwzCM7A7Clq5duyYPDw/FxsYyv/sRCR68ILtDAAA8oF0fdczuEAAAeCJYm2tm65xuAAAAAACeZiTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCMk3QAAAAAA2AhJNwAAAAAANkLSDQAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCMk3QAAAAAA2AhJNwAAAAAANkLSDQAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCMk3QAAAAAA2AhJNwAAAAAANkLSDQAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANhItibdI0aMkMlksniUKlXKvP/WrVvq3bu38uTJI1dXV7Vu3Vrnz5/PxogBAAAAALBetvd0lylTRmfPnjU/Nm7caN43YMAA/fjjj1qyZImioqJ05swZtWrVKhujBQAAAADAejmzPYCcOeXr65tie2xsrObMmaOFCxeqXr16kqS5c+cqKChIW7duVbVq1R51qAAAAAAAZEq293QfOXJEBQoUUNGiRdWhQwfFxMRIknbt2qXbt28rNDTUXLZUqVLy9/fXli1b0qwvPj5e165ds3gAAAAAAJAdsjXprlq1qubNm6dVq1Zp+vTpOn78uJ577jldv35d586dk4ODgzw9PS2O8fHx0blz59Ksc8yYMfLw8DA//Pz8bNwKAAAAAABSl63Dyxs3bmz+d/ny5VW1alUVLlxYixcvlrOz8wPVOWzYMA0cOND8/Nq1ayTeAAAAAIBske3Dy+/l6empEiVK6OjRo/L19VVCQoKuXr1qUeb8+fOpzgFP5ujoKHd3d4sHAAAAAADZ4bFKuuPi4nTs2DHlz59fwcHBsre319q1a837o6OjFRMTo5CQkGyMEgAAAAAA62Tr8PJBgwYpPDxchQsX1pkzZ/Tuu+8qR44cevHFF+Xh4aGuXbtq4MCB8vLykru7u/r27auQkBBWLgcAAAAAPBGyNek+ffq0XnzxRV26dEne3t6qWbOmtm7dKm9vb0nShAkTZGdnp9atWys+Pl5hYWGaNm1adoYMAAAAAIDVTIZhGNkdhC1du3ZNHh4eio2NZX73IxI8eEF2hwAAeEC7PuqY3SEAAPBEsDbXfKzmdAMAAAAA8DQh6QYAAAAAwEZIugEAAAAAsJFML6QWHx+vbdu26eTJk7p586a8vb1VsWJFBQQE2CI+AAAAAACeWFYn3Zs2bdKkSZP0448/6vbt2/Lw8JCzs7MuX76s+Ph4FS1aVD169FCvXr3k5uZmy5gBAAAAAHgiWDW8vHnz5mrXrp2KFCmiX375RdevX9elS5d0+vRp3bx5U0eOHNHbb7+ttWvXqkSJElqzZo2t4wYAAAAA4LFnVU9306ZNtXTpUtnb26e6v2jRoipatKg6deqkP//8U2fPns3SIAEAAAAAeBJZlXT37NnT6gpLly6t0qVLP3BAAAAAAAA8LTK9kNq99u/fr6ioKCUmJqpGjRoKDg7OqrgAAAAAAHjiPfAtw6ZOnar69esrKipK69atU7169fTBBx9kZWwAAAAAADzRrO7pPnXqlPz8/MzPp0yZogMHDihv3rySpC1btqh58+Z66623sj5KAAAAAACeQFb3dIeGhmrSpEkyDEOSlCdPHq1atUrx8fG6fv26IiMj5e3tbbNAAQAAAAB40liddO/YsUPR0dGqWrWq9u7dq1mzZmnChAlydnaWp6envvnmG82fP9+WsQIAAAAA8ESxeni5u7u7pk2bps2bNysiIkL16tXThg0blJiYqMTERHl6etowTAAAAAAAnjyZXkitevXq2rlzp3Lnzq2KFSvqt99+I+EGAAAAACAVVvd037lzR7NmzdLBgwdVoUIFvfnmm2rXrp169eqlefPmacqUKfLx8bFlrAAAAAAAPFGs7unu2rWrpkyZIhcXF82dO1cDBgxQiRIl9Ouvv6pRo0YKCQnR9OnTbRkrAAAAAABPFKuT7h9++EFLly7V2LFjtWbNGq1YscK8r2vXrtq6das2bNhgkyABAAAAAHgSWZ10+/j46JdfflFCQoJ+/fVX5cmTx2J/vnz5tHDhwiwPEAAAAACAJ5XVc7qnTJmiDh06aODAgcqfP78WL15sy7gAAAAAAHjiWZ10N2jQQOfPn9c///wjb29vW8YEAAAAAMBTIVO3DDOZTCTcAAAAAABYyaqku1GjRtq6dWuG5a5fv65x48Zp6tSpDx0YAAAAAABPOquGl7/wwgtq3bq1PDw8FB4ersqVK6tAgQJycnLSlStX9Oeff2rjxo36+eef1bRpU3300Ue2jhsAAAAAgMeeVUl3165d9fLLL2vJkiX65ptvNGvWLMXGxkq6O+S8dOnSCgsL044dOxQUFGTTgAEAAAAAeFJYvZCao6OjXn75Zb388suSpNjYWP3777/KkyeP7O3tbRYgAAAAAABPKquT7vt5eHjIw8MjK2MBAAAAAOCpkqnVywEAAAAAgPVIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAG3nghdR27dqlgwcPSpJKly6tSpUqZVlQAAAAAAA8DTKddF+4cEHt27fX+vXr5enpKUm6evWq6tatq0WLFsnb2zurYwQAAAAA4ImU6eHlffv21fXr13XgwAFdvnxZly9f1v79+3Xt2jX169fPFjECAAAAAPBEynRP96pVqxQZGamgoCDzttKlS2vq1Klq2LBhlgYHAAAAAMCTLNM93UlJSbK3t0+x3d7eXklJSVkSFAAAAAAAT4NMJ9316tXT66+/rjNnzpi3/f333xowYIDq16+fpcEBAAAAAPAky3TSPWXKFF27dk1FihRRsWLFVKxYMQUEBOjatWv69NNPbREjAAAAAABPpEzP6fbz89Pu3bsVGRmpQ4cOSZKCgoIUGhqa5cEBAAAAAPAke6D7dJtMJjVo0EANGjTI6ngAAAAAAHhqWJV0T548WT169JCTk5MmT56cblluGwYAAAAAwF1WJd0TJkxQhw4d5OTkpAkTJqRZzmQyPXDSPXbsWA0bNkyvv/66Jk6cKEm6deuW/ve//2nRokWKj49XWFiYpk2bJh8fnwc6BwAAAAAAj5JVSffx48dT/XdW2bFjh2bOnKny5ctbbB8wYIBWrFihJUuWyMPDQ3369FGrVq20adOmLI8BAAAAAICslunVy7NaXFycOnTooNmzZyt37tzm7bGxsZozZ47Gjx+vevXqKTg4WHPnztXmzZu1devWbIwYAAAAAADrWNXTPXDgQKsrHD9+fKYC6N27t5o2barQ0FCNGjXKvH3Xrl26ffu2xaropUqVkr+/v7Zs2aJq1apl6jwAAAAAADxqViXde/bssXi+e/du3blzRyVLlpQkHT58WDly5FBwcHCmTr5o0SLt3r1bO3bsSLHv3LlzcnBwkKenp8V2Hx8fnTt3Ls064+PjFR8fb35+7dq1TMUEAAAAAEBWsSrpXrdunfnf48ePl5ubm+bPn28eDn7lyhV17txZzz33nNUnPnXqlF5//XWtWbNGTk5OmQw7bWPGjNHIkSOzrD4AAAAAAB5Upud0f/LJJxozZozF/OvcuXNr1KhR+uSTT6yuZ9euXbpw4YIqVaqknDlzKmfOnIqKitLkyZOVM2dO+fj4KCEhQVevXrU47vz58/L19U2z3mHDhik2Ntb8OHXqVGabCAAAAABAlrCqp/te165d08WLF1Nsv3jxoq5fv251PfXr19e+ffsstnXu3FmlSpXSG2+8IT8/P9nb22vt2rVq3bq1JCk6OloxMTEKCQlJs15HR0c5OjpaHQcAAAAAALaS6aT7+eefV+fOnfXJJ5+oSpUqkqRt27Zp8ODBatWqldX1uLm5qWzZshbbXFxclCdPHvP2rl27auDAgfLy8pK7u7v69u2rkJAQFlEDAAAAADwRMp10z5gxQ4MGDdJLL72k27dv360kZ0517dpVH330UZYGN2HCBNnZ2al169aKj49XWFiYpk2blqXnAAAAAADAVkyGYRgPcuCNGzd07NgxSVKxYsXk4uKSpYFllWvXrsnDw0OxsbFyd3fP7nD+E4IHL8juEAAAD2jXRx2zOwQAAJ4I1uaamV5ILdnZs2d19uxZBQYGysXFRQ+YuwMAAAAA8NTKMOlOSkqyeH7p0iXVr19fJUqUUJMmTXT27FlJd+df/+9//7NNlAAAAAAAPIEyTLrHjx+vn3/+2fx8wIABsre3V0xMjHLlymXe3q5dO61atco2UQIAAAAA8ATKcCG1Bg0aqHXr1jp79qy6du2qX375RatXr1ahQoUsygUGBurkyZM2CxQAAAAAgCdNhj3dFSpU0Pbt27Vs2TJJdxdQu7eHO9nly5e5PzYAAAAAAPewaiE1Ly8v/fjjj5Kk5557TgsW/N/q1CaTSUlJSfrwww9Vt25d20QJAAAAAMATKNP36f7www9Vv3597dy5UwkJCRoyZIgOHDigy5cva9OmTbaIEQAAAACAJ1KmbxlWtmxZHT58WDVr1lSLFi1048YNtWrVSnv27FGxYsVsESMAAAAAAE+kTPd0S5KHh4feeuutrI4FAAAAAICnitVJd0xMjFXl/P39HzgYAAAAAACeJlYn3QEBAeZ/G4Yh6e4iavduM5lMSkxMzMLwAAAAAAB4clmddJtMJhUqVEgREREKDw9XzpwPNDIdAAAAAID/DKsz59OnT2v+/PmaO3euZsyYoZdfflldu3ZVUFCQLeMDAAAAAOCJZfXq5b6+vnrjjTd06NAhffvtt7py5YqqVq2qatWqafbs2UpKSrJlnAAAAAAAPHEyfcswSapZs6bmzJmjI0eOKFeuXOrVq5euXr2axaEBAAAAAPBke6Cke/PmzerWrZtKlCihuLg4TZ06VZ6enlkcGgAAAAAATzar53SfPXtWCxYs0Ny5c3XlyhV16NBBmzZtUtmyZW0ZHwAAAAAATyyrk25/f38VLFhQnTp1UvPmzWVvb6+kpCT98ccfFuXKly+f5UECAAAAAPAksjrpTkxMVExMjN5//32NGjVK0v/drzsZ9+kGAAAAAOD/WJ10Hz9+3JZxAAAAAADw1LE66S5cuLAt4wAAAAAA4KnzQKuXAwAAAACAjJF0AwAAAABgIyTdAAAAAADYSKaSbsMwFBMTo1u3btkqHgAAAAAAnhqZTrqLFy+uU6dO2SoeAAAAAACeGplKuu3s7BQYGKhLly7ZKh4AAAAAAJ4amZ7TPXbsWA0ePFj79++3RTwAAAAAADw1rL5Pd7KOHTvq5s2bqlChghwcHOTs7Gyx//Lly1kWHAAAAAAAT7JMJ90TJ060QRgAAAAAADx9Mp10d+rUyRZxAAAAAADw1Ml00i1JiYmJWrZsmQ4ePChJKlOmjJo3b64cOXJkaXAAAAAAADzJMp10Hz16VE2aNNHff/+tkiVLSpLGjBkjPz8/rVixQsWKFcvyIAEAAAAAeBJlevXyfv36qVixYjp16pR2796t3bt3KyYmRgEBAerXr58tYgQAAAAA4ImU6Z7uqKgobd26VV5eXuZtefLk0dixY1WjRo0sDQ4AAAAAgCdZpnu6HR0ddf369RTb4+Li5ODgkCVBAQAAAADwNMh00t2sWTP16NFD27Ztk2EYMgxDW7duVa9evdS8eXNbxAgAAAAAwBMp00n35MmTVaxYMYWEhMjJyUlOTk6qUaOGihcvrkmTJtkiRgAAAAAAnkiZntPt6empH374QUeOHNGhQ4ckSUFBQSpevHiWBwcAAAAAwJPsge7TLUmBgYEKDAzMylgAAAAAAHiqWJV0Dxw40OoKx48f/8DBAAAAAADwNLEq6d6zZ49VlZlMpocKBgAAAACAp4lVSfe6detscvLp06dr+vTpOnHihCSpTJkyGj58uBo3bixJunXrlv73v/9p0aJFio+PV1hYmKZNmyYfHx+bxAMAAAAAQFbK9OrlWalQoUIaO3asdu3apZ07d6pevXpq0aKFDhw4IEkaMGCAfvzxRy1ZskRRUVE6c+aMWrVqlZ0hAwAAAABgNZNhGEZmD9q5c6cWL16smJgYJSQkWOz77rvvHiogLy8vffTRR2rTpo28vb21cOFCtWnTRpJ06NAhBQUFacuWLapWrZpV9V27dk0eHh6KjY2Vu7v7Q8UG6wQPXpDdIQAAHtD3bh9ldwgAgIfgP3xfdofwn2Ftrpnpnu5FixapevXqOnjwoL7//nvdvn1bBw4c0K+//ioPD48HDjgxMVGLFi3SjRs3FBISol27dun27dsKDQ01lylVqpT8/f21ZcuWBz4PAAAAAACPSqZvGTZ69GhNmDBBvXv3lpubmyZNmqSAgAD17NlT+fPnz3QA+/btU0hIiG7duiVXV1d9//33Kl26tPbu3SsHBwd5enpalPfx8dG5c+fSrC8+Pl7x8fHm59euXct0TAAAAAAAZIVM93QfO3ZMTZs2lSQ5ODjoxo0bMplMGjBggGbNmpXpAEqWLKm9e/dq27ZtevXVV9WpUyf9+eefma4n2ZgxY+Th4WF++Pn5PXBdAAAAAAA8jEwn3blz59b169clSQULFtT+/fslSVevXtXNmzczHYCDg4OKFy+u4OBgjRkzRhUqVNCkSZPk6+urhIQEXb161aL8+fPn5evrm2Z9w4YNU2xsrPlx6tSpTMcEAAAAAEBWyHTSXatWLa1Zs0aS9MILL+j1119X9+7d9eKLL6p+/foPHVBSUpLi4+MVHBwse3t7rV271rwvOjpaMTExCgkJSfN4R0dHubu7WzwAAAAAAMgOVs/p3r9/v8qWLaspU6bo1q1bkqS33npL9vb22rx5s1q3bq233347UycfNmyYGjduLH9/f12/fl0LFy7U+vXrtXr1anl4eKhr164aOHCgvLy85O7urr59+yokJMTqlcsBAAAAAMhOVifd5cuX17PPPqtu3bqpffv2kiQ7OzsNHTr0gU9+4cIFdezYUWfPnpWHh4fKly+v1atXq0GDBpKkCRMmyM7OTq1bt1Z8fLzCwsI0bdq0Bz4fAAAAAACPktX36d6wYYPmzp2rb7/9VklJSWrdurW6deum5557ztYxPhTu0/3ocZ9uAHhycZ9uAHiycZ/uRyfL79P93HPP6fPPP9fZs2f16aef6sSJE6pdu7ZKlCihcePGpXsbLwAAAAAA/osyvZCai4uLOnfurKioKB0+fFgvvPCCpk6dKn9/fzVv3twWMQIAAAAA8ETKdNJ9r+LFi+vNN9/U22+/LTc3N61YsSKr4gIAAAAA4Iln9UJq9/vtt9/0+eefa+nSpbKzs1Pbtm3VtWvXrIwNAAAAAIAnWqaS7jNnzmjevHmaN2+ejh49qurVq2vy5Mlq27atXFxcbBUjAAAAAABPJKuT7saNGysyMlJ58+ZVx44d1aVLF5UsWdKWsQEAAAAA8ESzOum2t7fXt99+q2bNmilHjhy2jAkAAAAAgKeC1Un38uXLbRkHAAAAAABPnYdavRwAAAAAAKSNpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAAAAAAAbIekGAAAAAMBGSLoBAAAAALARkm4AAAAAAGyEpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAAAAAAAbIekGAAAAAMBGSLoBAAAAALARkm4AAAAAAGyEpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAAAAAAAbydake8yYMXr22Wfl5uamfPnyqWXLloqOjrYoc+vWLfXu3Vt58uSRq6urWrdurfPnz2dTxAAAAAAAWC9bk+6oqCj17t1bW7du1Zo1a3T79m01bNhQN27cMJcZMGCAfvzxRy1ZskRRUVE6c+aMWrVqlY1RAwAAAABgnZzZefJVq1ZZPJ83b57y5cunXbt2qVatWoqNjdWcOXO0cOFC1atXT5I0d+5cBQUFaevWrapWrVp2hA0AAAAAgFUeqzndsbGxkiQvLy9J0q5du3T79m2Fhoaay5QqVUr+/v7asmVLtsQIAAAAAIC1srWn+15JSUnq37+/atSoobJly0qSzp07JwcHB3l6elqU9fHx0blz51KtJz4+XvHx8ebn165ds1nMAAAAAACk57Hp6e7du7f279+vRYsWPVQ9Y8aMkYeHh/nh5+eXRRECAAAAAJA5j0XS3adPH/30009at26dChUqZN7u6+urhIQEXb161aL8+fPn5evrm2pdw4YNU2xsrPlx6tQpW4YOAAAAAECasjXpNgxDffr00ffff69ff/1VAQEBFvuDg4Nlb2+vtWvXmrdFR0crJiZGISEhqdbp6Ogod3d3iwcAAAAAANkhW+d09+7dWwsXLtQPP/wgNzc38zxtDw8POTs7y8PDQ127dtXAgQPl5eUld3d39e3bVyEhIaxcDgAAAAB47GVr0j19+nRJUp06dSy2z507VxEREZKkCRMmyM7OTq1bt1Z8fLzCwsI0bdq0RxwpAAAAAACZl61Jt2EYGZZxcnLS1KlTNXXq1EcQEQAAAAAAWeexWEgNAAAAAICnEUk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCMk3QAAAAAA2AhJNwAAAAAANkLSDQAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCMk3QAAAAAA2AhJNwAAAAAANkLSDQAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADZC0g0AAAAAgI2QdAMAAAAAYCMk3QAAAAAA2AhJNwAAAAAANkLSDQAAAACAjZB0AwAAAABgIyTdAAAAAADYCEk3AAAAAAA2QtINAAAAAICNkHQDAAAAAGAjJN0AAAAAANgISTcAAAAAADaSrUn3b7/9pvDwcBUoUEAmk0nLli2z2G8YhoYPH678+fPL2dlZoaGhOnLkSPYECwAAAABAJmVr0n3jxg1VqFBBU6dOTXX/hx9+qMmTJ2vGjBnatm2bXFxcFBYWplu3bj3iSAEAAAAAyLyc2Xnyxo0bq3HjxqnuMwxDEydO1Ntvv60WLVpIkhYsWCAfHx8tW7ZM7du3f5ShAgAAAACQaY/tnO7jx4/r3LlzCg0NNW/z8PBQ1apVtWXLlmyMDAAAAAAA62RrT3d6zp07J0ny8fGx2O7j42Pel5r4+HjFx8ebn1+7ds02AQIAAAAAkIHHtqf7QY0ZM0YeHh7mh5+fX3aHBAAAAAD4j3psk25fX19J0vnz5y22nz9/3rwvNcOGDVNsbKz5cerUKZvGCQAAAABAWh7bpDsgIEC+vr5au3atedu1a9e0bds2hYSEpHmco6Oj3N3dLR4AAAAAAGSHbJ3THRcXp6NHj5qfHz9+XHv37pWXl5f8/f3Vv39/jRo1SoGBgQoICNA777yjAgUKqGXLltkXNAAAAAAAVsrWpHvnzp2qW7eu+fnAgQMlSZ06ddK8efM0ZMgQ3bhxQz169NDVq1dVs2ZNrVq1Sk5OTtkVMgAAAAAAVsvWpLtOnToyDCPN/SaTSe+9957ee++9RxgVAAAAAABZ47Gd0w0AAAAAwJOOpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAAAAAAAbIekGAAAAAMBGSLoBAAAAALARkm4AAAAAAGyEpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAAAAAAAbIekGAAAAAMBGSLoBAAAAALARkm4AAAAAAGyEpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAAAAAAAbIekGAAAAAMBGSLoBAAAAALARkm4AAAAAAGyEpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAG3kiku6pU6eqSJEicnJyUtWqVbV9+/bsDgkAAAAAgAw99kn3N998o4EDB+rdd9/V7t27VaFCBYWFhenChQvZHRoAAAAAAOl67JPu8ePHq3v37urcubNKly6tGTNmKFeuXPr888+zOzQAAAAAANL1WCfdCQkJ2rVrl0JDQ83b7OzsFBoaqi1btmRjZAAAAAAAZCxndgeQnn/++UeJiYny8fGx2O7j46NDhw6lekx8fLzi4+PNz2NjYyVJ165ds12gsJAY/292hwAAeEDX7ROzOwQAwEMg73l0kq+1YRjplnusk+4HMWbMGI0cOTLFdj8/v2yIBgCAJ0vZ7A4AAPBwxnhkdwT/OdevX5eHR9rX/bFOuvPmzascOXLo/PnzFtvPnz8vX1/fVI8ZNmyYBg4caH6elJSky5cvK0+ePDKZTDaNFwCAJ9m1a9fk5+enU6dOyd3dPbvDAQDgsWYYhq5fv64CBQqkW+6xTrodHBwUHBystWvXqmXLlpLuJtFr165Vnz59Uj3G0dFRjo6OFts8PT1tHCkAAE8Pd3d3km4AAKyQXg93ssc66ZakgQMHqlOnTqpcubKqVKmiiRMn6saNG+rcuXN2hwYAAAAAQLoe+6S7Xbt2unjxooYPH65z587pmWee0apVq1IsrgYAAAAAwOPGZGS01BoAAPhPiI+P15gxYzRs2LAUU7UAAMCDIekGAAAAAMBG7LI7AAAAAAAAnlYk3QAAAAAA2AhJNwAAAAAANkLSDQDAU2TMmDF69tln5ebmpnz58qlly5aKjo62KHPr1i317t1befLkkaurq1q3bq3z589blOnXr5+Cg4Pl6OioZ555JsV5Tpw4IZPJlOKxdetWWzYPAIAnDkk3AABPkaioKPXu3Vtbt27VmjVrdPv2bTVs2FA3btwwlxkwYIB+/PFHLVmyRFFRUTpz5oxatWqVoq4uXbqoXbt26Z4vMjJSZ8+eNT+Cg4OzvE0AADzJWL0cAICn2MWLF5UvXz5FRUWpVq1aio2Nlbe3txYuXKg2bdpIkg4dOqSgoCBt2bJF1apVszh+xIgRWrZsmfbu3Wux/cSJEwoICNCePXtS7QkHAAB30dMNAMBTLDY2VpLk5eUlSdq1a5du376t0NBQc5lSpUrJ399fW7ZsyXT9zZs3V758+VSzZk0tX748a4IGAOApQtINAMBTKikpSf3791eNGjVUtmxZSdK5c+fk4OAgT09Pi7I+Pj46d+6c1XW7urrqk08+0ZIlS7RixQrVrFlTLVu2JPEGAOA+ObM7AAAAYBu9e/fW/v37tXHjxiyvO2/evBo4cKD5+bPPPqszZ87oo48+UvPmzbP8fAAAPKno6QYA4CnUp08f/fTTT1q3bp0KFSpk3u7r66uEhARdvXrVovz58+fl6+v7UOesWrWqjh49+lB1AADwtCHpBgDgKWIYhvr06aPvv/9ev/76qwICAiz2BwcHy97eXmvXrjVvi46OVkxMjEJCQh7q3Hv37lX+/Pkfqg4AAJ42DC8HAOAp0rt3by1cuFA//PCD3NzczPO0PTw85OzsLA8PD3Xt2lUDBw6Ul5eX3N3d1bdvX4WEhFisXH706FHFxcXp3Llz+vfff82rl5cuXVoODg6aP3++HBwcVLFiRUnSd999p88//1yfffbZI28zAACPM24ZBgDAU8RkMqW6fe7cuYqIiJAk3bp1S//73//09ddfKz4+XmFhYZo2bZrF8PI6deooKioqRT3Hjx9XkSJFNH/+fI0bN04nT55Uzpw5VapUKQ0ePNh8GzIAAHAXSTcAAAAAADbCnG4AAAAAAGyEpBsAAAAAABsh6QYAAAAAwEZIugEAAAAAsBGSbgAAAAAAbISkGwAAAAAAGyHpBgAAAADARki6AQAAAACwEZJuAAAAAABshKQbAID/gC1btihHjhxq2rRpdocCAMB/iskwDCO7gwAAALbVrVs3ubq6as6cOYqOjlaBAgWyOyQAAP4T6OkGAOApFxcXp2+++UavvvqqmjZtqnnz5pn3rV+/XiaTSWvXrlXlypWVK1cuVa9eXdHR0RZ1TJ8+XcWKFZODg4NKliypL7744hG3AgCAJxNJNwAAT7nFixfr/7VztyyxbmEYgG9hRNvO4x/wY0AwT1G0WQRhRLCowThBTP4EnSBYFEbBYhA0+JEGtEwRg4ow+BcsJrEMusMBwXPCPuXdmz1cF7ywWGuxeFa8eRbvyMhIhoeHs7S0lIODg/z7odvm5mYajUbu7u5SKpWysrLytXZ2dpZ6vZ719fU8PT1lbW0ty8vLub6+/t1XAYC/juflANDjqtVqarVa6vV6ut1uyuVyTk5OMjk5mZubm0xNTaXVamV6ejpJcnV1ldnZ2by/v2dwcDDVajWVSiX7+/tfZ9Zqtby9veXy8vJPXQsA/go63QDQw56fn3N7e5vFxcUkSalUysLCQprN5rd94+PjX+NyuZwkeXl5SZJ0Op1Uq9Vv+6vVajqdTpGlA0BPKP3pAgCA4jSbzXS73W8/Tvv8/MzAwEB2d3e/5vr7+7/GfX19SZKPj4/fVygA9CidbgDoUd1uN0dHR2k0Grm/v//6Hh4eMjQ0lOPj4/91zujoaNrt9re5drudsbGxIsoGgJ6i0w0APeri4iKvr69ZXV3Njx8/vq3Nz8+n2Wxma2vrl+dsbGykVqtlYmIiMzMzOT8/z+npaVqtVlGlA0DP0OkGgB7VbDYzMzPzn8Cd/BO67+7u8vj4+Mtz5ubmsrOzk+3t7VQqlezt7eXw8DCTk5MFVA0AvcXfywEAAKAgOt0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKMhPLy+MSa8/A2QAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "#estabelecendo tamanho da figura\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "#grafico de barras seaborn (data é de onde vem os dados) x e y estabelecem os eixos\n",
+        "#hue='Medida': Cria barras separadas para cada categoria da variável 'Medida', permitindo comparar as médias entre diferentes medidas.\n",
+        "sns.barplot(data=DF_grouped, x='Ano', y='Data_Value', hue='Medida')\n",
+        "\n",
+        "#definição rotulos e configurações visuais do grafico\n",
+        "plt.title('Média de Triagem de Colesterol e Pressão Alta (2015 e 2016)')\n",
+        "plt.ylabel('Valor Médio (%)')\n",
+        "plt.xlabel('Ano')\n",
+        "#definindo a legenda\n",
+        "plt.legend(title='Medida')\n",
+        "#ajusatando o grafico\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fUl7cdhRuN0j"
+      },
+      "source": [
+        "## Analise quais estados têm as taxas mais altas de pressão alta e triagem de colesterol. Você pode considerar um gráfico de barras ou um mapa de calor para visualização.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GaE3wXzxuN0j"
+      },
+      "source": [
+        "filtrando usando o metodo  isin"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RwdXU9zyuN0k",
+        "outputId": "a6aaef7e-6944-4000-b674-8beb5d285fa9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Ano</th>\n",
+              "      <th>SiglaEstado</th>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th>NomeCidade</th>\n",
+              "      <th>NivelGeografico</th>\n",
+              "      <th>FonteDados</th>\n",
+              "      <th>Categoria</th>\n",
+              "      <th>IDUnico</th>\n",
+              "      <th>Medida</th>\n",
+              "      <th>UnidadeValorDados</th>\n",
+              "      <th>...</th>\n",
+              "      <th>LimiteSuperiorConfianca</th>\n",
+              "      <th>SimboloNotaRodapeDados</th>\n",
+              "      <th>NotaRodapeDados</th>\n",
+              "      <th>ContagemPopulacional</th>\n",
+              "      <th>Geolocalizacao</th>\n",
+              "      <th>IDCategoria</th>\n",
+              "      <th>IDMedida</th>\n",
+              "      <th>FIPSCidade</th>\n",
+              "      <th>FIPSTrato</th>\n",
+              "      <th>TextoCurtoPergunta</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2015</td>\n",
+              "      <td>IN</td>\n",
+              "      <td>Indiana</td>\n",
+              "      <td>Fishers</td>\n",
+              "      <td>City</td>\n",
+              "      <td>BRFSS</td>\n",
+              "      <td>Health Outcomes</td>\n",
+              "      <td>1823278</td>\n",
+              "      <td>High blood pressure among adults aged &gt;=18 Years</td>\n",
+              "      <td>%</td>\n",
+              "      <td>...</td>\n",
+              "      <td>24.7</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>76794</td>\n",
+              "      <td>POINT (-85.969310702 39.9587635263)</td>\n",
+              "      <td>HLTHOUT</td>\n",
+              "      <td>BPHIGH</td>\n",
+              "      <td>1823278.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>High Blood Pressure</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1 rows × 24 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Ano SiglaEstado DescricaoEstado NomeCidade NivelGeografico FonteDados  \\\n",
+              "0  2015          IN         Indiana    Fishers            City      BRFSS   \n",
+              "\n",
+              "         Categoria  IDUnico                                            Medida  \\\n",
+              "0  Health Outcomes  1823278  High blood pressure among adults aged >=18 Years   \n",
+              "\n",
+              "  UnidadeValorDados  ... LimiteSuperiorConfianca SimboloNotaRodapeDados  \\\n",
+              "0                 %  ...                    24.7                    NaN   \n",
+              "\n",
+              "   NotaRodapeDados  ContagemPopulacional                       Geolocalizacao  \\\n",
+              "0              NaN                 76794  POINT (-85.969310702 39.9587635263)   \n",
+              "\n",
+              "  IDCategoria IDMedida  FIPSCidade FIPSTrato   TextoCurtoPergunta  \n",
+              "0     HLTHOUT   BPHIGH   1823278.0       NaN  High Blood Pressure  \n",
+              "\n",
+              "[1 rows x 24 columns]"
+            ]
+          },
+          "execution_count": 96,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "filtroporestado=df[df[\"Medida\"].isin([\"Cholesterol screening among adults aged >=18 Years\",\"High blood pressure among adults aged >=18 Years\"])]\n",
+        "filtroporestado.head(1)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ABMpl3x4uN0k"
+      },
+      "source": [
+        "## Descobrindo quais são os estados com mais casos de pressão alta e triagem de colesterol"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xpSrSXQpuN0k"
+      },
+      "source": [
+        "metodo unstack estaca valores\n",
+        "dessa forma estados não irão se repetir"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PqxSnaMouN0k",
+        "outputId": "4b0496a5-63d6-454e-b1a0-a042c3c8517e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Medida</th>\n",
+              "      <th>Cholesterol screening among adults aged &gt;=18 Years</th>\n",
+              "      <th>High blood pressure among adults aged &gt;=18 Years</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Alabama</th>\n",
+              "      <td>75.808683</td>\n",
+              "      <td>40.003922</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Alaska</th>\n",
+              "      <td>68.815789</td>\n",
+              "      <td>27.101754</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>73.059961</td>\n",
+              "      <td>27.579199</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arkansas</th>\n",
+              "      <td>71.525000</td>\n",
+              "      <td>35.117969</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>73.074238</td>\n",
+              "      <td>26.870608</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Colorado</th>\n",
+              "      <td>72.616433</td>\n",
+              "      <td>24.364185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Connecticut</th>\n",
+              "      <td>73.125847</td>\n",
+              "      <td>30.847034</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Delaware</th>\n",
+              "      <td>72.238462</td>\n",
+              "      <td>37.488462</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>District of C</th>\n",
+              "      <td>77.517778</td>\n",
+              "      <td>30.148889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Florida</th>\n",
+              "      <td>75.967083</td>\n",
+              "      <td>32.176267</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Georgia</th>\n",
+              "      <td>74.396137</td>\n",
+              "      <td>36.707296</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Hawaii</th>\n",
+              "      <td>71.085169</td>\n",
+              "      <td>31.267797</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Idaho</th>\n",
+              "      <td>70.779452</td>\n",
+              "      <td>28.161644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Illinois</th>\n",
+              "      <td>72.875776</td>\n",
+              "      <td>31.612491</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Indiana</th>\n",
+              "      <td>70.032463</td>\n",
+              "      <td>32.998321</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Iowa</th>\n",
+              "      <td>69.960396</td>\n",
+              "      <td>29.044554</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kansas</th>\n",
+              "      <td>70.904839</td>\n",
+              "      <td>31.245484</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Kentucky</th>\n",
+              "      <td>73.641729</td>\n",
+              "      <td>37.934211</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Louisiana</th>\n",
+              "      <td>72.681627</td>\n",
+              "      <td>39.635696</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Maine</th>\n",
+              "      <td>75.736364</td>\n",
+              "      <td>27.372727</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Maryland</th>\n",
+              "      <td>75.148259</td>\n",
+              "      <td>38.774129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Massachusetts</th>\n",
+              "      <td>73.170392</td>\n",
+              "      <td>28.443725</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Michigan</th>\n",
+              "      <td>73.591534</td>\n",
+              "      <td>37.941799</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Minnesota</th>\n",
+              "      <td>70.839815</td>\n",
+              "      <td>24.416358</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Mississippi</th>\n",
+              "      <td>70.582278</td>\n",
+              "      <td>43.716456</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Missouri</th>\n",
+              "      <td>71.426411</td>\n",
+              "      <td>33.241986</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Montana</th>\n",
+              "      <td>70.014894</td>\n",
+              "      <td>26.459574</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nebraska</th>\n",
+              "      <td>69.578440</td>\n",
+              "      <td>27.745413</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nevada</th>\n",
+              "      <td>71.756640</td>\n",
+              "      <td>26.907317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Hampshire</th>\n",
+              "      <td>75.780392</td>\n",
+              "      <td>28.768627</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Jersey</th>\n",
+              "      <td>70.618323</td>\n",
+              "      <td>33.687888</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New Mexico</th>\n",
+              "      <td>71.780841</td>\n",
+              "      <td>26.786449</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>New York</th>\n",
+              "      <td>75.043266</td>\n",
+              "      <td>29.447621</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>North Carolin</th>\n",
+              "      <td>76.143308</td>\n",
+              "      <td>31.425253</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>North Dakota</th>\n",
+              "      <td>67.381481</td>\n",
+              "      <td>24.888889</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Ohio</th>\n",
+              "      <td>70.497020</td>\n",
+              "      <td>36.936234</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oklahoma</th>\n",
+              "      <td>70.859619</td>\n",
+              "      <td>33.824524</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>70.809375</td>\n",
+              "      <td>25.358807</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Pennsylvania</th>\n",
+              "      <td>71.062481</td>\n",
+              "      <td>33.353333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rhode Island</th>\n",
+              "      <td>75.939623</td>\n",
+              "      <td>31.631132</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Carolin</th>\n",
+              "      <td>72.799435</td>\n",
+              "      <td>32.155932</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>South Dakota</th>\n",
+              "      <td>70.896667</td>\n",
+              "      <td>27.333333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tennessee</th>\n",
+              "      <td>74.411663</td>\n",
+              "      <td>37.579732</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Texas</th>\n",
+              "      <td>69.596661</td>\n",
+              "      <td>29.855810</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>United States</th>\n",
+              "      <td>76.100000</td>\n",
+              "      <td>30.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Utah</th>\n",
+              "      <td>63.116335</td>\n",
+              "      <td>22.767729</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Vermont</th>\n",
+              "      <td>65.730769</td>\n",
+              "      <td>20.146154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Virginia</th>\n",
+              "      <td>76.357774</td>\n",
+              "      <td>33.164299</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Washington</th>\n",
+              "      <td>71.336723</td>\n",
+              "      <td>27.596987</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>West Virginia</th>\n",
+              "      <td>80.575000</td>\n",
+              "      <td>40.033333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wisconsin</th>\n",
+              "      <td>71.048792</td>\n",
+              "      <td>31.075362</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wyoming</th>\n",
+              "      <td>72.844444</td>\n",
+              "      <td>29.344444</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Medida           Cholesterol screening among adults aged >=18 Years  \\\n",
+              "DescricaoEstado                                                       \n",
+              "Alabama                                                  75.808683    \n",
+              "Alaska                                                   68.815789    \n",
+              "Arizona                                                  73.059961    \n",
+              "Arkansas                                                 71.525000    \n",
+              "California                                               73.074238    \n",
+              "Colorado                                                 72.616433    \n",
+              "Connecticut                                              73.125847    \n",
+              "Delaware                                                 72.238462    \n",
+              "District of C                                            77.517778    \n",
+              "Florida                                                  75.967083    \n",
+              "Georgia                                                  74.396137    \n",
+              "Hawaii                                                   71.085169    \n",
+              "Idaho                                                    70.779452    \n",
+              "Illinois                                                 72.875776    \n",
+              "Indiana                                                  70.032463    \n",
+              "Iowa                                                     69.960396    \n",
+              "Kansas                                                   70.904839    \n",
+              "Kentucky                                                 73.641729    \n",
+              "Louisiana                                                72.681627    \n",
+              "Maine                                                    75.736364    \n",
+              "Maryland                                                 75.148259    \n",
+              "Massachusetts                                            73.170392    \n",
+              "Michigan                                                 73.591534    \n",
+              "Minnesota                                                70.839815    \n",
+              "Mississippi                                              70.582278    \n",
+              "Missouri                                                 71.426411    \n",
+              "Montana                                                  70.014894    \n",
+              "Nebraska                                                 69.578440    \n",
+              "Nevada                                                   71.756640    \n",
+              "New Hampshire                                            75.780392    \n",
+              "New Jersey                                               70.618323    \n",
+              "New Mexico                                               71.780841    \n",
+              "New York                                                 75.043266    \n",
+              "North Carolin                                            76.143308    \n",
+              "North Dakota                                             67.381481    \n",
+              "Ohio                                                     70.497020    \n",
+              "Oklahoma                                                 70.859619    \n",
+              "Oregon                                                   70.809375    \n",
+              "Pennsylvania                                             71.062481    \n",
+              "Rhode Island                                             75.939623    \n",
+              "South Carolin                                            72.799435    \n",
+              "South Dakota                                             70.896667    \n",
+              "Tennessee                                                74.411663    \n",
+              "Texas                                                    69.596661    \n",
+              "United States                                            76.100000    \n",
+              "Utah                                                     63.116335    \n",
+              "Vermont                                                  65.730769    \n",
+              "Virginia                                                 76.357774    \n",
+              "Washington                                               71.336723    \n",
+              "West Virginia                                            80.575000    \n",
+              "Wisconsin                                                71.048792    \n",
+              "Wyoming                                                  72.844444    \n",
+              "\n",
+              "Medida           High blood pressure among adults aged >=18 Years  \n",
+              "DescricaoEstado                                                    \n",
+              "Alabama                                                 40.003922  \n",
+              "Alaska                                                  27.101754  \n",
+              "Arizona                                                 27.579199  \n",
+              "Arkansas                                                35.117969  \n",
+              "California                                              26.870608  \n",
+              "Colorado                                                24.364185  \n",
+              "Connecticut                                             30.847034  \n",
+              "Delaware                                                37.488462  \n",
+              "District of C                                           30.148889  \n",
+              "Florida                                                 32.176267  \n",
+              "Georgia                                                 36.707296  \n",
+              "Hawaii                                                  31.267797  \n",
+              "Idaho                                                   28.161644  \n",
+              "Illinois                                                31.612491  \n",
+              "Indiana                                                 32.998321  \n",
+              "Iowa                                                    29.044554  \n",
+              "Kansas                                                  31.245484  \n",
+              "Kentucky                                                37.934211  \n",
+              "Louisiana                                               39.635696  \n",
+              "Maine                                                   27.372727  \n",
+              "Maryland                                                38.774129  \n",
+              "Massachusetts                                           28.443725  \n",
+              "Michigan                                                37.941799  \n",
+              "Minnesota                                               24.416358  \n",
+              "Mississippi                                             43.716456  \n",
+              "Missouri                                                33.241986  \n",
+              "Montana                                                 26.459574  \n",
+              "Nebraska                                                27.745413  \n",
+              "Nevada                                                  26.907317  \n",
+              "New Hampshire                                           28.768627  \n",
+              "New Jersey                                              33.687888  \n",
+              "New Mexico                                              26.786449  \n",
+              "New York                                                29.447621  \n",
+              "North Carolin                                           31.425253  \n",
+              "North Dakota                                            24.888889  \n",
+              "Ohio                                                    36.936234  \n",
+              "Oklahoma                                                33.824524  \n",
+              "Oregon                                                  25.358807  \n",
+              "Pennsylvania                                            33.353333  \n",
+              "Rhode Island                                            31.631132  \n",
+              "South Carolin                                           32.155932  \n",
+              "South Dakota                                            27.333333  \n",
+              "Tennessee                                               37.579732  \n",
+              "Texas                                                   29.855810  \n",
+              "United States                                           30.650000  \n",
+              "Utah                                                    22.767729  \n",
+              "Vermont                                                 20.146154  \n",
+              "Virginia                                                33.164299  \n",
+              "Washington                                              27.596987  \n",
+              "West Virginia                                           40.033333  \n",
+              "Wisconsin                                               31.075362  \n",
+              "Wyoming                                                 29.344444  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "FiltroEstados=filtroporestado.groupby([\"DescricaoEstado\", \"Medida\"])[\"Data_Value\"].mean().unstack()\n",
+        "display(FiltroEstados)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_mYYud5xuN0k"
+      },
+      "source": [
+        "## Descobrindo os paises mais afetados em ambas as situações\n",
+        "#### Metodo nlargest retorna os 5 maiores notas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zAsXiyUmuN0k",
+        "outputId": "39f62813-9736-439c-db00-59a8daafa5f8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Top 5 estados com maiores taxas de triagem de colesterol:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "DescricaoEstado\n",
+              "West Virginia    80.575000\n",
+              "District of C    77.517778\n",
+              "Virginia         76.357774\n",
+              "North Carolin    76.143308\n",
+              "United States    76.100000\n",
+              "Name: Cholesterol screening among adults aged >=18 Years, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Top 5 estados com maiores taxas de pressão alta:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "DescricaoEstado\n",
+              "Mississippi      43.716456\n",
+              "West Virginia    40.033333\n",
+              "Alabama          40.003922\n",
+              "Louisiana        39.635696\n",
+              "Maryland         38.774129\n",
+              "Name: High blood pressure among adults aged >=18 Years, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Ordenar os valores de colesterol e pressão alta separadamente e pegar os 5 estados com maiores valores\n",
+        "top5_colesterol = FiltroEstados[\"Cholesterol screening among adults aged >=18 Years\"].nlargest(5)\n",
+        "top5_pressao = FiltroEstados[\"High blood pressure among adults aged >=18 Years\"].nlargest(5)\n",
+        "\n",
+        "# Exibir os 5 estados com maiores valores para colesterol\n",
+        "print(\"Top 5 estados com maiores taxas de triagem de colesterol:\")\n",
+        "display(top5_colesterol)\n",
+        "\n",
+        "# Exibir os 5 estados com maiores valores para pressão alta\n",
+        "print(\"\\nTop 5 estados com maiores taxas de pressão alta:\")\n",
+        "display(top5_pressao)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_dhqEbdnuN0k"
+      },
+      "source": [
+        "## Vendo qual Estado aparece no top  em ambas as situações\n",
+        "Assim definindo o maior alvo nas duas situações"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "adPr8w4xuN0k",
+        "outputId": "767712c6-27db-4f5e-df3d-c614c31574dd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Estados que aparecem no top 5 em ambas as categorias:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Medida</th>\n",
+              "      <th>Cholesterol screening among adults aged &gt;=18 Years</th>\n",
+              "      <th>High blood pressure among adults aged &gt;=18 Years</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>West Virginia</th>\n",
+              "      <td>80.575</td>\n",
+              "      <td>40.033333</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Medida           Cholesterol screening among adults aged >=18 Years  \\\n",
+              "DescricaoEstado                                                       \n",
+              "West Virginia                                               80.575    \n",
+              "\n",
+              "Medida           High blood pressure among adults aged >=18 Years  \n",
+              "DescricaoEstado                                                    \n",
+              "West Virginia                                           40.033333  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "TOP = FiltroEstados[FiltroEstados.index.isin(top5_colesterol.index) & FiltroEstados.index.isin(top5_pressao.index)]\n",
+        "print(\"\\nEstados que aparecem no top 5 em ambas as categorias:\")\n",
+        "display(TOP)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V_t8sx4JuN0k"
+      },
+      "source": [
+        "## Grafico para representar o estados com a maior taxa de pressão alta e triagem de colesterol"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y-6ESbQ8uN0l",
+        "outputId": "f62912be-45a1-496c-fdf2-430f0e59071d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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3NRoNRo0ahVGjRmViq4iIiIiIiIiIyNg+25pSRERERERERET0fxeTUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWU6JqWIiIiIiIiIiCjTMSlFRERERERERESZjkkpIiIiIiIiIiLKdExKERERERERERFRpmNSioiIiIiIiIiIMh2TUkRERERERERElOmYlCIiIiIiIiIiokzHpBQREREREREREWW6LyIpNXPmTHh5ecHKygqlS5fG8ePHP3WTiIiIiIiIiIhID599UmrVqlXo27cvhg8fjtOnTyMwMBBBQUG4f//+p24aERERERERERGp9NknpcLCwtCxY0e0a9cO/v7+mD17NmxsbDB//vxP3TQiIiIiIiIiIlLps05KvXnzBqdOnUK1atWUbSYmJqhWrRqOHDnyCVtGRERERERERET6MPvUDfiQhw8fIjExEdmyZdPani1bNly+fDnd73n9+jVev36t/P/Zs2cAgOfPn2t93Su8ynA73v3eDzFG3C+prbrE/ZLaqkvcL6mtusT9ktqqS9wvqa26xP2S2qpL3C+prbrE/ZLaqkvcL6mtusT9ktqqS9wvqa26xP2S2qpL3C+prbrE/ZLaqkvcL6mtusT9ktqqS9wvqa26xP2S2qpL3C+prbrE/ZLaml7clP+LyAe/TyMf+4pP6Pbt28iRIwcOHz6Mr776Stn+448/Ijw8HMeOHUvzPSNGjMDIkSMzs5lERERERERERPSOmJgY5MyZ873vf9YjpVxdXWFqaop79+5pbb937x7c3d3T/Z5Bgwahb9++yv+TkpLw+PFjuLi4QKPRfPDnPX/+HJ6enoiJiYGDg4P+v4CRYhor7pfUVmPFZVu/rLhs65cVl239suKyrV9WXLb1y4rLtn5ZcdnWLysu2/plxWVbv6y4usQUEbx48QLZs2f/4Nd91kkpCwsLFC9eHLt370bDhg0BJCeZdu/eje7du6f7PZaWlrC0tNTa5uTkpNPPdXBwMOjOYKyYxor7JbXVWHHZ1i8rLtv6ZcVlW7+suGzrlxWXbf2y4rKtX1ZctvXLisu2fllx2dYvK25GYzo6On70az7rpBQA9O3bF23atEGJEiVQqlQpTJkyBXFxcWjXrt2nbhoREREREREREan02SelmjZtigcPHmDYsGG4e/cuihQpgm3btqUpfk5ERERERERERF+Ozz4pBQDdu3d/73Q9Q7K0tMTw4cPTTP/73GIaK+6X1FZjxWVbv6y4bOuXFZdt/bLisq1fVly29cuKy7Z+WXHZ1i8rLtv6ZcVlW7+suMaI+VmvvkdERERERERERP83mXzqBhARERERERER0X8Pk1JERERERERERJTpmJQiIiIiIiIiIqJMx6QUEREREX3RXr16ZbBY169fN1gsY3v79u1733v48GEmtoSIiEgdJqWIPnOJiYk4d+4cXr58mea9+Ph4nDt3DklJSZ+gZURERJ9OUlISRo8ejRw5csDOzk5JJg0dOhTz5s1THTdv3ryoXLkyli5datBkFwA8ePAABw8exMGDB/HgwQO943333XdIb82ie/fuoVKlSnrHJzKGp0+fYseOHVi6dCkWL16s9focvXnzBleuXEFCQoLesU6fPo3z588r/9+4cSMaNmyIn376CW/evNE7fornz59jw4YNuHTpksFiGsr+/fvT/VsmJCRg//79n6BF9Klx9T0AFy9eRHR0dJoTQf369T+rmClevXqVJq6Dg4OqWKdPn4a5uTkCAgIAJJ8YFyxYAH9/f4wYMQIWFhafVXsTExO1TrAFCxZE/fr1YWpqqrp9VapUwbp16+Dk5KS1/fnz52jYsCH27NmjOnaKV69ewcrKStX3Lly4EDNmzMCxY8fS/J4JCQkoU6YMevfujZYtW+rdTmMw5rFA9KXR51xARNpGjRqFRYsWYdSoUejYsSMuXLgAHx8frFq1ClOmTMGRI0dUxT179iwWLFiAFStW4M2bN2jatClCQkJQqlQp1W2Ni4tDjx49sGTJEiQmJgIATE1N0bp1a0yfPh02Njaq4pYsWRKFCxfWSsLdvXsXlStXRsGCBbFmzRqdY967dw8//PADdu/ejfv376dJeqW0X41//vkHmzZtSrdfEBYWpjpu6vgAkDNnTr1j/Rf17ds3w1+r9vPavHkzWrRogdjYWDg4OECj0SjvaTQaPH78WOeYiYmJmDx5MlavXp3uvqUmJpD88LdHjx5YtGgRACAyMhI+Pj7o0aMHcuTIgYEDB+ocs2TJkhg4cCC++eYbXL9+HQULFkRwcDBOnDiBOnXqYMqUKara2qRJE1SoUAHdu3fHy5cvERgYiJs3b0JEsHLlSnzzzTeq4hqDqakp7ty5g6xZs2ptf/ToEbJmzarXOQYwzr3H1atXsXfvXty/fz/NYIBhw4apirlmzZr37rOnT59W3dYv0X86KXX9+nUEBwfj/Pnz0Gg0ykU35eSo5oAwRkwg+aT4448/YvXq1Xj06FGa99XGNdaJ0RjtjYqKQp06dfDPP//A19cXAHDlyhV4enpi69atyJMnj6q2mpiY4O7du2lOjPfv30eOHDk+ODT+Q5KSkvDzzz9j9uzZuHfvnnIhGzp0KLy8vBASEpKhOOXLl0e3bt3w3Xffpfv+6tWrMWPGDIM8WYiPj0/3xFi4cGGdYxn6WJg2bRo6deoEKysrTJs27YNf27NnT53bCwDh4eGYNGmSkvT09/dH//79Ub58eVXx0pOYmIjz588jd+7ccHZ21iuWIT+vzIib4vnz59izZw98fX3h5+enKkabNm0QEhKCChUqGKRN7zJkh8ZQ5wIAH933U1N7HBjrYUVSUhKioqLS7dDp+znq+3llxvnFmOLi4hAeHp7u30Bte40R09Dy5s2L3377DVWrVoW9vT0iIiLg4+ODy5cv46uvvsKTJ0/0ip+QkIBNmzZh4cKF2LZtG/Lnz4/27dujVatWcHNz0ynW999/j127dmHGjBkoW7YsAODgwYPo2bMnqlevjlmzZqlq44MHD1ChQgXUqlULYWFhuH37NipXrozAwECsXLkSJia6T4qoVasWoqOj0b17d3h4eGglDQCgQYMGqtq6e/du1K9fX/mMChUqpNw0FytWTPVDwKSkJIwZMwahoaGIjY0FANjb26Nfv34YPHiwqr9BCmMk0eLi4jB+/Hgl6ffu+fBTTh+tXLmy1v9Pnz6NhIQEpd8dGRkJU1NTFC9eXPXnlT9/ftSuXRtjx45VnYx917BhwzB37lz069cPQ4YMweDBg3Hz5k1s2LABw4YNU33O6tWrFw4dOoQpU6agZs2aOHfuHHx8fLBx40aMGDECZ86c0Tmmo6MjTp8+jTx58mDChAnYs2cPtm/fjkOHDuG7775DTEyMqra6u7tj+/btCAwMxPLlyzF8+HBERERg0aJFmDNnjqq2GouJiQnu3buX5jwaGRmJEiVK4Pnz56riGus+/Pfff0eXLl3g6uoKd3f3NIlUNQmkadOmYfDgwWjbti3mzJmDdu3a4dq1azhx4gS6deuGn3/+WVVbjfVQwdnZOc21AEj+/a2srJA3b160bdsW7dq1UxUf8h9Wt25dadCggTx48EDs7Ozk4sWLcuDAASlVqpTs37//s4kpItK1a1fx8/OTNWvWiLW1tcyfP19Gjx4tOXPmlKVLl6qO6+DgIFFRUSIiMn78eKlRo4aIiBw8eFBy5sz5WbW3Vq1aUrNmTXn06JGy7eHDh1KzZk2pXbu2zvEiIiIkIiJCNBqN7N27V/l/RESEnD59WsaOHSu5c+dW1VYRkZEjR4qPj48sXbpUrK2t5dq1ayIisnLlSilTpkyG47i5ucmNGzfe+/7169fF1dVVdTtFRO7fvy916tQRExOTdF9qGPpY8PLykocPHyr/ft/L29tbVXuXLFkiZmZm0qRJE5k6dapMnTpVmjRpIubm5rJs2TJVMUVEevXqJXPnzhURkYSEBClbtqxoNBqxtbWVvXv3qoppjM/LmHEbN24s06dPFxGR+Ph4yZcvn5ibm4uZmZmsWbNGVcwGDRqIubm55M2bV37++Wf5559/VLcvtWvXrknhwoVFo9GIiYmJaDQa5d9q/waGOheIpN33bW1tRaPRiLOzszg7Oyv7ltrjQESkRIkSyudy7do1sbKykmbNmknevHmlV69eqmIeOXJEvL29tf6mqf+2ahnq8zLW+SU4OFiePXum/PtDL7VOnz4t7u7u4uDgIKampuLm5qb3fmCMmCLJ58BffvlFSpYsKdmyZVP225SXrqysrOTmzZsiImJnZ6ccW3///bfY2tqqbue7Xr16JWFhYWJpaSkajUYsLS2lVatWcvv27QzHcHFxSfecv2fPHr2v4dHR0ZIrVy7p06eP5MuXT5o2bSoJCQmq49nZ2cmZM2f0alN6SpYsKcOGDVN+xrVr1+TFixdSv359+fXXX1XHHThwoLi5ucmvv/6q9ONmzpwpbm5u8tNPP6mOu2vXLrGxsZFChQqJmZmZFClSRJycnMTR0VEqV66sOu53330nHh4e8uOPP8rkyZNlypQpWi+1DH18hYaGSr169eTx48fKtsePH0uDBg1k0qRJqttpY2OjHKuG4uPjI1u2bBGR5H0r5d5m6tSp0qxZM9Vxc+XKJUeOHFHiprT76tWrYm9vryqmvb29REZGiohItWrVlM/81q1bYmVlpbqtVlZWEh0dLSIirVq1kgEDBihx9T0fxsbGypAhQ+Srr76SPHnyiLe3t9Yro1KudyYmJlK7dm2ta2D9+vXFy8tLgoKCVLfTWPfhuXLlkvHjx6v+/vT4+vrK8uXLRUR73xo6dKh069ZNddyaNWuKv7+//Prrr7J+/XrZsGGD1kutsLAwcXFxkZYtW8q0adNk2rRp0rJlS3F1dZWff/5ZOnToIJaWljJnzhxV8f/TSSkXFxeJiIgQkeTkzOXLl0VEZPfu3VKkSJHPJqaIiKenp9KZsbe3l6tXr4qIyOLFi6VWrVqq4xrrxGiM9trY2Mi5c+fSbD979qyqk23qG5d3b5I0Go3Y2NjIvHnzVLVVRCRPnjyya9cuEdE+2Vy6dEmcnJwyHMfGxkbZp9ITEREhNjY2qtspItK8eXMpW7asnDhxQmxtbWXHjh2yZMkS8fX1VS7yujLWsWAsBQoUkLCwsDTbQ0NDpUCBAqrj5siRQ06cOCEiIuvXr5fs2bPLlStXZMiQIfL111+rimmMz8uYcbNlyyZnz54VEZFly5ZJ3rx5JS4uTn799Ve99oX79+9LaGioFC5cWMzMzKRmzZryxx9/yJs3b1THNEaHxlDngnctW7ZMypYtqxxbIiKXL1+W8uXLf3YPKwIDA6Vx48Zy8eJFefLkiTx9+lTrpZaxOqCG0rZtW3n+/Lny7w+91KpYsaJ07NhREhMTlf0rOjpaKlSoIGvXrv1sYookd7Y9PDxk0qRJYmVlJaNHj5aQkBBxcXGRqVOn6hyvWLFismTJEhHRPrZGjhwp5cqVU93OFCdOnJAuXbqIs7Oz5MyZUwYPHizXr1+X/fv3S9WqVaVkyZIZjmVtbS0XL15Ms/3ChQt6X8NFRK5cuSJZs2aVFi1aSFJSkl6x/Pz85PTp03q36V2pkwVOTk5y4cIFEUnux+nzENDDw0M2btyYZvuGDRske/bsquMaK4nm6OgoBw8eVP3972Po4yt79uzKZ5Ta+fPnxcPDQ3U7g4ODZdWqVaq/Pz02NjZy69YtERFxd3eXU6dOiUjygwsHBwfVcVM/SEp9jjl79qzquJUrV5bWrVvL4sWLxdzcXLlH2rdvn17HQb58+WTVqlUSGxsrbm5usnv3bqWtLi4uquOKGC6RmnK902g00rRpU61rYKdOnWTs2LHy4MED1e001r2Hvb29wROp1tbWykMVNzc3pZ8cGRkpWbJkUR3XWA8VGjVqJLNmzUqzffbs2dKoUSMREZk2bZoUKlRIVfz/dFLKyclJrl+/LiLJGfY9e/aIiEhUVJRYW1t/NjFFRGxtbZWTbY4cOeTYsWMikjxKRp/st7FOjMZor7Ozsxw6dCjN9oMHD6p6AnTz5k25ceOGaDQaOXHihNy8eVN53b59W6+njCKGe4IbGBiY7kkgxcyZMyUwMFCvtrq7uyufkb29vVy5ckVERDZu3Chly5ZVFdNYx4KxWFhYKPt/alevXhVLS0vVcS0tLSUmJkZERDp27KiMNLl+/brqp2zG+LyMGdeYT+9SnDp1Srp37y5WVlbi6uoqvXv3VhLuujBGh8ZYozl8fHzSvXE8efKkeHl5qY5rjIcVNjY26R5f+jJ28jspKUnvG/yUOLdu3ZL4+Hi9Y73L0dFR+b0dHR2VxMfRo0fF19f3s4kpYvjRDBs2bBBHR0cZP3682NjYyC+//CIdOnQQCwsL2bFjh+p2hoaGSqFChcTc3FwaNGggmzdvlsTERK2viYmJEVNT0wzHrFKlijRu3FhevnypbIuPj5fGjRtL1apVdWqfk5NTmlEwzs7OYmlpKQ4ODnqNjhER2b59u9SoUeODo7TVyJYtm7Iv+fn5KYkktQ8XU1haWirXq9QuX76s1wNWYyXRvLy80k1Q6svQx5ednd17R/fZ2dmpbufcuXMlV65cMnz4cFmzZo1s3LhR66VG/vz55ejRoyIiUrZsWRk3bpyIJI9IdnNzU93W8uXLy7Rp00Qk+e+R0q/t3r276hE9ERERUqhQIXFwcJARI0Yo27t3767XqK6ZM2eKmZmZODk5SeHChZVz1rRp06RSpUqq44oYPpE6YsQIiY2NNVi8FMa692jfvv0H78XU8Pb2VvpwxYsXl9mzZ4tI8vlX7blbxHgPFWxtbd97n5Ry/o6KilL9kMVM3aS//xsKFSqEiIgIeHt7o3Tp0pg4cSIsLCwwZ84c+Pj4fDYxAcDHxwc3btxArly5UKBAAaxevRqlSpXC5s2b0xTo1sWUKVPQokULbNiwAYMHD0bevHkBJBde+/rrrz+r9tatWxedOnXCvHnzlGKjx44dQ+fOnVXVecmdOzcAGG3lOn9/fxw4cED5OSnWrFmDokWLZjhO8+bNMWTIEHz99ddpavpERERg2LBh+PHHH/Vqa1xcnFJTy9nZGQ8ePED+/PkREBCgutCeoY+Fvn37YvTo0bC1tf1oIU41dR48PT2xe/du5RhIsWvXLnh6euocL0W2bNlw8eJFeHh4YNu2bUrdkPj4eNUF+o3xeRkzrqenJ44cOYIsWbJg27ZtWLlyJQDgyZMnBin6fefOHezcuRM7d+6EqakpateujfPnz8Pf3x8TJ05Enz59MhwrMTER9vb2AABXV1fcvn0bvr6+yJ07N65cuaKqfYY6F7zrzp076a5ek5iYiHv37qmOW6JECYwZMwbVqlVDeHi4ss/euHED2bJlUxWzdOnSiIqKSnN86csYnxcAzJs3D5MnT8bVq1cBAPny5UPv3r3RoUMHVfFEBHnz5sXff/+NfPnyqW5XeszNzZWaOVmzZkV0dDT8/Pzg6OioujaJMWICyQW4U2qV2dnZ4dmzZwCSr+9Dhw7VOV6DBg2wefNmjBo1Cra2thg2bBiKFSuGzZs3o3r16qrbOWvWLLRv3x5t27aFh4dHul+TNWtWnVb4mzp1KoKCgpAzZ04EBgYCSL6GW1lZYfv27Tq1T229z4xq2rQp4uPjkSdPHtjY2MDc3FzrfbVFo8uUKYODBw/Cz88PtWvXRr9+/XD+/HmsW7cOZcqUUd3ewMBAzJgxI009uBkzZih/azVsbW2VOlIeHh64du0aChYsCAB4+PCh6rijR4/GsGHDsGjRIoPVVAIMf3wFBwejXbt2CA0N1ep39+/fH40aNVLdzo4dOwJIXqjgXRqNRlXNm+DgYOzevRulS5dGjx490LJlS8ybNw/R0dE69QHeNXbsWNSqVQsXL15EQkICpk6diosXL+Lw4cMIDw9XFbNw4cJaq++l+OWXX/RauKlr164oVaoUYmJiUL16deUc7uPjgzFjxqiOCyT3CbNkyaJXjNSGDx9usFipGes+PG/evBg6dCiOHj2KgICANOdENTXLqlSpgk2bNqFo0aJo164d+vTpgzVr1uDkyZN6HV9TpkzBwIED8dtvv8HLy0t1nHdlyZIFmzdvTnM8bd68Wdk34uLilD6ZzvTJmH3ptm3bpgxDv3r1qvj6+opGoxFXV1dlyKMhY6ZM31AjLCxMGXq7c+dOsbKyEktLSzExMdFr/vn7vHz5Uq8pMMZo75MnT6R+/fqi0WjEwsJCLCwsxMTERBo2bKjX9I9FixZ98KWWoZ7gvnnzRipVqqRMT+rdu7f07t1batasKWZmZlKxYkW9PiuR5Boy27ZtExGRevXqSatWreSff/6RH3/8UXx8fFTFNPTxValSJXny5Iny7/e91NZ5+PXXX8XCwkI6d+4sixcvlsWLF8v3338vlpaWytMLNYYPHy6Ojo5SoEAByZUrl7x69UpERObNm6dzPaEUxvi8jBk39dO7wMBAgzy9e/PmjaxZs0bq1Kkj5ubmUrx4cZk1a5ZSv0dEZN26dTpPjytXrpysX79eRESaNWsmNWvWlIMHD0rr1q2lYMGCqtpqrNEcdevWlaJFiyrTFESSR0kVK1ZM6tWrpzru2bNnDf4Ud926deLv7y8LFiyQkydPatXw+9D05I8xxuc1dOhQsbW1lYEDBypP7gcOHCh2dnYydOhQ1W319/dXapMYUvXq1ZW6dx06dJBSpUrJ0qVLJSgoSEqVKvXZxBQx7GiGt2/fysiRI5WRqF+CuLg4mTNnjvTt21f69u0rv//+u16j596+fSuLFi2Su3fvGrCVIgsXLvzgS61r164px3tsbKx8//33EhAQII0aNVJGk6qxb98+sbW1FT8/P2nfvr20b99e/Pz8xM7OTq9pvA0aNFDqo/Tr10/y5s0rY8aMkWLFiuk8ui21IkWKiL29vdjZ2UmhQoWkaNGiWi+1DD1aKC4uTrp06aL0301MTMTCwkK6dOlilFEuhnTkyBEJDQ2VTZs26R0rKipKOnToICVLlhQ/Pz9p0aJFuuVEPhevX7+Wy5cvy9u3bw0Wc8mSJfLtt99KXFycQeLdvXtXWrZsKR4eHmJqamqwGqbGuLcXMU4t28TERK3PaMWKFdKjRw+ZNm2avH79WnVbnZyclHtkOzs7vWvLpZgzZ46YmppKvXr1ZPTo0TJ69GipX7++mJmZKbVzJ02aJE2aNFEV/z+dlErPo0ePDDJUP72Yhox78+ZNWbt2rV6d+cxkyPZGRkbKpk2bZNOmTQaZDuLk5KT1SikcbGlpqdfBKyKyf/9+qVatmri5uYm1tbWULVtWtm/frnOcN2/eyIQJEyQwMFBsbGzE2tpaAgMDZcKECXqduFIsWbJEFixYICLJN7Wurq5iYmIiVlZWsnLlSr3jpzDG8WVI69atk7Jly0qWLFkkS5YsUrZsWb2KAqb4448/JCwsTOvmaeHChapjG+vzMuZ+cPLkSVm3bp28ePFC2bZlyxbVw8FdXFzE2dlZunbt+t6580+ePNF5GpuxOjSGOhekdv/+falVq1aaRH2tWrXk3r17esVOz8uXL1V3ctOr25dSz89YHVC1D4JcXV2V4qOpLV++XK+6HJs2bZJy5crJ+fPnVcdIz4kTJ5QpCvfu3ZOgoCCxt7eXYsWKKTUqDBlTn1oVAwYMkJ9//llEkm+UzczMJG/evGJhYaFM69WFra2twaeYpdi/f7+0aNFCypQpoyyksHjxYjlw4IBRfp5aqeuS/Jf9+++/8tNPP0mjRo2kUaNGMnjwYPn333/1immsJNqIESM++FLL0MdXitjYWOUBwueajAoPD0/3+vT27VsJDw//BC16P0MXpE8RFxcn7du3F1NTUzE1NVVKBXTv3l1JUOqiSJEiWslSQyZSjVWMOz2f+72HoRnroYJIcsmc7777Tvncv/vuu3RL66ihEXlnnUDSyy+//IL+/fun2Z6UlIQWLVpgxYoVn6BV75eYmIjJkydj9erV6S53q3aI9pfu6tWr6NKlC/r374+goCCdvz8hIQFjx45F+/btkTNnTiO00Lji4+Nx+fJl5MqVC66urp+6OfQRxvq8Puf9YPHixWjSpEm60/8ePnxo0PY+fvz4vUvhfg4iIyNx+fJlAECBAgWQP39+veL5+PjgxIkTcHFx0dr+9OlTFCtWTNVy5bdu3frg++9ObdSHvp+Xk5MTTpw4kWaaXWRkJEqVKoWnT5+qiuvs7Iz4+HgkJCTAwsIC1tbWadr9JXj58mWatqt15MgRHDlyBPny5UO9evV0/v4GDRqgUaNGaNOmjUHak2Lt2rVo1aoVWrRogSVLluDixYvw8fHBjBkz8Oeff+LPP//UOea4ceOQLVs2tG/fXmv7/Pnz8eDBAwwYMEBVWytVqoTevXujYcOGqr4/xfPnz+Hg4KD8+0NSvk5Xxji30Pvpe3wZ0rRp09CpUydYWVmlmWb5LjVToUxNTXHnzh2lBEGKR48eIWvWrKqmBKa4du0aFixYgOvXr2PKlCnImjUr/vrrL+TKlUuZzqmLYcOGYe7cuejXrx+GDBmCwYMH4+bNm9iwYQOGDRum6vcHgF69euHQoUOYMmUKatasiXPnzsHHxwcbN27EiBEjcObMGZ3ijRw5MsNfq+t0PHt7exw4cABFihTR6fs+BympEzV9jHPnzqFQoUIwMTHBuXPnPvi175Zs+b/uP52UevXqFaZPn469e/fi/v37aWoLqamhkjVrVowbNw4hISHKtsTERHz33Xe4cOECLl26pLq9u3fvxu7du9Nt6/z581XFNOSJ0RgXnI/VDkpNTR2hDzl58iRatmyp3Ozpys7ODhcuXDDofN7PXaNGjbBw4UI4ODh8dD70unXr9PpZJ0+efG8yVZ/Yb968SfcYy5UrV4ZjGLvzZSzXr1/Xa879h/zzzz/YtGlTup+XmmP3m2++wdq1a9Nsv3fvHqpWrYoLFy6obquxGGLfygwmJia4e/dums79vXv34Onpmebz+5Tat2+PqVOnpqlhEBcXhx49eqi6Nvbo0QPm5uZp9ssffvgBL1++xMyZM1W1ddGiRR9839CJFX307Nkz3XNXXFwc6tati717936CVqU1e/ZsjBw5Ei1atEDx4sVha2ur9b6aepMAULRoUfTp0wetW7eGvb09IiIi4OPjgzNnzqBWrVq4e/euzjG9vLywfPnyNPU6jx07hu+++w43btxQ1dbVq1dj0KBB6NOnT7p/g4ze2KS+qTcxMUn3hktEVNf8AT58bsmVKxdev36d4ViZfXMXGxub5tytNjmX4tSpU8p9QcGCBfWqMWgMcXFxGD9+/HvvPXRJInp7e+PkyZNwcXGBt7f3e79Oo9GoSk6amJjg3r17cHNz09oeGRmJEiVKfDTR+j7h4eGoVasWypYti/379+PSpUvw8fHB+PHjcfLkSaxZs0bnmHny5MG0adNQp04d2Nvb4+zZs8q2o0ePYvny5aramjt3bqxatQplypTROm9FRUWhWLFiqv8GxuDv749ly5YZZJ/PrHuPxYsX45dfflFqTebPnx/9+/dHq1atMhwj9Tkw5TybXipG1/NsZjxUAJIH2URFRaV7PqhQoYLquADwny50HhISgh07duDbb79FqVKlDPIUfOvWrahRowYcHR3x7bffIiEhAU2aNMHly5f16sSNHDkSo0aNQokSJeDh4WGwJ/bLli3D77//jjp16mDEiBFo1qwZ8uTJg8KFC+Po0aM63TBPnjwZLVq0gJWVFSZPnvzer9NoNBmO+25W//Tp00hISICvry+A5IuNqakpihcvnuF2ZpSZmRlu376t+vurVq2K8PDwzzYpZYyEn6Ojo7JvOjo6qmpXRqxcuRKtW7dGUFAQduzYgRo1aiAyMhL37t1DcHCwqphXr15F+/btcfjwYa3tajrhxjgWUktMTMTChQvf21Hcs2ePzjGB5EKOOXPmRMWKFVGpUiVUrFjRIIWpd+/ejfr168PHxweXL19GoUKFcPPmTYgIihUrpipmdHQ0QkJCtAoN3717F5UrV1b15DKFITvhKQy5b6Vm6P1g06ZNyr+3b9+udQwnJiZi9+7dH7yZyIiLFy+mm5hUmzxYtGgRxo8fnyYp9fLlSyxevFj1A5t58+Zhx44dSvHlY8eOITo6Gq1bt9Y6d+qSUDVW0unevXv44YcflP3g3Q6umv1r69atcHZ21npSHhsbi1q1aukcK/V+9TG67gddu3YFkP7noM+xdeXKlXQ72I6OjqpHyt29ezfdoulubm64c+eOqpgA8N133wHQfsCRcqOjy99gz549SrFaQycdM3Ju0bWvVKRIEeXmrkiRIga7uUvtxo0b6N69O/bt24dXr14p2/U9d9+/fx/fffcd9u3bpyz+8/TpU1SuXBkrV65Mk1j5kE2bNqFWrVowNzf/6LGm6/HVoUMHhIeHo1WrVnrfe6ROuqpNwKYnJQmh0WjQtm1bWFpaKu8lJibi3Llzei3cNHDgQIwZMwZ9+/bVus5UqVIFM2bMUBXT0AXpUzx48CBNwhdI7td8biO9DVmMOzPuPcLCwjB06FB0794dZcuWBQAcPHgQnTt3xsOHDzNcTP/GjRvK8W3I48DZ2Vl5qODk5GSUhwpHjx5F8+bNcevWrTTnWn3ipvhPJ6W2bNmCP//8U9m5DKFkyZJYu3YtGjZsCAsLC8ybNw9RUVHYu3ev6lWLgOSngQsXLtQpG5sRhjwxGuOCk7pjFBYWBnt7eyxatAjOzs4AklfvateuHcqXL6/6Z7x7ERcR3LlzBzNmzNBr36hVqxYGDhyI8+fPG/QJrqEYI+G3YMGCdP9taGPHjsXkyZPRrVs32NvbY+rUqfD29sb333//3pWSPqZt27YwMzPDli1bPtvOV4pevXph4cKFqFOnDgoVKmSwzkZMTAz27duH8PBwTJw4ER07dkT27NlRsWJFVK5cWfXKY4MGDcIPP/yAkSNHwt7eHmvXrkXWrFnRokUL1KxZU1XMP//8ExUqVEDfvn0RFhaG27dvo3LlyggMDFRW91PDkJ3wFIbct1Iz9H6QevrPuwkUc3NzeHl5ITQ0VFXs69evIzg4GOfPn9e6eUxps66dmefPn0OS62LixYsXWtM4ExMT8eeff6bbOc+ICxcuKMnSa9euAUhe2c/V1VVrBJ6uf+/o6OgPvq92xFzbtm0RHR2NoUOHGmz/2rFjB8qXLw9nZ2f07t0bL168QFBQEMzMzPDXX3/pFOvdaWXpJQ/U7gfGWj3X3d0dUVFRaW6WDh48qHo0qaenJw4dOpQmsXvo0CFkz55dbVMNdo2pWLFiuv82hJR9QKPRGOzcYqybu9RatmwJEcH8+fORLVs2g527e/TogRcvXuDvv/+Gn58fgOSEfZs2bdCzZ0+dSn00bNhQSc59aAqnmpvGv/76C1u3bjXofZKhpSQhRAT29vZaU4stLCxQpkwZZbU/Nc6fP5/uyKWsWbOqXoExZ86cuHPnDnLlyoU8efJgx44dKFasGE6cOKGVVNNViRIlsHXrVvTo0QPA/86rc+fOxVdffaU6LmD4ci+GXOEzM+49pk+fjlmzZqF169bKtvr166NgwYIYMWJEhpNSqUsVGLJsgTEfKqTo3Lmzso8Zsi+rMEhlqi+Un5+f0QqFr1+/XszMzCQgIEAePHigd7wsWbJIVFSUAVqmzdArdRhT9uzZ5cKFC2m2nz9/Xjw8PFTHTa/4brZs2aRZs2Zy+/Ztg8V992dkROqVxIwpNDRU6tWrJ48fP1a2PX78WBo0aCCTJk3KlDbowsbGRilumyVLFmUVlIsXL4q7u7vqmJcuXTJUE9NlqAUPXFxcZOvWrQZo0YdFRkZKmzZtxMzMTK9i1HZ2dsr5y8nJSTmOz549K7lz51YdNzo6WnLlyiV9+vSRfPnySdOmTSUhIUF1PBERR0dH1cXX38dY+5ax9gMvLy+DXLdSq1u3rjRo0EAePHggdnZ2cvHiRTlw4ICUKlVK1epYKefR971MTU1lzJgxBv0d9PWxNqtlZ2enV/Hx94mIiJAsWbLI1KlTpUyZMlKxYkW9ixzv3LlTihUrJtu2bZNnz57Js2fPZNu2bVKiRAm9VqI0tLFjx4q/v78cPXpU7O3t5cCBA7J06VJxc3OTadOmqYo5YcIEcXFxkfnz58vNmzfl5s2bMm/ePHFxcZGxY8ca+DfQ3bsrYn7opZYxzi3GZGtrK5cvXzZ4XAcHBzl+/Hia7ceOHRNHR0eD/zy1vLy85OLFiwaJ1adPnwy/1BgxYoRRirDnyJFDKeRsZ2enFA9ft26d6lWJjVWQ/sCBA2JnZyedO3cWKysr6dWrl1SvXl1sbW3l5MmTquOKJK9K6+HhIZMmTRIrKysZPXq0hISEiIuLi7Laui6MWYzbGCwtLdNdXCsyMlIsLS1Vx42MjJTffvtNRo8eLSNHjtR6fW5sbGwMssDY+/ynR0qFhoZiwIABmD17tl7ZyvfNX3Vzc4OTkxM6deqkbFM7l7VDhw5Yvny5XsM60xMcHIzdu3ejdOnS6NGjB1q2bIl58+YhOjo6w1nf9BhjetHz58/x4MGDNNsfPHiAFy9eqG6rsZ60GiJu6uGYVapUwbp165Sh3oYUGhqKHTt2KCPQUn72mDFjUKNGDfTr10/nmEWLFk03i67RaGBlZYW8efOibdu2qFy5ss6xnZ2dlc88R44cuHDhAgICAvD06VPEx8frHA9Int+u9qnXxxhiHnpqFhYWBplW9674+HgcPHgQ+/btw759+3DmzBkUKFAA3bt3R6VKlVTHtbW1VZ6qeXh44Nq1a8oUO33+5p6enti5cyfKly+P6tWrY8mSJXo/uXF2dlaeNhmKsfYtY+0HKSPa3vXmzRtl6qyujhw5gj179sDV1RUmJiYwMTFBuXLlMG7cOPTs2VPnAqx79+6FiKBKlSpYu3at1mdmYWGB3Llz6zX6JEVMTAyA5H1NX+/+jm/fvsWZM2cQFhaGn3/+WXVcT0/PdKct6atw4cLYsmULqlevjtKlS2PLli16Fzjv3bs3Zs+ejXLlyinbgoKCYGNjg06dOmWo7mZm1OwbOHAgkpKSULVqVcTHx6NChQqwtLTEDz/8oIxC0FX//v3x6NEjdO3aVTkfWllZYcCAARg0aJCqmCmuXbuGKVOmKH8/f39/9OrVC3ny5MlwjA9NgUtN3+lwxnL16tX31ogdNmyYqpglS5ZETEyMMoLcUJKSktKMDAGSR40Zq0+qxujRozFs2DAsWrQINjY2esXK6Dle7TU8pdD2gwcPcOXKFQCAr6+vTlMh0/Pdd99hwIAB+OOPP6DRaJCUlIRDhw7hhx9+UHUtBIDx48cr/27atCly586Nw4cP612Qvly5cjh79izGjx+PgIAAZQTWkSNHlFkxahmy3AvwZU1nB5LLW6xevRo//fST1vZVq1alWRQlo37//Xd06dIFrq6ucHd319r3NRqN6vPWtm3bYGdnp1xnZ86cid9//x3+/v6YOXOm1r2eLkqXLo2oqCij9DuB/3ih8wcPHqBJkybYv3+/XkMH27Vrl+GfqXZYYa9evbB48WIULlwYhQsXTtNWQxX5NtRKHd27d1emlaQ3xO9DdXbep3Xr1jhw4ABCQ0NRqlQpAMl1Pvr374/y5ct/tIjsl8jR0RFHjx6Fn5/fe4s4GoK9vT02b96cJvGwd+9e1K9fX1XSb9CgQZg1axYCAgKUz+vEiRM4d+4c2rZti4sXL2L37t1Yt24dGjRooFPs5s2bo0SJEujbty9Gjx6N6dOno0GDBti5cyeKFSuW4eRv6mKAJ0+exJAhQzB27FgEBASkOcbUFgZ83zz0mTNnYsyYMaqSv6Ghobh+/TpmzJhh0OGzFhYWcHZ2RosWLVCpUiVl+o6+GjZsiDp16qBjx4744YcfsHHjRrRt2xbr1q2Ds7Mzdu3alaE471tRLT4+HpaWljA1NVW2qV3JbOnSpdi4caNBOuEp9uzZY5R9y1j7gTFWMXJ2dsbp06fh7e2NPHnyYO7cuahcuTKuXbuGgIAA1cnkW7duwdPTEyYmJqq+Pz0JCQkYOXIkpk2bhtjYWADJ09t79OiB4cOHp3szqY+tW7fil19+wb59+1R9/44dOxAaGqp3bY73PUi4desWsmbNqpWQUrMQDABYW1vjxIkTKFSokNb2c+fOoXTp0nj58uVHYxi7YHJqb968QVRUFGJjY+Hv7w87Ozu94gHJtbkuXboEa2tr5MuXT6/pOkByjab69eujSJEiyjXm0KFDiIiIwObNm1G9evUMxfnYCpmp6fMgNzw8HJMmTdJKoKX049T62M2d2v312rVr6Ny5M1q2bIlChQqlOfbVFlBv0KABnj59ihUrVijJ83///RctWrSAs7Mz1q9fryouYNhFkYoWLYpr165BRODl5ZXm91f7dzWG+Ph4dO/eHYsXL1Z+b1NTU7Ru3RrTp09XfT1/8+YNunXrhoULFyIxMRFmZmZITExE8+bNsXDhQq1+hyHI/6/787mxtbXFpUuXkCtXLnh4eGDr1q3KiplFixZVyr/oIjExERs2bNAq9l+/fn29/qa1atVCdHQ0unfvnu79p673GynWrl2Lpk2bolq1alrn2d27d2P16tWq6tnmzp0bXbt2Vb3y6vsEBARgwoQJqF27Ns6fP48SJUqgX79+2Lt3LwoUKKA6F7F+/XoMGTIE/fv3T7cvq++CEv/ppFS1atWUYrnpzRX/nFbC+dBoEo1Go7qwsbG4urpi8eLFqF27tsFixsfH44cffsD8+fPx9u1bAMnFyENCQvDLL7+kqdmUUd988w1KlSqV5qQwceJEnDhxAn/88YfqNsfFxSE8PDzd+dcZearwzTff4NChQ/Dz80N4eDi+/vprWFhYpPu1+uwDxkj4dezYEbly5Uozum/MmDG4desWfv/9dwwfPhxbt27FyZMndYr9+PFjvHr1CtmzZ0dSUhImTpyoPGUaMmRIhhMp764wlF5nQPQsDOjt7Y2RI0emeaK2aNEijBgxQtWT4+DgYOzduxdZsmRBwYIF01wY1I7IbNiwIQ4ePAgLCwtUqlRJeeXPn19VvBTXr19HbGwsChcujLi4OPTr10/5vMLCwjJ8g6PLfqj2/G2MTnhKwsTQ+5ax9oP3JcAjIiJQuXJlVQm/8uXLo1+/fmjYsCGaN2+OJ0+eYMiQIZgzZw5OnTql12qJT58+xbx587Q6tu3bt1dd8LRLly5Yt24dRo0apdThOHLkCEaMGIGGDRti1qxZqtuanqioKAQGBiIuLk7V9zs7OyM+Ph4JCQl6PWAz5vLfKSpUqAArKyssWbJEqbN57949tG7dGq9evUJ4eLiquMb2/Plz7NmzB76+vkoNoM9F0aJFERQUpDX6Akge7bVjx47PKnGwdOlStGvXDo0aNdK6sVu/fj0WLlyI5s2bq4prrJu7lMK+N2/eVLapKSL/rpiYGNSvXx9///23MgozJiYGhQoVwqZNm5AzZ05VcT+2KJKuya6PnRPUngdS++effwBA9e+c4vvvv8euXbu06sEePHgQPXv2RPXq1fU+b8fExOD8+fOIjY1F0aJFVY+OAZLrAM6cOTPNfcvNmzfRqlUrHDhwQFVcYzxQSuHr64vFixejdOnSKFeuHOrWrYuBAwdi1apV6NGjB+7fv69TvKioKNSuXRv//vuvMhLxypUr8PT0xNatW3Ua5Zmavb09Dhw4gCJFiqj6/g85deoUJk+erPQ1/Pz80K9fP9UrCDo4OODs2bMGX/U69ervI0aMwIULF7BmzRqcPn0atWvXVrV6LIB0H/4Z4nyoxPovJ6VsbGxw5MgRBAYGGizmy5cvISJKRv7WrVtYv349/P39UaNGDYP9HENZtGgRXF1dUadOHQDAjz/+iDlz5sDf3x8rVqxQ/TQse/bs2Ldvn943s+mJi4tTis/myZNHdTIqhZubG/bs2ZNmaOv58+dRrVo13Lt3T1XcM2fOoHbt2oiPj0dcXByyZMmChw8fwsbGBlmzZs3QE9yXL19i0aJFuHbtGkJDQ9GxY8f3Pu1RM/oshTESfo6Ojjh16lSaYZ5RUVEoXrw4nj17hsuXL6NkyZJ6Tb/Uhy43QGoLv1pZWeHChQtp/g5Xr15FQECA1oo+GfWx0Zn6Fno8d+4cwsPDER4ejgMHDsDMzAyVKlXCsmXL9Ir7JTBGJ/xj+5nafcvQ+0HKSJmIiAgULFgQZmb/m+GfmJiIGzduoGbNmli9erXObd2+fTvi4uLQqFEjREVFoW7duoiMjISLiwtWrVqFKlWq6BwTSB7hGBQUBGtra60RmS9fvlSmLujK0dERK1euTLPS3J9//olmzZqpeiIMpF2mWf7/ohojRozA5cuXcfbsWVVxP5as/ZwesEVFRSE4OBiRkZFaN+P58uXDhg0bjDYtQFdNmjRBhQoV0L17d7x8+RJFihTBjRs3ICJYuXIlvvnmmwzFyYylyq2srHD+/Pk0N8mRkZEoXLiwqmsMYJgpge/y8/NDp06d0owQDgsLw++//56h6ZvpMdbNnb+/P/z8/PDjjz+m+/BanxFjIoJdu3bh8uXLAJL/NtWqVdOrvR4eHpg4caLBF0UytKSkJIwZMwahoaHKaFR7e3v069cPgwcPVjXy1dXVFWvWrEl3xH+TJk3SLf+hRmJiIs6fP4/cuXOrHkletGhRPH/+HEuXLlUefCxatAg9e/ZElSpVVI+UMzExUYrep3b79m3kyZMnQyNR32fgwIFwcHDATz/9hFWrVqFly5bw8vJSyr28mxT/mNq1a0NEsGzZMmX6/aNHj9CyZUuYmJhg69atqtrp7++PZcuWqU4UZaaQkBCULFkSnTt3NmjcLFmy4ODBg/D390e5cuXQunVrdOrUCTdv3oS/v79eI9M/RN/C7f/pmlIFChTQ6wBNT4MGDdCoUSN07twZT58+RalSpWBhYYGHDx8iLCwMXbp00ftnGOqpApC8ilnK04MjR45gxowZmDJlCrZs2YI+ffqo7iT169cPU6dONfi0EiB5CKm+QwRTi42NTXf0kbm5eZqbCF306dMH9erVw+zZs5VpeObm5mjZsiV69eqVoRjW1tbKyerkyZOYMGGCUWpK2djY4Ndff8Uvv/xisISflZUVDh8+nOYm4/Dhw8pKWUlJSVqrZmVU69atUblyZVSoUEGvDrKhVxhKjzHmoRtzZUMgeehvQkIC3rx5g1evXmH79u1YtWqV6qRUTEwMNBqNcs46fvw4li9fDn9/f62ae7r4888/YWpqiqCgIK3tO3bsQGJioqql6wHDPPl9l7H2M0PvBykrN509exZBQUFaU5UsLCzg5eWV4Zvxd6X+nPLmzYvLly/j8ePH752SmVF9+vRB/fr18fvvvytJtISEBHTo0AG9e/fG/v37dY5paWmZ7jQ4b2/v945UzYj0lmkWEXh6euq1YqQxkk7GOGaB5M/+3Llz2LlzZ5qbcTX7gTHqVwLA/v37MXjwYADJo0uSkpLw9OlTLFq0CGPGjMnwcZAZS5W7ubnh7Nmzaa4nZ8+eVb0C5fumBBYsWFCnKYHvun79erqlIerXr5/mGqmLxo0bY8eOHQa/ubt16xY2bdpklGSpRqNB9erVVf8t0/PmzRt8/fXXBotnLIMHD8a8efMwfvx4rVFNI0aMwKtXr1TV2IuPj093lfOsWbOqvgkHkuvgBQQEICQkBImJiahYsSIOHz4MGxsbbNmyRVW9zePHj+Onn35CpUqV0K9fP0RFReGvv/5CWFiYqpUCU2rraTQazJ07V+vanZiYiP3796NAgQI6x03N0HWwwsPDcfToUa16kC4uLlr7hBpTpkzBwIED9Z7O/i5jjELLmzcvhg4diqNHj6Y7HU5tTcRy5cqhb9++KFu2LI4fP45Vq1YBSH5QoU/uwJCrBabnPz1SaseOHRg5ciR+/vlng9X5cHV1RXh4OAoWLIi5c+di+vTpOHPmDNauXYthw4apfgpkjKcKQHIy4vLly8iVKxcGDBiAO3fuYPHixfj7779RqVIl1U8WjDWt5OTJk+9djlRtzFKlSqFu3bppCsqNGDECmzdvxqlTp1TFdXJywrFjx+Dr6wsnJyccOXIEfn5+OHbsGNq0aaN0yNWQd5ZT/xyNGTMGY8eORceOHVGyZEkAySMY5s6di59++gmDBw/G5MmT8eeff2Lnzp06xe7QoQP279+PqKgo5MiRAxUrVkSlSpVQsWJFvYZUA8kdm/T2L7WJUGPMQzeWsLAw7Nu3DwcPHsSLFy8QGBiIChUq6F1fqnz58ujUqRNatWqFu3fvIn/+/ChUqBCuXr2KHj16qCrmWLhwYYwfPz7NFOFt27ZhwIABiIiIUNVWQzl37hwKFSoEExMTnDt37oNfa8gkuyEsWrQITZs2VZUwzmzW1tZKQf7ULl68iBIlSqi6GRk1ahQuX76MBQsWKPV+Xr9+jZCQEOTLl0914vLdEXMmJiZwc3ND3rx5tUal6ePVq1dpzl1q+jLGOGaNwRj1K4Hk/SplNFfr1q2RPXt2jB8/HtHR0fD391f6YZ+DUaNGYfLkyRg4cKCSkDh06BAmTJiAvn37qlogx1hTAvPmzYv+/fvj+++/19o+e/ZshIaGKouBZETqIvdxcXEICwtD7dq10627qvbmrl69emjbtq3qZPz7vK9Af+qFYCpUqKBzbZ0BAwbAzs7OYIsiJSYmYvLkye/td6ut3Zg9e3bMnj0b9evX19q+ceNGdO3aFf/++6/OMatWrQoXFxcsXrxYuXa9fPkSbdq0wePHjzNcu/JdOXPmxIYNG1CiRAls2LABXbt2xb59+7BkyRLs2bMHhw4dUhUXSH4INnr0aJiZmSE8PFwZNaWrlNp6t27dQs6cObX2m5QHSqNGjULp0qVVt3X//v34+uuv01yrEhIScPjwYVSoUEGneFmyZMGWLVvSJFEPHTqEevXqqd63DDWd/V3GGIVmrJqI0dHR6Nq1K2JiYtCzZ0+EhIQASH6Il5iY+NEFQlLbtGkTatWqBXNzc2zatOmDX/vu8ayr/3RSyhh1PlIneZo0aYKCBQti+PDhyuodarP1gwYNwrx58zBy5Mg0TxU6duyoeuWerFmzYvv27ShatCiKFi2Kvn37olWrVrh27RoCAwNVd7yMMb0oZdWnoKAg7NixAzVq1EBkZCTu3buH4OBg1aMGNm/ejEaNGqF58+bKFJLdu3djxYoV+OOPP5TRA7pyc3NTniLkz58f06dPR1BQEC5fvozixYurqh9iyFXcMmNawbJlyzBjxgytlVB69Oih1I14+fKl0glT499//8X+/fuVqWaRkZHw8PBQRhPq4sGDB2jXrh3++uuvdN/XZ660oeehA8CaNWve21FUe8NQsmRJJcFXvnx5gz3Zd3Z2xtGjR+Hr64tp06Zh1apVOHTokPJkW82F19raGpcuXUrzJOzmzZsoWLCg6vo8huqEp+7ApNQuS+9yq+88fGPsB0BynaY1a9bg2rVr6N+/P7JkyYLTp08jW7ZsyJEjh87xgoODP7oaZ/PmzXVe5SpbtmxYsmRJmunx27dvR+vWrVVNv05ZldbS0lKZ3h8REYE3b96gatWqWl+r9txoSHFxcRgwYABWr16NR48epXlfbWF6Qx+zqdurT63F1IxRvxJIvraOGTMGderUgbe3N1auXIkqVaogIiICVatWVbWa5uXLl987WmH79u1pRn1+TJMmTfDbb7/ByckJU6ZMQWhoKG7fvg0g+aa/f//+6Nmzp6qHV8aaEjhr1iz07t0b7du310qgLVy4EFOnTk2TrPqQD93QpabPzd2cOXMwZswYtG/fPt2H12pvwry9vfHgwQPEx8crD3uePHkCGxsb2NnZ4f79+/Dx8cHevXt1WvnT0IsiDRs2DHPnzkW/fv0wZMgQDB48GDdv3sSGDRswbNgw1ck+KysrnDt3Lk2JjytXrqBIkSKqbvDPnz+PmjVr4vXr11rnbSsrK2zfvl1Z8VdNW6OiopAzZ0506tQJNjY2mDJlCm7cuIHAwEBVMyrevn2LgQMHYubMmejXrx8OHjyIyMhIzJs3T69zWeXKlZUFZAzN0COFWrdujdOnT2PevHlatWw7duyI4sWLY+HCharaaejp7CkJnD59+mD06NHpjkK7efOmzisIfyne7cu+jyFqSkH+w/bt2/fBlxoBAQEydepUiY6OFgcHBzl8+LCIiJw8eVKyZcumuq0eHh6ycePGNNs3bNgg2bNnVx23efPmUqxYMQkJCREbGxt5+PChiIhs3LhRChYsqCrm27dvZdGiRXLnzh3V7UpPQECAzJgxQ0RE7Ozs5Nq1a5KUlCQdO3aUYcOG6RV7y5Yt8vXXX4uNjY24uLhI5cqVVe8DKapXry7Lli0TEZEOHTpIqVKlZOnSpRIUFCSlSpXSOV5oaKjY2NjIjz/+KBs3bpSNGzdK//79xcbGRsLCwnSO17ZtW3n+/Lny7w+9dPX27VsZOXKkxMTE6Py9uoiLi5Pt27fLwIEDpUyZMmJhYSFFihRRFat58+ZStmxZOXHihNja2sqOHTtkyZIl4uvrK1u2bDFwy/UzdepUsbOzk+7du4uFhYV8//33Uq1aNXF0dJSffvrpUzcvDVtbW7lx44aIiNSrV0/Gjx8vIiK3bt0SKysrVTGzZcsmu3fvTrN9586d4ubmprqtQ4cOFQ8PD5k0aZJYWVnJ6NGjJSQkRFxcXGTq1KkZjnPz5k1JSkpS/v2hl1rG2g8iIiLEzc1N8ubNK2ZmZnLt2jURERk8eLC0atVKVcw2bdqIo6Oj5M6dWxo1aiSNGjUSLy8vcXJykiZNmoivr69YWlrKwYMHdYrbo0cPyZkzp6xcuVKio6MlOjpaVqxYITlz5pRevXqpauvHzof6nBujoqKke/fuUrVqValatar06NFDoqKiVLUzRdeuXcXPz0/WrFkj1tbWMn/+fBk9erTkzJlTli5dqiqmMY5ZEZHTp0+Lu7u7ODg4iKmpqbi5uYlGoxFbW1vx9vbWOZ6Hh4dcuXJFdXveZ+bMmWJmZiZOTk4SGBgoiYmJIiIybdo0qVSpkqqY1tbWSh8mxatXr6Rbt25iaWmpc7yvvvpKPDw8tK5Pz58/V67r+siZM6esXr06zfZVq1aJp6enXrHXrVsnZcuWlSxZskiWLFmkbNmysmHDBr1ipnjw4IE8ePDAILFERDQazXtfJiYmquMuX75cKlWqpHXsX716VapUqSIrV66UmJgYKVu2rHzzzTc6xa1UqdJ7X5UrV9a5nT4+Psr+ZWdnp7R36tSp0qxZM53jpShVqpT06NEjzfbu3btL6dKlVceNi4uTOXPmSN++faVv377y+++/S3x8vOp4IiK5cuWS7du3S0JCgnh6eip/jwsXLoiTk5OqmIULF5a8efPKkSNHREQkKSlJxo8fL5aWltKlSxe92mssGo1G7t+/n2b7lStXxN7eXud4T548kfr164tGoxELCwuxsLAQExMTadiwoTx9+tQQTTYILy8v8fLyEo1GI56ensr/vby8JH/+/FKjRg05evTop25mGomJiXLlyhU5cOCAhIeHa70+V//ppJQx/PHHH2Jubi4mJiZSvXp1ZfvYsWOlZs2aquNaWlqm2/G6fPmyXh3EJ0+eSLdu3aR+/fry119/KduHDRsmY8aMUR3X2tpar5ut9NjY2Cid5CxZssi5c+dEROTixYvi7u5u0J9lCCdOnJA9e/aIiMi9e/ckKChI7O3tpVixYnL27Fmd43l5ecmiRYvSbF+4cKF4eXmpbmdSUpLcunVL7wv3u1Lf1BjaoEGD5KuvvhIrKyspWrSo9O7dWzZs2CCPHz9WHdPd3V2OHTsmIiL29vbK8bZx40YpW7asXu019MXB19dXli9fLiL/S9CKJCdUunXrpldbnzx5IpMmTZKQkBAJCQmR0NBQvTsIpUqVkgEDBsj+/fvFyspK2f+PHDkiOXLkUBWzU6dOEhAQkKZTX7hwYQkJCVHdVmN1wo3BWPtBlSpVpH///mniHjp0SHLnzq0q5oABA6RLly7Kzb1I8nHRvXt3GTRokCQlJUmnTp10PtZev34tPXv2VDq0JiYmYmlpKb1795ZXr16paquxbNu2TSwsLKRUqVLSp08f6dOnj5QqVUosLS1lx44dquN6enrK3r17RST53HX16lUREVm8eLHUqlVLVUxjHLMiIhUrVpSOHTtKYmKism9FR0dLhQoVZO3atTrHmzRpknTt2lVJABvSyZMnZd26dfLixQtl25YtW3ROnKZYtWqVZMmSRWrVqiV3796VM2fOiJ+fn/j6+srx48d1jpeUlCQTJ04Ua2trCQkJkdjYWFXtSs/IkSPFyclJxo8fL/v375f9+/fLuHHjxMnJSUaNGqU67oceVKXcoOvqyZMn0rVrV3FxcVHOAS4uLtKtW7fP6uY2NR8fHzlz5kya7adPn1aSs4cOHfrkfVsbGxu5deuWiCT3kU6dOiUiIteuXRMHBwfVcfft2ye2trbi5+cn7du3l/bt24ufn5/Y2dnJ/v37VcVMuRam54cfflDbVBk+fLg4OjpKgQIFJFeuXMp1Zd68eVKmTBlVMdu3b5/u8Xr69GnVAwJSxMTEyMyZM2XAgAHKdSblpUZwcLAEBweLiYmJ1K5dW/l/cHCw1K9fX7y8vCQoKEh1eyMjI2XTpk2yadMm5dqlq2fPnmn9+0MvtSpVqqTXPUaKPn36KJ/9u5+PIT4vkeRzqbe3t5iYmBg0mW5sTEpJcmb90qVLEhERofVS686dO3L69GmtzvexY8fk0qVLqmMa66mCsVSsWFHWr19v0Jg5cuRQElEBAQHKBejw4cN6XRy/FJaWlumesCMjI1U9ZU2RmJgo5ubmEhkZqU/z0qhfv74sXLjQoDFTaDQayZo1q4wbN85gT8nt7e2VJFquXLmUG4/r16+LtbW16rjGuDikTvq6ubkpN4yRkZGSJUsW1W09ceKEZMmSRXLkyKF0OnLmzCkuLi5KZ1SNvXv3ipOTk5iYmEi7du2U7YMGDZLg4GBVMZ8+fSplypQRMzMz5amVmZmZVK5cWZ48eaK6rcbqhIuI/P333/LXX38pIx1TXmoZaz9wcHBQknGpk1I3b95Ufa5xdXVN91i9cuWKuLi4iIjIuXPnxNHRUVX8uLg4OXfunJw7d07i4uJUxTC2IkWKyIABA9JsHzBggBQtWlR1XFtbW2WfzZEjh5Jcv379utja2qqKaYxjVkTE0dFRLl++rPz74sWLIiJy9OhR8fX1zVCM1DdFwcHB4ujoKN7e3lK3bt0076nx5s0b8fHxUdpmSDExMVKtWjVxcXERKysr6dy5s97766VLl6RMmTLi5eUloaGhMnXqVK2XGklJSRIWFiY5cuRQrlc5cuSQKVOm6JUA9PPzk0ePHqXZfvDgQVXH/qNHjyR//vxia2srnTp1ksmTJ8vkyZOlY8eOYmtrKwUKFDDIjaShWVtby4kTJ9JsP378uNLfuHHjhurj11Dy58+vjAIpW7asjBs3TkREVq5cqdeIZBGRf//9V3766Sdl5OzgwYPl33//VR3P0dFR/vzzzzTbe/furXdy748//pCwsDCtpOrChQsNNsIvNX0epuzatUtsbGykUKFCYmZmJkWKFBEnJydxdHRUNVJO5H8jhzUajTRt2lRrpHCnTp1k7NixBh2dqIaJiYncu3dPRETpW7/7+lwSMpUqVVL6qIYe2ZgiMDBQGjduLBcvXpQnT57I06dPtV76OH78uEyYMEH69etnsCRaiv/06nvGqiHj7u4Od3d3rW0p82XVmjhxIurUqYNdu3YphfCOHDmCmJgY/Pnnn3rFfvr0KY4fP55m5RqNRqN6WdmuXbuiX79++Oeff1C8ePE0q7ipKexboUIF7Ny5EwEBAWjcuDF69eqFPXv2YOfOnWnqfHxMlixZEBkZCVdX14+u/qS2KN78+fNRuXLlDNc9+BhjrOIGJM8XzpcvHx49eqR3kfDUatWqhYEDB+L8+fPp7gP6FMQ7c+YMwsPDsW/fPoSGhsLCwkKphVSpUqU0dQoywtfXF1euXIGXlxcCAwOVlTtmz54NDw8P1W3t3LkzSpQoga1bt6ZbiFcNd3d3PH78GLlz50auXLlw9OhRBAYGKkuWq2WMlcwAoFKlSnj48CGeP3+uVesgpT6DGo6Ojjh8+DB27tyJiIgIWFtbo3DhwjoX23xXzpw5cefOHeTKlQt58uTBjh07UKxYMZw4cUIpeq2r69evIzg4GOfPn9eqLZWyL+hzrTHGfmBpaZlunYzIyEi4ubmpipmQkIDLly+nOTYvX76s/P5WVlaqjw8bGxsEBATg+fPn2LFjB3x9feHn55fh7y9WrBh2794NZ2dnFC1a9IPtUFur69KlS1i9enWa7e3bt8eUKVNUxQQAHx8f3LhxA7ly5UKBAgWwevVqlCpVCps3b1a9WqsxjlkgeVXblLoUWbNmRXR0NPz8/ODo6IiYmJgMxXi31p2hF4swNzdXXTMpI968eYPExEQkJibCw8ND7wUFChQogJCQEHTu3BmTJ0/WKkSs0Wh0rvuTkJCA5cuXo3nz5ujTpw9evHgBIHlxHX2VKVMGNWrUwN69e5V4+/fvR926dTFy5Eid440aNQoWFha4du1ampXXRo0ahRo1aiiF4NUKDw/HpEmTlJqQ/v7+6N+/P8qXL686ZuXKlfH9999j7ty5Sm3JM2fOoEuXLkpt0/Pnz6vqPxpyQaCU+nqlS5dGjx490LJlS8ybNw/R0dHo06ePzm0Dkusp1axZE7Nnz1ZdDzc9y5YtQ7NmzbBlyxaUK1cOANCjRw+sW7cOe/fu1Sv2t99+q/X/p0+f6r3q6ZIlSzB79mzcuHEDR44cQe7cuTFlyhR4e3ujQYMGqmIOGjQIP/zwA0aOHAl7e3usXbsWWbNmRYsWLVCzZk1VMVPq9Xp5eaF///56nf9TExGsWbMGe/fuTXflVF321T179iir+On7Wb9P3759092eui5mgwYNtFYTTE/q9hmrrVevXsWaNWsMvmro2LFjMWTIEPj6+iJbtmxa/SSDLLyld1rrC2asGjInTpyQ/v37S9OmTQ3y1C7FP//8Y9CnCiIimzZtEnt7e9FoNOLo6ChOTk7Ky9nZWXXc982/1ydT/ejRI+X3TUxMlHHjxkm9evWkb9++Oj8JW7hwofI0YsGCBbJw4cL3vtTKmzevmJiYiKenp7Rs2VJ+//131UNTRUTWrFkjpqamEhQUJKNGjZJRo0ZJUFCQmJmZybp161THFUneD8qVKyfnz5/XK05qxqrFkJ6zZ89KmzZtxMzMTHXsJUuWyIIFC0QkedqGq6urmJiYiJWVlaxcuVJ122xsbPT63NMTEhIiI0aMEBGRGTNmiLW1tVSrVk2cnJykffv2quNaWVmlO6Lz77//1mu02JdkwIAB8vPPP4tI8tNgMzMzyZs3r1hYWKQ7yiUj6tatKw0aNJAHDx6InZ2dXLx4UQ4cOCClSpVSPVVBxHj7QUhIiDRs2FDevHkjdnZ2cv36dbl165YULVpUdZ2mHj16iKurq4SFhcmBAwfkwIEDEhYWJq6urtKzZ08REfn99991nr7XuHFjmT59uoiIxMfHS758+cTc3FzMzMxkzZo1GY4zYsQIZcTKiBEjPvhSy1h1esLCwpQRMTt37hQrKyuxtLQUExMTmTJliuq4xmDoWovG8vPPP0ubNm3k7du3Bou5YsUKcXJyknr16sn9+/dlx44dkiNHDvn666+V0Yi6unv3rtStW1ecnJwMOjLZGCUYRJL7bsHBwVKxYkV59eqV7NmzR+zs7FTvp7lz55Zt27a99/2//vpL9ZRjkeR+gZmZmTRp0kQZedakSRMxNzdX9mM17ty5I9WqVUtTT6d69epy9+5dERHZs2ePbN++Xae4K1asEHNzc6lbt65YWFhI3bp1JX/+/OLo6KiqBt67Dh8+LKGhobJp0ya94ri6uhp8dL6IyLJly8TZ2VlOnjwpXbp0kezZs+s9mn78+PFafcDGjRuLiYmJ5MiRQ/Wsml9//VVcXV1lzJgxYm1trRz/CxYsUF2zTkS75ICTk5NcuHBBRJL7yGqPg5T7wXdfXl5eUqNGDdVTz3v27CmWlpZSs2ZNadOmjd71Go2tUqVK4uDgILa2tlKsWDEpVqyY2NnZiaOjo5QuXVr5O/3999+fuqlSuXJlrZI8hpI1a1blPskY/tNJKWPUkDH2BcHQ8uXLJ7169TL4dAdDF/ZNKZ6ecrH+Uvzzzz+ydOlS6dSpk/j6+ioXshYtWqiKd/LkSWnRooVyQmzRooWcPn1a73Y6OTkpnSIrK6s0F5/PTVJSkpw6dUpCQ0OlXr164uzsLKampkp9KUOIi4uTU6dO6T0s2RgXh8TERK2bpRUrVkiPHj1k2rRp8vr1a9Vxs2bNmm4neNu2bZI1a1bVce/evSstW7YUDw8PMTU1TTOsWo2RI0d+8GUohuiEu7i4KJ1XBwcHZfrS7t27VRfmFzHefvD06VMluWVqaiqenp5ibm4uFSpUUF23JiEhQcaMGSPu7u5Kctrd3V1+/vlnSUhIEJHkItq6Lo6QLVs2ZdrismXLJG/evBIXFye//vqrXn9bYzBWnZ533bx5U9auXatXGQKR5CkrjRs3ltKlS0vRokW1XmoZutZifHy8Vv/l5s2bMnnyZJ1v5t/VsGFDsbe3Fw8PD6lRo4ZBHjDa2NjIr7/+qrXt8ePH0rhxY1WFglesWCEuLi5StWpVZfqmoRijBEOK169fS7Vq1eTrr78WOzs7JamshoWFxQfPGTExMXqVNyhQoEC6C8mEhoZKgQIFVMdNcenSJWUad8p1QR/GXBDIkHr37q36Ic/HzJw5UywtLSVnzpwGeSDo5eUlhw4dEhGRHTt2iJOTk2zfvl1CQkK0agfrws/PTzm+Uk+RP3/+vDKdXY1s2bIp0479/PyU8gBnz55VPRX0fQ/sp0yZIq1atRILCwtV/SNnZ2fZunWrqjZ9yLuleFJe586dk8jISNXTIydPniyNGjXSqkv19OlT+fbbb2XKlCkSFxcnDRo0kBo1amQ4ZsOGDdNcW4KDg6VRo0bSvHlzGTZsmKrzwrp168Tf318WLFggJ0+eNFh5Ind3d6Mkk1NoRPQY4/+Fc3BwwLlz5+Dl5YXcuXNj+fLlKFu2LG7cuIGCBQsiPj5e55iFCxfG999/j27dusHe3h4RERHw9vbG999/Dw8PD1XDk4Hk4fkVK1bE7NmztaaQPHz4EKVKlVK93K2trS3Onz8PHx8fVd+fmWxsbHDp0iXkzp3boHENvcxpeuLj43HgwAGsWLECy5Ytg4ggISFB77iGYuglVI3N2dkZsbGxCAwMVKbtlS9fXvVUFWNav349hgwZgv79+6e7pLSaqazG0rNnT6xfvx6TJk3SWq67f//++Oabb1RPMapVqxaio6PRvXv3dKcwqhmqnjLlIcXbt29x48YNmJmZIU+ePKqnWBmDs7MzTp8+DW9vb+TJkwdz585F5cqVce3aNQQEBKi61mSGQ4cOISIiArGxsShWrBiqVatmkLgpUwMdHBz0jmVtbY3IyEh4enqidevWyJ49O8aPH4/o6Gj4+/sjNjZW55gxMTHQaDTImTMnAOD48eNYvnw5/P390alTJ9VtFRFMmTIFoaGhuH37NgAge/bs6N+/P3r27Kl6+PvixYvRtGnTNNNL37x5g5UrV6J169Y6x5w2bRoGDx6Mtm3bYs6cOWjXrh2uXbuGEydOoFu3bgaddqOPGjVqoFGjRujcuTOePn0KX19fWFhY4OHDhwgLC0OXLl1UxW3Xrt0H30+Z0qKLK1euwNfXN933lixZonO5BFtbW4wfPx49evTQuS0fs3r1agwaNAh9+vTRuwTDuXPn0mx78eIFmjVrhjp16mh9RrpeD3PkyIFVq1Yp07XedeDAATRt2lQ53nRlaWmJv//+O800mKioKBQqVEjvaZ5v3rzBjRs3kCdPHq1pl2rZ2tri77//hpeXF1xcXLBv3z4EBATg0qVLqFKlCu7cufPRGJs2bcrwz1NbhqFHjx5YvHgx8uXLl+7+FRYWlqE475tS9ccff6BYsWLIkyePzjHflfoa06tXL7x69Qq//fYbIiMjUbp0aTx58kRVzMuXLyN37tzKvaKPjw+uXr2KwoUL4+XLl6ra2rBhQ9SpUwcdO3bEDz/8gI0bN6Jt27ZYt24dnJ2dsWvXLlVxPyQsLAxr1qzB4cOHdfo+b29v/PXXXyhQoIBB22NiYvLBa6m5uTmaNm2K3377Taep0zly5MDOnTvh7++vtf3vv/9GjRo18O+//+L06dOoUaMGHj58mKGYbdu2xYYNG+Dk5ITixYsDSC4P8PTpU9SoUQMRERG4efMmdu/ejbJly2a4rSlT5FNLKR2h0WhU39dOnDgRt2/f1qvcwIf8p2tKGaOGzLVr11CnTh0AgIWFBeLi4qDRaNCnTx9UqVJFdVLq5s2bMDMzQ/ny5bFp0yalZlViYiJu3bqlKiYABAUF4eTJk0ZJSl27dg1TpkzRmoffq1cvrYuELkqVKoWzZ88aPCn1vrzs69evYWFhoTrujh07sG/fPuzbtw9nzpyBn58fKlasiDVr1uhd98bQjJV0MkYtBgBYunQpypcvr/dN7fs6NOlR26H55ptvACTXjUmh5uJw7tw5FCpUCCYmJul28lNTm+iaNGkSNBoNWrdujYSEBIgILCws0KVLF4wfP15VTAA4ePAgDhw4gCJFiqiO8a4zZ86k2fb8+XO0bdtW5xozmzZtQq1atWBubv7RDrmaTnihQoWUBxSlS5fGxIkTYWFhgTlz5uh87v3YZ5+avgnPsmXLKh2hp0+f6hUrNUMko1J4enriyJEjyJIlC7Zt24aVK1cCAJ48eaK6Vk/z5s3RqVMntGrVCnfv3kW1atVQqFAhLFu2DHfv3sWwYcNUxU3pCxi6Tk+7du1Qs2bNNA9WXrx4gXbt2qlKSv3666+YM2cOmjVrhoULF+LHH3+Ej48Phg0bprrOIgCMGTMGLVq0MFitxdOnTyv1gtasWQN3d3ecOXMGa9euxbBhw1QnpdQknT7G19cXCQkJ2LdvH65du4bmzZvD3t4et2/fVlUX6+zZswatA5nad999BwBa9ajU3tQUKVJEq5Ze6li//fYb5syZo/pmKSgoCIMHD8bOnTvT9Ndev36NoUOHqq6lAySfX3bv3p0mKbVr1y54enqqjhsfH48ePXooDwQjIyPh4+ODHj16IEeOHBg4cKCquM7Ozsq5JUeOHLhw4QICAgLw9OnTDD/8aNiwYYa+Tp+b2wsXLqBYsWIAkn/3d+NmVHr9ACC5Buvz58+V9/Wpd+Ps7IyYmBh4enpi27ZtGDNmDIDkewe1v7+3t3e69zPbtm3TqRbiu8LCwpQHMSNHjkRsbKxSd1ZtH/Zj6tatq/xNdDFixAiMHDkS8+fPh7W1tcHas379egwYMAD9+/dX6jkfP34coaGhGD58OBISEjBw4EAMGTIEkyZNynDcZ8+e4f79+2mSUg8ePFAetDk5OaWp4/Yh7u7uaN68OWbMmKEkkpKSktCrVy/Y29tj5cqV6Ny5MwYMGICDBw9mOO6NGzcy/LW6+OGHH1CnTh3kyZMH/v7+aR6y61qzLg2jjcH6AhijhoyxVogzMTGRa9euSXBwsGTPnl1ZPvju3bt61eeZO3eu5MqVS4YPHy5r1qwx2KpQxlj6etWqVeLj4yPTp0+Xw4cP6z0cMaU+gImJifz8889aq9WEhYVJw4YN9Zr+kbJC3IQJE/RaDSyzJCQkyJo1a2T06NEyevRoWbdunTKtRg1j1WIwpHdXu3BwcBAbGxtlioqtra04ODjotQqGoaayajSaNKuLGKtel6FXMvPz8zPINNOMOHfunM61E9792xq6Ftq2bduU5e4jIyPF19dXNBqNuLq6yq5du3Ru6/s+e0PtB++roZE9e3adplgVLVpUqfdXpEiRNFPADDEdbObMmWJmZiZOTk5SuHBhZdXbadOmqa7N4eTkpAyZnzp1qnz99dciIrJ9+3ZluXY1jDXVTKPRyP3799NsP3v2rOrp18Za2bFw4cJiYmIiX331lcycOVPv6dHW1tbK1LXGjRsrNb+io6P1roP39u1b2blzp8yePVueP38uIskrhr148UJVvJs3b0qBAgXExsZGTE1NlSk7PXv2lO+//16vthqaIUswfCyWPqUdYmJiJFu2bJIrVy6ZMGGCbNy4UTZs2CDjxo0TT09PyZo1q0RHR+scN8Wvv/4qFhYW0rlzZ1m8eLEsXrxYvv/+e7G0tJTZs2erjtuzZ08pXry4HDhwQGxtbZV9YcOGDXr1O5s1ayahoaEiIjJq1Chxc3OTDh06SO7cufWua/tf1a1bN8mdO7eyambK8b9ixQqdr10jR46UuLg4+f333yVHjhyycuVKsbW1lRUrVsiYMWOUf39Jzp07J9myZdP5++Lj4yUoKEjs7OykUKFCBusTlCxZMt06c9u2bZOSJUuKiMj69evFx8dHp7jNmzcXb29vWbduncTExEhMTIysW7dOfHx8pGXLliKSvE8UL148wzGNvSqxoXXr1s2odcD+00mpdxmihoyxLgipb5oGDhwo1tbWsmTJEr2TUsa6qTHG0teGLp6esoy8RqMRT09P5f9eXl6SP39+qVGjhrIUrhqTJ0+W4OBgcXFxkezZs0uzZs3kt99+07voojFcvXpV8uXLp5WQsbGxEV9fX6Vooq6MXYvB0AsKpNSnSl00//Hjx9KgQQOZNGmS3u3V182bN5WluA3dsU9vTnt6L7W2b98uNWrUkBs3bqiOkVEHDhwQJycno/8cfT169EjV0urGvMFLYagaGplRPFwk+Vywbt06rWTBli1b5ODBg6ri2draKvtqvXr1ZPz48SKSXPPKyspKdTurV68us2bNEhGRJ0+eSNasWSVnzpxiZWWVptZQRqQk+kxMTCQgIECrQ1+4cGGxt7eXxo0bq2qrt7e3kkguXry4cgO+fft2vesMXrhwQQYNGiTe3t5ibm4utWvXlmXLlqlKgAcEBMjUqVMlOjpaHBwc5PDhwyKS/KBRzY1SCmMkkBo0aCAtW7aU169fa9WR2bt3r+TNm1d1W//rrl+/LjVr1tRK1puYmEhQUJBBagqtW7dOypYtK1myZJEsWbJI2bJlZcOGDXrFzJUrlxw5ckREtGsKXb16VVV9sRSGXBDofb6Eh6yG9ObNG/nll1+kZ8+eWg/XwsLC5Pfff9cplomJiXIvt3TpUsmbN6+yz+bIkUPmzp2rV1u9vb3l4cOHabY/efJErwcqH9KrVy8JCgrS+fsaN24srq6u0rlzZxk+fLjB+gTvW7Dn0qVLyvX7xo0bOj+0ePHihXTo0EGpv2tiYiIWFhbSsWNHpdbmmTNn5MyZMxmO6eTklO4AkI0bNyr92MjISFV92qioKOnevbtUrVpVqlatKj169FB9P5fCzs5Or4XgPuY/XVMqNXlniW61Hj9+jFevXiF79uxISkrCxIkTcfjwYeTLlw9DhgzRWlpZF+/WPVq6dCk6duyIZs2aYdGiRQape2RIVlZWOH/+fJqh5ZGRkShcuLCqefgfm6aodlpf5cqVlfnWxnL+/HmEh4djz5492LJlC7JmzYp//vnHaD9PV7Vr14aIYNmyZcpypo8ePULLli1hYmKCrVu36hzTmLUYUuqkBAUFYceOHahRowYiIyNx7949BAcHq5p6kSNHDuzYsQMFCxbU2n7hwgXUqFFDp5oUxp4OZmgfq5+SQu2UFmdnZ8THxyMhIQE2NjZphvyqmQ40bdo0rf+LCO7cuYMlS5agYsWKWL58uaq2pufp06eq6pWlnrL5IfPnz9c5NgDExcWlqcVhCMaooWFshqzNUrp0aVSuXBl16tRBjRo1cPToUQQGBuLo0aP49ttvVZ+7XV1dER4ejoIFC2Lu3LmYPn261lSzlGnOGZVSDmDkyJHo168f7OzslPcsLCzg5eWFb775RtU09A4dOsDT0xPDhw/HzJkz0b9/f5QtWxYnT55Eo0aNMG/ePJ1jpufQoUNYvnw5/vjjD7x69UqZBpFRa9asQfPmzZGYmIgqVapg586dAIBx48Zh//79+Ouvv1S1q2HDhrC3t8e8efPg4uKi1HzZt28fOnbsiKtXr+oc08XFBYcPH4avr69WHZmbN2/C39//s6wtd/HiRURHR6eZkqLvdcsYcZ88eaJ8Lnnz5v3o0uyfko2NDS5cuAAfHx+tfSEiIgIVKlTAs2fPPnUTAQATJkyAl5cXmjZtCgBo3Lgx1q5dCw8PD/z5558IDAxUHfvkyZNYvXp1uvuB2mlAxohpSCYmJrh7967WVOv4+HjExsammX5tqPgAcO/ePeTKlQuvX7/WOeb7ylw8e/YMp0+fRmRkJPbv36/URMooW1tbbN++/b314NQqWrQoAgMDMWfOHOXa9/btW3Ts2BERERE4c+YMDh06hJYtW6qa5hYbG6vUcfbx8dG67uqqZ8+eWLFiBX766SeULFkSAHDixAmMHTsWzZs3x9SpUzF37lwsXLhQp+l727dvR/369VGkSBGlBENKjdDNmzejevXqqtqbO3dubN++3eB1wFL8p2tKAcC8efMwefJk5UKWL18+9O7dGx06dFAVL/VF0MTERPW88He9mzts2bIl8uTJo6oOQWZwc3NLt97B2bNnVZ94DV1LKsXevXuNEhdI/tzOnDmDffv2Ye/evTh48CCSkpLg5uamc6z27dtj6tSpaWqQxMXFoUePHqpvbIHk2k9Hjx7V2n9dXFwwfvx4nYrrpWasWgwAMHbsWEyePFlZUGDq1KlaCwqo8fz5czx48CDN9gcPHig1GjKqYcOGSsfgQ/UZdKnJYMzio8aon5KaMYoiptSRSWFiYgI3Nze0adMGgwYNUh3XkJ3whQsXInfu3ChatOh7a9fpI1u2bGjSpAnat29v0I6dMWpopHjz5g3u37+PpKQkre25cuVSFc8YtVkmTJiA4OBg/PLLL2jTpo3ymW/atEmpUaG2rSnn7x07dqBRo0YwMTFBmTJlVNWGHD58OAAo+6vaGlrpmTNnjvIZdevWTUmo1K9fH99//73Bfo6trS2sra1hYWGh83kWAL799luUK1cOd+7c0To2q1atqlf/6MCBAzh8+HCahJ6Xlxf+/fdfVTGTkpLSPX7++ecfg9QWM6Tr168jODgY58+f16oHlfLgVu15wFhxgeTzlj7H54cY+rxVokQJbN26VSlSn/L7z507F1999ZXqdlarVg0tW7ZEo0aNDFK7b/bs2Vi2bBkAYOfOndi1axe2bduG1atXo3///tixY4equB97sPi5xEyxZMkS/Pbbb7h+/TqOHDmC3LlzY8qUKfD29tZ5oZZ3Bz/Y2NjAxsZGr/al7h9u374djo6Oyv8TExOxe/dueHl5qYr9vrpdDg4OqF69OtatW6eqRqCnp6dB60ummDlzJurXr4+cOXMqdTXPnz+PxMREbNmyBUDyeahr166q4tvZ2RlsgaLJkycjW7ZsmDhxIu7duwcguV/Xp08fDBgwAEDyYh661sUbOHAg+vTpk6YW7MCBAzFgwADVSakRI0Zg+PDhWLBggd77bLqMNgbrCzB06FCxtbWVgQMHKjWUBg4cKHZ2djJ06NAMx3n27FmGX4Z29+5d2bdvn14xYmNjZevWrTJr1iytukpTp05VHdNYS18bYzhio0aNlOkZqU2YMEG+/fZb1XHr1q0rzs7OYmpqKsWKFZO+ffvKxo0bVQ99Tj3sN7UHDx6Iqamp6naKJC/NmjJdJ7WDBw+qnqphrFoMIslLa6dMr8mSJYtSx+3ixYvi7u6uKmarVq3Ey8tL1q5dq8wXX7NmjXh7e0vr1q31aq8hvG/qaur/p7xIPUMu/9y1a1dxdnaWIkWKyNSpU+XRo0cGbev69eulQYMGYm5uLvny5ZNx48YpUzf0YcgaGimuXLki5cqV09pP9Zl+ncJYtVkSEhLSTHe5ceNGuufgjDLWVLMUr1+/lpiYGLl165bWS1dv376VkSNHSkxMjN5tSs/169dlzJgx4u/vL6amplKlShWZO3euPH36VK+40dHRetUPSs3JyUn+/vtvEdGeXnXgwAHJmjWrqphNmjSRjh07KjGvX78uL168kCpVquhVj2Po0KGyZ88eefnypeoY76pbt640aNBAHjx4IHZ2dnLx4kU5cOCAlCpVSvbv3//ZxTWWyMhIo5y3Dhw4IHZ2dtK5c2exsrKSXr16SfXq1cXW1lZOnjypOm7Pnj3F3d1drK2t5dtvv5UNGzbImzdvVMezsrJSjqmePXtKp06dRCT5fK7PNPmAgACZMWOGiPzv+EpKSpKOHTvKsGHDPpuYIsl9WVdXVxkzZoxYW1sr54IFCxboXLdQo9GIk5OTODs7f/Clq/f1CzUajVhYWEj+/Pll8+bNOsc1pi1btkhQUJBRyjo8f/5cZs2apdQ0Tl0XUK3Y2FgZMmSIfPXVV5InTx7x9vbWeunq7du3smjRIrl7966IiEHzBJaWlhIZGZlm+5UrV8TS0lJ13CJFioi9vb3B64Cl+E8npVxdXZVC5KktX75cKTCWEe/eEKb3MlQBYkM7ffq0uLu7i4ODg5iamoqbm5toNBqxtbXVa/5xUlKShIWFSY4cObTmS0+ZMkVVHRUR4xRPF0neD1KSGqmdO3dOdedTROSHH36QzZs3693RfvbsmTx9+lQ0Go1ERUVpJTkfP34sixYtEg8PD71+RqtWraRgwYJy9OhRSUpKkqSkJDly5IgUKlRI2rRpozquMWoxiBhnQYG4uDjp0qWLWFpaKseshYWFdOnSRZkv/rnYuXOnFCtWTLZt26bsC9u2bZMSJUrodSxkhpcvXxokWd+uXbt0OxmxsbHSrl071e0zdCf81atXsnz5cqlWrZrY2NhI48aNZdu2barPg+m5f/++hIaGSkBAgJiZmUmdOnVk7dq18vbtW1XxDFlDI8XXX38tFSpUkD///FPOnDkjZ8+e1XqpZYzaLO8rSJ5e8VRd/PHHH2Jubi4mJiZaCc6xY8dKzZo1Vcc1xo1z6rpahlS6dGkxMTGRIkWKyC+//CL//POPXvHevn0rQ4YMEQcHB+X3dnBwkMGDB+t1M26MBFJMTIz4+/uLn5+fmJmZSZkyZcTFxUV8fX31SnZWq1ZNbG1txdLSUsqVKyeDBw+WnTt3Snx8vOqYLi4uygIyDg4OSuH/3bt365XsNVZcYzHWeUtE5Nq1a9KhQwcpWbKk+Pn5SYsWLdLti+oqMTFRtm/fLm3atBEHBwdxdnaWjh07qnqA7eHhoTykyZ8/v6xevVpERC5fvqxX7StjPFg0RkyR5IVa1q9fLyLa15jz58/rdK8okny/OHXqVFm4cOEHX2p5eXnpvXhEZnFyclLqM9nZ2emdmBNJ7rv4+PjIxYsXDdxake+++048PDzkxx9/lMmTJ8uUKVO0XmqkXlDEkHLmzKkcq6mtWrVKPD09Vcc1Zm1Qkf94UsrR0fG9mURdKt3v27cvQ6/p06fr1L7MWLmoYsWK0rFjR0lMTFROttHR0VKhQgVltaiM2rhxY7qdwOfPn+udoRYxTvF0keSb0JSOUWqpi+J9Sh9LepqamsqYMWP0+hlPnjyR+vXrK4mYlAtFw4YN9U6qGYMxV5iJjY1VVnRUm4x6d8Thh15qFCxYUA4cOJBm+/79+w1SRN7QYmNjpVu3buLm5pbuPqyGsUYOGqsTLpKc3BgxYoT4+PhIrly5VK/i9SHTpk0TS0tL0Wg04ubmJkOHDjXICor6srGxSbf4qL5SP7lOfcNw9uxZ1QnqdwuSZ8uWTa+C5KnduXNHTp8+rawSKCJy7Ngxvf42xrhxrl+/vl43R+/z008/KSOQDKFz586SNWtWmT17tnLenj17tri7u0vnzp1VxzVWAunt27eyZMkS6d+/v3Tp0kV+//13vZJHqeMePHhQxo4dK0FBQWJvby8WFhZStmxZVfGcnJzk+vXrIiLi4+Mje/bsEZHk0er6rGporLjGYozz1ps3b6Rdu3bK38GYXr58KatXr5bAwEBV11pjjJoVMc6DRWOtfm5lZaUkDlJfYyIjI3W+R0i9aNV/nbESc9mzZzdKUsrR0VH14invU7FiRSXhaUjGmq1kbP/pmlKtWrXCrFmzEBYWprV9zpw5aNGiRYbjVKxY8b3vvXjxAitWrMDcuXNx6tQpdO/ePcNxGzRoAEtLSwD4YF0afZw9exa//fYbTExMYGpqitevX8PHxwcTJ05EmzZt0KhRowzHCg4Oxt27d+Hm5qZVmN1QtRIuXbqE1atXp9nevn17vWrWBAQEYNWqVRg2bJjW9pUrV8Lf3191XCC53lN4eHi6RRd79uyZoRh79+6FiKBKlSpYu3atVt0nCwsL5M6dG9mzZ9ernU5OTti4cSOuXr2Ky5cvAwD8/PzS1IPS1dOnT7FmzRpcv34dP/zwA7JkyYLTp08jW7ZsyJEjh+q4M2bMUAqlDx48GObm5jh8+DC++eYbDBkyRKdYGd3HdSmS+W69o/fRaDQZ3g9Su3btWrqFtx0dHXHz5k2d4xnbjz/+iL1792LWrFlo1aoVZs6ciX///Re//fZbmjnvH/P8+XNI8gMVvHjxQquOTmJiIv7880+9CoY2atQIzZs3R758+fDo0SPUqlULQHJdBX2PBxMTE6WOiiEXp7h37x4WLVqEhQsX4tatW/j2228REhKCf/75BxMmTMDRo0d1qv2xePHiD77funVrndvo7++Phw8f6vx9H2OM2iynT59WjuE1a9YgW7ZsWgXJu3Tporq97u7ucHd3B5C8L+/Zswe+vr56FQ49e/YsTp06ZdDio7Vq1cLAgQNx/vx5FC9ePE1BfbUFqX/++WcAhitMv3z5cqxcuVI5TgGgcOHC8PT0RLNmzTBr1ixVcXPmzImIiAisXLkS586dQ2xsLEJCQtCiRQtYW1urbq+ZmRlatmyp+vs/FLds2bJwc3NDlixZYG9vjw0bNijXc10VKlQIERER8Pb2RunSpTFx4kRYWFhgzpw58PHxUd1OY8U1FmOct8zNzbF27VoMHTrUoHHfdffuXaxcuRJLly7FuXPnVNXbmjx5Mry8vBATE4OJEycqRZ3v3LmjuiYPAFSoUAE7d+5EQEAAGjdujF69emHPnj3YuXMnqlat+tnEBABvb2+cPXs2TV3bbdu2wc/PT6dY+i6m9T5HjhzBo0ePULduXWXb4sWLMXz4cMTFxaFhw4aYPn26ck/5qb19+xbh4eEYOnSoqnpUH9KtWzdMmDABc+fO1XvRk9ScnZ0NvnhC165d0a9fP/zzzz/pXmfV1q4aOnQo7O3tERoaqtRYzZ49O0aMGKHqnuNdp06dUhZmKViwIIoWLap3TAD/vZpSKdO++vTpIz169BB7e3spWLCghISESEhIiBQqVEgcHByke/fuev2c8PBwad26tdja2kq+fPlkwIABcvz4cVWxEhISJDw83CjLsLq6uiqjxfLly6dMT7h06ZLY2NjoFCtbtmyyadMmEUl+GnD//n2DttVYwxE3bdokZmZm0rp1ayVD36pVKzEzM9Mrg23oqZE3b9406JQfY4uIiBA3NzfJmzevmJmZKU+XBg8eLK1atVIV0xi129q2bZuh1+ekfPnyUr16dWUuukhyfbkaNWpIhQoVPmHL0ufp6Sl79+4VERF7e3tlme7FixdLrVq1dIpl7JGDhp66lnr6npWVlXz77beydetWrZEyaq1du1bq1q0r5ubmEhgYKNOnT09znYiKihJzc3Od4jo5OWm9bG1tRaPRiKWlpU7D6lMfk7t375avvvpK9u7dKw8fPjRYvUVj1GaxtrZWajE1btxYGZYeHR2t12iOxo0bKyOm4+PjJV++fGJubi5mZmayZs0a1XFLlCiR7shJfbxbl+Td+nVqxcfHS/v27cXU1FRMTU2V60L37t1l3LhxOsdzc3NL96n4xYsXxdXVVXU7DSk8PDxDL7V+++03adasmWTPnl1cXFykYcOGMmXKFDl79qxe5RJSRstfvXpVfH19RaPRiKurq+zevVt1W40V15Ay47zVunVrCQsLM2Crkz179kzmz58v1apVEzMzM8mfP7+MHDlS79qrhvbo0SOl/mFiYqKMGzdO6tWrJ3379k1Ty+9TxhQR+f333yVHjhyycuVKsbW1lRUrVsiYMWOUf+vCWCOlatasqVUb99y5c2JmZiYdOnSQ0NBQcXd3l+HDhxv85+rDwcHBKKMFGzZsKPb29uLh4SE1atSQ4OBgrZdaS5YskW+//dagI8/fd301ZMkfQ81WEhG5d++eVK5cWTQajTLNUqPRSJUqVQxyz68RMcKSQJ+xypUrZ+jrNBoN9uzZo1Psu3fvYuHChZg3bx6eP3+OJk2aYPbs2YiIiNB7xI2VlRUuXbpk8IxyjRo10LZtWzRv3hwdO3bEuXPn0LNnTyxZsgRPnjzBsWPHMhxrxIgRGDVqVIaeBKgZJTBq1ChMnjwZAwcOxNdffw0geYnLCRMmoG/fvno9ddq6dSvGjh2Ls2fPwtraGoULF8bw4cM/OAruYypVqoT8+fNj9uzZcHR0REREBMzNzdGyZUv06tVLp1FoQPIKaXZ2dmjcuLHW9j/++APx8fFo06aNTvHet8xret4dTZgR1apVQ7FixTBx4kStJY8PHz6M5s2bqxrRkzLS5GMMOQrlcxQVFYXg4GBERkYqKxnGxMQgX7582LBhg94jegzNzs4OFy9eRK5cuZAzZ06sW7cOpUqVwo0bNxAQEIDY2NgMxwoPDzf6yEFD6dq1K1auXAlPT0+0b98eLVq0gKurq8HiOzo6olmzZggJCVGWE37Xy5cvMXHiRGWlNrWuXr2KLl26oH///ggKCsrQ97x7vIpImuM3ZZs+x+y1a9cwfvx4REREIDY2FsWKFcOAAQMQEBCgKl7hwoXRoUMHBAcHo1ChQti2bRu++uornDp1CnXq1MHdu3dVxXV3d8f27dsRGBiI5cuXY/jw4YiIiMCiRYswZ86c965y9DF79uzBkCFDMHbsWAQEBMDc3FzrfWOscKRWr169cOjQIUyZMgU1a9bEuXPn4OPjg40bN2LEiBE6/w1GjRqFy5cvY8GCBcoogNevXyMkJAT58uXTeb/PyCqnZmZmcHd3R6FChdKszpee1MfB+7rc+hwDKSuP9uvXD127dtVrifIPefz4MZydnQ0+2sNYcdXKjPPWmDFjEBoaiipVqqBEiRJpRkioHc1gbW0NZ2dnNG3aFC1atECJEiVUxcnoar9qR0x+aZYtW4YRI0bg2rVrAJJHnYwcORIhISGfuGXJPDw8sHnzZuXzHjx4MMLDw3Hw4EEAyfcJw4cPx8WLFz9lM7W0adMGRYoUQZ8+fQwat127dh98X+1q00WLFsW1a9cgIvDy8kpznT19+rTOMT+26q7aVedv3LiBhIQE5MuXT2v71atXYW5urnolxqZNm+L69etYvHixMkrw4sWLaNOmDfLmzYsVK1aoipviP5eUMpZ69eph//79qFOnDlq0aIGaNWvC1NQU5ubmBklKlShRAhMmTNBrCGp6Tp48iRcvXqBy5cq4f/8+WrdujcOHDyNfvnyYN28eihQpolO8y5cvIyoqCvXr18eCBQvSnWIEQOclVIHkTsCUKVMQGhqK27dvA0i+MPTv3x89e/b8bDo0KZycnHDs2DH4+vrCyckJR44cgZ+fH44dO4Y2bdroPKw+f/78+O2339IkVsPDw9GpUydcuXJFp3jG1NjlagAAYepJREFUTNACyTfMp0+fRp48ebSSUrdu3YKvr68y/U4X4eHhyr9FBLVr18bcuXPTTAXUJ5loCMZO+AHJv//OnTu1pltWq1btszsOgOSb/OnTp6NixYqoVq0aihQpgkmTJmHatGmYMGGCqiXWb926BU9PT5iYmOjdvk2bNqFWrVowNzf/aGdcl064iYkJcuXKhaJFi37wc9FlamiKhIQEzJkzB9988w2yZcum8/ercfLkSbRs2TLD567Ux+vHfOpjNrU1a9agefPmSExMRNWqVZWpj+PGjcP+/fvx119/qYprbW2tJJJbt26N7NmzY/z48YiOjoa/v79OydnUUo4BYyT8DC137txYtWoVypQpo3VdiIqKQrFixfD8+XOd4gUHB2P37t2wtLREYGAgACAiIgJv3rxJ01/KyHGmy/nE3d0dq1atQvny5T/4dS4uLrC3t0fbtm3RqlWr9yamUy/jrosNGzZg//792LdvHy5duoSiRYuiUqVKqFSpEsqVK6fT0t2JiYn4+++/kS9fvjTTFF++fImrV6+iUKFCep13RQSPHj2CRqOBi4uL6jjGkhnnrQ89YNZoNLh+/bqquClT1fS9Lmbk+9WcW27fvo2wsDAMGzYsTbL82bNnGDNmDH744QdV17Rnz55h586duHnzJjQaDXx8fFC1alWDJuXj4+MRGxurV4kAY7CyssLVq1eVh5TlypVDrVq1MHjwYADAzZs3ERAQgBcvXnzKZmpJScxWrVo13alrhphmZkgjR4784Pv6PvgzpIoVK6J9+/ZpBissXboUc+fOxb59+1TFdXR0xK5du9I8BD1+/Dhq1KiBp0+fqmxxMialDMTMzAw9e/ZEly5dtDKThkpKbdu2DYMGDcLo0aPTPXiN8ST05cuXqmsnjBw5Ev3799epM6SLlBOroepVGaP2kZubm5Lgy58/P6ZPn46goCBcvnwZxYsXR1xcnE7xrKyscPny5TQZ7ps3b8LPzw8vX77UuY3Xr1+Hl5eXQW7s35U1a1Zs374dRYsW1br52LlzJ9q3b4+YmBi9f0bquJ+TjCb8gOSaYf/XTZ48GaampujZsyd27dqFevXqQUTw5s0bjBw5UvUox6dPn+L48eO4f/8+kpKStN7Tpe6RiYkJ7t69i6xZs37wWNC1E962bdsMJQnVPrmzsbHBpUuXVD9N09XZs2dRoUIFnRMHxpaUlISoqKh094MKFSqoinn37l3cuXMHgYGByj5x/PhxODg4qK7dlD9/fowZMwZ16tSBt7c3Vq5ciSpVqiAiIgJVq1ZVXbvmYzfRam+cd+/ejcmTJyu1I/z8/NC7d29Uq1ZNVTwgeZ+9cOECfHx8tM7fERERqFChAp49e6ZTvI89FU9N7XH2LhHBvXv3MGbMGBw+fPijT8jfvHmD9evXY/78+Thw4ABq166NkJAQ1KxZ0+APEZ49e4YDBw7gjz/+wIoVK2BiYqLTA6CFCxdixowZOHbsGExNTbXeS0hIQJkyZdC7d29VdbHu3r2LH3/8EZs2bVL6cA4ODggODsa4ceMyLbn+OUk55g05gvZz9sMPP+D58+eYM2dOuu937twZjo6OmDBhgk5xly5diu7du6e5Njk6OmL27Nlo2rSp6jZ/CXLnzo0lS5agQoUKePPmDZycnLB582YlMX/+/HlUrFgRjx8//sQt/R9jJWY/d8Z6CJqag4MDTp8+nWbWRFRUFEqUKKE6eWRvb48DBw6kGbBy5swZVKxYUf++od4TAL9wJ06ckP79+0vTpk31mnd65MgR6dChg9jb20upUqVk+vTp8uDBAzEzMzPISjPvzjc1xJLPIiI9evRId3tsbKxUqlRJddwviTFqH4kkr+C0bNkyERHp0KGDlCpVSpYuXSpBQUFSqlQpneN5enrKxo0b02zfsGGD5MiRQ1Ub313BrEmTJlp1ivQREhIiDRs2lDdv3ihLat+6dUuKFi0qvXr1MsjPSL0Syn/Nrl27ZNCgQRISEiLt2rXTen0u3lcz4+bNm7J27VqJiIiQr7/+WlXsTZs2ib29vWg0GnF0dNSqgaR2OeEvjbFWbtm4caPWa8OGDTJr1iwpWLCg1KxZU1XM+fPnp1sTcPXq1XqttHPkyBHx9vZWroeGqn1kDDNnzhQzMzNxcnKSwMBApa7YtGnTPrvrbUpbv/vuO2Wl0GbNmom5ubnMmDFDddzy5cvLtGnTRESU64JIck2poKAgg7Q9s9y4cUMsLS11+p5bt27JyJEjxcfHR3LkyCE//fSTvH37Vu+2PHz4UNauXSs9evSQgIAAMTExUepL6aJcuXIfrJOzatUqKV++vM7te/bsmXh7e4ubm5v07t1bZs+eLbNmzZIePXqIq6ur5MuXzyirkRpKXFycXLp0SVnhMeWlxpMnT6Rr167i4uKi9OVdXFykW7duqlY7/tjq3IZYqdtQ3rdycIpDhw6Jv7+/TjFPnTolZmZm0qZNGzl79qy8evVKXr58KadOnZJWrVqJubm56lVIRZLrdbZs2VI8PDzE1NTUIKsHG1rnzp3lq6++kv3790vfvn3FxcVFXr9+rby/dOlSKVGixCdsoXFlxmr1hpK6rpixajc6ODho1URNcfLkSbGzs1Mdt379+lKhQgWldpuIyD///CMVK1bU+VqTnv/0SKmVK1eidevWCAoKwo4dO1CjRg1ERkbi3r17CA4OVvVULS4uDqtWrcL8+fNx/PhxJCYmIiwsDO3bt9drVM+HnoSeP39ep1X9UsuTJw9atmypNSwxNjZWWcnmwIEDGY5VrFgx7N69G87Ozh+drpLRubcfi6Mm5ruMUfsI+PDUyPnz5ytTDTJqwIABWLVqFRYsWKA8/Q8PD0f79u3x7bffYtKkSTq3MfUIEcCwI4+ePXuGb7/9Vvk7ZM+eHXfv3kWZMmXw119/pRntp8bnOlIKyNiqfhqNBmvXrtU59siRIzFq1CiUKFECHh4eaY6R9evX6xzTGKytrfHbb7+lO2opNjYWNWvWxMOHD1WtEJU/f37Url0bY8eONdqIzM/d6tWrMWjQIPTp08egK7e8O1pMo9HAzc0NVapUQWhoKDw8PHSOaejpxymKFCmC/PnzY+TIkekeCxmdEtWoUSMsXLgQDg4OHz121Uy3THHy5EnExMSgevXqSu2frVu3wsnJCWXLllUd9+nTp5g3b57Wijjt27dXPSUsZ86cGDhwYJq+xcyZMzF27FhVU24B4ODBg6hVqxZatmyJhQsX4vvvv8fFixdx+PBhhIeHo3jx4qrifirPnj1T9Te+ceMGQkJCEB4ejgcPHui1qlNAQAAuXboEZ2dnVKhQAZUqVULFihVVHf9Zs2bF8ePH31tz5MaNGyhVqhQePHigU9zRo0dj8eLFOHz4MNzc3LTeu3//PsqWLYt27drhp59+0rnNxvTgwQO0a9fuvVN2dZ2+9vjxY3z11Vf4999/0aJFC626LMuXL4enpycOHz4MZ2fnDMf82LSi1D71FCNbW1tcunQJuXLlSvf96Oho+Pn56TSToF27doiNjcUff/yR7vvffvstHBwcMH/+fFVtrlWrFqKjo9G9e/d0rzFqypEY2sOHD/H/2rvzuJry/w/gr26JNioxJWlRWVpk39skWbKONZIyxlL2JcaShpBBpBnZWhjJWMq+RLvIWkQRSYMaXxRC6/n94dcZqeiee27dpvfz8ejx0L31uZ/Svfec93kvI0eORFxcHBQVFREUFIQRI0aw9/fr1w89evRgp59KEj4msa5evRo//vgjjIyMeC2zU1VVxYMHD6CmpvbdvneSlIVmb28POTk5hISEsBmvJSUlGDt2LPLz8zm3IMjKysLQoUORkpJSrp+tsbExjh8/jpYtW4q073odlDI1NcXPP/+MWbNmsSe3urq6+Pnnn6GhoSHUC31l0tLSsGfPHuzbtw+5ubno379/tZsHfs+7d+8QEhKC3bt348aNG5x7Rjx69Ah9+/bF4sWLMXfuXLx79w4DBgyAjIyM0IGDL0v2+HpRqIk3W3H0PhKHwsJCTJo0CX/99Rf7wl1aWgpHR0fs2LGjWg1XvybOoFSZ+Pj4cg2IRSn9+JqSkhKSk5N5HwDAh+qWlXAJfmtoaMDb2xuTJk0S+ntr0uHDhzFp0iSEhoaWS0MuC3zn5OQgKiqKU2NyBQUF3Llzh/eApKen5zfvX7lyJa+PJ4rKSg2lpKQkspeQOMqPgc9/B0lJSSI3958yZQq2bdsGJSUlsTVKFZfr169jwIABkJOTY8e+X7t2DR8/fsT58+fRqVMnoddUVFTE7du3K/xeHz58iI4dO3LufwXw25j+1atXWLlyJSIjIyst35SkE4WCggIcOXIEe/fuRUJCAgYPHgxnZ2fY2dmJtK6fnx8sLCxgbGws8h4VFBSQkJBQZUArOTkZPXv2FLr9QI8ePfDzzz9X+dzau3cvdu3ahYSEBKH3LE4ODg7IzMyEj48PLC0tcezYMbZ0c9OmTRg8eLBQ682dOxcXL15EREREhXLF7Oxs2Nraol+/ftiyZQufP4bEUFNTw9GjR6ssq46JicHIkSOFKmU2NDTE77//XuWxZUREBGbOnIkHDx5w2nNVJUuSKC8vD4qKihVKb1+/fg1FRUVO5wni8uHDB7i5uSEoKAgA8ODBA+jp6cHNzQ2amppwd3cXaj2BQICuXbvCxcUF48eP56W9S1BQEMaNG4eGDRuy+6yKMMOmfvzxR0ydOhUDBgwQSx/Ye/fuwdzcHMrKymzPw9jYWLx9+xaXLl0S6b2CYRhERERU6GfLC5FzreoweXl5JiMjg2EYhlFVVWWSk5MZhvk8SlhdXZ23xykuLmaOHTvG2Nvbi7xWdHQ04+joyCgoKDAGBgbMkiVLmMTERJHWTEpKYlRVVZmtW7cyPXr0YCwsLJj379+LvNe6olmzZmya45flYOfPn2datmxZm1urVFpaGnPo0CHmxIkTzJMnT0RaSyAQlBvj+WU5BVcfPnxgTpw4wX7u7u7OzJs3j/1YtGgR8/HjR05rf11iKyMjw+vI17pCVVVV4kY8V2XXrl2MvLw8ExkZyTDM59LgPn36MPr6+szff//Ned0RI0YwoaGhPO3yX2ZmZuU+jIyMGHl5eaZx48YSkfr9pSdPnnzzQ5KIo/yYYRjGysqKOXPmjChbE6t58+ax76dfvg5W9sFVnz59GCcnp3JlYEVFRczkyZM5lVoxDMOMHz+e8fb2rnD7xo0bmbFjx3LeK98GDhzIGBgYMOvXr2cCAgKYwMDAch+S4OrVq8z06dMZZWVlxszMjNm6dSvz6tUr3h+noKCASU1NFakcsEOHDswff/xR5f1+fn5Mhw4dhF5XRUWFSU1NrfL++/fvS2TZtbq6OnP16lWGYRhGSUmJSUtLYxjmc4lz7969hV5PW1ubOXv2bJX3nzlzhtHW1ua0V4ZhGEdHRyY6Oprz94vboEGDmKlTp1Z5v4uLCzNw4ECh1lRQUGAyMzOrvD8zM5ORl5cXas0vtWvXrtJSKCKa2bNnM507d2ZiY2MZBQUF9twrLCyMMTMzE3q9mJgYZsqUKYySkhKjoKDATJ48mYmJieF727ywtrZmBAIB07JlS2bFihViaUPy7NkzZunSpcygQYOYUaNGMatXrxb5fUfc7VK45cn9R6ioqLDNFjU1NXH37l2YmJggNzcXHz584O1xpKWlMXz4cAwfPpzT92dnZyMwMBB79uzB27dvMWbMGBQUFCAsLEzkBurA54yxkydPon///ujevTtOnjzJucF5Tbhx40a5EoWOHTuKtN7QoUPh6emJQ4cOAficafD06VMsWbIEo0aNEmqtmig3NDQ0hKGhIafv/RrDMHBycmJHaX/69AnTp0+vkCEnTLlKUFAQTp06hSFDhgAAtm/fDiMjI/ZvKjU1FRoaGpzGwH5dJsGl2ep/wdSpU3HgwAHODcJr0tSpU/H69WsMGzYM4eHhWLlyJZ4/f47o6GjOQwQAYPDgwVi0aBHu3bsHExOTCuN5uTaIrGwk/du3b+Hk5FQuHV4SiKvBeUlJCQIDA3Hx4sVKs0+4TOMcP348Zs+eDSUlpXLlx3PmzMG4ceM479XNzQ0LFixAdnZ2pX8HXEqYPn78CIZh2LLQzMxMHDt2DO3bt4etra1Qa926dQtFRUXsv8Xh+vXr2LVrV7nSBxkZGSxevFiokfDbtm1j/92+fXusXbsWUVFR6NmzJwDgypUriI+Px4IFC4Teo0Ag+O57o5SUFIqLi4VaNzY2FnFxcUKXw9ekHj16oFWrVpg9ezZbnlg2qv1LXF+zPn78CFdXV14yDiZMmIDly5ejV69eFZ47SUlJWLlyJRYvXiz0Ht++fVvlNGbg87RiSRugAHxuyVGWSa6iooKXL1/C0NAQJiYmnI7hXrx4ASMjoyrvNzY2RnZ2Nuf95uXlwcbGBtra2pgyZQomT54s0vss3xYuXIj+/fujSZMmWLRoEZstlpOTA29vbwQGBrLTTqvrw4cPaNSoUZX3N2zYUKSKBx8fH7i7u8Pf37/KslYivLCwMHYS65fvDUZGRnj06JHQ6/Xt2xd9+/aFr68vDh06hMDAQFhYWEBfXx8uLi6YPHky1NXVRdozX0NVLl68iMzMTAQEBCA4OBhr166FhYUFpk6dilGjRrHnZKJo0aIFvLy8RF7nS/r6+rCwsICLiwt+/PHHbz7vuKjX5XsTJkxAly5dMH/+fPz666/w9fXFsGHDcOHCBXTq1EmkvhF8sbe3R0xMDAYPHgwHBwfY2dlBWlpapKl+VQVOMjMz0bx583IBKWHedL9Xb/slLin1//zzD8aNG4eoqCj24CY3NxdWVlY4ePBghT4F1VVV76OePXvi9OnTQpcwVld1yg3L/jYVFBQwf/78b37t5s2bq/3YZcRRYlZWDmpvbw+gYkng/v374efnJ3Fp+nXJnDlzEBwcDFNTU5iamlY4EefytyBu7u7u2LhxI3R0dBAVFcXWo3PF55S86rhz5w7s7e0595gTp3v37uHp06coLCwsdzvXk1xXV1cEBgZi8ODBlfbQ4FJaIo7yY0A8JYy2trYYOXIkpk+fjtzcXLRp0waysrL43//+h82bN2PGjBmc9iouP/zwA/bt21chYHbu3Dk4OjoiJyenWutUtwyay2Sk8PDwKu9LSEjAtm3bUFpaKvTJY9euXeHr64sePXoI9X3fs3LlSlhZWaFnz54iH3hXZ7qtKK9Zc+bMQXx8PHx8fGBnZ4fk5GTo6ekhPDwcHh4eQgVDi4qKYGtri7i4ONjY2LCTJlNTUxEREYHevXvjwoULFd5zvkdaWhrZ2dlVHqfl5OSgRYsWElVyDHz++1qzZg0GDBiAoUOHQllZGevWrcO2bdtw+PBhoU+eNTU1ERoaij59+lR6f2xsLMaOHYvnz59z3vPLly+xb98+BAUF4d69e7CxsYGLiwuGDRsm9P8b8O906kePHmHRokUiT6f29/fHnDlzUFRUhMaNG0NKSgp5eXlo0KABtmzZIvTrq0AgQFBQUJW93XJzczFlyhTOf1sqKir48OEDiouLIS8vX+F3KEklwnUJ35NYK5Oeno6AgADs27cP2dnZsLOz49xG58qVK5gwYQIyMzPxdehE1GPOS5cuYe/evTh27BgaNmyI8ePHw9nZmXOPxbNnz0JRUZF9nfHz88OuXbvQvn17+Pn5CdWz7ku3b99GQEAAQkJCUFhYiLFjx8LZ2Rndu3fntN7X6nVQ6vXr1/j06RNatGiB0tJSeHt7s42oly9fzvk/jU8yMjKYPXs2ZsyYAQMDA/Z2UYJS4urT9L162y8JU3tbZuzYsXj8+DGCg4PLNYecPHky9PX1ERISIvSaX4qLi0NycrJYeh9xZWVlhWPHjkFZWRmWlpZVBv2kpKQ4ZS+Ig4aGBhISEtgrSs2aNcO1a9fYzx88eICuXbvy8oZTX33dLPprkZGRNbSTb/u6YfTp06fRoUOHCgeyknAB4Hvi4uJgb2+PN2/e1PZWWI8fP8aIESNw584dNhADgH2d4HqQpKamhuDgYAwaNIi3vZZ58OABkpKSICcnBxMTE5GzvTIzM795P5f11dTUEB0dDSMjI+zevRu+vr64desWjhw5gpUrV7KZutXl7Oz83a+RkpLCnj17hN4rAMyePRvHjh3Db7/9hl69egH43Mtv0aJFGDVqFHx8fDitK25paWlwd3fHiRMn4ODgAE9PT6H/v65duwZ3d3esXLkSxsbGFU4YGzduzGlv/fv3R0JCAoqLi9G1a1dYWFjA0tISvXv3lrhMcm1tbTbj4MuTu/T0dHTq1EnoDKSioiJs2bIFBw4cwMOHD8EwDAwNDTFhwgTMnTuXc//KJk2aVHkMwzAM3r59K3FBqf3796O4uBhOTk64ceMG7Ozs8Pr1a8jKyiIwMBBjx44Vaj1nZ2c8evQIFy5cqPB7LCgowIABA6Cnp8e5KffXbt68iYCAAOzevRuKioqYOHEiZs6cWe584luSk5NhY2ODJk2a4MmTJ0hLS4Oenh6WL1+Op0+fIjg4mNO+nj17hkOHDiE9PZ39+/rxxx85NUoWd9CXz15C5F/m5uYYPXo03NzcyvWHdXNzw8OHD3H27FleHic/Px9//vknli5ditzcXM5/B3wNVfmWd+/e4cCBA1i2bBny8vKEzhwuY2Jigg0bNmDQoEG4c+cOunTpggULFiAyMhJt27YVuS9mcXExjh8/jsDAQJw9exaGhoZwdnbGpEmTOCeIAPU8KFUXXLlyBXv27EFoaCjatWuHSZMmYdy4cdDQ0OAclCpTUlKC+Ph4mJqafjOtWlI0adIEERER6Nq1a7nbExMTYWtri9zc3NrZ2DfwfYWpLpCTk8Pt27fRpk2bSu9PTU2FmZmZxDSQJ+IjzmbvX/r06RNvacRfljABn0+WXrx4gX379sHCwgIHDhzg5XH4YG9vD2lpaezevRu6urpITEzEq1evsGDBAvz2229sg0thtWjRAlFRUbyVCdc18vLySE1NRatWrTBmzBgYGRlh1apVyMrKQps2bYQu7xcIBNDW1kbHjh0rXGH9EtepmYWFhVi0aBF27NiB4uJiMAwDWVlZzJgxA+vXrxepFICPyUhfe/78OVatWoWgoCAMGDAA69at49x49eHDh5gwYUKFrG5RMuXKFBcX4+rVq4iJiUF0dDQuX76MgoICdO3atdLyu9pSExkHoqruRUtJP8H/8OED+9qgpqYm9Pf//fff6NKlCxo2bIhZs2ahbdu2YBgG9+/fx++//46CggJcv35d5Exi4HOpYHBwMAICAvD3339j1KhRePbsGaKjo+Ht7V2tFgrimk5N6q+7d+/C2NgY8fHxsLOzw8SJExEQEIDp06fzOok1JiYGe/fuxZEjRyAQCDBmzBi4uLhwzqrla6hKVTIyMhAYGIjAwEA8e/YMNjY2nANzioqKuHv3LnR0dODh4YG7d+/i8OHDuHnzJgYNGiRSifCXCgoK8Pvvv2Pp0qUoLCyErKwsxowZgw0bNnCa0lzvekoJc8WI6xU2PvXo0QM9evSAj48PQkNDsXfvXsyfPx+lpaW4cOECtLS0OE8YkJaWhq2tLe7fvy/WoNSnT58qlJVw+d2WlpZWmnrcoEGDCrW9wrp48WKV/VO4XrH6+grTTz/9BFVVVRw9elToK0xFRUVssIePCTvi1LJlS9y9e7fKoFRycrLIY0Prq++Nqgc+XxE8cuRIDezm+8Q5paykpAReXl7YsWMHcnJy2D4qK1asgI6ODlxcXDit+3VpmkAgQLNmzTB58mQsXbqUj63zJiEhAZcuXYKamhoEAgEEAgH69OmDdevWYfbs2Zx7GC1YsABbt27F9u3bRZoMI+7y4zIPHz6scvoal2mJ+vr6CAsLw4gRI3Du3Dn25O2ff/7h9N41Y8YMhISEICMjA1OmTMHEiROhqqoq9DpVkZWVxdatW7Fu3Tq2nKh169ZsTywu+J6MBHwulffy8oKvry/MzMxw8eJFzoHTMg4ODmjQoAEOHDiAH374gddJRjIyMujduzeaNWsGVVVVKCkpISwsjJ06JCm6dOmCU6dOwc3NDcC/mZK7d+9m+4HVNkkPNlWXvLw8p2mWZVq2bImEhATMnDkTS5cuLZfd2r9/f2zfvl2kgFRRURGOHz+OgIAAnD9/Hqamppg7dy4mTJjAvnYdO3YMzs7O1QpKXbt2Df7+/hVu19TU5O3Eti7gq5eQOAhTjsa1pJ9Ppqam6Nq1K6ZOnYr4+Hj4+vrC1NSUnRSbkJDAaRIr8PmCR1lgJz09Hb169cK2bdswZswYoVqxVKZ79+5IT0/nNSj16dMnHD58GHv37kVMTAy0tLTg4uKCKVOmiPQ6ICsry148i4iIgKOjIwBAVVWVl959169fx969e3Hw4EEoKChg4cKFcHFxwd9//43Vq1dj2LBhSExMFHrdeheUUlZW/u5BCx9X2PimoKAAZ2dnODs7Iy0tDXv27MH69evh7u6O/v37c66RNTY2xuPHj6vdS6K68vPzsWTJEhw6dAivXr2qcD+X3621tTXmzJmDkJAQdoT8s2fPMG/ePPTr14/zXlevXg1PT0906dKl0pRMrubPnw8nJyf2ClOZQYMGYcKECUKt1aBBA7Rq1Uqi/iarMmjQIKxcuRKDBw+ukL3y8eNHrF69WugxyuQzPtKD/yvWrl2LoKAgeHt746effmJvNzY2ho+PD+egVEZGBl9bFLuSkhL2tUVNTQ3Pnz9HmzZtoK2tjbS0NKHW+jrgeenSJZw5cwZGRkYVLgZUt9yyJpp879q1CzNmzICamhrU1dXLvX5LSUlxCkqtXLkSEyZMYN9byk7sz58/z2mwhp+fHzZv3oyjR49i7969WLp0KQYPHgwXFxfY2tpyfs+pTlkgwO3CytKlS5GUlISoqCjY2dmxt9vY2MDDw0PooJS3tzc2bNgAdXV1hISEYNiwYULvqTJ3797FrVu3qrwIwtXOnTsRFRWF6OhoFBQUoG/fvrC0tMTy5cs5Nc8XJy8vLwwcOBD37t1DcXExtm7dWi7jgAjnewH0L3EJpuvq6uLMmTN48+YNHj58COBzIJyPQLWGhgZKS0sxfvx4JCYmwszMrMLXWFlZVftCdMOGDSs9iX3w4IFIZTp1iTh7CfHh6yFaX5byl31eprb3CnwecBIQEIAFCxagtLQUo0aNwm+//SZycG/gwIGIiIiAmpoaHB0d4ezsLPL7QnJyMvtvPoeqJCYmYu/evQgNDcWnT58wYsQInD17Fv369ePlHLRPnz6YP38+evfujcTERISGhgL4/LwVJSlg8+bNCAgIQFpaGgYNGsS2eSgrodXV1UVgYCDngQD1rnyvum/Qd+7cgaurq5h3I5qSkhKcOHECe/fu5RyUOnv2LJYuXYpff/0VnTt3rhBJ5potNmvWLERGRuLXX3/FpEmT4Ofnh2fPnsHf3x/r16+Hg4OD0GtmZWVh6NChSElJYSPIWVlZMDY2xvHjxzk/0TQ0NODt7Y1JkyZx+v6qNGnSBDdv3kTr1q3LpT1nZmaiTZs2Qpev7dmzB0ePHsW+fft4vcrOt5ycHJiZmUFWVhaurq5sCVBaWhq2b9+O4uJi3Lp1i526QggX+vr68Pf3R79+/co9v1JTU9GzZ09eej/9/fffACCxmX19+/bFggULMHz4cEyYMAFv3rzB8uXLsXPnTty4cQN3796t9lrVLbUExJsBJyxtbW3MnDkTS5Ys4XXd7OxsvHjxAh06dGAPuBITE9G4cWO2+TNXmZmZCAwMRHBwMIqLi5GSkgJFRUWh1xFnWSDffYoEAgHk5ORgY2MDaWnpKr9O2P5y5ubmWLlyJe89IMsyJBcsWICZM2dy+v+pSY8ePcL69euRlJTE9sVcsmQJ54yD+ux7PRu/JCn9G8vs27cPo0eP5q2cferUqXj16hUOHToEVVVVJCcnsxPFzc3NJbZfHZ9qopcQXyIiIrBkyRJ4eXmxF1MSEhKwfPlyeHl5oX///rW8w3/l5+ezU/JiY2NFnpI3dOhQuLi4YMiQId98jxFG2dTYqt5fuQ5VEQgE6NChA1xcXODg4MB7D+unT59i5syZyMrKwuzZs9mLtPPmzUNJSUmFNhXfk5GRAV1dXRgYGLD/R1WV5xUWFiIkJIRTdmy9C0p9y7t37xASEoLdu3fjxo0bEhFRFrcvGwR++UIrarZYq1atEBwcDEtLSzRu3Bg3b96Evr4+9u3bh5CQEJw+fZrTugzDICIigk2fb9euncgHo02bNkViYiJat24t0jpfa968Oc6dO4eOHTuWO6i/cOECnJ2dkZWVJdR6HTt2RHp6OoqKiqCtrV0hgMhlPLG4ZGRkYMaMGbhw4UKF9PTff/+dncRHCFdycnJITU2FtrZ2uefXvXv30K1bN7x//57TuqWlpVizZg02bdrErqGkpIQFCxbgl19+qVZT1Zpy7tw55OfnY+TIkXj48CHs7e3x4MEDNG3aFAcPHhQpg5Qv4m7y3bhxY9y+fVusrylv377FpUuX0KZNG3bIhiiysrIQEBCAwMBAFBYWIjU1lVPQY9asWQgJCWHHv/NZFsh3nyInJ6dqXQEWNuD5119/wcPDA4sWLRL56vWXwsLCEBMTg6ioKNy/fx8dO3aEpaUlLC0t0adPH5FKIwmpKaK+dvE5nbquEncvIT4ZGxtjx44dFaY7xsbGYtq0aUIP6agpfE7J49P3Bql8SZghHTdv3hSpDLimlV0As7S0hLW1NaytrcXSF5mCUvjcDG3Pnj04cuQIWrRogZEjR2LUqFEVGmr/F30rc0yUbDFFRUXcu3cPrVq1QsuWLXH06FF069YNGRkZMDExEeqE8dKlS3B1dcWVK1cqZG7l5eWhV69e2LFjB+feFEuWLIGioiJWrFjB6furwvcVJg8Pj28e1AszKbGmvH79Gunp6QD4S08nBAA6d+6MefPmYeLEieVOmj09PXH+/HnOjYiXLl2KPXv2YPXq1ejduzeAz5P3PDw88NNPP2Ht2rV8/hi8e/36NVRUVERKAc/IyEBxcXGFCU0PHz5EgwYNhErNFneTbxcXF3Tt2hXTp0/n9P2VGTNmDMzNzeHq6oqPHz+iQ4cOePLkCRiGwcGDBzFq1Cih1ywoKGDL9+Li4jBkyBBMmTIFdnZ2IgU6v1z38uXLvJQFAjU3GUlUlf3uuF69rkpeXh5iY2Px119/ISQkBAKBgNOgjpUrV8LKygo9e/bkLZOFiFdkZGSVmVN+fn6YNWtWDe/o28Tx2gWIbzp1YWFhpX2aWrVqxcv6fLC2tsbixYvLlTFLKjk5OVy7dq1C79nk5GR0794dHz9+rKWdfR9fU/Lqq7dv37LnyN/LZBa2CioqKor9uHr1KgoLC6Gnpwdra2tYWVnBysqKl+qXehuUys7ORmBgIPbs2YO3b99izJgx2LFjh8gT7eo6vrLFTE1N4evrCwsLC9jY2MDMzAy//fYbtm3bBm9vb7YspjqGDh0KKyurKpsybtu2DZGRkZxPaubMmYPg4GCYmprC1NS0wpVWrg14y64wXbt2De/fv6+XV5gIEZfw8HC2+binpydWr16NtLQ0BAUF4ciRI7C3t+e0bosWLbBjx44KDUHDw8Mxc+ZMPHv2jI/ti0ScvYQAwMLCAs7OzhXSr/fv34/du3cjKiqq2muJM5sHANatW4fNmzdj8ODBlWbKzJ49W+g11dXVce7cOXTo0AEHDhzAqlWrkJSUhKCgIOzcuVPo/lgzZ87EwYMHoaWlBWdnZzg4OHCa3PU9fJUFAp9PQgcOHIiJEyciMDAQP//8M6+TkfjyvSvZwly9/tqrV68QHR3NHoynpKRARUUFffv25XS80b9/fyQkJKC4uBhdu3aFhYUFLC0t0bt3b8jJyQm9XllpybdISUlxGiteUlKCwMDAKgfAXLp0Seg16yIVFRVERERU+HvfunUrVqxYwUvTYD7x/dolLg8fPoSzszMuX75c7nY+gsl8B7qOHTuG5cuX856NKQ7m5uZo1KgR9u3bxwYJcnJy4OjoiE+fPklkjzm+p+SJS1BQENTU1NieuIsXL8bOnTvRvn179hinNklLS+PFixdo3rx5le8NfDy/Pn36hMuXL7Pvi4mJiSgqKkLbtm2RkpIiyo9QP4NS9vb2iImJweDBg+Hg4AA7OztIS0ujQYMG9TYoxXe22JYtWyAtLY3Zs2cjIiIC9vb2YBgGhYWF2LJlC+bMmVPttbS1tXH27NkqU49TU1Nha2uLp0+fctrrt/oHSElJiXzwFR8fX67PA9crTHp6erh27RqaNm1a7vbc3Fx06tQJjx8/FmmfhNQFW7ZsYQPUsbGx8PT0LPf8Wrx4MTZs2ID4+HhO6zdq1AjJyclsL7QyaWlpMDMzk4grjeLOPvqy5PpL6enp6NKlC3Jzc4VaT1zZPAC+OaRDSkqK0+uinJwcHjx4AC0tLTg6OqJFixZYv349nj59ivbt2wtdGioQCNCqVSt07Njxmz+vsP2UvsZXWWCZ+tynyMTEBPfv34eKigrMzc1haWkJCwsLkU9Ai4uLcfXqVcTExCA6OhqXL19GQUEBunbtKnR2Z3h4eJX3JSQkYNu2bSgtLeWU1eXq6orAwEAMHjy40j46X08pra66FuzavXs3li1bhpiYGLaX3KZNm+Dp6YmTJ0+KPD2Sb3y8dgnTb4ZL0B8AevfuDRkZGbi7u1f699WhQweh1xRXoKsmsjH5kp6ejhEjRrB/A8Dn9wUDAwOEhYVJTAliZVPyXFxceJmSJy5t2rTBH3/8AWtrayQkJKBfv37w8fHByZMnISMjI/L7t6iio6PRokULGBgYfDf4aGFhIfLjFRYWIj4+HmfOnIG/vz/ev38v8nOh3k3fA4AzZ85g9uzZmDFjRoXyhPqksmyxgoIChIWFiRyY+zKrycbGBqmpqbhx4wYMDAyEPqDNycmpcGXiSzIyMnj58iXnvYqjUWVpaSkCAwNx9OhRPHnyBFJSUtDV1YW6ujr7RiasJ0+eVPqELygoECrzjJC6bNmyZWjatCkcHR3Rt29fXLhwgb3v/fv3GDBgQKUTP6urQ4cO2L59e4UD8+3bt0vMFdEZM2YgJCQEGRkZYsk+kpKSwrt37yrcnpeXx+mgo2HDhhg/fjzGjx/PZvPMnDlT5GweQDzTErW0tJCQkABVVVWcPXsWBw8eBAC8efOGU9mVo6Mjb1Ndv1ZZWeD27dtFLgsEgNatW2PXrl087VS87t27h6dPn6KwsLDc7VxHoE+fPh0WFhYVymBEJSMjg969e6NZs2ZQVVWFkpISwsLC2D6ZwqhsgmFaWhrc3d1x4sQJODg4wNPTk9M+Dx48iEOHDmHQoEGcvr8qc+bMYYNdxsbGYnte8GXq1Kl4/fo1bGxsEBcXh9DQUHh5eeH06dNsebck4eO16+uA48uXL/Hhwwd2Yl9ubi7k5eXRvHlzzkGp27dv48aNGyIPjfiSk5MTZGRkcPLkSV4nadelibz6+vpITk7GhQsXKvTelZTnmjim5NWErKwsNqgXFhaGH3/8EdOmTUPv3r1haWlZu5vD50BT2QXLspI6Kysr3gb1FBYW4sqVK4iMjGTL+LS0tGBubo7t27fzEugCUw8lJCQwU6dOZZSUlJhu3boxvr6+zMuXLxkZGRkmJSWltrdXI4YMGcI0btyYGT9+PHPy5EmmuLiYYRhG5N/BxYsXmXbt2jF5eXkV7svNzWXat2/PxMTECLWmnp4ec+zYsSrvP3LkCKOrqyvsVsWmtLSUGTx4MCMlJcWYmZkx48aNY8aOHcuYmpoyUlJSzLBhw4RaLzw8nAkPD2ekpKSY4OBg9vPw8HDm6NGjzKxZsxhDQ0Px/DCESJi//vqLadSoERMeHl7u9vfv3zN9+vRhDA0NmWfPnnFePyoqilFQUGDatWvHODs7M87Ozky7du0YRUVFoV+7xOnTp0/MgQMHGBsbG0ZeXp4ZPXo0c/bsWaa0tFTktYcMGcKMHj2afV9gGIYpLi5mRo0axdjZ2Ym09tOnT5nVq1czurq6jKamJvPu3TtRt8s7Pz8/RkZGhlFWVmY6dOjAlJSUMAzDMNu2bWMsLS1reXf/mjFjBqOiosKYmpoyPj4+zMuXL0VeU0pKihEIBN/8kJaW5mH3/Hj06BH73ioQCBgpKalyP4OoCgoKmNTUVKaoqEjktfz9/Znx48czLVq0YJo2bcoMHz6c8fHxYW7fvi3y8/bZs2fM1KlTmQYNGjBDhgxh7ty5I9J6GhoaTFpamkhrVKZp06bMqVOneF9X3BYvXsw0bdqUUVZWZhISEmp7O1Xi+7Xrzz//ZHr37s2kpqayt6WmpjJ9+/Zl9u/fz3mfXbp0YWJjYzl/f2Xk5eWZ+/fv87pmXfbx40dejgf4Zm9vz4SFhZU7vqgLmjVrxty8eZNhGIYxMzNjgoODGYZhmPT0dEZBQYHTmlZWVsybN28q3J6Xl8dYWVkJvV5kZCSzatUqxsLCgmnUqBEjEAgYfX19Ztq0aUxISAiTnZ3NeZ/y8vKMkZERM3PmTCYkJIR5/vw5p7W+pV6W75XJz89HaGgo9u7di8TERJSUlGDz5s1wdnaGkpJSbW9PrGRkZCrNFhO1hFEc/Z/c3NwQFRWFa9euVbjS8/HjR3Tr1g1WVlZCj7gsk5+fj/Xr11eZTi5s+UdAQADmzJmD8PDwCqWBly5dwvDhw7F9+3Y4OjpWa72yq92VjSUtazq8adMmDBkyRKh9ElJX7d69G3PmzMGpU6dgaWmJ/Px82NnZITs7G1FRUSJPBXn+/Dn8/PzKXWmcNm0a1qxZg507d/LxI/CKz15CwOesE3NzcygrK7PlKbGxsewkJ2GzR/hu8j1//nz8+uuvUFBQwPz587/5tVx7Al6/fh1ZWVno378/+7s8deoUlJWVJSZDQhxlgeIsCRMHe3t7SEtLY/fu3dDV1UViYiJevXqFBQsW4LfffuNcXvXx40e4uroiKCgIAPDgwQPo6enBzc0NmpqacHd3F3pNgUCAZs2aYcGCBZg5c6ZIz9EyeXl58PLygq+vL8zMzLBhwwZeSso2bdqEx48fY/v27bxmWLRo0QJRUVEVyqMlSVXHkr/99hvMzc3RrVs39jaumULixOdrV+vWrXH48GF07Nix3O03btzAjz/+KFQW0Zf9t65fv47ly5fDy8ur0j5NwjZiBoCuXbtiy5YtFSbP8WHfvn3YsWMHMjIykJCQAG1tbfj4+EBXV7fSjMXaUlpairVr12LHjh3IyclhX7dWrFgBHR0duLi41PYW6ywHBwekpqaiY8eOCAkJwdOnT9G0aVMcP34cy5Ytw927d4VeUyAQIDs7G82bNy93+z///ANNTU0UFRVx3i+fvZ8aNGgADQ0NDB8+nC1l/7qVDB/qdVDqS2lpadizZw/27duH3Nxc9O/fXyLGUYrLlStXsGfPHoSGhqJdu3aYNGkSxo0bBw0NDZGCUuLo/5STk4NOnTpBWloarq6ubJpnamoq/Pz8UFJSgps3b3Lu/D9+/HhER0dj0qRJlab8CtP/CgBsbW1hbW1d5UGrl5cXoqOjce7cOaHW1dXVxbVr18TSIJeQusbb2xtr165FeHg4Vq5ciWfPniE6Opq3VOWvJSUloVOnThLVP6IM372EgM+Bue3btyMpKQlycnIwNTWFq6ur0GWC4mjybWVlhWPHjkFZWfmbPQEB8ZRnSwonJ6dqBQwCAgJEepzKSsJqu6lrGTU1NVy6dAmmpqZo0qQJEhMT0aZNG1y6dAkLFizg3Nh5zpw5iI+Ph4+PD+zs7JCcnAw9PT2Eh4fDw8OD07phYWGIiYlBVFQU7t+/j44dO8LS0hKWlpbo06cP5OXlhVrP29sbGzZsgLq6Ory8vEQ+OR45cmS5zy9dugRVVVUYGRlVCBpw7Z8irmAXn77Vp+5LXHvW1SXy8vKIjo6u0F82MTERlpaW+PDhQ7XX+rr5MlNJKwtGyD5N4g50AcAff/yBlStXYu7cuVi7di3u3r0LPT09BAYGIigoSKLeYzw9PREUFARPT0/89NNP7F5DQ0Ph4+ODhISE2t5inZWbm4vly5cjKysLM2bMYKcxrlq1CrKysvjll1+qvVZycjIAwMzMjH2dLVNSUoKzZ8/C398fT548EXnffPR+ys/PR2xsLKKiohAZGYnbt2/D0NCQHdZhYWGBZs2aibxXCkp9paSkBCdOnMDevXv/00GpMnxnizVq1Ah3796tspleeno6TExMhG4WnJmZiRkzZuDcuXNstpCUlBQGDBgAPz+/ah9EVEZZWRmnTp3i7eq3uro6zp49CzMzs0rvv3XrFgYOHIjs7GyRHys3N5et8yekvnF3d8fGjRuho6ODqKgotrGnOEhaUIrv7CNxqakm36Kqieyruub58+dYtWoVgoKCMGDAAKxbt473HkuiUlFRwc2bN6Grq4vWrVtj9+7dsLKywqNHj2BiYiLUSfOXtLW1ERoaih49ekBJSQlJSUnQ09NDeno6OnXqJPLUtby8PMTGxuKvv/5CSEgIBAKB0NlnAoEAcnJysLGxgbS0dJVfV93n1pQpU6r92MIEOmsi2CVuL1++hJSUlMRfFOS7kby9vT2ePXuG3bt3o1OnTgA+Z0lNmzYNmpqaQp0nCTP5rbr9acQR6Ppa+/bt4eXlheHDh5d7Lbh79y4sLS3xv//9j9O64qCvrw9/f3/069ev3F5TU1PRs2dPvHnzpra3SFD+77ayMIycnBx8fX2rPWn5S9/q/WRubg4LCwvOkyjLvHv3DnFxcexjJCUlwcDAgFO22JfqZaPzb5GWlsbw4cMxfPjw2t5KjVBQUICzszOcnZ3ZbLH169fD3d2dU7aYpqbmN4NSycnJ0NDQEHqf2traOH36NN68eYP09HQwDAMDAwOoqKgIvdbXVFRUeG0S/Pr1629mbf3www+c3hg2bNgAHR0djB07FgAwevRoHDlyBBoaGjh9+jSnaSWE1DVfn+A0aNAAampqFTIaJfXkhg9fZx+FhISI5WTpw4cPlTaPFqbhu7iafFfnYE1KSgp79uyp1nq3bt1iU+UlZWx6bfm6JOzixYsSN2WsjLGxMZKSkqCrq4vu3bvD29sbsrKy2LlzJ/T09Div+/LlywolFcDnC3mi/D2/evUK0dHRbElFSkoKVFRUOP1++X5uiZpRV5UmTZqU+3zEiBFieRy+5ebm4pdffkFoaCh7zKaiooJx48Zh7dq1FX4uScB3I/m9e/di8uTJ6NKlCxtALC4uxoABA7B7926h1voy0PT06VNoaWlVGkDKysqq9po1kaWUkZFRoXwR+DzAIz8/X+yPL4xnz55Vev5VWloqUikY+Rcfx0UZGRlgGAZ6enpITEwsl2UkKyuL5s2bf/NCQ1Wsra1x9epV6OrqwsLCAj///DMOHDjA6bz7WxQUFKCqqgpVVVWoqKhARkYG9+/fF3ldypQiFYiSLSbu/k/isH//foSHhyMoKEjo9PnKSEtLIzs7u8pUxpycHLRo0ULoqza6urr4888/0atXL1y4cAFjxoxBaGgoDh06hKdPn+L8+fMi750QSVfdq/l8n2BJUqaUuLOPXr58iSlTpuDMmTOV3i8pvwNtbW107Nix0iuNZYTpX0j4LwkTt3PnziE/Px8jR45Eeno6hgwZggcPHqBp06YIDQ2FtbU1p3XNzc0xevRouLm5QUlJCcnJydDV1YWbmxsePnyIs2fPCr2miYkJ7t+/DxUVFZibm7NlD5Iy1fNL1tbWOHr0aIVM7Ldv32L48OFCZ9zUNa9fv0bPnj3x7NkzODg4sC0p7t27hwMHDkBLSwuXL1/m5cIon9TU1BAcHMz71MQHDx7g/v37kJKSQtu2bUXuCSYtLY0XL15UCPy+evUKzZs35/Qe871AF9fskPbt22PdunUYNmxYuewjX19fBAQE4ObNm5zWFYfOnTtj3rx5mDhxYrm9enp64sKFC4iNja3tLdZZL1++hJOTU5Wv/ZJwXCSu3k+lpaW4fv06W74XHx+P/Px8aGpqlpv0J2pZP2VKkQpEyRZbvnw5jh49CkNDwyr7PwlTd1sTNm3ahEePHuGHH36Ajo5OhXRyYd9wGIaBk5MTGjZsWOn9BQUFnPaZnZ3NliedPHkSY8aMga2tLXR0dNC9e3dOaxJS14jrav7XGVhfy83NFcvjciGu7KMyc+fORW5uLq5evQpLS0scO3YMOTk5WLNmDTZt2iS2xxXGjBkzEBISgoyMDEyZMgUTJ04UOeOV7+yrusjd3R1ycnLQ19dHUFAQ2+j7a5KSiThgwAD23/r6+khNTcXr16+hoqIi0nPEy8sLAwcOxL1791BcXIytW7fi3r17uHz5slBlSF+aPn06LCwsJK4EsjJRUVEVMgGAz81zRTmxrSvBLk9PT8jKyrLHhl/fZ2trC09PT2zZsqWWdlg5WVnZKisVRGFoaMgOReLjvaeyMjsAeP/+fYUL2tWlq6tbaaDr9evX0NXVFTpo4OnpiYULF2L+/PmYNWsWPn36BIZhkJiYiJCQEKxbt07obDFxW7lyJSZPnoxnz56htLQUR48eRVpaGoKDg3Hy5Mna3l6dNnfuXOTl5fF6XBQUFAQ1NTUMHjwYALB48WLs3LkT7du3R0hIiNBBntzcXLb304YNGzB+/Hheej8pKysjPz8f6urqsLKywpYtW2BpaYnWrVsLvda3UKYU4Z04+z+Jw+rVq795/6pVq4RaT1yZHC1atMDhw4fRq1cvtGnTBmvWrMHo0aORlpaGrl27itzjgpD6rLYysCSRhoYGwsPD0a1bNzRu3BjXr1+HoaEhjh8/Dm9vb8TFxdX2FgGU76t1+fJlDB48GC4uLrC1teV04kTZVzXXPL0uePToEdavX4+kpCS8f/8enTp1wpIlS2BiYiLSuoWFhcjIyEDr1q0hIyNZ14bF3YBXnNOm+KSjowN/f/9yQc8vnT17FtOnT+elETGfxNFIPjg4GBs3bsTDhw8BfA5QLVq0CJMmTRJ6rbJ+fVu3bsVPP/1UrjqhpKQEV69ehbS0NOLj44VeWyAQICcnp8JJd2ZmJtq3by90qd2X2Vx//vknPDw88OjRIwCfj8dXr14tkdPsYmNj4enpWe51a+XKlbC1ta3trdVp4jguatOmDf744w9YW1sjISEB/fr1g4+PD06ePAkZGRmRL/7w1fvJ398fVlZWYp+aSkEpIjbi6P9Un7m6uuLkyZMwMDDArVu38OTJEygqKuLgwYPw9vaWqBRiQkjd1bhxYyQnJ0NHRwfa2to4cOAAevfujYyMDBgZGXFuHi1OmZmZCAwMRHBwMIqLi5GSkiL0BMJZs2axVyf5yr4i4vG9zMYykpLRBXxuYeDq6spmn5WNa3dzc4OmpmaVE3trkrga8NbktCk+NGzYEI8ePapymuvff/8NfX19oZvTi9uIESMQGRnJWyP5zZs3Y8WKFXB1dWWHAcXFxcHPzw9r1qzBvHnzhFqvbFpqdHQ0evbsCVlZWfY+WVlZ6OjoYOHChWxWVnWIK9BVWQD1w4cPeP/+faX95sh/mziOi+Tl5ZGamopWrVphyZIlePHiBYKDg5GSkgJLS0u8fPlSpD2Xlpbi2rVriIyMRGRkJOLi4vDp0yeJKDWsjGRdoiH/KSoqKhXGyEqyGzdusI3ajIyMKm1sWJu2bNkCHR0dZGVlwdvbmz3hevHiBWbOnFnLuyOE/Fe0adMGaWlp0NHRQYcOHeDv7w8dHR3s2LGD94aZfCk7mWYYhvMBl5+fHzZv3sxmXy1dulTk7CsiHl83mT5w4ADs7e05TQ2uKe7u7khKSkJUVBQ7ThwAbGxs4OHhIRFBKXE14DUzM4OUlBSkpKQq7fNVFuySFGpqanjy5EmVQamMjAyJDFgrKyvz2kje19cXf/zxBxwdHdnbhg4dCiMjI3h4eAgdlCprTD5lyhRs3boVjRs3FnmPZYMpGIbBnTt3KgS6OnTogIULF3Ja++vXfHl5eV56z4qLnp4erl27VqGPUG5uLjp16oTHjx/X0s7qPnEcFykqKuLVq1do1aoVzp8/zwZYGzVqJPSUeuD7vZ/8/PzYwLAkokwpUu/9888/GDduHKKiotg+B7m5ubCyssLBgwc51d8SQkhdtX//fhQXF8PJyQk3btyAnZ0dXr9+DVlZWQQGBrITQGvbl+V7cXFxGDJkCKZMmQI7OzsIBAKR1+cj+4rUjC+b+ori6xHzlZGSkkJxcbHQa2trayM0NBQ9evQot9/09HR06tRJYkrwi4qKMG3aNKxcuZK3dguZmZliCXaJi7OzMx49eoQLFy6UC3IAn193BgwYAD09Pezdu7eWdlgzGjVqVOlE7YcPH8LExESiMsX4DHQBn18LmjRp8t3Xg9evX/PyeHyoqjw2JycHrVq14tzTtj7LyMiArq6uWI6LHBwckJqaio4dOyIkJARPnz5F06ZNcfz4cSxbtkzoMrvGjRuX6/1kZWUllt5P4kKZUqTec3Nzw7t375CSklJuwsrkyZMxe/ZshISE1Nrejh8/joEDB6JBgwbfnYQ4dOjQGtoVIeS/bOLEiey/O3fujMzMTDbFXE1NrRZ39q+ZM2fi4MGD0NLSgrOzM0JCQnjfGx/ZV6Ru+Va/sISEBGzbtg2lpaWc1n758mWlZT/5+fkSlYXXoEEDHDt2DCtXruRtTW1tbRQVFWHy5Mlo2rSpyFOaxM3T0xNdunSBgYEBZs2ahbZt24JhGNy/fx+///47CgoKsG/fvtreptjp6+vj0KFDWLZsWbnbQ0NDhSqx+9r3pmJyaXgvjj53q1evrpCVKYm+PD84d+5cuT2XlJTg4sWL0NHRqYWd1X2tW7eGtrY2G+T5+++/eTsu8vPzw/Lly5GVlYUjR46wGW43btzA+PHjhV5v48aNNdL7SVwoU4rUe02aNEFERESFUsPExETY2trW6tStL696fOvKv5SUFJ00EULEoqSkBHfu3IG2trbE9AYUCARo1aoVOnbs+M0TemF7qIg7+4qIB1+ZUpVJS0uDu7s7Tpw4AQcHB3h6enIKqpibm2P06NFwc3ODkpISkpOToaurCzc3Nzx8+LDKUeO1YfLkyTAzMxO6POt7lJWVcevWLYkbeFOZjIwMzJw5E+fPny83tKd///7Yvn27WKbciSonJwcLFy7ExYsX8c8//1ToCybsceKRI0cwduxY2NjYsD2l4uPjcfHiRRw6dIhzqeDXf1dFRUW4ffs27t69i8mTJ2Pr1q1Cr8l3oKuqrCNJVPbeVHYR5UsNGjSAjo4ONm3ahCFDhtTG9uq0qKgo9uPq1asoLCyEnp4erK2t2UDV1xM6CTeUKUXqvdLS0grNIIHPL+Rcr4jy5cvHr+29EELqh7lz58LExAQuLi4oKSmBubk5EhISIC8vj5MnT8LS0rK2twhHR0fes0tqIvuK1B3Pnz/HqlWrEBQUhAEDBuD27dswNjbmvJ6XlxcGDhyIe/fuobi4GFu3bsW9e/dw+fJlREdH87hz0RkYGMDT0xPx8fHo3LkzFBQUyt0/e/ZsTusOGzYMYWFhvAe7xEFXVxdnzpzBmzdv2Mlz+vr6EtlLqoyTkxOePn2KFStWQENDQ+TXyFGjRuHq1avYsmULwsLCAADt2rVDYmKiSH1Xt2zZUuntHh4eeP/+Pac1O3ToUO7zrwNdwpKk7MXvKTs/0NXVxbVr1+h9i0eWlpbsMc+nT59w+fJlNkgVFBSEoqIitG3bFikpKZzWj42Nhb+/Px4/foy//voLmpqa2LdvH3R1ddGnTx8efxLJR5lSpN4bNmwYcnNzERISghYtWgAAnj17BgcHB6ioqPynx38TQsjXWrZsibCwMHTp0gVhYWGYNWsWIiMjsW/fPly6dInTuO66QFzZV4R/X5ezjx8/Hj4+PhWuWHMpa8/Ly4OXlxd8fX1hZmaGDRs2oG/fviLtt8yjR4+wfv36cuPalyxZAhMTE17W58u3MpmkpKQ4N0xes2YNNm3ahH79+vEa7CKfKSkpITY2FmZmZrW9FU7S09PRrVs3Xvs0lQW6fvvtN6G+ry5lSpGaVVhYiPj4eJw5cwb+/v54//49p2qVI0eOYNKkSXBwcMC+fftw79496OnpYfv27Th9+jROnz4tht1LLgpKkXovKysLQ4cORUpKCrS0tNjbjI2Ncfz48Sqnr9Sk0tJSBAYG4ujRo3jy5AmkpKSgq6uLH3/8EZMmTapTV3QIIZKtUaNGSE9PR8uWLTFt2jTIy8vDx8cHGRkZ6NChg8Q0ZOabk5NTtV5LxdG7hAinOqWUXMravb29sWHDBqirq8PLywvDhg3jukVSCXEFu8hn7du3x59//snr9OjS0lKkp6fjn3/+qZCxb25uztvjAMC+ffuwZMkSPH/+nLc1xRHokiTbtm3DtGnT0KhRI2zbtu2bX0tBX24KCwtx5coVREZGsmV8WlpaMDc3h7m5OSwsLNCqVSuh1+3YsSPmzZsHR0fHciXot27dwsCBA5GdnS2Gn0ZyUVCKEHweJRsREYHU1FQAn9OTbWxsanlXnzEMA3t7e5w+fRodOnQo13Dzzp07GDp0KJtWTQghotLW1sauXbvQr18/6Orq4o8//sDgwYORkpKCPn364M2bN7W9RULEQiAQQE5ODjY2Nt+cBlefsuW+7KdEJNv58+exadMmdly9qK5cuYIJEyaw0xO/JEov05EjR5b7nGEYvHjxAtevX8eKFSuwatUqznv+mjgCXZJEV1cX169fR9OmTSnoKwbW1ta4evUqdHV1YWFhgb59+8LCwgIaGhoiry0vL4979+5BR0enXFDq8ePHaN++vURNt6wJ1FOK1FuXLl2Cq6srrly5gsaNG6N///7o378/gM/p+0ZGRtixYwdvaftcBQYGIiYmBhcvXoSVlVW5+y5duoThw4cjODgYjo6OtbRDQsh/yZQpUzBmzBi2J0lZgP7q1ato27ZtLe+OEPERR6+ysimO3yIlJYXi4mJeH1dUwcHB2LhxI9tPydDQEIsWLcKkSZN4WZ+CXfwbO3YsPnz4gNatW0NeXr5Cv1Rhs4WmT5+OLl264NSpU7z0qCrz9UQ7gUCANm3awNPTE7a2tpzW/F6g678qIyOj0n8TfsTGxkJDQwPW1tawtLSEhYUFOyVPVOrq6khPT68QQI6LixPL0A5JR5lSpN4aOnQorKysqmy4uW3bNkRGRtZ6TylbW1tYW1vD3d290vu9vLwQHR2Nc+fO1fDOCCH/VYcPH0ZWVhZGjx7NljAHBQVBWVmZSpoIEUJ4eHiV9yUkJGDbtm0oLS2VqKvimzdvxooVK+Dq6spOXYuLi4Ofnx/WrFkjUqNycQe76rOgoKBv3i9sw28FBQUkJSVJ5KTBr02ZMqXc5wKBAM2aNYO1tTXnQBch+fn5iI2NRVRUFCIjI3H79m0YGhrCwsKCDVI1a9aM09rr1q3D/v37sXfvXvTv3x+nT59GZmYm5s2bhxUrVsDNzY3nn0ayUVCK1Fva2to4e/Ys2rVrV+n9qampsLW1xdOnT2t4Z+Wpq6vj7NmzVTaurK+1x4SQmpObmwtlZeXa3gYh/wlpaWlwd3fHiRMn4ODgAE9PT2hra9f2tli6urpYvXp1hQzsoKAgeHh4cM7IEGewi/DP2toaixcvhp2dnVjWv3HjBu7fvw8AMDIy4rUXVn1UUlKCwMBAXLx4sdIeYJcuXaqlnf13vHv3DnFxcWx/qaSkJBgYGODu3btCr8UwDLy8vLBu3Tp8+PABANCwYUMsXLgQv/76K99bl3hUvkfqrZycnAqpzV+SkZHBy5cva3BHlXv9+nWFiUJf+uGHH6jHCyGENxs2bICOjg7Gjh0LABgzZgyOHDkCDQ0NnD59GqamprW8Q0LqpufPn2PVqlUICgrCgAEDcPv2bRgbG9f2tip48eIFevXqVeH2Xr164cWLF5zX9fX1xR9//FEu2DV06FAYGRnBw8ODglI8ePToEQICAvDo0SNs3boVzZs3x5kzZ9CqVSsYGRl99/uTk5PZf7u5uWHBggXIzs6GiYlJhWNmru8F//zzD8aNG4eoqCj2Ykdubi6srKxw8OBBzpknQP0OdM2ZMweBgYEYPHgwjI2NqTRWDBQUFKCqqgpVVVWoqKhARkaG/XurroyMDOjq6kJKSgq//PILFi1ahPT0dLx//x7t27eHoqKimHYv2SgoReotTU1N3L17t8q05OTkZF4a2YmqpKQEMjJVP1WlpaUlrhcFIaTu2rFjB/78808AwIULF3DhwgWcOXMGhw4dwsKFC3H+/Pla3iEhdUteXh68vLzg6+sLMzMzXLx4sdb7VX6Lvr4+Dh06hGXLlpW7PTQ0FAYGBpzXFVewi3wWHR2NgQMHonfv3oiJicHatWvRvHlzJCUlYc+ePTh8+PB31zAzM4OUlFS5xubOzs7sv8vuE6XRuZubG969e4eUlBS2WuHevXuYPHkyZs+ejZCQEKHXFGegq644ePAgDh06hEGDBtX2Vv4zSktLcf36dbZ8Lz4+Hvn5+dDU1ISVlRX8/Pwq9Pv9ntatW0NbWxtWVlawtraGlZUV2rdvL6afoO6goBSptwYNGoQVK1bAzs4OjRo1Knffx48fsWrVKgwZMqSWdvcvhmHg5OSEhg0bVnp/QUFBDe+IEPJflp2dDS0tLQDAyZMnMWbMGNja2kJHRwfdu3ev5d0R8q+VK1fCysoKPXv2rPA+Lim8vb2xYcMGqKurIyQkpE70ZFu9ejXGjh2LmJgYtswuPj4eFy9exKFDhzivK65gF/nM3d0da9aswfz586GkpMTebm1tje3bt1drjZpoln327FlERESUa5/Rvn17+Pn5ce7/JI5AV10jKytbJ/p/1SXKysrIz8+Huro6rKyssGXLFlhaWqJ169ac17x06RKioqIQFRWFkJAQFBYWQk9Pjw1QWVlZfbNC5r+KekqReisnJwedOnWCtLQ0XF1d0aZNGwCfe0n5+fmhpKQEN2/erPUXhq+bN1YlICBAzDshhNQHLVq0wOHDh9GrVy+0adMGa9aswejRo5GWloauXbvi7du3tb1FQgAA/fv3R0JCAoqLi9G1a1e2+Wzv3r0hJydX29sD8LnhspycHGxsbCAtLV3l1x09erQGd/V9N27cwJYtW9jSlHbt2mHBggUilUMdOXIEY8eOhY2NTaXBrhEjRvCy9/pKUVERd+7cga6ubrkR80+ePEHbtm15bab/8eNHzs8xJSUlxMbGVuiVeuvWLVhYWHB6j2nSpAkiIiLQtWvXcrcnJibC1tYWubm5nPZal2zatAmPHz/G9u3bqXSPJ/7+/rCysoKhoaFY1v/06RMuX77MBqkSExNRVFSEtm3bIiUlRSyPKakoKEXqtczMTMyYMQPnzp0rN554wIAB8PPzg66ubi3vkBBCaparqytOnjwJAwMD3Lp1C0+ePIGioiIOHjwIb29v3Lx5s7a3SAiruLgYV69eRUxMDKKjo3H58mUUFBSga9euiIuLq+3twcnJqVoniPXlwpI4gl3ks5YtW+LQoUPo1atXuaDUsWPHsHDhQjx69EjkxygoKMD27duxceNGzgN2hg0bhtzcXISEhKBFixYAgGfPnsHBwQEqKiqcpl6LI9BV14wYMQKRkZFQVVWFkZFRhR5gkhb4Jv8qLCxEfHw8zpw5A39/f7x//55zeWxdRUEpQgC8efMG6enpYBgGBgYGUFFRqe0tEUJIrSgqKsLWrVuRlZUFJycn9mRxy5YtUFJSwtSpU2t5h4RU9ODBA0RGRiIiIgJhYWFo0qQJ/ve//9X2tgipMQsXLsTVq1fx119/wdDQEDdv3kROTg4cHR3h6OiIVatWVWudgoICeHh44MKFC5CVlcXixYsxfPhwBAQE4JdffmErDJYsWcJpn1lZWRg6dChSUlLYUvGsrCwYGxvj+PHjaNmypdBriiPQVdd8r7KivgS+64LCwkJcuXKFneJ39epVaGlpwdzcHObm5rCwsECrVq1qe5s1ioJShBBCCCGkztm5cyeioqIQHR2NgoIC9O3bF5aWlrC0tISpqSmVsAhJIBB893cmJSVFw1UkVGFhIVxdXREYGIji4mLIyMigpKQEEyZMQGBg4DfLR7+0ZMkS+Pv7w8bGBpcvX8bLly8xZcoUXLlyBcuWLcPo0aOrvVZVGIZBREQEUlNTAXzOmLOxseG8njgCXYSIg7W1Na5evQpdXV1YWFigb9++sLCwkIjhWrWJglKEEEIIqeDevXt4+vQpCgsLy90+dOjQWtoRIeUJBAI0a9YMCxYswMyZM+vtKG2+hIeHV3lfQkICtm3bhtLSUqF7E1GwS7xKS0uxceNGHD9+HIWFhTA1NcWoUaPw/v17dOzYUegm8np6evDx8cHQoUNx9+5dmJqawsnJCXv27JHoQC/fgS5CxKFBgwbQ0NDA8OHDYWlpCQsLCzRt2rS2t1XrKChFCCGEENbjx48xYsQI3Llzp9xo8LKTkfrW54BIrrCwMMTExCAqKgr3799Hx44d2UypPn36QF5evra3WOelpaXB3d0dJ06cgIODAzw9PaGtrS3UGuIKdpHPfv31V3h4eMDGxgZycnI4d+4cxo8fj71793JaT1ZWFhkZGdDU1AQAyMnJITExESYmJiLtMyEhAa9evSo32To4OBirVq1Cfn4+hg8fDl9f3yqnTZPKqaioVBosbNKkCQwNDbFw4UL079+/FnZGKpOfn4/Y2FhERUUhMjISt2/fhqGhITuow8LCAs2aNavtbdY4CkoRQgghhGVvbw9paWns3r0burq6SExMxKtXr7BgwQL89ttv6Nu3b21vkZAK8vLyEBsbi7/++gshISEQCAQU5BDB8+fPsWrVKgQFBWHAgAFYt24djI2NeVufj2AX+czAwAALFy7Ezz//DACIiIjA4MGD8fHjRwgEAqHXk5aWRnZ2NntirKSkhOTkZJGH/wwcOBCWlpZsL6o7d+6gU6dOcHJyQrt27bBx40b8/PPP8PDwqPaaFOgCgoKCKr09NzcXN27cQGhoKA4fPgx7e/sa3hmpjnfv3iEuLo7tL5WUlAQDAwPcvXu3trdWo2RqewOEEEIIkRwJCQm4dOkS1NTUIBAIIBAI0KdPH6xbtw6zZ8/GrVu3anuLhLBevXqF6OhodqR2SkoKVFRUKHjKUV5eHry8vODr6wszMzNcvHiR19/l18Gu27dv8xrsqo+ePn2KQYMGsZ/b2NhASkoKz58/59RLiWEYODk5sYGcT58+Yfr06VBQUCj3dcJOc7t9+zZ+/fVX9vODBw+ie/fu2LVrFwBAS0sLq1atEioo5enpCUtLSzYodefOHbi4uJQLdLVo0UKoNeuayZMnf/N+MzMzrFu3joJSEkpBQQGqqqpQVVWFiooKZGRk2Omk9QkFpQghhBDCKikpgZKSEgBATU0Nz58/R5s2baCtrY20tLRa3h0h/zIxMcH9+/ehoqICc3Nz/PTTT7CwsICpqWltb61O8vb2xoYNG6Curo6QkBAMGzaMt7XFHeyqz4qLi9GoUaNytzVo0ABFRUWc1vs6yDFx4kTOe/vSmzdv8MMPP7CfR0dHY+DAgeznXbt2RVZWllBriiPQ9V8zZMgQrFmzpra3Qf5faWkprl+/zpbvxcfHIz8/H5qamrCysoKfnx+srKxqe5s1joJShBBCCGEZGxsjKSkJurq66N69O7y9vSErK4udO3dCT0+vtrdHCGv69OmwsLCgTBueuLu7Q05ODvr6+ggKCqqyLEjYDBlxBrtIxcwmoPLspur+vwUEBPC+RwD44YcfkJGRAS0tLRQWFuLmzZtYvXo1e/+7d+/QoEEDodYUR6Drv6agoACysrK1vQ3y/5SVlZGfnw91dXVYWVlhy5YtsLS0ROvWrWt7a7WKglKEEEIIYS1fvhz5+fkAgNWrV8Pe3h59+/ZF06ZNcfDgwVreHSH/mjVrFgCgsLAQGRkZaN26NWRk6NCWK0dHR7FMVxNXsIt8Vln5Fl/ZTXwaNGgQ3N3dsWHDBoSFhUFeXr5ctlxycrLQJ+biCHT91+zZswdmZma1vQ3y/zZu3AgrKysYGhrW9lYkCjU6J4QQQsg3vX79usoJP4TUlo8fP8LV1ZUNcjx48AB6enpwc3ODpqYm3N3da3mHBACcnJyq9dohrgwdIhn+97//YeTIkYiLi4OioiKCgoIwYsQI9v5+/fqhR48eWLt2bbXXnDFjBpKSkthAV1BQEJ4/f85mBv3555/w8fHBtWvXeP95JMX8+fMrvT0vLw83b97EgwcPEBMTg86dO9fwzgipPgpKEUIIIQTOzs7V+jquY8YJ4ducOXMQHx8PHx8f2NnZITk5GXp6eggPD4eHhwc15SdEAuXl5UFRURHS0tLlbn/9+jUUFRWFKjUTR6Crrqmq/1Djxo3Rpk0bzJgxQ+TJiYSIGwWlCCGEEAKBQABtbW107NgR3zo0OHbsWA3uipCqaWtrIzQ0FD169ICSkhKSkpKgp6eH9PR0dOrUCW/fvq3tLRJCagCfgS5CSM2jwntCCCGEYMaMGQgJCUFGRgamTJmCiRMnQlVVtba3RUiVXr58iebNm1e4PT8/n0pNCalHmjRpUunt9B5GSN0gqO0NEEIIIaT2+fn54cWLF1i8eDFOnDgBLS0tjBkzBufOnftm5hQhtaVLly44deoU+3lZIGr37t3o2bNnbW2LEEIIIUKg8j1CCCGEVJCZmYnAwEAEBwejuLgYKSkpUFRUrO1tEcKKi4vDwIEDMXHiRAQGBuLnn3/GvXv3cPnyZURHR1NjX0IIIaQOoEwpQgghhFQgEAggJSUFhmFQUlJS29shpII+ffrg9u3bKC4uhomJCc6fP4/mzZsjISGBAlKEEEJIHUGZUoQQQggBABQUFODo0aPYu3cv4uLiMGTIEEyZMgV2dnYQCOg6FiGEEEII4Rc1OieEEEIIZs6ciYMHD0JLSwvOzs4ICQmBmppabW+LEEIIIYT8h1GmFCGEEEIgEAjQqlUrdOzY8ZuTy44ePVqDuyKkorLS0m+RkpJCcXFxDe2IEEIIIVxRphQhhBBC4Ojo+N0TfUIkwbFjx6q8LyEhAdu2bUNpaWkN7ogQQgghXFGmFCGEEEIIqdPS0tLg7u6OEydOwMHBAZ6entDW1q7tbRFCCCHkO6hrKSGEEEIIqZOeP3+On376CSYmJiguLsbt27cRFBREASlCCCGkjqCgFCGEEEIIqVPy8vKwZMkS6OvrIyUlBRcvXsSJEydgbGxc21sjhBBCiBCopxQhhBBCCKkzvL29sWHDBqirqyMkJATDhg2r7S0RQgghhCPqKUUIIYQQQuoMgUAAOTk52NjYQFpausqvo0mRhBBCiOSjTClCCCGEEFJn0KRIQggh5L+DMqUIIYQQQgghhBBCSI2jRueEEEIIIYQQQgghpMZRUIoQQgghhBBCCCGE1DgKShFCCCGEEEIIIYSQGkdBKUIIIYQQQgghhBBS4ygoRQghhBBCCCGEEEJqHAWlCCGEEELqMEtLS8ydO7e2t0EIIYQQIjQKShFCCCGEcODk5AQpKakKH3Z2dt/9Xh0dHfj4+Ih/k4QQQgghEkymtjdACCGEEFJX2dnZISAgoNxtDRs2rKXdEEIIIYTULZQpRQghhBDCUcOGDaGurl7uQ0VFBQzDwMPDA61atULDhg3RokULzJ49G8DncrvMzEzMmzePza4CgFevXmH8+PHQ1NSEvLw8TExMEBISUu7x8vPz4ejoCEVFRWhoaGDTpk0V9vTmzRs4OjpCRUUF8vLyGDhwIB4+fMjen5mZCXt7e6ioqEBBQQFGRkY4ffq0GH9LhBBCCCGVo6AUIYQQQgjPjhw5gi1btsDf3x8PHz5EWFgYTExMAABHjx5Fy5Yt4enpiRcvXuDFixcAgE+fPqFz5844deoU7t69i2nTpmHSpElITExk1120aBGio6MRHh6O8+fPIyoqCjdv3iz32E5OTrh+/TqOHz+OhIQEMAyDQYMGoaioCAAwa9YsFBQUICYmBnfu3MGGDRugqKhYQ78ZQgghhJB/UfkeIYQQQghHJ0+erBDQWbZsGRo1agR1dXXY2NigQYMGaNWqFbp16wYAUFVVhbS0NJSUlKCurs5+n6amJhYuXMh+7ubmhnPnzuHQoUPo1q0b3r9/jz179mD//v3o168fACAoKAgtW7Zkv+fhw4c4fvw44uPj0atXLwDAn3/+CS0tLYSFhWH06NF4+vQpRo0axQbJ9PT0xPPLIYQQQgj5DgpKEUIIIYRwZGVlhT/++KPcbaqqqsjPz4ePjw/09PRgZ2eHQYMGwd7eHjIyVR96lZSUwMvLC4cOHcKzZ89QWFiIgoICyMvLAwAePXqEwsJCdO/evdxjtWnThv38/v37kJGRKfc1TZs2RZs2bXD//n0AwOzZszFjxgycP38eNjY2GDVqFExNTXn5fRBCCCGECIPK9wghhBBCOFJQUIC+vn65D1VVVWhpaSEtLQ2///475OTkMHPmTJibm7MldJXZuHEjtm7diiVLliAyMhK3b9/GgAEDUFhYyOuep06disePH2PSpEm4c+cOunTpAl9fX14fgxBCCCGkOigoRQghhBAiBnJycrC3t8e2bdsQFRWFhIQE3LlzBwAgKyuLkpKScl8fHx+PYcOGYeLEiejQoQP09PTw4MED9v7WrVujQYMGuHr1Knvbmzdvyn1Nu3btUFxcXO5rXr16hbS0NLRv3569TUtLC9OnT8fRo0exYMEC7Nq1i/efnxBCCCHke6h8jxBCCCGEo4KCAmRnZ5e7TUZGBidPnkRJSQm6d+8OeXl57N+/H3JyctDW1gYA6OjoICYmBuPGjUPDhg2hpqYGAwMDHD58GJcvX4aKigo2b96MnJwcNpikqKgIFxcXLFq0CE2bNkXz5s3xyy+/QCD49xqjgYEBhg0bhp9++gn+/v5QUlKCu7s7NDU1MWzYMADA3LlzMXDgQBgaGuLNmzeIjIxEu3btaug3RgghhBDyL8qUIoQQQgjh6OzZs9DQ0Cj30adPHygrK2PXrl3o3bs3TE1NERERgRMnTqBp06YAAE9PTzx58gStW7dGs2bNAADLly9Hp06dMGDAAFhaWkJdXR3Dhw8v93gbN25E3759YW9vDxsbG/Tp0wedO3cu9zUBAQHo3LkzhgwZgp49e4JhGJw+fRoNGjQA8Ll31axZs9CuXTvY2dnB0NAQv//+u/h/WYQQQgghX5FiGIap7U0QQgghhBBCCCGEkPqFMqUIIYQQQgghhBBCSI2joBQhhBBCCCGEEEIIqXEUlCKEEEIIIYQQQgghNY6CUoQQQgghhBBCCCGkxlFQihBCCCGEEEIIIYTUOApKEUIIIYQQQgghhJAaR0EpQgghhBBCCCGEEFLjKChFCCGEEEIIIYQQQmocBaUIIYQQQgghhBBCSI2joBQhhBBCCCGEEEIIqXEUlCKEEEIIIYQQQgghNY6CUoQQQgghhBBCCCGkxv0fAUfrxrRw9sQAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10,8))\n",
+        "GraficoPorEstado=FiltroEstados.plot(kind='bar', color=[\"Purple\", \"Black\"],figsize=(12,8))\n",
+        "plt.title(\"Taxa de pressão alta e triagem de colesterol por estados \")\n",
+        "plt.xlabel(\"Estados\")\n",
+        "plt.ylabel(\"Medias de taxas %\")\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.legend(title=\"Medida\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhQSri9uN0l"
+      },
+      "source": [
+        "## Gráfico de mapa de calor"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YBm9DE38uN0l"
+      },
+      "source": [
+        " annot=True' exibe os valores dentro das células.\n",
+        " 'cmap=\"coolwarm\"' usa uma paleta de cores que vai do azul ao vermelho.\n",
+        " 'linewidths=.5' define a espessura das linhas de separação entre as células."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Dj8l2WqZuN0l",
+        "outputId": "496c0ca8-48bd-4319-bed3-b62fdb8ef16c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1800x1000 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(18,10))\n",
+        "sns.heatmap(FiltroEstados, annot= True, cmap= \"coolwarm\", linewidths=.5, cbar_kws={'label': 'Valores Médios'})  # Rótulo da barra de cores\n",
+        "\n",
+        "\n",
+        "plt.title(\"Gráfico Mapa de calor: Taxa pressão alta e triagem de colesterol por estados\")\n",
+        "plt.xticks(rotation=360)\n",
+        "\n",
+        "\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dvrki8TkuN0l",
+        "outputId": "27648ee1-9e24-44b9-92d6-b46301c5f75b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Estados que aparecem no top 5 em ambas as categorias:\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Medida</th>\n",
+              "      <th>Cholesterol screening among adults aged &gt;=18 Years</th>\n",
+              "      <th>High blood pressure among adults aged &gt;=18 Years</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DescricaoEstado</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>West Virginia</th>\n",
+              "      <td>80.575</td>\n",
+              "      <td>40.033333</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Medida           Cholesterol screening among adults aged >=18 Years  \\\n",
+              "DescricaoEstado                                                       \n",
+              "West Virginia                                               80.575    \n",
+              "\n",
+              "Medida           High blood pressure among adults aged >=18 Years  \n",
+              "DescricaoEstado                                                    \n",
+              "West Virginia                                           40.033333  "
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "TOP = FiltroEstados[FiltroEstados.index.isin(top5_colesterol.index) & FiltroEstados.index.isin(top5_pressao.index)]\n",
+        "print(\"\\nEstados que aparecem no top 5 em ambas as categorias:\")\n",
+        "display(TOP)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pCQPZOkQuN0l"
+      },
+      "source": [
+        "# Correlação entre pressão alta e triagem de colesterol\n",
+        "## O gráfico de correlação, ou a matriz de correlação, é uma ferramenta que mede o grau de relação linear entre duas variáveis. Os valores dessa correlação variam entre -1 e 1:\n",
+        "### O comando .corr() é uma função do pandas usada para calcular a correlação entre as colunas de um DataFrame.\n",
+        "Quando usamos esse comando, ele nos dá uma matriz de correlação, que mostra o quão relacionadas estão as variáveis numéricas entre si."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rHwPiqtQuN0l",
+        "outputId": "079029b9-b476-4858-99ce-9642ec24c1a2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Correlação entre pressão alta e triagem de colesterol:\n",
+            "Medida                                              Cholesterol screening among adults aged >=18 Years  \\\n",
+            "Medida                                                                                                   \n",
+            "Cholesterol screening among adults aged >=18 Years                                           1.000000    \n",
+            "High blood pressure among adults aged >=18 Years                                             0.455418    \n",
+            "\n",
+            "Medida                                              High blood pressure among adults aged >=18 Years  \n",
+            "Medida                                                                                                \n",
+            "Cholesterol screening among adults aged >=18 Years                                          0.455418  \n",
+            "High blood pressure among adults aged >=18 Years                                            1.000000  \n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1300x1000 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "    # Calcular a correlação entre pressão alta e triagem de colesterol\n",
+        "correlacao = FiltroEstados.corr()\n",
+        "\n",
+        "print(\"Correlação entre pressão alta e triagem de colesterol:\")\n",
+        "print(correlacao)\n",
+        "\n",
+        "# Visualizando a correlação em um heatmap\n",
+        "plt.figure(figsize=(13, 10))\n",
+        "sns.heatmap(correlacao, annot=True, cmap=\"Blues\")\n",
+        "\n",
+        "plt.title(\"Correlação entre Pressão Alta e Triagem de Colesterol\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FzN1hN02uN0l"
+      },
+      "source": [
+        "como conseguimos ver, existe uma correlação, mas uma correlção fraca(1 para 0.46)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9H8VvIyeuN0l"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.6"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file