Untitled.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX8UDGqMl7EG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4drf3idnPjg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2277ffa6-2831-4764-fd62-7a2e27dd1db6"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/Data.csv\")\n",
        "df = df.dropna()\n",
        "df = df.reset_index(drop=True)\n",
        "df.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sl_no</th>\n",
              "      <th>gender</th>\n",
              "      <th>ssc_p</th>\n",
              "      <th>ssc_b</th>\n",
              "      <th>hsc_p</th>\n",
              "      <th>hsc_b</th>\n",
              "      <th>hsc_s</th>\n",
              "      <th>degree_p</th>\n",
              "      <th>degree_t</th>\n",
              "      <th>workex</th>\n",
              "      <th>etest_p</th>\n",
              "      <th>specialisation</th>\n",
              "      <th>mba_p</th>\n",
              "      <th>status</th>\n",
              "      <th>salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>M</td>\n",
              "      <td>67.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>91.00</td>\n",
              "      <td>Others</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>58.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>No</td>\n",
              "      <td>55.0</td>\n",
              "      <td>Mkt&amp;HR</td>\n",
              "      <td>58.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>270000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>M</td>\n",
              "      <td>79.33</td>\n",
              "      <td>Central</td>\n",
              "      <td>78.33</td>\n",
              "      <td>Others</td>\n",
              "      <td>Science</td>\n",
              "      <td>77.48</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>86.5</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>66.28</td>\n",
              "      <td>Placed</td>\n",
              "      <td>200000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
              "      <td>65.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>68.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Arts</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>75.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>57.80</td>\n",
              "      <td>Placed</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>M</td>\n",
              "      <td>85.80</td>\n",
              "      <td>Central</td>\n",
              "      <td>73.60</td>\n",
              "      <td>Central</td>\n",
              "      <td>Commerce</td>\n",
              "      <td>73.30</td>\n",
              "      <td>Comm&amp;Mgmt</td>\n",
              "      <td>No</td>\n",
              "      <td>96.8</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>55.50</td>\n",
              "      <td>Placed</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>M</td>\n",
              "      <td>82.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>64.00</td>\n",
              "      <td>Central</td>\n",
              "      <td>Science</td>\n",
              "      <td>66.00</td>\n",
              "      <td>Sci&amp;Tech</td>\n",
              "      <td>Yes</td>\n",
              "      <td>67.0</td>\n",
              "      <td>Mkt&amp;Fin</td>\n",
              "      <td>62.14</td>\n",
              "      <td>Placed</td>\n",
              "      <td>252000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sl_no gender  ssc_p    ssc_b  ...  specialisation  mba_p  status    salary\n",
              "0      1      M  67.00   Others  ...          Mkt&HR  58.80  Placed  270000.0\n",
              "1      2      M  79.33  Central  ...         Mkt&Fin  66.28  Placed  200000.0\n",
              "2      3      M  65.00  Central  ...         Mkt&Fin  57.80  Placed  250000.0\n",
              "3      5      M  85.80  Central  ...         Mkt&Fin  55.50  Placed  425000.0\n",
              "4      8      M  82.00  Central  ...         Mkt&Fin  62.14  Placed  252000.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GeMexEwCIWQg",
        "colab_type": "text"
      },
      "source": [
        "***Comparison of GPA by salary***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZphLk1D-WG3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gpa_salary(data):\n",
        "  gpa_salary = data.loc[:, ['degree_p', 'salary']]\n",
        "  gpa_salary.plot.scatter(x='degree_p', y='salary', alpha=0.3)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P26Toc5uIekz",
        "colab_type": "text"
      },
      "source": [
        "***Comparison of GPA and Salary by Increase***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScI8RavRId6O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gpa_salary_inc(data):\n",
        "  row_data = data.loc[:, ['degree_p', 'salary']]\n",
        "  select = row_data['salary'] > 360000\n",
        "  gpa_salary = row_data[select]\n",
        "  gpa_salary.plot.bar(x='degree_p', y='salary', alpha=0.3)\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctnAW_rq-YPQ",
        "colab_type": "text"
      },
      "source": [
        "***Comparison of gender by salary***\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYgJXjT69q72",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gen_sal_proportion(data):\n",
        "  data.plot.scatter(x='gender', y='salary', alpha=0.3)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k5yJ_AN8NTs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gender_ratio(data):\n",
        "  # Find quantities\n",
        "  female_count = data['gender'].value_counts()[\"F\"]\n",
        "  male_count = data['gender'].value_counts()[\"M\"]\n",
        "\n",
        "  labels = 'Female', 'Male'\n",
        "  sizes = [female_count, male_count]\n",
        "  explode = (0, 0.1)\n",
        "\n",
        "  fig1, ax1 = plt.subplots()\n",
        "  ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "          shadow=True, startangle=90)\n",
        "  ax1.axis('equal')\n",
        "\n",
        "  plt.show()  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLX9jSGw80lT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gender_salary(dataFrame):\n",
        "  row_data = dataFrame.loc[:, ['gender', 'salary']]\n",
        "  select = (row_data['salary'] > 240000) & (row_data['salary'] < 360000)\n",
        "  gender_salary = row_data[select]\n",
        "  gender_ratio(gender_salary)\n",
        "  gen_sal_proportion(gender_salary)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5nf1C9vRgym",
        "colab_type": "text"
      },
      "source": [
        "***Results of Work Experience***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cfuypl2MRXfH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "98ed206a-7008-4efe-a5a6-0f8b996eaac9"
      },
      "source": [
        "row_data = df.loc[:, ['workex']]\n",
        "\n",
        "have_workex = row_data['workex'].value_counts()['Yes']\n",
        "not_have_workex = row_data['workex'].value_counts()['No']\n",
        "labels = 'Have Work Experience', 'Don\\'t Work Experience'\n",
        "sizes = [have_workex, not_have_workex]\n",
        "explode = (0, 0.1)\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "shadow=True, startangle=90)\n",
        "ax1.axis('equal')\n",
        "plt.show()"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlghKJmbOEvg",
        "colab_type": "text"
      },
      "source": [
        "***Final Result Call Each Function***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzZQJHBN-Qqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f9170e36-bb63-4daa-fbcd-3b1655541725"
      },
      "source": [
        "gpa_salary(df)\n",
        "gpa_salary_inc(df)\n",
        "gender_salary(df)"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2jtd4evcYe8drnLOi35P6ZmZMf+TyRxHxZUln0fjF4b8ErgHekFvPOsCCvjKXnLbYV4uZme1nukfDyYmEtwKfi4h/BHJ7AFcn6S4V6Ct3OVjMzJpM94j4lKT/Q+OZLt9Mvy/mo6mZmR3QdAPiHcAdwAXpF47nA3+YW6/MzKyjTWvOJSL2Arc0fd4GbMurU53Ed+ibmb3QtH/+xV7Id+ibmR2Y/6vdIt+hb2Y2NYdLi3yHvpnZ1BwuLWq+Qx/wHfpmZk0cLi3yHfpmZlPzhP4M+A59M+t0eV3x6nCZoe5SwaFiZh0pzytefVQ0M3sZyvuKV4eLmdnLUN5XvDpcZqhaqzM8Ou77W8yso+R9xavnXGbAd+ibWaeavOJ1zYYKz+0bf/4YltUcssOlRc3nKyefRLlmQ4VLTlvsCX4z6wh5XvHqo2CLfIe+mR0J8nomlcOlRb5D38xsag6XFvkOfTOzqXnOZQZ8h76Z2YE5XGbId+ibmb2Qj4pmZpa53MJF0hJJd0t6VNJ6Se9N9VMl3SfpIUmDkk5PdUm6WtKQpEckva6prcslbUyvy5vqr5f0/bTN1ZKU6vMl3ZnWv1PSvLz208zMXijPkUsN+IOIOBE4A7hK0onAXwAfjYhTgf+RPgNcBCxPryuBa6ARFMBHgDcApwMfaQqLa4DfbNruwlT/IHBXRCwH7kqfc+E79M2sk+V1DMttziUitgHb0vvdkh4DFgEBHJVW6wN+mN5fDNwYEQHcJ+loSQuBc4E7I2IngKQ7gQsl3QMcFRH3pfqNwNuA21Nb56Z2bwDuAT6Q9T76Dn0z62Qd/6vIkpYBpwH3A+8DPiFpC/CXwIfSaouALU2bbU21g9W3HqAOcFwKN4DtwHEZ7crz8v5FUTOzPHX8ryJL6gW+ArwvIp4Dfhv4/YhYAvw+8Ld5fn8aCcUUfbsyzfsMViqVQ2rXd+ibWSfr6F9FltRFI1i+EBG3pPLlwOT7L9OYRwF4CljStPniVDtYffEB6gBPp1NqpL87DtS/iLg2IgYiYqC/v/+Q9s136JtZJ5s8hu0eG2dPtcbusfFMj2F5Xi0mGqOSxyLik02Lfgick96fB2xM728DVqWrxs4AhtOprTuAN0ualyby3wzckZY9J+mM9F2rgK81tTV5VdnlTfXM+A59M+tk3aUCr1kwl+8M/Yi7Hnua7wz9iNcsmNsRv4p8JvBrwPclPZRqH6Zxddf/llQCxmhcGQbwTeAtwBCwF/h1gIjYKelPgO+m9T42ObkPvAf4PFCmMZF/e6r/OXCTpHcDm4F35LGDvkPfzDpVtVbnse27OfNVr6BYFBMTwWPbd/PqhX2ZHMvyvFrsXkBTLH79AdYP4Kop2roeuP4A9UHgtQeoPwOcfyj9bZXv0DezTjQ553LMnK7na0/vHmN0fCKTY5qPijM0MlZjy669jIzV2t0VM7Np85MoD2OPbNnFZ771A3aN7mNeeRbvOe9VnLzEPwZgZoc/P4nyMDUyVuPDtz7Mxqf3EAgRbB0eYfWVZ9Hb439WMzv8+UmUh6HHfvgsj2/fQ6lQYE53kVKhwOPb9/DYD59td9fMzKbNT6I8zFT27AOgVCz8xN/JupnZy5nP37TolEXzmNVVYN/4BMVigYmJOrO6CpyyyHMuZmYeubRo0fzZvO/8E+jpLlAQ9HQXeN/5J7Bo/ux2d83MrO08cpmBK85ezkWvXcSGym5W9M91sJiZJQ6XGVo0f7ZDxcxsPz4tZmZmmXO4mJlZ5hwuM7RjeIwHNj3DjuGxdnfFzOyw4TmXGbh5cDNX3/UDJiIoSvze+a/i0oGl7e6WmVnbeeTSoh3DY1x91w+Y3V1kYV+Z2d1Frr7rBx7BmJnhcGnZpl17mIigt6fxc9W9PV1MRLBp154298zMrP0cLi1aNm8ORYnnRqvU6sFzo1WKEsvmzWl318zM2s7h0qJj+3pYtXIJlZEqT/5ohMpIlVUrl3BsX0+7u2Zm1nae0G9RtVZnbnkW73/TCewem2BuT5HuUolqre4nU5rZy57DpUWTjwhd2DeHhX2NWpaPCDUz62Q+CrYo70eEmpl1ModLiyYfEbp3vMbTu8fYO17L9BGhZmadzEfCGVjQV+Z1S46muwivW3I0C/rK7e6SmdlhwXMuM/DJOx7luns3U6eR0lectZT3X3Biu7tlZtZ2Hrm0aFNlhOvu3UxXUfT1lOgqiuvu3cymyki7u2Zm1nYOlxat3zZMrV4nEKPjjb+1ep3124bb3TUzs7ZzuLRoxbFziYDaxARFNf5GNOpmZi93DpcWHdtX5sxXzaNWh5FqjVodznzVPI71pL6ZmSf0W1XuKnLRyYv5hRULqOweo39uD709Jd/nYmaGRy4tm7zPpbenxJJj5tDbU/J9LmZmiUcuM7Cgr8wlpy1mdHyCclfRwWJmljhcZqi7VHComJntJ7ejoqQlku6W9Kik9ZLe27TsdyX9e6r/RVP9Q5KGJD0u6YKm+oWpNiTpg0314yXdn+pfktSd6rPS56G0fFle+/nUzr3c/fjTPLVzb15fYWaWm5GxGlt27WVkrJZpu3mOXGrAH0TEg5LmAusk3QkcB1wMnBIR+yQdCyDpROAy4CTglcA/S1qR2vo08CZgK/BdSbdFxKPAx4FPRcRqSZ8F3g1ck/7uiogTJF2W1ntn1jt43bc38ul7nqQeQUHiqnOP54qzl2f9NWZmuXhkyy5uXLuZ6kSd7mKBVSuXcvKSeZm0ndvIJSK2RcSD6f1u4DFgEfDbwJ9HxL60bEfa5GJgdUTsi4gngSHg9PQaiognIqIKrAYuliTgPODmtP0NwNua2rohvb8ZOD+tn5mndu7l0/c8SU9Xgf7eWfR0Ffj0PU96BGNmHWFkrMaNazfTO6uLn5o/h95ZXdy4dnNmI5iXZLIgnZY6DbgfWAH8QjpdtUbSz6XVFgFbmjbbmmpT1Y8Bno2I2n71n2grLR9O6+/fryslDUoarFQqh7RPGyq7qUcwp7sx+JvTXaIewYbK7kNqx8ysHXaNVqlO1OntaRzDentKVCfq7BqtZtJ+7uEiqRf4CvC+iHiOxqm4+cAZwB8CN2U9qpiuiLg2IgYiYqC/v/+Qtl3RP5eCxJ5qI9v2VGsUJFb0+w59Mzv8zSt3010sPD9SGRmr0V0sMK/cnUn7uYaLpC4awfKFiLgllbcCt0TDA0AdeAXwFLCkafPFqTZV/RngaEml/eo0b5OW96X1M7No/myuOvd4xsbrVEb2MTZe56pzj2fR/NlZfo2ZWS56e0qsWrmUkX3j/MfOPYzsG2fVyqXPj2RmKrcJ/TQa+VvgsYj4ZNOirwJvBO5OE/bdwI+A24B/kPRJGhP6y4EHAAHLJR1PIzQuA34lIkLS3cClNOZhLge+lr7jtvR5bVr+rYiIrPfxirOXc9FrF7GhspsV/XMdLGbWUU5eMo8/7p/LrtEq88rdmQUL5Hu12JnArwHfl/RQqn0YuB64XtK/AVXg8nTgXy/pJuBRGleaXRUREwCSfge4AygC10fE+tTeB4DVkv4U+B6NMCP9/TtJQ8BOGoGUi0XzZztUzKxj9faUMg2VScrhP/QdaWBgIAYHB9vdDTOzjiJpXUQM7F/3reVmZpY5h8sMVWt1hkfHqdbq7e6Kmdlhw78tNgPbh0dZs6FCrR6UCuKcFf0s8PNczMw8cmlVtVZnzYYKs7tKHDe3h9ldJdZsqHgEY2aGw6Vlo+MT1OpBubvxcLByd5FaPRgdn2hzz8zM2s/h0qJyV5FSQYxWG2EyWp2gVJCfRGlmhsOlZZNPotw7XuPp3WPsHa/5SZRmZokn9GdgQV+Zt/7sK3O5u9XM7KVQrdVzeZquj4Yz4KvFzKyT5XkM8zmcFvlqMTPrZHkfwxwuLfLVYmbWyfI+hvm0WIsmrxZ7eMtONj69m+XHzWXx/Dm+WszMOsLkMeyJym62DY+ysK9M3+zuzI5hDpcWdZcKfPn+JxjcugeAf3q0wsDiObzz536qzT0zM3tx3aUC656scPOD25+vXfq6BZkdw3xarEVrh3Y8HyyF9BzNwa17WDu0o429MjObno3bn+PWh7YzqwRze4rMKsGtD21n4/bnMmnf4dKib2+sAD8Olsm/k3Uzs8PZQ1t3EUBPV4mCRE9XiUj1LDhcWnT28n4A6ulxOJN/J+tmZoezUxfPQ8C+WmMCf19tAqV6FhwuLVp5wrGcefxRwI+D5czjj2LlCce2sVdmZtOzfMFR/OobFjM+EezeV2N8IvjVNyxm+YKjMmnfT6JMWn0S5dqhHXx7Y4Wzl/c7WMys42zc/hwPbd3FqYvntRQsUz2J0leLzdDKE451qJhZx1q+4KjMRivNfFrMzMwy53AxM7PMOVzMzCxzDhczM8ucw8XMzDLnS5ETSRVgc4ubvwL4UYbdMTN7Kc3kGLY0Il5w97jDJQOSBg90nbeZWSfI4xjm02JmZpY5h4uZmWXO4ZKNa9vdATOzGcj8GOY5FzMzy5xHLmZmljmHi5mZZc7h0iJJIenvmz6XJFUkfaOd/TIzmy5JE5Ieanoty6pt/+R+6/YAr5VUjohR4E3AU23uk5nZoRiNiFPzaNgjl5n5JvDW9P5dwBfb2Bczs8OGw2VmVgOXSeoBTgbub3N/zMwORbnplNitWTbs02IzEBGPpHOU76IxijEz6yS5nRZzuMzcbcBfAucCx7S3K2ZmhweHy8xdDzwbEd+XdG67O2NmdjhwuMxQRGwFrm53P8zMDif++RczM8ucrxYzM7PMOVzMzCxzDhczM8ucw8XMzDLncDEzs8w5XMw6kKTPS7q03f0wm4rDxexlQJLvabOXlMPFLGeS/kjS45LulfRFSf9V0qsk/ZOkdZL+RdKr07qfl3S1pH+V9MTk6EQNf5Pa+Wfg2Kb2Xy9pTWrrDkkLU/0eSX8laRB4bzv23V6+/L8ZsxxJ+jngPwOnAF3Ag8A64FrgtyJio6Q3AJ8BzkubLQTOAl5N47frbgYuAX4GOBE4DngUuF5SF/DXwMURUZH0TuDPgN9IbXVHxEDuO2q2H4eLWb7OBL4WEWPAmKSvAz3AzwNfljS53qymbb4aEXXgUUnHpdrZwBcjYgL4oaRvpfrPAK8F7kxtFYFtTW19KYd9MntRDhezl16Bxo+dTvVT5/ua3muKdZqXr4+IlVMs33OonTPLgudczPL1HeCXJPVI6gV+EdgLPCnp7fD8fMopL9LOt4F3SiqmOZU3pvrjQL+klamtLkkn5bInZofA4WKWo4j4Lo15k0eA24HvA8PAfwHeLelhYD1w8Ys0dSuwkcZcy43A2tR+FbgU+Hhq6yEap9zM2sq/imyWM0m9ETEiaTaNEciVEfFgu/tllifPuZjl71pJJ9KYyL/BwWIvBx65mJlZ5jznYmZmmXO4mJlZ5hwuZmaWOYeLmZllzuFiZmaZ+/8LNRo+Bu8GzQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}