poverty.ipynb

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        "<a href=\"https://colab.research.google.com/github/sehbaw/data_visualization/blob/main/poverty.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import seaborn.objects as so"
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/poverty.csv\",index_col=False)\n",
        "df.head()"
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            "text/plain": [
              "  Unnamed: 0 Bangladesh - poverty headcount ratio at $1.90 a day (2011 PPP)  \\\n",
              "0       year                                    % of population               \n",
              "1       1991                                                 44               \n",
              "2       2016                                                 15               \n",
              "\n",
              "   Unnamed: 2                                         Unnamed: 3  \n",
              "0      source                                                URL  \n",
              "1  World Bank  https://data.worldbank.org/indicator/SI.POV.DD...  \n",
              "2  World Bank  https://data.worldbank.org/indicator/SI.POV.DD...  "
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              "      <td>year</td>\n",
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              "      <td>1991</td>\n",
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              "      <td>https://data.worldbank.org/indicator/SI.POV.DD...</td>\n",
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              "      <th>2</th>\n",
              "      <td>2016</td>\n",
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      "source": [
        "header_list = ['year', 'population' , 'source', 'url']\n",
        "\n",
        "df.to_csv(\"/poverty1.csv\", header=header_list, index=False)\n",
        "\n",
        "df2 = pd.read_csv(\"/poverty1.csv\")\n",
        "\n"
      ],
      "metadata": {
        "id": "9gr16QKid0MK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "FwNu2AJdeheT"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "df2 = df2.drop(index=0)"
      ],
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        "id": "6X0_QGjP5spc"
      },
      "execution_count": 16,
      "outputs": []
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        "df2.head()"
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              "   year population      source  \\\n",
              "1  1991         44  World Bank   \n",
              "2  2016         15  World Bank   \n",
              "\n",
              "                                                 url  \n",
              "1  https://data.worldbank.org/indicator/SI.POV.DD...  \n",
              "2  https://data.worldbank.org/indicator/SI.POV.DD...  "
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9587b69c-08d8-47f2-a41d-0b7803e01524')\"\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-9587b69c-08d8-47f2-a41d-0b7803e01524 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": "df2",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "so.Plot(df2, x='year', color=\"population\").add(so.Bar(), so.Count(), so.Stack())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "9GR2AW864xOy",
        "outputId": "d9fcd032-93ee-487f-e695-c1c63563753b"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn._core.plot.Plot at 0x7892db48f730>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 601.375,
              "height": 378.25
            }
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iMEwi0_r48_9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}