Code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyGQcIp30x01"
      },
      "source": [
        "## Prerequisities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E0UP-ZEuorSN"
      },
      "outputs": [],
      "source": [
        "# install prerequisities\n",
        "\n",
        "import gdown\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n",
        "from sklearn.experimental import enable_iterative_imputer\n",
        "from sklearn.impute import IterativeImputer\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from scipy import stats\n",
        "from scipy.stats import pearsonr\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "from statsmodels.graphics.tsaplots import plot_acf\n",
        "from statsmodels.graphics.tsaplots import plot_pacf\n",
        "from sklearn.metrics import r2_score\n",
        "from statsmodels.tsa.api import ARIMA\n",
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bb-JYV0VDkLP"
      },
      "outputs": [],
      "source": [
        "# Load data from drive\n",
        "def load_data(link, index= None):\n",
        "  # Download data from gdrive\n",
        "  meta_url = 'https://drive.google.com/uc?export=download&confirm=pbef&id='\n",
        "  url = meta_url + link\n",
        "  output = 'data.xlsx'\n",
        "  gdown.download(url, output, quiet=True)\n",
        "  data = pd.read_excel(output)\n",
        "  return data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Formal Employer Data"
      ],
      "metadata": {
        "id": "cFuGFJq9wUqz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the employment data\n",
        "emp_df = load_data('13ZCJJ8QCcMc-encEBvbmY1Cp_mPOcAkY')\n",
        "emp_df.drop(['Series Code','Country Code'], axis=1, inplace=True)\n",
        "emp_df.columns = ['Series', 'Country'] + [str(i) for i in range(2003,2019)]"
      ],
      "metadata": {
        "id": "3_R5EPMswgh4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emp_df.tail(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2R9oRHy3wnrt",
        "outputId": "756d1750-9063-4bc1-8bc5-e68734fabefe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Series   Country       2003  \\\n",
              "111  Self-employed, male (% of male employment) (mo...  Zimbabwe  50.139999   \n",
              "\n",
              "      2004       2005   2006       2007   2008   2009   2010  2011       2012  \\\n",
              "111  50.16  50.830002  52.18  54.560001  58.48  57.66  56.93  56.0  56.509998   \n",
              "\n",
              "      2013       2014       2015   2016   2017   2018  \n",
              "111  56.84  56.689999  57.060001  57.48  57.25  56.98  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7e36d330-6d53-4b58-adfc-cc11857794fb\">\n",
              "    <div 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>Series</th>\n",
              "      <th>Country</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>Self-employed, male (% of male employment) (mo...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>50.139999</td>\n",
              "      <td>50.16</td>\n",
              "      <td>50.830002</td>\n",
              "      <td>52.18</td>\n",
              "      <td>54.560001</td>\n",
              "      <td>58.48</td>\n",
              "      <td>57.66</td>\n",
              "      <td>56.93</td>\n",
              "      <td>56.0</td>\n",
              "      <td>56.509998</td>\n",
              "      <td>56.84</td>\n",
              "      <td>56.689999</td>\n",
              "      <td>57.060001</td>\n",
              "      <td>57.48</td>\n",
              "      <td>57.25</td>\n",
              "      <td>56.98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e36d330-6d53-4b58-adfc-cc11857794fb')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7e36d330-6d53-4b58-adfc-cc11857794fb 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-7e36d330-6d53-4b58-adfc-cc11857794fb');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melt the data to transform data structure\n",
        "melted_emp_df = pd.melt(emp_df, id_vars=['Series', 'Country'], \n",
        "                    var_name='Year', value_name='Value')\n",
        "melted_emp_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buPxnSSRxReJ",
        "outputId": "239bf1a8-3987-4c37-99d7-19b02654b0c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Series   Country  Year  \\\n",
              "1787  Self-employed, male (% of male employment) (mo...   Senegal  2018   \n",
              "1788  Self-employed, male (% of male employment) (mo...  Tanzania  2018   \n",
              "1789  Self-employed, male (% of male employment) (mo...    Uganda  2018   \n",
              "1790  Self-employed, male (% of male employment) (mo...    Zambia  2018   \n",
              "1791  Self-employed, male (% of male employment) (mo...  Zimbabwe  2018   \n",
              "\n",
              "          Value  \n",
              "1787  59.840000  \n",
              "1788  80.720001  \n",
              "1789  71.959999  \n",
              "1790  66.660004  \n",
              "1791  56.980000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7bb772ac-c6d9-4eea-aded-48ee10cc04b1\">\n",
              "    <div 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>Series</th>\n",
              "      <th>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1787</th>\n",
              "      <td>Self-employed, male (% of male employment) (mo...</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>2018</td>\n",
              "      <td>59.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1788</th>\n",
              "      <td>Self-employed, male (% of male employment) (mo...</td>\n",
              "      <td>Tanzania</td>\n",
              "      <td>2018</td>\n",
              "      <td>80.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1789</th>\n",
              "      <td>Self-employed, male (% of male employment) (mo...</td>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>71.959999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1790</th>\n",
              "      <td>Self-employed, male (% of male employment) (mo...</td>\n",
              "      <td>Zambia</td>\n",
              "      <td>2018</td>\n",
              "      <td>66.660004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1791</th>\n",
              "      <td>Self-employed, male (% of male employment) (mo...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>56.980000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7bb772ac-c6d9-4eea-aded-48ee10cc04b1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-7bb772ac-c6d9-4eea-aded-48ee10cc04b1 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-7bb772ac-c6d9-4eea-aded-48ee10cc04b1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pivot the data with the rows: year and columns: indicator\n",
        "\n",
        "pivoted_emp_df = pd.pivot_table(melted_emp_df, index=['Country','Year'], \n",
        "                            columns='Series', values='Value')\n",
        "pivoted_emp_df.columns = ['Informal female', 'Informal male', \n",
        "                          'Formal female', 'Formal male']\n",
        "pivoted_emp_df = pivoted_emp_df.reset_index()\n",
        "pivoted_emp_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVs1zoh2xXv_",
        "outputId": "5f02dce4-b0fb-402e-d5f9-ff21aa5f3df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country  Year  Informal female  Informal male  Formal female  Formal male\n",
              "0  Armenia  2003        53.880001      43.919998      46.119999    56.080002\n",
              "1  Armenia  2004        53.020000      43.160000      46.980000    56.840000\n",
              "2  Armenia  2005        52.200001      41.939999      47.799999    58.060001\n",
              "3  Armenia  2006        50.889999      40.590000      49.110001    59.410000\n",
              "4  Armenia  2007        49.169998      38.860001      50.840000    61.139999"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1c49963-bdfc-488e-8988-8e97a5b7d537\">\n",
              "    <div 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>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>Informal female</th>\n",
              "      <th>Informal male</th>\n",
              "      <th>Formal female</th>\n",
              "      <th>Formal male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2003</td>\n",
              "      <td>53.880001</td>\n",
              "      <td>43.919998</td>\n",
              "      <td>46.119999</td>\n",
              "      <td>56.080002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2004</td>\n",
              "      <td>53.020000</td>\n",
              "      <td>43.160000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>56.840000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2005</td>\n",
              "      <td>52.200001</td>\n",
              "      <td>41.939999</td>\n",
              "      <td>47.799999</td>\n",
              "      <td>58.060001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2006</td>\n",
              "      <td>50.889999</td>\n",
              "      <td>40.590000</td>\n",
              "      <td>49.110001</td>\n",
              "      <td>59.410000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2007</td>\n",
              "      <td>49.169998</td>\n",
              "      <td>38.860001</td>\n",
              "      <td>50.840000</td>\n",
              "      <td>61.139999</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1c49963-bdfc-488e-8988-8e97a5b7d537')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e1c49963-bdfc-488e-8988-8e97a5b7d537 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-e1c49963-bdfc-488e-8988-8e97a5b7d537');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_emp_df.to_excel(\"Clean_formal_employer.xlsx\", index=False)"
      ],
      "metadata": {
        "id": "trws0Ejpyesn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RxNDsZr_M6z"
      },
      "source": [
        "## Prepare Decision Making Factor Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "yqDd7Ll3ePT7",
        "outputId": "a898c6f0-a5c0-47f0-9cce-38b5f9cc977c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unknown extension is not supported and will be removed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Series Name        Series Code  \\\n",
              "0  Employers, female (% of female employment) (mo...  SL.EMP.MPYR.FE.ZS   \n",
              "\n",
              "  Country Name Country Code  2003 [YR2003]  2004 [YR2004]  2005 [YR2005]  \\\n",
              "0      Armenia          ARM           0.05           0.06           0.07   \n",
              "\n",
              "   2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  2010 [YR2010]  \\\n",
              "0           0.08           0.09           0.09           0.09           0.13   \n",
              "\n",
              "   2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  2014 [YR2014]  2015 [YR2015]  \\\n",
              "0           0.29           0.23           0.18           0.56           0.34   \n",
              "\n",
              "   2016 [YR2016]  2017 [YR2017]  2018 [YR2018]  \n",
              "0            0.4           0.52           0.31  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-432342b5-c78d-41ea-8b9e-189fa8655ec7\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Employers, female (% of female employment) (mo...</td>\n",
              "      <td>SL.EMP.MPYR.FE.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-432342b5-c78d-41ea-8b9e-189fa8655ec7')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-432342b5-c78d-41ea-8b9e-189fa8655ec7 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-432342b5-c78d-41ea-8b9e-189fa8655ec7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df_factor = load_data(\"1tFGpPXsRu4VSi0NO_nrSKjRe0xQCgzyn\") # Decision making factor\n",
        "df_factor.head(1) # Preview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7vZh890m6UpN"
      },
      "outputs": [],
      "source": [
        "# Split dataframe to 9 different indicators\n",
        "# These indicators have a high correlation with decision making\n",
        "# And will be used in imputation regression\n",
        "factors = {\"employers\":0, \"literacy\":0, \"parliement\":0, \"ministerial\":0, \"primary\":0, \n",
        "            \"secondary\":0, \"tertiary\":0, \"startup_proc\":0, \"time_business\":0}\n",
        "\n",
        "for key ,n in zip(factors.keys(),range(df_factor['Series Name'].nunique())):\n",
        "    factor = df_factor['Series Name']\n",
        "    factors[key] = df_factor[factor == factor.unique()[n]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivn_8Gwk7USm",
        "outputId": "c94aa88a-c427-4f48-d9db-d7335c031184"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "employers : 0\n",
            "literacy : 33\n",
            "parliement : 323\n",
            "ministerial : 15\n",
            "primary : 229\n",
            "secondary : 208\n",
            "tertiary : 236\n",
            "startup_proc : 132\n",
            "time_business : 33\n"
          ]
        }
      ],
      "source": [
        "# These indicators have some missing value\n",
        "for i,j in factors.items():\n",
        "  print(i,\":\",j.isnull().sum().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "2Xwbr2DqGfsl",
        "outputId": "10161823-57da-4d58-e46a-bb1acf3345fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Series Name, Series Code, Country Name, Country Code, 2003 [YR2003], 2004 [YR2004], 2005 [YR2005], 2006 [YR2006], 2007 [YR2007], 2008 [YR2008], 2009 [YR2009], 2010 [YR2010], 2011 [YR2011], 2012 [YR2012], 2013 [YR2013], 2014 [YR2014], 2015 [YR2015], 2016 [YR2016], 2017 [YR2017], 2018 [YR2018]]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2ad6a0d-a31e-41d9-b58e-7a1ada5a8778\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2ad6a0d-a31e-41d9-b58e-7a1ada5a8778')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c2ad6a0d-a31e-41d9-b58e-7a1ada5a8778 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-c2ad6a0d-a31e-41d9-b58e-7a1ada5a8778');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "# There is no row that is empty on all of it\n",
        "df_factor[df_factor.iloc[:,4:].isnull().sum(axis=1)==df_factor.shape[1]-4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDxYbOdE-n1u",
        "outputId": "8271be7e-b0c8-44f1-f701-ee8a9419ab4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[IterativeImputer] Early stopping criterion not reached.\n",
            "[IterativeImputer] Early stopping criterion not reached.\n",
            "[IterativeImputer] Early stopping criterion not reached.\n",
            "[IterativeImputer] Early stopping criterion not reached.\n",
            "[IterativeImputer] Early stopping criterion not reached.\n",
            "[IterativeImputer] Early stopping criterion not reached.\n"
          ]
        }
      ],
      "source": [
        "# Apply iterative imputer with linear regression to impute missing value\n",
        "imputed_factors = {}\n",
        "\n",
        "for key,value in factors.items():\n",
        "  tmp = value.iloc[:,4:].T\n",
        "  imp_mean = IterativeImputer(LinearRegression(), random_state=0, max_iter=1000,\n",
        "                              min_value=0, max_value=100)\n",
        "  clean_tmp = pd.DataFrame(imp_mean.fit_transform(tmp), \n",
        "                          columns=tmp.columns, \n",
        "                          index=tmp.index)\n",
        "  imputed_factors[key] = clean_tmp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X6kennEya37Y"
      },
      "outputs": [],
      "source": [
        "# Merge all imputed dataframe\n",
        "imputed_value = pd.concat([imputed_factors['employers'].T, \n",
        "                imputed_factors['literacy'].T, imputed_factors['parliement'].T, \n",
        "                imputed_factors['ministerial'].T, imputed_factors['primary'].T,\n",
        "                imputed_factors['secondary'].T, imputed_factors['tertiary'].T,\n",
        "                imputed_factors['startup_proc'].T, imputed_factors['time_business'].T])\n",
        "imputed_df_factor = pd.concat([df_factor.iloc[:,:4], imputed_value], axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rRcrVSG5j3Se"
      },
      "outputs": [],
      "source": [
        "# Save it\n",
        "imputed_df_factor.to_excel(\"imputed_decision_making_factor.xlsx\", index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Decision Making Data"
      ],
      "metadata": {
        "id": "ttJk_26oXtlP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kjjmAB-i1nlM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08956dd4-dad7-49fa-94fe-81d6155f6d53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unknown extension is not supported and will be removed\n"
          ]
        }
      ],
      "source": [
        "df_factor_clean = load_data(\"1wrGSl4DOW1Es5wrkoTqGa-pRPRGsGJwo\") # Load imputed decision making factor\n",
        "df = load_data(\"1PLb0Ayg5fRLQiBn2bD7J52m_T8ahjXKm\") # Load decision making\n",
        "df_all = pd.concat([df_factor_clean, df], ignore_index=True) # Merge it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "yG4JfH2j1rvo",
        "outputId": "be377e4d-1442-4de1-ee83-1e9cecd44426"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Series Name     Series Code  \\\n",
              "923  Women, Business and the Law: Mobility Indicato...  SG.LAW.INDX.MO   \n",
              "\n",
              "    Country Name Country Code  2003 [YR2003]  2004 [YR2004]  2005 [YR2005]  \\\n",
              "923     Zimbabwe          ZWE          100.0          100.0          100.0   \n",
              "\n",
              "     2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  \\\n",
              "923          100.0          100.0          100.0          100.0   \n",
              "\n",
              "     2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  \\\n",
              "923          100.0          100.0          100.0          100.0   \n",
              "\n",
              "     2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  2018 [YR2018]  \n",
              "923          100.0          100.0          100.0          100.0          100.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-76a2d3ea-037d-49cd-81d4-ebcf9f882293\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>923</th>\n",
              "      <td>Women, Business and the Law: Mobility Indicato...</td>\n",
              "      <td>SG.LAW.INDX.MO</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76a2d3ea-037d-49cd-81d4-ebcf9f882293')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-76a2d3ea-037d-49cd-81d4-ebcf9f882293 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-76a2d3ea-037d-49cd-81d4-ebcf9f882293');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df_all.tail(1) # Preview"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# No rows that contain all missing value\n",
        "df[df.iloc[:,4:].isnull().sum(axis=1)==df.shape[1]-4]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "x7AFLjjOs68t",
        "outputId": "be665c2f-92d1-4a03-8d7d-1197d2d8e54f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Series Name, Series Code, Country Name, Country Code, 2003 [YR2003], 2004 [YR2004], 2005 [YR2005], 2006 [YR2006], 2007 [YR2007], 2008 [YR2008], 2009 [YR2009], 2010 [YR2010], 2011 [YR2011], 2012 [YR2012], 2013 [YR2013], 2014 [YR2014], 2015 [YR2015], 2016 [YR2016], 2017 [YR2017], 2018 [YR2018]]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9548d42a-feb2-4c3a-af5c-9b7d9ea19d51\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9548d42a-feb2-4c3a-af5c-9b7d9ea19d51')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9548d42a-feb2-4c3a-af5c-9b7d9ea19d51 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-9548d42a-feb2-4c3a-af5c-9b7d9ea19d51');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PNl0aINbkDTl"
      },
      "outputs": [],
      "source": [
        "# Split dataframe based on countries\n",
        "countries = {}\n",
        "for i in df['Country Name'].unique():\n",
        "  countries[i] = df_all[df_all['Country Name']==i]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UKSUC2GcDy0n",
        "outputId": "1dc81b14-f0be-4400-9dfe-6f341aa65197"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[IterativeImputer] Early stopping criterion not reached.\n",
            "[IterativeImputer] Early stopping criterion not reached.\n"
          ]
        }
      ],
      "source": [
        "# Apply iterative imputer to impute missing value with decision making factor\n",
        "\n",
        "imputed_countries = {}\n",
        "\n",
        "for key,value in countries.items():\n",
        "  tmp = value.iloc[:,4:].T\n",
        "  imp_mean = IterativeImputer(LinearRegression(), random_state=0, max_iter=1000,\n",
        "                              min_value=0, max_value=100)\n",
        "  clean_tmp = pd.DataFrame(imp_mean.fit_transform(tmp), \n",
        "                          columns=tmp.columns, \n",
        "                          index=tmp.index)\n",
        "  imputed_countries[key] = clean_tmp.T"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example imputed data\n",
        "temp = imputed_countries[list(imputed_countries.keys())[0]]\n",
        "temp.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "SLV3SC52v0eG",
        "outputId": "01db1665-55d0-4cd0-a99a-3658b3d58311"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   2003 [YR2003]  2004 [YR2004]  2005 [YR2005]  2006 [YR2006]  2007 [YR2007]  \\\n",
              "0       0.050000       0.060000       0.070000       0.080000       0.090000   \n",
              "1      99.689119      99.691432      99.686960      99.691262      99.696851   \n",
              "2       4.580153       5.343511       5.343511       5.343511       9.160305   \n",
              "3       9.876856       9.876856      11.114847       9.876856       9.876856   \n",
              "4      83.048990      85.151363      82.180230      84.907010      89.249420   \n",
              "\n",
              "   2008 [YR2008]  2009 [YR2009]  2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  \\\n",
              "0       0.090000       0.090000       0.130000       0.290000       0.230000   \n",
              "1      99.690515      99.691436      99.680781      99.690353      99.695075   \n",
              "2       8.396947       9.160305       9.160305       8.396947      10.687023   \n",
              "3       5.900000       9.876856      11.100000       9.876856      10.500000   \n",
              "4      87.073452      86.938194      90.478953      95.761670      96.881210   \n",
              "\n",
              "   2013 [YR2013]  2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  \\\n",
              "0       0.180000       0.560000       0.340000       0.400000       0.520000   \n",
              "1      99.688376      99.690998      99.688031      99.682833      99.677162   \n",
              "2      10.687023      10.687023      10.687023      10.687023      18.095238   \n",
              "3       9.876856      11.100000      11.100000      11.100000       9.876856   \n",
              "4      95.294510      94.175950      93.291420      92.500960      91.915110   \n",
              "\n",
              "   2018 [YR2018]  \n",
              "0       0.310000  \n",
              "1      99.680177  \n",
              "2      18.095238  \n",
              "3       7.100000  \n",
              "4      90.545800  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7ec5fe7-1e20-4079-880f-f959a3f3650c\">\n",
              "    <div 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>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.130000</td>\n",
              "      <td>0.290000</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>0.180000</td>\n",
              "      <td>0.560000</td>\n",
              "      <td>0.340000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.310000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>99.689119</td>\n",
              "      <td>99.691432</td>\n",
              "      <td>99.686960</td>\n",
              "      <td>99.691262</td>\n",
              "      <td>99.696851</td>\n",
              "      <td>99.690515</td>\n",
              "      <td>99.691436</td>\n",
              "      <td>99.680781</td>\n",
              "      <td>99.690353</td>\n",
              "      <td>99.695075</td>\n",
              "      <td>99.688376</td>\n",
              "      <td>99.690998</td>\n",
              "      <td>99.688031</td>\n",
              "      <td>99.682833</td>\n",
              "      <td>99.677162</td>\n",
              "      <td>99.680177</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.580153</td>\n",
              "      <td>5.343511</td>\n",
              "      <td>5.343511</td>\n",
              "      <td>5.343511</td>\n",
              "      <td>9.160305</td>\n",
              "      <td>8.396947</td>\n",
              "      <td>9.160305</td>\n",
              "      <td>9.160305</td>\n",
              "      <td>8.396947</td>\n",
              "      <td>10.687023</td>\n",
              "      <td>10.687023</td>\n",
              "      <td>10.687023</td>\n",
              "      <td>10.687023</td>\n",
              "      <td>10.687023</td>\n",
              "      <td>18.095238</td>\n",
              "      <td>18.095238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.876856</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>11.114847</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>5.900000</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>10.500000</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>11.100000</td>\n",
              "      <td>9.876856</td>\n",
              "      <td>7.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>83.048990</td>\n",
              "      <td>85.151363</td>\n",
              "      <td>82.180230</td>\n",
              "      <td>84.907010</td>\n",
              "      <td>89.249420</td>\n",
              "      <td>87.073452</td>\n",
              "      <td>86.938194</td>\n",
              "      <td>90.478953</td>\n",
              "      <td>95.761670</td>\n",
              "      <td>96.881210</td>\n",
              "      <td>95.294510</td>\n",
              "      <td>94.175950</td>\n",
              "      <td>93.291420</td>\n",
              "      <td>92.500960</td>\n",
              "      <td>91.915110</td>\n",
              "      <td>90.545800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7ec5fe7-1e20-4079-880f-f959a3f3650c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b7ec5fe7-1e20-4079-880f-f959a3f3650c 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-b7ec5fe7-1e20-4079-880f-f959a3f3650c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge the countries dataframe into one\n",
        "clean_df = imputed_countries[list(imputed_countries.keys())[0]]\n",
        "for key,value in list(imputed_countries.items())[1:]:\n",
        "  clean_df = clean_df.append(pd.DataFrame(value, columns=clean_df.columns))"
      ],
      "metadata": {
        "id": "Q29v7YdPvV6K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split into the only imputed decision making\n",
        "imputed_decision_making = pd.merge(df_all.iloc[:,:4], clean_df, left_index=True, right_index=True)[252:]\n",
        "imputed_decision_making = imputed_decision_making.sort_values(by=['Country Name','Series Name'])\n",
        "imputed_decision_making.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "F1ftE3wVwLlw",
        "outputId": "2a9fbb67-4476-4bae-9b48-ef2f2c71503a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Series Name        Series Code  \\\n",
              "252  Decision maker about a woman's own health care...  SG.DMK.HLTH.HB.ZS   \n",
              "\n",
              "    Country Name Country Code  2003 [YR2003]  2004 [YR2004]  2005 [YR2005]  \\\n",
              "252      Armenia          ARM       7.987721       7.465325            7.6   \n",
              "\n",
              "     2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  \\\n",
              "252       6.876721       5.854465       6.016163       5.030415   \n",
              "\n",
              "     2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  \\\n",
              "252            4.5       2.971544       3.340955       3.154099   \n",
              "\n",
              "     2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  2018 [YR2018]  \n",
              "252       3.083432       3.110971            2.6       1.946455       1.540362  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1f45b28-1fc4-411e-aa14-488c01fa91d7\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>Decision maker about a woman's own health care...</td>\n",
              "      <td>SG.DMK.HLTH.HB.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>7.987721</td>\n",
              "      <td>7.465325</td>\n",
              "      <td>7.6</td>\n",
              "      <td>6.876721</td>\n",
              "      <td>5.854465</td>\n",
              "      <td>6.016163</td>\n",
              "      <td>5.030415</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2.971544</td>\n",
              "      <td>3.340955</td>\n",
              "      <td>3.154099</td>\n",
              "      <td>3.083432</td>\n",
              "      <td>3.110971</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1.946455</td>\n",
              "      <td>1.540362</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1f45b28-1fc4-411e-aa14-488c01fa91d7')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a1f45b28-1fc4-411e-aa14-488c01fa91d7 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-a1f45b28-1fc4-411e-aa14-488c01fa91d7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exclude row that contain this code because we won't use it\n",
        "exclude = [\"SG.DMK.HLTH.OT.ZS\", \"SG.DMK.HLTH.SE.ZS\",\n",
        "           \"SG.DMK.VISI.OT.ZS\", \"SG.DMK.VISI.SE.ZS\",\n",
        "           \"SG.DMK.PRCH.OT.ZS\", \"SG.DMK.PRCH.SE.ZS\",\n",
        "           \"SG.DMK.DPCH.FN.ZS\"]\n",
        "imputed_decision_making = imputed_decision_making[~imputed_decision_making['Series Code'].isin(exclude)]"
      ],
      "metadata": {
        "id": "QgMH9YKCeHmb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Xoj2E0oERhT"
      },
      "outputs": [],
      "source": [
        "# Save it\n",
        "imputed_decision_making.to_excel(\"imputed_decision_making.xlsx\", index=False) "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Process Decision Making Data"
      ],
      "metadata": {
        "id": "7OCwvAUI0fvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "imputed_decision_making = load_data(\"1eGWaYuIFaj1JF7jPuJLf0Alcqbnljztf\") # Imputed decision making"
      ],
      "metadata": {
        "id": "VahvkrryEn7w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6617463-4861-4a39-e209-f24302c197d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unknown extension is not supported and will be removed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "imputed_decision_making.tail(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "73-jtGtLetEh",
        "outputId": "9a945608-3ca7-4184-9899-17ac42d9fd15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Series Name     Series Code  \\\n",
              "475  Women, Business and the Law: Mobility Indicato...  SG.LAW.INDX.MO   \n",
              "\n",
              "    Country Name Country Code  2003 [YR2003]  2004 [YR2004]  2005 [YR2005]  \\\n",
              "475     Zimbabwe          ZWE          100.0          100.0          100.0   \n",
              "\n",
              "     2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  \\\n",
              "475          100.0          100.0          100.0          100.0   \n",
              "\n",
              "     2010 [YR2010]  2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  \\\n",
              "475          100.0          100.0          100.0          100.0   \n",
              "\n",
              "     2014 [YR2014]  2015 [YR2015]  2016 [YR2016]  2017 [YR2017]  2018 [YR2018]  \n",
              "475          100.0          100.0          100.0          100.0          100.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e2f5488b-a85a-4e94-babe-16ae0869629d\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>Women, Business and the Law: Mobility Indicato...</td>\n",
              "      <td>SG.LAW.INDX.MO</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>ZWE</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2f5488b-a85a-4e94-babe-16ae0869629d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e2f5488b-a85a-4e94-babe-16ae0869629d 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-e2f5488b-a85a-4e94-babe-16ae0869629d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataframe that contains series name decision maker\n",
        "process_df = imputed_decision_making[imputed_decision_making['Series Name']\\\n",
        "                                     .str.contains(\"Decision maker\")]\n",
        "process_df['Series Code'] = process_df['Series Code'].str[:-6] # Shorten series code"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vA91Uazrz8ve",
        "outputId": "8109bd8d-3a3f-49ed-c5e7-3f8be2a2e1ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The indicator sum must be 100, if it is not 100 , we will process it\n",
        "# We group by name and code and sum it\n",
        "tmp = (process_df.groupby(['Country Name','Series Code'])[process_df.columns[4:]].sum()).reset_index()\n",
        "tmp.columns = ['Country Name','Series Code'] + ['Total '+str(i) for i in range(2003,2019)]\n",
        "tmp = pd.concat([tmp for i in range(3)], ignore_index=True).sort_values(by=['Country Name','Series Code'])"
      ],
      "metadata": {
        "id": "3Hj3as_q0kt8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the indicator with sum to 100\n",
        "standardize_df = pd.DataFrame(process_df.iloc[:,4:].values/tmp.iloc[:,2:].values*100, \n",
        "                               columns=process_df.iloc[:,4:].columns)\n",
        "standardize_df = pd.concat([imputed_decision_making[imputed_decision_making['Series Name'].str\\\n",
        "                            .contains(\"Decision maker\")].reset_index(drop=True)\\\n",
        "                            .iloc[:,:4],standardize_df],axis=1)\n",
        "standardize_df = pd.concat([imputed_decision_making[imputed_decision_making['Series Code']\\\n",
        "                             .isin([\"SG.VAW.REAS.ZS\",\"SG.LAW.INDX.MR\",\"SG.LAW.INDX.MO\"])],\n",
        "                             standardize_df])\n",
        "standardize_df = pd.concat([imputed_decision_making[imputed_decision_making['Series Name'].str\\\n",
        "                            .contains(\"Women participating\")],\n",
        "                             standardize_df]).sort_values(by=['Country Name','Series Name'])\n",
        "\n",
        "standardize_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "okqsSXdjPSG-",
        "outputId": "5b407667-27e6-4fe7-daa7-6e84789f8eaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Series Name        Series Code  \\\n",
              "0  Decision maker about a woman's own health care...  SG.DMK.HLTH.HB.ZS   \n",
              "1  Decision maker about a woman's own health care...  SG.DMK.HLTH.WF.ZS   \n",
              "2  Decision maker about a woman's own health care...  SG.DMK.HLTH.WH.ZS   \n",
              "3  Decision maker about a woman's visits to her f...  SG.DMK.VISI.HB.ZS   \n",
              "4  Decision maker about a woman's visits to her f...  SG.DMK.VISI.WF.ZS   \n",
              "\n",
              "  Country Name Country Code  2003 [YR2003]  2004 [YR2004]  2005 [YR2005]  \\\n",
              "0      Armenia          ARM       7.954604       7.624269       7.794872   \n",
              "1      Armenia          ARM      33.875563      35.788608      34.153846   \n",
              "2      Armenia          ARM      58.169833      56.587123      58.051282   \n",
              "3      Armenia          ARM       8.184104       7.921424       7.959641   \n",
              "4      Armenia          ARM      13.996048      14.931040      14.237668   \n",
              "\n",
              "   2006 [YR2006]  2007 [YR2007]  2008 [YR2008]  2009 [YR2009]  2010 [YR2010]  \\\n",
              "0       7.020363       5.768802       6.376112       5.111847       4.559271   \n",
              "1      28.376986      21.965737      25.125584      20.637859      19.148936   \n",
              "2      64.602652      72.265461      68.498304      74.250295      76.291793   \n",
              "3       7.107316       6.024526       6.312088       5.385531       4.907306   \n",
              "4      12.411880      10.533510      11.549129      10.350202      10.032715   \n",
              "\n",
              "   2011 [YR2011]  2012 [YR2012]  2013 [YR2013]  2014 [YR2014]  2015 [YR2015]  \\\n",
              "0       2.905030       2.794120       2.576851       2.648035       2.823902   \n",
              "1      19.504959      20.170085      22.958984      24.461449      29.242618   \n",
              "2      77.590011      77.035795      74.464165      72.890516      67.933480   \n",
              "3       3.767840       4.119928       4.120061       4.142967       4.340213   \n",
              "4      10.783107      10.926026      12.014511      12.582855      14.379541   \n",
              "\n",
              "   2016 [YR2016]  2017 [YR2017]  2018 [YR2018]  \n",
              "0       2.636917       1.758326       1.823633  \n",
              "1      28.397566      22.532840      31.488574  \n",
              "2      68.965517      75.708835      66.687793  \n",
              "3       3.881279       3.225282       3.133291  \n",
              "4      14.269406      12.295661      15.969965  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c15936dd-c806-41de-b46b-b94e3af0668b\">\n",
              "    <div 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>Series Name</th>\n",
              "      <th>Series Code</th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>2003 [YR2003]</th>\n",
              "      <th>2004 [YR2004]</th>\n",
              "      <th>2005 [YR2005]</th>\n",
              "      <th>2006 [YR2006]</th>\n",
              "      <th>2007 [YR2007]</th>\n",
              "      <th>2008 [YR2008]</th>\n",
              "      <th>2009 [YR2009]</th>\n",
              "      <th>2010 [YR2010]</th>\n",
              "      <th>2011 [YR2011]</th>\n",
              "      <th>2012 [YR2012]</th>\n",
              "      <th>2013 [YR2013]</th>\n",
              "      <th>2014 [YR2014]</th>\n",
              "      <th>2015 [YR2015]</th>\n",
              "      <th>2016 [YR2016]</th>\n",
              "      <th>2017 [YR2017]</th>\n",
              "      <th>2018 [YR2018]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision maker about a woman's own health care...</td>\n",
              "      <td>SG.DMK.HLTH.HB.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>7.954604</td>\n",
              "      <td>7.624269</td>\n",
              "      <td>7.794872</td>\n",
              "      <td>7.020363</td>\n",
              "      <td>5.768802</td>\n",
              "      <td>6.376112</td>\n",
              "      <td>5.111847</td>\n",
              "      <td>4.559271</td>\n",
              "      <td>2.905030</td>\n",
              "      <td>2.794120</td>\n",
              "      <td>2.576851</td>\n",
              "      <td>2.648035</td>\n",
              "      <td>2.823902</td>\n",
              "      <td>2.636917</td>\n",
              "      <td>1.758326</td>\n",
              "      <td>1.823633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision maker about a woman's own health care...</td>\n",
              "      <td>SG.DMK.HLTH.WF.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>33.875563</td>\n",
              "      <td>35.788608</td>\n",
              "      <td>34.153846</td>\n",
              "      <td>28.376986</td>\n",
              "      <td>21.965737</td>\n",
              "      <td>25.125584</td>\n",
              "      <td>20.637859</td>\n",
              "      <td>19.148936</td>\n",
              "      <td>19.504959</td>\n",
              "      <td>20.170085</td>\n",
              "      <td>22.958984</td>\n",
              "      <td>24.461449</td>\n",
              "      <td>29.242618</td>\n",
              "      <td>28.397566</td>\n",
              "      <td>22.532840</td>\n",
              "      <td>31.488574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision maker about a woman's own health care...</td>\n",
              "      <td>SG.DMK.HLTH.WH.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>58.169833</td>\n",
              "      <td>56.587123</td>\n",
              "      <td>58.051282</td>\n",
              "      <td>64.602652</td>\n",
              "      <td>72.265461</td>\n",
              "      <td>68.498304</td>\n",
              "      <td>74.250295</td>\n",
              "      <td>76.291793</td>\n",
              "      <td>77.590011</td>\n",
              "      <td>77.035795</td>\n",
              "      <td>74.464165</td>\n",
              "      <td>72.890516</td>\n",
              "      <td>67.933480</td>\n",
              "      <td>68.965517</td>\n",
              "      <td>75.708835</td>\n",
              "      <td>66.687793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decision maker about a woman's visits to her f...</td>\n",
              "      <td>SG.DMK.VISI.HB.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>8.184104</td>\n",
              "      <td>7.921424</td>\n",
              "      <td>7.959641</td>\n",
              "      <td>7.107316</td>\n",
              "      <td>6.024526</td>\n",
              "      <td>6.312088</td>\n",
              "      <td>5.385531</td>\n",
              "      <td>4.907306</td>\n",
              "      <td>3.767840</td>\n",
              "      <td>4.119928</td>\n",
              "      <td>4.120061</td>\n",
              "      <td>4.142967</td>\n",
              "      <td>4.340213</td>\n",
              "      <td>3.881279</td>\n",
              "      <td>3.225282</td>\n",
              "      <td>3.133291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Decision maker about a woman's visits to her f...</td>\n",
              "      <td>SG.DMK.VISI.WF.ZS</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>ARM</td>\n",
              "      <td>13.996048</td>\n",
              "      <td>14.931040</td>\n",
              "      <td>14.237668</td>\n",
              "      <td>12.411880</td>\n",
              "      <td>10.533510</td>\n",
              "      <td>11.549129</td>\n",
              "      <td>10.350202</td>\n",
              "      <td>10.032715</td>\n",
              "      <td>10.783107</td>\n",
              "      <td>10.926026</td>\n",
              "      <td>12.014511</td>\n",
              "      <td>12.582855</td>\n",
              "      <td>14.379541</td>\n",
              "      <td>14.269406</td>\n",
              "      <td>12.295661</td>\n",
              "      <td>15.969965</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c15936dd-c806-41de-b46b-b94e3af0668b')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c15936dd-c806-41de-b46b-b94e3af0668b 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-c15936dd-c806-41de-b46b-b94e3af0668b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the clean decision making\n",
        "standardize_df.to_excel(\"Clean_decision_making.xlsx\", index=False)"
      ],
      "metadata": {
        "id": "88l0e__qVFoN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transform Decision Making Data"
      ],
      "metadata": {
        "id": "YylyNyvtoztQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clean_decision_making = load_data(\"1XWIKfYJcDBBJCd4Rx-lFUGQ0WLVEWL7F\") # Cleaned decision making\n",
        "clean_decision_making.drop(['Series Code','Country Code'], axis=1, inplace=True)\n",
        "clean_decision_making.columns = ['Series', 'Country'] + [str(i) for i in range(2003,2019)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htjzm-dbo6Bd",
        "outputId": "ea73ca81-7bb8-4828-8dbf-e8fe11e4123a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unknown extension is not supported and will be removed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_decision_making.tail(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "HGdMVAqvpD5q",
        "outputId": "4881574f-d58f-4dfe-86e7-d51742c8aa36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Series   Country   2003  \\\n",
              "475  Women, Business and the Law: Mobility Indicato...  Zimbabwe  100.0   \n",
              "\n",
              "      2004   2005   2006   2007   2008   2009   2010   2011   2012   2013  \\\n",
              "475  100.0  100.0  100.0  100.0  100.0  100.0  100.0  100.0  100.0  100.0   \n",
              "\n",
              "      2014   2015   2016   2017   2018  \n",
              "475  100.0  100.0  100.0  100.0  100.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e7546bb-f79b-4e99-abb3-f6a542b58038\">\n",
              "    <div 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>Series</th>\n",
              "      <th>Country</th>\n",
              "      <th>2003</th>\n",
              "      <th>2004</th>\n",
              "      <th>2005</th>\n",
              "      <th>2006</th>\n",
              "      <th>2007</th>\n",
              "      <th>2008</th>\n",
              "      <th>2009</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>Women, Business and the Law: Mobility Indicato...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e7546bb-f79b-4e99-abb3-f6a542b58038')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9e7546bb-f79b-4e99-abb3-f6a542b58038 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-9e7546bb-f79b-4e99-abb3-f6a542b58038');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melt the data to be transformed to another data structure\n",
        "melted_df = pd.melt(clean_decision_making, id_vars=['Series', 'Country'], \n",
        "                    var_name='Year', value_name='Value')\n",
        "melted_df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UA7WXRNWpF_2",
        "outputId": "afe3abf5-0636-4201-8571-b360cfc8c0b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Series   Country  Year  \\\n",
              "7611  Women participating in own health care decisio...  Zimbabwe  2018   \n",
              "7612  Women participating in the three decisions (ow...  Zimbabwe  2018   \n",
              "7613  Women who believe a husband is justified in be...  Zimbabwe  2018   \n",
              "7614  Women, Business and the Law: Marriage Indicato...  Zimbabwe  2018   \n",
              "7615  Women, Business and the Law: Mobility Indicato...  Zimbabwe  2018   \n",
              "\n",
              "           Value  \n",
              "7611   76.506525  \n",
              "7612   79.964010  \n",
              "7613   38.602836  \n",
              "7614   80.000000  \n",
              "7615  100.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2c2a455-196c-4252-8975-307effe59bc8\">\n",
              "    <div 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>Series</th>\n",
              "      <th>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7611</th>\n",
              "      <td>Women participating in own health care decisio...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>76.506525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7612</th>\n",
              "      <td>Women participating in the three decisions (ow...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>79.964010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7613</th>\n",
              "      <td>Women who believe a husband is justified in be...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>38.602836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7614</th>\n",
              "      <td>Women, Business and the Law: Marriage Indicato...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>80.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7615</th>\n",
              "      <td>Women, Business and the Law: Mobility Indicato...</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>2018</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2c2a455-196c-4252-8975-307effe59bc8')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c2c2a455-196c-4252-8975-307effe59bc8 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-c2c2a455-196c-4252-8975-307effe59bc8');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pivot the data with rows: year and columns: indicator, and rename the columns\n",
        "\n",
        "pivoted_df = pd.pivot_table(melted_df, index=['Country','Year'], \n",
        "                            columns='Series', values='Value')\n",
        "pivoted_df.columns = ['Health decision: husband','Health decision: wife','Health decision: jointly',\n",
        "                      'Visit decision: husband','Visit decision: wife','Visit decision: jointly',\n",
        "                      'Purchase decision: husband','Purchase decision: wife','Purchase decision: jointly',\n",
        "                      'Visit participation', 'Purchase participation',\n",
        "                      'None participation', 'Health participation', 'All participation',\n",
        "                      'Beating wife', 'Marriage score', 'Mobility score']\n",
        "pivoted_df = pivoted_df.reset_index()\n",
        "pivoted_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "vCiJXX_Tqg81",
        "outputId": "f3756b17-c298-46f7-b158-f28c8d8e4e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country  Year  Health decision: husband  Health decision: wife  \\\n",
              "0  Armenia  2003                  7.954604              33.875563   \n",
              "1  Armenia  2004                  7.624269              35.788608   \n",
              "2  Armenia  2005                  7.794872              34.153846   \n",
              "3  Armenia  2006                  7.020363              28.376986   \n",
              "4  Armenia  2007                  5.768802              21.965737   \n",
              "\n",
              "   Health decision: jointly  Visit decision: husband  Visit decision: wife  \\\n",
              "0                 58.169833                 8.184104             13.996048   \n",
              "1                 56.587123                 7.921424             14.931040   \n",
              "2                 58.051282                 7.959641             14.237668   \n",
              "3                 64.602652                 7.107316             12.411880   \n",
              "4                 72.265461                 6.024526             10.533510   \n",
              "\n",
              "   Visit decision: jointly  Purchase decision: husband  \\\n",
              "0                81.449924                   13.428266   \n",
              "1                83.341575                   13.546999   \n",
              "2                83.295964                   13.071201   \n",
              "3                85.195641                   10.487837   \n",
              "4                86.144927                    7.662980   \n",
              "\n",
              "   Purchase decision: wife  Purchase decision: jointly  Visit participation  \\\n",
              "0                25.640579                   57.428238            86.635522   \n",
              "1                24.418831                   56.201414            87.008241   \n",
              "2                24.442083                   57.279490            87.000000   \n",
              "3                21.930142                   63.079471            88.251409   \n",
              "4                19.327260                   70.377934            89.815449   \n",
              "\n",
              "   Purchase participation  None participation  Health participation  \\\n",
              "0               75.801956            5.418878             90.356630   \n",
              "1               75.780843            5.170527             90.025250   \n",
              "2               76.800000            5.200000             89.900000   \n",
              "3               80.488663            4.511838             91.000906   \n",
              "4               83.493757            3.630303             93.289386   \n",
              "\n",
              "   All participation  Beating wife  Marriage score  Mobility score  \n",
              "0          71.309578     23.243818            80.0           100.0  \n",
              "1          70.738159     22.487122            80.0           100.0  \n",
              "2          71.600000     22.100000            80.0           100.0  \n",
              "3          75.274737     18.049168            80.0           100.0  \n",
              "4          79.240723     13.649558            80.0           100.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-59d2eb9b-e4bc-4aa2-b35d-f8bc6a0a072e\">\n",
              "    <div 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>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>Health decision: husband</th>\n",
              "      <th>Health decision: wife</th>\n",
              "      <th>Health decision: jointly</th>\n",
              "      <th>Visit decision: husband</th>\n",
              "      <th>Visit decision: wife</th>\n",
              "      <th>Visit decision: jointly</th>\n",
              "      <th>Purchase decision: husband</th>\n",
              "      <th>Purchase decision: wife</th>\n",
              "      <th>Purchase decision: jointly</th>\n",
              "      <th>Visit participation</th>\n",
              "      <th>Purchase participation</th>\n",
              "      <th>None participation</th>\n",
              "      <th>Health participation</th>\n",
              "      <th>All participation</th>\n",
              "      <th>Beating wife</th>\n",
              "      <th>Marriage score</th>\n",
              "      <th>Mobility score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2003</td>\n",
              "      <td>7.954604</td>\n",
              "      <td>33.875563</td>\n",
              "      <td>58.169833</td>\n",
              "      <td>8.184104</td>\n",
              "      <td>13.996048</td>\n",
              "      <td>81.449924</td>\n",
              "      <td>13.428266</td>\n",
              "      <td>25.640579</td>\n",
              "      <td>57.428238</td>\n",
              "      <td>86.635522</td>\n",
              "      <td>75.801956</td>\n",
              "      <td>5.418878</td>\n",
              "      <td>90.356630</td>\n",
              "      <td>71.309578</td>\n",
              "      <td>23.243818</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2004</td>\n",
              "      <td>7.624269</td>\n",
              "      <td>35.788608</td>\n",
              "      <td>56.587123</td>\n",
              "      <td>7.921424</td>\n",
              "      <td>14.931040</td>\n",
              "      <td>83.341575</td>\n",
              "      <td>13.546999</td>\n",
              "      <td>24.418831</td>\n",
              "      <td>56.201414</td>\n",
              "      <td>87.008241</td>\n",
              "      <td>75.780843</td>\n",
              "      <td>5.170527</td>\n",
              "      <td>90.025250</td>\n",
              "      <td>70.738159</td>\n",
              "      <td>22.487122</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2005</td>\n",
              "      <td>7.794872</td>\n",
              "      <td>34.153846</td>\n",
              "      <td>58.051282</td>\n",
              "      <td>7.959641</td>\n",
              "      <td>14.237668</td>\n",
              "      <td>83.295964</td>\n",
              "      <td>13.071201</td>\n",
              "      <td>24.442083</td>\n",
              "      <td>57.279490</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>76.800000</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>89.900000</td>\n",
              "      <td>71.600000</td>\n",
              "      <td>22.100000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2006</td>\n",
              "      <td>7.020363</td>\n",
              "      <td>28.376986</td>\n",
              "      <td>64.602652</td>\n",
              "      <td>7.107316</td>\n",
              "      <td>12.411880</td>\n",
              "      <td>85.195641</td>\n",
              "      <td>10.487837</td>\n",
              "      <td>21.930142</td>\n",
              "      <td>63.079471</td>\n",
              "      <td>88.251409</td>\n",
              "      <td>80.488663</td>\n",
              "      <td>4.511838</td>\n",
              "      <td>91.000906</td>\n",
              "      <td>75.274737</td>\n",
              "      <td>18.049168</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2007</td>\n",
              "      <td>5.768802</td>\n",
              "      <td>21.965737</td>\n",
              "      <td>72.265461</td>\n",
              "      <td>6.024526</td>\n",
              "      <td>10.533510</td>\n",
              "      <td>86.144927</td>\n",
              "      <td>7.662980</td>\n",
              "      <td>19.327260</td>\n",
              "      <td>70.377934</td>\n",
              "      <td>89.815449</td>\n",
              "      <td>83.493757</td>\n",
              "      <td>3.630303</td>\n",
              "      <td>93.289386</td>\n",
              "      <td>79.240723</td>\n",
              "      <td>13.649558</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59d2eb9b-e4bc-4aa2-b35d-f8bc6a0a072e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-59d2eb9b-e4bc-4aa2-b35d-f8bc6a0a072e 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-59d2eb9b-e4bc-4aa2-b35d-f8bc6a0a072e');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivoted_df.to_excel(\"Final_decision_making.xlsx\", index=False)"
      ],
      "metadata": {
        "id": "7LFkgPOlwreE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Forecast Woman Participation"
      ],
      "metadata": {
        "id": "j5Pqa79Nw7xn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_df = load_data(\"1w-YMkDywP_Wcz-cVbb3GpkKn85f-f8Rk\") # Participation data to be forecasted\n",
        "forecast_df = final_df[['Country','Year','All participation','None participation']]\n",
        "forecast_df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "857XOOfQxAHs",
        "outputId": "1fb2fd05-cbfc-4e43-dd60-201fe0821ccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Country  Year  All participation  None participation\n",
              "0  Armenia  2003          71.309578            5.418878\n",
              "1  Armenia  2004          70.738159            5.170527"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d644e6da-335f-4935-a4b8-0df4572e5166\">\n",
              "    <div 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>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>All participation</th>\n",
              "      <th>None participation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2003</td>\n",
              "      <td>71.309578</td>\n",
              "      <td>5.418878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>2004</td>\n",
              "      <td>70.738159</td>\n",
              "      <td>5.170527</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d644e6da-335f-4935-a4b8-0df4572e5166')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-d644e6da-335f-4935-a4b8-0df4572e5166 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-d644e6da-335f-4935-a4b8-0df4572e5166');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the data \n",
        "indonesia = forecast_df.query('Country==\"Indonesia\"')\n",
        "indonesia_all = indonesia[[\"Year\",\"All participation\"]].set_index(\"Year\")\n",
        "indonesia_none = indonesia[[\"Year\",\"None participation\"]].set_index(\"Year\")"
      ],
      "metadata": {
        "id": "d65zGl_dx4i7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Stationary Test\n",
        "\n",
        "for i,j in [(indonesia_all,'All participation:'), (indonesia_none,'None participation:')]:\n",
        "  result = adfuller(i.iloc[:,0].dropna())\n",
        "  pvalue = result[1]\n",
        "  if pvalue < 0.05:\n",
        "      print(j,'Stationary')\n",
        "  else:\n",
        "      print(j,'Not Stationary')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiO4jegWyPRy",
        "outputId": "2ace78ff-d22f-40d2-d074-04b11c9ef0df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All participation: Stationary\n",
            "None participation: Not Stationary\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the autocorrelation and the partial autocorrelation\n",
        "plot_acf(indonesia_all.diff().dropna(), lags=indonesia_all.shape[0]-2)\n",
        "plot_pacf(indonesia_all.diff().dropna(), lags=indonesia_all.shape[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "4ko63c0R2xf9",
        "outputId": "87617e72-400c-4242-db2d-fcd2beae8069"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "invalid value encountered in sqrt\n",
            "invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_acf(indonesia_none.diff().dropna(), lags=indonesia_none.shape[0]-2)\n",
        "plot_pacf(indonesia_none.diff().dropna(), lags=indonesia_none.shape[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "w0iBs7TvUrei",
        "outputId": "0dd3098e-59af-4bbb-a6c1-26cc9dd86b31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "invalid value encountered in sqrt\n",
            "invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEICAYAAABCnX+uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAW40lEQVR4nO3dfXBddZ3H8fcnNwZS2higtFAaCE+TbWEkMB0rPnapD6AirOMq6CKsdRlXQWXccRVclnFc1ll3F0TcdRh5sgI+oKzIioLVDsooaylBaWstICVtoSkPMUIDoTff/eOeCzchaZLmnpz8yOc1c6c5597zO9+c3vvJ7/7O756riMDMzNLVUHQBZmY2OQ5yM7PEOcjNzBLnIDczS5yD3MwscQ5yM7PEOchtSkh6WtLh43hcu6SQ1DgVdU1Xks6W9MtJbH+bpLPqWZNNXw5yA0DSw5L6s8DdLulaSbP3sK3Vkj5cuy4iZkfEQ/Wp9oV9PCVprwluF5KOrFcd04GkiyV9s3ZdRJwcEdcVVZNNLQe51TolImYDxwNLgM9NZGNV5P6cktQOvAEI4F1572+yRnp3MdPfcVh9OcjtJSJiK3AbcIykfSXdKmlH1gO+VdLC6mOznvG/SLoL2AmspBKyV2S9+yuyx73QE5b0Dkn3SuqT1C3p4gmW+EHg18C1wJDhg+HvBmqHKCTdma2+L6vtfdn6v5P0gKQnJd0iaUHN9kdLuiO7b7ukC7L1e0m6TNK27HZZ9d2BpGWStkj6R0mPAddkveabJH1TUh9wtqRXSrpK0qOStkr6gqTSSL+wpC9nx6pP0j2S3pCtPwm4AHhf9jvdN/w4SGqQ9DlJmyX1SPqGpFdm91WHss6S9IikxyVdOMH/DyuYg9xeQlIb8HbgXirPkWuAQ4FDgH7gimGbnAmcA8wBzgZ+AZybDaecO8IunqESxq3AO4C/l3TaBEr8IHB9dnubpPnj2Sgi3pj9eGxW27clnQj8K/Be4CBgM/AtAElzgJ8CPwYWAEcCq7I2LgReA3QCxwKvZug7mAOB/agct3OydacCN2W/9/VU/hDtyto9DngrMGRIqsZvsn3tB9wAfFfS3hHxY+AS4NvZ73TsCNuend3+EjgcmM1L/w9fD3QAy4GLJC0apQ6bjiLCN98AHgaeBnqphNl/Ac0jPK4TeKpmeTXw+WGPWQ18eNi6AI4cZd+XAZdmP7dnj20c5bGvB54H5mbLvwfOH23fVALsl6PVAVwF/FvN8uys/XbgDODeUep4EHh7zfLbgIezn5cBA8DeNfdfDNxZszwfeK72GGf7+/lIdY+w/6eo/EGqtv3N0f4PqPzx+WjNfR3Z79hYc7wX1tz/f8DpRT8nfRv/zeN0Vuu0iPhp7QpJs4BLgZOAfbPVcySVIqKcLXdPZCeSlgJfBI4BmoC9gO+Oc/OzgNsj4vFs+YZs3aUTqaHGAmBtdSEinpb0BHAw0EYlsEfbbnPN8uZsXdWOiHh22Da1x+lQ4BXAo5Kq6xoY5VhK+gdgRbaPAFqAuaP+VmPX2kjlj0nVYzU/76TyB80S4aEVG8unqPTglkZEC1AdnlDNY4ZfQnOsS2reANwCtEXEK4GvDWtvRJKaqQyBvEnSY9n48/nAsZKqQwrPALNqNjtwjGa3UQnV6j72AfYHtlIJ1dGmTA7Zjsqw07aa5ZGOQe26bio98rkR0ZrdWiLi6OEbZePhn6byu+8bEa3An3jxmI11vEeqdRewfYztLBEOchvLHCrj4r2S9gP+eRzbbGf0AKy2+WREPCvp1cD7x1nLaUAZWExliKcTWERlTP6D2WO6gHdLmpWdXF0xRm03An8rqTM7WXkJcHdEPAzcChwk6ZPZyc052buJ6nafk3SApLnARcCQKYC7ExGPArcD/yGpJTsheYSkN43w8DlUgncH0CjpIio98trfqX03M4ZuBM6XdJgqU0qrY+q7xluvTW8OchvLZUAz8DiVmSI/Hsc2Xwbek81yuXyE+z8KfF7Sn6kE4HfGWctZwDUR8UhEPFa9UTlx94FsSt+lVMantwPXUTmpWOti4DpJvZLemw0l/RPwPeBR4AjgdICI+DPwFuAUKkMPm6icMAT4ArAG+C3wOyrDM18Y5+9R9UEqQ0vrqYx530TlhOtwP6Fy3P9AZVjkWYYOwVSHpZ6QtJaXuprKbKI7gT9m2583wVptGlOEv1jCzCxl7pGbmSXOQW5mljgHuZlZ4hzkZmaJK+QDQXPnzo329vYidm1mlqx77rnn8Yg4YPj6QoK8vb2dNWvWFLFrM7NkSdo80noPrZiZJc5BbmaWOAe5mVniHORmZolzkJuZJc7XIzczq1EeDFZv7GHdtj6OXtDCso55lBrGvMpyoRzkZmaZ8mBw5lV309XdS/9AmeamEp1traxcsXRah7mHVszMMqs39tDV3cvOgTIB7Bwo09Xdy+qNPUWXtlsOcjOzzLptffQPlIes6x8os35bX0EVjY+D3Mwsc/SCFpqbSkPWNTeVWLygZZQtpgcHuZlZZlnHPDrbWlF5AGKQWdkY+bKOeUWXtlsOcjOzTKlBrFyxlAM2/ZDWLXfxlTOOm/YnOsGzVszMhig1iFm9DzGr9yGWL5pfdDnj4h65mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJa4uQS7pakk9ku6vR3sjKQ8GqzZs5/JVm1i1YTvlwchrV2ZmSWmsUzvXAlcA36hTe0OUB4Mzr7qbru5e+gfKNDeV6GxrZeWKpZQalMcuzcySUZceeUTcCTxZj7ZGsnpjD13dvewcKBPAzoEyXd29rN7Yk9cuzcySMWVj5JLOkbRG0podO3ZMaNt12/roHygPWdc/UGb9tr56lmhmlqQpC/KIuDIilkTEkgMOOGBC2x69oIXmptKQdc1NJRYvaKlniWZmSUpi1sqyjnl0trWi8gDEILOyMfJlHfOKLs3MrHBJBHmpQaxcsZQDNv2Q1i138ZUzjvOJTjOzTL2mH94I/ArokLRF0op6tFur1CBm9T5E69Zfs3zRfIe4mVmmLtMPI+KMerRjZmYTl8TQipmZjc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZomr13d2mpnZbpQHg9Ube1i3rY+jF7SwrGNe3a7i6iA3M8tZ3l8g76EVM7Oc5f0F8g5yM7Oc5f0F8i/roZVfPfhE0SWYWYL6+p8H6pchJYmmxgae2zX4wrp6foG8e+RmZjnrbGvlyHmzYVc+XyDvIDczy1lDg7jg5EXMXv8/uXyB/Mt6aMXMbLpoaBBNTzxAy87NLF80v75t17U1MzObcg5yM7PEOcjNzBLnIDczS5yD3MwscTN+1srgYNDV3cvDTzxD+/770NnWSkOdpgSZmU2FGR3kg4PBJbdt4IGepxnYNUhTYwNHzpvNBScvcpibWTJm9NBKV3cvD/Q8zXO7BgnguV2DPNDzNF3dvUWXZmY2bjM6yB9+4hkGaq59ADCwa5CHn3imoIrMzCZuRgd5+/770NQ49BA0NTbQvv8+BVVkZjZxMzrIh1/IZq9sjLyzrbXo0szMxm1GB3nthWya//gLPn7iUT7RaWbJmdFBDi9eyKZ5810cf+i+DnEzS86MD3Izs9Q5yM3MEucgNzNLXF2CXNJJkjZKekDSZ+rRptloBgeDtZuf4vtrt7B281MMDkbRJZkVatIf0ZdUAr4KvAXYAvxG0i0RsX6ybZsN58sqmL2UIibXm5F0AnBxRLwtW/4sQET862jb7HfoonjLBVdPeF9d93UB0Hls57ge3/fs8+N63Kb19wNw1OJjJlyTTa0/P7uLrb391D5tJTi4tZk5e8/oSwdZHeWVCZvW30+ppHFn2HDf+chr74mIJcPX1+OZfzDQXbO8BVg6/EGSzgHOAZh90BF7tKM9/eXHkleA5/FkyPMJlkK7zz5fZnjfIwKee7486SBP5Rik2G5KtebRXm27LXu/ou7t1qNH/h7gpIj4cLZ8JrA0Is4dbZslS5bEmjVrJrXf8fjVg0/kvo/d+dj73wXAV2+4ZVq3mVK7azc/xeU/28RzNdfI2auxgY+feBTHH7rvpNpO5Rik2G5KtebthCP23+NtJY3YI6/Hyc6tQFvN8sJsnc1wg4PBwP5H0n/o6+p2UrJ6WYW9GhsQ+LIKZtRnaOU3wFGSDqMS4KcD769Du5aw6knJpxefBqVGLv/ZprqclKxeVsFfBmL2okkHeUTsknQu8BOgBFwdEesmXZlNmWrPuTx7Pms3P1WXYKxe653GJmDotd4nOwTS0CCOP3TfSbdTK49jYDZV6nKaPyJ+BPyoHm3Z1Mqr57y7a73XM4DrIa9jYDZV/MnOGW5Iz1kNdfuWpJSu9Z7XMTCbKg7yGS6vb0lK6aSkvynKUudPUMxw1Z5z7XS+evScUzopmdcxyJPH9K2Wgzwhebx4qz3n4R95r0fPOY+TknnI8xjkwWP6NpyDPBGezpef1I5BnjOCLE0O8kSkNp0vNSkdg5RmBNnU8MnORPiEnFWlNCPIpoaDPCf1/ni6X7xWldKMIJsaHlrJQR7j2amdkLP8pDamb/lzkOcgj/Fsv3itVkpj+pY/B3kO8joZ5RevmY3EY+Q58Hi2VeVxKV+z4RzkOfDJKIOh50r6D3sDl/9sE5fctsFhbnXnoZUceDzbwB/csanjIM+Jx7PNH9yxqeKhFbOc+FyJTZWXdY98Ml9yajZZrz5sP+568HG6unvpHyjT3FSis62Vjyw7gtI0HmZraa58y3s9Xz95tJlnu6l5WQe5WZFKDWLliqWs3tjD+m19LF7QwrKOedM6xMuDwc7WwxnYZz6rNmyf9vVahYPcLEelBrF80XyWL5pfdCljKg8GZ151NzuOOoVoaOS8G++ls62VlSuWOsynOY+RmxkAqzf20NXdS5QqX3m3c6BMV3cvqzf2FF2ajcFBbmYArNvWR/9Aeci6/oEy67f1FVSRjZeD3MwAOHpBC81NpSHrmptKLF7QUlBFNl4OcjMDYFnHPDrbWpnVVELArGyWzbKOeUWXZmPwyU4zA9KcZWMVDnIze0FKs2zsRR5aMbNcVeem9x58Aqs2bKfsi4bVnXvkZpYbz02fGu6Rm1luPDd9ajjIzSw3nps+NRzkZpYbz02fGg5yM8uN56ZPDZ/sNLPceG761HCQm1muPDc9fx5aMTNLnIPczCxxkwpySX8taZ2kQUlL6lWUmZmN32R75PcD7wburEMtZma2ByZ1sjMiNgBIPgNtZlYUj5GbmSVuzB65pJ8CB45w14UR8YPx7kjSOcA5AIcccsi4CzQzs90bM8gj4s312FFEXAlcCbBkyRJfx9LMrE48tGJmlrjJTj/8K0lbgBOA/5X0k/qUZWZm4zXZWSs3AzfXqRYzM9sDHloxM0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwS5yA3M0ucg9zMLHEOcjOzxDnIzcwSN6kgl/QlSb+X9FtJN0tqrVdhZmY2PpPtkd8BHBMRrwL+AHx28iWZmdlETCrII+L2iNiVLf4aWDj5kszMbCLqOUb+IeC20e6UdI6kNZLW7Nixo467NTOb2RrHeoCknwIHjnDXhRHxg+wxFwK7gOtHaycirgSuBFiyZEnsUbVmZvYSYwZ5RLx5d/dLOht4J7A8IhzQZmZTbMwg3x1JJwGfBt4UETvrU5KZmU3EZMfIrwDmAHdI6pL0tTrUZGZmEzCpHnlEHFmvQszMbM/4k51mZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmZolzkJuZJc5BbmaWOAe5mVniHORmlqTyYLCz9XB6Dz6BVRu2Ux6MoksqTGPRBZiZTVR5MDjzqrvZcdQpREMj5914L51traxcsZRSg4oub8q5R25myVm9sYeu7l6i1ARqYOdAma7uXlZv7Cm6tEI4yM0sOeu29dE/UB6yrn+gzPptfQVVVCwHuZkl5+gFLTQ3lYasa24qsXhBS0EVFctBbmbJWdYxj862VmY1lRAwq6lEZ1sryzrmFV1aIXyy08ySU2oQK1csZfXGHtZv62PxghaWdcybkSc6wUFuZokqNYjli+azfNH8okspnIdWzMwS5yA3M0ucg9zMLHEOcjOzxDnIzcwSp4ipv9CMpB3A5j3cfC7weB3LyVtK9aZUK6RVb0q1Qlr1plQrTK7eQyPigOErCwnyyZC0JiKWFF3HeKVUb0q1Qlr1plQrpFVvSrVCPvV6aMXMLHEOcjOzxKUY5FcWXcAEpVRvSrVCWvWmVCukVW9KtUIO9SY3Rm5mZkOl2CM3M7MaDnIzs8QlFeSSTpK0UdIDkj5TdD2jkdQm6eeS1ktaJ+kTRdc0FkklSfdKurXoWsYiqVXSTZJ+L2mDpBOKrml3JJ2fPQ/ul3SjpL2LrqlK0tWSeiTdX7NuP0l3SNqU/btvkTXWGqXeL2XPhd9KullSa5E1Vo1Ua819n5IUkubWY1/JBLmkEvBV4GRgMXCGpMXFVjWqXcCnImIx8BrgY9O41qpPABuKLmKcvgz8OCL+AjiWaVy3pIOBjwNLIuIYoAScXmxVQ1wLnDRs3WeAVRFxFLAqW54uruWl9d4BHBMRrwL+AHx2qosaxbW8tFYktQFvBR6p146SCXLg1cADEfFQRAwA3wJOLbimEUXEoxGxNvv5z1SC5uBiqxqdpIXAO4CvF13LWCS9EngjcBVARAxERG+xVY2pEWiW1AjMArYVXM8LIuJO4Mlhq08Frst+vg44bUqL2o2R6o2I2yNiV7b4a2DhlBc2glGOLcClwKeBus00SSnIDwa6a5a3MI3DsUpSO3AccHexlezWZVSeWINFFzIOhwE7gGuyoaCvS9qn6KJGExFbgX+n0vt6FPhTRNxebFVjmh8Rj2Y/Pwak9M0NHwJuK7qI0Ug6FdgaEffVs92Ugjw5kmYD3wM+GRHT8uu9Jb0T6ImIe4quZZwageOB/46I44BnmF5v/YfIxpdPpfIHaAGwj6S/Kbaq8YvK/OQk5ihLupDKsOb1RdcyEkmzgAuAi+rddkpBvhVoq1lemK2bliS9gkqIXx8R3y+6nt14HfAuSQ9TGa46UdI3iy1pt7YAWyKi+g7nJirBPl29GfhjROyIiOeB7wOvLbimsWyXdBBA9m9PwfWMSdLZwDuBD8T0/XDMEVT+oN+Xvd4WAmslHTjZhlMK8t8AR0k6TFITlRNGtxRc04gkicoY7oaI+M+i69mdiPhsRCyMiHYqx/RnETFte4wR8RjQLakjW7UcWF9gSWN5BHiNpFnZ82I50/jkbOYW4Kzs57OAHxRYy5gknURlaPBdEbGz6HpGExG/i4h5EdGevd62AMdnz+lJSSbIs5MZ5wI/ofJC+E5ErCu2qlG9DjiTSu+2K7u9veiiXkbOA66X9FugE7ik4HpGlb1zuAlYC/yOymtu2nykXNKNwK+ADklbJK0Avgi8RdImKu8ovlhkjbVGqfcKYA5wR/Za+1qhRWZGqTWffU3fdyFmZjYeyfTIzcxsZA5yM7PEOcjNzBLnIDczS5yD3MwscQ5yM7PEOcjNzBL3/w41LvtkS6foAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### All participation"
      ],
      "metadata": {
        "id": "Pix1IgksSi7B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop for searching the fit parameter AR & MA with RMSE metrics\n",
        "data_array = indonesia_all.values\n",
        "\n",
        "d=0\n",
        "for p in range(6):\n",
        "        for q in range(4):\n",
        "            try:\n",
        "              step = 9\n",
        "              X_train, X_test = data_array[:-step], data_array[-step:]\n",
        "              mod = ARIMA(X_train, order=(p,d,q))\n",
        "              res = mod.fit()\n",
        "              print((p,d,q),round(mean_squared_error(X_test, \n",
        "                                                     res.forecast(steps=step)[0], squared=False),3))\n",
        "            except:\n",
        "              pass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjE1S_1X5P-M",
        "outputId": "15e7a811-c928-4776-aeb9-596563df720b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 0, 0) 0.97\n",
            "(0, 0, 1) 0.992\n",
            "(1, 0, 0) 0.973\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "divide by zero encountered in log\n",
            "divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 0, 0) 0.987\n",
            "(2, 0, 1) 0.995\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "divide by zero encountered in log\n",
            "divide by zero encountered in log\n",
            "invalid value encountered in subtract\n",
            "invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3, 0, 0) 0.993\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3, 0, 1) 1.064\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "divide by zero encountered in log\n",
            "Inverting hessian failed, no bse or cov_params available\n",
            "invalid value encountered in sqrt\n",
            "divide by zero encountered in log\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(3, 0, 2) 1.015\n",
            "(4, 0, 1) 1.184\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inverting hessian failed, no bse or cov_params available\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the ARMA model\n",
        "step = 5\n",
        "X_train, X_test = data_array[:-step], data_array[-step:]\n",
        "mod = ARIMA(X_train, order=(3,0,0))\n",
        "res = mod.fit()\n",
        "\n",
        "plt.plot(indonesia_all)  \n",
        "plt.plot(pd.Series(res.forecast(steps=step)[0], index=indonesia_all[-step:].index))\n",
        "plt.legend(['Actual', 'Predicted'])\n",
        "plt.xlabel('Time steps of test data')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Wm0dKjqgExhE",
        "outputId": "4a69017f-aed7-4b40-979a-7bad87bf1b90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Forecast the all participation until 2025 with ARMA\n",
        "train = indonesia_all.iloc[:,0].values\n",
        "\n",
        "model = ARIMA(train, order=(3, 0, 0))  \n",
        "fitted = model.fit()  \n",
        "\n",
        "# Forecast\n",
        "step=7\n",
        "fc, se, conf = fitted.forecast(step, alpha=0.05)  # 95% conf\n",
        "\n",
        "# Make as pandas series\n",
        "idx = list(range(max(indonesia_all.index)+1, max(indonesia_all.index)+step+1))\n",
        "fc_series_all = pd.Series(fc, idx)\n",
        "lower_series_all = pd.Series(conf[:, 0], idx)\n",
        "upper_series_all = pd.Series(conf[:, 1], idx)\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(6,3), dpi=100)\n",
        "plt.plot(pd.Series(train, index=indonesia_all.index), label='training')\n",
        "plt.plot(fc_series_all, label='forecast')\n",
        "plt.fill_between(lower_series_all.index, lower_series_all, upper_series_all, \n",
        "                 color='k', alpha=.15)\n",
        "plt.legend(loc='upper left', fontsize=8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "s12mCa7kIMLx",
        "outputId": "8ea9d454-788e-46e9-e224-438187e763c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### None participation"
      ],
      "metadata": {
        "id": "4PnCopuBSmGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loop for searching the fit parameter AR, I & MA with RMSE metrics\n",
        "data_array = indonesia_none.values\n",
        "\n",
        "d=0\n",
        "for p in range(4):\n",
        "    for d in range(2):\n",
        "        for q in range(4):\n",
        "            try:\n",
        "              step = 3\n",
        "              X_train, X_test = data_array[:-step], data_array[-step:]\n",
        "              mod = ARIMA(X_train, order=(p,d,q))\n",
        "              res = mod.fit()\n",
        "              print((p,d,q),round(mean_squared_error(X_test, \n",
        "                                                     res.forecast(steps=step)[0], squared=False),3))\n",
        "            except:\n",
        "              pass"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UsBeX_pASrdy",
        "outputId": "5903f1fa-d0ef-41dc-d0b8-0f206d08f49f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 0, 0) 0.359\n",
            "(0, 0, 1) 0.279\n",
            "(0, 1, 0) 0.321\n",
            "(0, 1, 1) 0.262\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inverting hessian failed, no bse or cov_params available\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 1, 2) 1.029\n",
            "(0, 1, 3) 0.917\n",
            "(1, 0, 0) 0.302\n",
            "(1, 0, 1) 0.253\n",
            "(1, 0, 2) 0.258\n",
            "(1, 0, 3) 0.491\n",
            "(1, 1, 0) 0.272\n",
            "(2, 0, 0) 0.241\n",
            "(2, 0, 1) 0.24\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inverting hessian failed, no bse or cov_params available\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 0, 2) 0.232\n",
            "(2, 0, 3) 0.512\n",
            "(2, 1, 0) 0.4\n",
            "(3, 0, 0) 0.24\n",
            "(3, 0, 1) 0.43\n",
            "(3, 0, 2) 0.45\n",
            "(3, 0, 3) 0.507\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the ARIMA model\n",
        "step = 3\n",
        "X_train, X_test = data_array[:-step], data_array[-step:]\n",
        "mod = ARIMA(X_train, order=(3,0,3))\n",
        "res = mod.fit()\n",
        "\n",
        "plt.plot(indonesia_none)  \n",
        "plt.plot(pd.Series(res.forecast(steps=step)[0], index=indonesia_none[-step:].index))\n",
        "plt.legend(['Actual', 'Predicted'])\n",
        "plt.xlabel('Time steps of test data')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3p4rigEUECZ",
        "outputId": "4c8f0c61-ef26-478a-be7a-b6da0c5776ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Forecast the none participation until 2025 with ARIMA\n",
        "train = indonesia_none.iloc[:,0].values\n",
        "\n",
        "model = ARIMA(train, order=(3, 0, 3))  \n",
        "fitted = model.fit()  \n",
        "\n",
        "# Forecast\n",
        "step=7\n",
        "fc, se, conf = fitted.forecast(step, alpha=0.05)  # 95% conf\n",
        "\n",
        "# Make as pandas series\n",
        "idx = list(range(max(indonesia_none.index)+1, max(indonesia_none.index)+step+1))\n",
        "fc_series_none = pd.Series(fc, idx)\n",
        "lower_series_none = pd.Series(conf[:, 0], idx)\n",
        "upper_series_none = pd.Series(conf[:, 1], idx)\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(6,3), dpi=100)\n",
        "plt.plot(pd.Series(train, index=indonesia_none.index), label='training')\n",
        "plt.plot(fc_series_none, label='forecast')\n",
        "plt.fill_between(lower_series_none.index, lower_series_none, upper_series_none, \n",
        "                 color='k', alpha=.15)\n",
        "plt.legend(loc='upper left', fontsize=8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "CS0ypEhOURLa",
        "outputId": "1cf6d0ac-136f-420f-a385-6fb0d2cf5d65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Inverting hessian failed, no bse or cov_params available\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Save Forecasting"
      ],
      "metadata": {
        "id": "rp3_cXC3H8ti"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge all participation data with bounds\n",
        "forecast_all = pd.Series(fc_series_all, name='Forecast')\n",
        "real_all = indonesia_all.iloc[:,0].rename('Actual')\n",
        "lb_all = pd.Series(lower_series_all, name='Lower')\n",
        "ub_all = pd.Series(upper_series_all, name='Upper')\n",
        "pd.concat([real_all, forecast_all, lb_all, ub_all], axis=1).\\\n",
        "          to_excel('All_forecast.xlsx')"
      ],
      "metadata": {
        "id": "M1iEFfczKg3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge none participation data with bounds\n",
        "forecast_none = pd.Series(fc_series_none, name='Forecast')\n",
        "real_none = indonesia_none.iloc[:,0].rename('Actual')\n",
        "lb_none = pd.Series(lower_series_none, name='Lower')\n",
        "ub_none = pd.Series(upper_series_none, name='Upper')\n",
        "pd.concat([real_none, forecast_none, lb_none, ub_none], axis=1).\\\n",
        "          to_excel('None_forecast.xlsx')"
      ],
      "metadata": {
        "id": "9sn_swc4OrXr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation with Decision Making"
      ],
      "metadata": {
        "id": "_aBEKMnW35Kq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gender = load_data('1lCrYzpGmOWwaLnYDJvdW6v6gc8CerQVl') # Decision Making Aspects data"
      ],
      "metadata": {
        "id": "UWAYuBT-4BCf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Concat the female and male data to find correlation\n",
        "female = gender[gender.columns[gender.columns.str.endswith('_f')]]\n",
        "male = gender[gender.columns[gender.columns.str.endswith('_m')]]\n",
        "female.columns = np.arange(female.shape[1]).astype(str)\n",
        "male.columns = np.arange(male.shape[1]).astype(str)\n",
        "\n",
        "gender_df = pd.DataFrame(pd.concat([female, male], axis=0, ignore_index=True))\n",
        "gender_df.columns = gender.columns[gender.columns.str.contains('_f')].str[:-2]"
      ],
      "metadata": {
        "id": "Qph0nKLb4sNZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation on decision maker with other aspects\n",
        "plt.figure(figsize=(6,8))\n",
        "sns.heatmap(gender_df.corr()[['decision_maker']].sort_values(\n",
        "    by='decision_maker', ascending=False), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "5K4rucV489a5",
        "outputId": "ac3c181b-df31-40e2-bf39-ee38b815e845"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f714de71710>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x576 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the pearson correlation with p-value stastical significance\n",
        "new_df = {}\n",
        "for i in np.setdiff1d(gender_df.columns.values, ['decision_maker']):\n",
        "  new_df[i] = pearsonr(gender_df['decision_maker'],gender_df[i])\n",
        "new_df = pd.DataFrame(new_df, index=['decision_maker','p_value']).T"
      ],
      "metadata": {
        "id": "Tyuho6zI_yOB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Show the correlation and p-value\n",
        "plt.figure(figsize=(6,8))\n",
        "sns.heatmap(new_df.sort_values(\n",
        "    by='decision_maker', ascending=False), annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "0821Ow9fCsJF",
        "outputId": "f1d5e4c0-2188-4a49-af0e-b7370c20afb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f714db7dad0>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x576 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.to_excel(\"Correlation.xlsx\")"
      ],
      "metadata": {
        "id": "owgLSwMjHsuI"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "SyGQcIp30x01"
      ],
      "name": "Household_Authority_Inequality",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}