Milestone1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc9e9ba8-d261-4124-8fc8-c9f3ae59946c",
   "metadata": {},
   "source": [
    "# First data exploration for the school world exploration project\n",
    "\n",
    "This notebook contains the pre-processing of our chosen data sets and some basic statistics to get insights about the data.\n",
    "The overall project goal can be expressed as:\n",
    "\n",
    "> Exploring global education through interactive visuals, revealing the diversity and disparities within school systems across urban and rural landscapes.\n",
    "\n",
    "Data sets:\n",
    "- [World Bank EdStats](https://datacatalog.worldbank.org/search/dataset/0038480/education-statistics)\n",
    "- ...\n",
    " \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4126e776-6417-437a-80ec-eddad9ac2fbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:18.088974Z",
     "iopub.status.busy": "2024-03-28T22:58:18.088427Z",
     "iopub.status.idle": "2024-03-28T22:58:18.698567Z",
     "shell.execute_reply": "2024-03-28T22:58:18.697464Z",
     "shell.execute_reply.started": "2024-03-28T22:58:18.088924Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1ac566-7ba9-42f6-8b59-a24eb4bb0571",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50c1341a-3e0f-41f7-b736-8bb7f2dc8cfc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:18.700739Z",
     "iopub.status.busy": "2024-03-28T22:58:18.700374Z",
     "iopub.status.idle": "2024-03-28T22:58:25.149938Z",
     "shell.execute_reply": "2024-03-28T22:58:25.148079Z",
     "shell.execute_reply.started": "2024-03-28T22:58:18.700713Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "PATH = \"./data/\"\n",
    "\n",
    "# Loading the EdStats data set from the World Bank\n",
    "countries = pd.read_csv(PATH+ \"EdStatsCountry.csv\")\n",
    "country_series = pd.read_csv(PATH+\"EdStatsCountry-Series.csv\")\n",
    "data = pd.read_csv(PATH+\"EdStatsData.csv\")\n",
    "foot_note = pd.read_csv(PATH+\"EdStatsFootNote.csv\")\n",
    "series = pd.read_csv(PATH+\"EdStatsSeries.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff061576-81c0-44ed-82ab-fa7aba7cbcb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:25.151305Z",
     "iopub.status.busy": "2024-03-28T22:58:25.151062Z",
     "iopub.status.idle": "2024-03-28T22:58:25.163118Z",
     "shell.execute_reply": "2024-03-28T22:58:25.160718Z",
     "shell.execute_reply.started": "2024-03-28T22:58:25.151282Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Countries\n",
      "Index(['Country Code', 'Short Name', 'Table Name', 'Long Name', '2-alpha code',\n",
      "       'Currency Unit', 'Special Notes', 'Region', 'Income Group', 'WB-2 code',\n",
      "       'National accounts base year', 'National accounts reference year',\n",
      "       'SNA price valuation', 'Lending category', 'Other groups',\n",
      "       'System of National Accounts', 'Alternative conversion factor',\n",
      "       'PPP survey year', 'Balance of Payments Manual in use',\n",
      "       'External debt Reporting status', 'System of trade',\n",
      "       'Government Accounting concept', 'IMF data dissemination standard',\n",
      "       'Latest population census', 'Latest household survey',\n",
      "       'Source of most recent Income and expenditure data',\n",
      "       'Vital registration complete', 'Latest agricultural census',\n",
      "       'Latest industrial data', 'Latest trade data',\n",
      "       'Latest water withdrawal data', 'Unnamed: 31'],\n",
      "      dtype='object')\n",
      "\n",
      "Country Series\n",
      "Index(['CountryCode', 'SeriesCode', 'DESCRIPTION', 'Unnamed: 3'], dtype='object')\n",
      "\n",
      "Data\n",
      "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n",
      "       '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977', '1978',\n",
      "       '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986', '1987',\n",
      "       '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996',\n",
      "       '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005',\n",
      "       '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014',\n",
      "       '2015', '2016', '2017', '2020', '2025', '2030', '2035', '2040', '2045',\n",
      "       '2050', '2055', '2060', '2065', '2070', '2075', '2080', '2085', '2090',\n",
      "       '2095', '2100', 'Unnamed: 69'],\n",
      "      dtype='object')\n",
      "\n",
      "Foot Note\n",
      "Index(['CountryCode', 'SeriesCode', 'Year', 'DESCRIPTION', 'Unnamed: 4'], dtype='object')\n",
      "\n",
      "Series\n",
      "Index(['Series Code', 'Topic', 'Indicator Name', 'Short definition',\n",
      "       'Long definition', 'Unit of measure', 'Periodicity', 'Base Period',\n",
      "       'Other notes', 'Aggregation method', 'Limitations and exceptions',\n",
      "       'Notes from original source', 'General comments', 'Source',\n",
      "       'Statistical concept and methodology', 'Development relevance',\n",
      "       'Related source links', 'Other web links', 'Related indicators',\n",
      "       'License Type', 'Unnamed: 20'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Print available attributes\n",
    "def print_columns(df, df_name):\n",
    "    print(\"\\n\" + df_name)\n",
    "    print(df.columns)\n",
    "\n",
    "\n",
    "print_columns(countries, \"Countries\")\n",
    "print_columns(country_series, \"Country Series\")\n",
    "print_columns(data, \"Data\")\n",
    "print_columns(foot_note, \"Foot Note\")\n",
    "print_columns(series, \"Series\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8573ae1e-fe1e-402a-83f3-21dfd7d852b0",
   "metadata": {},
   "source": [
    "**The 'Country Code' and 'Series Code' attributes can be used as ids accross the dfs to link data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f66269ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:25.166390Z",
     "iopub.status.busy": "2024-03-28T22:58:25.165608Z",
     "iopub.status.idle": "2024-03-28T22:58:27.890224Z",
     "shell.execute_reply": "2024-03-28T22:58:27.888968Z",
     "shell.execute_reply.started": "2024-03-28T22:58:25.166320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Indicator Code       Country Code\n",
       "BAR.NOED.1519.FE.ZS  ABW             0\n",
       "                     AFG             9\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Selecting data attributes with sufficient data points\n",
    "number_of_years_with_data = data.groupby([\"Indicator Code\", \"Country Code\"]).count().sum(axis=1)-2\n",
    "display(number_of_years_with_data.head(2))\n",
    "number_of_years_with_data.sort_values(ascending=False).hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35bf79d",
   "metadata": {},
   "source": [
    "**If we choose to select only data for which there are > 18 years availlable**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c87daa01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:27.891944Z",
     "iopub.status.busy": "2024-03-28T22:58:27.891492Z",
     "iopub.status.idle": "2024-03-28T22:58:28.014584Z",
     "shell.execute_reply": "2024-03-28T22:58:28.013145Z",
     "shell.execute_reply.started": "2024-03-28T22:58:27.891915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Short Name</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Long Name</th>\n",
       "      <th>2-alpha code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>Special Notes</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>WB-2 code</th>\n",
       "      <th>...</th>\n",
       "      <th>IMF data dissemination standard</th>\n",
       "      <th>Latest population census</th>\n",
       "      <th>Latest household survey</th>\n",
       "      <th>Source of most recent Income and expenditure data</th>\n",
       "      <th>Vital registration complete</th>\n",
       "      <th>Latest agricultural census</th>\n",
       "      <th>Latest industrial data</th>\n",
       "      <th>Latest trade data</th>\n",
       "      <th>Latest water withdrawal data</th>\n",
       "      <th>Unnamed: 31</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CHE</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CH</td>\n",
       "      <td>Swiss franc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>High income: OECD</td>\n",
       "      <td>CH</td>\n",
       "      <td>...</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Expenditure survey/budget survey (ES/BS), 2000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>IND</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>Republic of India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Indian rupee</td>\n",
       "      <td>Fiscal year end: March 31; reporting period fo...</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>IN</td>\n",
       "      <td>...</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2011</td>\n",
       "      <td>Demographic and Health Survey (DHS), 2005/06</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Japan</td>\n",
       "      <td>JP</td>\n",
       "      <td>Japanese yen</td>\n",
       "      <td>Fiscal year end: March 31; reporting period fo...</td>\n",
       "      <td>East Asia &amp; Pacific</td>\n",
       "      <td>High income: OECD</td>\n",
       "      <td>JP</td>\n",
       "      <td>...</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Income survey (IS), 1993</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>MEX</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>United Mexican States</td>\n",
       "      <td>MX</td>\n",
       "      <td>Mexican peso</td>\n",
       "      <td>The new base year is 2008.</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>MX</td>\n",
       "      <td>...</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>National Survey of Demographic Dynamics (ENADI...</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>US</td>\n",
       "      <td>U.S. dollar</td>\n",
       "      <td>Fiscal year end: September 30; reporting perio...</td>\n",
       "      <td>North America</td>\n",
       "      <td>High income: OECD</td>\n",
       "      <td>US</td>\n",
       "      <td>...</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Labor force survey (LFS), 2000</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2012</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country Code     Short Name     Table Name                 Long Name  \\\n",
       "34           CHE    Switzerland    Switzerland               Switzerland   \n",
       "98           IND          India          India         Republic of India   \n",
       "107          JPN          Japan          Japan                     Japan   \n",
       "141          MEX         Mexico         Mexico     United Mexican States   \n",
       "227          USA  United States  United States  United States of America   \n",
       "\n",
       "    2-alpha code Currency Unit  \\\n",
       "34            CH   Swiss franc   \n",
       "98            IN  Indian rupee   \n",
       "107           JP  Japanese yen   \n",
       "141           MX  Mexican peso   \n",
       "227           US   U.S. dollar   \n",
       "\n",
       "                                         Special Notes  \\\n",
       "34                                                 NaN   \n",
       "98   Fiscal year end: March 31; reporting period fo...   \n",
       "107  Fiscal year end: March 31; reporting period fo...   \n",
       "141                         The new base year is 2008.   \n",
       "227  Fiscal year end: September 30; reporting perio...   \n",
       "\n",
       "                        Region         Income Group WB-2 code  ...  \\\n",
       "34       Europe & Central Asia    High income: OECD        CH  ...   \n",
       "98                  South Asia  Lower middle income        IN  ...   \n",
       "107        East Asia & Pacific    High income: OECD        JP  ...   \n",
       "141  Latin America & Caribbean  Upper middle income        MX  ...   \n",
       "227              North America    High income: OECD        US  ...   \n",
       "\n",
       "                IMF data dissemination standard  Latest population census  \\\n",
       "34   Special Data Dissemination Standard (SDDS)                      2010   \n",
       "98   Special Data Dissemination Standard (SDDS)                      2011   \n",
       "107  Special Data Dissemination Standard (SDDS)                      2010   \n",
       "141  Special Data Dissemination Standard (SDDS)                      2010   \n",
       "227  Special Data Dissemination Standard (SDDS)                      2010   \n",
       "\n",
       "                               Latest household survey  \\\n",
       "34                                                 NaN   \n",
       "98        Demographic and Health Survey (DHS), 2005/06   \n",
       "107                                                NaN   \n",
       "141  National Survey of Demographic Dynamics (ENADI...   \n",
       "227                                                NaN   \n",
       "\n",
       "    Source of most recent Income and expenditure data  \\\n",
       "34     Expenditure survey/budget survey (ES/BS), 2000   \n",
       "98            Integrated household survey (IHS), 2012   \n",
       "107                          Income survey (IS), 1993   \n",
       "141           Integrated household survey (IHS), 2012   \n",
       "227                    Labor force survey (LFS), 2000   \n",
       "\n",
       "    Vital registration complete Latest agricultural census  \\\n",
       "34                          Yes                       2008   \n",
       "98                          NaN                       2011   \n",
       "107                         Yes                       2010   \n",
       "141                         NaN                       2007   \n",
       "227                         Yes                       2012   \n",
       "\n",
       "    Latest industrial data Latest trade data Latest water withdrawal data  \\\n",
       "34                  2007.0            2012.0                         2000   \n",
       "98                  2009.0            2012.0                         2010   \n",
       "107                 2010.0            2012.0                         2001   \n",
       "141                 2010.0            2012.0                         2009   \n",
       "227                 2008.0            2012.0                         2005   \n",
       "\n",
       "    Unnamed: 31  \n",
       "34          NaN  \n",
       "98          NaN  \n",
       "107         NaN  \n",
       "141         NaN  \n",
       "227         NaN  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we want data that includes at least : USA, Mexico, Japan, India, and Switzerland\n",
    "countries_code_we_want = [\"USA\", \"MEX\", \"JPN\", \"IND\", \"CHE\"]\n",
    "countries[countries[\"Country Code\"].isin(countries_code_we_want)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cf788566",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.016858Z",
     "iopub.status.busy": "2024-03-28T22:58:28.016405Z",
     "iopub.status.idle": "2024-03-28T22:58:28.283567Z",
     "shell.execute_reply": "2024-03-28T22:58:28.282256Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.016808Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country Code</th>\n",
       "      <th>ABW</th>\n",
       "      <th>AFG</th>\n",
       "      <th>AGO</th>\n",
       "      <th>ALB</th>\n",
       "      <th>AND</th>\n",
       "      <th>ARB</th>\n",
       "      <th>ARE</th>\n",
       "      <th>ARG</th>\n",
       "      <th>ARM</th>\n",
       "      <th>ASM</th>\n",
       "      <th>...</th>\n",
       "      <th>VIR</th>\n",
       "      <th>VNM</th>\n",
       "      <th>VUT</th>\n",
       "      <th>WLD</th>\n",
       "      <th>WSM</th>\n",
       "      <th>XKX</th>\n",
       "      <th>YEM</th>\n",
       "      <th>ZAF</th>\n",
       "      <th>ZMB</th>\n",
       "      <th>ZWE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indicator Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAR.NOED.1519.FE.ZS</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAR.NOED.1519.ZS</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 242 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country Code         ABW  AFG  AGO  ALB  AND  ARB  ARE  ARG  ARM  ASM  ...  \\\n",
       "Indicator Code                                                         ...   \n",
       "BAR.NOED.1519.FE.ZS    0    9    0    9    0    0    9    9    9    0  ...   \n",
       "BAR.NOED.1519.ZS       0    9    0    9    0    0    9    9    9    0  ...   \n",
       "\n",
       "Country Code         VIR  VNM  VUT  WLD  WSM  XKX  YEM  ZAF  ZMB  ZWE  \n",
       "Indicator Code                                                         \n",
       "BAR.NOED.1519.FE.ZS    0    9    0    0    0    0    9    9    9    9  \n",
       "BAR.NOED.1519.ZS       0    9    0    0    0    0    9    9    9    9  \n",
       "\n",
       "[2 rows x 242 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_years_with_data_df = number_of_years_with_data.unstack(level=1)\n",
    "number_of_years_with_data_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5fd40b49",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.286825Z",
     "iopub.status.busy": "2024-03-28T22:58:28.286541Z",
     "iopub.status.idle": "2024-03-28T22:58:28.299856Z",
     "shell.execute_reply": "2024-03-28T22:58:28.298316Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.286801Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "647\n"
     ]
    }
   ],
   "source": [
    "limit = 18\n",
    "indicator_has_all_countries_wanted = (number_of_years_with_data_df[countries_code_we_want]>limit).all(axis=1)\n",
    "indicators_with_countries_wanted = indicator_has_all_countries_wanted[indicator_has_all_countries_wanted].index.to_list()\n",
    "print(len(indicators_with_countries_wanted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71a797c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.301354Z",
     "iopub.status.busy": "2024-03-28T22:58:28.301092Z",
     "iopub.status.idle": "2024-03-28T22:58:28.583228Z",
     "shell.execute_reply": "2024-03-28T22:58:28.581086Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.301330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Number of Countries with sufficient data</th>\n",
       "      <th>Cumuled number of years availlable</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indicator Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G1</th>\n",
       "      <td>Enrolment in Grade 1 of primary education, bot...</td>\n",
       "      <td>188</td>\n",
       "      <td>7109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G1.F</th>\n",
       "      <td>Enrolment in Grade 1 of primary education, fem...</td>\n",
       "      <td>180</td>\n",
       "      <td>6699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G2</th>\n",
       "      <td>Enrolment in Grade 2 of primary education, bot...</td>\n",
       "      <td>185</td>\n",
       "      <td>7020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G2.F</th>\n",
       "      <td>Enrolment in Grade 2 of primary education, fem...</td>\n",
       "      <td>177</td>\n",
       "      <td>6615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G3</th>\n",
       "      <td>Enrolment in Grade 3 of primary education, bot...</td>\n",
       "      <td>186</td>\n",
       "      <td>7028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.1.MA</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.1.MF</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.3.FE</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.3.MA</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.3.MF</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>647 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Indicator Name  \\\n",
       "Indicator Code                                                        \n",
       "UIS.E.1.G1        Enrolment in Grade 1 of primary education, bot...   \n",
       "UIS.E.1.G1.F      Enrolment in Grade 1 of primary education, fem...   \n",
       "UIS.E.1.G2        Enrolment in Grade 2 of primary education, bot...   \n",
       "UIS.E.1.G2.F      Enrolment in Grade 2 of primary education, fem...   \n",
       "UIS.E.1.G3        Enrolment in Grade 3 of primary education, bot...   \n",
       "...                                                             ...   \n",
       "PRJ.POP.ALL.1.MA  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.1.MF  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.3.FE  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.3.MA  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.3.MF  Wittgenstein Projection: Population in thousan...   \n",
       "\n",
       "                  Number of Countries with sufficient data  \\\n",
       "Indicator Code                                               \n",
       "UIS.E.1.G1                                             188   \n",
       "UIS.E.1.G1.F                                           180   \n",
       "UIS.E.1.G2                                             185   \n",
       "UIS.E.1.G2.F                                           177   \n",
       "UIS.E.1.G3                                             186   \n",
       "...                                                    ...   \n",
       "PRJ.POP.ALL.1.MA                                       167   \n",
       "PRJ.POP.ALL.1.MF                                       167   \n",
       "PRJ.POP.ALL.3.FE                                       167   \n",
       "PRJ.POP.ALL.3.MA                                       167   \n",
       "PRJ.POP.ALL.3.MF                                       167   \n",
       "\n",
       "                  Cumuled number of years availlable  \n",
       "Indicator Code                                        \n",
       "UIS.E.1.G1                                      7109  \n",
       "UIS.E.1.G1.F                                    6699  \n",
       "UIS.E.1.G2                                      7020  \n",
       "UIS.E.1.G2.F                                    6615  \n",
       "UIS.E.1.G3                                      7028  \n",
       "...                                              ...  \n",
       "PRJ.POP.ALL.1.MA                                3173  \n",
       "PRJ.POP.ALL.1.MF                                3173  \n",
       "PRJ.POP.ALL.3.FE                                3173  \n",
       "PRJ.POP.ALL.3.MA                                3173  \n",
       "PRJ.POP.ALL.3.MF                                3173  \n",
       "\n",
       "[647 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators_df = data.loc[data[\"Indicator Code\"].isin(indicators_with_countries_wanted), [\"Indicator Code\",\"Indicator Name\"]].drop_duplicates().set_index(\"Indicator Code\")\n",
    "indicators_df[\"Number of Countries with sufficient data\"] = (number_of_years_with_data_df>limit).sum(axis=1)\n",
    "indicators_df[\"Cumuled number of years availlable\"] = number_of_years_with_data_df.sum(axis=1)\n",
    "indicators_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d6912c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.584474Z",
     "iopub.status.busy": "2024-03-28T22:58:28.584229Z",
     "iopub.status.idle": "2024-03-28T22:58:28.686394Z",
     "shell.execute_reply": "2024-03-28T22:58:28.684798Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.584453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Enrolment in Grade 1 of primary education, both sexes (number)',\n",
       "       'Enrolment in Grade 1 of primary education, female (number)',\n",
       "       'Enrolment in Grade 2 of primary education, both sexes (number)',\n",
       "       'Enrolment in Grade 2 of primary education, female (number)',\n",
       "       'Enrolment in Grade 3 of primary education, both sexes (number)',\n",
       "       'Enrolment in Grade 3 of primary education, female (number)',\n",
       "       'Enrolment in Grade 4 of primary education, both sexes (number)',\n",
       "       'Enrolment in Grade 4 of primary education, female (number)',\n",
       "       'Enrolment in Grade 5 of primary education, both sexes (number)',\n",
       "       'Enrolment in Grade 5 of primary education, female (number)',\n",
       "       'Enrolment in pre-primary education, both sexes (number)',\n",
       "       'Enrolment in pre-primary education, female (number)',\n",
       "       'Enrolment in primary education, both sexes (number)',\n",
       "       'Enrolment in primary education, female (number)',\n",
       "       'Enrolment in secondary education, both sexes (number)',\n",
       "       'Enrolment in secondary education, female (number)',\n",
       "       'Enrolment in secondary general, both sexes (number)',\n",
       "       'Enrolment in secondary general, female (number)',\n",
       "       'Enrolment in secondary vocational, both sexes (number)',\n",
       "       'Enrolment in secondary vocational, female (number)',\n",
       "       'Enrolment in tertiary education per 100,000 inhabitants, both sexes',\n",
       "       'Enrolment in tertiary education per 100,000 inhabitants, female',\n",
       "       'Enrolment in tertiary education per 100,000 inhabitants, male',\n",
       "       'Enrolment in tertiary education, all programmes, both sexes (number)',\n",
       "       'Enrolment in tertiary education, all programmes, female (number)',\n",
       "       'GDP at market prices (constant 2005 US$)',\n",
       "       'GDP at market prices (current US$)',\n",
       "       'GDP per capita (constant 2005 US$)',\n",
       "       'GDP per capita (current US$)',\n",
       "       'GDP per capita, PPP (constant 2011 international $)',\n",
       "       'GDP per capita, PPP (current international $)',\n",
       "       'GDP, PPP (constant 2011 international $)',\n",
       "       'GDP, PPP (current international $)', 'GNI (current US$)',\n",
       "       'GNI per capita, Atlas method (current US$)',\n",
       "       'GNI per capita, PPP (current international $)',\n",
       "       'GNI, PPP (current international $)',\n",
       "       'Gross enrolment ratio, lower secondary, both sexes (%)',\n",
       "       'Gross enrolment ratio, lower secondary, female (%)',\n",
       "       'Gross enrolment ratio, lower secondary, male (%)',\n",
       "       'Gross enrolment ratio, pre-primary, both sexes (%)',\n",
       "       'Gross enrolment ratio, pre-primary, female (%)',\n",
       "       'Gross enrolment ratio, pre-primary, gender parity index (GPI)',\n",
       "       'Gross enrolment ratio, pre-primary, male (%)',\n",
       "       'Gross enrolment ratio, primary and secondary, both sexes (%)',\n",
       "       'Gross enrolment ratio, primary and secondary, female (%)',\n",
       "       'Gross enrolment ratio, primary and secondary, gender parity index (GPI)',\n",
       "       'Gross enrolment ratio, primary and secondary, male (%)',\n",
       "       'Gross enrolment ratio, primary to tertiary, both sexes (%)',\n",
       "       'Gross enrolment ratio, primary to tertiary, female (%)',\n",
       "       'Gross enrolment ratio, primary to tertiary, gender parity index (GPI)',\n",
       "       'Gross enrolment ratio, primary to tertiary, male (%)',\n",
       "       'Gross enrolment ratio, primary, both sexes (%)',\n",
       "       'Gross enrolment ratio, primary, female (%)',\n",
       "       'Gross enrolment ratio, primary, gender parity index (GPI)',\n",
       "       'Gross enrolment ratio, primary, male (%)',\n",
       "       'Gross enrolment ratio, secondary, both sexes (%)',\n",
       "       'Gross enrolment ratio, secondary, female (%)',\n",
       "       'Gross enrolment ratio, secondary, gender parity index (GPI)',\n",
       "       'Gross enrolment ratio, secondary, male (%)',\n",
       "       'Gross enrolment ratio, tertiary, both sexes (%)',\n",
       "       'Gross enrolment ratio, tertiary, female (%)',\n",
       "       'Gross enrolment ratio, tertiary, gender parity index (GPI)',\n",
       "       'Gross enrolment ratio, tertiary, male (%)',\n",
       "       'Gross enrolment ratio, upper secondary, both sexes (%)',\n",
       "       'Gross enrolment ratio, upper secondary, female (%)',\n",
       "       'Gross enrolment ratio, upper secondary, male (%)',\n",
       "       'Internet users (per 100 people)',\n",
       "       'Labor force, female (% of total labor force)',\n",
       "       'Labor force, total', 'Mortality rate, under-5 (per 1,000)',\n",
       "       'Official entrance age to lower secondary education (years)',\n",
       "       'Official entrance age to pre-primary education (years)',\n",
       "       'Official entrance age to primary education (years)',\n",
       "       'Percentage of female students in secondary education enrolled in general programmes, female (%)',\n",
       "       'Percentage of female students in secondary education enrolled in vocational programmes, female (%)',\n",
       "       'Percentage of male students in secondary education enrolled in general programmes, male (%)',\n",
       "       'Percentage of male students in secondary education enrolled in vocational programmes, male (%)',\n",
       "       'Percentage of students in pre-primary education who are female (%)',\n",
       "       'Percentage of students in primary education who are female (%)',\n",
       "       'Percentage of students in secondary education enrolled in general programmes, both sexes (%)',\n",
       "       'Percentage of students in secondary education enrolled in vocational programmes, both sexes (%)',\n",
       "       'Percentage of students in secondary education who are female (%)',\n",
       "       'Percentage of students in secondary general education who are female (%)',\n",
       "       'Percentage of students in tertiary education who are female (%)',\n",
       "       'Personal computers (per 100 people)',\n",
       "       'Population growth (annual %)',\n",
       "       'Population of the official age for lower secondary education, both sexes (number)',\n",
       "       'Population of the official age for lower secondary education, female (number)',\n",
       "       'Population of the official age for lower secondary education, male (number)',\n",
       "       'Population of the official age for pre-primary education, both sexes (number)',\n",
       "       'Population of the official age for pre-primary education, female (number)',\n",
       "       'Population of the official age for pre-primary education, male (number)',\n",
       "       'Population of the official age for primary education, both sexes (number)',\n",
       "       'Population of the official age for primary education, female (number)',\n",
       "       'Population of the official age for primary education, male (number)',\n",
       "       'Population of the official age for secondary education, both sexes (number)',\n",
       "       'Population of the official age for secondary education, female (number)',\n",
       "       'Population of the official age for secondary education, male (number)',\n",
       "       'Population of the official age for tertiary education, both sexes (number)',\n",
       "       'Population of the official age for tertiary education, female (number)',\n",
       "       'Population of the official age for tertiary education, male (number)',\n",
       "       'Population of the official age for the last grade of primary education, both sexes (number)',\n",
       "       'Population of the official age for the last grade of primary education, female (number)',\n",
       "       'Population of the official age for the last grade of primary education, male (number)',\n",
       "       'Population of the official age for upper secondary education, both sexes (number)',\n",
       "       'Population of the official age for upper secondary education, female (number)',\n",
       "       'Population of the official age for upper secondary education, male (number)',\n",
       "       'Population of the official entrance age to primary education, both sexes (number)',\n",
       "       'Population of the official entrance age to primary education, female (number)',\n",
       "       'Population of the official entrance age to primary education, male (number)',\n",
       "       'Population of the official entrance age to secondary general education, both sexes (number)',\n",
       "       'Population of the official entrance age to secondary general education, female (number)',\n",
       "       'Population of the official entrance age to secondary general education, male (number)',\n",
       "       'Population, age 0, female', 'Population, age 0, male',\n",
       "       'Population, age 0, total', 'Population, age 1, female',\n",
       "       'Population, age 1, male', 'Population, age 1, total',\n",
       "       'Population, age 10, female', 'Population, age 10, male',\n",
       "       'Population, age 10, total', 'Population, age 11, female',\n",
       "       'Population, age 11, male', 'Population, age 11, total',\n",
       "       'Population, age 12, female', 'Population, age 12, male',\n",
       "       'Population, age 12, total', 'Population, age 13, female',\n",
       "       'Population, age 13, male', 'Population, age 13, total',\n",
       "       'Population, age 14, female', 'Population, age 14, male',\n",
       "       'Population, age 14, total', 'Population, age 15, female',\n",
       "       'Population, age 15, male', 'Population, age 15, total',\n",
       "       'Population, age 16, female', 'Population, age 16, male',\n",
       "       'Population, age 16, total', 'Population, age 17, female',\n",
       "       'Population, age 17, male', 'Population, age 17, total',\n",
       "       'Population, age 18, female', 'Population, age 18, male',\n",
       "       'Population, age 18, total', 'Population, age 19, female',\n",
       "       'Population, age 19, male', 'Population, age 19, total',\n",
       "       'Population, age 2, female', 'Population, age 2, male',\n",
       "       'Population, age 2, total', 'Population, age 20, female',\n",
       "       'Population, age 20, male', 'Population, age 20, total',\n",
       "       'Population, age 21, female', 'Population, age 21, male',\n",
       "       'Population, age 21, total', 'Population, age 22, female',\n",
       "       'Population, age 22, male', 'Population, age 22, total',\n",
       "       'Population, age 23, female', 'Population, age 23, male',\n",
       "       'Population, age 23, total', 'Population, age 24, female',\n",
       "       'Population, age 24, male', 'Population, age 24, total',\n",
       "       'Population, age 25, female', 'Population, age 25, male',\n",
       "       'Population, age 25, total', 'Population, age 3, female',\n",
       "       'Population, age 3, male', 'Population, age 3, total',\n",
       "       'Population, age 4, female', 'Population, age 4, male',\n",
       "       'Population, age 4, total', 'Population, age 5, female',\n",
       "       'Population, age 5, male', 'Population, age 5, total',\n",
       "       'Population, age 6, female', 'Population, age 6, male',\n",
       "       'Population, age 6, total', 'Population, age 7, female',\n",
       "       'Population, age 7, male', 'Population, age 7, total',\n",
       "       'Population, age 8, female', 'Population, age 8, male',\n",
       "       'Population, age 8, total', 'Population, age 9, female',\n",
       "       'Population, age 9, male', 'Population, age 9, total',\n",
       "       'Population, ages 0-14 (% of total)',\n",
       "       'Population, ages 0-14, female', 'Population, ages 0-14, male',\n",
       "       'Population, ages 0-14, total', 'Population, ages 10-14, female',\n",
       "       'Population, ages 10-14, male', 'Population, ages 10-14, total',\n",
       "       'Population, ages 10-15, female', 'Population, ages 10-15, male',\n",
       "       'Population, ages 10-15, total', 'Population, ages 10-16, female',\n",
       "       'Population, ages 10-16, male', 'Population, ages 10-16, total',\n",
       "       'Population, ages 10-17, female', 'Population, ages 10-17, male',\n",
       "       'Population, ages 10-17, total', 'Population, ages 10-18, female',\n",
       "       'Population, ages 10-18, male', 'Population, ages 10-18, total',\n",
       "       'Population, ages 11-15, female', 'Population, ages 11-15, male',\n",
       "       'Population, ages 11-15, total', 'Population, ages 11-16, female',\n",
       "       'Population, ages 11-16, male', 'Population, ages 11-16, total',\n",
       "       'Population, ages 11-17, female', 'Population, ages 11-17, male',\n",
       "       'Population, ages 11-17, total', 'Population, ages 11-18, female',\n",
       "       'Population, ages 11-18, male', 'Population, ages 11-18, total',\n",
       "       'Population, ages 12-15, female', 'Population, ages 12-15, male',\n",
       "       'Population, ages 12-15, total', 'Population, ages 12-16, female',\n",
       "       'Population, ages 12-16, male', 'Population, ages 12-16, total',\n",
       "       'Population, ages 12-17, female', 'Population, ages 12-17, male',\n",
       "       'Population, ages 12-17, total', 'Population, ages 12-18, female',\n",
       "       'Population, ages 12-18, male', 'Population, ages 12-18, total',\n",
       "       'Population, ages 13-16, female', 'Population, ages 13-16, male',\n",
       "       'Population, ages 13-16, total', 'Population, ages 13-17, female',\n",
       "       'Population, ages 13-17, male', 'Population, ages 13-17, total',\n",
       "       'Population, ages 13-18, female', 'Population, ages 13-18, male',\n",
       "       'Population, ages 13-18, total', 'Population, ages 13-19, female',\n",
       "       'Population, ages 13-19, male', 'Population, ages 13-19, total',\n",
       "       'Population, ages 14-18, female', 'Population, ages 14-18, male',\n",
       "       'Population, ages 14-18, total', 'Population, ages 14-19, female',\n",
       "       'Population, ages 14-19, male', 'Population, ages 14-19, total',\n",
       "       'Population, ages 15-24, female', 'Population, ages 15-24, male',\n",
       "       'Population, ages 15-24, total',\n",
       "       'Population, ages 15-64 (% of total)',\n",
       "       'Population, ages 15-64, female', 'Population, ages 15-64, male',\n",
       "       'Population, ages 15-64, total', 'Population, ages 3-5, female',\n",
       "       'Population, ages 3-5, male', 'Population, ages 3-5, total',\n",
       "       'Population, ages 4-6, female', 'Population, ages 4-6, male',\n",
       "       'Population, ages 4-6, total', 'Population, ages 5-10, female',\n",
       "       'Population, ages 5-10, male', 'Population, ages 5-10, total',\n",
       "       'Population, ages 5-11, female', 'Population, ages 5-11, male',\n",
       "       'Population, ages 5-11, total', 'Population, ages 5-9, female',\n",
       "       'Population, ages 5-9, male', 'Population, ages 5-9, total',\n",
       "       'Population, ages 6-10, female', 'Population, ages 6-10, male',\n",
       "       'Population, ages 6-10, total', 'Population, ages 6-11, female',\n",
       "       'Population, ages 6-11, male', 'Population, ages 6-11, total',\n",
       "       'Population, ages 6-12, female', 'Population, ages 6-12, male',\n",
       "       'Population, ages 6-12, total', 'Population, ages 6-9, female',\n",
       "       'Population, ages 6-9, male', 'Population, ages 6-9, total',\n",
       "       'Population, ages 7-10, female', 'Population, ages 7-10, male',\n",
       "       'Population, ages 7-10, total', 'Population, ages 7-11, female',\n",
       "       'Population, ages 7-11, male', 'Population, ages 7-11, total',\n",
       "       'Population, ages 7-12, female', 'Population, ages 7-12, male',\n",
       "       'Population, ages 7-12, total', 'Population, ages 7-13, female',\n",
       "       'Population, ages 7-13, male', 'Population, ages 7-13, total',\n",
       "       'Population, ages 7-9, female', 'Population, ages 7-9, male',\n",
       "       'Population, ages 7-9, total', 'Population, female',\n",
       "       'Population, female (% of total)', 'Population, male',\n",
       "       'Population, male (% of total)', 'Population, total',\n",
       "       'School life expectancy, pre-primary, both sexes (years)',\n",
       "       'School life expectancy, pre-primary, female (years)',\n",
       "       'School life expectancy, pre-primary, gender parity index (GPI)',\n",
       "       'School life expectancy, pre-primary, male (years)',\n",
       "       'School life expectancy, primary and secondary, both sexes (years)',\n",
       "       'School life expectancy, primary and secondary, female (years)',\n",
       "       'School life expectancy, primary and secondary, gender parity index (GPI)',\n",
       "       'School life expectancy, primary and secondary, male (years)',\n",
       "       'School life expectancy, primary to tertiary, both sexes (years)',\n",
       "       'School life expectancy, primary to tertiary, female (years)',\n",
       "       'School life expectancy, primary to tertiary, gender parity index (GPI)',\n",
       "       'School life expectancy, primary to tertiary, male (years)',\n",
       "       'School life expectancy, primary, both sexes (years)',\n",
       "       'School life expectancy, primary, female (years)',\n",
       "       'School life expectancy, primary, gender parity index (GPI)',\n",
       "       'School life expectancy, primary, male (years)',\n",
       "       'School life expectancy, secondary, both sexes (years)',\n",
       "       'School life expectancy, secondary, female (years)',\n",
       "       'School life expectancy, secondary, gender parity index (GPI)',\n",
       "       'School life expectancy, secondary, male (years)',\n",
       "       'School life expectancy, tertiary, both sexes (years)',\n",
       "       'School life expectancy, tertiary, female (years)',\n",
       "       'School life expectancy, tertiary, gender parity index (GPI)',\n",
       "       'School life expectancy, tertiary, male (years)',\n",
       "       'Theoretical duration of lower secondary education (years)',\n",
       "       'Theoretical duration of pre-primary education (years)',\n",
       "       'Theoretical duration of primary education (years)',\n",
       "       'Theoretical duration of secondary education (years)',\n",
       "       'Theoretical duration of upper secondary education (years)',\n",
       "       'Unemployment, female (% of female labor force)',\n",
       "       'Unemployment, male (% of male labor force)',\n",
       "       'Unemployment, total (% of total labor force)',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 0-19. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 0-19. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 0-19. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 15+. Female',\n",
       "       'Wittgenstein Projection: Mean Years of Schooling. Age 15+. Gender Gap',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 15+. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 15+. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 15-19. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 15-19. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 15-19. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-24. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-24. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-24. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-39. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-39. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-39. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-64. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-64. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 20-64. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 25+. Female',\n",
       "       'Wittgenstein Projection: Mean Years of Schooling. Age 25+. Gender Gap',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 25+. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 25+. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 25-29. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 25-29. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 25-29. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 40-64. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 40-64. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 40-64. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 60+. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 60+. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 60+. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 65+. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 65+. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 65+. Total',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 80+. Female',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 80+. Male',\n",
       "       'Wittgenstein Projection: Mean years of schooling. Age 80+. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15+ by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 15-19 by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-24 by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-39 by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 20-64 by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25+ by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 25-29 by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 40-64 by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 60+ by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the population age 80+ by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Percentage of the total population by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 15-19 in thousands by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 20-24 in thousands by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Population age 25-29 in thousands by highest level of educational attainment. Upper Secondary. Total',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Female',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Male',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Incomplete Primary. Total',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Female',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Male',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Lower Secondary. Total',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Female',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Male',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. No Education. Total',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Female',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Male',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Post Secondary. Total',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Female',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Male',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Primary. Total',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Female',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Male',\n",
       "       'Wittgenstein Projection: Population in thousands by highest level of educational attainment. Upper Secondary. Total'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data[\"Indicator Code\"].isin(indicators_with_countries_wanted), \"Indicator Name\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a58355",
   "metadata": {},
   "source": [
    "we see there are multiple linked indicators. We will try to link them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0807c09c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.687768Z",
     "iopub.status.busy": "2024-03-28T22:58:28.687505Z",
     "iopub.status.idle": "2024-03-28T22:58:28.720726Z",
     "shell.execute_reply": "2024-03-28T22:58:28.719144Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.687743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Number of Countries with sufficient data</th>\n",
       "      <th>Cumuled number of years availlable</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indicator Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G1</th>\n",
       "      <td>Enrolment in Grade 1 of primary education, bot...</td>\n",
       "      <td>188</td>\n",
       "      <td>7109</td>\n",
       "      <td>UIS.E.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G1.F</th>\n",
       "      <td>Enrolment in Grade 1 of primary education, fem...</td>\n",
       "      <td>180</td>\n",
       "      <td>6699</td>\n",
       "      <td>UIS.E.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G2</th>\n",
       "      <td>Enrolment in Grade 2 of primary education, bot...</td>\n",
       "      <td>185</td>\n",
       "      <td>7020</td>\n",
       "      <td>UIS.E.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G2.F</th>\n",
       "      <td>Enrolment in Grade 2 of primary education, fem...</td>\n",
       "      <td>177</td>\n",
       "      <td>6615</td>\n",
       "      <td>UIS.E.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UIS.E.1.G3</th>\n",
       "      <td>Enrolment in Grade 3 of primary education, bot...</td>\n",
       "      <td>186</td>\n",
       "      <td>7028</td>\n",
       "      <td>UIS.E.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.1.MA</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "      <td>PRJ.POP.ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.1.MF</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "      <td>PRJ.POP.ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.3.FE</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "      <td>PRJ.POP.ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.3.MA</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "      <td>PRJ.POP.ALL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRJ.POP.ALL.3.MF</th>\n",
       "      <td>Wittgenstein Projection: Population in thousan...</td>\n",
       "      <td>167</td>\n",
       "      <td>3173</td>\n",
       "      <td>PRJ.POP.ALL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>647 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     Indicator Name  \\\n",
       "Indicator Code                                                        \n",
       "UIS.E.1.G1        Enrolment in Grade 1 of primary education, bot...   \n",
       "UIS.E.1.G1.F      Enrolment in Grade 1 of primary education, fem...   \n",
       "UIS.E.1.G2        Enrolment in Grade 2 of primary education, bot...   \n",
       "UIS.E.1.G2.F      Enrolment in Grade 2 of primary education, fem...   \n",
       "UIS.E.1.G3        Enrolment in Grade 3 of primary education, bot...   \n",
       "...                                                             ...   \n",
       "PRJ.POP.ALL.1.MA  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.1.MF  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.3.FE  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.3.MA  Wittgenstein Projection: Population in thousan...   \n",
       "PRJ.POP.ALL.3.MF  Wittgenstein Projection: Population in thousan...   \n",
       "\n",
       "                  Number of Countries with sufficient data  \\\n",
       "Indicator Code                                               \n",
       "UIS.E.1.G1                                             188   \n",
       "UIS.E.1.G1.F                                           180   \n",
       "UIS.E.1.G2                                             185   \n",
       "UIS.E.1.G2.F                                           177   \n",
       "UIS.E.1.G3                                             186   \n",
       "...                                                    ...   \n",
       "PRJ.POP.ALL.1.MA                                       167   \n",
       "PRJ.POP.ALL.1.MF                                       167   \n",
       "PRJ.POP.ALL.3.FE                                       167   \n",
       "PRJ.POP.ALL.3.MA                                       167   \n",
       "PRJ.POP.ALL.3.MF                                       167   \n",
       "\n",
       "                  Cumuled number of years availlable        Group  \n",
       "Indicator Code                                                     \n",
       "UIS.E.1.G1                                      7109      UIS.E.1  \n",
       "UIS.E.1.G1.F                                    6699      UIS.E.1  \n",
       "UIS.E.1.G2                                      7020      UIS.E.1  \n",
       "UIS.E.1.G2.F                                    6615      UIS.E.1  \n",
       "UIS.E.1.G3                                      7028      UIS.E.1  \n",
       "...                                              ...          ...  \n",
       "PRJ.POP.ALL.1.MA                                3173  PRJ.POP.ALL  \n",
       "PRJ.POP.ALL.1.MF                                3173  PRJ.POP.ALL  \n",
       "PRJ.POP.ALL.3.FE                                3173  PRJ.POP.ALL  \n",
       "PRJ.POP.ALL.3.MA                                3173  PRJ.POP.ALL  \n",
       "PRJ.POP.ALL.3.MF                                3173  PRJ.POP.ALL  \n",
       "\n",
       "[647 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators_df[\"Group\"] = indicators_df.index.str.split(\".\", n=3).to_list()\n",
    "indicators_df[\"Group\"] = indicators_df.Group.apply(lambda x : \".\".join(x[:3]))\n",
    "indicators_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f30de0d4-0e39-4195-8c20-fde79304078e",
   "metadata": {},
   "source": [
    "## Visual exploration \n",
    "The [World Bank]() website was use to explore big chuncks of the data set. Indeed, the data set contains very rich but sparse data accross **XXX** countries, over **XXX** years for **XXX** categories of data. This notebook explores the data related to the following key features selected from the world bank website. \n",
    "\n",
    "- feature 1\n",
    "- feature 2\n",
    "- ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f23fd829-9b01-485e-aeb1-29215f6800cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.722281Z",
     "iopub.status.busy": "2024-03-28T22:58:28.721989Z",
     "iopub.status.idle": "2024-03-28T22:58:28.780082Z",
     "shell.execute_reply": "2024-03-28T22:58:28.777729Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.722256Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# TODO explore data youpi !\n",
    "# idea of things to look at\n",
    "# for numerical var:\n",
    "    # histograms, box plots, scatter plots, correlation matrices... \n",
    "# for categorical var:\n",
    "    # bar charts ...\n",
    "# basic stats on data\n",
    "# get insight on distribution, outliers ect.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "552d91b1-8f71-42cd-863b-adb558c187a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce7e514d-2316-48aa-ad31-7dcfc88341b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8eab643e-63dd-4b75-9b49-031de0ddd755",
   "metadata": {},
   "source": [
    "## Draft pieces of code (to remove in the end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4322ffca-b228-4c29-a804-76986d52a99e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.783239Z",
     "iopub.status.busy": "2024-03-28T22:58:28.782567Z",
     "iopub.status.idle": "2024-03-28T22:58:28.915845Z",
     "shell.execute_reply": "2024-03-28T22:58:28.913927Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.783176Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "attendanceF = data[data[\"Indicator Code\"] == \"HH.DHS.NAR.1.F\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "071cd456-0b1a-4278-911c-d8695112776d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.917620Z",
     "iopub.status.busy": "2024-03-28T22:58:28.917362Z",
     "iopub.status.idle": "2024-03-28T22:58:28.964006Z",
     "shell.execute_reply": "2024-03-28T22:58:28.961641Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.917597Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "subset = attendanceF.dropna(subset=['2002','2003','2004','2005'], how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32293432-488c-4468-9812-58fe6424d0ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T22:58:28.967777Z",
     "iopub.status.busy": "2024-03-28T22:58:28.967070Z",
     "iopub.status.idle": "2024-03-28T22:58:29.032946Z",
     "shell.execute_reply": "2024-03-28T22:58:29.030877Z",
     "shell.execute_reply.started": "2024-03-28T22:58:28.967715Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121515</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.246757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147170</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.883621</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176490</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.205487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202145</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.879824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213140</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.633008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country Name  2002       2003       2004       2005\n",
       "121515       Armenia   NaN        NaN        NaN  98.246757\n",
       "147170    Bangladesh   NaN        NaN  84.883621        NaN\n",
       "176490       Bolivia   NaN  82.205487        NaN        NaN\n",
       "202145  Burkina Faso   NaN  28.879824        NaN        NaN\n",
       "213140      Cambodia   NaN        NaN        NaN  76.633008"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset[['Country Name','2002','2003','2004','2005']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cca1a63-44b5-47a3-ae41-d631b55eb918",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}