EDA_vegetarian_indictaors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size:3em;\"> Analysis of the global plant-based meat market </span> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an analysis of the global plant-based meat market, that take into consideration the following three axes for each country: water shortages, the meat consumption in relation to the production and the percentage of vegetarians, creating a coefficient to predict which country will be more suited to the market. \n",
    "\n",
    "The following notebook is the cleaning and prep of the vegetarian population part. \n",
    "This CSV file, along to the other parts of the analysis, has been used to build a dashbord in Tableau, that you can find here:\n",
    "https://public.tableau.com/app/profile/rossana.coro/viz/VegetalMeatProject/Accueil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>place</th>\n",
       "      <th>pop2023</th>\n",
       "      <th>growthRate</th>\n",
       "      <th>area</th>\n",
       "      <th>country</th>\n",
       "      <th>cca3</th>\n",
       "      <th>cca2</th>\n",
       "      <th>ccn3</th>\n",
       "      <th>region</th>\n",
       "      <th>subregion</th>\n",
       "      <th>unMember</th>\n",
       "      <th>officialName</th>\n",
       "      <th>landAreaKm</th>\n",
       "      <th>density</th>\n",
       "      <th>densityMi</th>\n",
       "      <th>Rank</th>\n",
       "      <th>vegetarianismByCountry_percVeg</th>\n",
       "      <th>vegetarianismByCountry_totVeg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>356</td>\n",
       "      <td>1428627663</td>\n",
       "      <td>0.00808</td>\n",
       "      <td>3287590</td>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>IN</td>\n",
       "      <td>356</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia, South Central Asia</td>\n",
       "      <td>True</td>\n",
       "      <td>Republic of India</td>\n",
       "      <td>2973190.0</td>\n",
       "      <td>480.5033</td>\n",
       "      <td>1244.5036</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>419000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>156</td>\n",
       "      <td>1425671352</td>\n",
       "      <td>-0.00015</td>\n",
       "      <td>9706961</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>CN</td>\n",
       "      <td>156</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>True</td>\n",
       "      <td>People's Republic of China</td>\n",
       "      <td>9424702.9</td>\n",
       "      <td>151.2696</td>\n",
       "      <td>391.7884</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>840</td>\n",
       "      <td>339996563</td>\n",
       "      <td>0.00505</td>\n",
       "      <td>9372610</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>US</td>\n",
       "      <td>840</td>\n",
       "      <td>North America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>True</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>9147420.0</td>\n",
       "      <td>37.1686</td>\n",
       "      <td>96.2666</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76</td>\n",
       "      <td>216422446</td>\n",
       "      <td>0.00515</td>\n",
       "      <td>8515767</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>BR</td>\n",
       "      <td>76</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America, Latin America</td>\n",
       "      <td>True</td>\n",
       "      <td>Federative Republic of Brazil</td>\n",
       "      <td>8358140.0</td>\n",
       "      <td>25.8936</td>\n",
       "      <td>67.0645</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>643</td>\n",
       "      <td>144444359</td>\n",
       "      <td>-0.00186</td>\n",
       "      <td>17098242</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>RU</td>\n",
       "      <td>643</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>True</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>16376870.0</td>\n",
       "      <td>8.8200</td>\n",
       "      <td>22.8439</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   place     pop2023  growthRate      area        country cca3 cca2  ccn3  \\\n",
       "0    356  1428627663     0.00808   3287590          India  IND   IN   356   \n",
       "1    156  1425671352    -0.00015   9706961          China  CHN   CN   156   \n",
       "2    840   339996563     0.00505   9372610  United States  USA   US   840   \n",
       "3     76   216422446     0.00515   8515767         Brazil  BRA   BR    76   \n",
       "4    643   144444359    -0.00186  17098242         Russia  RUS   RU   643   \n",
       "\n",
       "          region                          subregion  unMember  \\\n",
       "0           Asia  Southern Asia, South Central Asia      True   \n",
       "1           Asia                       Eastern Asia      True   \n",
       "2  North America                   Northern America      True   \n",
       "3  South America       South America, Latin America      True   \n",
       "4         Europe                     Eastern Europe      True   \n",
       "\n",
       "                    officialName  landAreaKm   density  densityMi  Rank  \\\n",
       "0              Republic of India   2973190.0  480.5033  1244.5036     1   \n",
       "1     People's Republic of China   9424702.9  151.2696   391.7884     2   \n",
       "2       United States of America   9147420.0   37.1686    96.2666     3   \n",
       "3  Federative Republic of Brazil   8358140.0   25.8936    67.0645     7   \n",
       "4             Russian Federation  16376870.0    8.8200    22.8439     9   \n",
       "\n",
       "   vegetarianismByCountry_percVeg  vegetarianismByCountry_totVeg  \n",
       "0                            24.0                      419000000  \n",
       "1                             5.0                       50000000  \n",
       "2                             5.0                       16000000  \n",
       "3                            14.0                       29260000  \n",
       "4                             1.0                        1400000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import csv file from folder\n",
    "dataset_brut = pd.read_csv(\"/Users/rossana/Desktop/demoday/fichiers bruts/vegetarianism-by-country-2023.csv\")\n",
    "dataset_brut.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop2023</th>\n",
       "      <th>country</th>\n",
       "      <th>cca3</th>\n",
       "      <th>region</th>\n",
       "      <th>subregion</th>\n",
       "      <th>Rank</th>\n",
       "      <th>vegetarianismByCountry_percVeg</th>\n",
       "      <th>vegetarianismByCountry_totVeg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1428627663</td>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia, South Central Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>419000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1425671352</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>339996563</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>216422446</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America, Latin America</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>144444359</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop2023        country cca3         region  \\\n",
       "0  1428627663          India  IND           Asia   \n",
       "1  1425671352          China  CHN           Asia   \n",
       "2   339996563  United States  USA  North America   \n",
       "3   216422446         Brazil  BRA  South America   \n",
       "4   144444359         Russia  RUS         Europe   \n",
       "\n",
       "                           subregion  Rank  vegetarianismByCountry_percVeg  \\\n",
       "0  Southern Asia, South Central Asia     1                            24.0   \n",
       "1                       Eastern Asia     2                             5.0   \n",
       "2                   Northern America     3                             5.0   \n",
       "3       South America, Latin America     7                            14.0   \n",
       "4                     Eastern Europe     9                             1.0   \n",
       "\n",
       "   vegetarianismByCountry_totVeg  \n",
       "0                      419000000  \n",
       "1                       50000000  \n",
       "2                       16000000  \n",
       "3                       29260000  \n",
       "4                        1400000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Delete unuseful column\n",
    "dataset = dataset_brut.drop(columns=[\"place\",\"growthRate\",\"area\",\"cca2\",\"ccn3\",\"unMember\",\"officialName\",\"landAreaKm\",\"density\",\"densityMi\"])\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population_2023</th>\n",
       "      <th>Country</th>\n",
       "      <th>Location_code</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Sub_continent</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Vegetarianism_percentage</th>\n",
       "      <th>Vegetarian_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1428627663</td>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern Asia, South Central Asia</td>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>419000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1425671352</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>339996563</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>Northern America</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>216422446</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BRA</td>\n",
       "      <td>South America</td>\n",
       "      <td>South America, Latin America</td>\n",
       "      <td>7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>29260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>144444359</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Eastern Europe</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population_2023        Country Location_code      Continent  \\\n",
       "0       1428627663          India           IND           Asia   \n",
       "1       1425671352          China           CHN           Asia   \n",
       "2        339996563  United States           USA  North America   \n",
       "3        216422446         Brazil           BRA  South America   \n",
       "4        144444359         Russia           RUS         Europe   \n",
       "\n",
       "                       Sub_continent  Rank  Vegetarianism_percentage  \\\n",
       "0  Southern Asia, South Central Asia     1                      24.0   \n",
       "1                       Eastern Asia     2                       5.0   \n",
       "2                   Northern America     3                       5.0   \n",
       "3       South America, Latin America     7                      14.0   \n",
       "4                     Eastern Europe     9                       1.0   \n",
       "\n",
       "   Vegetarian_population  \n",
       "0              419000000  \n",
       "1               50000000  \n",
       "2               16000000  \n",
       "3               29260000  \n",
       "4                1400000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename columns\n",
    "dataset = dataset.rename(columns={\"pop2023\": \"Population_2023\", \"country\": \"Country\",  \"cca3\": \"Location_code\", \"region\": \"Continent\",\"subregion\": \"Sub_continent\",\"vegetarianismByCountry_percVeg\": \"Vegetarianism_percentage\",\"vegetarianismByCountry_totVeg\":\"Vegetarian_population\"})\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ARG', 'BEL', 'BRA', 'CAN', 'CHE', 'CHL', 'CHN', 'CZE', 'DEU', 'DNK', 'ESP', 'EST', 'FIN', 'FRA', 'GRC', 'IND', 'IRL', 'ISR', 'ITA', 'JAM', 'JPN', 'KOR', 'LTU', 'LVA', 'MEX', 'NLD', 'NOR', 'NZL', 'PHL', 'POL', 'PRT', 'RUS', 'SGP', 'SVN', 'SWE', 'THA', 'TWN', 'UKR', 'USA', 'VNM']\n"
     ]
    }
   ],
   "source": [
    "#Checking the countries code in order to join them to the main dataset\n",
    "country_water= dataset['Location_code'].unique()\n",
    "print(sorted(country_water))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 8)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Keeping just the country that also are in the main dataset in order to join them with the main dataset\n",
    "country_list = ['AFR', 'ARG', 'ASP', 'AUS', 'BRA', 'BRICS', 'CAN', 'CHE', 'CHL', 'CHN', 'COL', 'DVD', 'DVG', 'EGY', 'ETH', 'EUN', 'EUR', 'GBR', 'IDN', 'IND', 'IRN', 'ISR', 'JPN', 'KAZ', 'KOR', 'LAC', 'MEX', 'MYS', 'NGA', 'NOA', 'NOR', 'NZL', 'OCD', 'OECD', 'PAK', 'PER', 'PHL', 'PRY', 'RUS', 'SAU', 'THA', 'TUR', 'UKR', 'USA', 'VNM', 'WLD', 'ZAF']\n",
    "\n",
    "dataset = dataset[dataset['Location_code'].isin(country_list)]\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ARG' 'BRA' 'CAN' 'CHE' 'CHL' 'CHN' 'IND' 'ISR' 'JPN' 'KOR' 'MEX' 'NOR'\n",
      " 'NZL' 'PHL' 'RUS' 'THA' 'UKR' 'USA' 'VNM']\n"
     ]
    }
   ],
   "source": [
    "#Check if the these countries correspond to the main file countries\n",
    "location = np.unique(dataset['Location_code'])\n",
    "\n",
    "sorted_location = np.sort(location)\n",
    "\n",
    "print(sorted_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population_2023</th>\n",
       "      <th>Country</th>\n",
       "      <th>Location_code</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Sub_continent</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Vegetarianism_percentage</th>\n",
       "      <th>Vegetarian_population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Eastern Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.271735e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.947368</td>\n",
       "      <td>8.710526</td>\n",
       "      <td>3.039111e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.310776e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.343377</td>\n",
       "      <td>5.770316</td>\n",
       "      <td>9.499412e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.228100e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.250000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.818711e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.223000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.180128e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.300000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.364500e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.358000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.428628e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>4.190000e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Population_2023 Country Location_code Continent Sub_continent  \\\n",
       "count      1.900000e+01      19            19        19            19   \n",
       "unique              NaN      19            19         5            11   \n",
       "top                 NaN   India           IND      Asia  Eastern Asia   \n",
       "freq                NaN       1             1         8             3   \n",
       "mean       2.271735e+08     NaN           NaN       NaN           NaN   \n",
       "std        4.310776e+08     NaN           NaN       NaN           NaN   \n",
       "min        5.228100e+06     NaN           NaN       NaN           NaN   \n",
       "25%        2.818711e+07     NaN           NaN       NaN           NaN   \n",
       "50%        7.180128e+07     NaN           NaN       NaN           NaN   \n",
       "75%        1.364500e+08     NaN           NaN       NaN           NaN   \n",
       "max        1.428628e+09     NaN           NaN       NaN           NaN   \n",
       "\n",
       "              Rank  Vegetarianism_percentage  Vegetarian_population  \n",
       "count    19.000000                 19.000000           1.900000e+01  \n",
       "unique         NaN                       NaN                    NaN  \n",
       "top            NaN                       NaN                    NaN  \n",
       "freq           NaN                       NaN                    NaN  \n",
       "mean     38.947368                  8.710526           3.039111e+07  \n",
       "std      41.343377                  5.770316           9.499412e+07  \n",
       "min       1.000000                  1.000000           4.250000e+05  \n",
       "25%       9.500000                  5.000000           1.223000e+06  \n",
       "50%      20.000000                  7.000000           2.300000e+06  \n",
       "75%      53.000000                 11.000000           1.358000e+07  \n",
       "max     123.000000                 24.000000           4.190000e+08  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Does the values are consistent?\n",
    "dataset.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes, they are\n"
     ]
    }
   ],
   "source": [
    "print(\"Yes, they are\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Export new file csv in order to join it to the main table in the dashboard\n",
    "dataset.to_csv(r\"/Users/rossana/Desktop/demoday/Vegetarianism_per_country.csv\")"
   ]
  }
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