MarketExtensionStrategyAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load CSV file needed for analysis and import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will load following files downloaded from FAO website, those are the same files we used for project 4. \n",
    "\n",
    "1. first file: food_balance_total.csv\n",
    "2. second file: food_balance_item_group.csv\n",
    "\n",
    "Also in terms of data cleaning and customizing, we will initially use same code that we used for project 4 below and then do manipulation based on our needs for this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>country</th>\n",
       "      <th>item_code</th>\n",
       "      <th>item</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>domestic_supply_quantity</th>\n",
       "      <th>export_quantity</th>\n",
       "      <th>fat_supply_quantity_gcapitaday</th>\n",
       "      <th>feed</th>\n",
       "      <th>...</th>\n",
       "      <th>import_quantity</th>\n",
       "      <th>losses</th>\n",
       "      <th>other_uses</th>\n",
       "      <th>processing</th>\n",
       "      <th>production</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>residuals</th>\n",
       "      <th>seed</th>\n",
       "      <th>stock_variation</th>\n",
       "      <th>tourist_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2014</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>639.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.90</td>\n",
       "      <td>110.0</td>\n",
       "      <td>...</td>\n",
       "      <td>344.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>33.01</td>\n",
       "      <td>46.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2015</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>748.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.02</td>\n",
       "      <td>125.0</td>\n",
       "      <td>...</td>\n",
       "      <td>324.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>33.07</td>\n",
       "      <td>106.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-63.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2016</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>650.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.22</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>312.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>34.70</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2017</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>547.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.28</td>\n",
       "      <td>105.0</td>\n",
       "      <td>...</td>\n",
       "      <td>337.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>34.36</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>2511</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2018</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>571.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.40</td>\n",
       "      <td>118.0</td>\n",
       "      <td>...</td>\n",
       "      <td>379.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>34.25</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   country_code  country  item_code                item  year   origin  \\\n",
       "0             1  Armenia       2511  Wheat and products  2014  vegetal   \n",
       "1             1  Armenia       2511  Wheat and products  2015  vegetal   \n",
       "2             1  Armenia       2511  Wheat and products  2016  vegetal   \n",
       "3             1  Armenia       2511  Wheat and products  2017  vegetal   \n",
       "4             1  Armenia       2511  Wheat and products  2018  vegetal   \n",
       "\n",
       "   domestic_supply_quantity  export_quantity  fat_supply_quantity_gcapitaday  \\\n",
       "0                     639.0              1.0                            3.90   \n",
       "1                     748.0              2.0                            4.02   \n",
       "2                     650.0              1.0                            4.22   \n",
       "3                     547.0              1.0                            4.28   \n",
       "4                     571.0              1.0                            4.40   \n",
       "\n",
       "    feed  ...  import_quantity  losses  other_uses  processing  production  \\\n",
       "0  110.0  ...            344.0    30.0         0.0        16.0       338.0   \n",
       "1  125.0  ...            324.0    59.0         0.0        16.0       363.0   \n",
       "2  128.0  ...            312.0    64.0         0.0        17.0       350.0   \n",
       "3  105.0  ...            337.0    15.0         0.0        16.0       176.0   \n",
       "4  118.0  ...            379.0    16.0         0.0        16.0       187.0   \n",
       "\n",
       "   protein_supply_quantity_gcapitaday  residuals  seed  stock_variation  \\\n",
       "0                               33.01       46.0  32.0             42.0   \n",
       "1                               33.07      106.0  33.0            -63.0   \n",
       "2                               34.70      -22.0  33.0             11.0   \n",
       "3                               34.36      -42.0  25.0            -34.0   \n",
       "4                               34.25      -27.0  20.0             -5.0   \n",
       "\n",
       "   tourist_consumption  \n",
       "0                  NaN  \n",
       "1                  NaN  \n",
       "2                  NaN  \n",
       "3                  NaN  \n",
       "4                  NaN  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = pd.read_csv(\"food_balance_total.csv\",encoding = \"ISO-8859-1\", engine='python')\n",
    "item_group = pd.read_csv(\"food_balance_item_group.csv\")\n",
    "\n",
    "\n",
    "total=total.merge(item_group, on='Item Code', how='left')\n",
    "animal=total.loc[total['Item Group Code'] ==2941]\n",
    "vegetal=total.loc[total['Item Group Code'] ==2903]\n",
    "animal.to_csv('food_balance_animal.csv')\n",
    "vegetal.to_csv('food_balance_vegetal.csv')\n",
    "ani = pd.read_csv(\"food_balance_animal.csv\")\n",
    "veg = pd.read_csv(\"food_balance_vegetal.csv\")\n",
    "ani=ani.drop(['Unnamed: 0','Item Group Code', 'Item Group', 'Item_y', 'Factor', 'HS Code','HS07 Code', 'HS12 Code', 'CPC Code'], axis=1)\n",
    "veg=veg.drop(['Unnamed: 0','Item Group Code', 'Item Group', 'Item_y', 'Factor', 'HS Code','HS07 Code', 'HS12 Code', 'CPC Code'], axis=1)\n",
    "# Adds variable ‘origin’\n",
    "veg[\"origin\"] = \"vegetal\"\n",
    "ani[\"origin\"] = \"animal\"\n",
    "# Adds variable ‘origin’\n",
    "veg[\"origin\"] = \"vegetal\"\n",
    "ani[\"origin\"] = \"animal\"\n",
    "# Appends veg and ani to one dataframe\n",
    "temp = ani.append(veg)\n",
    "# Deletion of ani and veg file\n",
    "del ani, veg\n",
    "# Renaming of temp’s columns\n",
    "temp.columns = [\"country_code\", \"country\",'item_code','item','x1','element','x2','year','unit','value','x3','origin']\n",
    "# Transformation of ‘temp’ to a pivot table\n",
    "data = temp.pivot_table(\n",
    "index=[\"country_code\", \"country\", \"item_code\", \"item\", \"year\", \"origin\"],\n",
    "columns = [\"element\"], values=[\"value\"], aggfunc=sum)\n",
    "# Renaming of data’s columns (be careful, the order of the columns needs\n",
    "# to be adapted to your own data !)\n",
    "data.columns = ['domestic_supply_quantity', 'export_quantity','fat_supply_quantity_gcapitaday','feed',\n",
    "'food', 'food_supply_kcalcapitaday','food_supply_quantity_kgcapitayr', 'import_quantity','losses', 'other_uses',\n",
    "'processing', 'production', 'protein_supply_quantity_gcapitaday','residuals','seed', 'stock_variation', 'tourist_consumption']\n",
    "# Index columns need to be normal columns\n",
    "\n",
    "## \"data\" dataframe below will be the combined dataframe which will be manipulated into smaller dataframe based on needs\n",
    "\n",
    "data = data.reset_index()\n",
    "data.columns\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only take columns needed for analysis later\n",
    "data.columns\n",
    "\n",
    "data=data[['country_code', 'country','item', 'year', 'origin',\n",
    "       'domestic_supply_quantity','food_supply_kcalcapitaday', 'food_supply_quantity_kgcapitayr',\n",
    "       'protein_supply_quantity_gcapitaday']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98729 entries, 0 to 98728\n",
      "Data columns (total 9 columns):\n",
      "country_code                          98729 non-null int64\n",
      "country                               98729 non-null object\n",
      "item                                  98729 non-null object\n",
      "year                                  98729 non-null int64\n",
      "origin                                98729 non-null object\n",
      "domestic_supply_quantity              98729 non-null float64\n",
      "food_supply_kcalcapitaday             89580 non-null float64\n",
      "food_supply_quantity_kgcapitayr       90231 non-null float64\n",
      "protein_supply_quantity_gcapitaday    89694 non-null float64\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "## check if we don't have any missing value \n",
    "\n",
    "data.info()\n",
    "\n",
    "## yes it seems there are missing values so let's get rid of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 89580 entries, 0 to 98728\n",
      "Data columns (total 9 columns):\n",
      "country_code                          89580 non-null int64\n",
      "country                               89580 non-null object\n",
      "item                                  89580 non-null object\n",
      "year                                  89580 non-null int64\n",
      "origin                                89580 non-null object\n",
      "domestic_supply_quantity              89580 non-null float64\n",
      "food_supply_kcalcapitaday             89580 non-null float64\n",
      "food_supply_quantity_kgcapitayr       89580 non-null float64\n",
      "protein_supply_quantity_gcapitaday    89580 non-null float64\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "data=data.dropna()\n",
    "data.info()\n",
    "## now there is no NA value in this dataframe!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To get population dataframe, we use same code that was used for project 4. We know already that there is a duplicate value in China, so we are going to keep this code line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33371000.000000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>34414000.000000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>35383000.000000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296000.000000</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country      population  year\n",
       "0  Afghanistan 33371000.000000  2014\n",
       "1  Afghanistan 34414000.000000  2015\n",
       "2  Afghanistan 35383000.000000  2016\n",
       "3  Afghanistan 36296000.000000  2017\n",
       "4  Afghanistan 37172000.000000  2018"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = total [total.Flag != 'A']\n",
    "population = population.rename(columns = {'Area Code': 'Area_Code'})\n",
    "# Get rid of data on China using area code 351\n",
    "population= population[population.Area_Code!= 351]\n",
    "population= population[population.Element== 'Total Population - Both sexes']\n",
    "population=population[['Area', 'Value','Year']]\n",
    "population['Value']=population['Value']*1000\n",
    "population = population.rename(columns = {'Area': 'country', 'Value': 'population','Year':'year'}, inplace = False)\n",
    "population_by_year=population.groupby('year')['population'].sum()\n",
    "population_by_year=population_by_year.reset_index()\n",
    "pd.set_option('float_format', '{:f}'.format)\n",
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Congo                                 5\n",
       "Armenia                               5\n",
       "China, Hong Kong SAR                  5\n",
       "Albania                               5\n",
       "Solomon Islands                       5\n",
       "                                     ..\n",
       "Venezuela (Bolivarian Republic of)    5\n",
       "Lebanon                               5\n",
       "China, mainland                       5\n",
       "Viet Nam                              5\n",
       "Saint Lucia                           5\n",
       "Name: country, Length: 173, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2014   7131485000.000000\n",
       "2015   7212776000.000000\n",
       "2016   7293574000.000000\n",
       "2017   7373798000.000000\n",
       "2018   7453133000.000000\n",
       "Name: population, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.groupby('year')['population'].sum()\n",
    "\n",
    "## seems that population data here seems correct, we will use this dataframe to clean up data dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## now let's just put aside of countries dataframe so that we have data for later on in case we need country list\n",
    "countries=population['country'].value_counts().to_frame()\n",
    "countries['country']=countries['country'].astype(str)\n",
    "countries=countries.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'country'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Congo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>China, Hong Kong SAR</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>China, mainland</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>Saint Lucia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                country number\n",
       "0                                 Congo      5\n",
       "1                               Armenia      5\n",
       "2                  China, Hong Kong SAR      5\n",
       "3                               Albania      5\n",
       "4                       Solomon Islands      5\n",
       "..                                  ...    ...\n",
       "168  Venezuela (Bolivarian Republic of)      5\n",
       "169                             Lebanon      5\n",
       "170                     China, mainland      5\n",
       "171                            Viet Nam      5\n",
       "172                         Saint Lucia      5\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries=countries.rename(columns={\"index\": \"country\", \"country\": \"number\"})\n",
    "countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 72974 entries, 0 to 72973\n",
      "Data columns (total 10 columns):\n",
      "country_code                          72974 non-null int64\n",
      "country                               72974 non-null object\n",
      "item                                  72974 non-null object\n",
      "year                                  72974 non-null int64\n",
      "origin                                72974 non-null object\n",
      "domestic_supply_quantity              72974 non-null float64\n",
      "food_supply_kcalcapitaday             72974 non-null float64\n",
      "food_supply_quantity_kgcapitayr       72974 non-null float64\n",
      "protein_supply_quantity_gcapitaday    72974 non-null float64\n",
      "number                                72974 non-null object\n",
      "dtypes: float64(4), int64(2), object(4)\n",
      "memory usage: 6.1+ MB\n"
     ]
    }
   ],
   "source": [
    "## clean the data based on country list \n",
    "\n",
    "data_cleaned=pd.merge(data,countries,on='country')\n",
    "data_cleaned.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Preparing Samples of Countries\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Difference in Poluation dataframe\\\n",
    "- Difference in population between a previous year (elective) and the current year, expressed as a percentage.= we will manipulate polulation dataframe we made previously.\n",
    "\n",
    "For below 5 variables, we will you 'data_cleaned' dataframe that we previously obtained and manipulate to get what we need.\\\n",
    "2) Proportion of animal proteins compared to the total amount of proteins available in the country.\\\n",
    "3) Food availability in protein per capita.\\\n",
    "4) Food availability in calories per capita.\\\n",
    "5) Poultry consmuption trend (previous 5 years)\\\n",
    "6) Poultry domestic supply\n",
    "\n",
    "For GDP variable, we will use dowloaded data from FAO\\\n",
    "7) GDP per capita- each country\\\n",
    "After we gain each of data frame, we will put them together and filter data to make short list of targeted countries.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Difference in population between previous year and current year and expressed as a percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33371000.000000</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>34414000.000000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>35383000.000000</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296000.000000</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country      population  year\n",
       "0  Afghanistan 33371000.000000  2014\n",
       "1  Afghanistan 34414000.000000  2015\n",
       "2  Afghanistan 35383000.000000  2016\n",
       "3  Afghanistan 36296000.000000  2017\n",
       "4  Afghanistan 37172000.000000  2018"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking out population dataframe to have an idea how to manipulate data to get population difference, it seems we have population per year 2014,2015,2016,2017,2018\n",
    "\n",
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 865 entries, 0 to 2962069\n",
      "Data columns (total 3 columns):\n",
      "country       865 non-null object\n",
      "population    865 non-null float64\n",
      "year          865 non-null int64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 27.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# check if there is missing or NA value\n",
    "\n",
    "population.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Congo                                 5\n",
       "Armenia                               5\n",
       "China, Hong Kong SAR                  5\n",
       "Albania                               5\n",
       "Solomon Islands                       5\n",
       "                                     ..\n",
       "Venezuela (Bolivarian Republic of)    5\n",
       "Lebanon                               5\n",
       "China, mainland                       5\n",
       "Viet Nam                              5\n",
       "Saint Lucia                           5\n",
       "Name: country, Length: 173, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.country.value_counts()\n",
    "\n",
    "## 173 countries in the list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we will only get 2017, 2018 population data to calculate population difference\n",
    "\n",
    "population_cleaned = population[(population['year'] >=2017) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "population_cleaned['population_difference']=population_cleaned.population.pct_change()*100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "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</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "      <th>population_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36296000.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.413489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15673</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2884000.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>-92.241472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15674</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2883000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>-0.034674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31141</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>41389000.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>1335.622615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2924846</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>28499000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.385486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2943590</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>16854000.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>-40.861083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2943591</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>17352000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.954788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2962068</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14237000.000000</td>\n",
       "      <td>2017</td>\n",
       "      <td>-17.951821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2962069</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14439000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.418838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             country      population  year  population_difference\n",
       "3        Afghanistan 36296000.000000  2017                    nan\n",
       "4        Afghanistan 37172000.000000  2018               2.413489\n",
       "15673        Albania  2884000.000000  2017             -92.241472\n",
       "15674        Albania  2883000.000000  2018              -0.034674\n",
       "31141        Algeria 41389000.000000  2017            1335.622615\n",
       "...              ...             ...   ...                    ...\n",
       "2924846        Yemen 28499000.000000  2018               2.385486\n",
       "2943590       Zambia 16854000.000000  2017             -40.861083\n",
       "2943591       Zambia 17352000.000000  2018               2.954788\n",
       "2962068     Zimbabwe 14237000.000000  2017             -17.951821\n",
       "2962069     Zimbabwe 14439000.000000  2018               1.418838\n",
       "\n",
       "[346 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## we will see now year 2017,2018 and population difference column, now we will only extract last year 2018 to get most recent year for population and keep population difference percentage\n",
    "\n",
    "population_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_difference_per_country= population_cleaned[(population_cleaned['year'] >=2018) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* data frame made with population difference(2017VS2018) for each country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "      <th>population_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.413489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15674</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2883000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>-0.034674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31142</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>42228000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>2.027109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47562</td>\n",
       "      <td>Angola</td>\n",
       "      <td>30810000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>3.330315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65563</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>96000.000000</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.052632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country      population  year  population_difference\n",
       "4              Afghanistan 37172000.000000  2018               2.413489\n",
       "15674              Albania  2883000.000000  2018              -0.034674\n",
       "31142              Algeria 42228000.000000  2018               2.027109\n",
       "47562               Angola 30810000.000000  2018               3.330315\n",
       "65563  Antigua and Barbuda    96000.000000  2018               1.052632"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## checking out last result!\n",
    "\n",
    "population_difference_per_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37172000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15674</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2883000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31142</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>42228000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47562</td>\n",
       "      <td>Angola</td>\n",
       "      <td>30810000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65563</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>96000.000000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country      population  year\n",
       "4              Afghanistan 37172000.000000  2018\n",
       "15674              Albania  2883000.000000  2018\n",
       "31142              Algeria 42228000.000000  2018\n",
       "47562               Angola 30810000.000000  2018\n",
       "65563  Antigua and Barbuda    96000.000000  2018"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## meantime, let's just put aside 'popualtion' dataframe, in case we only want to have population data, not a percentage for later on.\n",
    "\n",
    "population=population_cleaned[(population_cleaned['year'] >=2018) ]\n",
    "population=population[['country','population','year']]\n",
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.240687651475247\n"
     ]
    }
   ],
   "source": [
    "# check out mean growth of population worldwide to see mean value\n",
    "\n",
    "population_difference_per_country[[\"population_difference\"]].boxplot(whis=3,showmeans=True)\n",
    "mean = population_difference_per_country['population_difference'].mean()\n",
    "plt.show()\n",
    "print(mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15674</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31142</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47562</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65563</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2887962</td>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2906731</td>\n",
       "      <td>Viet Nam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2924846</td>\n",
       "      <td>Yemen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2943591</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2962069</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    country\n",
       "4                               Afghanistan\n",
       "15674                               Albania\n",
       "31142                               Algeria\n",
       "47562                                Angola\n",
       "65563                   Antigua and Barbuda\n",
       "...                                     ...\n",
       "2887962  Venezuela (Bolivarian Republic of)\n",
       "2906731                            Viet Nam\n",
       "2924846                               Yemen\n",
       "2943591                              Zambia\n",
       "2962069                            Zimbabwe\n",
       "\n",
       "[173 rows x 1 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_list=population_difference_per_country['country']\n",
    "country_list=country_list.to_frame()\n",
    "country_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Proportion of animal proteins compared to the total amount of protein available in the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country_code', 'country', 'item', 'year', 'origin',\n",
       "       'domestic_supply_quantity', 'food_supply_kcalcapitaday',\n",
       "       'food_supply_quantity_kgcapitayr', 'protein_supply_quantity_gcapitaday',\n",
       "       'number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## let's check data_cleaned data frame's columns to see how we could filter out only columns we need for variable.\n",
    "data_cleaned.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## filter only column that is needed and also filter only 2018 to get most recent year data only\n",
    "\n",
    "animal_pro_vs_all=data_cleaned[['country', 'item','year','origin','protein_supply_quantity_gcapitaday']]\n",
    "animal_pro_vs_all= animal_pro_vs_all[animal_pro_vs_all['year']==2018]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "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</th>\n",
       "      <th>item</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2018</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>34.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Barley and products</td>\n",
       "      <td>2018</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Maize and products</td>\n",
       "      <td>2018</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Rye and products</td>\n",
       "      <td>2018</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>Oats</td>\n",
       "      <td>2018</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>0.230000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country                 item  year   origin  \\\n",
       "4   Armenia   Wheat and products  2018  vegetal   \n",
       "9   Armenia  Barley and products  2018  vegetal   \n",
       "14  Armenia   Maize and products  2018  vegetal   \n",
       "19  Armenia     Rye and products  2018  vegetal   \n",
       "24  Armenia                 Oats  2018  vegetal   \n",
       "\n",
       "    protein_supply_quantity_gcapitaday  \n",
       "4                            34.250000  \n",
       "9                             0.200000  \n",
       "14                            0.030000  \n",
       "19                            0.030000  \n",
       "24                            0.230000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_pro_vs_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "animal_protein_proportion=animal_pro_vs_all.groupby(['country', 'origin'])['protein_supply_quantity_gcapitaday'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "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</th>\n",
       "      <th>origin</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>animal</td>\n",
       "      <td>10.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>44.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Albania</td>\n",
       "      <td>animal</td>\n",
       "      <td>61.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Albania</td>\n",
       "      <td>vegetal</td>\n",
       "      <td>53.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>animal</td>\n",
       "      <td>24.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country   origin  protein_supply_quantity_gcapitaday\n",
       "0  Afghanistan   animal                           10.790000\n",
       "1  Afghanistan  vegetal                           44.710000\n",
       "2      Albania   animal                           61.740000\n",
       "3      Albania  vegetal                           53.930000\n",
       "4      Algeria   animal                           24.720000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_protein_proportion=animal_protein_proportion.to_frame()\n",
    "animal_protein_proportion=animal_protein_proportion.reset_index()\n",
    "animal_protein_proportion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "animal_protein_proportion=pd.pivot_table(animal_protein_proportion,index=[\"country\"],values=[\"protein_supply_quantity_gcapitaday\"],\n",
    "               columns=[\"origin\"],aggfunc=[np.sum])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('sum', 'protein_supply_quantity_gcapitaday',  'animal'),\n",
       "            ('sum', 'protein_supply_quantity_gcapitaday', 'vegetal')],\n",
       "           names=[None, None, 'origin'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_protein_proportion.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "animal_protein_proportion.columns = ['animal_protein_gcapitaday', 'vegetal_protein_gcapitaday']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "animal_protein_proportion['animal_protein_vs_total_protein']=animal_protein_proportion['animal_protein_gcapitaday']/(animal_protein_proportion['animal_protein_gcapitaday']+animal_protein_proportion['vegetal_protein_gcapitaday'])\n",
    "animal_protein_proportion=animal_protein_proportion.fillna(0)\n",
    "animal_protein_proportion = pd.merge(left=animal_protein_proportion, right=country_list, left_on='country', right_on='country')\n",
    "animal_protein_proportion['animal_protein_vs_total_protein']=animal_protein_proportion['animal_protein_vs_total_protein']*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.92482169040531\n"
     ]
    }
   ],
   "source": [
    "animal_protein_proportion[[\"animal_protein_vs_total_protein\"]].boxplot(whis=3,showmeans=True)\n",
    "mean_animal_protein_proportion=animal_protein_proportion[\"animal_protein_vs_total_protein\"].mean()\n",
    "plt.show()\n",
    "print(mean_animal_protein_proportion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Food Availability in Protein Per Capita\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "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</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>55.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>115.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>91.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>53.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>79.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>54.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>98.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>54.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>60.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>44.130000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                country  protein_supply_quantity_gcapitaday\n",
       "0                           Afghanistan                           55.500000\n",
       "1                               Albania                          115.670000\n",
       "2                               Algeria                           91.820000\n",
       "3                                Angola                           53.050000\n",
       "4                   Antigua and Barbuda                           79.910000\n",
       "..                                  ...                                 ...\n",
       "168  Venezuela (Bolivarian Republic of)                           54.170000\n",
       "169                            Viet Nam                           98.570000\n",
       "170                               Yemen                           54.500000\n",
       "171                              Zambia                           60.500000\n",
       "172                            Zimbabwe                           44.130000\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_availability_protein_capita= data_cleaned[(data_cleaned['year'] >=2018)]\n",
    "food_availability_protein_capita=food_availability_protein_capita[['country','origin','year','protein_supply_quantity_gcapitaday']]\n",
    "food_availability_protein_capita=food_availability_protein_capita.groupby('country')['protein_supply_quantity_gcapitaday'].sum()\n",
    "food_availability_protein_capita=food_availability_protein_capita.to_frame()\n",
    "food_availability_protein_capita=food_availability_protein_capita.reset_index()\n",
    "food_availability_protein_capita = pd.merge(left=food_availability_protein_capita, right=country_list, left_on='country', right_on='country')\n",
    "food_availability_protein_capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.79687861271674\n"
     ]
    }
   ],
   "source": [
    "food_availability_protein_capita[[\"protein_supply_quantity_gcapitaday\"]].boxplot(whis=3,showmeans=True)\n",
    "food_availability_protein_capita_mean=food_availability_protein_capita[\"protein_supply_quantity_gcapitaday\"].mean()\n",
    "plt.show()\n",
    "print(food_availability_protein_capita_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Food Availability in Kcal Per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "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</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2038.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>3361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>3321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2383.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2442.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>2120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>3023.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1904.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                country  food_supply_kcalcapitaday\n",
       "0                           Afghanistan                2038.000000\n",
       "1                               Albania                3361.000000\n",
       "2                               Algeria                3321.000000\n",
       "3                                Angola                2383.000000\n",
       "4                   Antigua and Barbuda                2442.000000\n",
       "..                                  ...                        ...\n",
       "168  Venezuela (Bolivarian Republic of)                2120.000000\n",
       "169                            Viet Nam                3023.000000\n",
       "170                               Yemen                2152.000000\n",
       "171                              Zambia                2000.000000\n",
       "172                            Zimbabwe                1904.000000\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_availability_kcal_capita= data_cleaned[(data_cleaned['year'] >=2018)]\n",
    "food_availability_kcal_capita=food_availability_kcal_capita[['country','origin','year','food_supply_kcalcapitaday']]\n",
    "food_availability_kcal_capita=food_availability_kcal_capita.groupby('country')['food_supply_kcalcapitaday'].sum()\n",
    "food_availability_kcal_capita=food_availability_kcal_capita.to_frame()\n",
    "food_availability_kcal_capita=food_availability_kcal_capita.reset_index()\n",
    "food_availability_kcal_capita = pd.merge(left=food_availability_kcal_capita, right=country_list, left_on='country', right_on='country')\n",
    "food_availability_kcal_capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2867.843930635838\n"
     ]
    }
   ],
   "source": [
    "food_availability_kcal_capita[[\"food_supply_kcalcapitaday\"]].boxplot(whis=3,showmeans=True)\n",
    "food_availability_kcal_capita_mean=food_availability_kcal_capita[\"food_supply_kcalcapitaday\"].mean()\n",
    "plt.show()\n",
    "print(food_availability_kcal_capita_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5 GDP per country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Download GDP CSV file from FAO website"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "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</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15674</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31142</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47562</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65563</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country\n",
       "4              Afghanistan\n",
       "15674              Albania\n",
       "31142              Algeria\n",
       "47562               Angola\n",
       "65563  Antigua and Barbuda"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# again let's bring country list so that we could do join later so that we only get GDP data for countires that we will study\n",
    "country_list.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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</th>\n",
       "      <th>gdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>120137.955478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>China, Macao SAR</td>\n",
       "      <td>86248.547881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>82530.174862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Norway</td>\n",
       "      <td>81101.658048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>79661.622685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>527.386601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>482.063443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>473.499434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>437.118973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>375.490370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country           gdp\n",
       "0                  Luxembourg 120137.955478\n",
       "1            China, Macao SAR  86248.547881\n",
       "2                 Switzerland  82530.174862\n",
       "3                      Norway  81101.658048\n",
       "4                     Ireland  79661.622685\n",
       "..                        ...           ...\n",
       "167                Madagascar    527.386601\n",
       "168                Mozambique    482.063443\n",
       "169  Central African Republic    473.499434\n",
       "170                   Liberia    437.118973\n",
       "171                    Malawi    375.490370\n",
       "\n",
       "[172 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_per_country = pd.read_csv(\"gdp_per_country.csv\")\n",
    "gdp_per_country=gdp_per_country[['Area','Value']]\n",
    "gdp_per_country.rename(columns={'Area': 'country', 'Value': 'gdp'}, inplace=True)\n",
    "gdp_per_country=gdp_per_country.sort_values(by='gdp', ascending=False)\n",
    "gdp_per_country = pd.merge(left=gdp_per_country, right=country_list, left_on='country', right_on='country')\n",
    "gdp_per_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp_per_country.to_csv('gdp_check.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14738.507210906968\n"
     ]
    }
   ],
   "source": [
    "gdp_per_country[[\"gdp\"]].boxplot(whis=15,showmeans=True)\n",
    "gdp_mean=gdp_per_country[\"gdp\"].mean()\n",
    "plt.show()\n",
    "print(gdp_mean)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 Poultry Supply Trend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* we will make average of 2014-2018 poulty consumption trend so that we see how latest poultry consumption trend could be guessed.\n",
    "* Here we are using column 'domestic_supply_quantity' which equals to production+import-export of poultry meat. This does not tell us directly how many are consumped, but based on supply q'ty we could guess probably consumption trend as well indirectly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "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>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>five_year_average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <td>Afghanistan</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.040541</td>\n",
       "      <td>-0.112676</td>\n",
       "      <td>-0.095238</td>\n",
       "      <td>-0.070175</td>\n",
       "      <td>-0.079658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>-0.181818</td>\n",
       "      <td>-0.047151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.023026</td>\n",
       "      <td>0.028939</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>-0.029605</td>\n",
       "      <td>-0.006910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Angola</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.367397</td>\n",
       "      <td>-0.057692</td>\n",
       "      <td>0.240816</td>\n",
       "      <td>0.190789</td>\n",
       "      <td>0.001629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.279635</td>\n",
       "      <td>-0.303797</td>\n",
       "      <td>-0.263636</td>\n",
       "      <td>-0.072016</td>\n",
       "      <td>-0.229771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.059970</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052705</td>\n",
       "      <td>0.033119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Yemen</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.342105</td>\n",
       "      <td>-0.120000</td>\n",
       "      <td>-0.075758</td>\n",
       "      <td>0.516393</td>\n",
       "      <td>-0.005367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zambia</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.032705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>nan</td>\n",
       "      <td>-0.126582</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>-0.026573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    2014      2015      2016      2017  \\\n",
       "country                                                                  \n",
       "Afghanistan                          nan -0.040541 -0.112676 -0.095238   \n",
       "Albania                              nan  0.000000  0.147059 -0.153846   \n",
       "Algeria                              nan  0.023026  0.028939 -0.050000   \n",
       "Angola                               nan -0.367397 -0.057692  0.240816   \n",
       "Antigua and Barbuda                  nan  0.000000  0.000000  0.000000   \n",
       "...                                  ...       ...       ...       ...   \n",
       "Venezuela (Bolivarian Republic of)   nan -0.279635 -0.303797 -0.263636   \n",
       "Viet Nam                             nan  0.059970  0.019802  0.000000   \n",
       "Yemen                                nan -0.342105 -0.120000 -0.075758   \n",
       "Zambia                               nan  0.019608  0.038462  0.037037   \n",
       "Zimbabwe                             nan -0.126582  0.086957  0.000000   \n",
       "\n",
       "                                        2018  five_year_average  \n",
       "country                                                          \n",
       "Afghanistan                        -0.070175          -0.079658  \n",
       "Albania                            -0.181818          -0.047151  \n",
       "Algeria                            -0.029605          -0.006910  \n",
       "Angola                              0.190789           0.001629  \n",
       "Antigua and Barbuda                 0.000000           0.000000  \n",
       "...                                      ...                ...  \n",
       "Venezuela (Bolivarian Republic of) -0.072016          -0.229771  \n",
       "Viet Nam                            0.052705           0.033119  \n",
       "Yemen                               0.516393          -0.005367  \n",
       "Zambia                              0.035714           0.032705  \n",
       "Zimbabwe                           -0.066667          -0.026573  \n",
       "\n",
       "[173 rows x 6 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poultry_supply_trend = data_cleaned[(data_cleaned['year'] >=2013) ]\n",
    "poultry_supply_trend=poultry_supply_trend[(poultry_supply_trend['item'] =='Poultry Meat')]\n",
    "poultry_supply_trend=poultry_supply_trend[['country', 'year','domestic_supply_quantity']]\n",
    "\n",
    "\n",
    "poultry_supply_trend=pd.pivot_table(poultry_supply_trend,index=[\"country\"],values=[\"domestic_supply_quantity\"],columns=[\"year\"],aggfunc=[np.sum])\n",
    "\n",
    "poultry_supply_trend=poultry_supply_trend.reset_index()\n",
    "\n",
    "poultry_supply_trend.columns = ['country', '2014','2015','2016','2017','2018']\n",
    "poultry_supply_trend=poultry_supply_trend.set_index('country')\n",
    "poultry_supply_trend=poultry_supply_trend.pct_change(axis='columns')\n",
    "poultry_supply_trend['five_year_average']=(poultry_supply_trend['2015']+poultry_supply_trend['2016']+poultry_supply_trend['2017']+poultry_supply_trend['2018'])/4\n",
    "poultry_supply_trend['five_year_average']\n",
    "poultry_supply_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "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</th>\n",
       "      <th>five_year_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>-0.079658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>-0.047151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>-0.006910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0.001629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Venezuela (Bolivarian Republic of)</td>\n",
       "      <td>-0.229771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>0.033119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>-0.005367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.032705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>172</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-0.026573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                country  five_year_average\n",
       "0                           Afghanistan          -0.079658\n",
       "1                               Albania          -0.047151\n",
       "2                               Algeria          -0.006910\n",
       "3                                Angola           0.001629\n",
       "4                   Antigua and Barbuda           0.000000\n",
       "..                                  ...                ...\n",
       "168  Venezuela (Bolivarian Republic of)          -0.229771\n",
       "169                            Viet Nam           0.033119\n",
       "170                               Yemen          -0.005367\n",
       "171                              Zambia           0.032705\n",
       "172                            Zimbabwe          -0.026573\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poultry_supply_trend=poultry_supply_trend.reset_index()\n",
    "poultry_supply_trend=poultry_supply_trend[['country', 'five_year_average']]\n",
    "poultry_supply_trend = pd.merge(left=poultry_supply_trend, right=country_list, left_on='country', right_on='country')\n",
    "poultry_supply_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.02944372192541258\n"
     ]
    }
   ],
   "source": [
    "poultry_supply_trend[[\"five_year_average\"]].boxplot(whis=15,showmeans=True)\n",
    "trend_mean=poultry_supply_trend[\"five_year_average\"].mean()\n",
    "plt.show()\n",
    "\n",
    "print(trend_mean)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7 Poultry Domestic Supply Per Capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7453133000.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_2018 = population_cleaned[population_cleaned['year']==2018]\n",
    "population_2018.population.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "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</th>\n",
       "      <th>item</th>\n",
       "      <th>domestic_supply_quantity</th>\n",
       "      <th>population</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>Poultry Meat</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>211000.000000</td>\n",
       "      <td>9.478673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Kiribati</td>\n",
       "      <td>Poultry Meat</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>116000.000000</td>\n",
       "      <td>17.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>Poultry Meat</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>653000.000000</td>\n",
       "      <td>4.594181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Djibouti</td>\n",
       "      <td>Poultry Meat</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>959000.000000</td>\n",
       "      <td>3.128259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>Poultry Meat</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1874000.000000</td>\n",
       "      <td>2.134472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country          item  domestic_supply_quantity  \\\n",
       "0  Sao Tome and Principe  Poultry Meat                  2.000000   \n",
       "1               Kiribati  Poultry Meat                  2.000000   \n",
       "2        Solomon Islands  Poultry Meat                  3.000000   \n",
       "3               Djibouti  Poultry Meat                  3.000000   \n",
       "4          Guinea-Bissau  Poultry Meat                  4.000000   \n",
       "\n",
       "      population  poultry_supply_per_cap  \n",
       "0  211000.000000                9.478673  \n",
       "1  116000.000000               17.241379  \n",
       "2  653000.000000                4.594181  \n",
       "3  959000.000000                3.128259  \n",
       "4 1874000.000000                2.134472  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take out domestic supply quantity by country which is equal to production+import-export\n",
    "\n",
    "poultry_data=data[['country','item','year','domestic_supply_quantity']]\n",
    "poultry_data= poultry_data[(poultry_data['year'] >=2018)]\n",
    "poultry_data=poultry_data[(poultry_data['item'] =='Poultry Meat')]\n",
    "population_2018 = population[(population['year'] >=2018) ]\n",
    "poultry_data=pd.merge(left=poultry_data, right=population_2018, left_on='country', right_on='country')\n",
    "#poultry_data=poultry_data.sort_values(by='food', ascending=False).head(35)\n",
    "poultry_data=poultry_data.sort_values(by='domestic_supply_quantity')\n",
    "poultry_data=poultry_data[['country','item','domestic_supply_quantity']]\n",
    "poultry_data = pd.merge(left=poultry_data, right=population_2018, left_on='country', right_on='country')\n",
    "poultry_data=poultry_data[['country','item','domestic_supply_quantity','population']]\n",
    "poultry_data['poultry_supply_per_cap']=poultry_data['domestic_supply_quantity']*1000000/poultry_data['population']\n",
    "poultry_data.head()\n",
    "\n",
    "#poultry_data.to_csv('poultry_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.327365704386697"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poultry_data['poultry_supply_per_cap'].mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(24.56010227482537, 34.019999999999996, 'Mean: 22.33')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Let's figure out mean figure for domestic supply of poultry meat in order to filter data\n",
    "## normally world average poultry consumption is around 15.6kg according to google search\n",
    "\n",
    "\n",
    "result = plt.hist(poultry_data[\"poultry_supply_per_cap\"], bins=20, color='c', edgecolor='k', alpha=0.65)\n",
    "plt.axvline(poultry_data[\"poultry_supply_per_cap\"].mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "\n",
    "min_ylim, max_ylim = plt.ylim()\n",
    "plt.text(poultry_data[\"poultry_supply_per_cap\"].mean()*1.1, max_ylim*0.9, 'Mean: {:.2f}'.format(poultry_data[\"poultry_supply_per_cap\"].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Making Dendogram containing all the studies countries - split into Five Groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Dendogram -with seven variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* First we will create one integrated dataframe based on varialbes we collected from 2.1 to 2.6 before we create dendogram\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1.932367</td>\n",
       "      <td>30.451542</td>\n",
       "      <td>54.480000</td>\n",
       "      <td>2447.000000</td>\n",
       "      <td>1968.732492</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.478673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kiribati</td>\n",
       "      <td>1.754386</td>\n",
       "      <td>49.014008</td>\n",
       "      <td>73.530000</td>\n",
       "      <td>3078.000000</td>\n",
       "      <td>1592.082641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>2.672956</td>\n",
       "      <td>28.929042</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>2412.000000</td>\n",
       "      <td>2039.282045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.594181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1.588983</td>\n",
       "      <td>21.401313</td>\n",
       "      <td>65.510000</td>\n",
       "      <td>2695.000000</td>\n",
       "      <td>3009.407574</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>3.128259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>2.516411</td>\n",
       "      <td>21.304650</td>\n",
       "      <td>43.230000</td>\n",
       "      <td>2229.000000</td>\n",
       "      <td>764.899506</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.134472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       population_difference  animal_protein_vs_total_protein  \\\n",
       "country                                                                         \n",
       "Sao Tome and Principe               1.932367                        30.451542   \n",
       "Kiribati                            1.754386                        49.014008   \n",
       "Solomon Islands                     2.672956                        28.929042   \n",
       "Djibouti                            1.588983                        21.401313   \n",
       "Guinea-Bissau                       2.516411                        21.304650   \n",
       "\n",
       "                       protein_supply_quantity_gcapitaday  \\\n",
       "country                                                     \n",
       "Sao Tome and Principe                           54.480000   \n",
       "Kiribati                                        73.530000   \n",
       "Solomon Islands                                 53.130000   \n",
       "Djibouti                                        65.510000   \n",
       "Guinea-Bissau                                   43.230000   \n",
       "\n",
       "                       food_supply_kcalcapitaday         gdp  \\\n",
       "country                                                        \n",
       "Sao Tome and Principe                2447.000000 1968.732492   \n",
       "Kiribati                             3078.000000 1592.082641   \n",
       "Solomon Islands                      2412.000000 2039.282045   \n",
       "Djibouti                             2695.000000 3009.407574   \n",
       "Guinea-Bissau                        2229.000000  764.899506   \n",
       "\n",
       "                       five_year_average  poultry_supply_per_cap  \n",
       "country                                                           \n",
       "Sao Tome and Principe           0.000000                9.478673  \n",
       "Kiribati                        0.000000               17.241379  \n",
       "Solomon Islands                 0.000000                4.594181  \n",
       "Djibouti                        0.125000                3.128259  \n",
       "Guinea-Bissau                   0.000000                2.134472  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from functools import reduce\n",
    "\n",
    "df1=population_difference_per_country\n",
    "df2=animal_protein_proportion\n",
    "df3=food_availability_protein_capita\n",
    "df3=df3.groupby('country')['protein_supply_quantity_gcapitaday'].sum()\n",
    "df3= pd.DataFrame(data = df3)\n",
    "df4=food_availability_kcal_capita\n",
    "df4=df4.groupby('country')['food_supply_kcalcapitaday'].sum()\n",
    "df4= pd.DataFrame(data = df4) \n",
    "df5=gdp_per_country\n",
    "df6=poultry_supply_trend\n",
    "\n",
    "total_data=pd.merge(poultry_data,df1,on='country',how='inner')\n",
    "total_data=pd.merge(total_data,df2,on='country',how='inner')\n",
    "total_data=pd.merge(total_data,df3,on='country',how='inner')\n",
    "total_data=pd.merge(total_data,df4,on='country',how='inner')\n",
    "total_data=pd.merge(total_data,df5,on='country',how='inner')\n",
    "total_data=pd.merge(total_data,df6,on='country',how='inner')\n",
    "\n",
    "\n",
    "total_data=total_data[['country','population_difference','animal_protein_vs_total_protein','protein_supply_quantity_gcapitaday','food_supply_kcalcapitaday','gdp','five_year_average','poultry_supply_per_cap']]\n",
    "#Missing value Taiwan GDP- fillna\n",
    "total_data=total_data.fillna(30981)\n",
    "total_data=total_data.set_index('country')\n",
    "total_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Standardize data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.61960089, -0.78065082, -1.31621295, ..., -0.62260072,\n",
       "        -0.44612718, -0.67719892],\n",
       "       [ 0.45878085,  0.38526051, -0.39747687, ..., -0.64096458,\n",
       "        -0.44612718, -0.26557756],\n",
       "       [ 1.28878135, -0.87627927, -1.38132023, ..., -0.61916101,\n",
       "        -0.44612718, -0.93620154],\n",
       "       ...,\n",
       "       [-0.41561287,  0.83655336,  0.66980027, ..., -0.2865367 ,\n",
       "        -0.11951994,  1.3149086 ],\n",
       "       [-0.56748469,  1.36960089,  1.53403914, ...,  2.3521207 ,\n",
       "         0.05225337,  1.84272862],\n",
       "       [-0.70512082, -0.2218115 ,  0.91479656, ..., -0.24971512,\n",
       "        -0.24053851, -0.46601807]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the hierarchical clustering algorithm\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "# Import functions created for this course\n",
    "# Import the sklearn function\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X=total_data\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "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>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.619601</td>\n",
       "      <td>-0.780651</td>\n",
       "      <td>-1.316213</td>\n",
       "      <td>-0.920060</td>\n",
       "      <td>-0.622601</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-0.677199</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kiribati</td>\n",
       "      <td>0.458781</td>\n",
       "      <td>0.385261</td>\n",
       "      <td>-0.397477</td>\n",
       "      <td>0.461685</td>\n",
       "      <td>-0.640965</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-0.265578</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>1.288781</td>\n",
       "      <td>-0.876279</td>\n",
       "      <td>-1.381320</td>\n",
       "      <td>-0.996702</td>\n",
       "      <td>-0.619161</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-0.936202</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Djibouti</td>\n",
       "      <td>0.309326</td>\n",
       "      <td>-1.349097</td>\n",
       "      <td>-0.784262</td>\n",
       "      <td>-0.376997</td>\n",
       "      <td>-0.571862</td>\n",
       "      <td>1.445651</td>\n",
       "      <td>-1.013933</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>1.147331</td>\n",
       "      <td>-1.355169</td>\n",
       "      <td>-1.858774</td>\n",
       "      <td>-1.397430</td>\n",
       "      <td>-0.681295</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-1.066629</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mexico</td>\n",
       "      <td>-0.102490</td>\n",
       "      <td>0.382622</td>\n",
       "      <td>0.466280</td>\n",
       "      <td>0.634677</td>\n",
       "      <td>-0.262421</td>\n",
       "      <td>0.074432</td>\n",
       "      <td>0.647221</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>-0.999784</td>\n",
       "      <td>0.750372</td>\n",
       "      <td>0.947109</td>\n",
       "      <td>1.039784</td>\n",
       "      <td>-0.156230</td>\n",
       "      <td>-0.085074</td>\n",
       "      <td>0.489175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brazil</td>\n",
       "      <td>-0.415613</td>\n",
       "      <td>0.836553</td>\n",
       "      <td>0.669800</td>\n",
       "      <td>0.947814</td>\n",
       "      <td>-0.286537</td>\n",
       "      <td>-0.119520</td>\n",
       "      <td>1.314909</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United States of America</td>\n",
       "      <td>-0.567485</td>\n",
       "      <td>1.369601</td>\n",
       "      <td>1.534039</td>\n",
       "      <td>1.992334</td>\n",
       "      <td>2.352121</td>\n",
       "      <td>0.052253</td>\n",
       "      <td>1.842729</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>China, mainland</td>\n",
       "      <td>-0.705121</td>\n",
       "      <td>-0.221812</td>\n",
       "      <td>0.914797</td>\n",
       "      <td>0.709129</td>\n",
       "      <td>-0.249715</td>\n",
       "      <td>-0.240539</td>\n",
       "      <td>-0.466018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          population_difference  \\\n",
       "country                                           \n",
       "Sao Tome and Principe                  0.619601   \n",
       "Kiribati                               0.458781   \n",
       "Solomon Islands                        1.288781   \n",
       "Djibouti                               0.309326   \n",
       "Guinea-Bissau                          1.147331   \n",
       "...                                         ...   \n",
       "Mexico                                -0.102490   \n",
       "Russian Federation                    -0.999784   \n",
       "Brazil                                -0.415613   \n",
       "United States of America              -0.567485   \n",
       "China, mainland                       -0.705121   \n",
       "\n",
       "                          animal_protein_vs_total_protein  \\\n",
       "country                                                     \n",
       "Sao Tome and Principe                           -0.780651   \n",
       "Kiribati                                         0.385261   \n",
       "Solomon Islands                                 -0.876279   \n",
       "Djibouti                                        -1.349097   \n",
       "Guinea-Bissau                                   -1.355169   \n",
       "...                                                   ...   \n",
       "Mexico                                           0.382622   \n",
       "Russian Federation                               0.750372   \n",
       "Brazil                                           0.836553   \n",
       "United States of America                         1.369601   \n",
       "China, mainland                                 -0.221812   \n",
       "\n",
       "                          protein_supply_quantity_gcapitaday  \\\n",
       "country                                                        \n",
       "Sao Tome and Principe                              -1.316213   \n",
       "Kiribati                                           -0.397477   \n",
       "Solomon Islands                                    -1.381320   \n",
       "Djibouti                                           -0.784262   \n",
       "Guinea-Bissau                                      -1.858774   \n",
       "...                                                      ...   \n",
       "Mexico                                              0.466280   \n",
       "Russian Federation                                  0.947109   \n",
       "Brazil                                              0.669800   \n",
       "United States of America                            1.534039   \n",
       "China, mainland                                     0.914797   \n",
       "\n",
       "                          food_supply_kcalcapitaday       gdp  \\\n",
       "country                                                         \n",
       "Sao Tome and Principe                     -0.920060 -0.622601   \n",
       "Kiribati                                   0.461685 -0.640965   \n",
       "Solomon Islands                           -0.996702 -0.619161   \n",
       "Djibouti                                  -0.376997 -0.571862   \n",
       "Guinea-Bissau                             -1.397430 -0.681295   \n",
       "...                                             ...       ...   \n",
       "Mexico                                     0.634677 -0.262421   \n",
       "Russian Federation                         1.039784 -0.156230   \n",
       "Brazil                                     0.947814 -0.286537   \n",
       "United States of America                   1.992334  2.352121   \n",
       "China, mainland                            0.709129 -0.249715   \n",
       "\n",
       "                          five_year_average  poultry_supply_per_cap  cluster  \n",
       "country                                                                       \n",
       "Sao Tome and Principe             -0.446127               -0.677199        1  \n",
       "Kiribati                          -0.446127               -0.265578        2  \n",
       "Solomon Islands                   -0.446127               -0.936202        1  \n",
       "Djibouti                           1.445651               -1.013933        1  \n",
       "Guinea-Bissau                     -0.446127               -1.066629        1  \n",
       "...                                     ...                     ...      ...  \n",
       "Mexico                             0.074432                0.647221        4  \n",
       "Russian Federation                -0.085074                0.489175        0  \n",
       "Brazil                            -0.119520                1.314909        0  \n",
       "United States of America           0.052253                1.842729        0  \n",
       "China, mainland                   -0.240539               -0.466018        2  \n",
       "\n",
       "[172 rows x 8 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a hierarchical clustering model\n",
    "hiercluster = AgglomerativeClustering(affinity='euclidean', linkage='ward', compute_full_tree=True)\n",
    "\n",
    "# Fit the data to the model and determine which clusters each data point belongs to:\n",
    "hiercluster.set_params(n_clusters=5)\n",
    "clusters = hiercluster.fit_predict(X_scaled) \n",
    "np.bincount(clusters) # count of data points in each cluster\n",
    "# Read off 5 clusters:\n",
    "hiercluster.set_params(n_clusters=5)\n",
    "clusters = hiercluster.fit_predict(X_scaled) \n",
    "np.bincount(clusters)\n",
    "# Add cluster number to the original data\n",
    "X_scaled_clustered = pd.DataFrame(X_scaled, columns=X.columns, index=X.index)\n",
    "X_scaled_clustered['cluster'] = clusters\n",
    "\n",
    "X_scaled_clustered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    57\n",
       "0    47\n",
       "2    35\n",
       "4    27\n",
       "3     6\n",
       "Name: cluster, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the size of the clusters\n",
    "X_scaled_clustered[\"cluster\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we are going to export CSV file for countries-cluster connection as it's required for project deliverables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>country</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Kiribati</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>China, mainland</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      country  cluster\n",
       "0       Sao Tome and Principe        1\n",
       "1                    Kiribati        2\n",
       "2             Solomon Islands        1\n",
       "3                    Djibouti        1\n",
       "4               Guinea-Bissau        1\n",
       "..                        ...      ...\n",
       "167                    Mexico        4\n",
       "168        Russian Federation        0\n",
       "169                    Brazil        0\n",
       "170  United States of America        0\n",
       "171           China, mainland        2\n",
       "\n",
       "[172 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_cluster=X_scaled_clustered.reset_index()\n",
    "country_cluster=country_cluster[['country','cluster']]\n",
    "country_cluster\n",
    "#country_cluster.to_csv('country_cluster.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here below we are going to make dataframe of centroide and export in CSV as it is needed for project deliverable \n",
    "\n",
    "1) first we will make inner join with X=dendrogram dataframe and country_cluster so that we could add cluster on dataframe\n",
    "2) second we will group data frame by cluster and check mean of it so that we could see characteristic of each cluster and decide which cluster to use for our further market study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroide=pd.merge(total_data,country_cluster, on='country',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "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>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</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",
       "      <td>0</td>\n",
       "      <td>0.462468</td>\n",
       "      <td>59.074212</td>\n",
       "      <td>106.413830</td>\n",
       "      <td>3378.106383</td>\n",
       "      <td>39636.719441</td>\n",
       "      <td>0.012880</td>\n",
       "      <td>29.258402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.146201</td>\n",
       "      <td>26.526263</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>2469.684211</td>\n",
       "      <td>2086.192444</td>\n",
       "      <td>0.019745</td>\n",
       "      <td>6.345442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.844431</td>\n",
       "      <td>42.823153</td>\n",
       "      <td>85.275714</td>\n",
       "      <td>2980.371429</td>\n",
       "      <td>7315.000619</td>\n",
       "      <td>0.026821</td>\n",
       "      <td>17.607037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.431819</td>\n",
       "      <td>28.760810</td>\n",
       "      <td>53.370000</td>\n",
       "      <td>2221.333333</td>\n",
       "      <td>1631.457992</td>\n",
       "      <td>0.282331</td>\n",
       "      <td>9.642548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.970668</td>\n",
       "      <td>52.427690</td>\n",
       "      <td>81.118889</td>\n",
       "      <td>2813.629630</td>\n",
       "      <td>10643.358801</td>\n",
       "      <td>0.026173</td>\n",
       "      <td>52.445946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population_difference  animal_protein_vs_total_protein  \\\n",
       "cluster                                                           \n",
       "0                     0.462468                        59.074212   \n",
       "1                     2.146201                        26.526263   \n",
       "2                     0.844431                        42.823153   \n",
       "3                     2.431819                        28.760810   \n",
       "4                     0.970668                        52.427690   \n",
       "\n",
       "         protein_supply_quantity_gcapitaday  food_supply_kcalcapitaday  \\\n",
       "cluster                                                                  \n",
       "0                                106.413830                3378.106383   \n",
       "1                                 62.600000                2469.684211   \n",
       "2                                 85.275714                2980.371429   \n",
       "3                                 53.370000                2221.333333   \n",
       "4                                 81.118889                2813.629630   \n",
       "\n",
       "                 gdp  five_year_average  poultry_supply_per_cap  \n",
       "cluster                                                          \n",
       "0       39636.719441           0.012880               29.258402  \n",
       "1        2086.192444           0.019745                6.345442  \n",
       "2        7315.000619           0.026821               17.607037  \n",
       "3        1631.457992           0.282331                9.642548  \n",
       "4       10643.358801           0.026173               52.445946  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroide=centroide.groupby(\"cluster\").mean()\n",
    "centroide\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here we will see total piture of Dendrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see dendrogram tree for all studies countries : 172 countries\n",
    "# Import the hierarchical clustering algorithm\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "# Import functions created for this course\n",
    "from functions import *\n",
    "from scipy.cluster.hierarchy import linkage, fcluster \n",
    "\n",
    "sample = X_scaled_clustered\n",
    "Z = linkage(sample, 'ward') \n",
    "names = sample.index \n",
    "plot_dendrogram(Z, names, figsize=(10,30))\n",
    "\n",
    "#plt.savefig('dendrogram.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Make Short List Countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Now in previous chapter we were able to make dataframe with all countries(172) with their cluster. \n",
    "\n",
    "1) we want to investigate characteristic of each cluster, to see based on which features they were classified \\\n",
    "2) then we want to short list only 10-15 targeted countries so that we could narrow down out marketing extension target. \\\n",
    "3) lastly, we are going to visualize short list of countires with PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* in order to perform 1) lets bring centroide data frame and try to figure out each cluster's characteristic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "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>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</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",
       "      <td>0</td>\n",
       "      <td>0.462468</td>\n",
       "      <td>59.074212</td>\n",
       "      <td>106.413830</td>\n",
       "      <td>3378.106383</td>\n",
       "      <td>39636.719441</td>\n",
       "      <td>0.012880</td>\n",
       "      <td>29.258402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.146201</td>\n",
       "      <td>26.526263</td>\n",
       "      <td>62.600000</td>\n",
       "      <td>2469.684211</td>\n",
       "      <td>2086.192444</td>\n",
       "      <td>0.019745</td>\n",
       "      <td>6.345442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.844431</td>\n",
       "      <td>42.823153</td>\n",
       "      <td>85.275714</td>\n",
       "      <td>2980.371429</td>\n",
       "      <td>7315.000619</td>\n",
       "      <td>0.026821</td>\n",
       "      <td>17.607037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.431819</td>\n",
       "      <td>28.760810</td>\n",
       "      <td>53.370000</td>\n",
       "      <td>2221.333333</td>\n",
       "      <td>1631.457992</td>\n",
       "      <td>0.282331</td>\n",
       "      <td>9.642548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.970668</td>\n",
       "      <td>52.427690</td>\n",
       "      <td>81.118889</td>\n",
       "      <td>2813.629630</td>\n",
       "      <td>10643.358801</td>\n",
       "      <td>0.026173</td>\n",
       "      <td>52.445946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         population_difference  animal_protein_vs_total_protein  \\\n",
       "cluster                                                           \n",
       "0                     0.462468                        59.074212   \n",
       "1                     2.146201                        26.526263   \n",
       "2                     0.844431                        42.823153   \n",
       "3                     2.431819                        28.760810   \n",
       "4                     0.970668                        52.427690   \n",
       "\n",
       "         protein_supply_quantity_gcapitaday  food_supply_kcalcapitaday  \\\n",
       "cluster                                                                  \n",
       "0                                106.413830                3378.106383   \n",
       "1                                 62.600000                2469.684211   \n",
       "2                                 85.275714                2980.371429   \n",
       "3                                 53.370000                2221.333333   \n",
       "4                                 81.118889                2813.629630   \n",
       "\n",
       "                 gdp  five_year_average  poultry_supply_per_cap  \n",
       "cluster                                                          \n",
       "0       39636.719441           0.012880               29.258402  \n",
       "1        2086.192444           0.019745                6.345442  \n",
       "2        7315.000619           0.026821               17.607037  \n",
       "3        1631.457992           0.282331                9.642548  \n",
       "4       10643.358801           0.026173               52.445946  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroide.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "cluster 1,2,3,== GDP is lower than average=> not considered for this study\n",
    "\n",
    "Cluster 4 = GDP is slightly lower than average, but food supply but food supply kcal is little low and protein supply quantity as well, we will look into them\n",
    "\n",
    "cluster 0: GDP is very high => let's take them and further look into how we could filter countries with other variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here below, let's get list of country with clsuter  0 and 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "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</th>\n",
       "      <th>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.222684</td>\n",
       "      <td>77.060000</td>\n",
       "      <td>2579.000000</td>\n",
       "      <td>18098.875957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.923077</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Dominica</td>\n",
       "      <td>1.408451</td>\n",
       "      <td>55.548526</td>\n",
       "      <td>79.030000</td>\n",
       "      <td>2953.000000</td>\n",
       "      <td>7413.637732</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>69.444444</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>64.747841</td>\n",
       "      <td>79.910000</td>\n",
       "      <td>2442.000000</td>\n",
       "      <td>15629.054576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.683967</td>\n",
       "      <td>71.290000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>10787.400403</td>\n",
       "      <td>0.008929</td>\n",
       "      <td>63.063063</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Saint Vincent and the Grenadines</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.138008</td>\n",
       "      <td>89.850000</td>\n",
       "      <td>2967.000000</td>\n",
       "      <td>7362.068966</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>81.818182</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>Japan</td>\n",
       "      <td>-0.236073</td>\n",
       "      <td>55.681558</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2707.000000</td>\n",
       "      <td>39087.235938</td>\n",
       "      <td>0.013822</td>\n",
       "      <td>19.221396</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1.133222</td>\n",
       "      <td>48.972003</td>\n",
       "      <td>91.440000</td>\n",
       "      <td>3157.000000</td>\n",
       "      <td>9356.145765</td>\n",
       "      <td>0.034396</td>\n",
       "      <td>34.455706</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>0.140177</td>\n",
       "      <td>54.826940</td>\n",
       "      <td>101.410000</td>\n",
       "      <td>3342.000000</td>\n",
       "      <td>11534.175152</td>\n",
       "      <td>0.023857</td>\n",
       "      <td>31.475153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>0.786686</td>\n",
       "      <td>56.199038</td>\n",
       "      <td>95.660000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>8861.531858</td>\n",
       "      <td>0.021581</td>\n",
       "      <td>47.047534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>0.618607</td>\n",
       "      <td>64.685684</td>\n",
       "      <td>113.580000</td>\n",
       "      <td>3777.000000</td>\n",
       "      <td>62981.386956</td>\n",
       "      <td>0.032931</td>\n",
       "      <td>57.001614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              country  population_difference  \\\n",
       "5               Saint Kitts and Nevis               0.000000   \n",
       "8                            Dominica               1.408451   \n",
       "9                 Antigua and Barbuda               1.052632   \n",
       "10                            Grenada               0.000000   \n",
       "17   Saint Vincent and the Grenadines               0.000000   \n",
       "..                                ...                    ...   \n",
       "165                             Japan              -0.236073   \n",
       "167                            Mexico               1.133222   \n",
       "168                Russian Federation               0.140177   \n",
       "169                            Brazil               0.786686   \n",
       "170          United States of America               0.618607   \n",
       "\n",
       "     animal_protein_vs_total_protein  protein_supply_quantity_gcapitaday  \\\n",
       "5                          64.222684                           77.060000   \n",
       "8                          55.548526                           79.030000   \n",
       "9                          64.747841                           79.910000   \n",
       "10                         56.683967                           71.290000   \n",
       "17                         56.138008                           89.850000   \n",
       "..                               ...                                 ...   \n",
       "165                        55.681558                           87.300000   \n",
       "167                        48.972003                           91.440000   \n",
       "168                        54.826940                          101.410000   \n",
       "169                        56.199038                           95.660000   \n",
       "170                        64.685684                          113.580000   \n",
       "\n",
       "     food_supply_kcalcapitaday          gdp  five_year_average  \\\n",
       "5                  2579.000000 18098.875957           0.000000   \n",
       "8                  2953.000000  7413.637732           0.062500   \n",
       "9                  2442.000000 15629.054576           0.000000   \n",
       "10                 2401.000000 10787.400403           0.008929   \n",
       "17                 2967.000000  7362.068966           0.031250   \n",
       "..                         ...          ...                ...   \n",
       "165                2707.000000 39087.235938           0.013822   \n",
       "167                3157.000000  9356.145765           0.034396   \n",
       "168                3342.000000 11534.175152           0.023857   \n",
       "169                3300.000000  8861.531858           0.021581   \n",
       "170                3777.000000 62981.386956           0.032931   \n",
       "\n",
       "     poultry_supply_per_cap  cluster  \n",
       "5                 76.923077        4  \n",
       "8                 69.444444        4  \n",
       "9                 62.500000        4  \n",
       "10                63.063063        4  \n",
       "17                81.818182        4  \n",
       "..                      ...      ...  \n",
       "165               19.221396        0  \n",
       "167               34.455706        4  \n",
       "168               31.475153        0  \n",
       "169               47.047534        0  \n",
       "170               57.001614        0  \n",
       "\n",
       "[74 rows x 9 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "potential_country=pd.merge(total_data,country_cluster,on='country')\n",
    "potential_country.to_csv('potential_country.csv')\n",
    "short_list_country=potential_country.loc[potential_country['cluster'].isin([0,4])]\n",
    "short_list_country\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We are going to make short list of countries below\n",
    "- filter only country with positive population growth in recent year\n",
    "- filter only country with poultry supply is less than global average\n",
    "- filter only country where they have five year supply less than global average\n",
    "- filter only country with GRP more than mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "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</th>\n",
       "      <th>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2.027027</td>\n",
       "      <td>62.289935</td>\n",
       "      <td>108.300000</td>\n",
       "      <td>3470.000000</td>\n",
       "      <td>120137.955478</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>18.211921</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>0.303260</td>\n",
       "      <td>62.219475</td>\n",
       "      <td>105.160000</td>\n",
       "      <td>3247.000000</td>\n",
       "      <td>23528.804365</td>\n",
       "      <td>0.027803</td>\n",
       "      <td>21.919879</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0.349142</td>\n",
       "      <td>53.531728</td>\n",
       "      <td>86.360000</td>\n",
       "      <td>3198.000000</td>\n",
       "      <td>17177.139135</td>\n",
       "      <td>-0.110281</td>\n",
       "      <td>9.857930</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>Norway</td>\n",
       "      <td>0.793051</td>\n",
       "      <td>59.957608</td>\n",
       "      <td>113.230000</td>\n",
       "      <td>3366.000000</td>\n",
       "      <td>81101.658048</td>\n",
       "      <td>0.011639</td>\n",
       "      <td>20.794305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>Finland</td>\n",
       "      <td>0.217746</td>\n",
       "      <td>63.934565</td>\n",
       "      <td>117.980000</td>\n",
       "      <td>3340.000000</td>\n",
       "      <td>49955.341172</td>\n",
       "      <td>0.018269</td>\n",
       "      <td>20.822017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0.827815</td>\n",
       "      <td>62.801679</td>\n",
       "      <td>95.300000</td>\n",
       "      <td>3355.000000</td>\n",
       "      <td>82530.174862</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>15.482055</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0.542907</td>\n",
       "      <td>58.763921</td>\n",
       "      <td>99.670000</td>\n",
       "      <td>3764.000000</td>\n",
       "      <td>47225.757205</td>\n",
       "      <td>0.013532</td>\n",
       "      <td>14.109040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0.804989</td>\n",
       "      <td>60.357634</td>\n",
       "      <td>109.050000</td>\n",
       "      <td>3694.000000</td>\n",
       "      <td>52047.260459</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>19.795299</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>0.676426</td>\n",
       "      <td>63.997346</td>\n",
       "      <td>105.520000</td>\n",
       "      <td>3182.000000</td>\n",
       "      <td>55704.968354</td>\n",
       "      <td>0.018070</td>\n",
       "      <td>18.953069</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0.229129</td>\n",
       "      <td>65.567766</td>\n",
       "      <td>106.470000</td>\n",
       "      <td>3297.000000</td>\n",
       "      <td>53505.050866</td>\n",
       "      <td>-0.116643</td>\n",
       "      <td>13.716295</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>0.148740</td>\n",
       "      <td>51.652851</td>\n",
       "      <td>100.130000</td>\n",
       "      <td>3417.000000</td>\n",
       "      <td>33626.657541</td>\n",
       "      <td>0.014068</td>\n",
       "      <td>16.903776</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0.563769</td>\n",
       "      <td>60.893961</td>\n",
       "      <td>105.150000</td>\n",
       "      <td>3554.000000</td>\n",
       "      <td>47993.473332</td>\n",
       "      <td>0.012731</td>\n",
       "      <td>20.042346</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  population_difference  \\\n",
       "23          Luxembourg               2.027027   \n",
       "40             Estonia               0.303260   \n",
       "49             Uruguay               0.349142   \n",
       "89              Norway               0.793051   \n",
       "93             Finland               0.217746   \n",
       "98         Switzerland               0.827815   \n",
       "105            Belgium               0.542907   \n",
       "106            Austria               0.804989   \n",
       "108             Sweden               0.676426   \n",
       "116        Netherlands               0.229129   \n",
       "140  Republic of Korea               0.148740   \n",
       "156            Germany               0.563769   \n",
       "\n",
       "     animal_protein_vs_total_protein  protein_supply_quantity_gcapitaday  \\\n",
       "23                         62.289935                          108.300000   \n",
       "40                         62.219475                          105.160000   \n",
       "49                         53.531728                           86.360000   \n",
       "89                         59.957608                          113.230000   \n",
       "93                         63.934565                          117.980000   \n",
       "98                         62.801679                           95.300000   \n",
       "105                        58.763921                           99.670000   \n",
       "106                        60.357634                          109.050000   \n",
       "108                        63.997346                          105.520000   \n",
       "116                        65.567766                          106.470000   \n",
       "140                        51.652851                          100.130000   \n",
       "156                        60.893961                          105.150000   \n",
       "\n",
       "     food_supply_kcalcapitaday           gdp  five_year_average  \\\n",
       "23                 3470.000000 120137.955478           0.025000   \n",
       "40                 3247.000000  23528.804365           0.027803   \n",
       "49                 3198.000000  17177.139135          -0.110281   \n",
       "89                 3366.000000  81101.658048           0.011639   \n",
       "93                 3340.000000  49955.341172           0.018269   \n",
       "98                 3355.000000  82530.174862           0.003860   \n",
       "105                3764.000000  47225.757205           0.013532   \n",
       "106                3694.000000  52047.260459           0.004482   \n",
       "108                3182.000000  55704.968354           0.018070   \n",
       "116                3297.000000  53505.050866          -0.116643   \n",
       "140                3417.000000  33626.657541           0.014068   \n",
       "156                3554.000000  47993.473332           0.012731   \n",
       "\n",
       "     poultry_supply_per_cap  cluster  \n",
       "23                18.211921        0  \n",
       "40                21.919879        0  \n",
       "49                 9.857930        0  \n",
       "89                20.794305        0  \n",
       "93                20.822017        0  \n",
       "98                15.482055        0  \n",
       "105               14.109040        0  \n",
       "106               19.795299        0  \n",
       "108               18.953069        0  \n",
       "116               13.716295        0  \n",
       "140               16.903776        0  \n",
       "156               20.042346        0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_list_country=short_list_country.loc[(short_list_country['population_difference'] >0)                       \n",
    "        & (short_list_country['poultry_supply_per_cap']  <= poultry_data['poultry_supply_per_cap'].mean())\n",
    "        & (short_list_country['five_year_average']  <= poultry_supply_trend[\"five_year_average\"].mean())\n",
    "        & (short_list_country['gdp']  >= gdp_per_country[\"gdp\"].mean())]\n",
    "short_list_country\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "short_list_country.to_csv('short_list_country.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Perform PCA"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Bring scaled data that was previously used in chapter 3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.61960089, -0.78065082, -1.31621295, ..., -0.62260072,\n",
       "        -0.44612718, -0.67719892],\n",
       "       [ 0.45878085,  0.38526051, -0.39747687, ..., -0.64096458,\n",
       "        -0.44612718, -0.26557756],\n",
       "       [ 1.28878135, -0.87627927, -1.38132023, ..., -0.61916101,\n",
       "        -0.44612718, -0.93620154],\n",
       "       ...,\n",
       "       [-0.41561287,  0.83655336,  0.66980027, ..., -0.2865367 ,\n",
       "        -0.11951994,  1.3149086 ],\n",
       "       [-0.56748469,  1.36960089,  1.53403914, ...,  2.3521207 ,\n",
       "         0.05225337,  1.84272862],\n",
       "       [-0.70512082, -0.2218115 ,  0.91479656, ..., -0.24971512,\n",
       "        -0.24053851, -0.46601807]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot data before sclaing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x222d7c20788>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(X_scaled,columns=X.columns).plot(kind='density',sharex=True,figsize=(12,5),layout=(10,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform a PCA on our standardised data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=7, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the PCA function from sklearn\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Select the number of principal components we will return\n",
    "num_components = 7\n",
    "\n",
    "# Create the PCA model\n",
    "pca = PCA(n_components=num_components)\n",
    "\n",
    "# Fit the model with the standardised data\n",
    "pca.fit(X_scaled)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Array of the variance of the data explained by each of the 10 principal components, starting with PC1, the principal component that explains most of the variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5426636 , 0.14419034, 0.12070736, 0.09192886, 0.05911337,\n",
       "       0.02806805, 0.01332842])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Express the explained variance ratio as a cumulative sum:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5426636 , 0.68685394, 0.8075613 , 0.89949016, 0.95860354,\n",
       "       0.98667158, 1.        ])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pca.explained_variance_ratio_.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Screeplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_scree_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pc1 = pca.components_[0]\n",
    "pc2 = pca.components_[1]\n",
    "pc3 = pca.components_[2]\n",
    "# etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['population_difference', 'animal_protein_vs_total_protein',\n",
       "       'protein_supply_quantity_gcapitaday', 'food_supply_kcalcapitaday',\n",
       "       'gdp', 'five_year_average', 'poultry_supply_per_cap'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.34812328,  0.4533004 ,  0.46273663,  0.44073395,  0.38326453,\n",
       "       -0.12108552,  0.32156479])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.29904542, 0.02362032, 0.13287234, 0.11747521, 0.17026755,\n",
       "       0.91815161, 0.08102476])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc2 = pca.components_[1]\n",
    "pc2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation Circle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Generate a correlation circle\n",
    "pcs = pca.components_ \n",
    "display_circles(pcs, num_components, pca, [(0,1)], labels = np.array(X.columns),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Generate a correlation circle\n",
    "pcs = pca.components_ \n",
    "display_circles(pcs, num_components, pca, [(1,2)], labels = np.array(X.columns),)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "New Factorial Plane Repesentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Transform the original scaled data to the new vector space\n",
    "X_projected = pca.transform(X_scaled) \n",
    "\n",
    "# Display a scatter plot of the data points in this new vector space\n",
    "display_factorial_planes(X_projected, num_components, pca, [(0,1)])\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "display_factorial_planes(X_projected, 7, pca, [(0,1)], \n",
    "                         illustrative_var = X_scaled_clustered[\"cluster\"], alpha = 0.99)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 K-Means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import standard libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Import the kmeans algorithm\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Import functions created for this course\n",
    "from functions import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform Kmeans clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create a k-means clustering model\n",
    "kmeans = KMeans(init='random', n_clusters=3, n_init=10)\n",
    "\n",
    "# Fit the data to the model\n",
    "kmeans.fit(X_scaled)\n",
    "\n",
    "# Determine which clusters each data point belongs to:\n",
    "clusters =  kmeans.predict(X_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Put the cluster number on as a new column on the original data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "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>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.619601</td>\n",
       "      <td>-0.780651</td>\n",
       "      <td>-1.316213</td>\n",
       "      <td>-0.920060</td>\n",
       "      <td>-0.622601</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-0.677199</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kiribati</td>\n",
       "      <td>0.458781</td>\n",
       "      <td>0.385261</td>\n",
       "      <td>-0.397477</td>\n",
       "      <td>0.461685</td>\n",
       "      <td>-0.640965</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-0.265578</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>1.288781</td>\n",
       "      <td>-0.876279</td>\n",
       "      <td>-1.381320</td>\n",
       "      <td>-0.996702</td>\n",
       "      <td>-0.619161</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-0.936202</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Djibouti</td>\n",
       "      <td>0.309326</td>\n",
       "      <td>-1.349097</td>\n",
       "      <td>-0.784262</td>\n",
       "      <td>-0.376997</td>\n",
       "      <td>-0.571862</td>\n",
       "      <td>1.445651</td>\n",
       "      <td>-1.013933</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>1.147331</td>\n",
       "      <td>-1.355169</td>\n",
       "      <td>-1.858774</td>\n",
       "      <td>-1.397430</td>\n",
       "      <td>-0.681295</td>\n",
       "      <td>-0.446127</td>\n",
       "      <td>-1.066629</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       population_difference  animal_protein_vs_total_protein  \\\n",
       "country                                                                         \n",
       "Sao Tome and Principe               0.619601                        -0.780651   \n",
       "Kiribati                            0.458781                         0.385261   \n",
       "Solomon Islands                     1.288781                        -0.876279   \n",
       "Djibouti                            0.309326                        -1.349097   \n",
       "Guinea-Bissau                       1.147331                        -1.355169   \n",
       "\n",
       "                       protein_supply_quantity_gcapitaday  \\\n",
       "country                                                     \n",
       "Sao Tome and Principe                           -1.316213   \n",
       "Kiribati                                        -0.397477   \n",
       "Solomon Islands                                 -1.381320   \n",
       "Djibouti                                        -0.784262   \n",
       "Guinea-Bissau                                   -1.858774   \n",
       "\n",
       "                       food_supply_kcalcapitaday       gdp  five_year_average  \\\n",
       "country                                                                         \n",
       "Sao Tome and Principe                  -0.920060 -0.622601          -0.446127   \n",
       "Kiribati                                0.461685 -0.640965          -0.446127   \n",
       "Solomon Islands                        -0.996702 -0.619161          -0.446127   \n",
       "Djibouti                               -0.376997 -0.571862           1.445651   \n",
       "Guinea-Bissau                          -1.397430 -0.681295          -0.446127   \n",
       "\n",
       "                       poultry_supply_per_cap  cluster  \n",
       "country                                                 \n",
       "Sao Tome and Principe               -0.677199        2  \n",
       "Kiribati                            -0.265578        1  \n",
       "Solomon Islands                     -0.936202        2  \n",
       "Djibouti                            -1.013933        2  \n",
       "Guinea-Bissau                       -1.066629        2  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Add cluster number to the original data\n",
    "X_scaled_clustered = pd.DataFrame(X_scaled, columns=X.columns, index=X.index)\n",
    "X_scaled_clustered['cluster'] = clusters\n",
    "\n",
    "X_scaled_clustered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_scaled_clustered.to_csv('centroid.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run a number of tests, for 1, 2, ... num_clusters\n",
    "num_clusters = 8\n",
    "kmeans_tests = [KMeans(n_clusters=i, init='random', n_init=10) for i in range(1, num_clusters)]\n",
    "score = [kmeans_tests[i].fit(X_scaled).score(X_scaled) for i in range(len(kmeans_tests))]\n",
    "\n",
    "# Plot the curve\n",
    "plt.plot(range(1, num_clusters),score)\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Elbow Curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Create a PCA model to reduce our data to 2 dimensions for visualisation\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(X_scaled)\n",
    "\n",
    "# Transfor the scaled data to the new PCA space\n",
    "X_reduced = pca.transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>-1.986493</td>\n",
       "      <td>-0.686614</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kiribati</td>\n",
       "      <td>-0.242560</td>\n",
       "      <td>-0.392547</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>-2.408673</td>\n",
       "      <td>-0.526812</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Djibouti</td>\n",
       "      <td>-1.968559</td>\n",
       "      <td>1.059946</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>-3.039815</td>\n",
       "      <td>-0.712086</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            PC1       PC2  cluster\n",
       "country                                           \n",
       "Sao Tome and Principe -1.986493 -0.686614        2\n",
       "Kiribati              -0.242560 -0.392547        1\n",
       "Solomon Islands       -2.408673 -0.526812        2\n",
       "Djibouti              -1.968559  1.059946        2\n",
       "Guinea-Bissau         -3.039815 -0.712086        2"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to a data frame\n",
    "X_reduceddf = pd.DataFrame(X_reduced, index=X.index, columns=['PC1','PC2'])\n",
    "X_reduceddf['cluster'] = clusters\n",
    "X_reduceddf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans Scaled Silhouette Score: 0.45772630360328614\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('KMeans Scaled Silhouette Score: {}'.format(silhouette_score(X_reduceddf, kmeans.labels_, metric='euclidean')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "centres_reduced = pca.transform(kmeans.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x222d15c5a08>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_factorial_planes(X_reduced, 2, pca, [(0,1)], illustrative_var = clusters, alpha = 0.8)\n",
    "plt.scatter(centres_reduced[:, 0], centres_reduced[:, 1],\n",
    "            marker='x', s=169, linewidths=3,\n",
    "            color='k', zorder=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Statistical Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Adequecy Test-find data fitting normal distribution among 7 variables found above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Use Kolmogorov-Smirnov test for goodness of fit. The Kolmogorov-Smirnov tests the null hypothesis that two distributions are identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 172 entries, Sao Tome and Principe to China, mainland\n",
      "Data columns (total 7 columns):\n",
      "population_difference                 172 non-null float64\n",
      "animal_protein_vs_total_protein       172 non-null float64\n",
      "protein_supply_quantity_gcapitaday    172 non-null float64\n",
      "food_supply_kcalcapitaday             172 non-null float64\n",
      "gdp                                   172 non-null float64\n",
      "five_year_average                     172 non-null float64\n",
      "poultry_supply_per_cap                172 non-null float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 10.8+ KB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run test on population difference variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.046307411222500705, pvalue=0.8544497116257054)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm, kstest\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#perform Kolmogorov-Smirnov test\n",
    "data_population_difference = X.population_difference\n",
    "loc, scale = norm.fit(data_population_difference)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_population_difference, bins=np.arange(data_population_difference.min(), data_population_difference.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_population_difference.min(), data_population_difference.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_population_difference, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is more than .05, we accept the null hypothesis.\n",
    "# data fits normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rune test on animal protein portion variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.09564995309042579, pvalue=0.08043313394984095)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_protein = X.animal_protein_vs_total_protein\n",
    "loc, scale = norm.fit(data_protein)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_protein, bins=np.arange(data_protein.min(), data_protein.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_protein.min(), data_protein.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_protein, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is more than .05, we accept the null hypothesis.\n",
    "# data fits normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reun test on protein supply quantity per capita variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.08267072420842447, pvalue=0.18021004014548658)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_protein_supply= X.protein_supply_quantity_gcapitaday\n",
    "loc, scale = norm.fit(data_protein_supply)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_protein_supply, bins=np.arange(data_protein_supply.min(), data_protein_supply.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_protein_supply.min(), data_protein_supply.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_protein_supply, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is more than .05, we accept the null hypothesis.\n",
    "# data fits normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run test on food supply in terms of kcal variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAc7klEQVR4nO3dfZRU1Z3u8e9D86a8SEM3L/LWICQRFVE7oKOJJlEk3puQrOTe4EwSJtHLXUmcyczk3jV6s0YTXbOWmVl3cpOJiSGGMa+aN5MwSxKDmmhixNAoooiEBlSaRmlpEBAEG373j33aLpvq7oIuaPr081nrrDq1zz7Vu06feurUqV37KCIwM7P86tfTDTAzs+PLQW9mlnMOejOznHPQm5nlnIPezCzn+vd0A4qpqqqKmpqanm6GmVmvsWrVqpcjorrYspMy6Gtqaqirq+vpZpiZ9RqSnu9omU/dmJnlnIPezCznHPRmZjnnoDczyzkHvZlZzjnozcxyrsuglzRR0m8lrZO0VtJni9SRpK9Kqpe0RtL5BcsWStqQTQvL/QTMzKxzpfSjbwE+FxGPSxoGrJK0PCKeKajzXmB6Ns0BvgHMkTQSuAmoBSJbd2lE7CzrszAzsw51eUQfEdsi4vFsfg+wDhjfrtp84LuRrABGSBoHXAksj4jmLNyXA/PK+gzMzKxTR3WOXlINcB7wWLtF44EtBfcbsrKOyos99iJJdZLqmpqajqZZljM119/b000wy5WSg17SUOBnwN9FxO72i4usEp2UH1kYsTgiaiOitrq66HANZmZ2DEoKekkDSCH/g4i4p0iVBmBiwf0JQGMn5WZmdoKU0utGwLeBdRHxbx1UWwp8POt9cyHwSkRsA+4D5kqqlFQJzM3KzMzsBCml183FwMeApyStzsr+DzAJICJuB5YBVwH1wD7gE9myZkm3ACuz9W6OiObyNd/MzLrSZdBHxB8ofq69sE4An+lg2RJgyTG1zszMus2/jDUzyzkHvZlZzjnozcxyzkFvZpZzDnozs5xz0JuZ5ZyD3sws5xz0ZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcc9GZmOdflhUckLQH+K7A9Is4usvx/A39V8HhnAtXZ1aWeA/YAh4CWiKgtV8PNzKw0pRzR3wnM62hhRPxrRMyKiFnADcBD7S4X+K5suUPezKwHdBn0EfEwUOp1Xq8G7upWi8zMrKzKdo5e0qmkI/+fFRQH8BtJqyQtKtffMjOz0nV5jv4ovA94pN1pm4sjolHSaGC5pGezTwhHyN4IFgFMmjSpjM0yM+vbytnrZgHtTttERGN2ux34OTC7o5UjYnFE1EZEbXV1dRmbZWbWt5Ul6CWdBlwK/LKgbIikYa3zwFzg6XL8PTMzK10p3SvvAi4DqiQ1ADcBAwAi4vas2geB30TEqwWrjgF+Lqn17/wwIn5dvqabmVkpugz6iLi6hDp3krphFpZtAs491oaZmVl5+JexZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcc9GZmOeegNzPLOQe9mVnOOejNzHLOQW9mlnMOejOznHPQm5nlnIPezCznHPRmZjnXZdBLWiJpu6Si13uVdJmkVyStzqYbC5bNk7ReUr2k68vZcDMzK00pR/R3AvO6qPP7iJiVTTcDSKoAbgPeC8wArpY0ozuNNTOzo9dl0EfEw0DzMTz2bKA+IjZFxEHgbmD+MTyOmZl1Q7nO0V8k6UlJv5J0VlY2HthSUKchKytK0iJJdZLqmpqaytQsMzMrR9A/DkyOiHOBfwd+kZWrSN3o6EEiYnFE1EZEbXV1dRmaZWZmUIagj4jdEbE3m18GDJBURTqCn1hQdQLQ2N2/Z2ZmR6fbQS9prCRl87Ozx9wBrASmS5oiaSCwAFja3b9nZmZHp39XFSTdBVwGVElqAG4CBgBExO3Ah4FPSWoB9gMLIiKAFknXAfcBFcCSiFh7XJ6FmZl1qMugj4iru1j+NeBrHSxbBiw7tqaZmVk5+JexZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcc9GZmOeegNzPLOQe9mVnOOejNzHLOQW9mlnMOejOznHPQm5nlnIPezCznHPRmZjnXZdBLWiJpu6SnO1j+V5LWZNMfJZ1bsOw5SU9JWi2prpwNNzOz0pRyRH8nMK+T5ZuBSyNiJnALsLjd8ndFxKyIqD22JpqZWXeUcs3YhyXVdLL8jwV3VwATut8sMzMrl3Kfo78G+FXB/QB+I2mVpEWdrShpkaQ6SXVNTU1lbpaZWd/V5RF9qSS9ixT0lxQUXxwRjZJGA8slPRsRDxdbPyIWk532qa2tjXK1y8ysryvLEb2kmcAdwPyI2NFaHhGN2e124OfA7HL8PTMzK123g17SJOAe4GMR8eeC8iGShrXOA3OBoj13zMzs+Ony1I2ku4DLgCpJDcBNwACAiLgduBEYBXxdEkBL1sNmDPDzrKw/8MOI+PVxeA5mZtaJUnrdXN3F8muBa4uUbwLOPXINMzM7kfzLWDOznHPQm5nlnIPezCznHPRmZjnnoDczyzkHvZlZzjnozcxyzkFvZpZzDnozs5xz0JuZ5ZyD3sws5xz0ZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7OcKynoJS2RtF1S0Wu+KvmqpHpJaySdX7BsoaQN2bSwXA03M7PSlHpEfycwr5Pl7wWmZ9Mi4BsAkkaSrjE7B5gN3CSp8lgba2ZmR6+koI+Ih4HmTqrMB74byQpghKRxwJXA8ohojoidwHI6f8MwM7MyK9c5+vHAloL7DVlZR+VHkLRIUp2kuqampjI1y9qruf7enm6CWa9T6uvmZH19lSvoVaQsOik/sjBicUTURkRtdXV1mZplZmblCvoGYGLB/QlAYyflZmZ2gpQr6JcCH89631wIvBIR24D7gLmSKrMvYedmZWZmdoL0L6WSpLuAy4AqSQ2knjQDACLidmAZcBVQD+wDPpEta5Z0C7Aye6ibI6KzL3XNzKzMSgr6iLi6i+UBfKaDZUuAJUffNDMzKwf/MtbMLOcc9GZmOeegNzPLOQe9mVnOOejNzHLOQW9mlnMOejOznHPQm5nlnIPezCznHPRmZjnnoDczyzkHvZlZzjnozcxyzkFvZpZzDnozs5xz0JuZ5ZyD3sws50oKeknzJK2XVC/p+iLLvyxpdTb9WdKugmWHCpYtLWfjzcysa11eSlBSBXAbcAXQAKyUtDQinmmtExF/X1D/b4DzCh5if0TMKl+TzczsaJRyRD8bqI+ITRFxELgbmN9J/auBu8rRODMz675Sgn48sKXgfkNWdgRJk4EpwIMFxYMl1UlaIekDHf0RSYuyenVNTU0lNMvMzEpRStCrSFl0UHcB8NOIOFRQNikiaoG/BP6fpDOKrRgRiyOiNiJqq6urS2iWmZmVopSgbwAmFtyfADR2UHcB7U7bRERjdrsJ+B1vPn9vZmbHWSlBvxKYLmmKpIGkMD+i94yktwKVwKMFZZWSBmXzVcDFwDPt1zUzs+Ony143EdEi6TrgPqACWBIRayXdDNRFRGvoXw3cHRGFp3XOBL4p6TDpTeXWwt46ZmZ2/HUZ9AARsQxY1q7sxnb3v1BkvT8C53SjfWZm1k3+ZayZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcc9GZmOeegNzPLOQe9mVnOOejNzHLOQW9mlnMOejOznHPQm5nlnIPezCznHPRmZjnnoDczyzkHvZlZzpUU9JLmSVovqV7S9UWW/7WkJkmrs+nagmULJW3IpoXlbLyZmXWty0sJSqoAbgOuABqAlZKWFrn2648i4rp2644EbgJqgQBWZevuLEvrzcysS6Uc0c8G6iNiU0QcBO4G5pf4+FcCyyOiOQv35cC8Y2uqmZkdi1KCfjywpeB+Q1bW3ockrZH0U0kTj3JdJC2SVCeprqmpqYRmmZlZKUoJehUpi3b3/xOoiYiZwP3Ad45i3VQYsTgiaiOitrq6uoRmmZlZKUoJ+gZgYsH9CUBjYYWI2BERB7K73wIuKHVdMzM7vkoJ+pXAdElTJA0EFgBLCytIGldw9/3Aumz+PmCupEpJlcDcrMzMzE6QLnvdRESLpOtIAV0BLImItZJuBuoiYinwt5LeD7QAzcBfZ+s2S7qF9GYBcHNENB+H52FmZh3oMugBImIZsKxd2Y0F8zcAN3Sw7hJgSTfaaGZm3eBfxpqZ5ZyD3sws5xz0ZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcc9GZmOeegNzPLOQe9mVnOlTR6pVmfEAH7d8K+ZnhtF+zfld3uhAN74NBBaDmQblunCOjXH/pVpFv1g4oBMGgYDBoOA4dm88Pg1JEwZDQMqUr1zU4QB731HS0HYdcL0LwRmjfBKw2wuxH2bMtuX4RDBzp/jH79oWJg2yTB4UNwuAXicLo9dDDddkT94NQqGDo6TadNhMoaqJycbkfUpDcFFbsSp9nRc9Bb/hzcB03r4MWnYfsz8PKGFO67tkAcaqvX/xQYPg6Gj4eJc9L8sHFw6igYPAIGnwanjEjzg4ZB/0GlHYlHpCP/g3vhwO70aeC13bC/GfZuz6aXstsXYdsa2Pfymx9j4DCofgtUnwmj39Z2O3y83wDsqDnorXfb1wxbV0Hjanjp6TTt2Mgb16AfMASqpsHp58M5/w1GngGjzoCRU1OgH4/QlGDA4DQNqSptnQN7YdfzsPM52Pl8+sTx8nrY8BtY/f22eoOGw9hz4PTz2qbKKdDPX7dZx0oKeknzgK+QLiV4R0Tc2m75PwDXki4l2AR8MiKez5YdAp7Kqr4QEe8vU9utr2k5CC89BQ2rYGsdNKxMgdiqcgqMPTsF+piz0jSipneE4KChbW1ub18zbF+XPqVsXwfbnoSVd0DLa9m6p8G4mTD+fJh4Yfp0MmTUiW2/ndS6DHpJFcBtwBVAA7BS0tKIeKag2hNAbUTsk/Qp4F+Aj2TL9kfErDK32/qCg6/ClsfguUfg+Udg6+Nt59CHjoUJtXDex2DC2+H0Wen0Sh6dOhJqLk5Tq0OvQ9N6aHyibXr06/DIV9LyqrfCpAvbpsopPuXTh5VyRD8bqI+ITQCS7gbmA28EfUT8tqD+CuCj5Wyk9REH9sALj/GP/e+CO76cwutwC6giBfns/5FCfUKtz1VXDEifXsaeDed/LJW9/lraZi88Ci+sgGd+AY9/Jy0bOgZqLoEpl8LUS9OXvtZnlBL044EtBfcbgDmd1L8G+FXB/cGS6kindW6NiF8UW0nSImARwKRJk0polvV6hw+lo/T6+9PU+ATEIa6tqAAugL/4mxROE+fk92i9nAYMhskXpQng8GFoejYL/kdh88Pw9M/SshGTU+BPyaah1T3XbjvuSgn6YodNUbSi9FGgFri0oHhSRDRKmgo8KOmpiNh4xANGLAYWA9TW1hZ9fMuBvduh/gGoXw4bH0x91BGMvwAu+XuouYSZ32pm3bUf6umW9n79+sGYGWl6+zWpN1DTetj8EGx6CNb+Eh7/bqo7+iyYehlMew9Mvji9aVhulBL0DcDEgvsTgMb2lSRdDnweuDQi3uiMHBGN2e0mSb8DzgOOCHrLqUMt0PCndMS+YTm8uCaVD6mGt8yDaZfDGe9O56Ez+7m3hxqbc1Lqojn6bTDnf6b/zbYnYfPvUvCvvANW3Ja6nU55R/rfTLs89VKyXq2UoF8JTJc0BdgKLAD+srCCpPOAbwLzImJ7QXklsC8iDkiqAi4mfVFrefbKVtj4QAr2TQ/BgVfSefaJs+Hd/5TCY+zM3tEbJs8q+sOEC9L0js+l3x88/0j6v9Xfn7p2Qvoid9rlMP2KdCpt4JCebbcdtS6DPiJaJF0H3EfqXrkkItZKuhmoi4ilwL8CQ4GfKH1B1tqN8kzgm5IOk8bVubVdbx3Lg5aD6Rxw67n27dm/eNjpMOP9KSCmXJp+fGQnr4Gnpv/V9CvS/eZN6TTbhuWw+gew8ltQMQgm/0Xb0X71W/v2l+K9REn96CNiGbCsXdmNBfOXd7DeH4FzutNAO0ntfD6dZ69/IB21v/4q9BuQuvJdcXMKgdEzHAK92cipMHtq6u30+mtvfjP/zefTdNrEtqP9Ke/0l+YnKf8y1krz+mvw/B/ajvB2bEjlp02Ccz8C065I53X9Qs+nAYPhjHel6cp/TmMG1d+f9oenfgKr/qPtjX76FWl/GH2m3+hPEg56Ky4i++iefYn63B+gZX/66F5zCdR+Mh3JVU33i7kvGjEp7QO1n0yn7rasyM7tPwDLb0zT8PGpF8+0K1KPnsHDe7rVfZaD3toc2AObf5/CfeMDadwVSOPDXLAwBfvki9O5XLNW/Qem0zZT3glzb0lfxtffn07trf1F6sLZr3/6PUTraZ4xZ/sA4QRy0Pdlhw+nsWPqH0h92l9YAYdfTwOBTXknXHRdOiIbObWnW2q9yWnj04HBBQvTUA1b/pRCf8P98MAX0zRsXHa0fzlMfZe/qD/OHPR9zEh2w5qfZEftD8KrWW/YMefARZ9OL7yJc9KQvGbdVTGgbZyey78Au7e1db195j/hie+3db1t7cnjrrdl56DPu4Ovtv38fdND1A16Eu4JOGVk+qHStPek22Fje7ql1hcMHwfnfTRNh1rSCKStp3kevCVNQ0anc/qtp4MqJ/d0q3s9B33etByAhroU7JsfSvOHX089IibU8uWWD/G5T38axs3y5eysZ1X0bxub5z3/BHteSp8y65fDpt/CUz9O9UZMzkL/0nQ7bEzPtrsXctD3di0HYdvq9IvGTQ+l8+wt+9Pl6sadCxd9Jr04Jl0IA4fw79ffy+fGX9DTrTY70rAxMOvqNEWkAdk2P5ymdUvhie+letVvS/t0zTvSj7dKvbhLH+ag723270ofd1uHot26qu0CFKNnpC/AprwzvQBOqezZtpodKyn1wx99ZhqX5/ChNE5Sa/A/8X340+JUd9R0mDQnXXRl0kVpbB736HkTB/3JLAJ2bk5XVNqyIgX7S2uBSF9gjTsXaq9pu7jE0NE93WKz46NfRdulEy/+bPok2/h4dsDzGDx7bwp/SBdenzin7XUxdmafH43TQX8y2fNiGp9966q0Ezc+kQ3jCwwcmnomzJifduIJtR5cyvqu/gPbghxSV+EdG9LB0Asr0oHR+mwU1H7906fd8eenaweffl76pFAxoOfaf4I56HtCBLyyBV58Oh2hb1udAn5PNvqzKtKOeeb70o45/oJ0v8L/LrOi+vVLA6xVvzWdvoR07YMtj6XXVuPjsPbnsOrOtKz/4HSkf/p56eplY85Kl1/M6ZG/k+N4O7A3XdD5pSzUW28P7G6rM/KM1M/49PPTUcfYmf71qVl3DR2dDpbOfF+63zqsR+s1drc+np3r/2ZargoYNa3tIu1jzk4XbTltYsd/o5dw0JfD4cPpaPzlP8PL9el2x4Y0v7uhrd6g4WkHmvnf23ak0Wd6IDCzE0FKX9SOOgPO+XAqO3wohf8bB2JrYWsdrL2nbb1Bw/nFwCq455fpi9+qael21Bkw4JSeeS5HyUFfqtf3w64tsOv5NO3Mbps3w456eH1fW91Bw9ORQc0laacYnR0hjJjk3gBmJ5N+FWlgvqrpcNYH28pf2932SXz7M+xd8Sg89wis+VHBykpH+6POSMOEVE7mqn4vw9axqe//qaNOmte7gx7Sj4z2vAh7tmVTNr+7sS3Q97705nUqBsGIienqOzWXpB1l1HSoekv6yHiS/IPN7BgMHp66bE6aA8BHf38vz/3Df0m/NN+xse0T+476NL/2Hti/k68PBL711fQYA4akg7vWafjpaYyfYWPbpsEjTkhW5DPoDx+G13bBvmbYtwP2vZzdZtOrO1Jwtwb6/uYjH6NiUPpHVE6G6XPTO3Tl5OyfNhmGjvF4HGZ9zcAhMG5mmtp77RXmffGH/Hrh5DRe/67n225fWJEuqdle/8FZ6GdvACMmpQv3lFlJQS9pHvAV0qUE74iIW9stHwR8F7gA2AF8JCKey5bdAFwDHAL+NiLuK1vrC0XANy5Og3Tta4Y4VLxe/8Gpn+2QqhTck+akS961buzh49LtKZU+Kjez0g0+jWdjErztquLLD76aHVy+2HbmYO+LbWXb1kDT+p4JekkVwG3AFUADsFLS0nbXfr0G2BkR0yQtAL4EfETSDNLFxM8CTgful/SWiI5SuBuk1E2qYmA6N9Y6DRn15vsDTnWAm9mJN3BI25fBJ1gpR/SzgfqI2AQg6W5gPlAY9POBL2TzPwW+pnSV8PnA3RFxANgsqT57vEfL0/x2PvD14/KwZma9WSlBPx7YUnC/AZjTUZ2IaJH0CjAqK1/Rbt3xxf6IpEXAouzuXknrS2jbyaYKeLmnG9EVfalH/3xJ26iH29iTesU+1IN6bPuUuk/24L7b4XjOpQR9sfMcUWKdUtZNhRGLgcUltOekJakuImp7uh0nM2+jznn7dM7b59iU0m2kASj8adgEoLGjOpL6A6cBzSWua2Zmx1EpQb8SmC5piqSBpC9Xl7arsxTIBpjgw8CDERFZ+QJJgyRNAaYDfypP083MrBRdnrrJzrlfB9xH6l65JCLWSroZqIuIpcC3ge9lX7Y2k94MyOr9mPTFbQvwmePS4+bk0atPPZ0g3kad8/bpnLfPMVA68DYzs7zyTzvNzHLOQW9mlnMO+k5Imijpt5LWSVor6bNZ+UhJyyVtyG4rs3JJ+qqkeklrJJ1f8FgLs/obJC3s6G/2Np1soy9I2ippdTZdVbDODdk2Wi/pyoLyeVlZvaTre+L5lJukwZL+JOnJbPt8MSufIumxbH/4UdbRgazjwo+ybfCYpJqCxyq63Xq7TrbRnZI2F+xDs7LyPvc667aI8NTBBIwDzs/mhwF/BmYA/wJcn5VfD3wpm78K+BXp9wMXAo9l5SOBTdltZTZf2dPP7zhvoy8A/6tI/RnAk8AgYAqwkfQlf0U2PxUYmNWZ0dPPrwzbR8DQbH4A8Fi2b/wYWJCV3w58Kpv/NHB7Nr8A+FFn262nn99x3kZ3Ah8uUr/Pvc66O/mIvhMRsS0iHs/m9wDrSL/snQ98J6v2HeAD2fx84LuRrABGSBoHXAksj4jmiNgJLAfmncCnctx0so068sawGBGxGWgdFuONoTYi4iDQOtRGr5btC3uzuwOyKYB3k4YLgSP3odZ966fAe9oPJ9Juu/V6nWyjjvS511l3OehLlH2EPo90tDEmIrZBCjpgdFat2HAR4zspz5V22wjguuyj9ZLW01v0wW0kqULSamA7KXw2ArsioiWrUvhc3zScCFA4nEgutw8cuY0ionUf+udsH/pyNkou9MF9qLsc9CWQNBT4GfB3EbG7s6pFyo5qKIjeqsg2+gZwBjAL2Ab839aqRVbP9TaKiEMRMYv0y/DZwJnFqmW3fW77wJHbSNLZwA3A24C3k07H/GNWvU9uo+5w0HdB0gBSgP0gIlovJPlS9lGR7HZ7Vt7RkA+5Hgqi2DaKiJeyF+9h4Fu0nWbok9sIICJ2Ab8jnVcekQ0XAm9+rn16OJGCbTQvOy0YkUa//Q+8Dx0zB30nsnOj3wbWRcS/FSwqHPJhIfDLgvKPZ70CLgReyU7t3AfMlVSZncKYm5X1eh1to9Y3wswHgaez+Y6GxShlqI1eR1K1pBHZ/CnA5aTvMX5LGi4EjtyH+tRwIh1so2cLDqZE+g6jcB/qU6+zbuvpb4NP5gm4hPTRbw2wOpuuIp0zfQDYkN2OzOqLdJGWjcBTQG3BY32S9AVaPfCJnn5uJ2AbfS/bBmtIL8xxBet8PttG64H3FpRfReq1sxH4fE8/tzJtn5nAE9l2eBq4MSufSgrqeuAnwKCsfHB2vz5bPrWr7dbbp0620YPZPvQ08H3aeub0uddZdycPgWBmlnM+dWNmlnMOejOznHPQm5nlnIPezCznHPRmZjnnoDczyzkHvZlZzv1/fUGGYa8zrKcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.07680492370666236, pvalue=0.24972162199518655)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_food_supply= X.food_supply_kcalcapitaday\n",
    "loc, scale = norm.fit(data_food_supply)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_food_supply, bins=np.arange(data_food_supply.min(), data_food_supply.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_food_supply.min(), data_food_supply.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_food_supply, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is more than .05, we accept the null hypothesis.\n",
    "# data fits normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run test on five year average of poultry supply variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=1.0, pvalue=0.0)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_poultry_consumption_trend= X.five_year_average\n",
    "loc, scale = norm.fit(data_poultry_consumption_trend)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_food_supply, bins=np.arange(data_food_supply.min(), data_food_supply.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_food_supply.min(), data_food_supply.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_food_supply, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is less than .05, we accept the null hypothesis.\n",
    "# data does not normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run test on GDP variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.24626618281249313, pvalue=1.1318865642574712e-09)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_gdp= X.gdp\n",
    "loc, scale = norm.fit(data_gdp)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_gdp, bins=np.arange(data_gdp.min(), data_gdp.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_gdp.min(), data_gdp.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_gdp, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is less than .05, we accept the null hypothesis.\n",
    "# data does not normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run test on Pultry Domestic Supply Per Capita variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.12457768152616977, pvalue=0.008733357205001985)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_poultry_per_cap= X.poultry_supply_per_cap\n",
    "loc, scale = norm.fit(data_poultry_per_cap)\n",
    "# create a normal distribution with loc and scale\n",
    "n = norm(loc=loc, scale=scale)\n",
    "plt.hist(data_poultry_per_cap, bins=np.arange(data_poultry_per_cap.min(), data_poultry_per_cap.max()+0.2, 0.2), rwidth=0.5)\n",
    "x = np.arange(data_poultry_per_cap.min(), data_poultry_per_cap.max()+0.2, 0.2)\n",
    "plt.plot(x, 350*n.pdf(x))\n",
    "plt.show()\n",
    "kstest(data_poultry_per_cap, n.cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since the p-value is less than .05, we accept the null hypothesis.\n",
    "# data does not normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Guassian Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    " Have a look on clustomer 0 and 1\n",
    " \n",
    "H0: the sample has a Gaussian distribution.\n",
    "H1: the sample does not have a Gaussian distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "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>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>1.932367</td>\n",
       "      <td>30.451542</td>\n",
       "      <td>54.480000</td>\n",
       "      <td>2447.000000</td>\n",
       "      <td>1968.732492</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.478673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kiribati</td>\n",
       "      <td>1.754386</td>\n",
       "      <td>49.014008</td>\n",
       "      <td>73.530000</td>\n",
       "      <td>3078.000000</td>\n",
       "      <td>1592.082641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Solomon Islands</td>\n",
       "      <td>2.672956</td>\n",
       "      <td>28.929042</td>\n",
       "      <td>53.130000</td>\n",
       "      <td>2412.000000</td>\n",
       "      <td>2039.282045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.594181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Djibouti</td>\n",
       "      <td>1.588983</td>\n",
       "      <td>21.401313</td>\n",
       "      <td>65.510000</td>\n",
       "      <td>2695.000000</td>\n",
       "      <td>3009.407574</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>3.128259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>2.516411</td>\n",
       "      <td>21.304650</td>\n",
       "      <td>43.230000</td>\n",
       "      <td>2229.000000</td>\n",
       "      <td>764.899506</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.134472</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       population_difference  animal_protein_vs_total_protein  \\\n",
       "country                                                                         \n",
       "Sao Tome and Principe               1.932367                        30.451542   \n",
       "Kiribati                            1.754386                        49.014008   \n",
       "Solomon Islands                     2.672956                        28.929042   \n",
       "Djibouti                            1.588983                        21.401313   \n",
       "Guinea-Bissau                       2.516411                        21.304650   \n",
       "\n",
       "                       protein_supply_quantity_gcapitaday  \\\n",
       "country                                                     \n",
       "Sao Tome and Principe                           54.480000   \n",
       "Kiribati                                        73.530000   \n",
       "Solomon Islands                                 53.130000   \n",
       "Djibouti                                        65.510000   \n",
       "Guinea-Bissau                                   43.230000   \n",
       "\n",
       "                       food_supply_kcalcapitaday         gdp  \\\n",
       "country                                                        \n",
       "Sao Tome and Principe                2447.000000 1968.732492   \n",
       "Kiribati                             3078.000000 1592.082641   \n",
       "Solomon Islands                      2412.000000 2039.282045   \n",
       "Djibouti                             2695.000000 3009.407574   \n",
       "Guinea-Bissau                        2229.000000  764.899506   \n",
       "\n",
       "                       five_year_average  poultry_supply_per_cap  \n",
       "country                                                           \n",
       "Sao Tome and Principe           0.000000                9.478673  \n",
       "Kiribati                        0.000000               17.241379  \n",
       "Solomon Islands                 0.000000                4.594181  \n",
       "Djibouti                        0.125000                3.128259  \n",
       "Guinea-Bissau                   0.000000                2.134472  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_info=X.reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cluster=X_scaled_clustered.reset_index()\n",
    "X_cluster=X_cluster[['country','cluster']]\n",
    "#X_cluster=X_cluster[(X_cluster['cluster'] >=3)]\n",
    "X_cluster=pd.merge(X_cluster,X_info,on='country',how='inner')\n",
    "X_cluster_0=X_cluster[(X_cluster['cluster'] ==0)]\n",
    "X_cluster_1=X_cluster[(X_cluster['cluster'] ==1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>cluster</th>\n",
       "      <th>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>0</td>\n",
       "      <td>0.898204</td>\n",
       "      <td>71.668487</td>\n",
       "      <td>146.480000</td>\n",
       "      <td>3656.000000</td>\n",
       "      <td>76624.485693</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>29.673591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>0</td>\n",
       "      <td>2.027027</td>\n",
       "      <td>62.289935</td>\n",
       "      <td>108.300000</td>\n",
       "      <td>3470.000000</td>\n",
       "      <td>120137.955478</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>18.211921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Malta</td>\n",
       "      <td>0</td>\n",
       "      <td>0.228311</td>\n",
       "      <td>56.781846</td>\n",
       "      <td>116.340000</td>\n",
       "      <td>3419.000000</td>\n",
       "      <td>33671.589348</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>31.890661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>China, Macao SAR</td>\n",
       "      <td>0</td>\n",
       "      <td>1.444623</td>\n",
       "      <td>64.850037</td>\n",
       "      <td>109.360000</td>\n",
       "      <td>3326.000000</td>\n",
       "      <td>86248.547881</td>\n",
       "      <td>0.011905</td>\n",
       "      <td>34.810127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.546573</td>\n",
       "      <td>62.327089</td>\n",
       "      <td>126.510000</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>18583.568442</td>\n",
       "      <td>0.015709</td>\n",
       "      <td>29.632274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>Norway</td>\n",
       "      <td>0</td>\n",
       "      <td>0.793051</td>\n",
       "      <td>59.957608</td>\n",
       "      <td>113.230000</td>\n",
       "      <td>3366.000000</td>\n",
       "      <td>81101.658048</td>\n",
       "      <td>0.011639</td>\n",
       "      <td>20.794305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>Finland</td>\n",
       "      <td>0</td>\n",
       "      <td>0.217746</td>\n",
       "      <td>63.934565</td>\n",
       "      <td>117.980000</td>\n",
       "      <td>3340.000000</td>\n",
       "      <td>49955.341172</td>\n",
       "      <td>0.018269</td>\n",
       "      <td>20.822017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>0</td>\n",
       "      <td>1.388597</td>\n",
       "      <td>61.302682</td>\n",
       "      <td>117.450000</td>\n",
       "      <td>3885.000000</td>\n",
       "      <td>79661.622685</td>\n",
       "      <td>0.010937</td>\n",
       "      <td>26.146503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>0</td>\n",
       "      <td>0.827815</td>\n",
       "      <td>62.801679</td>\n",
       "      <td>95.300000</td>\n",
       "      <td>3355.000000</td>\n",
       "      <td>82530.174862</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>15.482055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>0</td>\n",
       "      <td>0.348918</td>\n",
       "      <td>68.139475</td>\n",
       "      <td>117.010000</td>\n",
       "      <td>3399.000000</td>\n",
       "      <td>61809.750887</td>\n",
       "      <td>0.060332</td>\n",
       "      <td>26.947149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "      <td>0.542907</td>\n",
       "      <td>58.763921</td>\n",
       "      <td>99.670000</td>\n",
       "      <td>3764.000000</td>\n",
       "      <td>47225.757205</td>\n",
       "      <td>0.013532</td>\n",
       "      <td>14.109040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106</td>\n",
       "      <td>Austria</td>\n",
       "      <td>0</td>\n",
       "      <td>0.804989</td>\n",
       "      <td>60.357634</td>\n",
       "      <td>109.050000</td>\n",
       "      <td>3694.000000</td>\n",
       "      <td>52047.260459</td>\n",
       "      <td>0.004482</td>\n",
       "      <td>19.795299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>0</td>\n",
       "      <td>0.676426</td>\n",
       "      <td>63.997346</td>\n",
       "      <td>105.520000</td>\n",
       "      <td>3182.000000</td>\n",
       "      <td>55704.968354</td>\n",
       "      <td>0.018070</td>\n",
       "      <td>18.953069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>0</td>\n",
       "      <td>1.997041</td>\n",
       "      <td>49.403781</td>\n",
       "      <td>103.150000</td>\n",
       "      <td>3471.000000</td>\n",
       "      <td>33760.739169</td>\n",
       "      <td>0.038570</td>\n",
       "      <td>48.344211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0.871969</td>\n",
       "      <td>54.987212</td>\n",
       "      <td>93.840000</td>\n",
       "      <td>3192.000000</td>\n",
       "      <td>43776.465705</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>44.064938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>0</td>\n",
       "      <td>0.229129</td>\n",
       "      <td>65.567766</td>\n",
       "      <td>106.470000</td>\n",
       "      <td>3297.000000</td>\n",
       "      <td>53505.050866</td>\n",
       "      <td>-0.116643</td>\n",
       "      <td>13.716295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>China, Hong Kong SAR</td>\n",
       "      <td>0</td>\n",
       "      <td>0.903367</td>\n",
       "      <td>73.110748</td>\n",
       "      <td>128.490000</td>\n",
       "      <td>3267.000000</td>\n",
       "      <td>48820.505446</td>\n",
       "      <td>-0.035367</td>\n",
       "      <td>40.151926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>127</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.320731</td>\n",
       "      <td>63.629363</td>\n",
       "      <td>116.880000</td>\n",
       "      <td>3479.000000</td>\n",
       "      <td>23397.874237</td>\n",
       "      <td>0.010031</td>\n",
       "      <td>32.761310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0</td>\n",
       "      <td>1.517867</td>\n",
       "      <td>45.813658</td>\n",
       "      <td>110.120000</td>\n",
       "      <td>3312.000000</td>\n",
       "      <td>43407.635143</td>\n",
       "      <td>0.112976</td>\n",
       "      <td>62.921815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>Israel</td>\n",
       "      <td>0</td>\n",
       "      <td>1.673945</td>\n",
       "      <td>58.894496</td>\n",
       "      <td>126.820000</td>\n",
       "      <td>3527.000000</td>\n",
       "      <td>43841.825316</td>\n",
       "      <td>0.021292</td>\n",
       "      <td>73.848723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>140</td>\n",
       "      <td>Republic of Korea</td>\n",
       "      <td>0</td>\n",
       "      <td>0.148740</td>\n",
       "      <td>51.652851</td>\n",
       "      <td>100.130000</td>\n",
       "      <td>3417.000000</td>\n",
       "      <td>33626.657541</td>\n",
       "      <td>0.014068</td>\n",
       "      <td>16.903776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>Italy</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.077463</td>\n",
       "      <td>53.479853</td>\n",
       "      <td>106.470000</td>\n",
       "      <td>3500.000000</td>\n",
       "      <td>35163.573799</td>\n",
       "      <td>0.006395</td>\n",
       "      <td>19.149884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>1.273134</td>\n",
       "      <td>66.792631</td>\n",
       "      <td>105.850000</td>\n",
       "      <td>3391.000000</td>\n",
       "      <td>58689.490194</td>\n",
       "      <td>0.019996</td>\n",
       "      <td>48.638445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>Spain</td>\n",
       "      <td>0</td>\n",
       "      <td>0.098613</td>\n",
       "      <td>62.186597</td>\n",
       "      <td>107.290000</td>\n",
       "      <td>3324.000000</td>\n",
       "      <td>30599.422680</td>\n",
       "      <td>0.012955</td>\n",
       "      <td>30.754074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0</td>\n",
       "      <td>0.933791</td>\n",
       "      <td>54.348659</td>\n",
       "      <td>104.400000</td>\n",
       "      <td>3567.000000</td>\n",
       "      <td>46343.371942</td>\n",
       "      <td>0.031981</td>\n",
       "      <td>40.404585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>0.242628</td>\n",
       "      <td>59.578402</td>\n",
       "      <td>108.160000</td>\n",
       "      <td>3501.000000</td>\n",
       "      <td>41168.651446</td>\n",
       "      <td>0.002705</td>\n",
       "      <td>24.233913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "      <td>0.563769</td>\n",
       "      <td>60.893961</td>\n",
       "      <td>105.150000</td>\n",
       "      <td>3554.000000</td>\n",
       "      <td>47993.473332</td>\n",
       "      <td>0.012731</td>\n",
       "      <td>20.042346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.621937</td>\n",
       "      <td>55.827394</td>\n",
       "      <td>103.820000</td>\n",
       "      <td>3343.000000</td>\n",
       "      <td>42889.386317</td>\n",
       "      <td>0.021429</td>\n",
       "      <td>31.708915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>0</td>\n",
       "      <td>0.618607</td>\n",
       "      <td>64.685684</td>\n",
       "      <td>113.580000</td>\n",
       "      <td>3777.000000</td>\n",
       "      <td>62981.386956</td>\n",
       "      <td>0.032931</td>\n",
       "      <td>57.001614</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               country  cluster  \\\n",
       "19                                             Iceland        0   \n",
       "23                                          Luxembourg        0   \n",
       "25                                               Malta        0   \n",
       "34                                    China, Macao SAR        0   \n",
       "84                                           Lithuania        0   \n",
       "89                                              Norway        0   \n",
       "93                                             Finland        0   \n",
       "95                                             Ireland        0   \n",
       "98                                         Switzerland        0   \n",
       "103                                            Denmark        0   \n",
       "105                                            Belgium        0   \n",
       "106                                            Austria        0   \n",
       "108                                             Sweden        0   \n",
       "109                                             Kuwait        0   \n",
       "112                                        New Zealand        0   \n",
       "116                                        Netherlands        0   \n",
       "124                               China, Hong Kong SAR        0   \n",
       "127                                           Portugal        0   \n",
       "136                               United Arab Emirates        0   \n",
       "137                                             Israel        0   \n",
       "140                                  Republic of Korea        0   \n",
       "144                                              Italy        0   \n",
       "146                                          Australia        0   \n",
       "149                                              Spain        0   \n",
       "150                                             Canada        0   \n",
       "154                                             France        0   \n",
       "156                                            Germany        0   \n",
       "162  United Kingdom of Great Britain and Northern I...        0   \n",
       "170                           United States of America        0   \n",
       "\n",
       "     population_difference  animal_protein_vs_total_protein  \\\n",
       "19                0.898204                        71.668487   \n",
       "23                2.027027                        62.289935   \n",
       "25                0.228311                        56.781846   \n",
       "34                1.444623                        64.850037   \n",
       "84               -1.546573                        62.327089   \n",
       "89                0.793051                        59.957608   \n",
       "93                0.217746                        63.934565   \n",
       "95                1.388597                        61.302682   \n",
       "98                0.827815                        62.801679   \n",
       "103               0.348918                        68.139475   \n",
       "105               0.542907                        58.763921   \n",
       "106               0.804989                        60.357634   \n",
       "108               0.676426                        63.997346   \n",
       "109               1.997041                        49.403781   \n",
       "112               0.871969                        54.987212   \n",
       "116               0.229129                        65.567766   \n",
       "124               0.903367                        73.110748   \n",
       "127              -0.320731                        63.629363   \n",
       "136               1.517867                        45.813658   \n",
       "137               1.673945                        58.894496   \n",
       "140               0.148740                        51.652851   \n",
       "144              -0.077463                        53.479853   \n",
       "146               1.273134                        66.792631   \n",
       "149               0.098613                        62.186597   \n",
       "150               0.933791                        54.348659   \n",
       "154               0.242628                        59.578402   \n",
       "156               0.563769                        60.893961   \n",
       "162               0.621937                        55.827394   \n",
       "170               0.618607                        64.685684   \n",
       "\n",
       "     protein_supply_quantity_gcapitaday  food_supply_kcalcapitaday  \\\n",
       "19                           146.480000                3656.000000   \n",
       "23                           108.300000                3470.000000   \n",
       "25                           116.340000                3419.000000   \n",
       "34                           109.360000                3326.000000   \n",
       "84                           126.510000                3410.000000   \n",
       "89                           113.230000                3366.000000   \n",
       "93                           117.980000                3340.000000   \n",
       "95                           117.450000                3885.000000   \n",
       "98                            95.300000                3355.000000   \n",
       "103                          117.010000                3399.000000   \n",
       "105                           99.670000                3764.000000   \n",
       "106                          109.050000                3694.000000   \n",
       "108                          105.520000                3182.000000   \n",
       "109                          103.150000                3471.000000   \n",
       "112                           93.840000                3192.000000   \n",
       "116                          106.470000                3297.000000   \n",
       "124                          128.490000                3267.000000   \n",
       "127                          116.880000                3479.000000   \n",
       "136                          110.120000                3312.000000   \n",
       "137                          126.820000                3527.000000   \n",
       "140                          100.130000                3417.000000   \n",
       "144                          106.470000                3500.000000   \n",
       "146                          105.850000                3391.000000   \n",
       "149                          107.290000                3324.000000   \n",
       "150                          104.400000                3567.000000   \n",
       "154                          108.160000                3501.000000   \n",
       "156                          105.150000                3554.000000   \n",
       "162                          103.820000                3343.000000   \n",
       "170                          113.580000                3777.000000   \n",
       "\n",
       "              gdp  five_year_average  poultry_supply_per_cap  \n",
       "19   76624.485693           0.027778               29.673591  \n",
       "23  120137.955478           0.025000               18.211921  \n",
       "25   33671.589348           0.019231               31.890661  \n",
       "34   86248.547881           0.011905               34.810127  \n",
       "84   18583.568442           0.015709               29.632274  \n",
       "89   81101.658048           0.011639               20.794305  \n",
       "93   49955.341172           0.018269               20.822017  \n",
       "95   79661.622685           0.010937               26.146503  \n",
       "98   82530.174862           0.003860               15.482055  \n",
       "103  61809.750887           0.060332               26.947149  \n",
       "105  47225.757205           0.013532               14.109040  \n",
       "106  52047.260459           0.004482               19.795299  \n",
       "108  55704.968354           0.018070               18.953069  \n",
       "109  33760.739169           0.038570               48.344211  \n",
       "112  43776.465705           0.045952               44.064938  \n",
       "116  53505.050866          -0.116643               13.716295  \n",
       "124  48820.505446          -0.035367               40.151926  \n",
       "127  23397.874237           0.010031               32.761310  \n",
       "136  43407.635143           0.112976               62.921815  \n",
       "137  43841.825316           0.021292               73.848723  \n",
       "140  33626.657541           0.014068               16.903776  \n",
       "144  35163.573799           0.006395               19.149884  \n",
       "146  58689.490194           0.019996               48.638445  \n",
       "149  30599.422680           0.012955               30.754074  \n",
       "150  46343.371942           0.031981               40.404585  \n",
       "154  41168.651446           0.002705               24.233913  \n",
       "156  47993.473332           0.012731               20.042346  \n",
       "162  42889.386317           0.021429               31.708915  \n",
       "170  62981.386956           0.032931               57.001614  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cluster_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "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</th>\n",
       "      <th>cluster</th>\n",
       "      <th>population_difference</th>\n",
       "      <th>animal_protein_vs_total_protein</th>\n",
       "      <th>protein_supply_quantity_gcapitaday</th>\n",
       "      <th>food_supply_kcalcapitaday</th>\n",
       "      <th>gdp</th>\n",
       "      <th>five_year_average</th>\n",
       "      <th>poultry_supply_per_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Kiribati</td>\n",
       "      <td>1</td>\n",
       "      <td>1.754386</td>\n",
       "      <td>49.014008</td>\n",
       "      <td>73.530000</td>\n",
       "      <td>3078.000000</td>\n",
       "      <td>1592.082641</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>64.222684</td>\n",
       "      <td>77.060000</td>\n",
       "      <td>2579.000000</td>\n",
       "      <td>18098.875957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Dominica</td>\n",
       "      <td>1</td>\n",
       "      <td>1.408451</td>\n",
       "      <td>55.548526</td>\n",
       "      <td>79.030000</td>\n",
       "      <td>2953.000000</td>\n",
       "      <td>7413.637732</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>69.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>64.747841</td>\n",
       "      <td>79.910000</td>\n",
       "      <td>2442.000000</td>\n",
       "      <td>15629.054576</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.683967</td>\n",
       "      <td>71.290000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>10787.400403</td>\n",
       "      <td>0.008929</td>\n",
       "      <td>63.063063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>165</td>\n",
       "      <td>Japan</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.236073</td>\n",
       "      <td>55.681558</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2707.000000</td>\n",
       "      <td>39087.235938</td>\n",
       "      <td>0.013822</td>\n",
       "      <td>19.221396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>167</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>1.133222</td>\n",
       "      <td>48.972003</td>\n",
       "      <td>91.440000</td>\n",
       "      <td>3157.000000</td>\n",
       "      <td>9356.145765</td>\n",
       "      <td>0.034396</td>\n",
       "      <td>34.455706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>168</td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>1</td>\n",
       "      <td>0.140177</td>\n",
       "      <td>54.826940</td>\n",
       "      <td>101.410000</td>\n",
       "      <td>3342.000000</td>\n",
       "      <td>11534.175152</td>\n",
       "      <td>0.023857</td>\n",
       "      <td>31.475153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>169</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "      <td>0.786686</td>\n",
       "      <td>56.199038</td>\n",
       "      <td>95.660000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>8861.531858</td>\n",
       "      <td>0.021581</td>\n",
       "      <td>47.047534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>China, mainland</td>\n",
       "      <td>1</td>\n",
       "      <td>0.466284</td>\n",
       "      <td>39.348819</td>\n",
       "      <td>100.740000</td>\n",
       "      <td>3191.000000</td>\n",
       "      <td>9616.756315</td>\n",
       "      <td>0.013584</td>\n",
       "      <td>13.461301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country  cluster  population_difference  \\\n",
       "1                 Kiribati        1               1.754386   \n",
       "5    Saint Kitts and Nevis        1               0.000000   \n",
       "8                 Dominica        1               1.408451   \n",
       "9      Antigua and Barbuda        1               1.052632   \n",
       "10                 Grenada        1               0.000000   \n",
       "..                     ...      ...                    ...   \n",
       "165                  Japan        1              -0.236073   \n",
       "167                 Mexico        1               1.133222   \n",
       "168     Russian Federation        1               0.140177   \n",
       "169                 Brazil        1               0.786686   \n",
       "171        China, mainland        1               0.466284   \n",
       "\n",
       "     animal_protein_vs_total_protein  protein_supply_quantity_gcapitaday  \\\n",
       "1                          49.014008                           73.530000   \n",
       "5                          64.222684                           77.060000   \n",
       "8                          55.548526                           79.030000   \n",
       "9                          64.747841                           79.910000   \n",
       "10                         56.683967                           71.290000   \n",
       "..                               ...                                 ...   \n",
       "165                        55.681558                           87.300000   \n",
       "167                        48.972003                           91.440000   \n",
       "168                        54.826940                          101.410000   \n",
       "169                        56.199038                           95.660000   \n",
       "171                        39.348819                          100.740000   \n",
       "\n",
       "     food_supply_kcalcapitaday          gdp  five_year_average  \\\n",
       "1                  3078.000000  1592.082641           0.000000   \n",
       "5                  2579.000000 18098.875957           0.000000   \n",
       "8                  2953.000000  7413.637732           0.062500   \n",
       "9                  2442.000000 15629.054576           0.000000   \n",
       "10                 2401.000000 10787.400403           0.008929   \n",
       "..                         ...          ...                ...   \n",
       "165                2707.000000 39087.235938           0.013822   \n",
       "167                3157.000000  9356.145765           0.034396   \n",
       "168                3342.000000 11534.175152           0.023857   \n",
       "169                3300.000000  8861.531858           0.021581   \n",
       "171                3191.000000  9616.756315           0.013584   \n",
       "\n",
       "     poultry_supply_per_cap  \n",
       "1                 17.241379  \n",
       "5                 76.923077  \n",
       "8                 69.444444  \n",
       "9                 62.500000  \n",
       "10                63.063063  \n",
       "..                      ...  \n",
       "165               19.221396  \n",
       "167               34.455706  \n",
       "168               31.475153  \n",
       "169               47.047534  \n",
       "171               13.461301  \n",
       "\n",
       "[73 rows x 9 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cluster_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Compare Population Difference between clsuter 0 and clstuer 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "clsuter_0_pop = X_cluster_0.population_difference.values\n",
    "clsuter_1_pop = X_cluster_1.population_difference.values\n",
    "\n",
    "sns.distplot(clsuter_0_pop, label ='Cluster 0')\n",
    "sns.distplot(clsuter_1_pop, label = 'Cluster 1')\n",
    "plt.legend()\n",
    "plt.title('Histogram of Cluster 0 and 1- Population Difference')\n",
    "plt.xlabel('Population difference')\n",
    "sns.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=0.948, p=0.158\n",
      "Probably Gaussian, fail to reject H0\n",
      "stat=0.989, p=0.778\n",
      "Probably Gaussian, fail to reject H0\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "stat, p = shapiro(clsuter_0_pop)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably Gaussian, fail to reject H0')\n",
    "else:\n",
    "\tprint('Probably not Gaussian, fail to reject H1')\n",
    "    \n",
    "stat, p = shapiro(clsuter_1_pop)\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('Probably Gaussian, fail to reject H0')\n",
    "else:\n",
    "\tprint('Probably not Gaussian,fail to reject H1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from scipy.stats import norm\n",
    "import pylab\n",
    "\n",
    "sm.qqplot(clsuter_0_pop, line='45')\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4358289450239874 1.5798600679711907e-05\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kstest, norm\n",
    "\n",
    "ks_statistic, p_value = kstest(clsuter_0_pop, 'norm')\n",
    "print(ks_statistic, p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from scipy.stats import norm\n",
    "import pylab\n",
    "\n",
    "sm.qqplot(clsuter_1_pop, line='45')\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2943889993703141 4.216943924579781e-06\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kstest, norm\n",
    "\n",
    "ks_statistic, p_value = kstest(clsuter_1_pop, 'norm')\n",
    "print(ks_statistic, p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "258.982px"
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