food_production_and_supply.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "86972c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.api.types import CategoricalDtype\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import awoc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "86fac251",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "\n",
    "# food production dataset\n",
    "food_prod_df = pd.read_csv('dataset/Africa Food Production (2004 - 2013).csv')\n",
    "\n",
    "# food supply/consumption dataset\n",
    "food_supp_df = pd.read_csv('dataset/Africa Food Supply (2004 - 2013).csv')\n",
    "\n",
    "# Dimensional table containing additional country information\n",
    "country_dim_df = pd.read_csv('dataset/country_dim.csv', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e93443f5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Item</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2004</td>\n",
       "      <td>2731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2005</td>\n",
       "      <td>2415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2006</td>\n",
       "      <td>2688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2007</td>\n",
       "      <td>2319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2008</td>\n",
       "      <td>1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2009</td>\n",
       "      <td>2953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2010</td>\n",
       "      <td>2605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2011</td>\n",
       "      <td>2555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2012</td>\n",
       "      <td>3432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2013</td>\n",
       "      <td>3299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Rice (Milled Equivalent)</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country                      Item  Year  Value\n",
       "0   Algeria        Wheat and products  2004   2731\n",
       "1   Algeria        Wheat and products  2005   2415\n",
       "2   Algeria        Wheat and products  2006   2688\n",
       "3   Algeria        Wheat and products  2007   2319\n",
       "4   Algeria        Wheat and products  2008   1111\n",
       "5   Algeria        Wheat and products  2009   2953\n",
       "6   Algeria        Wheat and products  2010   2605\n",
       "7   Algeria        Wheat and products  2011   2555\n",
       "8   Algeria        Wheat and products  2012   3432\n",
       "9   Algeria        Wheat and products  2013   3299\n",
       "10  Algeria  Rice (Milled Equivalent)  2004      0\n",
       "11  Algeria  Rice (Milled Equivalent)  2005      0\n",
       "12  Algeria  Rice (Milled Equivalent)  2006      0\n",
       "13  Algeria  Rice (Milled Equivalent)  2007      0\n",
       "14  Algeria  Rice (Milled Equivalent)  2008      0\n",
       "15  Algeria  Rice (Milled Equivalent)  2009      0\n",
       "16  Algeria  Rice (Milled Equivalent)  2010      0\n",
       "17  Algeria  Rice (Milled Equivalent)  2011      0\n",
       "18  Algeria  Rice (Milled Equivalent)  2012      0\n",
       "19  Algeria  Rice (Milled Equivalent)  2013      0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_prod_df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "922ffafc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Value</th>\n",
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       "      <td>Algeria</td>\n",
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       "      <td>2688</td>\n",
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       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
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       "      <td>1111</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>23105</th>\n",
       "      <td>Zimbabwe</td>\n",
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       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23106</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23107</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23108</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23109</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country                Item  Year  Value\n",
       "0       Algeria  Wheat and products  2004   2731\n",
       "1       Algeria  Wheat and products  2005   2415\n",
       "2       Algeria  Wheat and products  2006   2688\n",
       "3       Algeria  Wheat and products  2007   2319\n",
       "4       Algeria  Wheat and products  2008   1111\n",
       "...         ...                 ...   ...    ...\n",
       "23105  Zimbabwe         Crustaceans  2009      0\n",
       "23106  Zimbabwe         Crustaceans  2010      0\n",
       "23107  Zimbabwe         Crustaceans  2011      0\n",
       "23108  Zimbabwe         Crustaceans  2012      0\n",
       "23109  Zimbabwe         Crustaceans  2013      0\n",
       "\n",
       "[23110 rows x 4 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_prod_df.groupby(['Year', 'Item', 'Value']).head(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "86fc4715",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2004</td>\n",
       "      <td>2987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2005</td>\n",
       "      <td>2958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2006</td>\n",
       "      <td>3047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>3041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2008</td>\n",
       "      <td>3048</td>\n",
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       "      <th>445</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "      <td>2147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>2197</td>\n",
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       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>2110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  Value\n",
       "0     Algeria  2004   2987\n",
       "1     Algeria  2005   2958\n",
       "2     Algeria  2006   3047\n",
       "3     Algeria  2007   3041\n",
       "4     Algeria  2008   3048\n",
       "..        ...   ...    ...\n",
       "445  Zimbabwe  2009   2147\n",
       "446  Zimbabwe  2010   2168\n",
       "447  Zimbabwe  2011   2200\n",
       "448  Zimbabwe  2012   2197\n",
       "449  Zimbabwe  2013   2110\n",
       "\n",
       "[450 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b253a46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso',\n",
       "       'Cabo Verde', 'Cameroon', 'Central African Republic', 'Chad',\n",
       "       'Congo', \"Cote d'Ivoire\", 'Djibouti', 'Egypt', 'Ethiopia', 'Gabon',\n",
       "       'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya', 'Lesotho',\n",
       "       'Liberia', 'Madagascar', 'Malawi', 'Mali', 'Mauritania',\n",
       "       'Mauritius', 'Morocco', 'Mozambique', 'Namibia', 'Niger',\n",
       "       'Nigeria', 'Rwanda', 'Sao Tome and Principe', 'Senegal',\n",
       "       'Sierra Leone', 'South Africa', 'Sudan', 'Swaziland', 'Togo',\n",
       "       'Tunisia', 'Uganda', 'United Republic of Tanzania', 'Zambia',\n",
       "       'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supp_df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "ab9d68b0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Algeria',\n",
       " 'Angola',\n",
       " 'Benin',\n",
       " 'Botswana',\n",
       " 'Burkina Faso',\n",
       " 'Burundi',\n",
       " 'Cameroon',\n",
       " 'Cape Verde',\n",
       " 'Central African Republic',\n",
       " 'Chad',\n",
       " 'Comoros',\n",
       " 'Democratic Republic of the Congo',\n",
       " 'Djibouti',\n",
       " 'Egypt',\n",
       " 'Equatorial Guinea',\n",
       " 'Eritrea',\n",
       " 'Ethiopia',\n",
       " 'Gabon',\n",
       " 'Gambia',\n",
       " 'Ghana',\n",
       " 'Guinea',\n",
       " 'Guinea-Bissau',\n",
       " 'Ivory Coast',\n",
       " 'Kenya',\n",
       " 'Lesotho',\n",
       " 'Liberia',\n",
       " 'Libya',\n",
       " 'Madagascar',\n",
       " 'Malawi',\n",
       " 'Mali',\n",
       " 'Mauritania',\n",
       " 'Mauritius',\n",
       " 'Mayotte',\n",
       " 'Morocco',\n",
       " 'Mozambique',\n",
       " 'Namibia',\n",
       " 'Niger',\n",
       " 'Nigeria',\n",
       " 'Republic of the Congo',\n",
       " 'Reunion',\n",
       " 'Rwanda',\n",
       " 'Saint Helena',\n",
       " 'Sao Tome and Principe',\n",
       " 'Senegal',\n",
       " 'Seychelles',\n",
       " 'Sierra Leone',\n",
       " 'Somalia',\n",
       " 'South Africa',\n",
       " 'South Sudan',\n",
       " 'Sudan',\n",
       " 'Swaziland',\n",
       " 'Tanzania',\n",
       " 'Togo',\n",
       " 'Tunisia',\n",
       " 'Uganda',\n",
       " 'Western Sahara',\n",
       " 'Zambia',\n",
       " 'Zimbabwe']"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fetch additional data such as sub-region and ISO code for each country\n",
    "countries_list = awoc.AWOC()\n",
    "african_countries = countries_list.get_countries_list_of('Africa')\n",
    "african_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f4db88dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso',\n",
       "       'Cape Verde', 'Cameroon', 'Central African Republic', 'Chad',\n",
       "       'Republic of the Congo', 'Ivory Coast', 'Djibouti', 'Egypt',\n",
       "       'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau',\n",
       "       'Kenya', 'Lesotho', 'Liberia', 'Madagascar', 'Malawi', 'Mali',\n",
       "       'Mauritania', 'Mauritius', 'Morocco', 'Mozambique', 'Namibia',\n",
       "       'Niger', 'Nigeria', 'Rwanda', 'Sao Tome and Principe', 'Senegal',\n",
       "       'Sierra Leone', 'South Africa', 'Sudan', 'Swaziland', 'Togo',\n",
       "       'Tunisia', 'Uganda', 'Tanzania', 'Zambia', 'Zimbabwe'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slightly modify some country names in the food_supp_df and food_prod_df tables so that they can match \n",
    "# the ones obtained using the awoc module\n",
    "\n",
    "country_name_remapper = {\n",
    "    'Congo' : 'Republic of the Congo',\n",
    "    \"Cote d'Ivoire\" : 'Ivory Coast',\n",
    "    'Cabo Verde': 'Cape Verde',\n",
    "    'United Republic of Tanzania': 'Tanzania',\n",
    "}\n",
    "\n",
    "food_prod_df['Country'] = food_prod_df['Country'].replace(country_name_remapper)\n",
    "food_prod_df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2a20ea99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Algeria', 'Angola', 'Benin', 'Botswana', 'Burkina Faso',\n",
       "       'Cape Verde', 'Cameroon', 'Central African Republic', 'Chad',\n",
       "       'Republic of the Congo', 'Ivory Coast', 'Djibouti', 'Egypt',\n",
       "       'Ethiopia', 'Gabon', 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau',\n",
       "       'Kenya', 'Lesotho', 'Liberia', 'Madagascar', 'Malawi', 'Mali',\n",
       "       'Mauritania', 'Mauritius', 'Morocco', 'Mozambique', 'Namibia',\n",
       "       'Niger', 'Nigeria', 'Rwanda', 'Sao Tome and Principe', 'Senegal',\n",
       "       'Sierra Leone', 'South Africa', 'Sudan', 'Swaziland', 'Togo',\n",
       "       'Tunisia', 'Uganda', 'Tanzania', 'Zambia', 'Zimbabwe'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supp_df['Country'] = food_supp_df['Country'].replace(country_name_remapper)\n",
    "food_supp_df['Country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "bde3b622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set()\n"
     ]
    }
   ],
   "source": [
    "# Ensure that no country is missed out before merging\n",
    "# It should return empty set if true\n",
    "\n",
    "print(set(food_supp_df['Country']) - set(african_countries))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "82b86539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\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>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2004</td>\n",
       "      <td>2987</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2005</td>\n",
       "      <td>2958</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2006</td>\n",
       "      <td>3047</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>3041</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2008</td>\n",
       "      <td>3048</td>\n",
       "      <td>DZA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "      <td>2147</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2168</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "      <td>2200</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>2197</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>2110</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  Value ISO3\n",
       "0     Algeria  2004   2987  DZA\n",
       "1     Algeria  2005   2958  DZA\n",
       "2     Algeria  2006   3047  DZA\n",
       "3     Algeria  2007   3041  DZA\n",
       "4     Algeria  2008   3048  DZA\n",
       "..        ...   ...    ...  ...\n",
       "445  Zimbabwe  2009   2147  ZWE\n",
       "446  Zimbabwe  2010   2168  ZWE\n",
       "447  Zimbabwe  2011   2200  ZWE\n",
       "448  Zimbabwe  2012   2197  ZWE\n",
       "449  Zimbabwe  2013   2110  ZWE\n",
       "\n",
       "[450 rows x 4 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's pull additional country information from the module\n",
    "\n",
    "# Country ISO3\n",
    "food_supp_df['ISO3'] = food_supp_df.apply(lambda x: countries_list.get_country_ISO3(x.Country), axis=1)\n",
    "food_supp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "4823c2fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Sub-region</th>\n",
       "      <th>Intermediate region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2004</td>\n",
       "      <td>2987</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2005</td>\n",
       "      <td>2958</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2006</td>\n",
       "      <td>3047</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>3041</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2008</td>\n",
       "      <td>3048</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009</td>\n",
       "      <td>2147</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>2168</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "      <td>2200</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>2197</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
       "      <td>2110</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country  Year  Value ISO3          Sub-region Intermediate region\n",
       "0     Algeria  2004   2987  DZA     Northern Africa     Northern Africa\n",
       "1     Algeria  2005   2958  DZA     Northern Africa     Northern Africa\n",
       "2     Algeria  2006   3047  DZA     Northern Africa     Northern Africa\n",
       "3     Algeria  2007   3041  DZA     Northern Africa     Northern Africa\n",
       "4     Algeria  2008   3048  DZA     Northern Africa     Northern Africa\n",
       "..        ...   ...    ...  ...                 ...                 ...\n",
       "445  Zimbabwe  2009   2147  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "446  Zimbabwe  2010   2168  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "447  Zimbabwe  2011   2200  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "448  Zimbabwe  2012   2197  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "449  Zimbabwe  2013   2110  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "\n",
       "[450 rows x 6 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the ISO3 column, let's fetch additional data for each country from the country_dim table\n",
    "\n",
    "food_supp_df = pd.merge(food_supp_df, country_dim_df, on='ISO3', how='left')\n",
    "food_supp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d559eb1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3f27ce3a",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "2dbd179b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23110 entries, 0 to 23109\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Country  23110 non-null  object\n",
      " 1   Item     23110 non-null  object\n",
      " 2   Year     23110 non-null  int64 \n",
      " 3   Value    23110 non-null  int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 722.3+ KB\n"
     ]
    }
   ],
   "source": [
    "food_prod_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ff9c28bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Wheat and products', 'Rice (Milled Equivalent)',\n",
       "       'Barley and products', 'Maize and products', 'Oats',\n",
       "       'Sorghum and products', 'Cereals, Other', 'Potatoes and products',\n",
       "       'Sugar beet', 'Sugar (Raw Equivalent)', 'Honey', 'Beans', 'Peas',\n",
       "       'Pulses, Other and products', 'Nuts and products',\n",
       "       'Groundnuts (Shelled Eq)', 'Sunflower seed',\n",
       "       'Rape and Mustardseed', 'Cottonseed',\n",
       "       'Olives (including preserved)', 'Groundnut Oil',\n",
       "       'Sunflowerseed Oil', 'Rape and Mustard Oil', 'Olive Oil',\n",
       "       'Oilcrops Oil, Other', 'Tomatoes and products', 'Onions',\n",
       "       'Vegetables, Other', 'Oranges, Mandarines',\n",
       "       'Lemons, Limes and products', 'Grapefruit and products',\n",
       "       'Citrus, Other', 'Bananas', 'Apples and products', 'Dates',\n",
       "       'Grapes and products (excl wine)', 'Fruits, Other', 'Pimento',\n",
       "       'Wine', 'Beer', 'Beverages, Alcoholic', 'Bovine Meat',\n",
       "       'Mutton & Goat Meat', 'Pigmeat', 'Poultry Meat', 'Meat, Other',\n",
       "       'Offals, Edible', 'Butter, Ghee', 'Fats, Animals, Raw', 'Eggs',\n",
       "       'Milk - Excluding Butter', 'Freshwater Fish', 'Demersal Fish',\n",
       "       'Pelagic Fish', 'Marine Fish, Other', 'Crustaceans', 'Cephalopods',\n",
       "       'Molluscs, Other', 'Millet and products', 'Cassava and products',\n",
       "       'Sweet potatoes', 'Sugar cane', 'Soyabeans', 'Sesame seed',\n",
       "       'Palm kernels', 'Oilcrops, Other', 'Cottonseed Oil',\n",
       "       'Palmkernel Oil', 'Palm Oil', 'Pineapples and products',\n",
       "       'Coffee and products', 'Cocoa Beans and products',\n",
       "       'Beverages, Fermented', 'Fish, Body Oil', 'Yams', 'Roots, Other',\n",
       "       'Coconuts - Incl Copra', 'Soyabean Oil', 'Coconut Oil', 'Pepper',\n",
       "       'Aquatic Animals, Others', 'Sweeteners, Other', 'Cream',\n",
       "       'Fish, Liver Oil', 'Sesameseed Oil', 'Spices, Other',\n",
       "       'Aquatic Plants', 'Plantains', 'Tea (including mate)',\n",
       "       'Rye and products', 'Alcohol, Non-Food', 'Sugar non-centrifugal',\n",
       "       'Maize Germ Oil', 'Cloves'], dtype=object)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_prod_df['Item'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "a01d6b79",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2731,\n",
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      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_prod_df['Value'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b2410416",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 450 entries, 0 to 449\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   Country              450 non-null    object\n",
      " 1   Year                 450 non-null    int64 \n",
      " 2   Value                450 non-null    int64 \n",
      " 3   ISO3                 450 non-null    object\n",
      " 4   Sub-region           450 non-null    object\n",
      " 5   Intermediate region  450 non-null    object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 24.6+ KB\n"
     ]
    }
   ],
   "source": [
    "food_supp_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ce47c85",
   "metadata": {},
   "source": [
    "- Change column datatypes [Country, Item, Year, Sub-region, Intermediate region]\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "67822128",
   "metadata": {},
   "outputs": [],
   "source": [
    "# food supply table\n",
    "dtype_mapper = {\n",
    "    'Country': 'category',\n",
    "    'Sub-region': 'category',\n",
    "    'Intermediate region': 'category',\n",
    "}\n",
    "\n",
    "food_supp_df = food_supp_df.astype(dtype_mapper)\n",
    "food_supp_df['Year'] = pd.to_datetime(food_supp_df['Year'], yearfirst=True, format='%Y')\n",
    "# food_supp_df['Year'] = pd.DatetimeIndex(food_supp_df['Year']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "1941a751",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Year</th>\n",
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       "      <th>ISO3</th>\n",
       "      <th>Sub-region</th>\n",
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       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>2987</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2958</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>3047</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>3041</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>3048</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>2147</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>2168</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2200</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2197</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2110</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country       Year  Value ISO3          Sub-region Intermediate region\n",
       "0     Algeria 2004-01-01   2987  DZA     Northern Africa     Northern Africa\n",
       "1     Algeria 2005-01-01   2958  DZA     Northern Africa     Northern Africa\n",
       "2     Algeria 2006-01-01   3047  DZA     Northern Africa     Northern Africa\n",
       "3     Algeria 2007-01-01   3041  DZA     Northern Africa     Northern Africa\n",
       "4     Algeria 2008-01-01   3048  DZA     Northern Africa     Northern Africa\n",
       "..        ...        ...    ...  ...                 ...                 ...\n",
       "445  Zimbabwe 2009-01-01   2147  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "446  Zimbabwe 2010-01-01   2168  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "447  Zimbabwe 2011-01-01   2200  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "448  Zimbabwe 2012-01-01   2197  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "449  Zimbabwe 2013-01-01   2110  ZWE  Sub-Saharan Africa      Eastern Africa\n",
       "\n",
       "[450 rows x 6 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_supp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c55b0b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 450 entries, 0 to 449\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Country              450 non-null    category      \n",
      " 1   Year                 450 non-null    datetime64[ns]\n",
      " 2   Value                450 non-null    int64         \n",
      " 3   ISO3                 450 non-null    object        \n",
      " 4   Sub-region           450 non-null    category      \n",
      " 5   Intermediate region  450 non-null    category      \n",
      "dtypes: category(3), datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 17.1+ KB\n"
     ]
    }
   ],
   "source": [
    "food_supp_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b405209f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# food production table\n",
    "dtype_mapper = {\n",
    "    'Country': 'category',\n",
    "    'Item': 'category',\n",
    "}\n",
    "\n",
    "food_prod_df = food_prod_df.astype(dtype_mapper)\n",
    "food_prod_df['Year'] = pd.to_datetime(food_prod_df['Year'], yearfirst=True, format='%Y')\n",
    "# food_prod_df['Year'] = pd.DatetimeIndex(food_prod_df['Year']).year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b84c055",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "fd852a46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23110 entries, 0 to 23109\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype         \n",
      "---  ------   --------------  -----         \n",
      " 0   Country  23110 non-null  category      \n",
      " 1   Item     23110 non-null  category      \n",
      " 2   Year     23110 non-null  datetime64[ns]\n",
      " 3   Value    23110 non-null  int64         \n",
      "dtypes: category(2), datetime64[ns](1), int64(1)\n",
      "memory usage: 410.5 KB\n"
     ]
    }
   ],
   "source": [
    "food_prod_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a26585",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "542b8f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "food_prod_df2 = food_prod_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "bf13212b",
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>2731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23105</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23106</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23107</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23108</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23109</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country                Item       Year  Value\n",
       "0       Algeria  Wheat and products 2004-01-01   2731\n",
       "1       Algeria  Wheat and products 2005-01-01   2415\n",
       "2       Algeria  Wheat and products 2006-01-01   2688\n",
       "3       Algeria  Wheat and products 2007-01-01   2319\n",
       "4       Algeria  Wheat and products 2008-01-01   1111\n",
       "...         ...                 ...        ...    ...\n",
       "23105  Zimbabwe         Crustaceans 2009-01-01      0\n",
       "23106  Zimbabwe         Crustaceans 2010-01-01      0\n",
       "23107  Zimbabwe         Crustaceans 2011-01-01      0\n",
       "23108  Zimbabwe         Crustaceans 2012-01-01      0\n",
       "23109  Zimbabwe         Crustaceans 2013-01-01      0\n",
       "\n",
       "[23110 rows x 4 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_prod_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "601ccbde",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2ccd5242",
   "metadata": {},
   "source": [
    "### Preliminary Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "38c90196",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribution of food production over time\n",
    "\n",
    "def dist_for_year_x(df, val):\n",
    "    '''\n",
    "    This function sums up the total food production value for each country for a particular year\n",
    "    '''\n",
    "    year_df = df[df['Year'] == val]\n",
    "    year_df = year_df.groupby('Country')['Value'].sum().reset_index()\n",
    "    return year_df\n",
    "    \n",
    "# y2004 = food_prod_df2[food_prod_df2['Year'] == '2004']\n",
    "# y2004 = y2004.groupby(['Country', 'Year'])['Value'].sum().reset_index()\n",
    "# y2004\n",
    "# food_prod_df2.groupby(['Country', 'Year'])['Value'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "03807e5e",
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>15536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>13028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Benin</td>\n",
       "      <td>7963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>8323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>13739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>2242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chad</td>\n",
       "      <td>3660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>75989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>26246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>24040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>5891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>16675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>20664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>10360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>10917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mali</td>\n",
       "      <td>6275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>6081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>27244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>12867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Niger</td>\n",
       "      <td>5852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>149857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Republic of the Congo</td>\n",
       "      <td>2241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>6863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>4368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>3592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>54949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>28646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>5950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>27389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Togo</td>\n",
       "      <td>2765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>8503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>31819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>6310</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>8984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                     Country   Value\n",
       "0                    Algeria   15536\n",
       "1                     Angola   13028\n",
       "2                      Benin    7963\n",
       "3                   Botswana     461\n",
       "4               Burkina Faso    8323\n",
       "5                   Cameroon   13739\n",
       "6                 Cape Verde     148\n",
       "7   Central African Republic    2242\n",
       "8                       Chad    3660\n",
       "9                   Djibouti      55\n",
       "10                     Egypt   75989\n",
       "11                  Ethiopia   26246\n",
       "12                     Gabon    1211\n",
       "13                    Gambia     473\n",
       "14                     Ghana   24040\n",
       "15                    Guinea    5891\n",
       "16             Guinea-Bissau     637\n",
       "17               Ivory Coast   16675\n",
       "18                     Kenya   20664\n",
       "19                   Lesotho     371\n",
       "20                   Liberia    1382\n",
       "21                Madagascar   10360\n",
       "22                    Malawi   10917\n",
       "23                      Mali    6275\n",
       "24                Mauritania     906\n",
       "25                 Mauritius    6081\n",
       "26                   Morocco   27244\n",
       "27                Mozambique   12867\n",
       "28                   Namibia    1421\n",
       "29                     Niger    5852\n",
       "30                   Nigeria  149857\n",
       "31     Republic of the Congo    2241\n",
       "32                    Rwanda    6863\n",
       "33     Sao Tome and Principe     113\n",
       "34                   Senegal    4368\n",
       "35              Sierra Leone    3592\n",
       "36              South Africa   54949\n",
       "37                     Sudan   28646\n",
       "38                 Swaziland    5950\n",
       "39                  Tanzania   27389\n",
       "40                      Togo    2765\n",
       "41                   Tunisia    8503\n",
       "42                    Uganda   31819\n",
       "43                    Zambia    6310\n",
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   ],
   "source": [
    "# Let's find outliers for the year 2004\n",
    "px.box(year_2004['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "471ecc6b",
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1462d068",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detect outliers\n",
    "# Accept a dataframe, then for each year, searches for possible outliers in the value column\n",
    "# then return a list of affected countries.\n",
    "def filter_outliers(df, col, year):\n",
    "    table = df[df['Year'] == year]    \n",
    "    table.groupby('')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d4d275",
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   "source": []
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  {
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   "id": "7ff7acbe",
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    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 145,
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   "source": [
    "temp = food_prod_df[food_prod_df['Year'] == '2012']\n",
    "temp\n",
    "temp.groupby('Country')['Value'].sum().reset_index()['Value'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "f3581fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to help compute the outliers in the data for each year\n",
    "def compute_outliers(df, year_filter, col_name):\n",
    "    table = df[df['Year'] == year_filter]\n",
    "    groupby_country = table.groupby('Country')[col_name].sum().reset_index()  \n",
    "    \n",
    "    q1 = groupby_country[col_name].quantile(0.25)\n",
    "    q3 = groupby_country[col_name].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lower_fence = q1 - (1.5 * iqr)\n",
    "    upper_fence = q3 + (1.5 * iqr)\n",
    "    top_outliers = groupby_country[groupby_country['Value'] > upper_fence]\n",
    "    bottom_outliers = groupby_country[groupby_country['Value'] < lower_fence]\n",
    "    return {'Quartile 1': q1, 'Quartile 3': q3, 'Interquartile range': iqr, 'Top outliers': top_outliers, 'Bottom outliers': bottom_outliers}"
   ]
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      "2004:\n",
      "High production          Country   Value\n",
      "10         Egypt   75989\n",
      "30       Nigeria  149857\n",
      "36  South Africa   54949\n",
      "------------------------------------------\n",
      "2005:\n",
      "High production          Country   Value\n",
      "10         Egypt   80422\n",
      "30       Nigeria  158149\n",
      "36  South Africa   59577\n",
      "------------------------------------------\n",
      "2006:\n",
      "High production          Country   Value\n",
      "10         Egypt   83191\n",
      "30       Nigeria  168987\n",
      "36  South Africa   54024\n",
      "------------------------------------------\n",
      "2007:\n",
      "High production          Country   Value\n",
      "10         Egypt   86828\n",
      "30       Nigeria  157273\n",
      "36  South Africa   53795\n",
      "------------------------------------------\n",
      "2008:\n",
      "High production          Country   Value\n",
      "10         Egypt   89489\n",
      "30       Nigeria  167935\n",
      "36  South Africa   61162\n",
      "------------------------------------------\n",
      "2009:\n",
      "High production          Country   Value\n",
      "10         Egypt   90375\n",
      "30       Nigeria  141270\n",
      "36  South Africa   59590\n",
      "------------------------------------------\n",
      "2010:\n",
      "High production          Country   Value\n",
      "10         Egypt   88450\n",
      "30       Nigeria  158709\n",
      "36  South Africa   56863\n",
      "------------------------------------------\n",
      "2011:\n",
      "High production          Country   Value\n",
      "10         Egypt   90767\n",
      "30       Nigeria  167403\n",
      "36  South Africa   56788\n",
      "------------------------------------------\n",
      "2012:\n",
      "High production          Country   Value\n",
      "10         Egypt   96139\n",
      "30       Nigeria  178816\n",
      "36  South Africa   59581\n",
      "------------------------------------------\n",
      "2013:\n",
      "High production          Country   Value\n",
      "10         Egypt   95848\n",
      "30       Nigeria  179631\n",
      "36  South Africa   63263\n",
      "------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Compute outliers for all years in terms of food production:\n",
    "for year in np.arange(2004, 2014, 1):\n",
    "    outliers = compute_outliers(food_prod_df, str(year), 'Value')\n",
    "    print('{}:'.format(year))\n",
    "    if len(outliers['Top outliers']) > 0:\n",
    "        print('High production', outliers['Top outliers'])\n",
    "    if len(outliers['Bottom outliers']) > 0:\n",
    "        print('Low production', outliers['Bottom outliers'])\n",
    "    print('------------------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7eb628aa",
   "metadata": {},
   "outputs": [
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       "      <th>Country</th>\n",
       "      <th>Item</th>\n",
       "      <th>Year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>2731</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Wheat and products</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2415</td>\n",
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       "    <tr>\n",
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       "      <th>3</th>\n",
       "      <td>Algeria</td>\n",
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       "      <td>2007-01-01</td>\n",
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       "    <tr>\n",
       "      <th>23106</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23107</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Crustaceans</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>23108</th>\n",
       "      <td>Zimbabwe</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>23110 rows × 4 columns</p>\n",
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      "text/plain": [
       "        Country                Item       Year  Value\n",
       "0       Algeria  Wheat and products 2004-01-01   2731\n",
       "1       Algeria  Wheat and products 2005-01-01   2415\n",
       "2       Algeria  Wheat and products 2006-01-01   2688\n",
       "3       Algeria  Wheat and products 2007-01-01   2319\n",
       "4       Algeria  Wheat and products 2008-01-01   1111\n",
       "...         ...                 ...        ...    ...\n",
       "23105  Zimbabwe         Crustaceans 2009-01-01      0\n",
       "23106  Zimbabwe         Crustaceans 2010-01-01      0\n",
       "23107  Zimbabwe         Crustaceans 2011-01-01      0\n",
       "23108  Zimbabwe         Crustaceans 2012-01-01      0\n",
       "23109  Zimbabwe         Crustaceans 2013-01-01      0\n",
       "\n",
       "[23110 rows x 4 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "food_prod_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "06578fce",
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       "      Country       Year       Value\n",
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       "2     Algeria 2006-01-01  283.051724\n",
       "3     Algeria 2007-01-01  254.534483\n",
       "4     Algeria 2008-01-01  238.637931\n",
       "..        ...        ...         ...\n",
       "445  Zimbabwe 2009-01-01   89.906250\n",
       "446  Zimbabwe 2010-01-01  105.890625\n",
       "447  Zimbabwe 2011-01-01  117.984375\n",
       "448  Zimbabwe 2012-01-01  127.703125\n",
       "449  Zimbabwe 2013-01-01  123.656250\n",
       "\n",
       "[450 rows x 3 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "box_data = food_prod_df.groupby(['Country', 'Year'])['Value'].mean().reset_index()\n",
    "box_data"
   ]
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   "execution_count": 152,
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   "source": [
    "### For all the years under study, Egypt, Nigeria and South Africa consistently produced outrageously higher quantity of food than other countries"
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     "text": [
      "2004:\n",
      "High consumption     Country  Value\n",
      "10    Egypt   3309\n",
      "26  Morocco   3263\n",
      "41  Tunisia   3248\n",
      "------------------------------------------\n",
      "2005:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3367\n",
      "------------------------------------------\n",
      "2006:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3389\n",
      "------------------------------------------\n",
      "2007:\n",
      "------------------------------------------\n",
      "2008:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3490\n",
      "------------------------------------------\n",
      "2009:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3441\n",
      "------------------------------------------\n",
      "2010:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3507\n",
      "------------------------------------------\n",
      "2011:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3549\n",
      "------------------------------------------\n",
      "2012:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3561\n",
      "------------------------------------------\n",
      "2013:\n",
      "High consumption    Country  Value\n",
      "10   Egypt   3522\n",
      "------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# IN TERMS OF CONSUMPTION\n",
    "# Compute outliers for all years in terms of food consumption:\n",
    "for year in np.arange(2004, 2014, 1):\n",
    "    outliers = compute_outliers(food_supp_df, str(year), 'Value')\n",
    "    print('{}:'.format(year))\n",
    "    if len(outliers['Top outliers']) > 0:\n",
    "        print('High consumption', outliers['Top outliers'])\n",
    "    if len(outliers['Bottom outliers']) > 0:\n",
    "        print('Low consumption', outliers['Bottom outliers'])\n",
    "    print('------------------------------------------')"
   ]
  },
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   "cell_type": "markdown",
   "id": "9ea89f67",
   "metadata": {},
   "source": [
    "### Even though Egypt, Nigeria and South Africa had the highest food production, Egypt was the only country out of the three that equally had higher food consumption among the three countries.\n",
    "\n",
    "## In the year 2007, no outliers were observed in terms of food consumption"
   ]
  },
  {
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   "execution_count": null,
   "id": "c5fbd25d",
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   "cell_type": "code",
   "execution_count": 33,
   "id": "aa68a687",
   "metadata": {},
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   "source": [
    "# Average consumption and production for all countries for each year\n",
    "def avg_prod_and_cons(prod_df, cons_df):\n",
    "    prod = prod_df.groupby('Year')['Value'].mean().reset_index()\n",
    "    prod.rename(columns = {'Value': 'Average production (kt)'}, inplace=True)\n",
    "    \n",
    "    cons = cons_df.groupby('Year')['Value'].mean().reset_index()\n",
    "    cons.rename(columns = {'Value': 'Average consumption (kcal/person/day)'}, inplace=True)\n",
    "    average_table = pd.merge(prod, cons, on='Year')\n",
    "    return average_table"
   ]
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    "average_production_and_suppy = avg_prod_and_cons(food_prod_df, food_supp_df)\n",
    "average_production_and_suppy"
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    "fig = px.line(average_production_and_suppy, x='Year', y=average_production_and_suppy['Average production (kt)'], markers = True,\n",
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    "Since 2004, there has been an upward trend in food production.\n",
    "\n",
    "This pattern however, was broken in 2007 when a noticeable downward trend was recorded. \n",
    "\n",
    "> A little research revealed that in 2007, there was a global food crises leading to world food riot. Thus, this downward trend affected many other nations of the world outside of Africa.\n",
    "\n",
    "A significant drop in the rate of change was also observed from 2007 onwards."
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    "Food consumption also followed an almost linear pattern indicating a year-on-year increase in average food consumption rate.\n",
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   "source": [
    "### Comparing average food production and consumption/supply for all years"
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   "id": "f1435fcb",
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          "2007-01-01T00:00:00",
          "2008-01-01T00:00:00",
          "2009-01-01T00:00:00",
          "2010-01-01T00:00:00",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of average production b/w 2004 and 2013\n",
    "# This showcases trends over time\n",
    "fig = px.line(average_production_and_suppy, x='Year', y=average_production_and_suppy.columns[[1, 2]],\n",
    "             title='Trends in Food Production & Consumption Over time', hover_name='Year', template='none', markers=True)\n",
    "fig.add_annotation(text='Drop in production did not affect consumption', x='2007', y=1500, showarrow=True, arrowhead=1, yshift=5)\n",
    "fig.add_vline(x='2007', line_width=1, line_dash='dash', line_color='green')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "171c6eca",
   "metadata": {},
   "source": [
    "Since 2004, there has been an upward trend in both average food production and consumption, although no rapid rate of change was recorded. \n",
    "\n",
    "Surprisingly, for the same year 2007, the average food consumption rate was not affected by the downward trend recorded in terms of production."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a186664a",
   "metadata": {},
   "source": [
    "## Distribution by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "13e291ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>3111.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2255.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Benin</td>\n",
       "      <td>2537.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>2226.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2607.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>2460.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>2551.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>2071.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2051.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2416.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>3458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2029.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>2729.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>2569.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>2918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>2296.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>2766.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2145.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>2558.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2182.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>2060.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>2292.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2750.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>2744.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>3054.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>3296.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>2170.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>2160.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Niger</td>\n",
       "      <td>2502.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2698.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Republic of the Congo</td>\n",
       "      <td>2153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2130.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>2446.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>2378.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2229.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>2962.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>2323.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>2317.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>2155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Togo</td>\n",
       "      <td>2333.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>3305.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2220.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1870.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2120.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Country   Value\n",
       "0                    Algeria  3111.8\n",
       "1                     Angola  2255.6\n",
       "2                      Benin  2537.8\n",
       "3                   Botswana  2226.3\n",
       "4               Burkina Faso  2607.2\n",
       "5                   Cameroon  2460.3\n",
       "6                 Cape Verde  2551.4\n",
       "7   Central African Republic  2071.9\n",
       "8                       Chad  2051.1\n",
       "9                   Djibouti  2416.5\n",
       "10                     Egypt  3458.0\n",
       "11                  Ethiopia  2029.2\n",
       "12                     Gabon  2729.9\n",
       "13                    Gambia  2569.5\n",
       "14                     Ghana  2918.0\n",
       "15                    Guinea  2518.0\n",
       "16             Guinea-Bissau  2296.3\n",
       "17               Ivory Coast  2766.6\n",
       "18                     Kenya  2145.3\n",
       "19                   Lesotho  2558.8\n",
       "20                   Liberia  2182.7\n",
       "21                Madagascar  2060.8\n",
       "22                    Malawi  2292.5\n",
       "23                      Mali  2750.2\n",
       "24                Mauritania  2744.3\n",
       "25                 Mauritius  3054.3\n",
       "26                   Morocco  3296.7\n",
       "27                Mozambique  2170.2\n",
       "28                   Namibia  2160.2\n",
       "29                     Niger  2502.4\n",
       "30                   Nigeria  2698.8\n",
       "31     Republic of the Congo  2153.0\n",
       "32                    Rwanda  2130.9\n",
       "33     Sao Tome and Principe  2446.2\n",
       "34                   Senegal  2378.4\n",
       "35              Sierra Leone  2229.1\n",
       "36              South Africa  2962.9\n",
       "37                     Sudan  2323.8\n",
       "38                 Swaziland  2317.1\n",
       "39                  Tanzania  2155.0\n",
       "40                      Togo  2333.9\n",
       "41                   Tunisia  3305.5\n",
       "42                    Uganda  2220.5\n",
       "43                    Zambia  1870.1\n",
       "44                  Zimbabwe  2120.9"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# food consumption\n",
    "countries_average_consumption = food_supp_df.groupby('Country')['Value'].mean().reset_index()\n",
    "countries_average_consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "d41ed541",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Value</th>\n",
       "      <th>Average Consumption</th>\n",
       "      <th>ISO3</th>\n",
       "      <th>Sub-region</th>\n",
       "      <th>Intermediate region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>3111.8</td>\n",
       "      <td>2822.84 - 3140.42 kcal</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>2255.6</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Benin</td>\n",
       "      <td>2537.8</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>BEN</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>2226.3</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>BWA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2607.2</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>BFA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>2460.3</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>CMR</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>2551.4</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>CPV</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>2071.9</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>CAF</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chad</td>\n",
       "      <td>2051.1</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>TCD</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>2416.5</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>DJI</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Egypt</td>\n",
       "      <td>3458.0</td>\n",
       "      <td>3140.42 - 3458.0 kcal</td>\n",
       "      <td>EGY</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2029.2</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>ETH</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>2729.9</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>GAB</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>2569.5</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>GMB</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ghana</td>\n",
       "      <td>2918.0</td>\n",
       "      <td>2822.84 - 3140.42 kcal</td>\n",
       "      <td>GHA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2518.0</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>GIN</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>2296.3</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>GNB</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ivory Coast</td>\n",
       "      <td>2766.6</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>CIV</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kenya</td>\n",
       "      <td>2145.3</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>KEN</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>2558.8</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>LSO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2182.7</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>LBR</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>2060.8</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>MDG</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Malawi</td>\n",
       "      <td>2292.5</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>MWI</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2750.2</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>MLI</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>2744.3</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>MRT</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>3054.3</td>\n",
       "      <td>2822.84 - 3140.42 kcal</td>\n",
       "      <td>MUS</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>3296.7</td>\n",
       "      <td>3140.42 - 3458.0 kcal</td>\n",
       "      <td>MAR</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>2170.2</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>MOZ</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Namibia</td>\n",
       "      <td>2160.2</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>NAM</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Niger</td>\n",
       "      <td>2502.4</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>NER</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2698.8</td>\n",
       "      <td>2505.26 - 2822.84 kcal</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Republic of the Congo</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>COG</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2130.9</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>RWA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>2446.2</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>STP</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>2378.4</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>SEN</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2229.1</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>SLE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>2962.9</td>\n",
       "      <td>2822.84 - 3140.42 kcal</td>\n",
       "      <td>ZAF</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>2323.8</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>SDN</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>2317.1</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>SWZ</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Southern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>TZA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Togo</td>\n",
       "      <td>2333.9</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>TGO</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Western Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>3305.5</td>\n",
       "      <td>3140.42 - 3458.0 kcal</td>\n",
       "      <td>TUN</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>2220.5</td>\n",
       "      <td>2187.68 - 2505.26 kcal</td>\n",
       "      <td>UGA</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1870.1</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2120.9</td>\n",
       "      <td>1868.512 - 2187.68 kcal</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Eastern Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Country   Value      Average Consumption ISO3  \\\n",
       "0                    Algeria  3111.8   2822.84 - 3140.42 kcal  DZA   \n",
       "1                     Angola  2255.6   2187.68 - 2505.26 kcal  AGO   \n",
       "2                      Benin  2537.8   2505.26 - 2822.84 kcal  BEN   \n",
       "3                   Botswana  2226.3   2187.68 - 2505.26 kcal  BWA   \n",
       "4               Burkina Faso  2607.2   2505.26 - 2822.84 kcal  BFA   \n",
       "5                   Cameroon  2460.3   2187.68 - 2505.26 kcal  CMR   \n",
       "6                 Cape Verde  2551.4   2505.26 - 2822.84 kcal  CPV   \n",
       "7   Central African Republic  2071.9  1868.512 - 2187.68 kcal  CAF   \n",
       "8                       Chad  2051.1  1868.512 - 2187.68 kcal  TCD   \n",
       "9                   Djibouti  2416.5   2187.68 - 2505.26 kcal  DJI   \n",
       "10                     Egypt  3458.0    3140.42 - 3458.0 kcal  EGY   \n",
       "11                  Ethiopia  2029.2  1868.512 - 2187.68 kcal  ETH   \n",
       "12                     Gabon  2729.9   2505.26 - 2822.84 kcal  GAB   \n",
       "13                    Gambia  2569.5   2505.26 - 2822.84 kcal  GMB   \n",
       "14                     Ghana  2918.0   2822.84 - 3140.42 kcal  GHA   \n",
       "15                    Guinea  2518.0   2505.26 - 2822.84 kcal  GIN   \n",
       "16             Guinea-Bissau  2296.3   2187.68 - 2505.26 kcal  GNB   \n",
       "17               Ivory Coast  2766.6   2505.26 - 2822.84 kcal  CIV   \n",
       "18                     Kenya  2145.3  1868.512 - 2187.68 kcal  KEN   \n",
       "19                   Lesotho  2558.8   2505.26 - 2822.84 kcal  LSO   \n",
       "20                   Liberia  2182.7  1868.512 - 2187.68 kcal  LBR   \n",
       "21                Madagascar  2060.8  1868.512 - 2187.68 kcal  MDG   \n",
       "22                    Malawi  2292.5   2187.68 - 2505.26 kcal  MWI   \n",
       "23                      Mali  2750.2   2505.26 - 2822.84 kcal  MLI   \n",
       "24                Mauritania  2744.3   2505.26 - 2822.84 kcal  MRT   \n",
       "25                 Mauritius  3054.3   2822.84 - 3140.42 kcal  MUS   \n",
       "26                   Morocco  3296.7    3140.42 - 3458.0 kcal  MAR   \n",
       "27                Mozambique  2170.2  1868.512 - 2187.68 kcal  MOZ   \n",
       "28                   Namibia  2160.2  1868.512 - 2187.68 kcal  NAM   \n",
       "29                     Niger  2502.4   2187.68 - 2505.26 kcal  NER   \n",
       "30                   Nigeria  2698.8   2505.26 - 2822.84 kcal  NGA   \n",
       "31     Republic of the Congo  2153.0  1868.512 - 2187.68 kcal  COG   \n",
       "32                    Rwanda  2130.9  1868.512 - 2187.68 kcal  RWA   \n",
       "33     Sao Tome and Principe  2446.2   2187.68 - 2505.26 kcal  STP   \n",
       "34                   Senegal  2378.4   2187.68 - 2505.26 kcal  SEN   \n",
       "35              Sierra Leone  2229.1   2187.68 - 2505.26 kcal  SLE   \n",
       "36              South Africa  2962.9   2822.84 - 3140.42 kcal  ZAF   \n",
       "37                     Sudan  2323.8   2187.68 - 2505.26 kcal  SDN   \n",
       "38                 Swaziland  2317.1   2187.68 - 2505.26 kcal  SWZ   \n",
       "39                  Tanzania  2155.0  1868.512 - 2187.68 kcal  TZA   \n",
       "40                      Togo  2333.9   2187.68 - 2505.26 kcal  TGO   \n",
       "41                   Tunisia  3305.5    3140.42 - 3458.0 kcal  TUN   \n",
       "42                    Uganda  2220.5   2187.68 - 2505.26 kcal  UGA   \n",
       "43                    Zambia  1870.1  1868.512 - 2187.68 kcal  ZMB   \n",
       "44                  Zimbabwe  2120.9  1868.512 - 2187.68 kcal  ZWE   \n",
       "\n",
       "            Sub-region Intermediate region  \n",
       "0      Northern Africa     Northern Africa  \n",
       "1   Sub-Saharan Africa       Middle Africa  \n",
       "2   Sub-Saharan Africa      Western Africa  \n",
       "3   Sub-Saharan Africa     Southern Africa  \n",
       "4   Sub-Saharan Africa      Western Africa  \n",
       "5   Sub-Saharan Africa       Middle Africa  \n",
       "6   Sub-Saharan Africa      Western Africa  \n",
       "7   Sub-Saharan Africa       Middle Africa  \n",
       "8   Sub-Saharan Africa       Middle Africa  \n",
       "9   Sub-Saharan Africa      Eastern Africa  \n",
       "10     Northern Africa     Northern Africa  \n",
       "11  Sub-Saharan Africa      Eastern Africa  \n",
       "12  Sub-Saharan Africa       Middle Africa  \n",
       "13  Sub-Saharan Africa      Western Africa  \n",
       "14  Sub-Saharan Africa      Western Africa  \n",
       "15  Sub-Saharan Africa      Western Africa  \n",
       "16  Sub-Saharan Africa      Western Africa  \n",
       "17  Sub-Saharan Africa      Western Africa  \n",
       "18  Sub-Saharan Africa      Eastern Africa  \n",
       "19  Sub-Saharan Africa     Southern Africa  \n",
       "20  Sub-Saharan Africa      Western Africa  \n",
       "21  Sub-Saharan Africa      Eastern Africa  \n",
       "22  Sub-Saharan Africa      Eastern Africa  \n",
       "23  Sub-Saharan Africa      Western Africa  \n",
       "24  Sub-Saharan Africa      Western Africa  \n",
       "25  Sub-Saharan Africa      Eastern Africa  \n",
       "26     Northern Africa     Northern Africa  \n",
       "27  Sub-Saharan Africa      Eastern Africa  \n",
       "28  Sub-Saharan Africa     Southern Africa  \n",
       "29  Sub-Saharan Africa      Western Africa  \n",
       "30  Sub-Saharan Africa      Western Africa  \n",
       "31  Sub-Saharan Africa       Middle Africa  \n",
       "32  Sub-Saharan Africa      Eastern Africa  \n",
       "33  Sub-Saharan Africa       Middle Africa  \n",
       "34  Sub-Saharan Africa      Western Africa  \n",
       "35  Sub-Saharan Africa      Western Africa  \n",
       "36  Sub-Saharan Africa     Southern Africa  \n",
       "37     Northern Africa     Northern Africa  \n",
       "38  Sub-Saharan Africa     Southern Africa  \n",
       "39  Sub-Saharan Africa      Eastern Africa  \n",
       "40  Sub-Saharan Africa      Western Africa  \n",
       "41     Northern Africa     Northern Africa  \n",
       "42  Sub-Saharan Africa      Eastern Africa  \n",
       "43  Sub-Saharan Africa      Eastern Africa  \n",
       "44  Sub-Saharan Africa      Eastern Africa  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorise each country depending on the average consumption\n",
    "\n",
    "labels = ['1868.512 - 2187.68 kcal', '2187.68 - 2505.26 kcal', '2505.26 - 2822.84 kcal', '2822.84 - 3140.42 kcal', '3140.42 - 3458.0 kcal']\n",
    "food_supp_df3 = food_supp_df.copy()\n",
    "food_supp_df3 = food_supp_df3.groupby('Country')['Value'].mean().reset_index()\n",
    "food_supp_df3['Average Consumption'] = pd.cut(countries_average_consumption['Value'], 5, labels = labels)\n",
    "\n",
    "food_supp_df3['ISO3'] = food_supp_df3.apply(lambda x: countries_list.get_country_ISO3(x.Country), axis=1)\n",
    "food_supp_df3 = pd.merge(food_supp_df3, country_dim_df, on='ISO3', how='left')\n",
    "food_supp_df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "4aa33f27",
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         "text": "Food Consumption by Country"
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   "source": [
    "# let's plot the map for food production\n",
    "prod_map = px.choropleth(data_frame=food_supp_df3, locations='ISO3', locationmode='ISO-3', color='Average Consumption',\n",
    "                        hover_name='Country', scope='africa', \n",
    "                         color_discrete_sequence = ['#ffffcc', '#fed976', '#fd8d3c', '#e31a1c', '#800026'],\n",
    "                        category_orders = {'Average Consumption': ['1868.512 - 2187.68 kcal', '2187.68 - 2505.26 kcal', '2505.26 - 2822.84 kcal', '2822.84 - 3140.42 kcal', '3140.42 - 3458.0 kcal']},\n",
    "                         title='Food Consumption by Country')\n",
    "\n",
    "prod_map"
   ]
  },
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   "id": "b4121a74",
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