{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abstract\n",
    "We would like to see if there is any connection between the products (names) and price, as well as existing patterns. This is set a-priori. When we do the exploration further question will arise. Some of the data will be removed as it will not be used. There will be plots, groupings and hypothesis testing via ANOVA to find potential connections. In this notebook we will explore the nature of the products, the last one went from the countries to their creator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import mstats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author\n",
    "## Stefan Panev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "For the second dataset we will choose the wfp_market_food_prices from here: https://www.kaggle.com/jboysen/global-food-prices#wfp_market_food_prices.csv. The file is saved in the data folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents:\n",
    "* [Introduction](#first-bullet)\n",
    "* [Reading the Data and Preprocessing](#second-bullet)\n",
    "* [EDA - grouping and plotting](#fourth-bullet)\n",
    "* [Hypothesis testing via Z test](#fifth-bullet)\n",
    "* [Conclusion](#sixth-bullet)\n",
    "* [Further Development](#seventh-bullet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_price_data = pd.read_csv('data\\wfp_market_food_prices.csv', sep = ',', encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is read and stored as food_price_data, the separator is a  ','  , there is an UTF8 encoding that has successfully been fixed with iso-8859-1 encoding. We are having 743914 rows and 18 columns at the beginning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adm0_id</th>\n",
       "      <th>adm0_name</th>\n",
       "      <th>adm1_id</th>\n",
       "      <th>adm1_name</th>\n",
       "      <th>mkt_id</th>\n",
       "      <th>mkt_name</th>\n",
       "      <th>cm_id</th>\n",
       "      <th>cm_name</th>\n",
       "      <th>cur_id</th>\n",
       "      <th>cur_name</th>\n",
       "      <th>pt_id</th>\n",
       "      <th>pt_name</th>\n",
       "      <th>um_id</th>\n",
       "      <th>um_name</th>\n",
       "      <th>mp_month</th>\n",
       "      <th>mp_year</th>\n",
       "      <th>mp_price</th>\n",
       "      <th>mp_commoditysource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>272</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>266</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>55</td>\n",
       "      <td>Bread</td>\n",
       "      <td>87</td>\n",
       "      <td>AFN</td>\n",
       "      <td>15</td>\n",
       "      <td>Retail</td>\n",
       "      <td>5</td>\n",
       "      <td>KG</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>272</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>266</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>55</td>\n",
       "      <td>Bread</td>\n",
       "      <td>87</td>\n",
       "      <td>AFN</td>\n",
       "      <td>15</td>\n",
       "      <td>Retail</td>\n",
       "      <td>5</td>\n",
       "      <td>KG</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>272</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>266</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>55</td>\n",
       "      <td>Bread</td>\n",
       "      <td>87</td>\n",
       "      <td>AFN</td>\n",
       "      <td>15</td>\n",
       "      <td>Retail</td>\n",
       "      <td>5</td>\n",
       "      <td>KG</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>272</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>266</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>55</td>\n",
       "      <td>Bread</td>\n",
       "      <td>87</td>\n",
       "      <td>AFN</td>\n",
       "      <td>15</td>\n",
       "      <td>Retail</td>\n",
       "      <td>5</td>\n",
       "      <td>KG</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>272</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>266</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>55</td>\n",
       "      <td>Bread</td>\n",
       "      <td>87</td>\n",
       "      <td>AFN</td>\n",
       "      <td>15</td>\n",
       "      <td>Retail</td>\n",
       "      <td>5</td>\n",
       "      <td>KG</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   adm0_id    adm0_name  adm1_id   adm1_name  mkt_id  mkt_name  cm_id cm_name  \\\n",
       "0        1  Afghanistan      272  Badakhshan     266  Fayzabad     55   Bread   \n",
       "1        1  Afghanistan      272  Badakhshan     266  Fayzabad     55   Bread   \n",
       "2        1  Afghanistan      272  Badakhshan     266  Fayzabad     55   Bread   \n",
       "3        1  Afghanistan      272  Badakhshan     266  Fayzabad     55   Bread   \n",
       "4        1  Afghanistan      272  Badakhshan     266  Fayzabad     55   Bread   \n",
       "\n",
       "   cur_id cur_name  pt_id pt_name  um_id um_name  mp_month  mp_year  mp_price  \\\n",
       "0      87      AFN     15  Retail      5      KG         1     2014      50.0   \n",
       "1      87      AFN     15  Retail      5      KG         2     2014      50.0   \n",
       "2      87      AFN     15  Retail      5      KG         3     2014      50.0   \n",
       "3      87      AFN     15  Retail      5      KG         4     2014      50.0   \n",
       "4      87      AFN     15  Retail      5      KG         5     2014      50.0   \n",
       "\n",
       "  mp_commoditysource  \n",
       "0                WFP  \n",
       "1                WFP  \n",
       "2                WFP  \n",
       "3                WFP  \n",
       "4                WFP  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_price_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(743914, 18)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_price_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['adm0_id', 'adm0_name', 'adm1_id', 'adm1_name', 'mkt_id', 'mkt_name',\n",
       "       'cm_id', 'cm_name', 'cur_id', 'cur_name', 'pt_id', 'pt_name', 'um_id',\n",
       "       'um_name', 'mp_month', 'mp_year', 'mp_price', 'mp_commoditysource'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_price_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of ids tell us little information so they will be removed. We are left with 11 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "food_price_data = food_price_data.drop(['adm0_id', 'adm1_id', 'cm_id', 'mkt_id', 'cur_id', 'pt_id', 'um_id'] , axis = 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(743914, 11)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_price_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adm0_name</th>\n",
       "      <th>adm1_name</th>\n",
       "      <th>mkt_name</th>\n",
       "      <th>cm_name</th>\n",
       "      <th>cur_name</th>\n",
       "      <th>pt_name</th>\n",
       "      <th>um_name</th>\n",
       "      <th>mp_month</th>\n",
       "      <th>mp_year</th>\n",
       "      <th>mp_price</th>\n",
       "      <th>mp_commoditysource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>Bread</td>\n",
       "      <td>AFN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>KG</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>Bread</td>\n",
       "      <td>AFN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>KG</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>Bread</td>\n",
       "      <td>AFN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>KG</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>Bread</td>\n",
       "      <td>AFN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>KG</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Badakhshan</td>\n",
       "      <td>Fayzabad</td>\n",
       "      <td>Bread</td>\n",
       "      <td>AFN</td>\n",
       "      <td>Retail</td>\n",
       "      <td>KG</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>50.0</td>\n",
       "      <td>WFP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     adm0_name   adm1_name  mkt_name cm_name cur_name pt_name um_name  \\\n",
       "0  Afghanistan  Badakhshan  Fayzabad   Bread      AFN  Retail      KG   \n",
       "1  Afghanistan  Badakhshan  Fayzabad   Bread      AFN  Retail      KG   \n",
       "2  Afghanistan  Badakhshan  Fayzabad   Bread      AFN  Retail      KG   \n",
       "3  Afghanistan  Badakhshan  Fayzabad   Bread      AFN  Retail      KG   \n",
       "4  Afghanistan  Badakhshan  Fayzabad   Bread      AFN  Retail      KG   \n",
       "\n",
       "   mp_month  mp_year  mp_price mp_commoditysource  \n",
       "0         1     2014      50.0                WFP  \n",
       "1         2     2014      50.0                WFP  \n",
       "2         3     2014      50.0                WFP  \n",
       "3         4     2014      50.0                WFP  \n",
       "4         5     2014      50.0                WFP  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food_price_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA - grouping and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "products = food_price_data.groupby(['cm_name'], axis=0)['mp_price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cm_name\n",
       "Apples                                             106.001701\n",
       "Apples (red)                                      1425.104470\n",
       "Avocados                                           209.374762\n",
       "Bananas                                            210.652722\n",
       "Bananas (medium size)                                5.596964\n",
       "Beans                                            35930.450876\n",
       "Beans (black)                                      364.444558\n",
       "Beans (butter)                                      71.097794\n",
       "Beans (catarino)                                    72.322850\n",
       "Beans (dry)                                        418.211878\n",
       "Beans (fava, dry)                                   13.133533\n",
       "Beans (green, fresh)                               467.466260\n",
       "Beans (haricot)                                      8.607755\n",
       "Beans (kidney red)                                 434.279540\n",
       "Beans (kidney white)                                51.792416\n",
       "Beans (kidney)                                       3.065130\n",
       "Beans (magnum)                                      71.964810\n",
       "Beans (mung)                                        75.452179\n",
       "Beans (niebe)                                     2296.531770\n",
       "Beans (niebe, white)                              8898.305085\n",
       "Beans (red)                                        733.763038\n",
       "Beans (red, fresh)                                2045.286885\n",
       "Beans (silk red)                                    59.308950\n",
       "Beans (string)                                    1721.960000\n",
       "Beans (sugar)                                       22.982903\n",
       "Beans (sugar-red)                                    9.534655\n",
       "Beans (white)                                     1579.556356\n",
       "Beans(mash)                                        203.113371\n",
       "Beetroots                                            5.622410\n",
       "Blackberry                                        2458.680000\n",
       "                                                     ...     \n",
       "Sugar (brown, local)                                32.419809\n",
       "Sugar (jaggery/gur)                                 42.288625\n",
       "Sugar (white)                                      221.785206\n",
       "Sweet potatoes                                     115.756188\n",
       "Tamarillos/tree tomatoes                          1572.700000\n",
       "Tea                                               1643.685634\n",
       "Tea (black)                                        147.713260\n",
       "Tea (green)                                         38.743100\n",
       "Tea (sahm)                                         149.687500\n",
       "Tomatoes                                           256.326868\n",
       "Tomatoes (greenhouse)                                3.667646\n",
       "Tomatoes (paste)                                  2120.817106\n",
       "Tortilla (maize)                                     4.132389\n",
       "Transport (public)                                 681.439704\n",
       "Wage (non-qualified labour)                       8565.931882\n",
       "Wage (non-qualified labour, non-agricultural)    26907.457486\n",
       "Wage (qualified labour)                           1984.690185\n",
       "Water (drinking)                                     3.426732\n",
       "Wheat                                              151.581749\n",
       "Wheat flour                                        685.088476\n",
       "Wheat flour (first grade)                           13.121940\n",
       "Wheat flour (high quality)                           5.964929\n",
       "Wheat flour (imported)                              92.782934\n",
       "Wheat flour (local)                                 26.893728\n",
       "Wheat flour (locally processed)                     12.180345\n",
       "Yam                                               4756.290341\n",
       "Yam (Abuja)                                      11794.671429\n",
       "Yam (florido)                                      152.297532\n",
       "Yogurt                                             226.100620\n",
       "Zucchini                                           273.379846\n",
       "Name: mp_price, Length: 321, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start the EDA by grouping the product names by price. We see that there are 321 products, a lot of the product have several types. Sorting the values by price shows that the livestock (sheep, one-year-old alive female) have the highest price tag significantly higher then the rest. Exploring further we see that mostly meat products, rice and fish occupy the top 20 spots. Some studies show that a significant part of the biosphere biomass is made by livestock animals. This could potentially be an evidence for this study, for further reading read the abstract here:\n",
    "http://www.pnas.org/content/early/2018/05/15/1711842115"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cm_name\n",
       "Livestock (sheep, one-year-old alive female)      1.786756e+06\n",
       "Rice (white)                                      9.213260e+05\n",
       "Livestock (cattle)                                1.762069e+05\n",
       "Rice (paddy)                                      1.378682e+05\n",
       "Livestock (sheep, two-year-old male)              6.701173e+04\n",
       "Meat (beef, first quality)                        6.384423e+04\n",
       "Meat (buffalo, first quality)                     6.341970e+04\n",
       "Meat (beef, second quality)                       5.583045e+04\n",
       "Meat (buffalo, second quality)                    5.490517e+04\n",
       "Livestock (pig)                                   4.648268e+04\n",
       "Fish (snake head)                                 4.546479e+04\n",
       "Meat (pork, first quality)                        3.780716e+04\n",
       "Beans                                             3.593045e+04\n",
       "Meat (pork, second quality)                       3.390452e+04\n",
       "Meat (chicken, broiler)                           3.161585e+04\n",
       "Chili (green)                                     2.887728e+04\n",
       "Wage (non-qualified labour, non-agricultural)     2.690746e+04\n",
       "Livestock (sheep, medium-sized castrated male)    2.643287e+04\n",
       "Fish (tilapia, farmed)                            2.459370e+04\n",
       "Fish                                              2.450000e+04\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cm_name\n",
       "Eggs (imported)                  1.790909\n",
       "Maize meal (white, roller)       2.156225\n",
       "Maize meal (white, breakfast)    2.923677\n",
       "Onions (dry, local)              2.975524\n",
       "Beans (kidney)                   3.065130\n",
       "Bread (common)                   3.149557\n",
       "Meat (chicken, imported)         3.258333\n",
       "Potatoes (medium size)           3.337528\n",
       "Water (drinking)                 3.426732\n",
       "Tomatoes (greenhouse)            3.667646\n",
       "Tortilla (maize)                 4.132389\n",
       "Cassava meal                     4.431776\n",
       "Potatoes (black)                 4.939310\n",
       "Cheese (white, boiled)           5.286899\n",
       "Bananas (medium size)            5.596964\n",
       "Beetroots                        5.622410\n",
       "Sorghum (food aid)               5.923578\n",
       "Wheat flour (high quality)       5.964929\n",
       "Rice (medium grain)              6.212385\n",
       "Rice (carolina 2da)              6.356250\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.sort_values(ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the opposite suggest that mostly plant food have the cheapest price, as well as water and eggs. We will try to further aggregate the data, because the product categories are to many. We removed the many of the similar types and created the new variable product2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "product2 = food_price_data.groupby([x.split(' ')[0] for x in food_price_data['cm_name']], axis=0)['mp_price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Livestock     72486.875456\n",
       "Sesame        19660.346536\n",
       "Wage          15081.809960\n",
       "Gari          11428.958842\n",
       "Rice          11138.845516\n",
       "Meat           9334.460052\n",
       "Fish           8296.532474\n",
       "Beans          7441.436568\n",
       "Charcoal       5465.269476\n",
       "Cowpeas        5311.199394\n",
       "Yam            4376.368489\n",
       "Chili          4210.502747\n",
       "Garlic         3368.442551\n",
       "Soybeans       3145.146410\n",
       "Blackberry     2458.680000\n",
       "Peas           2282.405504\n",
       "Broccoli       2280.546667\n",
       "Coffee         2100.743724\n",
       "Groundnuts     2081.877997\n",
       "Fuel           1770.573768\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7H5bks/nalNP1rPd+w74Z7WNj9zndfWJ3n7hx48bdVw0AAMBCw+L2JNu7+x3T9p9lFh4/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\nSZIR/A+H7eczKabOMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e18eff8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"Top 20 products by price\")\n",
    "plt.bar(product2.sort_values(ascending=False).head(20).index, product2.sort_values(ascending=False).head(20).values, align='center', alpha=0.5)\n",
    "plt.xticks(product2.sort_values(ascending=False).head(20).index, rotation=90)\n",
    "plt.ylabel('price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have reduced the number of products to 86. The top 20 confirms our suspicion that the livestock mean price is significantly higher then the rest, after that are various plant species some meat and fish. The visualization is telling us how much higher is the price of the livestock. Interesting the 20th potions is Fuel, it can be suggested that some of the food produced is used as a biofuel, see here https://en.wikipedia.org/wiki/Biofuel. Unfortunately looking at the data there is not something useful for further exploration on the topic. Now lets view the bottom 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bananas        205.641361\n",
       "Beans(mash)    203.113371\n",
       "Potatoes       171.549511\n",
       "Poultry        153.028182\n",
       "Carrots        146.618798\n",
       "Cabbage        119.939404\n",
       "Sweet          115.756188\n",
       "Butter          95.931737\n",
       "Ghee            88.872130\n",
       "Curd            66.996478\n",
       "Parsley         65.931530\n",
       "Cashew          56.336731\n",
       "Fat             35.001130\n",
       "Sour            29.473698\n",
       "Buckwheat       21.979291\n",
       "Noodles          9.330782\n",
       "Labaneh          8.011913\n",
       "Beetroots        5.622410\n",
       "Tortilla         4.132389\n",
       "Water            3.426732\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.sort_values(ascending=False).tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A4NJv28NfVe1Icp8kL91q6+6vdven5vMnJflgkkO2uzYAAID91RI9f3dL8r7uPnOroap2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\nXymllFJKKaWsA2rxV0oppZRSSinrgP8HPvvDYbi+7DsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e19060ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"Lowest 20 products by price\")\n",
    "plt.bar(product2.sort_values(ascending=False).tail(20).index, product2.sort_values(ascending=False).tail(20).values, align='center', alpha=0.5)\n",
    "plt.xticks(product2.sort_values(ascending=False).tail(20).index, rotation=90)\n",
    "plt.ylabel('price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lowest 20 are occupied with mostly plant food. Their mean price is significantly lower then the rest. The variance between the price is lower then in the top 20, but the lowest 6 are significantly lower then the rest.\n",
    "The lowest is water. It is hard to interpret why the price of water is the lowest, is it only as a by product for the process of creating other food products. Considering many countries are suffering dwindling water supply, the price of water should be higher. See here more on the topic https://www.theguardian.com/global-development-professionals-network/2017/mar/27/aquifers-worlds-reserve-water-tank-asia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BzlT+G3Aj8Kd11gXgUDEzs+f1GyoRsTB/8fG6iFg+RH0yM7NhasC7vyLi98DZQ9AXMzMb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\nrDIOFTMzq4xDxczMKuNQMTOzyvx/a//BEt7k8AgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e18f0d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([product2.sort_values(ascending=False).head(20).values, product2.sort_values(ascending=False).tail(20).values], labels = [\"top 20 with highest price\", \"lowest 20\"])\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Boxplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot proves that the variance is higher for the top 20, the live stock is is clearly an outlier. Lets try to remove the outlier, to see if there is a significant change. There is another lower outlierso it is also going to be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Qchsi4oaIWJlm72T1192siHg1IpYCLcAe9X5M+q2I8G0db8Bo4KHS/D9RfOoZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e250ae0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([product2.sort_values(ascending=False).iloc[2:].head(20).values, product2.sort_values(ascending=False).tail(20).values], labels = [\"top 20 with highest price\", \"lowest 20\"])\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Boxplot without the two outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even without the outliers there is still a large difference, now lets try to plot the lowest 20 separately because it is not very evident in this boxplot, the scale for the top 20 if far higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ShmCjAVFVJyR5DPAPVXXmo3TMJ0390a+qNUmm7um0M/DjgX6r2zYDQpJ6MOM5iKq6Hzh6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\npE4GhCSpkwEhSer0/wGh+PBVvoXiqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e3122df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([product2.sort_values(ascending=False).tail(20).values], labels = [\"lowest 20\"])\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Boxplot lowest 20')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the latest boxplot we can conclude that the lowest 20 products have a much more symmetric distribution compared with the top 20, who seem to be skewed to the right. Now a histogram will make things more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e313f3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(product2, edgecolor = 'black')\n",
    "plt.xlabel(\"products\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Histogram of the products')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.2542362732803678"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution is very skewed to the left. We will try to remove the outliers and increase the number of bins for a clear picture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XYFzDcE3Lhl7271blP5D4Gvot4sU6gq6V4WB/uh5AZQuWavT/Snq64PMdnvfe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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e31e512b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(product2.sort_values(ascending=False).iloc[2:], edgecolor = 'black', bins = 20)\n",
    "plt.xlabel(\"products\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Histogram of the products without the 2 biggest outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9428586892652127"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.sort_values(ascending=False).iloc[2:].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still the majority are cheaper then the rest, the more expensive products don't have a very high number. We will try to remove the top 15 products to see if the distribution will become smoother. Still the majority are concentrated in the lower part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e31e7b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(product2.sort_values(ascending=False).iloc[15:], edgecolor = 'black', bins = 20)\n",
    "plt.xlabel(\"products\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Histogram of the products without the 15 biggest outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0773168907536925"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.sort_values(ascending=False).iloc[15:].skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Apples', 'Avocados', 'Bananas', 'Beans', 'Beans(mash)', 'Beetroots',\n",
       "       'Blackberry', 'Bread', 'Broccoli', 'Buckwheat', 'Bulgur', 'Butter',\n",
       "       'Cabbage', 'Carrots', 'Cashew', 'Cassava', 'Cauliflower', 'Charcoal',\n",
       "       'Cheese', 'Chickpeas', 'Chili', 'Cocoa', 'Coffee', 'Cornstarch',\n",
       "       'Cotton', 'Cowpeas', 'Cucumbers', 'Curd', 'Dates', 'Eggplants', 'Eggs',\n",
       "       'Exchange', 'Fat', 'Fish', 'Fonio', 'Fuel', 'Gari', 'Garlic', 'Ghee',\n",
       "       'Groundnuts', 'Guava', 'Labaneh', 'Lentils', 'Lettuce', 'Livestock',\n",
       "       'Maize', 'Mangoes', 'Meat', 'Milk', 'Millet', 'Noodles', 'Oil',\n",
       "       'Onions', 'Oranges', 'Papaya', 'Parsley', 'Passion', 'Pasta', 'Peanut',\n",
       "       'Peas', 'Peppers', 'Plantains', 'Potatoes', 'Poultry', 'Pulses',\n",
       "       'Pumpkin', 'Rice', 'Salt', 'Sesame', 'Sorghum', 'Sour', 'Soybeans',\n",
       "       'Spinach', 'Sugar', 'Sweet', 'Tamarillos/tree', 'Tea', 'Tomatoes',\n",
       "       'Tortilla', 'Transport', 'Wage', 'Water', 'Wheat', 'Yam', 'Yogurt',\n",
       "       'Zucchini'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us consider that the product are divided into 2 groups, animal-derived products and plant derived products. When we gen the product2 index we see that some of the products are not a part of the 2 categories like water, salt, there is also other costs, that we have not come across earlier like Transport, Wage, Charcoal, Exchange or Fuel (it can be speculated if the fuel is plant derived or traditional fossil fuels, lets assume its plant derived and we will leave it hear), there is also oil (let as assume its plant oil and it will stay). We will omit the 'wage' and the 'transport', 'charcoal', 'exchange' from now own. It would be wise to do the same exploration as the above but without this 'additions'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wage         15081.809960\n",
       "Transport      681.439704\n",
       "Charcoal      5465.269476\n",
       "Exchange       538.113119\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2[['Wage', 'Transport','Charcoal','Exchange' ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we get the wage and the transport, charcoal, exchange separately we see that they are not outliers, none make it to the top 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       86.000000\n",
       "mean      2671.669273\n",
       "std       8331.367310\n",
       "min          3.426732\n",
       "25%        216.734536\n",
       "50%        636.871839\n",
       "75%       1614.026810\n",
       "max      72486.875456\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get the descriptive statistics, both wage and transport are below the mean, what if we remove the 15 biggest?\n",
    "The mean drops mean and standard deviation drop significantly. We don't think that the wage and the transport, charcoal, exchange are going to have a very big impact, considering the time, we will leave their removal for the above research for another time. But now they will be removed from now own. Livestock is also removed as being the largest outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      71.000000\n",
       "mean      655.756099\n",
       "std       617.595760\n",
       "min         3.426732\n",
       "25%       187.331441\n",
       "50%       489.675007\n",
       "75%      1022.681356\n",
       "max      2282.405504\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.sort_values(ascending=False).iloc[15:].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      67.000000\n",
       "mean      608.599672\n",
       "std       567.427670\n",
       "min         3.426732\n",
       "25%       162.288846\n",
       "50%       458.264684\n",
       "75%       903.947403\n",
       "max      2100.743724\n",
       "Name: mp_price, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product2.drop(['Wage', 'Transport','Charcoal' ,'Exchange']).sort_values(ascending=False).iloc[15:].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plant_derived = product2[['Apples', 'Avocados', 'Bananas', 'Beans', 'Beans(mash)', 'Beetroots',\n",
    "       'Blackberry', 'Bread', 'Broccoli', 'Buckwheat','Bulgur','Cabbage', 'Carrots', 'Cashew', 'Cassava', 'Cauliflower',\n",
    "       'Chili', 'Cocoa', 'Coffee', 'Cornstarch','Cotton','Cowpeas', 'Cucumbers','Dates', 'Eggplants','Fonio', 'Gari',\n",
    "        'Garlic','Guava','Lentils','Lettuce','Maize', 'Mangoes','Millet', 'Noodles', 'Oil','Onions', 'Oranges', 'Papaya',\n",
    "        'Parsley', 'Passion', 'Pasta', 'Peanut','Peas', 'Peppers', 'Plantains', 'Potatoes', 'Poultry', 'Pulses',\n",
    "        'Pumpkin', 'Rice','Sesame', 'Sorghum', 'Sour', 'Soybeans',\n",
    "       'Spinach', 'Sugar', 'Sweet', 'Tamarillos/tree', 'Tea', 'Tomatoes','Tortilla', 'Wheat', 'Yam', 'Yogurt',\n",
    "       'Zucchini']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "animal_derived = product2[['Butter','Cheese','Eggs','Curd','Fat', 'Fish','Labaneh','Meat', 'Milk']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plant_derived.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_derived.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing\n",
    "The number of animal derived products is far lower then the rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LpZ0t6deS7kqvd+SWfVbSekk/l/T2XPq8lLZe0pm59IMl/UzSOklXSJpcrX0xM7OhqeaZ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e31e87cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([plant_derived.values, animal_derived.values], labels = [\"plant derived\", \"animal derived\"])\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Boxplot for animal and plant derived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.9032296714241212"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plant_derived.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.523287907300602"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_derived.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sesame'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plant_derived.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Boxplot shows that the plant derived products are more skewed to the right. We will try to remove the biggest outlier in the plants derived in our case 'Sesame'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GloH2m+T7XMyGmo/NkWW0B5VO4Jjc/Axga0l1MTMb90Z7ULkbmCXpeEkHAucD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e31ed7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([plant_derived.sort_values(ascending=False).iloc[1:].values, animal_derived.values], labels = [\"plant derived\", \"animal derived\"])\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Boxplot for animal and plant derived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try a one way parametric ANOVA test from the stats package, to see if there is any statistical significant differences between the mean of the 2 groups, for more information of the test see here: https://www.marsja.se/four-ways-to-conduct-one-way-anovas-using-python/. We set the H0 hypothesis that there is no statistically significant difference between the 2 means, and the H1 that there is. We take the default value for the p-value of 0.05, if the it is higher we reject the H1 hypothesis else, we reject the H0 hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "F, p = stats.f_oneway(plant_derived.sort_values(ascending=False).iloc[1:].values, animal_derived.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.523497920564596, 0.22110442241658015)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F, p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is higher the 0.05 so, we will conclude that H0 is the correct hypothesis. This is good, but if we read the criteria for for one way ANOVA we can see that the two populations are not normally distributed and it is possible that they are dependent or and populations are not the same see here:\n",
    "https://en.wikipedia.org/wiki/Homogeneity_and_heterogeneity \n",
    "This leaves us that the results are not to be trusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cFRFXAEgaksperapiZmbWmRrd45hGERQ9RgA/aX51zMys0zUaHMMjYlVPT+repJoqmZlZ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\nYAsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e18f830f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(plant_derived.sort_values(ascending=False).iloc[1:], edgecolor = 'black', bins = 20)\n",
    "plt.xlabel(\"Plant products\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Histogram of plant based products')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IVRGxOg9fB4ySNK7KmMzMrLEq7z4ScD6wKCLOalDnjbkekg7I8TxdVUxmZtZclXcfvQM4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\nCH9c2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e18f48eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(animal_derived, edgecolor = 'black', bins = 20)\n",
    "plt.xlabel(\"Plantproducts\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.title('Histogram of plant based products')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will use another non-parametric test Kruskal-Wallis H-test.For more information for the test check here: https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.kruskalwallis.html The hypothesis are the same. Now we don't assume normal distributions and similar sizes to the population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "H, p_value = mstats.kruskalwallis(plant_derived.sort_values(ascending=False).iloc[1:].values, animal_derived.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.837606838416832e-05, 0.99340238187075891)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H, p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is significantly higher then 0.05 meaning we reject the H1 hypothesis, and conclude that the mean price of the plant and animal derived product are statistically similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2435.139785765948"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animal_derived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1637.1796028978513"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plant_derived.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately this test is also not the best, is assumes that the populations are independent. Some of the plant derived food can be used for food for animals. There could also be competition for land for example so there is likely some form of interdependence between the populations. The issue should be addressed for example with https://en.wikipedia.org/wiki/Multivariate_analysis_of_variance. But for now we conclude the work on this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "The focus of this notebook was on the the products and their prices, several grouping and preprocessing have been used. Finally the products were divided to plant and animal based. Some of the products were excluded, also several links have been added, if the reader is more interested on the subject. Finally an one way ANOVA test and the non-parametric test Kruskal-Wallis H-test were used. The results were inconclusive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further development\n",
    "In the future the time and the countries, sectors could also be checked. Some counties are exporters, others are importers, there could be seasonality based on the time of year.\n",
    "<br>\n",
    "Also there is a clear connection between the variables, this should also be addressed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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