{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8501b536",
   "metadata": {},
   "source": [
    "# Climate example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a3540a",
   "metadata": {},
   "source": [
    "## Example\n",
    "\n",
    "We will look at a dataset containing carbon emissions, GDP and population for 164 countries (data from 2018).\n",
    "\n",
    "These data are adapted from a dataset downloaded from <a href=\"https://ourworldindata.org/\">Our World in Data</a>, a fabulous Oxford-based organization that provides datasets and visualizations addressing global issues.\n",
    "\n",
    "\n",
    "### Set up Python libraries\n",
    "\n",
    "As usual, run the code cell below to import the relevant Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f1d34e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up Python libraries - you need to run this but you don't need to change it\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import pandas \n",
    "import seaborn as sns\n",
    "sns.set_theme() # use pretty defaults"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f633741",
   "metadata": {},
   "source": [
    "### Load and inspect the data\n",
    "\n",
    "Load the data from the file CO2vGDP.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "075f78c7",
   "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>CO2</th>\n",
       "      <th>GDP</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>1934.555054</td>\n",
       "      <td>36686788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.6422</td>\n",
       "      <td>11104.166020</td>\n",
       "      <td>2877019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>3.8241</td>\n",
       "      <td>14228.025390</td>\n",
       "      <td>41927008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.7912</td>\n",
       "      <td>7771.441895</td>\n",
       "      <td>31273538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>4.0824</td>\n",
       "      <td>18556.382810</td>\n",
       "      <td>44413592</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>160</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2.3415</td>\n",
       "      <td>6814.142090</td>\n",
       "      <td>94914328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>World</td>\n",
       "      <td>4.8022</td>\n",
       "      <td>15212.415040</td>\n",
       "      <td>7683789824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.3503</td>\n",
       "      <td>2284.889893</td>\n",
       "      <td>30790514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.4215</td>\n",
       "      <td>3534.033691</td>\n",
       "      <td>17835898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.8210</td>\n",
       "      <td>1611.405151</td>\n",
       "      <td>15052191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country     CO2           GDP  population\n",
       "0    Afghanistan  0.2245   1934.555054    36686788\n",
       "1        Albania  1.6422  11104.166020     2877019\n",
       "2        Algeria  3.8241  14228.025390    41927008\n",
       "3         Angola  0.7912   7771.441895    31273538\n",
       "4      Argentina  4.0824  18556.382810    44413592\n",
       "..           ...     ...           ...         ...\n",
       "160      Vietnam  2.3415   6814.142090    94914328\n",
       "161        World  4.8022  15212.415040  7683789824\n",
       "162        Yemen  0.3503   2284.889893    30790514\n",
       "163       Zambia  0.4215   3534.033691    17835898\n",
       "164     Zimbabwe  0.8210   1611.405151    15052191\n",
       "\n",
       "[165 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CO2vGDP = pandas.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook/main/data/CO2vGDP.csv')\n",
    "display(CO2vGDP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3cce993",
   "metadata": {},
   "source": [
    "<b>Aside</b> - \n",
    "I notice that the GDP values contain loads of decimal places which makes them hard to read. \n",
    "Let's just round those:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "732fb048",
   "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>CO2</th>\n",
       "      <th>GDP</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>1935.0</td>\n",
       "      <td>36686788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1.6422</td>\n",
       "      <td>11104.0</td>\n",
       "      <td>2877019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>3.8241</td>\n",
       "      <td>14228.0</td>\n",
       "      <td>41927008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>0.7912</td>\n",
       "      <td>7771.0</td>\n",
       "      <td>31273538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>4.0824</td>\n",
       "      <td>18556.0</td>\n",
       "      <td>44413592</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>160</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2.3415</td>\n",
       "      <td>6814.0</td>\n",
       "      <td>94914328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>World</td>\n",
       "      <td>4.8022</td>\n",
       "      <td>15212.0</td>\n",
       "      <td>7683789824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>0.3503</td>\n",
       "      <td>2285.0</td>\n",
       "      <td>30790514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>0.4215</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>17835898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.8210</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>15052191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country     CO2      GDP  population\n",
       "0    Afghanistan  0.2245   1935.0    36686788\n",
       "1        Albania  1.6422  11104.0     2877019\n",
       "2        Algeria  3.8241  14228.0    41927008\n",
       "3         Angola  0.7912   7771.0    31273538\n",
       "4      Argentina  4.0824  18556.0    44413592\n",
       "..           ...     ...      ...         ...\n",
       "160      Vietnam  2.3415   6814.0    94914328\n",
       "161        World  4.8022  15212.0  7683789824\n",
       "162        Yemen  0.3503   2285.0    30790514\n",
       "163       Zambia  0.4215   3534.0    17835898\n",
       "164     Zimbabwe  0.8210   1611.0    15052191\n",
       "\n",
       "[165 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CO2vGDP['GDP']=CO2vGDP['GDP'].round()\n",
    "display(CO2vGDP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2abcbb1a",
   "metadata": {},
   "source": [
    "It is easier to comapre the values now as the larger GDPs actually take up more space!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abc7e702",
   "metadata": {},
   "source": [
    "### Plot the data\n",
    "\n",
    "Let's plot the data. A scatterplot is a good choice for bivariate data such as these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "65493236",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CO2 emissions: tonnes/person/year')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=CO2vGDP, x='GDP', y='CO2')\n",
    "plt.xlabel('GDP: $/person/year')\n",
    "plt.ylabel('CO2 emissions: tonnes/person/year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44ab138",
   "metadata": {},
   "source": [
    "Let's find the UK on that graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "da409274",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CO2 emissions: tonnes/person/year')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=CO2vGDP, x='GDP', y='CO2')\n",
    "sns.scatterplot(data=CO2vGDP[CO2vGDP['Country']=='United Kingdom'], x='GDP', y='CO2',color='r') # see what I did there to plot the UK in red?\n",
    "plt.xlabel('GDP: $/person/year')\n",
    "plt.ylabel('CO2 emissions: tonnes/person/year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0bb52f",
   "metadata": {},
   "source": [
    "### Calculate the correlation\n",
    "\n",
    "We can calculate the correlation using the built in function <tt>pandas.df.corr()</tt>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7da276fe",
   "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>CO2</th>\n",
       "      <th>GDP</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.795121</td>\n",
       "      <td>-0.000190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>0.795121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>-0.000190</td>\n",
       "      <td>-0.027832</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CO2       GDP  population\n",
       "CO2         1.000000  0.795121   -0.000190\n",
       "GDP         0.795121  1.000000   -0.027832\n",
       "population -0.000190 -0.027832    1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2vGDP.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b528b58b",
   "metadata": {},
   "source": [
    "Humph, population was included in my correlation matrix, which I didn't really want. \n",
    "\n",
    "The function <tt>pandas.df.corr()</tt> returns the matrix of correlations between all pairs of variables in your dataframe. \n",
    "\n",
    "This isn't a big problem in the current case, but if you had a big dataframe with many irrelevant columns, it would be an issue, because we don't want the reader to have to search through a huge correlation matrix to find the correlation we are interested in.\n",
    "\n",
    "We have two options to avoid this - one is to create a new dataframe with only the columns you want to correlate, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b4e3fbea",
   "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>CO2</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.795121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>0.795121</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CO2       GDP\n",
       "CO2  1.000000  0.795121\n",
       "GDP  0.795121  1.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2vGDP_reduced = CO2vGDP[['CO2','GDP']] # new dataframe has only columns 'CO2' and 'GDP'\n",
    "CO2vGDP_reduced.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14907a2",
   "metadata": {},
   "source": [
    "The other is to correlate just the two columns we want, rather than getting the whole correlation matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4e998580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7951213612309438"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2vGDP['CO2'].corr(CO2vGDP['GDP'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3203dcc3",
   "metadata": {},
   "source": [
    "### Outliers\n",
    "\n",
    "The correlation between GDP and C02 looks quite high, 0.79.\n",
    "\n",
    "However, looking at our scatterplot, I can see a problem - there is one bad outlier with very high GDP and high CO2 emissions.\n",
    "\n",
    "Any guesses what this country is? \n",
    "\n",
    "We can find out by sorting the dataframe by GDP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "17f150d6",
   "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>CO2</th>\n",
       "      <th>GDP</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>38.4397</td>\n",
       "      <td>153764.0</td>\n",
       "      <td>2766743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Norway</td>\n",
       "      <td>8.3307</td>\n",
       "      <td>84580.0</td>\n",
       "      <td>5312321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>16.0112</td>\n",
       "      <td>76398.0</td>\n",
       "      <td>9140172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>7.9898</td>\n",
       "      <td>68402.0</td>\n",
       "      <td>5814543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Kuwait</td>\n",
       "      <td>23.1008</td>\n",
       "      <td>65521.0</td>\n",
       "      <td>4317190</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>108</th>\n",
       "      <td>Niger</td>\n",
       "      <td>0.0830</td>\n",
       "      <td>965.0</td>\n",
       "      <td>22577060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Democratic Republic of Congo</td>\n",
       "      <td>0.0331</td>\n",
       "      <td>859.0</td>\n",
       "      <td>87087352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>0.2252</td>\n",
       "      <td>818.0</td>\n",
       "      <td>4889396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Burundi</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>651.0</td>\n",
       "      <td>11493476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>0.0471</td>\n",
       "      <td>623.0</td>\n",
       "      <td>5094795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Country      CO2       GDP  population\n",
       "122                         Qatar  38.4397  153764.0     2766743\n",
       "112                        Norway   8.3307   84580.0     5312321\n",
       "154          United Arab Emirates  16.0112   76398.0     9140172\n",
       "133                     Singapore   7.9898   68402.0     5814543\n",
       "79                         Kuwait  23.1008   65521.0     4317190\n",
       "..                            ...      ...       ...         ...\n",
       "108                         Niger   0.0830     965.0    22577060\n",
       "39   Democratic Republic of Congo   0.0331     859.0    87087352\n",
       "85                        Liberia   0.2252     818.0     4889396\n",
       "21                        Burundi   0.0608     651.0    11493476\n",
       "26       Central African Republic   0.0471     623.0     5094795\n",
       "\n",
       "[165 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2vGDP.sort_values(by='GDP', ascending=False) # sort in descending order to put the richest country at the top"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cdcd6bf",
   "metadata": {},
   "source": [
    "It's Qatar - maybe not what you expected?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8038acab",
   "metadata": {},
   "source": [
    "### Remove outlier\n",
    "\n",
    "Let's exclude Qatar from our dataset and re-calculate the correlation.\n",
    "\n",
    "We erase the values for Qatar data values for CO2 and GDP for Qatar to <tt>Nan</tt> but in this case, since they are not misrecorded but just unusual values, let's not do that, as we don't want to hide the data point.\n",
    "\n",
    "Instead we conduct the correlation on the dataframe excluding Qatar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "951a05b8",
   "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>CO2</th>\n",
       "      <th>GDP</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.732323</td>\n",
       "      <td>0.005751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>0.732323</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.025626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.005751</td>\n",
       "      <td>-0.025626</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CO2       GDP  population\n",
       "CO2         1.000000  0.732323    0.005751\n",
       "GDP         0.732323  1.000000   -0.025626\n",
       "population  0.005751 -0.025626    1.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CO2vGDP[CO2vGDP['Country']!='Qatar'].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ff0ed2",
   "metadata": {},
   "source": [
    "Hm, the correlation went down from $r$=0.79 to $r$=0.073 - lower but still strong\n",
    "\n",
    "Here's a plot of the data with Qatar excluded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9dc42324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CO2 emissions: tonnes/person/year')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=CO2vGDP[CO2vGDP['Country']!='Qatar'], x='GDP', y='CO2')\n",
    "plt.xlabel('GDP: $/person/year')\n",
    "plt.ylabel('CO2 emissions: tonnes/person/year')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36a341c1",
   "metadata": {},
   "source": [
    "We no longer have an obvious outlier, but we do have a problem, called heteroscedasticity\n",
    "\n",
    "Heteroscedasticty is when the variance of the data in $y$ depends on the value in $x$. In this case, CO2 emissions are more variable for high income countries (which can be high- or low poluting) compared to low income countries\n",
    "\n",
    "This property violates the assumptions of Pearson's correlation coefficient, so for these dataset we would be better off using Spearman's rank correlation coefficient, as explored in the next section."
   ]
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