{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualisation with `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Increase figure and font sizes\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries = pd.read_csv('https://github.com/estimand/teaching-datasets/raw/master/world-development-indicators/wdi-countries.csv.gz')\n",
    "wdi = pd.read_csv('https://github.com/estimand/teaching-datasets/raw/master/world-development-indicators/wdi-data.csv.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "indicators = {\n",
    "    'NY.GNP.PCAP.CD': 'gni_per_capita',  # GNI per capita, Atlas method (current US$)\n",
    "    'SP.DYN.IMRT.IN': 'infant_mort',     # Mortality rate, infant (per 1,000 live births)\n",
    "    'SP.DYN.LE00.IN': 'life_exp'         # Life expectancy at birth, total (years)\n",
    "}\n",
    "\n",
    "wdi_subset = wdi[(wdi['indicator_code'].isin(indicators.keys())) & (wdi['year'] == 2015)].copy()\n",
    "wdi_subset.drop('year', axis=1, inplace=True)\n",
    "wdi_subset['indicator_code'].replace(indicators, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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_code</th>\n",
       "      <th>indicator_code</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7959</th>\n",
       "      <td>ABW</td>\n",
       "      <td>life_exp</td>\n",
       "      <td>75.594341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33132</th>\n",
       "      <td>AFG</td>\n",
       "      <td>gni_per_capita</td>\n",
       "      <td>590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37757</th>\n",
       "      <td>AFG</td>\n",
       "      <td>infant_mort</td>\n",
       "      <td>54.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37875</th>\n",
       "      <td>AFG</td>\n",
       "      <td>life_exp</td>\n",
       "      <td>63.298195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65965</th>\n",
       "      <td>AGO</td>\n",
       "      <td>gni_per_capita</td>\n",
       "      <td>4070.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country_code  indicator_code        value\n",
       "7959           ABW        life_exp    75.594341\n",
       "33132          AFG  gni_per_capita   590.000000\n",
       "37757          AFG     infant_mort    54.900000\n",
       "37875          AFG        life_exp    63.298195\n",
       "65965          AGO  gni_per_capita  4070.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi_subset = wdi_subset.pivot(index='country_code', columns='indicator_code', values='value')\n",
    "wdi_subset.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>indicator_code</th>\n",
       "      <th>gni_per_capita</th>\n",
       "      <th>infant_mort</th>\n",
       "      <th>life_exp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>590.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>63.298195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGO</th>\n",
       "      <td>4070.0</td>\n",
       "      <td>56.9</td>\n",
       "      <td>61.189341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALB</th>\n",
       "      <td>4300.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>78.203146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARE</th>\n",
       "      <td>42910.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>77.484244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARG</th>\n",
       "      <td>12430.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>76.293024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "indicator_code  gni_per_capita  infant_mort   life_exp\n",
       "country_code                                          \n",
       "AFG                      590.0         54.9  63.298195\n",
       "AGO                     4070.0         56.9  61.189341\n",
       "ALB                     4300.0         12.5  78.203146\n",
       "ARE                    42910.0          6.7  77.484244\n",
       "ARG                    12430.0         10.3  76.293024"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdi_subset = pd.merge(wdi_subset, countries, left_index=True, right_on='country_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>gni_per_capita</th>\n",
       "      <th>infant_mort</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>country_code</th>\n",
       "      <th>country_name</th>\n",
       "      <th>region</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>590.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>63.298195</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4070.0</td>\n",
       "      <td>56.9</td>\n",
       "      <td>61.189341</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4300.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>78.203146</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Europe &amp; Central Asia</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42910.0</td>\n",
       "      <td>6.7</td>\n",
       "      <td>77.484244</td>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12430.0</td>\n",
       "      <td>10.3</td>\n",
       "      <td>76.293024</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gni_per_capita  infant_mort   life_exp country_code          country_name  \\\n",
       "1           590.0         54.9  63.298195          AFG           Afghanistan   \n",
       "2          4070.0         56.9  61.189341          AGO                Angola   \n",
       "3          4300.0         12.5  78.203146          ALB               Albania   \n",
       "5         42910.0          6.7  77.484244          ARE  United Arab Emirates   \n",
       "6         12430.0         10.3  76.293024          ARG             Argentina   \n",
       "\n",
       "                       region         income_group  \n",
       "1                  South Asia           Low income  \n",
       "2          Sub-Saharan Africa  Lower middle income  \n",
       "3       Europe & Central Asia  Upper middle income  \n",
       "5  Middle East & North Africa          High income  \n",
       "6   Latin America & Caribbean  Upper middle income  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wdi_subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms and density plots\n",
    "\n",
    "**Usage**: show the distribution of a numerical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a0dd7e4e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a0c18f4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wdi_subset['life_exp'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a34111240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a0d944518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try changing the number of bins\n",
    "wdi_subset['life_exp'].plot.hist(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S40z2kiQ1zmQvSVLjTPaSJDXOZC9JUuNM9pIkNc5kL0lS40z2kiQ1zmQvSVLj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\niwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a3ca71358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add title and labels\n",
    "wdi_subset['life_exp'].plot.hist(20, title='Histogram of life expectancy')\n",
    "plt.xlabel('Life expectancy (years)')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a0d94e048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mIuHpN8t28Keyvdw6f6QGJUaYGUOyWb3nKM2tPq9LkQjQqzMIzrlaulgsyTn3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\nZ3PgJ7QyxAUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a0c8d4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare with the corresponding density plot\n",
    "wdi_subset['life_exp'].plot.density()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f99f05dbac8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99f05c1b38>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f99f05840b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99f053c5f8>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cOAjYXtKOEfFUoUgvB87LeX8APNDCxzgHmA18EXg7cDjwCLAPcAvwBeD9wBRJ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\nmnFwNzMzqxkHdzMzs5r5/9AplKwsOMtcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a0c9d8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped histograms\n",
    "wdi_subset.hist(column='life_exp', by='income_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f99f048b940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99f0442668>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f99f03fd550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99f03b6978>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A+vr6KiyCmVUpIi4qDkuaDfwZOBA4aaDJSsMaIL0xT9cHZhWr4sq9cS+u/K18E1b/Bg9A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99f048ec50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped histograms with shared x-axis\n",
    "wdi_subset.hist(column='life_exp', by='income_group', sharex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f99f0380e48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99f02679e8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f99f0221748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99f01dc3c8>]], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f23LH1RhaaUvpzlhiRh/vuR9DvaT0sqmE5j7oW6m/5tpPhZ5B/WW/b/M9tSOc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\nknrG4C5JUs8Y3CVJ6pn/DzUK1jtTD/mCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99f028d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped histograms with shared x- and y-axes\n",
    "wdi_subset.hist(column='life_exp', by='income_group', sharex=True, sharey=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plots\n",
    "\n",
    "**Usage**: show the relationship between two (or pairs of) numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99f01baa90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/I+P43QRz9iINqVGDWCM+iESVmCki0Sk12B8HvMfdkzZyb/AXCd/nXqRhNGoQ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\nbm9CBe4sqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99f0466c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wdi_subset.plot.scatter(x='infant_mort', y='life_exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99f00548d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eW4nnyL/3TcBNAOvWravhrftPq3vPnTgcXxzYKwVzrQoQkV5Wa7B/KvDahUAP\n4O7HzewvgYlqX8TM1gC/Dby54OkngAwwVnT6Gk7v7S+8923kbjIYHx8veUMgT2p177mThuOLA/tV\nG0a5Z8ehssFcVepEpJfV+in2CHBmiefXAttreJ0bgXng9oUn3D1JLjP/RUXnvgi4u6ZWStt0wvKw\nUgV97nrkIHPpTNklfqrJLyK9rNZP5D8H/tbMXmlm6/NfrwQ+DrzXzFYufJV7gXxi3huA2919pujw\nR4EbzewNZrbRzG4hd3Px9zW2U/pYqV56yAzPUjaYq0qdiPSyWofx78z/+TmenEe3/J9fLXjsnLqe\nvtDzgQuBVxcfcPcvmNkouZuKtcCDwP/l7rtrbKf0sVLJgoPREFefP8rdjx4qm1PQSdMQIiLNZO7V\nT3Wb2bXVnuvum+tqUZ3Gx8d9YqLqtAGpQjcvQyuXjLfwbwqbVVz/LyLSDcxsi7svmhxfU88+6AAu\n7RP0MrTiG4tGbzTK9dJjkRCHj89riZ2I9JVa19n/LfCn7j5f9Pwa4J/dfVMzGyftEfQytHJldhsN\nxqVWH2iJnYj0o1o/3V4CTJjZpQtPmNl/Ize3rq5Rjwhyc5rizPlYOMS/3LObWChU9Xa6taj137aw\nsU+z3l9EpB1qTdB7BvB3wL1m9l5yiXZ/CNwMfKi5TZN2CXJzmuLgGwoZyUyWcDiX99ns9e61/NtU\nUU9EekVNn57uftzd/xD4G3I17F8PvNjdP+i1ZPpJRwtyGVrx+vZs1omFQ2QyuR+nZt9oVPtvK7VW\nv5kjDJ1MoxkivaeejXDeAfwp8BlyW9beZma/7+5Khe8hQS1DK1Vm97VXncu2yRkOzMw1tezuQtLf\nyqGBRf9t/VpRT6MZIr2p1gS97wJPB17p7l81swFyPfwfmdn73P39rWiktEdQm9OUurG4eO1IU280\nag1iQU5lQGcsc1TyokjvqvU32IGnu/tXAdx93t3fArwM+ONmN076R3GZ3WaW3a1nSD7IqYzJ6QR3\nbN3L1+/fxx1b9zI5XdNu0U0TZGKmiASr1nX2Ly7z/LfM7LLmNEmkueodkg9iKqOTetNBj2aISHBq\n/jQxs0Ez+10z+zMzW55/7nxA2TzSkRrZ5KbVG/t0Um9a+wOI9K5a5+wvAL4HDAPLgS8BR4E/yj9+\nQ7MbKNKoUkmAnRLEOq03rf0BRHpTrdn4Hwe+Qy64Hy14/mvAPzerUSLN1qlBrBNvRIJKzBSR4NQa\n7K8GnuPumdxOtSftofQ+9yIdo1ODWKfeiEBnrBIQkcbVvM4eiJZ4bh0w3WBbRPpWJ96IaM29SO+o\n9dPlO8DbCx67mS0D/l/gG02AE6r7AAAbpElEQVRrlYi0VT9XEBTpRbUG+7cDzzWzXwKDwBeAXcAY\n8K7mNk1E2qWTVgmISONqXWe/z8yeAfwe8ExyNwu3Af/u7u2pBCIiTddpqwREpDE1z9nng/o/5b9K\nMrNvAG9w9/0NtE1E2qQTVwmISP3qSdCrxvPQ/vYiXa2TVwmISG1aFexFpAd04ioBEamdfotFRER6\nnIK9SICS6SzTiVTJJWyVjomINELD+NI36qkG18wKcpWK1KiAjYi0koK99LxkOsvuQ7P8bNdhwKoO\nps0MwJW2sgU6ZptbEelNrfok+QBwuEWvLVK1yekEX5p4jFt/8CgP7j1GLGRVVYNrdgW5SkVqgihg\noymC7qT/N2mWmnv2ZvYS4M3ABuB6d3/MzN4A7HT3uwDc/YPNbaZI7RYCdiRsDA1EWBKN8OC+Y1y1\nYZT0fC6Ylus5lwrAx+ZTFb+nksWK1LSygE03TBFow53TdcP/m3SPmn6rzOz3gS8CvwLO48lNccLA\nO5vbNJHGLATskXiUcH6Xxow704nUosG0MDgDDQfghSI1J1JpDszMcSKVPlmkptKxRnVDjfvJ6QR3\nbN3L1+/fxx1b9zI5rWKc3fD/Jt2l1p79O4E3uvvt+d78gp8Af9m8Zok0biFgp9LOpWeNcN/uI8yl\nM6Q9y3VPOaNiMG1FBblKRWpaVcCmlhGKdvSuK+Uy9HMPv9kjSyK1BvsLgXtKPD8LLGu8OSLNUxiw\n09lcwL/ivBWsHx2u6gOzFQG4UpGaVhSwqbbGfbuGjBXUStPeBNJstQb7fcBFwO6i558HPNqUFok0\nUaMBu9sryFUzQtHO3rWCWmnam0CardZgfxvwtwVD+OeY2TXAh4Gbm9kwkWZpZ8BebGg8iKHzxW54\n2tm7VlArT3sTSDPVusXth81sBPguuf3svw/MAx9x90+2oH0iXWuxofEgh84r3fC0u3etoFZet48s\nSedY9KfIzJ5nZidvCtz9vcAq4ErgOcBqd/+L1jVRpPtUyqZOprM8MTPPXY8c6Ihs61auBqilDSPx\nqAKbSItU07P/PrAWOGhmO4Ar3P0QMNHSlol0sXJD47sOzbJ1z1Fm5tI8tG+aqzeMEo+F256Ypt61\nSG+r5jf6CLk19QDrq/wekb5Wap0+OPfuPMKSaISzV8QZjIS5b89R0plsRySmqXct0ruq+a3+CrDZ\nzHYCDkyY2Y5SX4u9kJmtNbPPmtmUmc2Z2cNmdm3B8c+YmRd9/aT+f55Ie5QaGr9y/UqwXC8/Egrx\nzHNXMJfOsPdooi1D5yLSP6oZxv+fwNfIrbH/KPDPwEytb2Rmy4EfAz8CNgFT5EruHiw69XvAawoe\nJ2t9L5FOUDw0DnDfnqMnE+EGI2HGz13J9ZeOsWww+B61StSK9I9Fg727O/ANADN7OvC/3b3mYE+u\n+t5+d39twXM7S5w37+6Tdby+SMcpzqYuXmZ23cY1rBoeaHk7igO76q6L9Jdal979QQPv9TLgW2b2\nBeAF5Ar0/CPwyfwNxYLnmtlB4CiwGXivuxf3/kW6UjsS4YoD+1UbRrlnxyGVqBXpI4sGezP7GvBq\ndz+W/3tZ7v7fKxzeALwJ+BjwIeAZwN/lj30i/+e3gP8g1+NfD/wV8F9m9ix3ny/RtpuAmwDWrVu3\n2D9FpCMEuXa6VHW8ux45SCgEo0O5EYV4LMzhxDwHZuY4Y+mgAr5ID6qmZ3+IXGIe5Pao9wrnVhIC\nJtz93fnHW83sQnLb5X4CwN1vLzj/ATPbQq407yZyNwGncPfbyFX1Y3x8vN52ifSsUksAQycMz/rJ\n3IH9RxM8sHca3BiMhjSkL9KDqpmz/4OCv9/YwHvtBx4uem4b8CcV3nufme0llxwoIjUqVR1vMBri\n6vNHufvRQxxOzPPA3mmefs5y1o7ENaQv0qOqHcavhrv7b1c4/mPgKUXPldpUp/C9VwFnkbtREJEa\nlas9PzYS54ZlcQ7MzIEba/M9+XYX9xGR1qh2GL8ZPgbcbWbvBb4AXA68FXgPgJkNk9tM5yvkgvt6\n4IPklubd0aQ2iPSdckmBsUiIM5YOMhgN1VwXX8v2RLpLTcP4jXD3e83sZcAHgL8A9uT/vDV/Sga4\nDHgtsJxcwP8+8PI6l/qJSF65pMB6dp3Tsj2R7mOnrnrrXuPj4z4xoXL90l+a1cOu9nWS6Sx3bN17\nSnb/iVRac/wibWJmW9x9fLHzat3PXkQ6RDN72NUuByy3wY/m+EU6m347RbpQpS10W6nUBj/t3sBH\nRBanYC/ShUr1sNNZJ5HKtPR9S23wow18RDqfhvFFAtDs7PVS6+eD6mG3o+SviDRGwV6kxVqRvV5P\nFn0zBVnytxW0dFD6jYK9SAuVqk3frAp16mHXR0sHpR/p00GkhVo9tx6LhBiJRxXoS0ims0wnUqck\nLbYrsVGk3dSzF2mhds6t97NyvXctHZR+pZ9ukRZS9nrwSvXe73rkAE/MzBM209JB6Uvq2Yu0mObW\nG1dLQl1x730ulWZi1xESySxLByNsHFvKtsmZtiQ2irSLgr1IALo9e72dak2oK5w6iYaN+/YcZTAS\n5uwVcVJpZ9vkDJsuO5OMu26+pG/op1xEOlY9CXWFUyd7jyaYS2d45rkriIRCJxMkM+5KbJS+op69\niHSsehPqFqZOjs2liEfDDEZy3685eulXuq0V6VGllp51m0Zq8cciIVYND3DdxjVKkJS+p569SA9a\nbJ67WyrINaNSoBIkRRTsRXrOYlX7uq2CXDOCtRIkpd/pp1+kx1Sq2tetFeRUKVCkMfrNEekxlea5\n27U1roi0l4K9SJu0KoGuUtW+RhLeOkkvJB+KBElz9iJt0Op583Lz3O3eGrcZui3nQKQTKNiLBKyV\n294WKpeU1s3Z6UFdO5Feo98OkYB1wrx5tya8dcK1E+lG3fWbLtIDemXevB107UTqo2AvEjBte1s/\nXTuR+mjOXqQNumnevNOq7XXTtRPpFAr2Im3SDVXdOjXzvVnXrtNuZERaRcFeRErq9cz3Tr2REWmF\n7v+NFZGGlSpS08uZ791aNlikXurZi/S5cj3cwsz3hZ59r2S+l7qROTafIpHK9MSohUgx/VSL9LFK\nPdxeznzXEj7pN+rZi/SxxXq4vZr53gtlg0VqoWAv0seqGarvhlUD9ei0GxmtDJBWUrAX6WP93sPt\nlBsZrQyQVlOwF+lzndbD7Te9vsRROoN+kkT6SLl94Lt1Y5xe0MtLHKVzBP6bbWZrzeyzZjZlZnNm\n9rCZXVtw3MzsZjPbZ2YJM/uBmT016HaK9JrJ6QR3bN3L1+/fxx1b9zI5nWh3kwStDJBgBBrszWw5\n8GPAgE3ARuAtwMGC094JvCP//BX5Y981s6VBtlWkl6iITOfq5SWO0jmCnrN/J7Df3V9b8NzOhb+Y\nmQFvAz7k7l/JP/c6cgH/VcA/BNhWkZ7R7UVkqs1U79aMduVNSKsFHexfBnzLzL4AvADYB/wj8El3\nd+A8YAz4zsI3uHvCzH4IXI2CvUhdurkaXrWZ6t2e0d4pKwOkNwX9k7UBeBOwA7geuAX4EPDm/PGx\n/J8Hir7vQMGxk8zsJjObMLOJqamp1rRYpAd061Bx4fTD6FAMgLu2HTxt+kHTFCKVBd2zDwET7v7u\n/OOtZnYhuWD/iYLzvOj7rMRzuPttwG0A4+Pjpx0XkSd141DxwvTDXDrDxO5pMu4cn09z5XkruPCM\nZaed163TFCKtFvRvwX7g4aLntgHr8n+fzP9Z3Itfw+m9fZG+V24pXTndtsQuHg2Dw327jzAQCbMk\nGmEwEuZnuw6f8m9WRrtIZUH/xv8YeErRcxcBu/N/30ku4L9o4aCZDQLXAHcH0UCRbtEPS+likRBX\nnLeCuXSGE6k08+kMz1y3HLBT1qEHPU1R602WSLsFPYz/MeBuM3sv8AXgcuCtwHsA3N3N7OPAe83s\nEWA78OfALPC5gNsq0rH6qera+tFhxtevIGK5UYlUxsmm0qf12oOapuj2REDpT4F+Krj7veQy8l8O\nPAi8H/gL4NaC0z4MfBT4JDABrAVe7O4zQbZVpJP1U9W1WCTEdRefAQaHTiQr9tpbPU2hRECNanSr\nwGvju/s3gG9UOO7AzfkvESmhm5fS1aNTkgv7PRFQoxrdq/d/OkV6ULcupWtEJyQX9nMioEY1upt2\nvRPpUp3S2+0n/bwlcL+PanQ7BXuRLqaqa8Hr15usfps66jX98VMqItJEnTClELR+nDrqJerZi4hI\nVfp1VKMXKNiLiEjVNHXUnfQ/JiIi0uMU7EVERHqcgr2IiEiPU7AXERHpcQr2IiIiPU7BXkREpMdp\n6Z2I9JRkOntyHTigNeEiKNiLSA8p3JXt+FwKMIYGI32xQ1vhTU7hjU2556W/KNiLSE8o3JUtGjYe\n3DsNwLVPWU0q7WzePsUNl5/dkwGv3Naz2pJWFvTeT72I9KXCXdmSmSzhsBEOG/PpLPFYmHTWSaQy\n7W5m05XbenZ2Lq0taeUkBXsR6QmFu7LFwiEyGSeTcQYioZ7eoa3U1rPprHMkkSz5fC/e8MjiFOxF\npK2S6SzTiVTDPc7CXdkOnUhy3qolnLdqiEPHkz29Q1vhTQ5w8sZmRTxW8vlevOGRxWnOXkTappE5\n5VKJZ8W7skFzs/E7Mdlt4SZn8/Ypjs2nTl7H4cFIyec7pd0SLHP3drehKcbHx31iYqLdzRCRKiXT\nWe7Yupcl0QjxWJhEMsOJVLqqJLp2JJ51erKbsvH7k5ltcffxxc7T/7yItEW5uebF5pTLJaS1MvGs\nHe9Zq1gkxEg8elpAL/e89Bf974tIW5Sba15sTrnem4RGtOM9RZpJwV5E2qIwoe7AzFzVSXT13iQ0\noh3vKdJMStATkbYpTqirZqi5XEJaK4ep2/GeIs2kYC8ibRWLhGoOmvXcJDSq0nsqCU46nYK9iHSl\nem4SWvGenZ6lLwKasxcRqVs3ZOmLgIK9iEjdlKUv3ULBXkSkTsrSl26hYC8iUqd6lw+KBE0JeiIi\nDWjHygCRWinYi4g0qB0rA0RqoZ9OERGRHqdgLyIi0uMCDfZmdrOZedHXZMHxz5Q4/pMg2ygiItJr\n2jFn/0vg+QWPixekfg94TcHjZKsbJCIi0svaEezT7j5Z4fj8IsdFRESkBu2Ys99gZo+b2U4zu93M\nNhQdf66ZHTSz7Wb2KTNb04Y2ioiI9Iygg/1PgRuBlwBvBMaAu81sNH/8W8BrgeuAdwBXAv9lZgMB\nt1NERKRnBDqM7+7fLHycT77bAbwO+Ki7315w+AEz2wLsBjYB/1H8emZ2E3ATwLp161rVbBERka7W\n1qV37j4LPARcWOb4PmBvheO3ufu4u4+vXr26dQ0VERHpYm0N9mY2CFwM7C9zfBVwVrnjIiIisrig\n19l/xMyuNbPzzOzZwJeBIeCzZjacP36Vma03s+cDdwIHgTuCbKeIiEgvCXrp3dnA54FVwBTwE+A5\n7r7bzOLAZeQS9JaT681/H3i5u88E3E4REZGeEXSC3isrHEsA1wfYHBERkb6g2vgiIiI9TsFeRESk\nxynYi4iI9DgFexERkR6nYC8iInVJprNMJ1Ik09l2N0UW0Y5d70REpMtNTifYvH2KdNaJhIxrL1rN\n2Ei83c2SMtSzFxGRmiTTWTZvn2JJNMIZSwdZEo2wefuUevgdTMFeRERqkkhlSGedeCwMQDwWJp11\nEqlMm1sm5SjYi4h0uaDnzuPRMJGQkUjmgnsimSESMuLRcCDvL7XTnL2ISBcrN3eeTGdJpDLEo2Fi\nkeb262KRENdetJrN26c4Np86+b7Nfh9pHgV7EZEuVTh3Ho+FSSQzbN4+xVUbRrlnx6GWJs+NjcS5\n4fKzW3ZDIc2l/x0RkS5Vau58Lp3hrkcOBpI8F4uEGIlHFei7gP6HRES6VKm5c89CyEzJc3IKBXsR\nkS61MHd+IpXmwMwcJ1Jprtu4hsFoqKOS51R8p/00Zy8i0sVKzZ1fGw51TPKciu90BgV7EZEuF4uE\nTgnmnZI8Vy6B8IbLz9Y8f8B0tUVEelAnJM+p+E7nULAXEZGWUPGdzqFgLyIiLVEqgVDFd9pDc/Yi\nItIynZI/0O8U7EVEpKWKEwgleLr6IiIiPU7BXkREpMcp2IuIiOT1arU/zdmLiIjQ2mp/rdxyuBoK\n9iIi0vdaWe2vE0oGaxhfRET6Xquq/RXeRLR6y+FKFOxFRKTvtaraX6eUDFawFxGRvteqan+dUjJY\nc/YiIiK0ptrfwk1Eu7ccVrAXERHJa0W1v04oGaxgLyIi0mLtLhmsOXsREZEep2AvIiLS4xTsRURE\nelygwd7MbjYzL/qaLDhu+XP2mVnCzH5gZk8Nso0iIiK9ph09+18Cawu+Lis49k7gHcBbgCuAg8B3\nzWxp0I0UERHpFe3Ixk+7+2Txk2ZmwNuAD7n7V/LPvY5cwH8V8A+BtlJERKRHtKNnv8HMHjeznWZ2\nu5ltyD9/HjAGfGfhRHdPAD8Erm5DO0VERHpC0MH+p8CNwEuAN5IL7neb2Wj+7wAHir7nQMGxU5jZ\nTWY2YWYTU1NTrWmxiIhIlwt0GN/dv1n42Mx+AuwAXgf8ZOG0om+zEs8tvN5twG0A4+PjJc8RERHp\nd21deufus8BDwIXAwjx+cS9+Daf39kVERKRKbS2Xa2aDwMXA94Gd5AL+i4B7C45fA/zpYq+1ZcuW\nJ8xsd41NWAU8UeP3SO10nYOjax0cXevg6FqXd241JwUa7M3sI8CdwB5yPfa/AIaAz7q7m9nHgfea\n2SPAduDPgVngc4u9truvrqM9E+4+Xuv3SW10nYOjax0cXevg6Fo3Luie/dnA58ndpU2Rm6d/jrsv\n9Mg/DMSBTwIryCX0vdjdZwJup4iISM8IOkHvlYscd+Dm/JeIiIg0Qb/Xxr+t3Q3oE7rOwdG1Do6u\ndXB0rRtkuc60iIiI9Kp+79mLiIj0PAV7ERGRHteXwd7M3pSvzT9nZlvM7Jp2t6nbmdm7zexeMztm\nZlNmdqeZXVp0jrYwbjIze09+q+hPFDyn69wkZrbWzD6b/5meM7OHzezaguO61k1gZmEze1/B5/JO\nM/srM4sUnKNr3YC+C/Zm9grgFuADwOXA3cA3zWxdWxvW/Z4P3Epu06IXAmnge2a2suAcbWHcRGb2\nHHJ7TNxfdEjXuQnMbDnwY3IluzcBG8ld04MFp+laN8efAW8G3kqu0Nqf5B+/u+AcXetGuHtffZFb\nu/+poud+BXyw3W3rpS9gGMgA/y3/2ID9wHsLzokDM8D/3e72dtsXMAI8Su7G6gfAJ3Sdm36NPwD8\nuMJxXevmXeuvkyuuVvjcZ4Gv61o356uvevZmFgOeRcE2unnfQdvoNttSciNHR/KPtYVxc90GfNnd\n/6voeV3n5nkZ8FMz+4KZHTSzn5vZH5uZ5Y/rWjfPj4AXmNnFAGZ2Cbkb2f/MH9e1blBba+O3wSog\nTOltdH8z+Ob0tFuAnwP35B9X2sL4rKAa1QvM7I3ABcBrShzWdW6eDcCbgI8BHwKeAfxd/tgn0LVu\npr8m10F42Mwy5GLT+9391vxxXesG9VuwX1D1NrpSOzP7KPBc4Lnunik6rGvfADN7Crnh5WvcPVnh\nVF3nxoWACXdfmDfeamYXkptL/kTBebrWjXsF8FrgVeR2Qn0GcIuZ7XT3Txecp2tdp74axie3a1IG\nbaPbMmb2MeD3gBe6+46CQ9rCuDmuIjdC9aCZpc0sDVwLvCn/90P583SdG7cfeLjouW3AQjKvfqab\n52+Aj7j77e7+gLv/K/BRnkzQ07VuUF8F+3xPaAu5bXQLvYhcVr40wMxuIXdn/kJ3f6TocOEWxgvn\nL2xhrGtfvf8DXEau57PwNQHcnv/7dnSdm+XHwFOKnrsIWNi4Sz/TzbOEXEesUIYnY5SudYP6cRj/\no8C/mtnPyP0y/0/gTODv29qqLmdmnyQ3h/wy4IiZLdyBz7r7rHtjWxhLjrsfBY4WPmdmx4HD7v5g\n/rGuc3N8DLjbzN4LfIHcUt23Au+B3MZdutZNcyfwLjPbSW4Y/3Lg7cC/gK51U7R7OUA7vsgl3ewC\n5sn19J/X7jZ1+xe5ebNSXzcXnGPkdjTcD8wBm4FL2932bv+iYOmdrnPTr+0m4Bf567idXLA3Xeum\nX+elwMfJjZokgB3kclMGda2b86WNcERERHpcX83Zi4iI9CMFexERkR6nYC8iItLjFOxFRER6nIK9\niIhIj1OwFxER6XEK9iJdwMw+Y2Zfr+H8JWb2ZTObNjM3s/Wta52IdDoFe5Hu8CfAq2s4/w+B55Hb\nkGgt8FgzGmFmN5rZbDNeq1XyNze/2+52iHSSfiyXK9J13H26xm+5ANjm7g+0oj2dyMxiXnknQJG+\npZ69SBcoHMY3sx+Y2a1m9gEze8LMDprZR8wstHCc3EjA8/K93B/kn3+1md1rZjP57/mSmZ1V8B7P\nz59/nZn91MxOmNmEmT1z4Tjwz8BQ/jw3s5uraPsuM/t/8v+GGTN7zMxeYWbLzex2M5s1s1+Z2YuL\nvu95+XbMmdkBM/uYmcUKjv/AzP6//L99Cvixme3KH/5Svn27EBEFe5Eu9ftAGrga+GPgbeT2BAf4\nHXJB+R5yQ/i/k38+Bvwv4OnAS8ltlfv5Eq/9QeBdwDPJbZn772Zm5HYXextwIv+6a4GPVNnetwE/\ny7/mF4HPktvA5D/J7db3Q+Df8juZkb8J+SawldymKK8nt3XyB4te99XkaqZfQ24/9Cvyz78x374r\nEBHVxhfpBmb2GWCVu78031MfcPerCo5/F9jt7m/IP/4EuU1Cnl/hNS8mtz/7Oe6+N99z/z7wW+7+\n7fw5vwH8qOCcG8ltujNcQ9t3Afe4++/lHw8DM8Dfuftb88+tJ7eN6RXuPmFm7yd383KRu2fz59wI\n/AOwwt1P5K/DSnd/WtH7OfA/3P3L1bZRpNepZy/Sne4verwPWFPpG8zsmWb2VTPbbWYzwET+0LoK\nr70v/2fF167Cydd091lyowOF+QQHit5nI7kbhGzBOT8iNzpxQcFzWxpsl0hfULAX6U6posdOhd9n\nMxsCvk0uyL6G3PD2b+UPx4pOL3zthaG/Rj8rSrW30vtYwXPFCp8/3mC7RPqCgr1If7iY3Bz9e9z9\nh+7+CPX11pNAuKktK+1h4KqFpMO85+bf/9FFvjdFMG0U6RoK9iL9YQ8wD/yxmW0ws03A++p4nV3A\noJm9yMxWmdmSZjaywK3AmcCtZrYx394PkcsXOFFFG68zszEzW9Gi9ol0FQV7kT7g7lPA64CXkes1\n/y/g7XW8zt3A35PL4p8C3tnEZha+z+PAS8hl4v8c+Kf8e76nim9/B/ACcoWEtraifSLdRtn4IiIi\nPU49exERkR6ncrkiUjczu4Zc8ZuSalmPLyKto2F8EambmcWBs8odd/dfB9gcESlDwV5ERKTHac5e\nRESkxynYi4iI9DgFexERkR6nYC8iItLjFOxFRER63P8PPh4Yn4SgkNwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99f00937f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add transparency\n",
    "wdi_subset.plot.scatter(x='infant_mort', y='life_exp', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99f004b160>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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85bwnwq7JTH05a8HVT8gGQhglwQKu+NP+mQq+J2wp+PVlqaNYKdY7JJR8oEse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\noGBmZmY9OVAwMzOznv4f+G3gQOMmhJoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a340f8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vary point colour by GNI per capita\n",
    "wdi_subset.plot.scatter(x='infant_mort', y='life_exp', c='gni_per_capita', colormap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f99effa5668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99eff471d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99efeff240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f99efeb8240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99efe74240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99efe74278>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f99efdd4c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99efd99160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f99efd526a0>]], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4ygv0iyWLEyMJqn0uOheodXaliVSeYwNxumr9i96tYL159tQ4L52bJJMv8esH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\nKC/IOeecc85llBfknHPOOecy6v8BGD/utfgnE3sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99eff65b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter matrix of three (numerical) variables\n",
    "pd.plotting.scatter_matrix(wdi_subset[['infant_mort', 'life_exp', 'gni_per_capita']], figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plots\n",
    "\n",
    "**Usage**: show a numerical comparison across categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>life_exp</th>\n",
       "      <th>infant_mort</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>79.531873</td>\n",
       "      <td>4.914000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low income</th>\n",
       "      <td>60.462551</td>\n",
       "      <td>53.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>68.044967</td>\n",
       "      <td>32.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle income</th>\n",
       "      <td>73.179189</td>\n",
       "      <td>16.689362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      life_exp  infant_mort\n",
       "income_group                               \n",
       "High income          79.531873     4.914000\n",
       "Low income           60.462551    53.675000\n",
       "Lower middle income  68.044967    32.300000\n",
       "Upper middle income  73.179189    16.689362"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute average life expectancy and infant mortality for each country group\n",
    "wdi_subset.groupby('income_group')[['life_exp', 'infant_mort']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99f00c6a90>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UIS1JUk0Z0pIk1ZQhLUlSTRnSkiTVlCEtSVJNGdKSJNWUIS1JUk01e6tKrazx\nw3p5eU/07vIkSauNe9KSJNWUIS1JUk0Z0pIk1ZQhLUlSTRnSkiTVlCEtSVJNGdKSJNWUIS1JUk0Z\n0pIk1ZQhLUlSTRnSkiTVlCEtSVJNGdKSJNWUIS1JUk21NKQj4sSImBURz0bEnRGxbyu/X5KkdtKy\nkI6IdwOTgPOAXYFbgZ9GxBatWoYkSe2klXvSpwDfysyvZ+a9mXkSMBf4SAuXIUlS22hJSEfEQGB3\n4OaGSTcDe7diGZIktZvIzFX/kohNgdnA/pk5rUP7Z4GjM/NfGuY/Hji+fPsvwF9WuYj6Gg4srLoI\nrRTXXd/m+uvb+vP62zIzRzQz45otXnBj4kcnbWTmZGByi5ddSxExPTPHVF2Hes5117e5/vo211+h\nVeekFwIvAhs3tI8E5rVoGZIktZWWhHRmLgHuBMY2TBpL0ctbkiT1UCsPd18M/GdE/B74LfBhYFPg\n8hYuoy9qi8P6/ZTrrm9z/fVtrj9a1HHs5S+LOBE4HdgEuBv4RMeOZJIkqXktDWlJktQ6jt0tSVJN\nGdKSJNWUIb0aRMTwiNgzItauuhY1LyJ2iohLIuKnEbFJ2XZ4ROxadW3qXkRsFBGnRcTXImJ42bZP\nRGxddW3qnttf5wzpFoqIoRHxX8B8ikvPRpXtl0fE+Cpr04pFxMHAHRTr7CBgUDnp1cA5VdWl5kTE\n7hQjFx4NfABYr5w0FvhiVXWpOW5/XTOkW+vLFP/JdgMWd2i/EXhbJRWpWecCp2Tm24AlHdqnAq+r\npCL1xIXApMzcFXiuQ/vPgX2qKUk94PbXhVYPC9ru3gK8LTPvioiO3ebvBUZXVJOasyPwk07a/wFs\n2Mu1qOd2p9iDbjQX2KiXa1HPuf11wT3p1toA+Hsn7UMphk1VfT1OeXqiwW7A33q5FvXcYortr9F2\nFKefVG9uf10wpFvrDoq96aWW7k2fgMOj1t21wAURsRnFelszIvanOIz67UorUzN+BJzTobNmRsRW\nFKegrq+qKDXN7a8LDmbSQhGxN8U5sOuA9wLfoDiM8zpgv8ycUWF5WoGIWAv4FvAeiru3vVQ+XwuM\ny0yPhNRYRKxHcbh0Z2Bd4DGKw9y/BQ7NzGcqLE/dcPvrmiHdYhGxE3AaxTmyAcAM4MuZ+edKC1NT\nIuLVwK4U6+4PmflAxSWpByLiIIpDpAOAGZn5i4pLUg+4/S3PkJYkqabs3b0aRMSGFPfSXuacf2bO\nrKYiNSMi3gYcSOfr7ohKilLTykEvulp/p1dSlJrm9tc5Q7qFyl8SVwE7LW2i6ASx9HmNikpTNyLi\nIuAkinOY87A3fp8SEacDE4AD2oaTAAAZQ0lEQVRHKNZfx0OEHi6sObe/rnm4u4Ui4g/AHOB8lv9F\nQWb+pYq61L2IWAh8IDN/VHUt6rmImAuMz8wrqq5FPef21zX3pFtrW+Bdmflg1YWoxxYB91VdhFba\nAOCWqovQSnP764LXSbfWb4Dtqy5CK2UCcHpE+Idr3/Q14Liqi9BKc/vrgoe7WygiRlFcG/0z4G7g\n+Y7TM3NaFXWpe+V1mj+iuHTufpZfdwdVUZeaExFBcZ30JsCfWX79vb+KutQct7+u+VdLa20L7AIc\n0sk0O47V2+XAvhR/YC3Xn0C190XgYIpxCTbA9dfXuP11wT3pFoqIv1AMDfolOu841tm43qqBiHgK\neHtmTqm6FvVcRPwTOCEzv1d1Leo5t7+uuSfdWptRDEH416oLUY8tBGZXXYRW2mLgD1UXoZXm9tcF\nO4611hSKcyrqe84BPh8RQ6ouRCvlK8DJ5blp9T1uf13wcHcLRcSHgc8AV9N555UfVFGXuhcRfwa2\novjD9VGWX3c7V1CWmhQRNwD7Af8EZrL8+ntLZ59TPbj9dc3D3a11Wfn86U6m2XGs3r5fdQFaJQsB\n/wjuu9z+uuCetCRJNeWetNRBeavDHSiOfNyTmVOrrUg9ERGjeWX93ZuZD1VcknrA7W957km3WEQc\nBnyKV/6jzaS4n/RPKi1MK1QORPNDio5/c8rmTYHpwNsyc05Xn1X1ImI94JvAO4CXljYD11OMCf1U\nVbWpe25/XbN3dwtFxAcp/qP9lSKozwBmAT+MCEc8qrevUtx5Z5vM3DwzN6cYnObFcprqbRKwM8Wt\nDgeVj38r2yZWWJea4/bXBfekWygiHgAmZeYlDe0nASdl5muqqUzdiYgngQMyc0ZD+xjglswcVk1l\nakZE/B04PDN/3dC+H/DDzHxVNZWpGW5/XXNPurW2oBjWrtFPgS17uRa1xkvdz6IaGAR0NqLfP4B1\nerkWtU7bb3+GdGs9CoztpP1gipvRq75uAb4aEZsvbYiILSgOo3oLxPr7LXBuRAxe2hAR6wKfA26t\nrCo1y+2vCx7ubqGIOAH4vxSDmdxK0XHsDcD7KA53T66wPK1A+cvhR8BOFB1XEhgF/Al4a2b+rcLy\n1I2I+FeKo1jrUqyzBF4LPAMckpn3VFieuuH21zVDusUi4m3AqbxyX+l7gQsy80fVVaVmRcRYYDuK\nnsEzM/MXFZekJkXEIOC9dFh/wHcyc3Glhalpbn/LM6QlSaopz0m3UETsHxH7d9G+XxU1qTkRcWVE\nnNpJ+ykR8Y0qalLzIuKL5dj5je0fjohzq6hJzXP765oh3VpfobjhfKP1ymmqr0OB/+mk/X/Kaaq3\n99H5rSrvBI7p5VrUc25/XTCkW+tfgD920v7ncprqa33g6U7anwE27OVa1HMjgQWdtP8d2KiXa1HP\nuf11wZBurcUUQ9k12gxY0su1qGfup/O/2A8DHuzlWtRzjwL7dtK+H9C2PYP7ELe/LniDjdb6OTAh\nIt6SmY8DRMSGwHnlNNXXRcDlETGSVw67/RtwMvAflVWlZl0BfCUiBrLs+vsS8OXKqlKz3P66YO/u\nFoqITYBpFIfe/lQ27wzMB/Zv50Hi+4LyOvezKK7PBJgNfDEzL6+uKjUrIr5E8Ut9YNm0hGKY3jOq\nq0rNcvvrnCHdYuWIR0cDu1Bc6zcDuDYzF1VamJoWESMoto35VdeinilHGduBV66z7ew8p2rM7W9Z\nhrQkSTXlOekWK4e325fikPcyHfMy8+JKilK3yr4DX6Q4D9bZuluvirrUnIhYB/g4Xa+/nauoS81x\n++uaId1CEXE0cCXwAsXlIB0PUyRgSNfXN4Fdgcm8Mnaw+o7LgLcB/49Xxs1X3+H21wUPd7dQRPwV\n+B5wdma+WHU9al55P9uxmXl71bWo5yLiH8ARjvXcN7n9dc3rpFtrI+AbBnSfNJ/OB1NQ37AI+N+q\ni9BKc/vrgiHdWj8B9qy6CK2UzwCfj4ghVReilXI+cEpE+Dutb3L764KHu1soIj4EnA18m2Io0Oc7\nTs/MH1RRl7oXEX8GtgLWAB5h+XVnx6Mai4gbKDpsPkFxi8rG9feWKupSc9z+umbHsda6onz+dCfT\nkuI/oOrp+1UXoFWyEPhh1UVopbn9dcE9aUmSasrzN5Ik1ZSHu1dRRJwCXJaZz5avu+RgJvVSXvYx\nOjMXRsRTrODazHYeTKGuIuJPFGPiP16e01zR+mvbc5p15fbXHEN61Z0EXA08W77uioOZ1M9JwFPl\n649WWYhWyvXAc+Vrz2n2PW5/TfCctCRJNeU5aUmSasqQliSppgxpSZJqypCWJKmmDGkJiAhHg5NU\nO4b0ahIR60fEhh0fVdekFXoiIn4eEWdGxF6Gdt8TEW+OiBsjYmZEbF62fTAi/q3q2tS9iDgxIu6J\niEURMbpsOyMijqi6tioZ0i0UEVtGxE8j4lng78CC8rGwfFZ9vQ24AzgMmAr8s2NoV1qZuhURRwP/\nBTwAbA2sVU5aAzi9qrrUnIg4GTgLmAxEh0mzafNrqL1OuoUi4n+A9YELgTk0jKCTmb+qoi71TEQM\nAvYBjgbeCwzITPesaywi/gh8KTOvK0evem1mPhQRrwVuzsyNKi5RKxAR9wGnZuZNDetvR2BaZr6q\n4hIr44hjrfU64PWZeXfVhajnImIj4IDycRCwBfBb4JfVVaUmbQvc1kn700DbDinZh2wJdPZ783lg\nUC/XUiuGdGvNAtauugj1XETcQ3E/299THO4+AbgtM59bwcdUH3OA11Dci7ij/YC/9n456qGHgN1Y\nfv0dSnF/8LblOenW+jjwpYjYpupC1GPDgBeBRcAzFGMKL6m0IvXEZOCrEbFP+X7ziDgWOB/4WnVl\nqUkXApeUfQsC2CsizgG+CFxQaWUV85z0Kurk7i3rUHRWeQ54oeO87Xwnl76g/OPqgPKxPzAE+DXw\ny8z8SnWVqRkR8UXgExTbIBTb4IWZeXZ1ValZEfEhis5jm5dNs4HxmfnN6qqqniG9isq/1puSmVev\nzlrUGuXlV68DjseOY31KRAwGdqA4SjgzM5+uuCT1UEQMp9jm5lddSx0Y0hIQEXsAB5aPfSj6FvyB\n4vz0LzPz59VVJ6ldGdItFBFbdDEpgWcz02ulayoilgB3Ar+iCOZfZ+YzlRalFYqIHzc7b2a+ZXXW\nop6LiD/TcJlqVzJz59VcTm3Zu7u1HmYF/+ki4kngKuD0zHyhq/lUiQ0M5T7n71UXoFXy/aoL6Avc\nk26hiHg3RW/Sy4Hby+Y9Kc5tjqcY6OQs4LLMPKeKGrViEXEQxTnNpDin6TXSkipjSLdQREwFvpqZ\nP2hofzvw8czcPyKOBD6Xma+pokZ1LiJGAf9Nca3mnLJ5U2A68LbMnNPVZyVpdTGkWygiFgM7Z+YD\nDe2vAe7KzMERsRXFHtrgCkpUFyLieopQPiozZ5Vto4FrgDmZ+c4q69PyIuKXNH9O86DVXI56KCJm\n0fz6G72ay6ktz0m31iMUh7Y/2dD+IeDR8vUI4B+9WZSaMhY4YGlAA5RjB38MuKW6srQCHYeRXINi\nrPXHeOVU0+uATSj+0FL9XNLh9RDgFIoR/5YO77oXxTq8qJfrqhVDurVOBa6PiEMp7qiUwB7Aq4F3\nlPPsQXG3HvUNL1VdgDqXmSctfR0RXwGupjitlB3aJ7LsXZVUE5n5cvhGxLeAL2fmeR3niYgzgR17\nubRa8XB3i5WXYX0E+BeKXw73Apdn5qMr/KAqFRE/pDjKcWRm/m/ZtgXwHWBBZr69yvq0YhHxd2Cv\nzLy/of01wO8y0/u511h55ctumflgQ/s2wIx2Hq3RPekWK8P4zKrrUI99DPgR8FBELL3N6CjgTxRj\nsqveAtgJuL+hfacKalHPPUMxHO+DDe0HUIyn37YM6VUUEbtRdAp7qXzdpcyc0UtlqYfKvefdImIs\nsB3FL/2ZmfmLaitTk64EvhER2wK/K9teD5xOMTaB6u0rwKURMYZl19+xFJevti0Pd6+iiHgJ2Dgz\n55evk87PgaXjP/c9EbElcEFmHlF1LepaRAwATqM46rFJ2TwXmARclJkvVlWbmhMRR1Csv+3LpnuB\nSZnZ1n14DOlVVP4SfzQzs3zdpcxsvFeqai4iXktxTsw/sPqIiFgPIDOfrLoWaVV5uHsVdQxeQ1iq\nnuGs/sSQboEV3FhjGfbwllrHwTD6tg6nB7vVzkeyDOnWeJgV/2eLcnrb/keTVgMHw+jbjuCV35sb\nAZ8Hfsiy6+9woK3vc+A56RaIiN07vqW43eFRwN86zpeZd/ZmXepeE7c7XA/Yt53/ku8LysEw7u9q\nMIzMfG8lhakp5XZ4Q2Z+vaH9Q8DhmXlYNZVVz5BeDSLiKeC1mflQ1bVoxSKiqctzMvO41V2LVp6D\nYfRtEfE0sEsX6++PmbluNZVVz8PdamuGb7/hYBh920LgncCEhvZ3Agt6v5z6MKQl9QcOhtG3fRa4\nKiIO5JVz0q8H3gh8oLKqasDD3atBebh75453VJK0ejkYRt8WEXtSDM+7PeWIf8BXM/P2FX6wnzOk\nW6CTzkdvpug8tsxhtsx8S68VJUnq8zzc3Rp/b3jv/WslaQUiYsPM/MfS1yuad+l87cg9aUl9Utmj\ne3RmLixPMXX5y8ze3fUTES8CmzTc92C52Wjz+x64Jy2przoJeKp8/dEqC9FKOQhYuod8YJWF1Jl7\n0pIk1ZR70pL6lYhYBxjQsS0zvVa6DyjPTY9k+fU3s5qKqmdIS+rzytvEfpXisGlno1O17TnNviAi\ndgWuAnZa2kRxjrrt73tgSEvqD64B1qE4Tz2PJu+upNq4EphNcZ27668Dz0lL6vPKsZ/3yMx7q65F\nPdfV2N1qOO4vSX3UH4ERVRehlfYbXhkpTh24Jy2pz4uIHSnOSX8VuBt4vuP0zHy0irrUnIgYBXwD\n+Bmdr79pVdRVB56TltQfDKDoFfxDlj2f2fYdj/qIbYFdgEM6mdbW6889aUl9XkTMAP4JXEQnHY8y\n884q6lJzIuIvwB3Al+h8/TUOvdw2DGlJfV5ELKLoeHR/1bWo5yLiGYo7B/616lrqxo5jkvqD3wNb\nV12EVtoUYPeqi6gjz0lL6g++BkyMiIuAP7N8x6MZlVSlZv0MuCgidqbz9feDSqqqAQ93S+rzyrso\ndaWt76LUF7j+uuaetKT+wEPdfVhmeuq1C+5JS5JUU/71IklSTRnSkiTVlCEtSVJNGdKS+rSIGBAR\nO0REZ/eRVs1FxJoRcWhEvKrqWurIkJbU1yVwF7BJ1YWo5zLzBeAHwNCqa6kjQ1pSn5bFJSp/wVtV\n9mV/BLapuog6MqQl9QenAxdExC4REVUXox4bTzHi2OERsXlEbNjxUXVxVfI6aUl9XkQ8BaxDsePx\nAvBcx+mZuV4Vdak5DSOOLXerUUcck6S+7aNVF6BVcmDVBdSVe9KSJNWU56Ql9QsRsVFEnBYRX4uI\n4WXbPhHhuN59QETsFBGXRMRPI2KTsu3wiNi16tqqZEhL6vMiYneKHt5HAx8Alp6DHgt8saq61JyI\nOBi4AxgFHAQMKie9GjinqrrqwJCW1B9cCEzKzF1ZttPYz4F9qilJPXAucEpmvg1Y0qF9KvC6Siqq\nCUNaUn+wO3B1J+1zgY16uRb13I7ATzpp/wfQ1pdgGdKS+oPFwAadtG8HzO/lWtRzj1Mc6m60G/C3\nXq6lVgxpSf3Bj4BzImLt8n1GxFbAl4HrqypKTbuWYjCazSiuk14zIvanOI3x7Uorq5iXYEnq8yJi\nPYrDpTsD6wKPURzm/i1waGY+U2F56kZErAV8C3gPxQAmL5XP1wLjMvPF6qqrliEtqd+IiIMoDpEO\nAGZk5i8qLkk9EBGvBnalWH9/yMwHKi6pcoa0pD4vItZo572t/iQihgBk5tNV11IHnpOW1B88ERE/\nj4gzI+L1EdG2Yz33VRFxckQ8CjxBsT7/NyI+0e43THFPWlKfFxFjgf2BA4A9KK61vZXiOtupmXlb\nZcWpWxFxPnA8cAGwdF3tBZwGfD0zT6+qtqoZ0pL6lYgYRDGAydHAe4EB7XwXpb4gIv4BHJ+Z329o\nfydwRWa+qprKquddsCT1CxGxEcWe9AEUQ0tuQdG7+5fVVaUe+FMXbW19WtY9aUl9XkTcA2wF/J7i\nEPevgNsy87kVfEw1ERETKfLo4w3tXwHWyMyPVVNZ9dyTltQfDANeBBYBzwBPsewY0Kq3tYGjIuIQ\n4Hdl257ApsB3IuKrS2dst8B2T1pSvxAR2/DK4e79gSHAr4FfZuZXqqtM3YmIZk9JZGYetFqLqRlD\nWlK/Ul5+9TqK3sJ2HFOf5uFuSX1eROwBHFg+9qE4fPoH4CLsONZnlD3zX12+/WtmLq6ynjpwT1pS\nnxcRS4A7KTqMTQV+7XjdfUd5Y5QvAycAAynG7X4OmAx8KjOfrbC8SrknLak/2MBQ7tO+BhwMfJBl\nBzP5EjAUeH9FdVXOPWlJ/UZ5g40dKG53ODMzPdTdB0TEU8DbM3NKQ/tY4PrMXK+ayqrnnrSkPi8i\nRgH/TXEHrDll86YRMR14W2bO6fLDqoNngNmdtM8G2vq8dFuP5CKp3/gq8AKwTWZunpmbA9tSXDv9\n1RV+UnXwf4Fzyo5jwMudyM4up7UtD3dL6vMi4knggMyc0dA+BrglM4dVU5maERE3UFzb/gKvDA+6\nE8XR3l91nDcz39K71VXLw92S+rOXqi5ATVkIXN/QNquKQurGPWlJfV5E/BAYARyZmf9btm0BfAdY\nkJlvr7I+aWUZ0pL6vIjYHPgRxSHSORS9u0dRHDo9fGlwS32NIS2p3ygv2dmOYjCMmZn5i4pL0gqU\nl151FkJPAH8BLsjMn/duVfViSEvqtyJiS4pf9EdUXYuWFxHHdjFpfWB34N3AOzPzht6rql4MaUn9\nVkS8FpjhDTb6poj4BHBEZu5VdS1V8TppSVJd3URx+qJtGdKSpLpaB2jbm2uAIS1Jqq8PAndVXUSV\nHMxEUp8VET/uZpa2vTFDXxARXQ3ZOoxiHPbRwH69V1H9GNKS+rK/NzHdkavqa6cu2p8Efgp8LTPb\nev3Zu1uSpJrynLQkSTVlSEuSVFOGtCRJNWVIS5JUU4a0tJIi4lsRcWPVdUjqv+zdLa2kiBhGsQ39\ns+paJPVPhrSkloqIgZm5pOo6pP7Aw93SSup4uDsipkbEZRFxXkQsjIj5EXFhRAzoMP/AcvojEfFc\nRDwUER/rMH2/iLg9Ip6NiHkR8ZWIGNhh+tSI+FpEXBQR/4iIBRHx8YhYOyIujYh/RsSjEfG+hjpH\nRcR1EfF4+bgpIrbtwc95ZlnP0xHx7Yg4JyIebvx3iIhPRcTfgL+V7RtExNXlMhdHxC8iYscOnxsX\nEU83LOuAiMiIGN5xnoj4PxFxf/lv88uIGN1s/VJfZkhLrXM08AKwN/BR4GSK++EudTVwDHAKsD3w\nAeCfUAQpxQhLfwB2LacdCXypk2U8BewJTAAmAv8N3A+MKZfxjYjYtPzewcAvKW5SsD+wFzAX+EU5\nbYUi4j3AOcBnKIZpvLesv9H+wM7Am4B/K9u+Vdb5VuB1wCLgZxExqLvlNli7rOG4sv41gB9GRPTw\ne6S+JzN9+PCxEg+KELqxfD0VuK1h+hTgG+XrbYEE3tTFd30ReBAY0KFtHPAcMLizZQABLAB+3KFt\nLWAJ8M7y/fuBByhPbZVta1AMl3lEEz/jbcDlDW03Aw83/DssANbu0Lb0592vQ9sw4Anggx1+vqcb\nvvuA8nPDO8yTwD4d5tkSeBF4Y9X/B3z4WN0P96Sl1vlTw/s5wMjy9a7ASxR7tZ3ZniKAX+rQ9htg\nILBNZ8vIzATmA3/u0PY88HiH5e4ObA08VR42fpoiKDcAXt3Ez7Qd8PuGtts7me/uzHyu4ed5iSLk\nl9b2RFnrDk0st6OXOtaQmY9Q/Nv29HukPscbbEit83zD++SVU0rdHZqNcv7OdGzvbBkrWu4Ailv9\nvaeT7/1HNzV1tvyuPNPwfkU/79Lve6mT+dZqsiapLbgnLfWOGRTb24FdTJ8J7NWxoxnwBopD139d\nxeVuAyzMzAcbHs2E9H0U55M7anzfmZkUP+9eSxsiYj2Kux7NLJsWAIPL9qV26eS7BgB7dPieLYBN\nKc6PS/2aIS31gsx8APgvik5d74iIrSNi3w49sS+jCJ7LImL7iDiMomPYJZm5aBUW/R1gHvCjiNi/\nXO5+ZQ/xZnp4TwLGRcT7I2LbiDidojPYCveuy5/3R8AV5c+5E3ANxS0Iry1nu51iD/xLEbFNRLwD\nOLGTr3sBmBgRe0XELhSd4+4BftFE/VKfZkhLvecYioD6KsUe6rcoOlORmbOBN1Ocu74LuBL4LvDp\nVVlgGfD7AQ8B/69c7tUU56Qfb+Lz1wHnUvzB8AfgX4HLKXqLd+c4inPJPy6fB1N0nFtcfvc/KHqr\nj6U4V308cHYn3/McRce6b1ME+wDg7eU5ealfczATST0SET8E1szM/9MLyxpHcTRhyOpellRHdhyT\n1KXyWuqPAD+jOOz8Dorrnt9RZV1Su/Bwt9TGIuKepZdmdfI4muLc85uBaRSHu98NvC8zf1hl3VK7\n8HC31MYiYku6vuxpXmY+1Zv1SFqWIS1JUk15uFuSpJoypCVJqilDWpKkmjKkJUmqqf8PwC0kxqTe\nYSAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99f05cc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot side-by-side\n",
    "wdi_subset.groupby('income_group')[['life_exp', 'infant_mort']].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box plots\n",
    "\n",
    "**Usage**: show quartiles (and outliers) for numerical variables (also across categories)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    173.000000\n",
       "mean      71.532517\n",
       "std        8.020844\n",
       "min       51.377854\n",
       "25%       66.284049\n",
       "50%       73.301683\n",
       "75%       77.120268\n",
       "max       83.843659\n",
       "Name: life_exp, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Five-number summary (min, Q1, Q2 [median], Q3, max)\n",
    "wdi_subset['life_exp'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99efd68320>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SdyZ5dpJfAd4CfBygqgr4KLAuyT9P8jPA9cBDwKeOztiSJC19874Fq6ruTPIa4DeBdwN/\n3f15zdC2DwInMAj304GtwMurat/YJ5YkaUL0eZ80VbUF2HKI9QLWdzdJkjQGfna3JEmNMtKSJDXK\nSEuS1CgjLUlSo4y0JEmNMtKSJDXKSEuS1CgjLUlSo4y0JEmNMtKSJDXKSEuS1CgjLUlSo4y0JEmN\nMtKSJDXKSEuS1CgjLUlSo4y0JEmNMtKSJDXKSEuS1CgjLUlSo4y0JEmNMtKSJDXKSEuS1CgjLUlS\no4y0JEmNMtKSJDXKSEuS1CgjLUlSo4y0JEmNMtKSJDXKSEuS1CgjLUlSo4y0JEmNMtKSJDVq3kgn\nWZ+kRm67h9avn2P9jqM7tiRJS99xPfd9Gzhn6P6BkfVbgDcM3X/0CGaSJEn0j/SPqmr3IdYfmWdd\nkiQtUN/fSa9O8r0kO5JsSrJ6ZP2lSfYmuS/JdUmWj3tQSZImTZ9IbwUuBl4JXAqsAG5P8oxu/SvA\nhcAvAlcCLwK+muTJY59WkqQJMu/l7qq6cfh+96Kw7wAXAR+uqk1Dy3cn2Q7sBM4DvjDXcyZZC6wF\nWLly5eFNLknSErfgt2BV1UPAPcCag6w/AHz3YOvdno1VNV1V01NTUwsdQZKkibDgSCc5HjgN2HWQ\n9WXAKQdblyRJ/fR5n/TVSc5OcmqSFwN/DDwFuCHJU7v1lyRZleQcYDOwF/jiUZ1ckqQlrs9bsJ4J\nfBpYBswAdwBnVtXOJCcApzN44djTGJw93wq8tqr2HZ2RJUmaDH1eOPb6Q6ztB14x1okkSRLgZ3dL\nktQsIy1JUqOMtCRJjTLSkiQ1ykhLktQoIy1JUqOMtCRJjTLSkiQ1ykhLktQoIy1JUqOMtCRJjTLS\nkiQ1ykhLktQoIy1JUqOMtCRJjTLSkiQ1ykhLktQoIy1JUqOMtCRJjTLSkiQ1ykhLktQoIy1JUqOM\ntCRJjTLSkiQ1ykhLktQoIy1JUqOMtCRJjTpusQeQdHiSHPlzfODI56iqI38SSXMy0tIxyjhKS5+X\nuyVJapSRliSpUUZakqRGGWlJkhplpCVJapSRliSpUb0inWR9khq57R5aT7fngST7k3wtyfOO3tiS\nJC19CzmT/jZw8tDt9KG1q4ArgcuBM4C9wM1JThrTnJIkTZyFfJjJj6pq9+jBDD726ApgQ1V9vjt2\nEYNQnw9cO45BJUmaNAs5k16d5HtJdiTZlGR1d/xUYAVw0+zGqtoPfB04a3yjSpI0WfpGeitwMfBK\n4FIGUb49yTO6nwH2jDxmz9Daj0myNsm2JNtmZmYWPLQkSZOg1+Xuqrpx+H6SO4DvABcBd8xuG3lY\n5jg2+3wbgY0A09PTfgCxJElzOKy3YFXVQ8A9wBpg9vfUo2fNy3ns2bUkSerpsL4FK8nxwGnArcAO\nBqE+F7hzaP1lwL+d77m2b9/+YJKdhzOHpCOyDHhwsYeQJtCz+m7sFekkVwObgb9mcIb8buApwA1V\nVUk+CrwzybeA+4B3AQ8Bn5rvuatqqu+wksYnybaqml7sOSQdXN8z6WcCn2bwL+8ZBr+HPrOqZs+A\nPwicAHwceDqDF5q9vKr2jXdcSZImR/zieGkyeSYttc/P7pYm18bFHkDSoXkmLUlSozyTliSpUUZa\nakyS65P86ejP3f0nJLk2yf/uvo3unEUbVNJRd1jvk5b0uHkrg0/vm/Uq4I3AOQw+9e//LMJMkh4n\nRlpqWFV9f+TQs4FdVXX7Yswj6fHl5W6pYaOXvoGPACu7S933d8eT5Kok/zPJ/iR3J7lgAX/HKd03\n2/1Nd9uSZM3Qc9+c5Jbua2lJ8tQk/yPJ73b3z+nmeXWSu5I8nGR7kheO9/+GNHmMtHTseCvwHuC7\nwMnAGd3x9wGXAJcBzwV+C7g2yXnzPWGSExl8vO/DwNnAS4BdwC1JTqzB2z8uAp4P/Eb3sN8BHuWx\nH/t7NfB2YJrBpfgt3fNLOkxe7paOEVX1/ST7gANVtRsgyVOAtzH4hL//0m3dkeRFDKK9ZZ6nfT2D\n33m/sQsySX4V2Au8GvhsVT2Q5E3AZ5L8Q+BfAS/qvjd+2Hur6s+653gjg39MnA984oj+w6UJZqSl\nY9tzgeOBryQZ/tCDJwH393j8C4FTgX3d1exZJwL/ePZOVf3nJJ9i8Ln8V1XVX87xXP91aP9DSe7u\n5pN0mIy0dGyb/ZXVP2PwBTjDftjz8XcxOKMe9XevHO++2e4M4ACDF69JehwYaenYdi/wCPCsqvrq\nYTz+z4F/CTxYVf/3EPs+BDyZwVfS/lmSL1fVl0b2nMngd9Gzl+F/BvjkYcwkqeMLx6RjWPdNc1cD\nVyf510meneT5SX4tydoeT/FHwB7gS0nOTnJqkp9L8ttDr/D+JeBXgQuq6lZgPfCJJCtGnutdSc5N\n8jzgPzF4cdm8X1cr6eCMtHTsezeDcP4GcA9wM/AvgB3zPbCqfgD8HIMz4M8B3wJuYPCVs3+TZAq4\nHnhfVW3tHrah+3t+Pz/+i+x1wG8zODtfA7y6qv7fEf63SRPNL9iQdES6jya9FZiqqgcXeRxpSfFM\nWpKkRhlpaQlL8u+SPHSQ242LPZ+kQ/Nyt7SEJflJ4CcPsry/qr73eM4jaWGMtCRJjfJytyRJjTLS\nkiQ1ykhLktQoIy1JUqOMtCRJjfr/yBLjC0hkk1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99ee3e5780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare with box plot\n",
    "wdi_subset['life_exp'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99ee3b4940>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99efd41128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Include multiple variables\n",
    "wdi_subset[['life_exp', 'infant_mort']].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99ee330b70>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JgwIzMzMDHBSYmZlZ4qDAzMzMAAcFZmZmljgoMDMzM8BBgZmZmSUOCszMzAxw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\nhKzN3iYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99ee36a240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped box plots\n",
    "wdi_subset.boxplot('life_exp', by='income_group')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line plots\n",
    "\n",
    "**Usage**: show the trend of a numerical variable over time (don't use when there's no logical ordering!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_life_exp = wdi[(wdi['country_code'] == 'GBR') & (wdi['indicator_code'] == 'SP.DYN.LE00.IN')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_code</th>\n",
       "      <th>indicator_code</th>\n",
       "      <th>year</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2157818</th>\n",
       "      <td>GBR</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>71.126829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157819</th>\n",
       "      <td>GBR</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1961</td>\n",
       "      <td>70.878049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157820</th>\n",
       "      <td>GBR</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1962</td>\n",
       "      <td>70.926829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157821</th>\n",
       "      <td>GBR</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1963</td>\n",
       "      <td>70.826829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2157822</th>\n",
       "      <td>GBR</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1964</td>\n",
       "      <td>71.624390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country_code  indicator_code  year      value\n",
       "2157818          GBR  SP.DYN.LE00.IN  1960  71.126829\n",
       "2157819          GBR  SP.DYN.LE00.IN  1961  70.878049\n",
       "2157820          GBR  SP.DYN.LE00.IN  1962  70.926829\n",
       "2157821          GBR  SP.DYN.LE00.IN  1963  70.826829\n",
       "2157822          GBR  SP.DYN.LE00.IN  1964  71.624390"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uk_life_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99ee25cf98>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jh4v5fMkmHji3B+f0bul1SSJhSwePRKRSnv7fz7w1cz03DO3I6GPbe12OSFhT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\nZZa5y7/MBUAvYCK+cE0ItA3wnRWf5X9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99ee373550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uk_life_exp.plot.line(x='year', y='value')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}