04-ws_breakout_session2_solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to Python for Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Break out session 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this breakout session, you will explore some data from the Guardian(theguardian.com) that provides some\n",
    "data about key indicators by country from the World Economic Forum in 2013. They also have an open data explorer at http://open-platform.theguardian.com."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the wef csv data and look at the first five rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Emissions (tonnes per capita)</th>\n",
       "      <th>Unemployment rate (%)</th>\n",
       "      <th>GDP growth (%)</th>\n",
       "      <th>Government debt (% of GDP)</th>\n",
       "      <th>Gini coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>18.23</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.91</td>\n",
       "      <td>29.32</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>7.45</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.70</td>\n",
       "      <td>74.04</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>9.60</td>\n",
       "      <td>8.3</td>\n",
       "      <td>1.78</td>\n",
       "      <td>91.40</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.90</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>52.21</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>15.24</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.46</td>\n",
       "      <td>52.63</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country  Emissions (tonnes per capita)  Unemployment rate (%)  \\\n",
       "0  Australia                          18.23                    5.2   \n",
       "1    Austria                           7.45                    4.4   \n",
       "2    Belgium                           9.60                    8.3   \n",
       "3     Brazil                           1.90                    8.3   \n",
       "4     Canada                          15.24                    8.0   \n",
       "\n",
       "   GDP growth (%)  Government debt (% of GDP)  Gini coefficient  \n",
       "0            1.91                       29.32              0.34  \n",
       "1            2.70                       74.04              0.26  \n",
       "2            1.78                       91.40              0.26  \n",
       "3            2.73                       52.21              0.55  \n",
       "4            2.46                       52.63              0.32  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef = pd.read_csv('wef.csv')\n",
    "wef.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Emissions (tonnes per capita)', 'Unemployment rate (%)',\n",
       "       'GDP growth (%)', 'Government debt (% of GDP)', 'Gini coefficient'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many rows are in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(wef)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the column names lower case using a lambda function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>emissions (tonnes per capita)</th>\n",
       "      <th>unemployment rate (%)</th>\n",
       "      <th>gdp growth (%)</th>\n",
       "      <th>government debt (% of gdp)</th>\n",
       "      <th>gini coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>18.23</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.91</td>\n",
       "      <td>29.32</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>7.45</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.70</td>\n",
       "      <td>74.04</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  emissions (tonnes per capita)  unemployment rate (%)  \\\n",
       "0  Australia                          18.23                    5.2   \n",
       "1    Austria                           7.45                    4.4   \n",
       "\n",
       "   gdp growth (%)  government debt (% of gdp)  gini coefficient  \n",
       "0            1.91                       29.32              0.34  \n",
       "1            2.70                       74.04              0.26  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef.columns = wef.columns.map(lambda x: x.lower())\n",
    "wef.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the cut method to assign unemployment rate to four buckets and then obtain the value counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.58, 8.65]     27\n",
       "(8.65, 13.7]      8\n",
       "(18.75, 23.8]     2\n",
       "(13.7, 18.75]     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employrateranges = pd.cut(wef['unemployment rate (%)'], 4)\n",
    "pd.value_counts(employrateranges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column to the dataframe called employrateranges(created above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>emissions (tonnes per capita)</th>\n",
       "      <th>unemployment rate (%)</th>\n",
       "      <th>gdp growth (%)</th>\n",
       "      <th>government debt (% of gdp)</th>\n",
       "      <th>gini coefficient</th>\n",
       "      <th>employrateranges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>18.23</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.91</td>\n",
       "      <td>29.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>(3.58, 8.65]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>7.45</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.70</td>\n",
       "      <td>74.04</td>\n",
       "      <td>0.26</td>\n",
       "      <td>(3.58, 8.65]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  emissions (tonnes per capita)  unemployment rate (%)  \\\n",
       "0  Australia                          18.23                    5.2   \n",
       "1    Austria                           7.45                    4.4   \n",
       "\n",
       "   gdp growth (%)  government debt (% of gdp)  gini coefficient  \\\n",
       "0            1.91                       29.32              0.34   \n",
       "1            2.70                       74.04              0.26   \n",
       "\n",
       "  employrateranges  \n",
       "0     (3.58, 8.65]  \n",
       "1     (3.58, 8.65]  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef['employrateranges'] = employrateranges\n",
    "wef.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Index into the first three columns of the first row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          Australia\n",
       "emissions (tonnes per capita)        18.23\n",
       "unemployment rate (%)                  5.2\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef.iloc[0,0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Slice into the first five rows of unemployment rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5.2\n",
       "1    4.4\n",
       "2    8.3\n",
       "3    8.3\n",
       "4    8.0\n",
       "Name: unemployment rate (%), dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef['unemployment rate (%)'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort unemployment rate and return the first ten values in ascending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26    3.6\n",
       "21    3.7\n",
       "35    4.2\n",
       "6     4.3\n",
       "1     4.4\n",
       "22    4.4\n",
       "16    4.4\n",
       "24    4.5\n",
       "20    5.0\n",
       "0     5.2\n",
       "Name: unemployment rate (%), dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef['unemployment rate (%)'].sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return value counts for the gini coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26    6\n",
       "0.29    4\n",
       "0.32    4\n",
       "0.34    3\n",
       "0.33    3\n",
       "0.30    3\n",
       "0.25    2\n",
       "0.31    2\n",
       "0.37    1\n",
       "0.35    1\n",
       "0.40    1\n",
       "0.24    1\n",
       "0.48    1\n",
       "0.27    1\n",
       "0.63    1\n",
       "0.47    1\n",
       "0.49    1\n",
       "0.55    1\n",
       "0.38    1\n",
       "0.41    1\n",
       "Name: gini coefficient, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef['gini coefficient'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Return a boolean value indicating if all values for unemployment rate are above 5.2 percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(wef.ix[0:9,'unemployment rate (%)'] > 5.2).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return a dataframe where unemployment rate is greater than 10 and the gini coefficient is greater then .2. Only display these two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unemployment rate (%)</th>\n",
       "      <th>gini coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.9</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.5</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.5</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.8</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14.4</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>23.8</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>20.1</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>11.9</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    unemployment rate (%)  gini coefficient\n",
       "9                    16.9              0.32\n",
       "13                   12.5              0.31\n",
       "14                   11.2              0.27\n",
       "17                   13.5              0.29\n",
       "28                   10.8              0.35\n",
       "30                   14.4              0.26\n",
       "32                   23.8              0.63\n",
       "33                   20.1              0.32\n",
       "36                   11.9              0.41"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef[(wef['unemployment rate (%)'] > 10) & (wef['gini coefficient'] > .20)][['unemployment rate (%)', 'gini coefficient']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Return the first ten rows of a dataframe where unemployment rate is greater than 10 or the gini coefficient is greater then .2. Return a dataframe\n",
    "with country,unemployment rate, and gini coefficient columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>unemployment rate (%)</th>\n",
       "      <th>gini coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>8.3</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chile</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>China</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>16.9</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  unemployment rate (%)  gini coefficient\n",
       "0       Australia                    5.2              0.34\n",
       "1         Austria                    4.4              0.26\n",
       "2         Belgium                    8.3              0.26\n",
       "3          Brazil                    8.3              0.55\n",
       "4          Canada                    8.0              0.32\n",
       "5           Chile                    8.1              0.49\n",
       "6           China                    4.3              0.47\n",
       "7  Czech Republic                    7.3              0.26\n",
       "8         Denmark                    7.4              0.25\n",
       "9         Estonia                   16.9              0.32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef[(wef['unemployment rate (%)'] > 10) | (wef['gini coefficient'] > .20)][['country','unemployment rate (%)', 'gini coefficient']][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are any of the values for gdp growth null?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef['gdp growth (%)'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Obtain a summary of descriptive statistics for the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emissions (tonnes per capita)</th>\n",
       "      <th>unemployment rate (%)</th>\n",
       "      <th>gdp growth (%)</th>\n",
       "      <th>government debt (% of gdp)</th>\n",
       "      <th>gini coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.401026</td>\n",
       "      <td>8.602564</td>\n",
       "      <td>2.510000</td>\n",
       "      <td>63.842258</td>\n",
       "      <td>0.335128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.082954</td>\n",
       "      <td>4.417459</td>\n",
       "      <td>2.896838</td>\n",
       "      <td>37.120178</td>\n",
       "      <td>0.086659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.640000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>-7.100000</td>\n",
       "      <td>9.140000</td>\n",
       "      <td>0.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.690000</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.045000</td>\n",
       "      <td>41.580000</td>\n",
       "      <td>0.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.830000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>1.930000</td>\n",
       "      <td>52.210000</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.075000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>85.540000</td>\n",
       "      <td>0.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.380000</td>\n",
       "      <td>23.800000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>174.980000</td>\n",
       "      <td>0.630000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       emissions (tonnes per capita)  unemployment rate (%)  gdp growth (%)  \\\n",
       "count                      39.000000              39.000000       39.000000   \n",
       "mean                        8.401026               8.602564        2.510000   \n",
       "std                         4.082954               4.417459        2.896838   \n",
       "min                         1.640000               3.600000       -7.100000   \n",
       "25%                         5.690000               5.250000        1.045000   \n",
       "50%                         7.830000               7.800000        1.930000   \n",
       "75%                        10.075000               9.600000        3.755000   \n",
       "max                        20.380000              23.800000        9.300000   \n",
       "\n",
       "       government debt (% of gdp)  gini coefficient  \n",
       "count                   31.000000         39.000000  \n",
       "mean                    63.842258          0.335128  \n",
       "std                     37.120178          0.086659  \n",
       "min                      9.140000          0.240000  \n",
       "25%                     41.580000          0.280000  \n",
       "50%                     52.210000          0.320000  \n",
       "75%                     85.540000          0.345000  \n",
       "max                    174.980000          0.630000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wef.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display a bar plot of mean gdp growth by country with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd568c962e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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atAAAfPTRR5gxYwYqVqyo+JrMzCy4uDgjKysLUVFRNr/38vKy+cKVysqV11I2P9b9rO14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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5808352e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "wef.groupby(['country'])['gdp growth (%)'].mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}