Analyse Open Data Sets Using Pandas in a Python notebook.ipynb

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    "# Analyse Open Data Sets Using Pandas in a Python Notebook\n",
    "\n",
    "This notebook accompanies a tutorial on [Analyzing Open Data Sets with Pandas](https://wp.me/p6nwVO-280). It shows how to load open data sets, which are freely available on IBM's Analytics Exchange. Then combine and run analysis on them using Pandas in a python notebook. \n",
    "\n",
    "To understand how to get the data you want and run these commands in your own notebook, [follow the full tutorial](https://wp.me/p6nwVO-280). \n",
    "\n",
    "## Load Data into a DataFrame\n",
    "\n",
    "Paste the _URL link/access key_ you copied from the Life Expectancy data set into the following code (replacing the **\\<LINK-TO-DATA\\>** string). Then run the following code to load the data in a dataframe. This code keeps 3 columns and renames them.  "
   ]
  },
  {
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    {
     "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>year</th>\n",
       "      <th>life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>60.509122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>60.065366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>59.600098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2009</td>\n",
       "      <td>59.112341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2008</td>\n",
       "      <td>58.607098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year       life\n",
       "0  Afghanistan  2012  60.509122\n",
       "1  Afghanistan  2011  60.065366\n",
       "2  Afghanistan  2010  59.600098\n",
       "3  Afghanistan  2009  59.112341\n",
       "4  Afghanistan  2008  58.607098"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# life expectancy at birth in years\n",
    "life = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area','Year','Value'])\n",
    "life.columns = ['country','year','life']\n",
    "life[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Life expectancy figures might be more meaningful if we combine them with other data sets from the Analytics Exchange. Let's start by loading data set [Total Population](https://console.ng.bluemix.net/data/exchange/public/entry/view/889ca053a19986a4445839358a91963e) by country and year. To do so, enter and run the following code after you have copied your link to the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nr of countries in life: 246\n",
      "Nr of countries in population: 277\n"
     ]
    }
   ],
   "source": [
    "# population\n",
    "population = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area', 'Year','Value'])\n",
    "population.columns = ['country', 'year','population']\n",
    "\n",
    "print \"Nr of countries in life:\", np.size(np.unique(life['country']))\n",
    "print \"Nr of countries in population:\", np.size(np.unique(population['country']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining DataFrames\n",
    "\n",
    "These two data sets don't fit together perfectly. For instance, one lists more countries than the other. When we join the two dataframes we're sure to introduce nulls or NaNs into the new dataframe. We'll use the pandas `merge` function to handle this problem. This function includes [many options](http://pandas.pydata.org/pandas-docs/stable/merging.html). In the following code, `how='outer'` makes sure we keep all data from `life` and `population`. `on=['country','year']` specifies which columns to perform the merge on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
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   "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>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1998</td>\n",
       "      <td>72.973780</td>\n",
       "      <td>74206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1999</td>\n",
       "      <td>73.186024</td>\n",
       "      <td>76041.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2000</td>\n",
       "      <td>73.397293</td>\n",
       "      <td>77648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2001</td>\n",
       "      <td>73.606073</td>\n",
       "      <td>78972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2002</td>\n",
       "      <td>73.813390</td>\n",
       "      <td>80030.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  year       life  population\n",
       "400  Antigua and Barbuda  1998  72.973780     74206.0\n",
       "401  Antigua and Barbuda  1999  73.186024     76041.0\n",
       "402  Antigua and Barbuda  2000  73.397293     77648.0\n",
       "403  Antigua and Barbuda  2001  73.606073     78972.0\n",
       "404  Antigua and Barbuda  2002  73.813390     80030.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(life, population, how='outer', sort=True, on=['country','year'])\n",
    "df[400:405]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add more data to the dataframe in a similar way. For each data set, look up the link/keys for the data on Analytics Exchange and copy these into the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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     "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>year</th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>Interpolated using the Mid-year population for...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>Interpolated between 2000 and 2005 assuming co...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>Calculated from end of year population of 2011...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>Calculated from end of year population of 2010...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>Excluding Agalega and St. Brandon. Extrapolate...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>Excluding Agalega and St. Brandon.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>Adjusted to mid-year population by geometric m...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>Calculated backwards from 1995 using UN WPP201...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>Extrapolated assuming the same growth rate as ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>Extrapolated assuming the same growth rate as ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Derived using linearly extrapolated data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>National estimate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>Preliminary.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20</td>\n",
       "      <td>Estimated using geometric mean of January 1st ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21</td>\n",
       "      <td>Estimated using geometric mean of January 1st ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22</td>\n",
       "      <td>Interpolated between 2007 and 2009 using geome...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23</td>\n",
       "      <td>There is a discontinuity in 1991 caused by Sta...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>Data on Kosovo are excluded. Extrapolated assu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>25</td>\n",
       "      <td>Data on Kosovo are excluded.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26</td>\n",
       "      <td>Data on Kosovo are excluded. Break in series.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27</td>\n",
       "      <td>Break in series: revision of estimates from 20...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28</td>\n",
       "      <td>Break in series on Jan 1, 2008: average pop in...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29</td>\n",
       "      <td>Preliminary. Break in series.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
       "      <td>Derived using linearly interpolated data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>Extrapolated assuming the same growth rate as ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3</td>\n",
       "      <td>UNESCO Institute for Statistics (UIS) estimate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>30</td>\n",
       "      <td>Interpolated between 2004 and 2010 assuming co...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>31</td>\n",
       "      <td>Interpolated between 1997-2007.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>32</td>\n",
       "      <td>Interpolated between 1990-1997.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>Derived using extrapolated data assuming the s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>Interpolated using geometric mean between begi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>5</td>\n",
       "      <td>Break in series.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>5</td>\n",
       "      <td>Derived using interpolated data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6</td>\n",
       "      <td>Derived using extrapolated data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>6</td>\n",
       "      <td>Extrapolated assuming the same growth rate as ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>7</td>\n",
       "      <td>Derived using linearly extrapolated data assum...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7</td>\n",
       "      <td>Extrapolated assuming the same growth rate dur...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>8</td>\n",
       "      <td>Adjusted for Syrian refugees.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8</td>\n",
       "      <td>Derived using preliminary data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>9</td>\n",
       "      <td>Geometric mean of official estimates of end-ye...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country                                               year  life  \\\n",
       "0            10  Interpolated using the Mid-year population for...   NaN   \n",
       "1            11  Interpolated between 2000 and 2005 assuming co...   NaN   \n",
       "2            12  Calculated from end of year population of 2011...   NaN   \n",
       "3            13  Calculated from end of year population of 2010...   NaN   \n",
       "4            14  Excluding Agalega and St. Brandon. Extrapolate...   NaN   \n",
       "5            15                 Excluding Agalega and St. Brandon.   NaN   \n",
       "6            16  Adjusted to mid-year population by geometric m...   NaN   \n",
       "7            17  Calculated backwards from 1995 using UN WPP201...   NaN   \n",
       "8            18  Extrapolated assuming the same growth rate as ...   NaN   \n",
       "9            19  Extrapolated assuming the same growth rate as ...   NaN   \n",
       "10            2           Derived using linearly extrapolated data   NaN   \n",
       "11            2                                  National estimate   NaN   \n",
       "12            2                                       Preliminary.   NaN   \n",
       "13           20  Estimated using geometric mean of January 1st ...   NaN   \n",
       "14           21  Estimated using geometric mean of January 1st ...   NaN   \n",
       "15           22  Interpolated between 2007 and 2009 using geome...   NaN   \n",
       "16           23  There is a discontinuity in 1991 caused by Sta...   NaN   \n",
       "17           24  Data on Kosovo are excluded. Extrapolated assu...   NaN   \n",
       "18           25                       Data on Kosovo are excluded.   NaN   \n",
       "19           26      Data on Kosovo are excluded. Break in series.   NaN   \n",
       "20           27  Break in series: revision of estimates from 20...   NaN   \n",
       "21           28  Break in series on Jan 1, 2008: average pop in...   NaN   \n",
       "22           29                      Preliminary. Break in series.   NaN   \n",
       "23            3           Derived using linearly interpolated data   NaN   \n",
       "24            3  Extrapolated assuming the same growth rate as ...   NaN   \n",
       "25            3     UNESCO Institute for Statistics (UIS) estimate   NaN   \n",
       "26           30  Interpolated between 2004 and 2010 assuming co...   NaN   \n",
       "27           31                    Interpolated between 1997-2007.   NaN   \n",
       "28           32                    Interpolated between 1990-1997.   NaN   \n",
       "29            4  Derived using extrapolated data assuming the s...   NaN   \n",
       "30            4  Interpolated using geometric mean between begi...   NaN   \n",
       "31            5                                   Break in series.   NaN   \n",
       "32            5                    Derived using interpolated data   NaN   \n",
       "33            6                    Derived using extrapolated data   NaN   \n",
       "34            6  Extrapolated assuming the same growth rate as ...   NaN   \n",
       "35            7  Derived using linearly extrapolated data assum...   NaN   \n",
       "36            7  Extrapolated assuming the same growth rate dur...   NaN   \n",
       "37            8                      Adjusted for Syrian refugees.   NaN   \n",
       "38            8                     Derived using preliminary data   NaN   \n",
       "39            9  Geometric mean of official estimates of end-ye...   NaN   \n",
       "40  Afghanistan                                               1980   NaN   \n",
       "41  Afghanistan                                               1982   NaN   \n",
       "42  Afghanistan                                               1983   NaN   \n",
       "43  Afghanistan                                               1984   NaN   \n",
       "44  Afghanistan                                               1985   NaN   \n",
       "45  Afghanistan                                               1986   NaN   \n",
       "46  Afghanistan                                               1987   NaN   \n",
       "47  Afghanistan                                               1988   NaN   \n",
       "48  Afghanistan                                               1989   NaN   \n",
       "49  Afghanistan                                               1990   NaN   \n",
       "\n",
       "    population  poverty  school  employment  births  measles  \n",
       "0          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "1          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "2          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "3          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "4          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "5          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "6          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "7          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "8          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "9          NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "10         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "11         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "12         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "13         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "14         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "15         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "16         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "17         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "18         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "19         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "20         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "21         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "22         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "23         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "24         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "25         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "26         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "27         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "28         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "29         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "30         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "31         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "32         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "33         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "34         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "35         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "36         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "37         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "38         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "39         NaN      NaN     NaN         NaN     NaN      NaN  \n",
       "40         NaN      NaN     NaN         NaN     NaN     11.0  \n",
       "41         NaN      NaN     NaN         NaN     NaN      8.0  \n",
       "42         NaN      NaN     NaN         NaN     NaN      9.0  \n",
       "43         NaN      NaN     NaN         NaN     NaN     14.0  \n",
       "44         NaN      NaN     NaN         NaN     NaN     14.0  \n",
       "45         NaN      NaN     NaN         NaN     NaN     14.0  \n",
       "46         NaN      NaN     NaN         NaN     NaN     31.0  \n",
       "47         NaN      NaN     NaN         NaN     NaN     34.0  \n",
       "48         NaN      NaN     NaN         NaN     NaN     22.0  \n",
       "49         NaN      NaN     NaN         NaN     NaN     20.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# poverty (%)\n",
    "poverty = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area', 'Year','Value'])\n",
    "poverty.columns = ['country', 'year','poverty']\n",
    "df = pd.merge(df, poverty, how='outer', sort=True, on=['country','year'])\n",
    "\n",
    "# school completion (%)\n",
    "school = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area', 'Year','Value'])\n",
    "school.columns = ['country', 'year','school']\n",
    "df = pd.merge(df, school, how='outer', sort=True, on=['country','year'])\n",
    "\n",
    "# employment\n",
    "employmentin = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area', 'Year','Value','Sex','Subclassification'])\n",
    "employment = employmentin.loc[(employmentin.Sex=='Total men and women')&\n",
    "                              (employmentin.Subclassification=='Total.')]\n",
    "employment = employment.drop('Sex', 1)\n",
    "employment = employment.drop('Subclassification', 1)\n",
    "employment.columns = ['country', 'year','employment']\n",
    "df = pd.merge(df, employment, how='outer', sort=True, on=['country','year'])\n",
    "\n",
    "# births attended by skilled staff (%)\n",
    "births = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area', 'Year','Value'])\n",
    "births.columns = ['country', 'year','births']\n",
    "df = pd.merge(df, births, how='outer', sort=True, on=['country','year'])\n",
    "\n",
    "# measles immunization (%)\n",
    "measles = pd.read_csv(\"<LINK-TO-DATA>\",usecols=['Country or Area', 'Year','Value'])\n",
    "measles.columns = ['country', 'year','measles']\n",
    "df = pd.merge(df, measles, how='outer', sort=True, on=['country','year'])\n",
    "\n",
    "df[0:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting table looks kind of strange, as it contains incorrect values, like numbers in the `country` column and text in the `year` column. You can manually remove these errors from the dataframe. Also, we can now create a multiindex with country and year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Afghanistan</th>\n",
       "      <th>1980</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  life  population  poverty  school  employment  births  \\\n",
       "country     year                                                          \n",
       "Afghanistan 1980   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1982   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1983   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1984   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1985   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1986   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1987   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1988   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1989   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "            1990   NaN         NaN      NaN     NaN         NaN     NaN   \n",
       "\n",
       "                  measles  \n",
       "country     year           \n",
       "Afghanistan 1980     11.0  \n",
       "            1982      8.0  \n",
       "            1983      9.0  \n",
       "            1984     14.0  \n",
       "            1985     14.0  \n",
       "            1986     14.0  \n",
       "            1987     31.0  \n",
       "            1988     34.0  \n",
       "            1989     22.0  \n",
       "            1990     20.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.drop(df.index[0:40])\n",
    "\n",
    "df2 = df2.set_index(['country','year'])\n",
    "\n",
    "df2[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are curious about other variables, you can keep adding data sets from Analytics Exchange to this dataframe. Be aware that not all data is equally formatted and might need some clean-up before you add it. Use the code samples you just read about, and make sure you keep checking results with a quick look at each of your tables when you load or change them with commands like `df2[0:10]`. \n",
    "\n",
    "## Check the Data \n",
    "You can run a first check of the data with [`describe()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html), which calculates some basic statistics for each of the columns in the dataframe. It gives you the number of values (count), the [mean](https://en.wikipedia.org/wiki/Mean), the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) (std), the min and max, and some [percentiles](https://en.wikipedia.org/wiki/Percentile).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>life</th>\n",
       "      <th>population</th>\n",
       "      <th>poverty</th>\n",
       "      <th>school</th>\n",
       "      <th>employment</th>\n",
       "      <th>births</th>\n",
       "      <th>measles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11969.000000</td>\n",
       "      <td>1.309100e+04</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>5078.000000</td>\n",
       "      <td>2909.000000</td>\n",
       "      <td>1523.000000</td>\n",
       "      <td>6944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>63.156417</td>\n",
       "      <td>1.409922e+08</td>\n",
       "      <td>30.763209</td>\n",
       "      <td>78.018509</td>\n",
       "      <td>14337.147966</td>\n",
       "      <td>83.944882</td>\n",
       "      <td>76.452661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.290103</td>\n",
       "      <td>5.450133e+08</td>\n",
       "      <td>17.349350</td>\n",
       "      <td>25.675860</td>\n",
       "      <td>57236.797036</td>\n",
       "      <td>23.885349</td>\n",
       "      <td>22.153693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.504927</td>\n",
       "      <td>4.279000e+03</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>1.522030</td>\n",
       "      <td>0.663000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54.884268</td>\n",
       "      <td>8.189045e+05</td>\n",
       "      <td>17.245109</td>\n",
       "      <td>60.831905</td>\n",
       "      <td>954.300000</td>\n",
       "      <td>73.650000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.171191</td>\n",
       "      <td>5.366554e+06</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>88.120480</td>\n",
       "      <td>3256.500000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>71.691415</td>\n",
       "      <td>2.574550e+07</td>\n",
       "      <td>43.700000</td>\n",
       "      <td>97.417360</td>\n",
       "      <td>9463.000000</td>\n",
       "      <td>99.700000</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>83.480488</td>\n",
       "      <td>7.124544e+09</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>193.263340</td>\n",
       "      <td>737400.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               life    population     poverty       school     employment  \\\n",
       "count  11969.000000  1.309100e+04  651.000000  5078.000000    2909.000000   \n",
       "mean      63.156417  1.409922e+08   30.763209    78.018509   14337.147966   \n",
       "std       11.290103  5.450133e+08   17.349350    25.675860   57236.797036   \n",
       "min       19.504927  4.279000e+03    1.700000     1.522030       0.663000   \n",
       "25%       54.884268  8.189045e+05   17.245109    60.831905     954.300000   \n",
       "50%       66.171191  5.366554e+06   26.900000    88.120480    3256.500000   \n",
       "75%       71.691415  2.574550e+07   43.700000    97.417360    9463.000000   \n",
       "max       83.480488  7.124544e+09   96.000000   193.263340  737400.000000   \n",
       "\n",
       "            births      measles  \n",
       "count  1523.000000  6944.000000  \n",
       "mean     83.944882    76.452661  \n",
       "std      23.885349    22.153693  \n",
       "min       5.000000     1.000000  \n",
       "25%      73.650000    65.000000  \n",
       "50%      98.000000    84.000000  \n",
       "75%      99.700000    94.000000  \n",
       "max     100.000000    99.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "At this point, we have enough sample data to work with. Let's start by finding the correlation between different variables. First we'll create a scatter plot, and relate the values for two variables of each row. In our code, we also customize the look by defining the font and figure size and colours of the points with matplotlib. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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DEEsTCAQCLUaw1AQCji9/+Su88MJPgC8QZTddjc2EGkSDg7dt+11uvfWW5k00EAgEAom0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1139949d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['font.size']=11\n",
    "plt.rcParams['figure.figsize']=[8.0, 3.5]\n",
    "fig, axes=plt.subplots(nrows=1, ncols=2)\n",
    "df2.plot(kind='scatter', x='life', y='population', ax=axes[0], color='Blue');\n",
    "df2.plot(kind='scatter', x='life', y='school', ax=axes[1], color='Red');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure on the left shows that increased life expectancy leads to higher population. The figure on the right shows that the life expectancy increases with the percentage of school completion. But the percentage ranges from 0 to 200, which is odd for a percentage. You can remove the outliers by keeping the values within a specified range `df2[df2.school>100]=float('NaN')`. Even better, would be to check where these values in the original data came from. In some cases, a range like this could indicate an error in your code somewhere. In this case, the values are correct, see the [description](http://data.un.org/_Docs/WDIseries_2016_01_21.pdf) of the school completion data.\n",
    "  \n",
    "We don't have data for all the exact same years. So we'll group by country (be aware that we lose some information by doing so). Also because variables are percentages, we'll convert our employment figures to percent. Probably, we no longer need the `population` column, so let's drop it. Then we create scatter plots from the dataframe using `scatter_matrix`, which creates plots for all variables and also adds a histogram for each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11997cf90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119806850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11970d890>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1196eda90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119c24a10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119c8a6d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x119d0a650>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119d8f4d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119dfb6d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119e7f550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x119ee4490>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a067310>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a0c3fd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a158110>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a1cdf50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a23d9d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a2c0850>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a2555d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a38cb90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a511a10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a580550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a6033d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a667410>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a6ea390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11a8223d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a8dc150>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a950f90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11a9c1290>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11ab44110>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11ab9fdd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11ac29ed0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11acacd50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11af1c7d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11af9f650>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b803690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11b885610>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A7N37AdnZhQjCZUSxP4WFp9Fo5nLlynYaGy/i738TAwf6MWfOIyxYkIharWbP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InXMXVqma4tdCvzxtqBlT7R6YxeWxF+bMBunDhxkDPPPI+vfGUoU6dO6LYfyY4Rfs9mjdXQOVIV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mznbRDQ01VFRYkdLCyZN/YNSokbz55kbeeOMDIIZx44Y1PWsJgwf3pLr6lVZOrB2R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uTTKJhFpVIxbFgfQkN/JiDgCp061dOnjw8lJakMGxZEaemqNn8Ys0YurJUd+TAeGxtB9+4l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\n7SYMwnAvaXleOcJsfxs3wvXXw29+A//5n3DuuXaQSpT4V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119974190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "\n",
    "# group by country\n",
    "grouped = df2.groupby(level=0)\n",
    "dfgroup = grouped.mean()\n",
    "\n",
    "# employment in % of total population\n",
    "dfgroup['employment']=(dfgroup['employment']*1000.)/dfgroup['population']*100\n",
    "dfgroup=dfgroup.drop('population',1)\n",
    "\n",
    "scatter_matrix(dfgroup,figsize=(12, 12), diagonal='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You can see that the data is now in a pretty good state. There are no large outliers. We can even start to see some relationships: life expectancy increases with schooling, employment, safe births, and measles vaccination. You are deriving insights from the data and can now build a statistical model--for instance, have a look at an _ordinary least squares_ regression ([OLS](http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/ols.html)) from [StatsModels](http://statsmodels.sourceforge.net). \n",
    "\n",
    "## Summary\n",
    "In this tutorial, you learned how to use open data from Analytics Exchange in a Python notebook. You saw how to load, clean and explore data using pandas. As you can see from this example, data analysis entails lots of trial and error. This experimentation can be challenging, but is also a lot of fun!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}