Analyze open data sets with pandas DataFrames (DSX).ipynb

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    "# Analyze open data sets with pandas DataFrames\n",
    "\n",
    "In this notebook, you'll learn how to use open data from the data sets on the Data Science Experience home page in a Python notebook. You will load, clean, and explore the data with pandas DataFrames. \n",
    "\n",
    "Some familiarity with Python is recommended. This notebook runs on Python 2 with Spark 2.0.\n",
    "\n",
    "The data sets for this notebook are from the World Development Indicators (WDI) data set. The WDI data set is a statistical benchmark that helps measure the progress of human development. The WDI data set draws on data from the World Bank and more than 30 partners. You'll look at seven key development variables, sorted by country and year.\n",
    "\n",
    "## Learning goals\n",
    "\n",
    "1. Access data from the Data Science Experience home page.\n",
    "1. Merge multiple data sets in pandas DataFrames.\n",
    "1. Structure data for analysis.\n",
    "1. Generate summary statistics.\n",
    "1. Create charts with custom formatting.\n",
    "1. Show relationships between all variables."
   ]
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    "## Table of contents\n",
    "The main parts of this notebook are:\n",
    "1. [Load data into DataFrames](#load)\n",
    "    1. [Load the first data set](#first)\n",
    "    1. [Load another data set](#loadsecond)\n",
    "    1. [Join the DataFrames](#joindataframes)\n",
    "    1. [Add more data](#loadmore)\n",
    "    1. [Create a MultiIndex](#multiindex)\n",
    "1. [Check the data](#check)\n",
    "1. [Analyze the data](#analysis)\n",
    "    1. [Create scatter plots with matplotlib](#matplotlib)\n",
    "    1. [Create a scatter plot matrix](#matrix)\n",
    "1. [Next steps](#summary)"
   ]
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    "<a id=\"load\"></a>\n",
    "## 1. Load data into DataFrames\n",
    "\n",
    "To load the data, you obtain access key links and put them into the code to create pandas DataFrames. You will load data into multiple DataFrames and then merge the DataFrames and create a MultiIndex to structure the data for analysis."
   ]
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    "<a id=\"first\"></a>\n",
    "### A. Load the first data set\n",
    "\n",
    "The first data set that you'll load contains life expectancy data.\n",
    "\n",
    "To get your access key link and load the first data set:\n",
    "1. Find the data set and get the access key link:\n",
    "    1. On the Data Science Experience home page, search for \"life expectancy.\"\n",
    "    1. Click the card with the title [Life expectancy at birth by country in total years](https://apsportal.ibm.com/exchange/public/entry/view/f15be429051727172e0d0c226e2ce895).\n",
    "    1. Click the link button.\n",
    "    1. Hover above the link button next to the access key to display the link.\n",
    "    1. Double-click the link to select it, copy the link, and click **Close**.\n",
    "    1. Insert the access key link and run the cell to load the data:\n",
    "    1. In the cell below, replace the **LINK-TO-DATA** string in the `read_csv()` method with the link.\n",
    "    1. Run the following cell to import the pandas and numpy libraries, rename and load three columns of the data into the DataFrame, and show the first five rows of data."
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       "<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"
      ]
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   "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]"
   ]
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   "source": [
    "<a id=\"loadsecond\"></a>\n",
    "### B. Load another data set\n",
    "\n",
    "The life expectancy figures are more meaningful if you combine them with other data sets. Get an access key link for this data set: [Total population by country](https://apsportal.ibm.com/exchange/public/entry/view/889ca053a19986a4445839358a91963e). Paste your link in the following cell, which loads three columns of the data and prints the number of countries in each data set: "
   ]
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     "output_type": "stream",
     "text": [
      "Nr of countries in life: 246\n",
      "Nr of countries in population: 245\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']))"
   ]
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    "<a id=\"joindataframes\"></a>\n",
    "### C. Join DataFrames\n",
    "\n",
    "As you can see, the two data sets don't fit together perfectly: one contains more countries than the other. When you join two DataFrames, it's common to introduce nulls or NaNs (a value that's not a number when a number is expected) into the resulting DataFrame. \n",
    "\n",
    "Use the pandas `merge` function to make the data fit together. In the following cell, the `how='outer'` option specifies to keep the data from the `life` and `population` columns. The `on=['country','year']` option specifies which columns to merge on. To view the options for the `merge` function, see [Merge, join, and concatentate](http://pandas.pydata.org/pandas-docs/stable/merging.html). "
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      "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>Andorra</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "     country  year  life  population\n",
       "400  Andorra  1978   NaN       33746\n",
       "401  Andorra  1979   NaN       34819\n",
       "402  Andorra  1980   NaN       36063\n",
       "403  Andorra  1981   NaN       37502\n",
       "404  Andorra  1982   NaN       39112"
      ]
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   "source": [
    "df = pd.merge(life, population, how='outer', sort=True, on=['country','year'])\n",
    "df[400:405]"
   ]
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    "<a id=\"loadmore\"></a>\n",
    "### D. Add more data \n",
    "\n",
    "Add more data about development to the DataFrame. For each data set, you'll include the country and year columns, plus a column that contains the key development variable for that data set. For the employment data set, you'll derive a column from three original columns.\n",
    "\n",
    "For each of these data sets, get an access key link for the data and paste the link into the appropriate section of the following cell:\n",
    "* [Population below national poverty line, total, percentage](https://apsportal.ibm.com/exchange/public/entry/view/3a42de810e682d5a83e76fda9fea2bbe)\n",
    "* [Primary school completion rate % of relevant age group by country](https://apsportal.ibm.com/exchange/public/entry/view/97fc6c420265012ae9b5e9439b9ee293)\n",
    "* [Total employment, by economic activity (Thousands)](https://apsportal.ibm.com/exchange/public/entry/view/21c789ae4cfadf52f1bb14b5d4a0b99c)\n",
    "* [Births attended by skilled health staff (% of total) by country](https://apsportal.ibm.com/exchange/public/entry/view/87160cc8374805cb3b07dff4b4f9223e)\n",
    "* [Measles immunization % children 12-23 months by country](https://apsportal.ibm.com/exchange/public/entry/view/752e36ea889eca360e989baa785c2a5e)"
   ]
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       "<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>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>1</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>2</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>3</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>4</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>5</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>6</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>7</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>8</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>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8774440</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>Afghanistan</td>\n",
       "      <td>1961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8953544</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>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9141783</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>Afghanistan</td>\n",
       "      <td>1963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9339507</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>Afghanistan</td>\n",
       "      <td>1964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9547131</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>Afghanistan</td>\n",
       "      <td>1965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9765015</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>Afghanistan</td>\n",
       "      <td>1966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9990125</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>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10221902</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>Afghanistan</td>\n",
       "      <td>1968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10465770</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>Afghanistan</td>\n",
       "      <td>1969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10729191</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>Afghanistan</td>\n",
       "      <td>1970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11015621</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>Afghanistan</td>\n",
       "      <td>1971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11323446</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>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11644377</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>Afghanistan</td>\n",
       "      <td>1973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11966352</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>Afghanistan</td>\n",
       "      <td>1974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12273589</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>Afghanistan</td>\n",
       "      <td>1975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12551790</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>Afghanistan</td>\n",
       "      <td>1976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12806810</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>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13034460</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>Afghanistan</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13199597</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>Afghanistan</td>\n",
       "      <td>1979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13257128</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>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13180431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12963788</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>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12634494</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12241928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11854205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11528977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11262439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11063107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11013345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11215323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11731193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12612043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13811876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15175325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16485018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17586073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18415307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19021226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19496836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19987071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20595360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country                                               year  life  \\\n",
       "0             2           Derived using linearly extrapolated data   NaN   \n",
       "1             2                                  National estimate   NaN   \n",
       "2             3           Derived using linearly interpolated data   NaN   \n",
       "3             3     UNESCO Institute for Statistics (UIS) estimate   NaN   \n",
       "4             4  Derived using extrapolated data assuming the s...   NaN   \n",
       "5             5                    Derived using interpolated data   NaN   \n",
       "6             6                    Derived using extrapolated data   NaN   \n",
       "7             7  Derived using linearly extrapolated data assum...   NaN   \n",
       "8             8                     Derived using preliminary data   NaN   \n",
       "9   Afghanistan                                               1960   NaN   \n",
       "10  Afghanistan                                               1961   NaN   \n",
       "11  Afghanistan                                               1962   NaN   \n",
       "12  Afghanistan                                               1963   NaN   \n",
       "13  Afghanistan                                               1964   NaN   \n",
       "14  Afghanistan                                               1965   NaN   \n",
       "15  Afghanistan                                               1966   NaN   \n",
       "16  Afghanistan                                               1967   NaN   \n",
       "17  Afghanistan                                               1968   NaN   \n",
       "18  Afghanistan                                               1969   NaN   \n",
       "19  Afghanistan                                               1970   NaN   \n",
       "20  Afghanistan                                               1971   NaN   \n",
       "21  Afghanistan                                               1972   NaN   \n",
       "22  Afghanistan                                               1973   NaN   \n",
       "23  Afghanistan                                               1974   NaN   \n",
       "24  Afghanistan                                               1975   NaN   \n",
       "25  Afghanistan                                               1976   NaN   \n",
       "26  Afghanistan                                               1977   NaN   \n",
       "27  Afghanistan                                               1978   NaN   \n",
       "28  Afghanistan                                               1979   NaN   \n",
       "29  Afghanistan                                               1980   NaN   \n",
       "30  Afghanistan                                               1981   NaN   \n",
       "31  Afghanistan                                               1982   NaN   \n",
       "32  Afghanistan                                               1983   NaN   \n",
       "33  Afghanistan                                               1984   NaN   \n",
       "34  Afghanistan                                               1985   NaN   \n",
       "35  Afghanistan                                               1986   NaN   \n",
       "36  Afghanistan                                               1987   NaN   \n",
       "37  Afghanistan                                               1988   NaN   \n",
       "38  Afghanistan                                               1989   NaN   \n",
       "39  Afghanistan                                               1990   NaN   \n",
       "40  Afghanistan                                               1991   NaN   \n",
       "41  Afghanistan                                               1992   NaN   \n",
       "42  Afghanistan                                               1993   NaN   \n",
       "43  Afghanistan                                               1994   NaN   \n",
       "44  Afghanistan                                               1995   NaN   \n",
       "45  Afghanistan                                               1996   NaN   \n",
       "46  Afghanistan                                               1997   NaN   \n",
       "47  Afghanistan                                               1998   NaN   \n",
       "48  Afghanistan                                               1999   NaN   \n",
       "49  Afghanistan                                               2000   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      8774440      NaN     NaN         NaN     NaN      NaN  \n",
       "10     8953544      NaN     NaN         NaN     NaN      NaN  \n",
       "11     9141783      NaN     NaN         NaN     NaN      NaN  \n",
       "12     9339507      NaN     NaN         NaN     NaN      NaN  \n",
       "13     9547131      NaN     NaN         NaN     NaN      NaN  \n",
       "14     9765015      NaN     NaN         NaN     NaN      NaN  \n",
       "15     9990125      NaN     NaN         NaN     NaN      NaN  \n",
       "16    10221902      NaN     NaN         NaN     NaN      NaN  \n",
       "17    10465770      NaN     NaN         NaN     NaN      NaN  \n",
       "18    10729191      NaN     NaN         NaN     NaN      NaN  \n",
       "19    11015621      NaN     NaN         NaN     NaN      NaN  \n",
       "20    11323446      NaN     NaN         NaN     NaN      NaN  \n",
       "21    11644377      NaN     NaN         NaN     NaN      NaN  \n",
       "22    11966352      NaN     NaN         NaN     NaN      NaN  \n",
       "23    12273589      NaN     NaN         NaN     NaN      NaN  \n",
       "24    12551790      NaN     NaN         NaN     NaN      NaN  \n",
       "25    12806810      NaN     NaN         NaN     NaN      NaN  \n",
       "26    13034460      NaN     NaN         NaN     NaN      NaN  \n",
       "27    13199597      NaN     NaN         NaN     NaN      NaN  \n",
       "28    13257128      NaN     NaN         NaN     NaN      NaN  \n",
       "29    13180431      NaN     NaN         NaN     NaN       11  \n",
       "30    12963788      NaN     NaN         NaN     NaN      NaN  \n",
       "31    12634494      NaN     NaN         NaN     NaN        8  \n",
       "32    12241928      NaN     NaN         NaN     NaN        9  \n",
       "33    11854205      NaN     NaN         NaN     NaN       14  \n",
       "34    11528977      NaN     NaN         NaN     NaN       14  \n",
       "35    11262439      NaN     NaN         NaN     NaN       14  \n",
       "36    11063107      NaN     NaN         NaN     NaN       31  \n",
       "37    11013345      NaN     NaN         NaN     NaN       34  \n",
       "38    11215323      NaN     NaN         NaN     NaN       22  \n",
       "39    11731193      NaN     NaN         NaN     NaN       20  \n",
       "40    12612043      NaN     NaN         NaN     NaN       19  \n",
       "41    13811876      NaN     NaN         NaN     NaN       22  \n",
       "42    15175325      NaN     NaN         NaN     NaN       25  \n",
       "43    16485018      NaN     NaN         NaN     NaN       40  \n",
       "44    17586073      NaN     NaN         NaN     NaN       41  \n",
       "45    18415307      NaN     NaN         NaN     NaN       42  \n",
       "46    19021226      NaN     NaN         NaN     NaN       38  \n",
       "47    19496836      NaN     NaN         NaN     NaN       31  \n",
       "48    19987071      NaN     NaN         NaN     NaN       31  \n",
       "49    20595360      NaN     NaN         NaN    12.4       27  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Population below national poverty line, total, percentage\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",
    "# Primary school completion rate % of relevant age group by country\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",
    "# Total employment, by economic activity (Thousands)\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 health staff (% of total) by country\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 % children 12-23 months by country\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 strange because the first 40 rows contain incorrect values, such as numbers in the `country` column and text in the `year` column. Run the following cell to delete the first 40 rows from the DataFrame: "
   ]
  },
  {
   "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>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>40</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12612043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13811876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1993</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15175325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16485018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17586073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18415307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19021226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19496836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19987071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20595360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  life  population  poverty  school  employment  births  \\\n",
       "40  Afghanistan  1991   NaN    12612043      NaN     NaN         NaN     NaN   \n",
       "41  Afghanistan  1992   NaN    13811876      NaN     NaN         NaN     NaN   \n",
       "42  Afghanistan  1993   NaN    15175325      NaN     NaN         NaN     NaN   \n",
       "43  Afghanistan  1994   NaN    16485018      NaN     NaN         NaN     NaN   \n",
       "44  Afghanistan  1995   NaN    17586073      NaN     NaN         NaN     NaN   \n",
       "45  Afghanistan  1996   NaN    18415307      NaN     NaN         NaN     NaN   \n",
       "46  Afghanistan  1997   NaN    19021226      NaN     NaN         NaN     NaN   \n",
       "47  Afghanistan  1998   NaN    19496836      NaN     NaN         NaN     NaN   \n",
       "48  Afghanistan  1999   NaN    19987071      NaN     NaN         NaN     NaN   \n",
       "49  Afghanistan  2000   NaN    20595360      NaN     NaN         NaN    12.4   \n",
       "\n",
       "    measles  \n",
       "40       19  \n",
       "41       22  \n",
       "42       25  \n",
       "43       40  \n",
       "44       41  \n",
       "45       42  \n",
       "46       38  \n",
       "47       31  \n",
       "48       31  \n",
       "49       27  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(df.index[0:40])\n",
    "\n",
    "df[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"multiindex\"></a>\n",
    "### E. Create a MultiIndex\n",
    "\n",
    "You structure the data for analysis by creating a pandas MultiIndex. A MultiIndex is a hierarchical index that groups the data so that you can easily summarize it.\n",
    "For example, by grouping by country and then by year, you can plot the average value per country for all years instead of plotting a value for every country for every year.\n",
    "\n",
    "Run this cell to create a MultiIndex on the country and the year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>1991</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12612043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13811876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15175325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16485018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17586073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18415307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19021226</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19496836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19987071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20595360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.4</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  life  population  poverty  school  employment  births  \\\n",
       "country     year                                                          \n",
       "Afghanistan 1991   NaN    12612043      NaN     NaN         NaN     NaN   \n",
       "            1992   NaN    13811876      NaN     NaN         NaN     NaN   \n",
       "            1993   NaN    15175325      NaN     NaN         NaN     NaN   \n",
       "            1994   NaN    16485018      NaN     NaN         NaN     NaN   \n",
       "            1995   NaN    17586073      NaN     NaN         NaN     NaN   \n",
       "            1996   NaN    18415307      NaN     NaN         NaN     NaN   \n",
       "            1997   NaN    19021226      NaN     NaN         NaN     NaN   \n",
       "            1998   NaN    19496836      NaN     NaN         NaN     NaN   \n",
       "            1999   NaN    19987071      NaN     NaN         NaN     NaN   \n",
       "            2000   NaN    20595360      NaN     NaN         NaN    12.4   \n",
       "\n",
       "                  measles  \n",
       "country     year           \n",
       "Afghanistan 1991       19  \n",
       "            1992       22  \n",
       "            1993       25  \n",
       "            1994       40  \n",
       "            1995       41  \n",
       "            1996       42  \n",
       "            1997       38  \n",
       "            1998       31  \n",
       "            1999       31  \n",
       "            2000       27  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.set_index(['country','year'])\n",
    "\n",
    "df2[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that for the 1980s, the only data for Afghanistan is about measles immunizations. \n",
    "\n",
    "If you are curious about other variables, you can add more WDI data sets from the Data Science Experience home page to this DataFrame. Not all WDI data is properly formatted. You might need to clean the data before you add it. You can reuse the code samples that you just ran. Make sure that you keep checking the results by running commands like `df2[0:10]` on your tables when you load or change them. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"check\"></a>\n",
    "## 2. Check the data \n",
    "You can check the data with the [`describe()` method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html), which calculates basic summary statistics for each of the columns in the DataFrame (except the country and year columns). \n",
    "\n",
    "Run the following cell to show the number of values (count), the mean, the standard deviation (std), the minimum value, three percentiles, and the maximum value for the columns:  "
   ]
  },
  {
   "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>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>1.328800e+04</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>5078.000000</td>\n",
       "      <td>2909.000000</td>\n",
       "      <td>1523.000000</td>\n",
       "      <td>6934.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>63.156417</td>\n",
       "      <td>1.393916e+08</td>\n",
       "      <td>30.763209</td>\n",
       "      <td>78.018509</td>\n",
       "      <td>14337.147966</td>\n",
       "      <td>83.944882</td>\n",
       "      <td>76.537392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.290103</td>\n",
       "      <td>5.411527e+08</td>\n",
       "      <td>17.349350</td>\n",
       "      <td>25.675860</td>\n",
       "      <td>57236.797036</td>\n",
       "      <td>23.885349</td>\n",
       "      <td>22.054563</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>8.577282e+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>5.409290e+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>2.609458e+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  1.328800e+04  651.000000  5078.000000    2909.000000   \n",
       "mean      63.156417  1.393916e+08   30.763209    78.018509   14337.147966   \n",
       "std       11.290103  5.411527e+08   17.349350    25.675860   57236.797036   \n",
       "min       19.504927  4.279000e+03    1.700000     1.522030       0.663000   \n",
       "25%       54.884268  8.577282e+05   17.245109    60.831905     954.300000   \n",
       "50%       66.171191  5.409290e+06   26.900000    88.120480    3256.500000   \n",
       "75%       71.691415  2.609458e+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  6934.000000  \n",
       "mean     83.944882    76.537392  \n",
       "std      23.885349    22.054563  \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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"analysis\"></a>\n",
    "## 3. Analyze the data\n",
    "Now that you have enough sample data to work with, you can find the correlation between different variables. You'll create scatter plots that each compare two variables. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"matplotlib\"></a>\n",
    "### A. Create scatter plots with matplotlib\n",
    "\n",
    "Run the following cell to create two scatter plots that relate the values of population and amount of schooling to life expectancy. Use `matplotlib` library commands to create charts with custom font, figure size, and colors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h/tH/Avh5U6+W3qyZ+5wCPIVayJ4ERvnzj6C1W0B/Kf0QzRX0MjAz1r/Jaynu\n1SZftmEc6XS0oEF3MD6B5jR7G80J9nYLx7gVta7U+nGqXeo16KRYnyavJY3dDt9y12aoFxbhwVjm\nH+L9/UO9n3+Yh4d2HxF3VmGhm1hU5Prn5aV8EA8kCpwdnJvreiUJjYKePRMETJl/qMcDbgv8Q388\niYGxRyWJiCBwpiXNI8w9Pr9psfEKaCw4CnyfHv7eQSAMB9fHv/p7UVPehKg43Y9zeoprhUTBywV+\njr1ir/jn6k8kvsKc+xMFL6e6d1sHBrdmfTmcy+nhw1xvE5xzLwLjU5w/N/a+nig/UHK7Jq8ZhtFl\nuBuNx3yKKHaxRTjn5kJCjcBwPuUadLhrRvOkio+Jn6v5+OOoLeraCK6feahv73tEAbU9neOKHTsA\ndYmkqlR9OlFto8K6OrajLpjgBLnmo4+oQN0mELlTQM1v+4F8Ec5wjj1oVt1h6C/lXsBEP7eDqKun\n0WcG/gcNkl2f4vqYpLalRC6kBWhMTE8/51v8Z8wlCvAFdfEM8++X+jl+jJZC6IVmDY4XvJwH/DT2\nvYxBzY7noJW4D6B1o+ZDQ1Xwy9D8N8+gSn406p4KNaXmRMMzB43LGQ6sefDBjAUGH67adkVz1w3D\nMDqQGufc/ZmehJEeqYJ1L7/6am5furTh3ONED8ZVNN6REzLsrke3lCVfv4uo7ABED9vPogJkHvqQ\nXU0kaHqg5QMW++PkMa8AbnGOaj9+vBjlX6O7mXqjwucCourYQUCsIsotMzs29ki08NgC1KUwBw24\nvT7WphpV7Teh5Q76EdWKSo5x+Re0Dkg8I/AcNOh3FSqIQomGOjS2p8J/vpnA79HvX/x8Dvrx7vHX\nN6E7uT4mEgrD0ZwtW/25q9Cg4quAq/34mSyHkFZQsIj8AmiU09s59/U2n5FhGEZq/lNEznHO/SrT\nEzEOT6rt0ktuuinxHGouC1Wig3iZjT7ca9EHOSQGtwYOoVaV+eiD9S9oYG3YYrzQX9+DPmyDJaEa\nFTyp9vyPRoXDdKLt1oHlvs9fiIo8FhOJozJUIJT6e1/v7z8PLU55gp/n2+guoheT7r0JFROL0R1M\nqSxAgb1opt8fJM1xnv9bQmSRWYN+r3vQh/49fh4z0SR9B/1rje97j/97EC2suQP4o++7ncTSDw4V\nOkEsnTZiRDOzbl/S3eUUdz31BL6KViM3DMNoV0TkHXTdFOAfRWQ/+sNRUD/70Zmcn/HJGFlYyI49\ne6itqWna9vZPAAAgAElEQVQQLzNQq0JcUFST6Cuci+4SWosKmuFojpXk4o3z0ZwvlwIF6C6hPX7s\nahJdJ3OBLzYz191EOV4CJaj1YxUa1HUWatmZ5c+9gIqXi/z9gtXnIdTlFL//M/6V5+9zLLrbJZdE\nF1JwOR1oYp5zk9rOQt1Mc9Hv9RBwrp9LMZrL5jkid1cf1NLz1+hupjtj861FRWFOfj71Bw/yrbo6\nVqE7uA7m5nLtsszVrU5L0CS7nkRkDfBou8zIMAwjkXGZnoDRcsZOnEjZ44+zqr5etxvn53P5/Pks\nXLo0YQt1xcqVrF6xQksNxPpf17cv7N/fcDwGjY8JloZjUDfUYNSy8JUm5tEDFSJ5aADWchpbXuah\nrp1voQ/5BaiLKDlOZBa6PU5Qa0zYtv1jVHGH7czFqMXiEGoJGezvuxsVDdegAmwkMBktafABav0J\nBcbCFu0Q5/IRagFxqMvrXH//5DnWAN/3nxVUgKz1/ULcTwEaQzMGjbKfgVqsggg7hMbvrCexPlWF\nv74zP5+169YBsGzRIt7ZtYtPjRjBte1QpLIlpGuhScah4tEwDKNdcc7tAvCFKP/snKv1x93RNB1G\nJ2Pp0qXcfN11nOTFzD05OZRdfTVXX30148aNawgKrvCBwuE4znGjRnHls8+Cz1ETr15dgT5spxBZ\nYH6EVn++Mje3oU8QIT9HH/Cl/m816lICFRWnoNvXQB/yZehD/RCau6WOKKfMADS+JR63Uos+TB9H\nRckjqIAKbeYBf0aTpb2Cxt88jwqM4P6p930gEjMzUDdUnW97JrDFv69A3XE/899BMGH2QMXSYH89\nHkvzBioEX0AFUnDZVaBCa5P/jE1ZfkAtMfOvvrpBuGRSwCTTmhiaHNQd+Fh7TcowDCMFDwOfJypc\nnIdawm0HUieiqqqKFdddx8319YDGklxcX8+am25i88aNzC4r48FHEw38KZPfTZ/Os88+yyr0Afsh\nUWDrQv/3Td8/xIb07tGDwcOG8Y979vBhTQ21qLiIP6B70DjYt5hEjvFjz/T3udiP8110F1GyhWcu\n+oC8xR/PR11Md6EC5iAqSl5Es+9+EfgtGidzALW4nOGPn0PjVEpRgfFC+Gz+/A9Ri9Q41Fr0GVSU\nhezBJf7+H/t53YoKvzmoGHuRSIDh+9UAf4MGPJ/kx5jnP3fc1bXQf67NGzdCBkscNEVrYmjqgOXO\nuafaYT6GYRhN0cM592E4cM79RUR6ZnJCRmNWr1jBzfX1CQ/8VcDgffvUrZSiNEFJSQkV69Y1WGou\nnziRNTfdxC21tQ1btr9OVLvoYlRwhAfsJj/OKzU1vL1jB8v9PS9BLTnnEu2GyqGxIJlPVJ9oAWqx\nWeD7lqLWklFocGxdis/cncTdUtf4+3cjilkBFUNfJHJLPYwKlRCXcy66y2kQannZ7/vP9Md/Qa1L\nT6GWmiDKQvDzPf51kCiYeh5qualDhdoxqAXobtQF1t9/J19G6zf9ABVOQ/z3Gj7byUQicmeK76Az\n0KIYGhHp44+bs0gZhmG0CyIy2Dn3jn/fVsUpjXbmJeB+/NbpWHHIOCUlJQ15akqnTmVkTU2Da2gb\nUbHHs9EH7U6iB2wQNHXoludSEnPAjPFtV6O7g0BFUnDF9PXtt/m+8Qf2Jn/PSt/uIhK3is9FH/qB\nKtSaFPLN7CaR3/rPshZ1Cw0nEhjbibZMv+Xvd56/loMG6d7t+4EKsZOJ3EbD0R1UNxOJqzH+e3uZ\nKJ/MJqIYmSl+jvN8v2+jQcC7URfVt/zYC/AiMlbfqrOR1mIgIqNF5PdoEPc7IvI7EUkO9jYMw2hP\nbgM2icg1InItGs9482H6GB3M7LIyfegRVXX+PFEemGpg29NPM33yZKqqqhr1D9u9T0FdNlOAa/1Y\nVf79c/78m+iD9jl0d1A82+Jsf01Qd8ubvs8BfzzDH1+CVq6uRMXAXagLJwQGv4QKgAGohSWIo/Wo\nFeYg6rKZ488vJspDk0zYPbQJde1AFC8zE7XM3IMKm7/244Y6TsNQK8oJvt95aNwNvs2HaIBx8MfG\nedvf7wv+ewo1pWaj4mw4avUqy8nhD0VF/FtlJfud42eVlewsLuaUoiLWFBWxvrg47cKfmUA0w/Bh\nGok8hbru7vWnLgAuc85lne/a15jL9DQMI+sREZxz0sH3nIha5gEecs79uiPv3xy2tkTEswGPnTix\nIZneQ6i7JWyZXpuTw+lnnsmi2O6Y6ZMnM/Kxx1gLrCCyNIQdNsei4iMfFTDdUCGwB3XfHCTKk3IJ\nKkIGoA/8Q75fPVGQcBj7CtRqUY3GqOT4sbujrp5jUVdUmD+oVeNj9OEYilxCVB27nkSXU7fYHGYS\nuaVmEcXLfAhcjoqcs/2Yp6DxNXv9nH+Eulfi/XL9vU5D42RK0bift9D4nno/76GoZed1NL4mJPM7\nobCQH6xc2WnESmvWl3QFzTPOudOTzlU758Y01aezYouOYbQNHS1oROQB1CJei3oHBgH/4pxb3mzH\nDsLWlqapqqpiyaJFPLdlCzej7owVROa1K/PyOPm008gF3vngA/bs2MFoErPjVgBX5eZysK6OU4jc\nNMFV8EfgqD59OPGkkxrG2bVjB8ehVpkDqIVkHxoYfDONxdKTseP5qNg4iAqa7miOls+jbqMDqIC4\nAxUJoYRByDUzCxUUu1Ex4fwrFxVB+b5NiI3Bnz8H3S0VXE9bUXHVE3V37URFysv+/s7P06HVrl+M\n9cklylezH93ZdKe/1xwgV4QBAwZw0fz5CbvPQpmKTNKq9SWdgk/of9+zY8cTgDUtLRzVGV76kQ3D\n+KTQiuJxn+QFbPF/v4r+SO0F/LEj53CY+bXBt9p1mVZc3FDocGCK4oahkGM/XwSxDK2evTZ2fkLS\ntVBZu7+/Fio9V1ZWuj6+T7zoYg+0oGSq6tj9/bwG+zbxathHgevu+wzyxz18n7U0Lk5ZBu54XwQy\nuZJ3H7Sidk9/v6P92KFtKIR5ur9XL6IilckVuifECkNWJhWKHJ3i8/fz58eD65+X11C9vLKyMqEC\neahUnklas76ku8vpTGCjiGz3x6OAP4rI7/y/4s+0QEMZhmG0hhB7ORH4lXPuQxGpb66DkVnirqd3\n9+5lAhoMe1KKtsNQK0dIVhfysKxCrQ7FwO9Qd048R8uLaGzIY8A5EycCmuytPxobEqw8VahLKgQN\nh5IFL6KxKnf5eVyG/o8Wt+BU+3nXoy6hfD/Wl9EYlD5ovE08t80w1K0Td2M85MfI9WMEt9DHqPXl\nPqJClXNQ99QPfbvlaFxPSMr3ERpP9CaJW9rnAsehLqpTaVwD6nvAwYICHrj//gYrTKoyFakCtzs7\n6QqaRtVpDcMwOpjnRORXwKeA74lIfqYnZKSuqB3Ox4tTXibCM7m5HFNXxwQSdwqFpHmB+K4k0Afz\nCUTZbePXeqKumy8D6zdupGrcOKq3bm2oRh1YTWKwbrxkwXNEu5FyiQpCggqhNagQAhUaF/v3d6IC\n5X0a57bp58+H2krVqCvpDH8cXGa1aEKl+Ofa48dNjukYA/zSvx/u5z8PFTTBRfZFVCSFSuTVaFzN\nXtQN9T5w2fz5WSdW0qIl5hxUlPZuqRmoM70ws7BhtAl0vMspH81wP9IfHwv8TUfO4TDza4NvNbsI\nrorg+hmYk+PKy8udc+piWpvC3TIoP7/B7TPeu0F6xdwivWKunLXeZVLmzyW7oeLnysCdVVjoenqX\nTr9wP9+2MEX/PuAm+nkMJnKFVfqxx3uXUPLnmOiv9/OuoiEp2oxH3WPBxTPQ/01+DU8xr+AqSj4f\nvrd+3mXV33/WsticC/xnnY66xXokuar6geudm5vgUjqiXE4iciKaRuAswInIFmCGc+6V1ogowzCM\nluKcqyH6gYpz7nXUqm9kiNUrVjCjpiZyldTXM++66xg3LnX5rdMBamqozs1lVV0dgloNvoNaSl5G\ng2IfFeGWE0/k3bffpv7jj7nblzJItmKchrpixqAWivd37KA/6o6pA45HLROX+PtUELm1nkGtGBf5\nuQWrSth5JL7fqhSfI7lI5Y4UbV5ErUqvotvyXiKyygS3U7AYJX8u0Ij3+Hxf8vPaSbS76SUi604J\n8AC6W+tt1GK118+tnKgEwqnA9rq6BJdScmLDik4QFNwa0k1KdSf6XeSjgXh3EQVLG4ZhGEcom1Ax\nMxTNzXJKfT1LFi1idlkZl4kwBo3puBw1qQ0DLqmroxsqcG5DRcmT6M6n14HePXvyg5Ur6X/00dTV\n1nILuusnnk8m5FKZjrpVDhHtBMpFH2790YDPfDQW5T6ipHm5RNvCS1GRsB8VMYeIYmgWoy6xkFdn\nDlEw1/toaYO/JLWZi7qBXkHdU2uIYmRm+u/gQ3Rn1r6kzxWqXu9B3VEhaWAtalV4kMgVlUNiDpw3\nfb8vl5ayMD+fj327av9ZQt6dg8Bbe0OKQaWkpIQHH32UBx99NCvFDKQvaAY75+6JWYPWECUrNAzD\nMI5AZpeV8XJOTqMH5nPbtvGHP/yB7jk5LEAtBD2AXwFj0Yf0bdAozgXUsrG/pobzvvQleu/Y0VCm\nIG6JCcUeD6EP8B+hsSO90LIAx6PbqZ/0r95+7BI0mRyoBSbOJjQvS2nSvEr8uXlo7pp+qNXlOeBP\nqCUoiKWQbK8OLSNwa2zewQqzk6jcQA76IK31n+tKVMxc5udQT5ThF6LA35BMEKBnXh5TS0tZUlDA\nkoICriovZ+3atVSsW0e3oiL2olah64nE261EAbRVVVWcPXYsowYOZNLYsSmTHWYN6filgM3AKbHj\nk4GnW+rf6gwvjkA/t2G0B3RwDE1nfx2pa0t5ebkbkCKGpLCgIGVcSdhmvdbHfSTHjvSJtR0DboSP\nYxkQiwUpo+ktzMf5OJL4vSf4e46OXUseo3+sTyWJ2537+5iU/n4evfxYg4i2myd/zmnNjDfY3zts\nHy8j2mY93cfVFKBbv8P4lWjsznBwx/g+R+fmuolFRU3Gu1RWVrqzCgsbtrrH5zituNhVVla6/nl5\niXOLbefOJK1ZX9L9h/o3qFvuUf96C5jc0pt1hteRuugYRltjgsbWlsDEoqK0BU1hQYE7tqCg4SFa\n5h/eQTQMQ3Ow9PKvgljfMF5y3pcgZEIg7nD/N1m4JIuPMjSANgTnxsVVbz+n033/fmi+mBBwHMRM\nU4G7yWKttx9rGirU4mJnPFEg7wDfLgQ8J4uz8DmCIFpL6iDe5EDfePsBOTmusrKyITdQ8vghn08m\nac36kpbLyTlXicZf3QbcDpzmnHs0nb6GYRhG12bRsmUJ9ZsW5udz0fz5XJmX13BuAfBCXh4r77+f\nbgcPNhSQ3InGl4xG42eO921z0crQIW9MKvdUoBrNhgsas/IRGvw6H80fE9xWw3zb6f71KurieRH4\nPyTWaDoLLYVwsu/fD42d6eHHXo7G18Tz5cxDt+H9K+oemuHnMAd1Q+1Gc9TEI9lLUDfdYGAjGt+y\nAA3w3Ym60UKMTIjheRrNo1Ps5zuypoYlixYlfCcht0yIbToVjcFZBQwfOTJr42SaI908NAAhp7cl\nsjIMwzAaaGqXzLhx41i2aBG7d+3ilBEjuHbZMgDeO3CAH6OxHKA5aWagwbMNVbnRB3tgtm8DKgrm\n+Peh1EDIAXMFMBB4HjiaxIfcWOAGolpSd8Xe340KmxI0niYE2YZ7HEBzyByIjVdCVJByNzAEFVBV\nfu6vo0LmWN/vfd8+VcXuXqjougcVQTehMUkh9848NBZoGBq781u0OmtIxDdv2zaqqqoShEq1/z7C\njqw/o7u7jg/fx8SJPPpf/8UCF5XsuDIvj3s7aTXtw5KOGQeYhu4Aq0ITMr4NfKWl5qDO8OIINgsb\nRluCuZxsbWkF04qLXRlR6YDgtjnKu1wa5a1JcucEl9J0774pTHLfxNsP8q6e5txWcddQyHcTctYM\n8vfp5e8bn2v8PgV+rn1i5/rH2vaKua2Gx+47zY8ZctWEeJrevt/RubnumD59GuJ14p8rHvOTylVU\nWVnp+oo06jfau5xKS0vdgJychvidAjSPT2eIn3GudetLuv9QnwdOjh2fBDzf0pulGDcftay9jAZt\nn9tM21m+3cvAbbHzU4A/oDvgqoH5h7lnG37lhnHk0tUEDdHmlS3oRohif348Wu/vBbTQ86Am+rfV\nV9ulCfE2leBO9Q/S8eDKaZw0L9QyGu7bhMDfIELi8StNxdaE+JgBJMajhLaphM1aLz6GkhgrE+YU\nREAIVA6CJSQLLPCfJ8zhdDS4uQ+Jwc1BZIQg43g9piFEgbup4mgG5eQcNvZlmI+hCd/1ID+3UBPr\ncP0zSWvWl3RdTh8558K2f5xzL4tITZp9m2MB8IFz7iQRGQX8WkQKnXMfxhuJyAnAdcCZzrl9IlIp\nIjOcc/cBbwBfcs69KSL9gKdF5LfOuU0YhmGkTz0w3Tn3fNL5e4F/cM49KSJXo1b+mR0+uy5CHbrw\nL0eT6N1NVG9oHOq+eQnNa3MumgCtD+oOCiUJStC8NsvQZHNX+PMhKV2c94EPiFxLwVUV3Fan+uPV\nRFubA2VoHA+o2+cBdOv2z4iqbR8ECvzfe9BEdxcBV8fGeTs3l27du/Ptmhr+gP4Cnwv0RRPo7UZr\nLMWjWmpFGkpJHF9YCDsS0/cdM3IkC/fsAV9aYmF+PhUxV1FVVRU1H31ENXCp/55CBfBnSUwM2GVI\nR/Wg/49djf4/dgzwj6jAyAd6tVRFxcZ9BhgbO16PLijJ7RaQaJWZDjzUxJjrgfObuecnVo6GYXRJ\nC81O4NSkc+OIVfRGwzP2N9G/Lb7WrCHskglWhHQJLqfgLkreJTQEXKm3WsRdPmFbc3zHUnAhherU\nfWOWh7Wkds2EnUNhq3df1L3T1Pbr4EqKu24GoG6h+Jwr/di9aOyOKgPXV6TB0hLOT/Dfw/Qkq81R\nIg0lJMJ3PTgvr9HW6ub+G4TvuYCovEJ/IvdZ8vcedj51FlqzvqT7D72+mdehlt40Nu4HwMDY8Urg\nihTtbgPKYsd/BWxN0W40uqV8aDP3bMvv3DCOWLqooNmCupfuAI5C4wcfSmp3AOifon8bfbOdn09S\n+yfeN55jJWyfLvcP+RD/0ofGOVvC9ux4/3gdprN829A3WagMiYmU4D6Kt12L5ooJImC6Fz3D/dh9\nU4wZXFcjvFA52ousShJje5IF01o/dg8/r6ObyAPTUgEZammF3DUhVidsKw8uu5AXqLS0tKX/G7Qr\nrVlf0nI5OefSzSicgIg8jWa9TjgNONTa02aIyDFonZfvOufebK7t4sWLG95PmjSJSZMmteVUDKNL\nsmHDBjZs2JDpabQnZzvnXheR7ugGnDuAdSnaJSeZbaAt15amqlh3BsKW4Ab3TE1NQm2g5ojviNr1\n5JPMO3CAm9Htzj8CtqPuqG3+7y2oS2m9Pw73XIBuQQ7uq2Ni93jDt3sEeI3E3VJz0F/ix6NBU5vQ\nzL17gKv8ffagprgfo1vAH0PdNRVoeYXTUnyuPf4+tUS1gkaiLqpnUVdUMrvRHU3H+nvPB3rk5aX8\nHktKSlr0/8DssjJKn3iCGTU1/B5V4X3QrefJFcuHAvv3pHLYdRxtsr60VAG15Qt1acZdTg/RtMvp\n9thxgssJ3Z33DHBRGvf8xMrRMIyuZ6GJv9AyQzuATwPVsfOD6ACXU6aqH6drBUiupF2GJsxrqfup\nsrLS9USDb0M24DJvVQjvQ7XrVAG/E4h26AR3VDzZXdyCE3YUBctOb3B5vk+qTLpDY+OF+1aSujr2\nUX4ulTHLy4Sk+fTx7UKfkLm4V6zf6f57bCvCf88JRUVuSEFBw+6seDXzYKnpTAHBzrVufcn0ovF9\n4E7//iRUWPdO0W4kKrQHouUvKoEL/bWBqIn422nes42+bsM4sulKggZNA9IvdrwU+DfUGvMy8Dl/\n/hrg7ibGaKNvtrFgWBt74KQrOlrqomiJiAop84MbI+6qCf2S7x9/uE4sKnLTiotdeXl5Qt8+RDEn\nwRXSCxpET9iyHa7FhUDYkhyuNyWCCojKGARXUnBnhXnESykkjxHcUHHXV9huXk5UtqB3rH8QXb38\n/Qr9PQagu4/CPXtBQuxMW1NeXu56oO6tsLusDNzA3NxOFT/jXHYKml7Az/2C8Ty6Wylc+ydgdux4\nFmqNfBk1BYs/fwNa7HQz0XbL0mbu2XbfuGEcwXQxQTPSrx1bUcvxz4Ah/tp4NC3Ei2gursFNjNFm\n321TgiZd0dEaC09zIirV+CFINVUw7cSiooT7D8zNdX1EGgmHkAclWFAGkmj9KEPLDcRFT6h3FIRP\nsvgoIwoMTrUVe4K/z3QSty6HEgRh3Oa2c3dHBVYQRfHg5RCs3MuPU55i/r1ix8Fq0oeOiWOprKx0\npxYWuqNyctzgbt06Ve6ZOFknaDLxMkFjGG1DVxI0bfFqy7WlKUGSruhoiThpTZ9421RWkKbqOKVq\nezoagBvex91E/Uldyygk0zu6CfER3D1riawxwUIymshikkqMnU7khonXmxqIusLiuWZ6gjvhmGPc\nUTk5jVxKg/z14SnuMT7peFgHuRSzidasL60K9jUMwzDajxA4u764mPXFxVSsW9fuQcGzy8oa1WOa\nnUYK/NlokGO839ARI9K+727gRjSI9yjUDAYasDoaOIQG/05HzWMAIQnap9BaSiP9HEL9pOfR3DDz\ngV+h5Q9uAn6IBu++hboHjiWxTtJconIGO9GAqSvQwOGfABvQgNoDRLtb3n7jDbrX13M6GkleGnv1\nAIan8R2MP/vsThX0nbW0VAFl+wuz0BhGm4BZaDp8bWlPl1Pol258Tnz8/nl5DXExIV4mfj1U045X\nj16b5H4J1p5BRMG0yfE5Ic4kBACXEcW0jPDWlQFEcTFNWWGOJnLz5Pj2cfdRfH6Dk+YQLD3BtdVU\nvE7c0jQkRf+W/rc50mjN+hLiUI4YRMQdaZ/ZMNoDEcE51+QW5iONjlpb0t3O3d7bvsP4b+3dSy4w\ncNCghPssXbqUNTfdxMGDB3m3poZL6up4BLV85PsxatBtxN1Q60Y1ulX6W8BT6HbnW4i2aleg26D3\n+X59gXf9OINRa8iX0G3WzwKjfL9LksZY5c8tAD5ErUB3+jZVRNmKj/b3/4P/W+/b3kq0vTuMP9T3\nD8UiQ4HJ5DE/VVTEOdOns3njRqDzbcnvLLRmfTFBYxhGqzBBk8iRuLZUVVVROnUq1/v0+/Nycjj1\nzDM5Z/p0lv/zPzO6thaA53NzOXbECF7fuZPu9fXUooLkEOqW+Qu6T34Y8ArwBVT4/Ab4AYli5Ao0\nF0x/dFvst4hKERz0492E5qYpQUsr5PhzoCLmPiLX0ip0p8mKpPss920XElXeXuWvX4IKmpF+/Fzf\nPlTt7paby4G6OvJRIQTq2rqqvJyrr44XRTCawgRNGhyJi45htAcmaBI5EteW6ZMnM+WxxxpZP54V\nIdc5bvbn5wEf+/e5aJK5icDjaFGsVagQORm11lQTJbKrJxIjc1GLylGogDkWeBsVRzcRWV5KgbPR\neJyJwMNAb3/vC1HxEZ/vi/6+4fw84Kf+/WI0zmef71/v234LFS8z0QR+b6GJ62ry8+mVm8vX9u/n\nUX9egIGFhTy7fXu6X+0RT2vWl3SLUxqGYRjGYRkGbHcuIasvqBj5lH8/AQ3QPQp19XQjEi2XAScA\na1E30rnAGlRUhGzA5wJ3AdeSKGLWx+43BN1v/wugyN/zbtSlNca3CS6nXmgA8HWotehif70UDTLe\nhBa5BM0ZUu3nlOPHHB2bU11NDaP8PeLCaf2JJzbzrRltgQkawzAMo1WE9PoNFZ/Rh/fGFG27+b/v\now/+7qg7KS5IqtCHUihVcCUqGILbJriLVqMF/pJFzGxUgIC6g+4iqqb9CBpj8zqwCHVPfURkOdqD\nWo6+6T/DJj/WfWhcTHBBBZE2BrgqN5cL6+rYibrIZvl+i0kUc8mVsI32wQSNYRiGkTYh2PfD2lpc\nXh6HPv6YeWhg7rFojMvHRKKkGrXCdEczn/YA+gHlNBYkq0kMAga1hKxCY2tK0diX1bHrcREDanG5\nArWeFKOBuC/5+97m2yxEhUqIi3kD+DNRrahi1KW0CRUzQ32bZHrm5zNm//4ES8wmovicxcA7BQVU\n3H+/Bf52ACZoDMMwjGYJO5peeOUVdu/YwSy0kGQukUiYA5yFWmcK0NiROUAd6tI5hWjX0pLY2EGQ\nVAO/A6Yk3ftjNMalzPcdg1pf5sTa1KAiZiAa4yJoPM6XUYFS5K/HhdLq2L0OkJgvpgQ4D7gn1vab\nqBAKzAVq9+/nChHdqI3ubKpDxQzAzvx8EzMdiAkawzAMo0niO5l+hwqYu1CRkhwns8Sfm49uza4D\nzkAtN4+jsSYAF9FYkIQA23hl7HloLMwQVHB86Pv1Qx9eS1AX1iW+/T3oVu0FRNuoQXdFJROqY38I\nnIOKrTCnaj/WIeC7/jNMIapQvQcVTj8Cqp2jLCeH40aO5FP9+gFqVRo4aBAVtiW7QzFBYxiGYTTJ\n6hUruL6mhlJUQFQDz6G7hpriZFRkXIEKjt2omJmAWjmuR906V6DWlJDpN7h7Fvs+F6PiaRjqGhL/\n/ntEguWbRLuMHPCOn0Pc7fMiiQJqDhrTMwR4FbUM5aGi6VLUPRbidspyc/lgxAjm7dzJzfX1TEGF\n1myiuJ9N9fW88957/GDlShMwGcRKHxiGYRhpcRFqSbkZFSzxsgHB5bSAKCj2FlRoHEDFzH2oe2kV\n8N++7U+JdhCBCpG4xWUWWsrgSWClb1uGJs87G43P2ePnNNPfK8zrTWAHKlxqUTG1hGiX01uogHkN\nrXx8FZGYCeULVtTVMfrEE/npf/4na4qKKMvJYYgfo8q3uQS4dt8+SqdOpaoqFGgwOhqz0BiGYRhN\nEt/J9B5qBQEI6eGuQbc61wK/JQrcDRyLWkEq/LVNaJDuh8B7qOjYR6IFZSEqfH5GJD4CJ6Di6E70\nF3hlTScAABYBSURBVPnpRJl6F6Kipxq1/gwH/gG18jiiLL4LgL9GLTt3xe4xB7UCpaKkpISSkhKq\nqqpYsmgR87Zt45T6eq4n5narqWH1ihVmpckQJmgMwzCMJgmFMpcsWsRz27ZxTH19Q5zLcNQiMhTN\n3BusMEEgzEVjbfLQh/5O1GU0AS32eDdqEfkc8Gs09uZkVMz8K/D//BiBBahQuZ1IyIRdUqshQVyM\nQV1Dd6NWHvz4A9Dt2nuLiuj38sv0PXCAq9B4n8GowCrLzYW6OsBnP967l6VLlzaUK7h22TIALj3/\nfNi3r4XfqNFeWKZgwzBahWUKTqSrri1hh9O2p5/m2n37WI9aMf6HKLPvIRJdSpuAF4DT0FpLrxEF\nEYMKk49RYQLqQlqBCqMQdPs+sBetp9QPjY35CiqKpqBCZgpRLM1IGtdsitdTCueu7NaNex95BIAL\np0zhxtpaqlFLTdixdZkIo048kT/t3Mk36+sh6frC/Hwq1q0DSCj9EM6bheaTY5mCDcMwjE9MEDHv\n7t3LtmefZWZtLb9DE+HVomIjBP4OBb6BxqAEl9ILqCXmWaKdTXuIaiF9BHybSGiE8yVEwbzz0Zid\nIGDiwgWioNzrURG1mkRrzhw/h2ROPeMMSkpKmD55MjfW1lIKTCdK1AeAc1z79tusqK9Pfd27lh58\n9FEq1q1rKAJqu5oyiwkawzAMA6AhPuS5bdu4ub6eauBpoi3Vd6Huo5NJ3LE0E41puduPk4cG3N5C\n5BI6F61a/QzqqorHxhxLYgzNAuDz/n6z/PFyVLjc6dve5o9DrMxsdBt12ME0C90xFd9WfmVeHvd6\nd9HhOOAT5u0GttE4P04gxNYYmccEjWEYRhcmWFtAA3zDwzf5PKj7ZGRNDTcTBdmGhHP3omUEglUm\n1DlahVpkjkfdQv+AWlhO8v1mE1lwQkXqVSRaU/4HFSBL0DiWUA17ARoYfAqa2+Vjf59XUAvOMPQh\n9rq/fhD4Wmkp//Pv/86Y/fsbZey9N5bkbuzEicx7/HGor2+UqG8OUdzNDUSiKmClDDonFkNjGEar\nsBiaRDrj2hJPilcNrM3JYdhIddrs9nlVQB/Qo0eP5qItWxpiU9aj7p070cR0o/yY8bpLi4liW9ag\n8Sqh/25UDNxGVP6gF+qyqkGtKAWoCOmDFpoMfZOrdz8ZOw7zqsjPJ+fgQbp17073/v05uH8/dYcO\ncfCjj5jpHBVEMTvx2Ja4Feri+no2AS/n5NB3yBCGvvFGw+dZjrqawnwaPm9BASst+2+7YzE0hmEY\nRgMhKV6wtnyzvp6KHTsYheZticeELNm1C4gsKn3QeJj4tuhvkGipeBmNkdkEkJcHtbUN1zaTaHX5\nHhoEPBMSxMYcNCYnxMzEx59LYimBkHxvbW4uP4kJlG9MmUJubS2jiARXSNAXr6UU2vapreUU32Y5\nUFFfz11Dh7L9/fcZWVPTaKs4qMXoTWD9pz9tYqaTYon1DMMwujhhS/NO9AGeKli2x4ABXC7C19G0\n/ueh1pNACfAAMAjdlTTXX5+Fioi62louI8r6uweNkznTX9+cNIeQuO42NOvwRWiMjaBxMcvRApE9\ngMv8uY+Ah445hp88/HCDqFi9YgWja2sbfa6QoO/MmABZsmgRubW1lJNoaQIYMmgQFevW0a2oiHk5\nOVQQ1YwKyQMX5uc3uOeMzodZaAzDMLooISneSL+tuOE8icGyV+TlcWjnTsQ5coiCeUNm4LjV5E1U\nyHQHbk0a51rUWtMd2IWKnm/5/qNomu7+PregLqZgEVqNBiBXA2cWFbFo2bJmrSPJnyvEuoR4oZer\nqxvVn1qMLyLp44uCJSfEF101cSLrff4Z28XUubEYGsMwWoXF0CTSWdeW5JiR4O4JMTWnn3kmf/7g\nA3ru2AFo7Es5iXlelqAC5SAqTA75sZPzviwhMRZmKHA+6jaqR10Cs2jscnJ+zHz//iI0MDhk9r1C\nhAd+9auUYiLucgqfa40IY846i0V+R1OIIwpiKT7n6/r2ZfUvfmFCpZPRmvUlo4JGRPLRWLJPo/9W\nrnTOPdJE21loqQ2AXznn5iRd7wFsAQ445z7TzD075aJjGNnGkSRoROQk9PlXgOaK+wfn3I6kNp16\nbQlWh7f27iUXrQYddj2NGjiQwT7jbUhoV0qi8JiXk8PF9fXsRN1JE9BaSzf763PQmJSNSX2XE8W+\nnI0Kl26oe+BjYFefPnz2s59l7MSJbN64kbf27uX5rVu5ybkE4bG+uJgHH320yc+2ZNEi3ty1i+Ej\nRjQImXhCwOBemhH7TFfm5XHv+vUmZjohrVpfnHMZe6FifrV/Pwp4A+iVot0JwJ+AAn9cCcxIarMc\nTVvwu8Pc0xmG8cnx/5YyuoZ01At4HPh7//4C4PEUbT75l5ohJhQVuX6Q8BoPbjS4AeAmFhW58vJy\nNyQ/35X564PATQdX4NtOBzcQ3Ing+vu+p4PrK+IGdOvm1kJD37X+NSQ/31VWVjaaz8SiIrcWnPOv\nteCmFRen/XkqKyvdkPx8t9bPLT5WGbjCggI3rbg45b2NzkFr1pdMx9Cch6YtwDm3XUR+D5wDPJjU\n7qvAOudcKJpxF1o1/j4AEfm/qCC6CY1BMwzDaBNEZDBQhMbEghaIvkNEBjrn3s3czNqOa5ct4xtT\npjC0tpa9aPDta/n5nDR6NLfE4lbGjRvH6hUrGLN3Lx988AFb33uPYwcM4CNggy8TMAaNyRly2mkM\nHDSI5T6ItiUxKYuWLaN06lSIlxRoQTBu2N1Virq9ZsSu3Zef37DryehaZFrQHI+6ZgN/Ao5Lo91r\noZ2I9EaFzJfR/EuGYRhtyXHA6/5XI865ehHZ4893CUFTUlLCA+vXp0zAl9yuKSEQXFo7gQdS9E84\nvvpqmiMUxGyLkgIlqLtrSUEBZ3760xbY24VpV0EjIk/TWKAIGvc1tI1ucyOw0jn3poiMPmxrYPHi\nxQ3vJ02axKRJk9poKobRddmwYQMbNmzI9DQ6Ndm8tnzSFP5tXQLgk4wXdncFC49ZZTo/bbG+ZDoo\nuBoodc5t9scPAWudcw8mtVsAjHDOXe6PpwPfdM59WUS2AX1903y0OvwLzrmzmriny+RnNoyuwpES\nFOxdTi8CA51zTkRyUMvMqLjLydaWzkVTJR+M7CAbdzl9HxjmnPu230Xwv+gi8ZekdiPR4Pki4D3g\nP4GfOOfuTWo3EbjR2S4nw2h3jhRBAyAi/w3c7Zz7iYjMAC5yzn0hqY2tLYbRRrRmfcl0puAbgQEi\n8jKaumBWEDMi8k8iMhvAObcTTXHwW/SX0nZ8QLBhGEYH8B3gchF5AbgUTWdiGEYnwhLrGYbRKo4k\nC0062NpiGG1HNlpoDMMwDMMwPjEmaAzDMAzDyHpM0BiGYRiGkfWYoDEMwzAMI+sxQWMYhmEYRtZj\ngsYwDMMwjKzHBI1hGIZhGFmPCRrDMAzDMLIeEzSGYRiGYWQ9JmgMwzAMw8h6TNAYhmEYhpH1mKAx\nDMMwDCPrMUFjGIZhGEbWY4LGMAzDMIysxwSNYRiGYRhZjwkawzAMwzCyHhM0hmEYhmFkPSZoDMMw\nDMPIekzQGIZhGIaR9ZigMQzDMAwj6zFBYxiGYRhG1mOCxjAMwzCMrCejgkZE8kXkARF5WUSeE5H/\n396ZB0tR3HH88yV4PW85FCICQSBgBU8Ub5MUGkvRiqQ0KkosRE0qViVKWfG+yjIR0UTRMmISY7wD\nmuCFJqCW8QjI8cDEAwURjwSjGBVNlfJ++aN7fZPN7mPf7szO7nu/T9XUTh87v+909/T+drqn54gO\n8k6O+ZZLuq4obTdJT0r6m6QXJB2WvXrHcboCkn4jabWkRXE7N5HWV9Kjkl6WtFjS3nlqdRynPHnf\noZkCfGhmQ4GjgFsktRRnkjQIuAjYJ+YdJmlCTGsBZgFTzGwXYBQwvz7ya+OJJ57IW8IXNJIWaCw9\nrqVbcKWZ7RG3K5PxwJNmNhz4IXBHPvI6Jq924Xa7tt28bXeWvB2a44CbAMzsVWABcHiJfN8B7jez\n92N4RvwuwAnAU2a2IB6nzczWZqo6JRqpoTSSFmgsPa6lW6Ay8cfS3kc9DXwqac+6qaqQ7vZD63a7\nh+3OkrdDsxOwKhFeDQyoIN8biXwjgc8lPRRvF8+QtE0mah3H6ar8WFKrpPskfRVA0nYAiT9SUL6P\nchwnZ3pmeXBJC/n/i1+AATukZKYn8A1gjJmtkXQtMA2YlNLxHcdpYjbQD20PnGdm78S8JwFzJA2u\nr0rHcWpFZpafcWkZMNHMFsXwA8CtZjarKN8UYKCZnRnD44Hvmdk4SWcDI8zs1Jg2GviVmY0qYzO/\nE3acLoaZlRuqaVok/QvY3cxWS/qI0Pe8H9OWEfqehSW+532L46RIZ/uXTO/QVMBM4HTgdElDgb2A\n75bINwt4UtIlwFpgMu2T8+4FHpK0hZl9DHwLaC1nsCt2wI7jVI+k/mb2dtw/DPgceCsm/x74PnCF\npAOATUs5M+B9i+PkTd4OzVTgVknLCZ3IZDNbByDpUuAtM7vZzFZKuhz4K+E28aPA7QDxX9RVwLOS\n1gMrgdNyOBfHcZqT30rqS+hb/g2MM7O2mHYucLukicAnwIScNDqOswFyHXJyHMdxHMdJg7yfckqd\nRlqsLy0tMX2TeIyq19hJQ4+koyQ9L2mppGWSzuqE/aGSnpH0kqSnJQ0pkaeHpBskvSrpFUmTKknr\nLClouSC2i8WSFkg6tFotaehJ5BkuaV28a5mbFknHJtpIq6Q+1eppNCRtp/BU5YuSlkiaKalXTBsT\n416SNEdS75Rt3x/b3KLYP+0a4zdYZynZv1hSm6SRMZzp+UYbr8f+qnDeY+thO/a5N8b23Srpphif\nWVlLGpg4z0WSVirM6ULSsCzrWNKR0ebiWK7fjvGZty1JR0haGMv5cUkDq7JtZl1qAy4Ebo77OwPv\nAC0l8g0iPIK5XQzPASbE/RbgNWB0DPcAts1DSyLP1YT1d+bnXDajgR3i/lbAcmD/Cu3PBY6P+ycC\nc0vkOQl4JO73jjp2iuGTy6VVURa1ahlLmE8BYTHHtcAmNdRNTXoS7fRxwnDsVXlpIcyFewHoE8Nb\nAhtXq6fRNmBb4KBE+CpgRtxfDuwb988nPKCQpu0tE/tHAQsrrbMUbO8OPEwY1h9Zj/ONx11BePCj\nOD7rsr4OmJYIF9pz5mWdsHkt8It62AXeL5Qz8DXCorf1sLsN8C4wJGHjkWpsZ1IJeW6xI90jEZ4N\njC+RbwpwXSI8Hngg7p9KeNoqdy0xfCDwB+AganNoUtFTlHc2cEIFtvvEC6YwzNmD4AT0Ksr3IHBM\nInw9cPaG0jpZDjVrKXHMtUD/KuslFT3AecBZwMVU6dCkVE+3E54ESu26buQNOAZ4jODILU3E9wI+\nytDuyYRV0SuqsxptbQw8AwwkOjT1Ol8SDlQiLlPbwOaxDFuK4jMv64StjYA1wK51quN3aXcQDwJe\ninbXZmx3L2BZIrwtsL4a211uyInGWqyvZi2SNgeuAc6g/GqmddOTRGEBsn2AeRXYHkCY5G0QVnQG\n3i5x3I5sV6SrTlq+QGHC6GsWn5TJQ4+kUcChhH90tZBG2YwEhsQhkeclnV+jpoZFkghPQc2mqEzM\n7L2YJdWFPmN/tAq4HJhI5XVWC5cBt5lZss7rcr6RO+IwyHRJW9fB9hDgPeAShSHleZL2pz5lXeBo\n4E0za62T3eOA2ZJeB+6jvW29mbHdV4Ad1L4Cd2HifadtN51DE8fZ1hRt78bPtM6nsFjfKWa2B/Ax\nYbG+PLRMBW4ws3+wAYemTnoKtvoR7hr9IGrrlkg6GLiU0ssN1EtDT+Bm4IzCxZ8zPQm3rL8JHAIc\nrvjutS7IdMKdgell0lN/lNvMJpvZQMIduauzslNA0hjC8PtNyehy2TOQcICZ7U4Y7u5BKPOsbfcE\nvkIY0hsN/ITwI79FynY64hTg1/UwJOlLhCf6xpnZIMJw5r2E880UM/uQ4Ez9XGGOaG/gA6oo66Zz\naMxsTzPrW7T1iZ9tBK99YOIrOxHuRBTzBmGuSKl8q4B5ZrYmhu8kXEx5aNkfuEjSCuAuYJSkJSWO\nUS89KDzi+ifgZ1a0CGIHrAa+HP/REh2s/iXsv9GBxo7SOkMaWpC0L3AbcLSFd5FVS616+hE634cl\nrQR+BEwuTGKssxYI7WymmX1uYW2oPwJd7i3VkqYS/skfG6P+57qJk1TbzOyDLOyb2R3A16m8zqrl\nYGC4pBWxfe1ImFc3hEQbyOp8zeyt+PkZcCOwH6GNDcrQ9irgMzO7J9qeTxiS+RTon2FZE4/bjzDs\nU1hvLes63g3oZ2bPAZjZM8A64D8Z2yXam2dmB5rZ3sANwGaEocbO2U5rHKxRNsL8gV/G/aGEia+b\nl8g3mNAB9SI4dnOAk2LaAGApsEUMXwj8Lg8tRfkOprY5NGmUTS9gCXB6FfbnASfG/QmUnmw6EXiE\n4Jn3iToGbSgtBy2jCZ3e6JTabU16StRzLZOCay2b4wnDExDmAcwBJqVRTo2yAVcQJixumogTYaLq\nfjF8ASlOVCXM69gxER4HrK60zlLUsRIYkfX5xmO2AFsVlfvMOtmeA4yN+8MI81m2qkdZE+6+3V0U\nl5ldwitAPgCGxfAIwpDbNnU63+3jZw/gFuJk7M7azqTB57nFC+De2NhfBI5MpF0KnJYITwZejXmn\nEycfJQpvGeHH+37iDPc8tCTy1OrQ1KyH8ETHOmARsDh+TqzQ/nDgOcJks2eBnWP8Q8TJyrFB35iw\nPSnx/bJpVZRFrVrmA/8sKodd8tJTdKxaHZpay0aEIdq/x2toarVaGnEjzBFaH6+hxXGbFdP2JfwZ\nepmwAGin+40O7PaN9dEabf4Z2K1MnQ3N8PxX0P6U05iszjcef3C8tpbEtnQP7T9+9bD9eLTxPHBo\nvco6HntsUVymdgl/RJYm2vS4Op7vjNhfLCfcodm4Gtu+sJ7jOI7jOE1P082hcRzHcRzHKcYdGsdx\nHMdxmh53aBzHcRzHaXrcoXEcx3Ecp+lxh8ZxHMdxnKbHHRrHcRzHcZoed2icXJDUJqkl7j8oaXDc\n3zm+P2uhpOPzVek4TrPhfUv3xdehcXJB0npgSzP7pCj+HGCAmZ2ZjzLHcZoZ71u6L+7QOLkgqY3w\naolP4vthjiC8T2QaYaXZt4HxhHenXE94HcVmwF1m9tN8VDuO0+h439J96Zm3AMcpYGZ3ShpKeL/U\nOQCSHgMuM7O/SNoImCtpgZnNzVWs4zhNg/ct3QN3aJyGJY6DHwL0LrxxlfBK+RGEFwM6juN0Gu9b\nuibu0DiNTA+gDdjLzNryFuM4TpfB+5YuiD/l5DQsZvYx8BRwXiFO0o6Sts9PleM4zY73LV0Td2ic\nvLAy+8WcCIyU1CppKXA3sHWmyhzHaWa8b+mm+FNOjuM4juM0PX6HxnEcx3GcpscdGsdxHMdxmh53\naBzHcRzHaXrcoXEcx3Ecp+lxh8ZxHMdxnKbHHRrHcRzHcZoed2gcx3Ecx2l63KFxHMdxHKfp+S8m\nSls0bxReyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfe3b59310>"
      ]
     },
     "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 chart on the left shows that increased life expectancy leads to higher population. Each dot represents a value for one country for one year.\n",
    "\n",
    "The chart on the right shows that the life expectancy increases with the percentage of school completion. Each dot represents the average value for one country for all years that have data. Notice that the percentage goes up to 200% instead of 100%. This might indicate a problem with the data. You can remove the outliers and keep only the values less than 100% with a command like: `df2[df2.school>100]=float('NaN')`. Even better, you can check where these values in the original data came from. Otherwise, a range like this might indicate an error in your code. In this case, however, the values are correct because of the way the school completion data was calculated: read the description of [Primary school completion rate % of relevant age group by country](https://apsportal.ibm.com/exchange/public/entry/view/97fc6c420265012ae9b5e9439b9ee293). Percentages higher than 100 indicate that older people are finishing primary school."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"matrix\"></a>\n",
    "### B. Create a scatter plot matrix\n",
    "\n",
    "You can create charts that show the relationships between all variables with a scatter plot matrix. Each dot represents the average value for one country for all years that have data. \n",
    "\n",
    "Run the following cell to create scatter plots and histograms from the DataFrame with the `scatter_matrix()` method. Because the data does not cover the same years, group the data by country (with some loss of data). Many variables are percentages, so convert the employment figures to percentages as well. Drop the `population` column, because you don't need it anymore. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbfda173690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfda18a990>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfdc2cca10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfdea7c9d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfda0fc990>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfda0e1690>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbfda0657d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9fe8690>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9f56750>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9eda610>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9e3c590>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9d7f550>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9e567d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9cef2d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9c72190>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9bd3c10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9b55ad0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9beddd0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9aa0e90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfd9a24d50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb86b48d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8638790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb859e810>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb851f7d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8558950>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb848f6d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8412590>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8377710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb82fa5d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8263310>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb81ed450>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb816f310>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8152d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb80d5c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbfb8039cd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fbf7ff79c90>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Pz5fYvp2YeVr7Ci75URQFt7tZhHUIgOQcErE+63I18sQTB2hvV4AWFi6sA/qH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\nJ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deQUtLX9//Ze//4qKoz8f99kgmEgCggCaEtQhRdwTiFQaAW3KQCWldt7dqurrSlbndb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\nBo3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wxx1w/fXRZRg8eVKTce9ebUeOVMZbR4MRwjkyiXS/O7mwcAAi5YuPZYNwaQySdJFI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc0042f3c90>"
      ]
     },
     "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. You start to see some relationships: life expectancy increases with schooling, employment, safe births, and measles vaccination. \n",
    "\n",
    "<a id=\"summary\"></a>\n",
    "## 4. Next steps\n",
    "You are deriving insights from the data and can now build a statistical model, for example, try an _ordinary least squares_ regression ([OLS](http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/ols.html)) from [StatsModels](http://statsmodels.sourceforge.net). \n",
    "\n",
    "As you can see from this sample notebook, data analysis entails a lot of trial and error. This experimentation can be challenging, but also a lot of fun!\n",
    "\n",
    "### Author\n",
    "\n",
    "**Margriet Groenendijk**, PhD, is a Developer Advocate at IBM Cloud Data Services. Currently she is all about data: from storing, cleaning, munging, and analysing to visualising. She creates clear plots and figures to show new insights from diverse data. She uses a range of tools for this, such as IBM Cloudant, IBM dashDB, Apache Spark, and Python notebooks. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Copyright © 2017 IBM. This notebook and its source code are released under the terms of the MIT License."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2 with Spark 2.0",
   "language": "python",
   "name": "python2-spark20"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}