notebooks/unit04/unit04_07.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook was prepared by Cayetano Benavent, 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas 02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = \"../../data/world_emissions/undata_emisspcap.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "World CO<sub>2</sub> emissions data.\n",
    "\n",
    "Data source: United Nations, 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading a CSV (comma separated values) file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read and print only first rows with ```head``` (it is a big file with thousands of rows):"
   ]
  },
  {
   "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>Indicator Code</th>\n",
       "      <th>Country or Area Code</th>\n",
       "      <th>Country or Area</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.425262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.302936</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.249074</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.158962</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Indicator Code Country or Area Code Country or Area Time Code  Year  \\\n",
       "0  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2011  2011   \n",
       "1  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2010  2010   \n",
       "2  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2009  2009   \n",
       "3  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2008  2008   \n",
       "4  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2007  2007   \n",
       "\n",
       "      Value  Value Footnotes  \n",
       "0  0.425262              NaN  \n",
       "1  0.302936              NaN  \n",
       "2  0.249074              NaN  \n",
       "3  0.158962              NaN  \n",
       "4  0.088141              NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print only last rows with ```tail``` :"
   ]
  },
  {
   "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>Indicator Code</th>\n",
       "      <th>Country or Area Code</th>\n",
       "      <th>Country or Area</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10566</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>YR1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>1.309827</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10567</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>YR1967</td>\n",
       "      <td>1967</td>\n",
       "      <td>1.122961</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10568</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>YR1966</td>\n",
       "      <td>1966</td>\n",
       "      <td>1.323656</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>YR1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>1.179176</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10570</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>YR1964</td>\n",
       "      <td>1964</td>\n",
       "      <td>1.045374</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Indicator Code Country or Area Code Country or Area Time Code  Year  \\\n",
       "10566  EN.ATM.CO2E.PC                  ZWE        Zimbabwe    YR1968  1968   \n",
       "10567  EN.ATM.CO2E.PC                  ZWE        Zimbabwe    YR1967  1967   \n",
       "10568  EN.ATM.CO2E.PC                  ZWE        Zimbabwe    YR1966  1966   \n",
       "10569  EN.ATM.CO2E.PC                  ZWE        Zimbabwe    YR1965  1965   \n",
       "10570  EN.ATM.CO2E.PC                  ZWE        Zimbabwe    YR1964  1964   \n",
       "\n",
       "          Value  Value Footnotes  \n",
       "10566  1.309827              NaN  \n",
       "10567  1.122961              NaN  \n",
       "10568  1.323656              NaN  \n",
       "10569  1.179176              NaN  \n",
       "10570  1.045374              NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Counting rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Indicator Code          10571\n",
       "Country or Area Code    10571\n",
       "Country or Area         10571\n",
       "Time Code               10571\n",
       "Year                    10571\n",
       "Value                   10571\n",
       "Value Footnotes             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Indicator Code', 'Country or Area Code', 'Country or Area',\n",
       "       'Time Code', 'Year', 'Value', 'Value Footnotes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generating various summary statistics, excluding NaN values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10571.000000</td>\n",
       "      <td>10571.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1986.746949</td>\n",
       "      <td>4.311682</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.053139</td>\n",
       "      <td>7.193311</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1974.000000</td>\n",
       "      <td>0.411164</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1988.000000</td>\n",
       "      <td>1.590776</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>5.977060</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2011.000000</td>\n",
       "      <td>99.840439</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Year         Value  Value Footnotes\n",
       "count  10571.000000  10571.000000                0\n",
       "mean    1986.746949      4.311682              NaN\n",
       "std       15.053139      7.193311              NaN\n",
       "min     1960.000000      0.000580              NaN\n",
       "25%     1974.000000      0.411164              NaN\n",
       "50%     1988.000000      1.590776              NaN\n",
       "75%     2000.000000      5.977060              NaN\n",
       "max     2011.000000     99.840439              NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.describe?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.425262\n",
       "1    0.302936\n",
       "2    0.249074\n",
       "3    0.158962\n",
       "4    0.088141\n",
       "Name: Value, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10571\n",
       "Name: Value, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10571\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Year'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True    10571\n",
       "Name: Value Footnotes, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value Footnotes'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42526210494041705"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.rename(columns={'Value': 'emissions'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Indicator Code</th>\n",
       "      <th>Country or Area Code</th>\n",
       "      <th>Country or Area</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.425262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.302936</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.249074</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.158962</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Indicator Code Country or Area Code Country or Area Time Code  Year  \\\n",
       "0  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2011  2011   \n",
       "1  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2010  2010   \n",
       "2  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2009  2009   \n",
       "3  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2008  2008   \n",
       "4  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2007  2007   \n",
       "\n",
       "   emissions  Value Footnotes  \n",
       "0   0.425262              NaN  \n",
       "1   0.302936              NaN  \n",
       "2   0.249074              NaN  \n",
       "3   0.158962              NaN  \n",
       "4   0.088141              NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing data by attribute (China emissions):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "chn = df[df['Country or Area Code'] == 'CHN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Indicator Code</th>\n",
       "      <th>Country or Area Code</th>\n",
       "      <th>Country or Area</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1855</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.710302</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1856</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>YR2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>6.172489</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1857</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>YR2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>5.778143</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1858</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>YR2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>5.311152</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1859</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>CHN</td>\n",
       "      <td>China</td>\n",
       "      <td>YR2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>5.153564</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Indicator Code Country or Area Code Country or Area Time Code  Year  \\\n",
       "1855  EN.ATM.CO2E.PC                  CHN           China    YR2011  2011   \n",
       "1856  EN.ATM.CO2E.PC                  CHN           China    YR2010  2010   \n",
       "1857  EN.ATM.CO2E.PC                  CHN           China    YR2009  2009   \n",
       "1858  EN.ATM.CO2E.PC                  CHN           China    YR2008  2008   \n",
       "1859  EN.ATM.CO2E.PC                  CHN           China    YR2007  2007   \n",
       "\n",
       "      emissions  Value Footnotes  \n",
       "1855   6.710302              NaN  \n",
       "1856   6.172489              NaN  \n",
       "1857   5.778143              NaN  \n",
       "1858   5.311152              NaN  \n",
       "1859   5.153564              NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_ch = chn['Year']\n",
    "y_ch = chn['emissions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1855    2011\n",
       "1856    2010\n",
       "1857    2009\n",
       "1858    2008\n",
       "1859    2007\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1902    1964\n",
       "1903    1963\n",
       "1904    1962\n",
       "1905    1961\n",
       "1906    1960\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ch.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1855    6.710302\n",
       "1856    6.172489\n",
       "1857    5.778143\n",
       "1858    5.311152\n",
       "1859    5.153564\n",
       "Name: emissions, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ch.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1902    0.625646\n",
       "1903    0.640002\n",
       "1904    0.661428\n",
       "1905    0.836047\n",
       "1906    1.170381\n",
       "Name: emissions, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ch.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas and Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1960, 2011)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4uKR9JF1Z2wwAAACmSJXi6rqIuE6SbG8VET+RdJ9mp4Wuo6cgHzLLidzyIbOc2u65utT2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\nAADUiOIKAACgRhRXAAAANfr/KZE/T6o10xsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f27664a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x_ch,y_ch, alpha=0.5, linewidth=0)\n",
    "plt.grid(True)\n",
    "plt.title(\"China emissions - $CO_2$\")\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"metric tons per capita\")\n",
    "plt.xlim([x_ch.min(), x_ch.max()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "esp = df[df['Country or Area Code'] == 'ESP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_es = esp['Year']\n",
    "y_es = esp['emissions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2f274e4da0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cudhVg5tYz0BEAjF/vo18TJlifyQHDLAr1MaNg6eessuPo/WDJk60P+IdOwbd\n6vQVLSVQuzb077/p43r2tNpOzzwDI0bY1N9nn1l/1i1cknWvvSzw2mWXxOXO7bOPrdt3661w110W\nuI0caUFfmzYW4LVokZhzi1TBiy/ax1yDBjYdWKtWcG3R1YIi6eTLL60wXl6e5cTceefG00tgics3\n3GCJ0JmZcMklcPzxSnZPhEGD7BO/Xz/7d47FrFnWd999Z/v772+BWWUrnlfG8OE29RfVrp1dFViR\npHuRJPnxR7voNRKxJSr32Sex54tLKYbCtQU7Af9en+u9HxeXFqLgSqTKCgpg2DC7cgygRw+rlN24\n8aZfs3KlXRX27ru237OnLRGvQo3x8/PPcOqpNmr13nsV+7f1Hr791kaJglikuHgNoI4dLbAqOaUp\nkgKWLrWUxtxc+3Wr6HRgZVQ5uHLOnQUMBNoAk4G9gAne+7jVildwFT7Vvo5LJAJff221fYKeIsnN\ntWmcyZNtGvCCC6xAZYlL8TfZZ598YgVh8vKgSRMLsHr12vi45cttiqrk5r1VAN91V418lRQdtarC\nJ36gv2uRiJVc6NLFqspLTKr952MSRSI2IPzVVzZI/+STUDOWhKdSJGVtwWIGAnsAX3vv93PObQfc\nHtPZRdKR93DPPfD66xbA9OwJxxxjt7HWFoqXCRNsem/pUrsK8PbbLR+nIg480D6VbrzRikJecgkc\ncoiNtuTmWgA1b56NdG3K++9b4cp+/ez9Kvvplk6mT7fAqk4dC67CKCOj6os6iyTQc89ZYNWokV2L\nkSofPbGMXH3rvd/DOTcZ2NN7v8Y5N8173zVujdDIlYTJyy/bpH70t3j9ertt0QKOOspqBMUjLyUS\nsZVGowFOdCu5D5bofPPNVZvSi0QsI3TYsKKfqbjate1nbNnSbps3t9sFCyzQXLrUjmve3K6BPuaY\nYot4VUOXXgrjx9soohYcFom777+3aiGRiF1Q26NH8s4dj2nBUcB/sSsG9weWALW894fGsZEKriQc\nxo61JUy8tyrb3btbLs2oUTB7th2TkWHTasceC3vvXfZoViRiAdKsWbZ0ycyZ8OefNlK0YIE9X5bM\nTKuW99//xm/U7LffbMSlUSMLnqIBVYMGm572W7MGPvwQXnoJcnLssXr1LNg88US7fL86mT7dgqo6\ndez/R5MmQbdIJK0sXgwnnQSLFtnH34UXJvf8cV1b0Dm3L9AI+Mh7H7cCOQquwqda5hRMn26Xo6xZ\nY3lNZxSPhqoiAAAgAElEQVRbuzwSsSm1t96yAKygwB5v1coCjL597XWzZhUFUtH7q1Zt+pxNmxaN\nEBXfmje3gKdZs5grcielzyIRG6N/8UX79wAL+g44wCr77bBD2a9PFwMH2pWbcRi1qpa/ayGnPkus\nSMQqzEycaKmejz0Wn1JvSc25cs7tBUzz3ud77z93zjUEdgG+ibG9IuE3Z47lIq1ZA0ccYV+VisvI\nsFGs7t3tK9V771mg9c8/9pv/2GObfu+mTe1qrI4doX1721q3tgCqskuZBCU6aterl9V3euklGD0a\nPv7Ytm7dLEDt3j19k9+nTbPAqm5dC65EJK6GD7fAqkkTm0CIVw3deIplWvAHYNfo0JJzLgOY5L3f\nNW6N0MiVpLL8fJt6mznTgoKHHootazISsUvp33zTcm+aNCkKojp0KLptmOZ1eHNzbQ29t96yKw7B\nEujPPTc9g6yLL7bRu/797eu1iMTNxIlFU4CPPhrcKl7xyLma7L3vVuKxKd77UioTVo6CK0lZ69bZ\nFM/EiRYMPf205R5VlPfpF0RU1MqVlvg+YoQtpwIWZJ1zDuy5Z3r8+/z0k41q1qtn9cPKqjMmIjFb\nuxY++ggefhiWLLGPjXPOCa498VhbcKZz7mLnXK3CbSAwM35NlDCqFmtneW/X9k6caFN3Q4dWLrCC\nlAgcAu+zevXg9NNtyvSiiyxhfsoUG90580yrGxb2L1lPPWW3//d/cQusAu83qTD1WfwsWWK/Vocf\nbhdEL1liF0efdVb8z5XstQXPAx4Crgc88CkQYLwokiTPPmujD7VrW2BV3a54S5RokHX88UUjWdEg\nK5aRrOXL7WrKaCmKefMsuf+oo5JfZ6y4KVNsOrBePav3JSKVNmuWrUv+/vtF68t37my/Wn36BPur\nHgutLShSmtGj4brr7A/83XfDfvsF3aL0tanpwkMPtdpZxYOosoqZ9uxpRVTr109e24u76CIr6nrG\nGXY1qYhUSHTFp5desmtConr3tqAqlRaBiGsphkRRcCUpZfJkOP98y7e69FIrISCJV1qQVZratYsK\nmbZsaevdvfmmLd/TsWMwo4xTplhQVa+eTXs2apTc84uE2Lp1MGaMBVUzZthjmZk2FXjKKcEsrVke\nBVeSEGlbx2X2bLvKa9kym7a68srU+apURaHps5Ur7crCGTNsSZ9oEBUNqBo23LhPZs+2QDgnx3Kd\n7r3Xyj4kw5o1doXgd98lZNQqNP0m/1KfxW7SJFvONLrYRNOmlrJ47LHJvx4kaXWuCssuHOe9f61C\nLdzwPXKAZUAEWOe9D+jCSZFy/PorXHONBVY9e8Lll6dNYBUqlclZ2mory5G75hpLjD//fJvWPfzw\nxLQRbA5jzBi7fGnePJuOVK6VSEwiEUtUHz7cfpU6drRfn0MOCV95v9LEUophkvd+90qfwLmZwG7e\n+yVlHKORKwnOX3/B44/bH0qwrMnhw+2PvIRLQYGt+zhypO2ffroVxYl39utPP9l5fvrJ9jt1ssBu\np7hVqBFJW7m5cP31tjagczbge845qVkMdFPiUefqTmAhMBJYEX3ce784xgbMAnb33i8q4xgFV5J8\nubkWRL39tn2Nysy0qcAzz0z/wp7p7o037EKESAT23RduuSU+wfK8eTZSNXq07TdtatOARxyR+pcv\niaSAL76wacBlyyxd8pZbgisEWhXxCK5mlfKw9953jLEBM4GlQAHwpPf+qVKOUXAVMqHOKcjLg+ee\ns6rha9faH8W+fe2rU4sWQbcuYULdZ5UxcSJcdZVV2O/c2UaaWras3HutXGn/Z1580f7PZGZapm20\nYGgCVbt+SwPqs42tW2cV1V980fb32svqVjVtGmy7ikvq2oLe+w6xN61UPb33c51zWwAfO+d+9t5/\nUcX3FKm4VauscMqIEUXLsBxwgOXntG8faNMkAbp3t4Do0kstOf600+C++2DHHWN/j0jECu08+igs\nKhx879PHanKp7plITP75B6691pbdzMiwwd7TTkvvwd5YFm6uBwwC2nrvz3HOdQK29d6/H8sJvPdz\nC28XOOdGAd2BjYKr/v37077wD1zjxo3p1q3bvxFktGqq9lNrPypV2rPJ/U8+gfHjyZowARYvJjs/\nH7p0Iev222H77e34nJzUaa/247ffrh3ZZ54JTzxB1pw5cO65ZO+9N2y2GVk77ADOkT1tGji38X6X\nLvDGG2R/95293957w6BBZC9eDDNmkFUYXCX654k+lhL/ntqPeT8qVdoT1P5992UzYgTUqpVFy5Zw\n9NHZtG8PGRmp0b7i+1lZWWX2Z3Z2Njk5OcQilmnBkcB3wGne+x0Kg62vSq43uInX1gMyvPfLnXP1\ngTHAEO/9mBLHaVpQEmPePBuZmj3b9rt2tVGHPfYItl2SXOvXWw7WW29V/LUtWliB0DCUhRZJEWvW\n2Ez8m2/aflYW3Hhj+qSzxiPnapL3fnfn3A/e+10KH/vRe79zDCfvAIzCls2pCbzkvb+zlOMUXIVM\n8W/SKWvdOjj7bJg61ab9Bgyw5OZqWl4hFH2WSN7Dxx/DDz/Y/egWfa60x7beGo47DurUCazZ1b7f\nQqi691l+Ppx7rs3G16plM/PHH5/6H70V6bcq51wBa51zdbEACefc1sCaWE7uvZ8FJKmSn0gJDz5o\ngVXLlvD006qaXd05Z6NPffoE3RKRtBWJ2NWAM2ZAmzZw112w7bZBtyr5Yhm5OghbtHl7bFqvJ9Df\ne58dt0Zo5Eri7ZNP4OqroWZNC6y6dg26RSIiae+ZZ2DYMGjQwK4M3HLLoFuUGHFZ/sY5tzmwF+CA\nr733C+PXRAVXEmd//WWlfleuhCuusLUUREQkob7+2tITvbeJg549g25R4pQXXMWanbkvcACwH9A7\nHg2TcCt5RUzKWL3a1gNcuRIOPBBOOCHoFqWMlO0zKZP6LXyqY5/NnWsrTnlvJQPDGFjFs9/KDa6c\nc8OA84CfgKnAuc65R+PWApF4uvtu+P13aNsWbrgh9TMoRURCbu1aq9e7bBn06AFnnRV0i4IXS87V\nL0CX6Lxd4WLO07z3XeLWCE0LSjy8+66V/M3MhOeft/XeREQkoW67DUaNsrq6L76YPuUWyhKPacHf\ngbbF9rcqfEwkdfz2G9xZWOXj6qsVWImIJME771hglZkJ99xTPQKrWMQSXDUAfnbOZTvnxgLTgYbO\nuXedc+8mtnmSqlIqp2DFChuTXrvWFtA94oigW5SSUqrPJGbqt/CpLn32889WagFseZuwl1yIZ7/F\nUufqxridTSTevLdl1f/6y0arrroq6BaJiKS9Zcvs2qG1a+HYY+Hww4NuUWqJqRRDwhuhnCuprFdf\nhXvvhXr1bLK/bdvyXyMiIpUWicDAgTBhgpUQfOopmxasTuJVikEk9UydCkOH2v3BgxVYiYgkwZNP\nWmDVuLFdoF3dAqtYKLiSSgk8p2DZMpsCXL8eTjwRDjgg2PaEQOB9JpWifgufdO6z8eNh+HBbw/z2\n221d83SR1DpXxTnnmjjndorb2UUqY906q2E1fz7ssIONT4uISELNnm0fvQAXXgjduwfbnlQWS52r\nbOAILPn9OyAX+NJ7PyhujVDOlcRq9mwrAzx9ul3z+/LLtjCziIgkTH4+nH221Wjebz+bDqzONZrj\nkXPVyHufBxwDjPDe7wkcGK8GisRs9Gg45RQLrFq1gkceUWAlIpJgy5fDgAEWWLVrBzfdVL0Dq1jE\nElzVdM61Ak4A3k9weyQkkppTsGqVVV6/7jpbM/CAA2zEavvtk9eGNJDOeSDpTP0WPunUZytXwsUX\nw7RpVoF92DCoXz/oViVGsutc3QyMBr7w3n/rnOsI/Ba3FoiU5bff4JprICfHLkm5/HI4+mh9bRIR\nSbBVq+CSS2DKFEtcf/zx9EpgTyTVuZLU5D288QY88IBVqevYEe64A7beOuiWiYikvTVrLLD69lvY\nYgurZdWmTdCtSh3l5VyVO3LlnNsCOBtoX/x47/0Z8WigyEby8qzq+tixtn/00XDZZVCnTrDtEhGp\nBtautY/cb7+FzTeHJ55QYFVRseRcvQM0Aj4BPii2STWWsJyCKVPg5JMtsKpf3wqpXHedAqs4SKc8\nkOpE/RY+Ye6ztWvhiivg66+hSRObCqwu9ZmTnXNVz3uvBdsksbyHESPg0UdtbYWuXS2w2nLLoFsm\nIlItrF9vKa5ffgmNGsFjj0GHDkG3KpxiqXN1K/CV9/7DhDVCOVfy2mtWOAXgtNPg/POhVq1g2yQi\nUk0UFMC118Knn1oJwccfh86dg25V6iov5yqW4CofqA+sBdYVPuy99w3j2EgFV9XZ999bMFVQYCUX\nDj006BaJiFQbkQhcfz2MGQObbWYjVl26BN2q1FblIqLe+wbe+wzvfZ3C+w0qGlg55zKcc987596t\nyOskdcVtbnr+fFsjsKAATj1VgVUChTkPpDpTv4VPmPosErGioGPGQL16Vpu5ugZWyc65wjl3BLBP\n9Pze+4oWEx0ITAfiNtolaWDNGqtbtWSJLVI1YEDQLRIRqVbuvBM+/BDq1oWHH7blWqXqYpkWvBPY\nA3ip8KGTgEne+2tiOoFzbYBngduAQd77I0o5RtOC1Y339nXpgw+s7O8LL1gGpYiIJMXnn1vJhdq1\nLbDaddegWxQeVa5zBRwKdPPeRwrf8HngByCm4Ap4ALgCK+cgYkaOtMCqTh247z4FViIiSbRyZdE1\nRBdfrMAq3mKpcwXQuNj9mP8KOucOA+Z77ycDrnCTNFCluelJk+D+++3+4MHQqVNc2iRlC1MeiBRR\nv4VPGPrsiScs5XX77eH444NuTWpIds7VHcAPzrmxWHC0D7GPWvUEjnDOHQrUBRo450Z4708reWD/\n/v1p3749AI0bN6Zbt25kZWUBRT+w9lNnf/LkyZV7/dy5ZJ97LixfTtZFF8FBB6XEz1Md9qNSpT3a\nj21/8uTJKdUe7Ze/X+nPxyTt//UXvPJKFhkZcOCB2Ywbl1rtS8X96P2cnBxiEdPags65VljeFcBE\n7/28mN59w/fYF7hMOVfV2OrVcOaZ8Ouv0KMHDB0KGRlBt0pEpNqIRKB/f5g+HU46yXKupOKqXIrB\nOfep936u9/7dwm2ec+7T+DZT0p73cNttFli1aQO33qrASkQkyd54wwKr5s2tvKAkxib/ujnn6jjn\nmgLNnHNNnHNNC7f2QIXXJPHef17aqJWEU/Gh0pi8/DL87392ve9991kJYEmqCveZpAT1W/ikap8t\nWGArjIFVwalXL9j2pJp49ltZOVfnApcArYHvKEpGzwMeiVsLJP1NnAgPPmj3hwyBrbcOtj0iItXQ\n/ffDihXQuzfst1/QrUlvsdS5ush7/3BCG6Gcq/Q1Zw706wd5eXDGGXDBBUG3SESk2vnqKyu5UKcO\nvP46tGoVdIvCLR7L3yQ0sJI0tngxDBpkgVWvXnDeeUG3SESk2lm92iqxA5xzjgKrZFBGsVRKuXPT\nM2faJSm//w5t28IttyiBPWCpmgciZVO/hU+q9dnTT9skwjbbwMknB92a1BXPftNfO4m/SZNsCnDO\nHOjaFYYPhwYNgm6ViEi1M3MmjBgBzsG110LNmFYUlqqKJefqaOAz7/2ywv3GQJb3/u24NUI5V+nj\ngw9slGr9esuYvOUWm+QXEZGkikTg7LPhxx/h2GPhmljLf0u5ysu5iiW4muy971bisR+897vEqY0K\nrtKB9/DUU/Dkk7Z/8slwySWaChQRCcjbb1tJwaZN4c03NYEQT1VOaN/EMRpYrOY2mJtetw5uuskC\nq4wMuPJKS2RXYJVSUi0PRGKjfgufVOizxYvhoYfs/mWXKbCKRbLqXEVNcs7dDxSWHuNCrO6ViF0J\neOWVlmdVpw7ccYcVURERkcAMHWofz3vuCX36BN2a6ieWacH6wA3AgYUPfQzc6r1fEbdGaFownObM\ngYEDYdYs2Hxz+23u0iXoVomIVGvffmtL22RmwsiRsNVWQbco/ZQ3LVjuyFVhEHV1XFsl4TdtGlx6\nqY09d+xo488tWwbdKhGRam3duqKaVmeeqcAqKGWtLTi08PY959y7JbfkNVFSzuefk/1//2eBVffu\n8MwzCqxCIBXyQKTi1G/hE2SfvfEG/PmnlRc89dTAmhFKycq5eqHw9t64nU3Cb9YsuPpq+3p0wgkq\nnCIikiLy8uyibbCLtTMzg21PdVZmzpVzrgYwwnt/SkIboZyrcIhE4Nxz4YcfoG9fuPFGq0wnIiKB\nu/9+ePll2GMPGDZMH8+JVKVSDN77AqCdc07xr8C771pg1bSp5VvpN1dEJCX89Zclrzunj+dUEEsh\nopnAl865G5xzg6JbohsmKWbxYnjwQbt/+eVkf/99sO2RClPuTjip38IniD576CEoKLBJhc6dk376\ntJDstQX/AN4vPLZB4bZZ3Fog4XDvvZCfDz16wEEHBd0aEREp9N13kJ0NdetaCQYJXix1ro733r9e\n3mNVaoRyrlLbV1/BxRdD7drw+uvQunXQLRIRESwV9tRT4ddf4bzz4Kyzgm5R9RCP5W9KW+pRyz9W\nF6tWFRVNOfdcBVYiIinkww8tsGreHPr1C7o1ElVWnav/OOceBrZ0zj1UbHsOWJ+0FkqwnnrKKrF3\n7myLMRdSHkj4qM/CSf0WPsnqs1Wr4NHChekGDLAVyKTyklXnag4wCTiCDdcSzAcujVsLJHXNmAEv\nvmiXnVx3nepZiYikkBdegAULYPvt4ZBDgm6NFBdLzlUtLAhr673/NSGNUM5V6olEoH9/mD4d/u//\n4Iorgm6RiIgUys2FY46B1attgmGXXYJuUfUSj5yrQ4DJwEeFb9gt1uVvnHO1nXPfOOd+cM5Nc87d\nHlOrJXivv26BVfPmcMEFQbdGRESKeewxC6z231+BVSqKJbi6CegOLAXw3k8GOsTy5t77NcB+3vtd\ngJ2A/Z1zPSvXVEma3NyiifyrroL69Tc6RHkg4aM+Cyf1W/gkus9++QXef98yNS6+OKGnqlaSXedq\nnfd+WYnHYp7D896vLLxbu/B8S2J9rQTk7rth5UrYbz/Yd9+gWyMiIoW8hwcesNsTT4Q2bYJukZQm\nlpyrp4FPgauBY4GLgVre+/NiOoFzGVhC/NbA4977K0s5RjlXqWLsWMuvqlfPlldv3jzoFomISKHP\nP4fLLoNGjeDtt6FBg6BbVD3FI+fqIqArsAZ4BcgDLom1Ad77SOG0YBtgH+echkJS1YoVcM89dn/A\nAAVWIiIpZN26olXIzj1XgVUqK/fa+sJpvesKt0rz3uc55z4Adgc+L/l8//79ad++PQCNGzemW7du\nZGVlAUXzoNqv4H7v3rBgAdlTp0LNmuUfP3Ei5OaS3awZNGuGPVv68ZMnT+aSSy5JrZ9X+2XuRx9L\nlfZoP7b9oUOH6vMwZPuJ+nx84w2YNi2bFi3gmGNS5+dNl/3o/dKej97PyckhFrFMC+4OXAu0p1gw\n5r3fqdw3d64ZhTlbzrm6wGhgiPf+0xLHaVow3mbMgMsvtwKgAE2awBZb2Na8edFt8+bQrBksXQoX\nXmg1rV56CTp1KvPts7Oz//3PJ+GgPgsn9Vv4JKLP8vLgqKPs9oEHoHfvuL69ULF+K29aMJbg6lfg\nCuAnIBJ93Hv/Zwwn3xF4HnDYFOQL3vt7SzlOwVU8ffwx3HQTrFkDm21myemRSLkvA+C003T5iYhI\nClm0CO67D8aMge7d7WJut8k/65IM8QiuvvTeJ7R8goKrOIlE4PHH4ZlnbL9vX7jmGrted/FiK7Gw\nYEHRbfH7ubnQti08+aTWUBCRlOA9zJplA/F161qOUcOGRVvt2ukbZEQiMGkSvPkmZGdDQQFkZNii\nGZ07B906iUdwdRBwIvAJltQOgPf+rTg2UsFVVS1fDjfcAOPH22/gpZfadboJ+uTRVEX4qM/CqTr1\nWzSY+u67om1JGcV7atXaOODafHPo0wf22MM+CoNQlT5butRqWL31Fvz1lz2WkWFVcfr1g513jl87\nZUPxnBaMZbG4/sC2hcdG55Y8ELfgSqror79g0CDIybFPlzvvtLFjEZEU5r19bBUPphYv3vCYLbaA\nHXawkZtlyyA/3/KO8vJg7Vo7vuRr3nkHttrKlofp2xcaN07aj1Qp3sOPP9oo1Sef2FWBYCmxRx8N\nRx6pi7fDJqacK+/9tglthEauKu+rr+Daa23kqmNHuP9+VZUTkZQ2ZQq8+qpNe5UMjJo1g912g913\nt9utttr0APyaNRZkFQ+4Zsyw+k/z5tkxtWrBgQfCscfaqE+qTCOuXw/z58OXX1pJwZkz7XHnoEcP\na2/PnlCjRrDtlNLFY1rwWeAe7/30eDeu2DkUXFWU97Yk+iOP2OT8fvvBkCFW/FNEJAXNmmXJ2MWu\nbmfzzYuCqd13LzuYilUkYkHLm2/abfTPy9ZbW9By6KF2rU8iRSKwcKFdsD1nDvzzD8yda7dz5lhg\nVfw6o6ZN7WrAo46C1q0T2zapungEVz9j1dVnYTlXDvCxlGKoQCMVXFXEmjVwyy3w0Ue2f845cNZZ\nSU0wqE55IOlCfRZO6dBvubnwxBPw3nsWUNSpAyefbEFOu3aJHU2aM8dGst5+u2iUrG5dOOQQmzLc\nckurdl4zliSZEtassWDp77833H74IZuCgizWrt30a52zKc9ttoEjjrCcqlq1KvczSnwkO+fqkBjb\nJcmQm2trH/z8s31C3HyzjVqJiKSYvDx4/nl45RXLj8rIsJGjs8+26b9kaN0aLrjAzpmdbaNZkybB\nqFG2RTVoYLlZTZrYbXSL7tesuXEgtXBh6efMz7f3a9oUWrWyAK51a9ui91u0gMzMpPwTSADKHblK\nSiM0clU+763IyV132SdW69aWX7XNNkG3TERkA2vWwMiR8Nxz9nEFlvd0wQVW8SVoOTl2Nd7XX9vV\niMuWxV4KsLiaNe2juE2bjbdWrez7r6SnKk8LJoOCq3IsWWJXAH5aWNi+Rw+bFmzUKNh2iYgUU1AA\nH3xg5fZyc+2x3XeHiy6Crl2DbVtZIhEbbVq61LYlSza8XbrURt5atdowgGrZMrhyDxIsBVdhl50N\nt99uyQL16ln9qqOOCvySl3TIA6lu1GfhFIZ+W7jQFoZ46y1LWgcrdHnRRbDXXoF/XCVdGPpMNpbs\nnCsJQn4+3HMPfPih7e+2GwwerMtIRCQl5OXB2LEwerTlMEWn1Vq3hvPPh4MP1qiOVF8auUpFEybY\ntF9urq3vcNFFcMIJ+qQSkUCtXg3jxllA9dVXRcUua9a0mkwHHwxZWUrUlvSnkaswWbkShg61sXWA\nHXe02lWpkAEqItXS+vXwzTdW+SU7G1atssczMmwhiIMPtguWGzYMtJkiKUXBVar4/nu46SYrylKr\nFpx3Hpx6asqOVimnIHzUZ+EURL95D1OnWlbCmDF2NV3UDjtYQHXQQckrpxA2+l0Lp3j2m4KroOXm\nwlNPFRVc2XZbq1219dbBtktEqp05cyyg+vDDokWDwVbWOuQQWxBZq2uJlE85V0FZvBiefdYq2kWr\n6511FpxxRuVKBYuIVEJ+vi0W/OGH8MMPRY9vvrkFVIcdBp06Vb8r/kTKopyrVLNsGYwYYRX2Vq+2\nx/r0sSVs2rcPtGkiUj2sX28J6R98AOPH8+8yLbVrw/7727I03btr0WCRylJwlSzLl8PLL8OLL1ri\nOthiUuedZ18LQ0Y5BeGjPgunqvZbJGJLtfzyS9H28882YgU2ItW9uwVU+++vtd/jQb9r4aScqzBZ\ntQpee80W2IquA7H33hZUpXLJYhEJnfXrYeZM+PVXC6J+/RVmzCj6Pldcx4425fef/0Dz5slvq0g6\nU85Voqxda/lUzz5btBT7rrtadb1ddgm2bSISet7D3LkwZYptU6fC778XTfEV17w5bLedXS8TvW3R\nQnlUIpWlnKtkiy6u9cQTMH++Pda1q61Y2r27Ps1EpFJWr4bp0+GnnyyY+umnou9txW211YZB1Lbb\nQtOmyW+vSHWm4CpevLcKe8OGFS2u1amTjVT17p12QZVyCsJHfZZaCgosa2DVKgucorfF769YAaNH\nZ7NiRRYzZhQtMRPVqBHstJPVG95xR+jSBTbbLJifR4rody2clHOVaiZNgocfhmnTbH/LLS2o6tMn\nZYuAikjV5eTAZ5/ZkjC5ufZYJGLftaJb8f3o/bVrLT8qFvn50KCBfZR07mzBVDSgatMm7b63iaSF\nhOZcOefaACOAFkAEeMp7/1Apx4Uz5+qXX+CRR+Drr22/aVOrVXX00VZlXUTSivf2az92rAVVOTmV\nf6+MDKhTx7a6dW2L3i9+u9VWFkx16aIr+URSRXk5V4kOrloCLb33k51zmwHfAUd6738pcVy4gqu/\n/oLHH7d1IQDq14fTT4cTT9Snn0iaiURg8mQLqMaOhXnzip5r2BD22cfW1uvcuaguVEaGjShFt5L7\nmZn2/UujTiLhFGhCu/d+HjCv8P5y59zPwJbAL2W+MFXNnw9PPw1vv22fuJmZcMIJ8N//WvJDNaKc\ngvBRn8Vu1Sqb7f/8c9uWLCl6bostICvLAqpdd038ggrqt/BRn4VTKHOunHPtgW7AN8k6Z9z8/DO8\n9BJ8/LFloWZkwJFHWlX1Fi2Cbp2IVJH3Vh/qq69gwgRbBmbduqLnt9rKgqn99rOLf5VKKSJlSUqd\nq8IpwWzgFu/9O6U8n3rTgpEIfPGFVVT//nt7LCMDDjwQzj4bOnQItn0iUiV5eTBxogVUX39dlJAO\nNl3XtSv07GkB1dZbawpPRIoEXufKOVcTeAN4obTAKqp///60L1xbr3HjxnTr1u3f4bns7GyA2Pc/\n+wxyc8k6/HDYbLOKvX71arLvugs++YSsVavs+bVroXdvsq6/Hlq1suP//LPy7dO+9rWf9P3u3bP4\n9Vd46aVspk2DRYuyiEQgP9+eb98+i733hvr1s+nSBQ4/vOj1f/8dfPu1r33tB7cfvZ8T41UsCR+5\ncjEj9NwAABk7SURBVM6NABZ67weVcUx8Rq68t9GmRx6BP/6wx5o1s1GmDh1sYeT27e1+s2YbfhVd\nvNiWqXn9dVtcGaBlSzjpJDjqKEtal39lZ2f/+59PwqE69dnSpRsuAfPLL3YdSnE1akC3btCjh61I\n1alTao5OVad+Sxfqs3CqSL8FOnLlnOsJnAL85Jz7AfDAtd77j+J+silT4KGH7LIesMt4Vq+GhQtt\n+/bbDY+vX78o0PLe8qmi60Zsvz306wcHHKBl4UVS3LJl8OOPRUHUL78ULY5QXM2asM02Nt3Xowfs\nsYcu7hWRxAj/2oIzZ8Kjj9olPWBX7Z15Jhx3nH2azptnFdOjW06O3UYXUS5qhFVS79fP1v5Lxa+w\nIgLA8uWQnW3VUL75xq4zKa5OHSuNsN12RVuHDio/JyLxEWidq1hVKrjKzbVaU++/b8nnderAKafA\nqaeWv/6D93ZtdTTQWrbMEtXbtq30zyAiibVyJYwfbwHVV18VXc2XkWHTe127Fq2p17atrugTkcRJ\nv+AqLw+eew5efdWm8WrUsIroZ51leVSSFMopCJ8w9tmaNfDllxZQjR9v+2ADy7vtZitM7b8/NG4c\nbDsTKYz9Vt2pz8IpNDlXcbViBbzxhgVW+fn22EEH2Rp+GnESSRvr1llphDFjbLZ/5cqi53be2QKq\nAw7QdykRSV2pPXLlPUydCqNG2Sft6tX2ePfuMGCAJZ6LSOgVFMB338Ho0bbETPGUyO23t4DqwAPt\nAl4RkaCFc+QqLw/+9z94662ikgpga02ceSbsuWdwbRORuIhE4KefLKD65BOrhhLVqZMNTPfpA23a\nBNdGEZHKSJ3gynsrozBqlH3SRssiNGkCfftarSlN/6UM5RSETyr0mfdWKmHMGNuKl0xo29aCqT59\noGPH4NqYalKh36Ri1GfhFM9+S53g6vjj7eq9qD33tET1fffV9dMiIZWfD7//bttvv1m5udmzi55v\n0QIOPtgCqm23VQUUEUkPqZNztdtulqF6xBE2StW6ddDNEpEYrVtn342iQVQ0oCq+Xl9U06Y25Xfw\nwbDDDiqZICLhE55SDNnZ0KuXKqKLxMh724IITtauhUmTrDzCd9/Bn39uXMgTIDPTFj3eZhvLo9pu\nO7viT7/mIhJm4QmuUqAdEjvlFMRfJGIlCMaNs+m0NWvsAtniW8nHvIcttoAtt7TE7y23LLrfpo2l\nLEan2qraZ4sX29Kd48dbOwvXNQfsHG3aWBAVDaS22cYe08hU1eh3LXzUZ+FUPetciaSpBQvg3Xfh\n7bdh7tzKvX7BgqJlNYurW7co4Fq1yvKdmjWzgKxZM9vq1y8918l7W11q3Djbpk61x6I6d7YVo3r2\ntGCqbt2Kt11EkqN9+/b8+eefQTcjdNq1a0dO8XzwGGnkSiQAkYgt4TJqlI0ERSL2eOvWdnFsmza2\nolPt2nZb2la7tr1m/nz4+2/45x/bovf//ruo3m5ZatfeMNhq1sym+L78EubMKTquVi1b7Lh3b9tU\nc0okPApHWoJuRuhs6t9N04IiKWT+fHjnHduiZQhq1ICsLDjmGAte4jmNlpdXFGjNmQMLFxZtCxbY\nbbQ2b2maNCkKpvbcE+rVi1/bRCR5FFxVjoIrSSrlFMRu7VrLURo1ykaDoqNUbdpYtZG+fe0KukQr\nrc+8t+VligdbCxdam3ffXVfzpQL9roVPKvaZgqvKqWxwpZwrkQTIzbXk7y++gIkTi0aHata0ZVyO\nPtoWHg46cHHOcq7q14d27YJti4hIutDIlUgcRCKW8B0NqGbM2PD5zp3hP/+Bww+3qTYRkWQK88jV\nkCFD+P3333nhhRdKfX6HHXZg2LBh7LPPPnE/t0auRJIsL8+m+774wqb7li0req5OHVtfvFcv25o3\nD66dIiJh8PLLL/PAAw/wyy+/0LBhQ7p168a1114LWDCzKVOnTk1WE2Om4EoqJRVzCuJl1aqNc5Ci\n94vfrlix4eu23LIomNptNyugmUrSuc/SmfotfNRnFXf//fdz991388QTT9CnTx8yMzMZPXo07733\nHnVDWOdFwZUIduXea6/Be+9ZscxYZGbCTjsVBVTt2mltPBGRisrLy2Pw4ME8//zzHHnkkf8+fuih\nh3LooYcyZMgQ1qxZw+mnn86oUaNo164dzz//PLvuuisAHTp04Omnn2b//fdnyJAhTJ8+nTp16pR6\n7F133cVTTz1Fbm4ubdu25dZbb+Woo46K+8+k4EoqJR2+lXkPU6bAK6/AZ58VXcVXq9bGdZ+i+8Vv\nGzYMVzCVDn1WHanfwkd9VjETJkxgzZo1ZQY57733HqNGjeK5557juuuu48ILL2TChAkVPnabbbbh\nyy+/pEWLFrz++uv069ePP/74gxYtWsT1Z1JwJdXO2rXw8cfw6qvw88/2WI0a0KcPnHgi7LhjuIIm\nEZGq2H33+L3XpEkVf82iRYto1qwZGWVcPt2rVy8OPvhgAE499VQefPDBSh177LHH/nv/+OOP5/bb\nb2fixIn07du34g0vQ8pUsPn776BbIBWRnZ0ddBMqbNEiePJJu2Jv8GALrBo3hjPOsOnA22+3ab50\nDazC2Geifgsj9VnFbL755ixcuJBIdPqgFC2LLQlRr149Vq9evcnjyzp2xIgR7LLLLjRp0oQmTZow\nbdo0Fi5cGKefpEhCR66cc08DhwPzvfc7lXXsWWfBsGHQsWMiWyRh5L2NNq1caduKFZZ0XlBgz0eD\noeK3xQOk1avh/fdhzBhYv94e69QJTjoJDj64aBkZEZHqqDKjTfG09957U7t2bd5++22OOeaYhJ3n\nr7/+4pxzzmHs2LHsvffeAOyyyy4JKVGR6GnBZ4GHgRHlHbhwIZx9NjzyCHTpkuBWSZVVJadg3Tq7\n2i4317b584vu5+VtHEStWFGUD1UVGRmw334WVO2yS/qOUG2K8kDCSf0WPuqzimnYsCFDhgzhwgsv\npEaNGvTp04datWrxySefMHbsWOqVsu5WRQKi6LErVqwgIyODZs2aEYlEeP755xNWxiGhwZX3/gvn\nXEx1n3v1snpB554LDz5of/wkvLyH2bNhwgTIydkwkIr1arziatWyde3q14e6de1+zZp2nuj5it8W\nv++cTfedcIItjCwiIqll0KBBtGrViltvvZV+/frRoEEDdtttN6677jpGjx690fHF616VVQOr+PNd\nunThsssuY6+99qJGjRqcdtpp9OrVK74/SPScia7YWhhcvVfWtKBzzq9b57nxRpu6qV0b7rsP9tor\noU2TKiitjsvKlTa8/NVXts2ZU/prMzLsirvmzW1r0aLofuPGGwZR0dtatRL/M6U71d4JJ/Vb+KRi\nn4W5QnuQQl+hvWZNuPVWq2z97rtw6aWWYLzffkG3TDbFe/j9dxud+uormDy5KKcJoFEjC5C7doWW\nLYsCqM03t6vzRERE0lHKBFf9+/enffv2eA+tWzfm11+7cdVVWdx0E9Srlw0UzWNHr8TQfjD7jz6a\nzaRJcOedliuXn2/PN2qUxU47QdOm2XTtCqefnkXG/7d378FVlfcax7+/gNwCNSCXFKygIyAeilxD\nmnLGgnKZMiWFIxxueqA9w6AiOp0eseOxB5kzXmh1KDgqeGAIeIQZxCKdkhqYci0KchfKTYc0QENO\ni4BBwXB5zx/v2mQTScoO+7bC85lZs9dee69kJc8k+eV93/W+GZXnd+2aHtev53oepueRY+lyPXp+\nfc8j0u16JHaR7+G6desoLi6+rnOS0S3YAd8t+N0a3nPVws3OwZtvwvz5frzMM89A1NQUkiKlpfCr\nX8GGDZXHWraEvDz43vegb18/saaIiKQXdQvWTm27BRM6z5WZvQNsBjqZWYmZTby+8+DRR2HqVF9o\nvfgiLPqH9xtKoly86L//I0f6wqpJE+jffx1LlkBhIfzylzBwoAqrdKf/YMNJuYWPMpNE3y049kbO\nf+QRP6D5pZdg9mx/S/7kyTffLfSptHu3H/v22Wf++aBBfjzcvn1+rigRERG5WsK7Ba/rIqp0C1a1\nahVMn+7nOurZEyZNgl69VGQl0pkzMGcOrFjhn7dr57tng3nXREQkRNQtWDu17RYMRXEFsG4dPP88\nlJf75z16+ElH+/RRkRVPzsHvfw+zZsHp0/4uzgkTYOJEzWQuIhJWKq5qJy3HXMXTD37g13979FE/\ntmfnTnjsMfjpT+Gjj66ePFJq58gRP4nr9Om+sOrd2y9uPHnyNwsrjSkIH2UWTsotfJSZhKa4Amja\n1BdTv/sdPP64n0dpzx6YMsUvvrt5c/iKrK+/9jOYHzmSmmt3DvbuhZkz/bIwO3ZA8+YwYwa88QZ0\n6JD8axIREblRmzZtokuK1tMLTbfgtXz1Fbz7rr+T7fRpf+zee313Yb9+6dFdeOmSX/Ll+HE/Y3nk\nMbJ/8mTle+++G/Lz4Yc/9IVjojgHBw7A6tWwZs3VM6mPGOGLVd35JyJSd6R7t+CmTZuYNm0a+/bt\no379+nTp0oVZs2bRq1evlF5XnR9zVZNz53yRtXhx5bp1HTvCXXdBs2Z+a9q08jGyRV7LzPTdXhkx\ntuM55xcaLiuDEyf8Y/R24oTfalp0uF49P3v52bN+EDn4pV769/eFVp8+sV9Xddf66ae+oFq92q/7\nF9G6NTz4IAwdCp073/jnEhGR9JLOxVV5eTl33HEHc+fOZeTIkVRUVLBx40ays7Pp2rVrSq/tpi6u\nIs6fh/feg4KCq1uErlejRpVb48ZXP0Y2s8oFiMvKfLdeTcygVSu/YHDbtv6uu+jH1q198XThgp9D\nasWKq8eQffvbMGwY/OhHvgiL1ZEjvpgqKvLdjxEtWviCauBAuO++2Au46BmjJRyUWTgpt/BJx8zS\nubjavn07AwcO5PNI60iUgoIC3nrrLXr06MHixYtp27Ytr732GgMGDABg4cKFzJw5k2PHjtG6dWue\nfvppJk2aBMD69esZP348R4PWhDvvvJMpU6awaNEiSkpKGDJkCAUFBTRo0KDaawv92oLx0KgRjB3r\nZ3Pfvt13FZ49W7mVl1c+Ru9/+aUvks6f91ssMjP9wsPZ2f4xesvO9lsNuV1xyy3wwAN+O3HCjytb\nudLPij53Lsyb59fpy8/3j198AadO+SLy88+vvZ08WdkaBn5R5AED/FxVPXvGp0VMRETkRnTq1Il6\n9eoxYcIERo8eTW5uLllZWVde37JlC6NGjeLkyZMsX76cESNGUFxcTFZWFm3atGHVqlV06NCBjRs3\nMmTIEHJycujevTvgi6Boy5Yto6ioiIYNG5KXl8fChQuvFGPxVKdarm7E5cu+wDp3rrLIimzRxy5d\n8ku+RIqpzMzEXtO2bb41a+1a37oVq299y99pOWiQ72LUgskiIjefGluueveO3yfatq1Wpx08eJCX\nX36ZNWvWUFpaytChQ5k3bx6FhYU8++yzHDt27Mp7+/bty9SpUxk3btw3Ps7w4cMZMGAATzzxBOvX\nr+fhhx+mpKQE8C1XL7zwAmPGjAFg2rRplJeX8/rrr1d7XWq5ukEZGb4LsHHjVF9JpYwMyMnx25kz\nfqmZlSt9915WFtx2m+/ei2y33ebv9Is+npWlFioREUlvnTt3ZsGCBQAcOnSIcePG8dRTTzF48GDa\ntWt31Xvbt2/PX4M7sQoLC5kxYwaHDh3i8uXLnDt3jm7dulX7edq0aXNlv0mTJpSWlibgq1FxFRq3\n3gqjR/stHaTjmAKpmTILJ+UWPqHLrJatTYnSqVMnJkyYwLx58xg8eDDHjx+/6vWSkhLy8/OpqKjg\noYce4u233yY/P5+MjAyGDx+eFmPL1KYhIiIiKXPw4EFeffXVK0XU0aNHWbJkCbm5uQCUlZUxZ84c\nLl68yLJlyzhw4ABDhw6loqKCiooKWrZsSUZGBoWFhRQVFaXyS7lCxZXUSqj+KxNAmYWVcgsfZRab\nZs2asWXLFvr27UuzZs3Iy8ujW7duvPLKKwDk5uZy+PBhWrZsyXPPPcfy5cvJysqiadOmzJ49m5Ej\nR9KiRQuWLl1Kfn5+tZ+n6uD2RNKAdhERkTounadiqElBQQHz589nw4YNKfn8dX5tQUkvWjsrfJRZ\nOCm38FFmouJKREREJI7ULSgiIlLHhbVbMNXULSgiIiKSBlRcSa1oTEH4KLNwUm7ho8xExZWIiIhI\nHGnMlYiISB2nMVe1o7UFRURE5Jrat2+f1Ek064r27dvX6ryEdwua2RAzO2Bmh8xsWqI/nySHxhSE\njzILJ+UWPumYWXFxMc45bTVsa9eu/cax4uLiWn2/E1pcmVkG8BowGPgnYIyZ3ZPIzynJsWvXrlRf\ngsRImYWTcgsfZRZO8cwt0S1XOcBh59xfnHMXgKVA9Qv/SGicPn061ZcgMVJm4aTcwkeZhVM8c0t0\ncdUOOBr1/FhwTERERKRO0lQMUiu17YeW1FFm4aTcwkeZhVM8c0voVAxmlgtMd84NCZ4/Azjn3MtV\n3qf7Q0VERCQ0apqKIdHFVT3gIPAAUApsBcY45/Yn7JOKiIiIpFBC57lyzl0ysylAEb4Lcr4KKxER\nEanL0mKGdhEREZG6IiED2s1svpmVmdmeqGPdzGyzme02s/fNrOk1XtsbvN4gON7TzPYEE5DOSsS1\nihdLZmY21sx2mtmO4PGSmXULXuulzJInxtwamtk7QT77gjGQkXP0s5YkMWZ2i5ktCLLZaWb3R52j\nzJLEzG43sz8GPzefmNnU4HhzMysys4Nm9oGZ3Rp1zi/M7LCZ7TezQVHHlVuSxJqbmbUI3l9uZrOr\nfKzYckvELKdAP6A7sCfq2FagX7A/AZgR7NcDdgNdg+fNqWxR2wL0CfZXAYNTPYNrXd1iyazKeV3x\nc5lFniuzNM0N+DfgnWC/MXAEuEO5pXVmj+GHUwC0ArZFnaPMkpdZNtA92G+KH0t8D/Ay8HRwfBrw\nUrB/L7ATP/SmA/Cp/q6FIrcmQB4wCZhd5WPFlFtCWq6cc5uAU1UOdwyOA6wB/iXYHwTsds7tDc49\n5ZxzZpYNNHPOfRy8bxHw40Rcr8ScWbQx+MlhUWbJF2NuJ4DM4EaTJsDXwBfKLbmuM7MRwf69wB+D\n8/4GnDaz3sosuZxzJ5xzu4L9s8B+4Hb8pNgFwdsKqMxgGLDUOXfROVcMHAZylFtyxZqbc+4r59xm\n/O/GK2qTWzLnudpnZsOC/VH4LxCgE4CZ/cHMtpnZfwTH2+EnHY3QBKTJV11m0f4VWBLsK7P0cM3c\nnHMfAF/g79wtBn7tnDuNcksHVTP7TrC/GxhmZvXM7E6gV/CaMksRM+uAb3n8CGjjnCsD/4ccaB28\nreoE2seDY8otRa4zt+rEnFsyi6ufAI+b2cdAJlARHK8PfB/fAvLPwHAz65/E65LqVZcZAGaWA3zp\nnPtzKi5OqnXN3MxsPL47MBu4C/h58AtHUq+6n7UF+D/MHwOvAn8CLqXkCoVgLNy7wJNBS0jVO8J0\nh1gaSkVuCZ2KIZpz7hB+AWfMrCMwNHjpGLDBOXcqeG0V0BP4Xyr/ewP/3/fxZF2v1JhZxGgqW63A\n56PMUqyG3PKA3zrnLgN/M7M/Ab2BTSi3lKouM+fcJeBnkfcFmR0CTqPMksrM6uP/QC92zr0fHC4z\nszbOubKg6+j/guPV/S7U78gkizG36sScWyJbrizY/BOzVsFjBvCfwJvBSx8A3zWzRsE34X5gX9BU\nd8bMcszMgEeA95FEut7MCDIZRTDeCq40ryqz5PtHub0RvHQAP6EvZpYJ5AL7lVtKXNfPmpk1NrMm\nwf5A4IJz7oAyS4kFwJ+dc7+JOrYSfwMC+BtG3o86PtrMGgTduXcDW5VbSsSSW7QrP5+1yi1BI/Tf\nAf6KHxRWAkwEpuJH6h8AXqjy/rHAXmAP8GLU8V7AJ/jBgL9J1R0HN8NWi8zuBzZf4+MoszTNDWgI\nvB3ksxf4mXJL+8zaB8f24Sdj/o4yS0lm38d3x+7C3wW4AxgCtMDfgHAwyCcr6pxf4O8S3A8MUm6h\nye0I8Hf8+NQS4J7a5KZJREVERETiKJkD2kVERETqPBVXIiIiInGk4kpEREQkjlRciYiIiMSRiisR\nERGROFJxJSIiIhJHKq5ERERE4kjFlYjc9IKZ0UVE4kK/UEQkVMzseTN7Mur5f5vZVDP7uZltNbNd\nZvZfUa//1sw+NrNPzOzfo46Xm9mvzWwnfikgEZG4UHElImGzAL+2V2SNy9FAKdDROZcD9AB6m1m/\n4P0TnXN9gD7Ak2bWPDieCXzonOvhnNuc1K9AROq0+qm+ABGRWDjn/mJmfzez+4Bs/HphOcBAM9uB\nX3A1E+gIbAKeMrMfB6ffHhzfClwE3kv29YtI3afiSkTC6H/wCx5n41uyHsQv+v5W9JvM7H5gANDX\nOfe1ma0FGgUvn3daXFVEEkDdgiISRivwq9v3Bj4Itp+YWSaAmbU1s1bArcCpoLC6h6vHVlmSr1lE\nbhJquRKR0HHOXQhaoU4FrU+rg+LpQz8Mi3JgPPAHYLKZ7QMOAh9Gf5gkX7aI3CRMreIiEjbB1Anb\ngYecc5+l+npERKKpW1BEQsXMugCHgdUqrEQkHanlSkRERCSO1HIlIiIiEkcqrkRERETiSMWViIiI\nSBypuBIRERGJIxVXIiIiInGk4kpEREQkjv4f4wLN3iz5nCEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f27513f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(x_ch,y_ch, alpha=0.8, linewidth=2, label=\"China\")\n",
    "plt.plot(x_es,y_es, alpha=0.8, linewidth=2, color='red', label=\"Spain\")\n",
    "plt.grid(True)\n",
    "plt.title(\"$CO_2$ emissions\")\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"metric tons per capita\")\n",
    "plt.xlim([x_ch.min(), x_ch.max()])\n",
    "plt.legend(loc=4, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Indicator Code</th>\n",
       "      <th>Country or Area Code</th>\n",
       "      <th>Country or Area</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.425262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.607038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.316038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>ADO</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>5.968685</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.354008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Indicator Code Country or Area Code Country or Area Time Code  Year  \\\n",
       "0    EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2011  2011   \n",
       "52   EN.ATM.CO2E.PC                  ALB         Albania    YR2011  2011   \n",
       "104  EN.ATM.CO2E.PC                  DZA         Algeria    YR2011  2011   \n",
       "156  EN.ATM.CO2E.PC                  ADO         Andorra    YR2011  2011   \n",
       "173  EN.ATM.CO2E.PC                  AGO          Angola    YR2011  2011   \n",
       "\n",
       "     emissions  Value Footnotes  \n",
       "0     0.425262              NaN  \n",
       "52    1.607038              NaN  \n",
       "104   3.316038              NaN  \n",
       "156   5.968685              NaN  \n",
       "173   1.354008              NaN  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Year == df.Year.max()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Country or Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8745</th>\n",
       "      <td>2011</td>\n",
       "      <td>5.790764</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8746</th>\n",
       "      <td>2010</td>\n",
       "      <td>5.815787</td>\n",
       "      <td>YR2010</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8747</th>\n",
       "      <td>2009</td>\n",
       "      <td>6.216973</td>\n",
       "      <td>YR2009</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8748</th>\n",
       "      <td>2008</td>\n",
       "      <td>7.165452</td>\n",
       "      <td>YR2008</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8749</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.920891</td>\n",
       "      <td>YR2007</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8750</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.884196</td>\n",
       "      <td>YR2006</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8751</th>\n",
       "      <td>2005</td>\n",
       "      <td>8.097058</td>\n",
       "      <td>YR2005</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.908051</td>\n",
       "      <td>YR2004</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.611166</td>\n",
       "      <td>YR2003</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.591281</td>\n",
       "      <td>YR2002</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8755</th>\n",
       "      <td>2001</td>\n",
       "      <td>7.307637</td>\n",
       "      <td>YR2001</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8756</th>\n",
       "      <td>2000</td>\n",
       "      <td>7.312740</td>\n",
       "      <td>YR2000</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  emissions Time Code Country or Area\n",
       "8745  2011   5.790764    YR2011           Spain\n",
       "8746  2010   5.815787    YR2010           Spain\n",
       "8747  2009   6.216973    YR2009           Spain\n",
       "8748  2008   7.165452    YR2008           Spain\n",
       "8749  2007   7.920891    YR2007           Spain\n",
       "8750  2006   7.884196    YR2006           Spain\n",
       "8751  2005   8.097058    YR2005           Spain\n",
       "8752  2004   7.908051    YR2004           Spain\n",
       "8753  2003   7.611166    YR2003           Spain\n",
       "8754  2002   7.591281    YR2002           Spain\n",
       "8755  2001   7.307637    YR2001           Spain\n",
       "8756  2000   7.312740    YR2000           Spain"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esp[esp['Year'] >= 2000][['Year', 'emissions', 'Time Code', 'Country or Area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Year</th>\n",
       "      <th>emissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8749</th>\n",
       "      <td>2007</td>\n",
       "      <td>7.920891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8750</th>\n",
       "      <td>2006</td>\n",
       "      <td>7.884196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8751</th>\n",
       "      <td>2005</td>\n",
       "      <td>8.097058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8752</th>\n",
       "      <td>2004</td>\n",
       "      <td>7.908051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8753</th>\n",
       "      <td>2003</td>\n",
       "      <td>7.611166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>2002</td>\n",
       "      <td>7.591281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  emissions\n",
       "8749  2007   7.920891\n",
       "8750  2006   7.884196\n",
       "8751  2005   8.097058\n",
       "8752  2004   7.908051\n",
       "8753  2003   7.611166\n",
       "8754  2002   7.591281"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "esp[(esp['Year'] >= 1970) & (esp['emissions'] > 7.5) ][['Year', 'emissions']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pivot tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Indicator Code</th>\n",
       "      <th>Country or Area Code</th>\n",
       "      <th>Country or Area</th>\n",
       "      <th>Time Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>emissions</th>\n",
       "      <th>Value Footnotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.425262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.302936</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2009</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.249074</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2008</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.158962</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>YR2007</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Indicator Code Country or Area Code Country or Area Time Code  Year  \\\n",
       "0  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2011  2011   \n",
       "1  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2010  2010   \n",
       "2  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2009  2009   \n",
       "3  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2008  2008   \n",
       "4  EN.ATM.CO2E.PC                  AFG     Afghanistan    YR2007  2007   \n",
       "\n",
       "   emissions  Value Footnotes  \n",
       "0   0.425262              NaN  \n",
       "1   0.302936              NaN  \n",
       "2   0.249074              NaN  \n",
       "3   0.158962              NaN  \n",
       "4   0.088141              NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wld = df.pivot(index='Country or Area',columns='Year', values='emissions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country or Area</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>Afghanistan</th>\n",
       "      <td>0.046068</td>\n",
       "      <td>0.053615</td>\n",
       "      <td>0.073781</td>\n",
       "      <td>0.074251</td>\n",
       "      <td>0.086317</td>\n",
       "      <td>0.101499</td>\n",
       "      <td>0.107674</td>\n",
       "      <td>0.123782</td>\n",
       "      <td>0.115547</td>\n",
       "      <td>0.086863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041641</td>\n",
       "      <td>0.046108</td>\n",
       "      <td>0.040727</td>\n",
       "      <td>0.054855</td>\n",
       "      <td>0.065816</td>\n",
       "      <td>0.088141</td>\n",
       "      <td>0.158962</td>\n",
       "      <td>0.249074</td>\n",
       "      <td>0.302936</td>\n",
       "      <td>0.425262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1.258195</td>\n",
       "      <td>1.374186</td>\n",
       "      <td>1.439956</td>\n",
       "      <td>1.181681</td>\n",
       "      <td>1.111742</td>\n",
       "      <td>1.166099</td>\n",
       "      <td>1.333055</td>\n",
       "      <td>1.363746</td>\n",
       "      <td>1.519551</td>\n",
       "      <td>1.558968</td>\n",
       "      <td>...</td>\n",
       "      <td>1.229541</td>\n",
       "      <td>1.412697</td>\n",
       "      <td>1.376213</td>\n",
       "      <td>1.412498</td>\n",
       "      <td>1.291548</td>\n",
       "      <td>1.507536</td>\n",
       "      <td>1.580113</td>\n",
       "      <td>1.533178</td>\n",
       "      <td>1.515632</td>\n",
       "      <td>1.607038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.553764</td>\n",
       "      <td>0.531810</td>\n",
       "      <td>0.484954</td>\n",
       "      <td>0.452824</td>\n",
       "      <td>0.459569</td>\n",
       "      <td>0.522449</td>\n",
       "      <td>0.649481</td>\n",
       "      <td>0.632118</td>\n",
       "      <td>0.659263</td>\n",
       "      <td>0.797206</td>\n",
       "      <td>...</td>\n",
       "      <td>2.840028</td>\n",
       "      <td>2.856410</td>\n",
       "      <td>2.727017</td>\n",
       "      <td>3.220155</td>\n",
       "      <td>2.990267</td>\n",
       "      <td>3.189978</td>\n",
       "      <td>3.205183</td>\n",
       "      <td>3.428472</td>\n",
       "      <td>3.309912</td>\n",
       "      <td>3.316038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.484095</td>\n",
       "      <td>7.159140</td>\n",
       "      <td>7.208829</td>\n",
       "      <td>7.088128</td>\n",
       "      <td>6.553477</td>\n",
       "      <td>6.350868</td>\n",
       "      <td>6.296125</td>\n",
       "      <td>6.049173</td>\n",
       "      <td>6.124770</td>\n",
       "      <td>5.968685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>0.104357</td>\n",
       "      <td>0.084718</td>\n",
       "      <td>0.216025</td>\n",
       "      <td>0.206877</td>\n",
       "      <td>0.216174</td>\n",
       "      <td>0.206089</td>\n",
       "      <td>0.265164</td>\n",
       "      <td>0.166659</td>\n",
       "      <td>0.275655</td>\n",
       "      <td>0.451126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.786223</td>\n",
       "      <td>0.543084</td>\n",
       "      <td>1.086605</td>\n",
       "      <td>1.069417</td>\n",
       "      <td>1.200877</td>\n",
       "      <td>1.311096</td>\n",
       "      <td>1.369425</td>\n",
       "      <td>1.430873</td>\n",
       "      <td>1.401654</td>\n",
       "      <td>1.354008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Year                 1960      1961      1962      1963      1964      1965  \\\n",
       "Country or Area                                                               \n",
       "Afghanistan      0.046068  0.053615  0.073781  0.074251  0.086317  0.101499   \n",
       "Albania          1.258195  1.374186  1.439956  1.181681  1.111742  1.166099   \n",
       "Algeria          0.553764  0.531810  0.484954  0.452824  0.459569  0.522449   \n",
       "Andorra               NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "Angola           0.104357  0.084718  0.216025  0.206877  0.216174  0.206089   \n",
       "\n",
       "Year                 1966      1967      1968      1969    ...         2002  \\\n",
       "Country or Area                                            ...                \n",
       "Afghanistan      0.107674  0.123782  0.115547  0.086863    ...     0.041641   \n",
       "Albania          1.333055  1.363746  1.519551  1.558968    ...     1.229541   \n",
       "Algeria          0.649481  0.632118  0.659263  0.797206    ...     2.840028   \n",
       "Andorra               NaN       NaN       NaN       NaN    ...     7.484095   \n",
       "Angola           0.265164  0.166659  0.275655  0.451126    ...     0.786223   \n",
       "\n",
       "Year                 2003      2004      2005      2006      2007      2008  \\\n",
       "Country or Area                                                               \n",
       "Afghanistan      0.046108  0.040727  0.054855  0.065816  0.088141  0.158962   \n",
       "Albania          1.412697  1.376213  1.412498  1.291548  1.507536  1.580113   \n",
       "Algeria          2.856410  2.727017  3.220155  2.990267  3.189978  3.205183   \n",
       "Andorra          7.159140  7.208829  7.088128  6.553477  6.350868  6.296125   \n",
       "Angola           0.543084  1.086605  1.069417  1.200877  1.311096  1.369425   \n",
       "\n",
       "Year                 2009      2010      2011  \n",
       "Country or Area                                \n",
       "Afghanistan      0.249074  0.302936  0.425262  \n",
       "Albania          1.533178  1.515632  1.607038  \n",
       "Algeria          3.428472  3.309912  3.316038  \n",
       "Andorra          6.049173  6.124770  5.968685  \n",
       "Angola           1.430873  1.401654  1.354008  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wld.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transpose data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Arab World</th>\n",
       "      <th>Argentina</th>\n",
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       "      <th>Aruba</th>\n",
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       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela, RB</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <th>World</th>\n",
       "      <th>Yemen, Rep.</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1960</th>\n",
       "      <td>0.046068</td>\n",
       "      <td>1.258195</td>\n",
       "      <td>0.553764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104357</td>\n",
       "      <td>0.670617</td>\n",
       "      <td>0.643964</td>\n",
       "      <td>2.367473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.701583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.005107</td>\n",
       "      <td>0.215631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.096144</td>\n",
       "      <td>0.703403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>0.053615</td>\n",
       "      <td>1.374186</td>\n",
       "      <td>0.531810</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084718</td>\n",
       "      <td>0.860441</td>\n",
       "      <td>0.685501</td>\n",
       "      <td>2.442616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.602723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.136886</td>\n",
       "      <td>0.225435</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.067062</td>\n",
       "      <td>0.507631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>0.073781</td>\n",
       "      <td>1.439956</td>\n",
       "      <td>0.484954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.216025</td>\n",
       "      <td>1.823374</td>\n",
       "      <td>0.761148</td>\n",
       "      <td>2.522392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.540657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.594888</td>\n",
       "      <td>6.155057</td>\n",
       "      <td>0.258760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.138020</td>\n",
       "      <td>0.728004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>0.074251</td>\n",
       "      <td>1.181681</td>\n",
       "      <td>0.452824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.206877</td>\n",
       "      <td>1.470175</td>\n",
       "      <td>0.875124</td>\n",
       "      <td>2.316356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.639285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.471728</td>\n",
       "      <td>6.155748</td>\n",
       "      <td>0.247579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.242364</td>\n",
       "      <td>0.537606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>0.086317</td>\n",
       "      <td>1.111742</td>\n",
       "      <td>0.459569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.216174</td>\n",
       "      <td>1.567094</td>\n",
       "      <td>0.999248</td>\n",
       "      <td>2.538380</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.710101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.864247</td>\n",
       "      <td>5.973226</td>\n",
       "      <td>0.314058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.358696</td>\n",
       "      <td>0.658012</td>\n",
       "      <td>0.950434</td>\n",
       "      <td>1.045374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 230 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country or Area  Afghanistan   Albania   Algeria  Andorra    Angola  \\\n",
       "Year                                                                  \n",
       "1960                0.046068  1.258195  0.553764      NaN  0.104357   \n",
       "1961                0.053615  1.374186  0.531810      NaN  0.084718   \n",
       "1962                0.073781  1.439956  0.484954      NaN  0.216025   \n",
       "1963                0.074251  1.181681  0.452824      NaN  0.206877   \n",
       "1964                0.086317  1.111742  0.459569      NaN  0.216174   \n",
       "\n",
       "Country or Area  Antigua and Barbuda  Arab World  Argentina  Armenia  Aruba  \\\n",
       "Year                                                                          \n",
       "1960                        0.670617    0.643964   2.367473      NaN    NaN   \n",
       "1961                        0.860441    0.685501   2.442616      NaN    NaN   \n",
       "1962                        1.823374    0.761148   2.522392      NaN    NaN   \n",
       "1963                        1.470175    0.875124   2.316356      NaN    NaN   \n",
       "1964                        1.567094    0.999248   2.538380      NaN    NaN   \n",
       "\n",
       "Country or Area    ...      Uruguay  Uzbekistan   Vanuatu  Venezuela, RB  \\\n",
       "Year               ...                                                     \n",
       "1960               ...     1.701583         NaN       NaN       7.005107   \n",
       "1961               ...     1.602723         NaN       NaN       6.136886   \n",
       "1962               ...     1.540657         NaN  0.594888       6.155057   \n",
       "1963               ...     1.639285         NaN  0.471728       6.155748   \n",
       "1964               ...     1.710101         NaN  0.864247       5.973226   \n",
       "\n",
       "Country or Area   Vietnam  West Bank and Gaza     World  Yemen, Rep.  \\\n",
       "Year                                                                   \n",
       "1960             0.215631                 NaN  3.096144     0.703403   \n",
       "1961             0.225435                 NaN  3.067062     0.507631   \n",
       "1962             0.258760                 NaN  3.138020     0.728004   \n",
       "1963             0.247579                 NaN  3.242364     0.537606   \n",
       "1964             0.314058                 NaN  3.358696     0.658012   \n",
       "\n",
       "Country or Area    Zambia  Zimbabwe  \n",
       "Year                                 \n",
       "1960                  NaN       NaN  \n",
       "1961                  NaN       NaN  \n",
       "1962                  NaN       NaN  \n",
       "1963                  NaN       NaN  \n",
       "1964             0.950434  1.045374  \n",
       "\n",
       "[5 rows x 230 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wld.T.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting data to CSV file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wld.to_csv(\"/tmp/world_emissions.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting data to excel file (XLSX):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "writer = pd.ExcelWriter(\"/tmp/co2_emissions.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "esp.to_excel(writer, sheet_name='Spain')\n",
    "chn.to_excel(writer, sheet_name='China')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "writer.save()"
   ]
  }
 ],
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