World_Countries_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can connect few data tables and analyze them as one.\n",
    "\n",
    "From [2013 UN Energy Statistics](https://unstats.un.org/unsd/energy/yearbook/2013.htm) we take [Energy Indicators.xls](http://unstats.un.org/unsd/environment/excel_file_tables/2013/Energy%20Indicators.xls).\n",
    "\n",
    "Then, we take data from [World Bank](http://data.worldbank.org/indicator/NY.GDP.MKTP.CD).\n",
    "\n",
    "Then, we take data from [Energy Engineering and Power Technology](http://www.scimagojr.com/countryrank.php?category=2102).\n",
    "\n",
    "Then, we load numpy, pandas, reduce (from fuctools) and matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from functools import reduce\n",
    "import matplotlib as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we connect the files and clean them (we take only the first 15 countries and years 2006-2015):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def connected_table():\n",
    "    # load the energy file and prepare for analysing\n",
    "    energy = pd.read_excel('Energy_Indicators.xls', skiprows=17, skipfooter=38, names=['Delete', 'Delete', 'Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable'])\\\n",
    "    .iloc[:,2:6].apply(lambda x: [ x[0], x[1]*1000000, x[2], x[3]], axis=1, result_type='broadcast')\\\n",
    "    .replace(to_replace=r'^\\.', value=np.NaN, regex=True)\\\n",
    "    .replace(to_replace=[\"Republic of Korea\",\"United States of America\", \"United Kingdom of Great Britain and Northern Ireland\",\"China, Hong Kong Special Administrative Region\"], value=[\"South Korea\",\"United States\", \"United Kingdom\", \"Hong Kong\"], regex=True)\\\n",
    "    .replace(to_replace=r'\\d+', value='', regex=True).replace(to_replace=r\"\\s+\\(.*\\)\", value=\"\", regex=True)\n",
    "\n",
    "    # load the GDP file and prepare for analysing\n",
    "    GDP = pd.read_csv('world_bank.csv', skiprows=4)\\\n",
    "    .replace(to_replace=[\"Korea, Rep.\",\"Iran, Islamic Rep.\", \"Hong Kong SAR, China\"], value=[\"South Korea\",\"Iran\", \"Hong Kong\"], regex=True)\\\n",
    "    .rename(index=str, columns={\"Country Name\": \"Country\"})\n",
    "\n",
    "    # load the Energy Engineering and Power Technology file and prepare for analysing\n",
    "    ScimEn = pd.read_excel('scimagojr-3.xlsx')\n",
    "\n",
    "    # connect all the files and take only the first 15 countries and years 2006-2015\n",
    "    return reduce(lambda left,right: pd.merge(left,right,on='Country'), [ScimEn[ScimEn['Rank'] < 16], energy, GDP[GDP[[c for c in GDP if c not in ['Country']] + ['Country']].columns[-11:]]]).set_index('Country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the new table we created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Documents</th>\n",
       "      <th>Citable documents</th>\n",
       "      <th>Citations</th>\n",
       "      <th>Self-citations</th>\n",
       "      <th>Citations per document</th>\n",
       "      <th>H index</th>\n",
       "      <th>Energy Supply</th>\n",
       "      <th>Energy Supply per Capita</th>\n",
       "      <th>% Renewable</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",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</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>China</th>\n",
       "      <td>1</td>\n",
       "      <td>127050</td>\n",
       "      <td>126767</td>\n",
       "      <td>597237</td>\n",
       "      <td>411683</td>\n",
       "      <td>4.70</td>\n",
       "      <td>138</td>\n",
       "      <td>1.271910e+11</td>\n",
       "      <td>93.0</td>\n",
       "      <td>19.754910</td>\n",
       "      <td>3.992331e+12</td>\n",
       "      <td>4.559041e+12</td>\n",
       "      <td>4.997775e+12</td>\n",
       "      <td>5.459247e+12</td>\n",
       "      <td>6.039659e+12</td>\n",
       "      <td>6.612490e+12</td>\n",
       "      <td>7.124978e+12</td>\n",
       "      <td>7.672448e+12</td>\n",
       "      <td>8.230121e+12</td>\n",
       "      <td>8.797999e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2</td>\n",
       "      <td>96661</td>\n",
       "      <td>94747</td>\n",
       "      <td>792274</td>\n",
       "      <td>265436</td>\n",
       "      <td>8.20</td>\n",
       "      <td>230</td>\n",
       "      <td>9.083800e+10</td>\n",
       "      <td>286.0</td>\n",
       "      <td>11.570980</td>\n",
       "      <td>1.479230e+13</td>\n",
       "      <td>1.505540e+13</td>\n",
       "      <td>1.501149e+13</td>\n",
       "      <td>1.459484e+13</td>\n",
       "      <td>1.496437e+13</td>\n",
       "      <td>1.520402e+13</td>\n",
       "      <td>1.554216e+13</td>\n",
       "      <td>1.577367e+13</td>\n",
       "      <td>1.615662e+13</td>\n",
       "      <td>1.654857e+13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>3</td>\n",
       "      <td>30504</td>\n",
       "      <td>30287</td>\n",
       "      <td>223024</td>\n",
       "      <td>61554</td>\n",
       "      <td>7.31</td>\n",
       "      <td>134</td>\n",
       "      <td>1.898400e+10</td>\n",
       "      <td>149.0</td>\n",
       "      <td>10.232820</td>\n",
       "      <td>5.496542e+12</td>\n",
       "      <td>5.617036e+12</td>\n",
       "      <td>5.558527e+12</td>\n",
       "      <td>5.251308e+12</td>\n",
       "      <td>5.498718e+12</td>\n",
       "      <td>5.473738e+12</td>\n",
       "      <td>5.569102e+12</td>\n",
       "      <td>5.644659e+12</td>\n",
       "      <td>5.642884e+12</td>\n",
       "      <td>5.669563e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>4</td>\n",
       "      <td>20944</td>\n",
       "      <td>20357</td>\n",
       "      <td>206091</td>\n",
       "      <td>37874</td>\n",
       "      <td>9.84</td>\n",
       "      <td>139</td>\n",
       "      <td>7.920000e+09</td>\n",
       "      <td>124.0</td>\n",
       "      <td>10.600470</td>\n",
       "      <td>2.419631e+12</td>\n",
       "      <td>2.482203e+12</td>\n",
       "      <td>2.470614e+12</td>\n",
       "      <td>2.367048e+12</td>\n",
       "      <td>2.403504e+12</td>\n",
       "      <td>2.450911e+12</td>\n",
       "      <td>2.479809e+12</td>\n",
       "      <td>2.533370e+12</td>\n",
       "      <td>2.605643e+12</td>\n",
       "      <td>2.666333e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Federation</th>\n",
       "      <td>5</td>\n",
       "      <td>18534</td>\n",
       "      <td>18301</td>\n",
       "      <td>34266</td>\n",
       "      <td>12422</td>\n",
       "      <td>1.85</td>\n",
       "      <td>57</td>\n",
       "      <td>3.070900e+10</td>\n",
       "      <td>214.0</td>\n",
       "      <td>17.288680</td>\n",
       "      <td>1.385793e+12</td>\n",
       "      <td>1.504071e+12</td>\n",
       "      <td>1.583004e+12</td>\n",
       "      <td>1.459199e+12</td>\n",
       "      <td>1.524917e+12</td>\n",
       "      <td>1.589943e+12</td>\n",
       "      <td>1.645876e+12</td>\n",
       "      <td>1.666934e+12</td>\n",
       "      <td>1.678709e+12</td>\n",
       "      <td>1.616149e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>6</td>\n",
       "      <td>17899</td>\n",
       "      <td>17620</td>\n",
       "      <td>215003</td>\n",
       "      <td>40930</td>\n",
       "      <td>12.01</td>\n",
       "      <td>149</td>\n",
       "      <td>1.043100e+10</td>\n",
       "      <td>296.0</td>\n",
       "      <td>61.945430</td>\n",
       "      <td>1.564469e+12</td>\n",
       "      <td>1.596740e+12</td>\n",
       "      <td>1.612713e+12</td>\n",
       "      <td>1.565145e+12</td>\n",
       "      <td>1.613406e+12</td>\n",
       "      <td>1.664087e+12</td>\n",
       "      <td>1.693133e+12</td>\n",
       "      <td>1.730688e+12</td>\n",
       "      <td>1.773486e+12</td>\n",
       "      <td>1.792609e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>7</td>\n",
       "      <td>17027</td>\n",
       "      <td>16831</td>\n",
       "      <td>140566</td>\n",
       "      <td>27426</td>\n",
       "      <td>8.26</td>\n",
       "      <td>126</td>\n",
       "      <td>1.326100e+10</td>\n",
       "      <td>165.0</td>\n",
       "      <td>17.901530</td>\n",
       "      <td>3.332891e+12</td>\n",
       "      <td>3.441561e+12</td>\n",
       "      <td>3.478809e+12</td>\n",
       "      <td>3.283340e+12</td>\n",
       "      <td>3.417298e+12</td>\n",
       "      <td>3.542371e+12</td>\n",
       "      <td>3.556724e+12</td>\n",
       "      <td>3.567317e+12</td>\n",
       "      <td>3.624386e+12</td>\n",
       "      <td>3.685556e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>8</td>\n",
       "      <td>15005</td>\n",
       "      <td>14841</td>\n",
       "      <td>128763</td>\n",
       "      <td>37209</td>\n",
       "      <td>8.58</td>\n",
       "      <td>115</td>\n",
       "      <td>3.319500e+10</td>\n",
       "      <td>26.0</td>\n",
       "      <td>14.969080</td>\n",
       "      <td>1.265894e+12</td>\n",
       "      <td>1.374865e+12</td>\n",
       "      <td>1.428361e+12</td>\n",
       "      <td>1.549483e+12</td>\n",
       "      <td>1.708459e+12</td>\n",
       "      <td>1.821872e+12</td>\n",
       "      <td>1.924235e+12</td>\n",
       "      <td>2.051982e+12</td>\n",
       "      <td>2.200617e+12</td>\n",
       "      <td>2.367206e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>9</td>\n",
       "      <td>13153</td>\n",
       "      <td>12973</td>\n",
       "      <td>130632</td>\n",
       "      <td>28601</td>\n",
       "      <td>9.93</td>\n",
       "      <td>114</td>\n",
       "      <td>1.059700e+10</td>\n",
       "      <td>166.0</td>\n",
       "      <td>17.020280</td>\n",
       "      <td>2.607840e+12</td>\n",
       "      <td>2.669424e+12</td>\n",
       "      <td>2.674637e+12</td>\n",
       "      <td>2.595967e+12</td>\n",
       "      <td>2.646995e+12</td>\n",
       "      <td>2.702032e+12</td>\n",
       "      <td>2.706968e+12</td>\n",
       "      <td>2.722567e+12</td>\n",
       "      <td>2.729632e+12</td>\n",
       "      <td>2.761185e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>10</td>\n",
       "      <td>11983</td>\n",
       "      <td>11923</td>\n",
       "      <td>114675</td>\n",
       "      <td>22595</td>\n",
       "      <td>9.57</td>\n",
       "      <td>104</td>\n",
       "      <td>1.100700e+10</td>\n",
       "      <td>221.0</td>\n",
       "      <td>2.279353</td>\n",
       "      <td>9.410199e+11</td>\n",
       "      <td>9.924316e+11</td>\n",
       "      <td>1.020510e+12</td>\n",
       "      <td>1.027730e+12</td>\n",
       "      <td>1.094499e+12</td>\n",
       "      <td>1.134796e+12</td>\n",
       "      <td>1.160809e+12</td>\n",
       "      <td>1.194429e+12</td>\n",
       "      <td>1.234340e+12</td>\n",
       "      <td>1.266580e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>11</td>\n",
       "      <td>10964</td>\n",
       "      <td>10794</td>\n",
       "      <td>111850</td>\n",
       "      <td>26661</td>\n",
       "      <td>10.20</td>\n",
       "      <td>106</td>\n",
       "      <td>6.530000e+09</td>\n",
       "      <td>109.0</td>\n",
       "      <td>33.667230</td>\n",
       "      <td>2.202170e+12</td>\n",
       "      <td>2.234627e+12</td>\n",
       "      <td>2.211154e+12</td>\n",
       "      <td>2.089938e+12</td>\n",
       "      <td>2.125185e+12</td>\n",
       "      <td>2.137439e+12</td>\n",
       "      <td>2.077184e+12</td>\n",
       "      <td>2.040871e+12</td>\n",
       "      <td>2.033868e+12</td>\n",
       "      <td>2.049316e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>12</td>\n",
       "      <td>9428</td>\n",
       "      <td>9330</td>\n",
       "      <td>123336</td>\n",
       "      <td>23964</td>\n",
       "      <td>13.08</td>\n",
       "      <td>115</td>\n",
       "      <td>4.923000e+09</td>\n",
       "      <td>106.0</td>\n",
       "      <td>37.968590</td>\n",
       "      <td>1.414823e+12</td>\n",
       "      <td>1.468146e+12</td>\n",
       "      <td>1.484530e+12</td>\n",
       "      <td>1.431475e+12</td>\n",
       "      <td>1.431673e+12</td>\n",
       "      <td>1.417355e+12</td>\n",
       "      <td>1.380216e+12</td>\n",
       "      <td>1.357139e+12</td>\n",
       "      <td>1.375605e+12</td>\n",
       "      <td>1.419821e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>13</td>\n",
       "      <td>8896</td>\n",
       "      <td>8819</td>\n",
       "      <td>57470</td>\n",
       "      <td>19125</td>\n",
       "      <td>6.46</td>\n",
       "      <td>72</td>\n",
       "      <td>9.172000e+09</td>\n",
       "      <td>119.0</td>\n",
       "      <td>5.707721</td>\n",
       "      <td>3.895523e+11</td>\n",
       "      <td>4.250646e+11</td>\n",
       "      <td>4.289909e+11</td>\n",
       "      <td>4.389208e+11</td>\n",
       "      <td>4.677902e+11</td>\n",
       "      <td>4.853309e+11</td>\n",
       "      <td>4.532569e+11</td>\n",
       "      <td>4.445926e+11</td>\n",
       "      <td>4.639027e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>14</td>\n",
       "      <td>8831</td>\n",
       "      <td>8725</td>\n",
       "      <td>90765</td>\n",
       "      <td>15606</td>\n",
       "      <td>10.28</td>\n",
       "      <td>107</td>\n",
       "      <td>5.386000e+09</td>\n",
       "      <td>231.0</td>\n",
       "      <td>11.810810</td>\n",
       "      <td>1.021939e+12</td>\n",
       "      <td>1.060340e+12</td>\n",
       "      <td>1.099644e+12</td>\n",
       "      <td>1.119654e+12</td>\n",
       "      <td>1.142251e+12</td>\n",
       "      <td>1.169431e+12</td>\n",
       "      <td>1.211913e+12</td>\n",
       "      <td>1.241484e+12</td>\n",
       "      <td>1.272520e+12</td>\n",
       "      <td>1.301251e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>15</td>\n",
       "      <td>8668</td>\n",
       "      <td>8596</td>\n",
       "      <td>60702</td>\n",
       "      <td>14396</td>\n",
       "      <td>7.00</td>\n",
       "      <td>86</td>\n",
       "      <td>1.214900e+10</td>\n",
       "      <td>59.0</td>\n",
       "      <td>69.648030</td>\n",
       "      <td>1.845080e+12</td>\n",
       "      <td>1.957118e+12</td>\n",
       "      <td>2.056809e+12</td>\n",
       "      <td>2.054215e+12</td>\n",
       "      <td>2.208872e+12</td>\n",
       "      <td>2.295245e+12</td>\n",
       "      <td>2.339209e+12</td>\n",
       "      <td>2.409740e+12</td>\n",
       "      <td>2.412231e+12</td>\n",
       "      <td>2.319423e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Rank  Documents  Citable documents  Citations  \\\n",
       "Country                                                             \n",
       "China                  1     127050             126767     597237   \n",
       "United States          2      96661              94747     792274   \n",
       "Japan                  3      30504              30287     223024   \n",
       "United Kingdom         4      20944              20357     206091   \n",
       "Russian Federation     5      18534              18301      34266   \n",
       "Canada                 6      17899              17620     215003   \n",
       "Germany                7      17027              16831     140566   \n",
       "India                  8      15005              14841     128763   \n",
       "France                 9      13153              12973     130632   \n",
       "South Korea           10      11983              11923     114675   \n",
       "Italy                 11      10964              10794     111850   \n",
       "Spain                 12       9428               9330     123336   \n",
       "Iran                  13       8896               8819      57470   \n",
       "Australia             14       8831               8725      90765   \n",
       "Brazil                15       8668               8596      60702   \n",
       "\n",
       "                    Self-citations  Citations per document  H index  \\\n",
       "Country                                                               \n",
       "China                       411683                    4.70      138   \n",
       "United States               265436                    8.20      230   \n",
       "Japan                        61554                    7.31      134   \n",
       "United Kingdom               37874                    9.84      139   \n",
       "Russian Federation           12422                    1.85       57   \n",
       "Canada                       40930                   12.01      149   \n",
       "Germany                      27426                    8.26      126   \n",
       "India                        37209                    8.58      115   \n",
       "France                       28601                    9.93      114   \n",
       "South Korea                  22595                    9.57      104   \n",
       "Italy                        26661                   10.20      106   \n",
       "Spain                        23964                   13.08      115   \n",
       "Iran                         19125                    6.46       72   \n",
       "Australia                    15606                   10.28      107   \n",
       "Brazil                       14396                    7.00       86   \n",
       "\n",
       "                    Energy Supply  Energy Supply per Capita  % Renewable  \\\n",
       "Country                                                                    \n",
       "China                1.271910e+11                      93.0    19.754910   \n",
       "United States        9.083800e+10                     286.0    11.570980   \n",
       "Japan                1.898400e+10                     149.0    10.232820   \n",
       "United Kingdom       7.920000e+09                     124.0    10.600470   \n",
       "Russian Federation   3.070900e+10                     214.0    17.288680   \n",
       "Canada               1.043100e+10                     296.0    61.945430   \n",
       "Germany              1.326100e+10                     165.0    17.901530   \n",
       "India                3.319500e+10                      26.0    14.969080   \n",
       "France               1.059700e+10                     166.0    17.020280   \n",
       "South Korea          1.100700e+10                     221.0     2.279353   \n",
       "Italy                6.530000e+09                     109.0    33.667230   \n",
       "Spain                4.923000e+09                     106.0    37.968590   \n",
       "Iran                 9.172000e+09                     119.0     5.707721   \n",
       "Australia            5.386000e+09                     231.0    11.810810   \n",
       "Brazil               1.214900e+10                      59.0    69.648030   \n",
       "\n",
       "                            2006          2007          2008          2009  \\\n",
       "Country                                                                      \n",
       "China               3.992331e+12  4.559041e+12  4.997775e+12  5.459247e+12   \n",
       "United States       1.479230e+13  1.505540e+13  1.501149e+13  1.459484e+13   \n",
       "Japan               5.496542e+12  5.617036e+12  5.558527e+12  5.251308e+12   \n",
       "United Kingdom      2.419631e+12  2.482203e+12  2.470614e+12  2.367048e+12   \n",
       "Russian Federation  1.385793e+12  1.504071e+12  1.583004e+12  1.459199e+12   \n",
       "Canada              1.564469e+12  1.596740e+12  1.612713e+12  1.565145e+12   \n",
       "Germany             3.332891e+12  3.441561e+12  3.478809e+12  3.283340e+12   \n",
       "India               1.265894e+12  1.374865e+12  1.428361e+12  1.549483e+12   \n",
       "France              2.607840e+12  2.669424e+12  2.674637e+12  2.595967e+12   \n",
       "South Korea         9.410199e+11  9.924316e+11  1.020510e+12  1.027730e+12   \n",
       "Italy               2.202170e+12  2.234627e+12  2.211154e+12  2.089938e+12   \n",
       "Spain               1.414823e+12  1.468146e+12  1.484530e+12  1.431475e+12   \n",
       "Iran                3.895523e+11  4.250646e+11  4.289909e+11  4.389208e+11   \n",
       "Australia           1.021939e+12  1.060340e+12  1.099644e+12  1.119654e+12   \n",
       "Brazil              1.845080e+12  1.957118e+12  2.056809e+12  2.054215e+12   \n",
       "\n",
       "                            2010          2011          2012          2013  \\\n",
       "Country                                                                      \n",
       "China               6.039659e+12  6.612490e+12  7.124978e+12  7.672448e+12   \n",
       "United States       1.496437e+13  1.520402e+13  1.554216e+13  1.577367e+13   \n",
       "Japan               5.498718e+12  5.473738e+12  5.569102e+12  5.644659e+12   \n",
       "United Kingdom      2.403504e+12  2.450911e+12  2.479809e+12  2.533370e+12   \n",
       "Russian Federation  1.524917e+12  1.589943e+12  1.645876e+12  1.666934e+12   \n",
       "Canada              1.613406e+12  1.664087e+12  1.693133e+12  1.730688e+12   \n",
       "Germany             3.417298e+12  3.542371e+12  3.556724e+12  3.567317e+12   \n",
       "India               1.708459e+12  1.821872e+12  1.924235e+12  2.051982e+12   \n",
       "France              2.646995e+12  2.702032e+12  2.706968e+12  2.722567e+12   \n",
       "South Korea         1.094499e+12  1.134796e+12  1.160809e+12  1.194429e+12   \n",
       "Italy               2.125185e+12  2.137439e+12  2.077184e+12  2.040871e+12   \n",
       "Spain               1.431673e+12  1.417355e+12  1.380216e+12  1.357139e+12   \n",
       "Iran                4.677902e+11  4.853309e+11  4.532569e+11  4.445926e+11   \n",
       "Australia           1.142251e+12  1.169431e+12  1.211913e+12  1.241484e+12   \n",
       "Brazil              2.208872e+12  2.295245e+12  2.339209e+12  2.409740e+12   \n",
       "\n",
       "                            2014          2015  \n",
       "Country                                         \n",
       "China               8.230121e+12  8.797999e+12  \n",
       "United States       1.615662e+13  1.654857e+13  \n",
       "Japan               5.642884e+12  5.669563e+12  \n",
       "United Kingdom      2.605643e+12  2.666333e+12  \n",
       "Russian Federation  1.678709e+12  1.616149e+12  \n",
       "Canada              1.773486e+12  1.792609e+12  \n",
       "Germany             3.624386e+12  3.685556e+12  \n",
       "India               2.200617e+12  2.367206e+12  \n",
       "France              2.729632e+12  2.761185e+12  \n",
       "South Korea         1.234340e+12  1.266580e+12  \n",
       "Italy               2.033868e+12  2.049316e+12  \n",
       "Spain               1.375605e+12  1.419821e+12  \n",
       "Iran                4.639027e+11           NaN  \n",
       "Australia           1.272520e+12  1.301251e+12  \n",
       "Brazil              2.412231e+12  2.319423e+12  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connected_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to get the average GDP for each country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "United States         1.536434e+13\n",
       "China                 6.348609e+12\n",
       "Japan                 5.542208e+12\n",
       "Germany               3.493025e+12\n",
       "France                2.681725e+12\n",
       "United Kingdom        2.487907e+12\n",
       "Brazil                2.189794e+12\n",
       "Italy                 2.120175e+12\n",
       "India                 1.769297e+12\n",
       "Canada                1.660647e+12\n",
       "Russian Federation    1.565459e+12\n",
       "Spain                 1.418078e+12\n",
       "Australia             1.164043e+12\n",
       "South Korea           1.106715e+12\n",
       "Iran                  4.441558e+11\n",
       "Name: avgGDP, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def averageGDP():\n",
    "    Top15 = connected_table()\n",
    "    return Top15.iloc[:,-10:].apply(lambda row: np.mean(row), axis=1).sort_values(ascending=False).rename(\"avgGDP\")\n",
    "averageGDP()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to know how much had the GDP changed for the country with the sixth largest average GDP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246702696075.3999"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15.loc[averageGDP().index[5]].loc['2015']-Top15.loc[averageGDP().index[5]].loc['2006']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to find the mean energy supply per capita:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157.6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15['Energy Supply per Capita'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to find the country that has the maximum '% Renewable' and the percentage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Brazil', 69.64803)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "(Top15['% Renewable'].idxmax(),Top15['% Renewable'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we are creating a new column that is the ratio of 'Self-Citation' to 'Total Citation', and we want to find the maximum value for this column and the country that has this value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('China', 0.6893126179389422)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "(Top15.assign(ratio=Top15['Self-citations']/Top15['Citations'])['ratio'].idxmax(),Top15.assign(ratio=Top15['Self-citations']/Top15['Citations'])['ratio'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we are creating a new column that estimates the number of citable documents per capita, and we want to find the correlation between the 'Number of Citable Documents per Capita' and the 'Energy Supply per Capita':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7940010435442943"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15.assign(CITPER=Top15['Citable documents']/Top15.assign(ESTPOP=Top15['Energy Supply']/Top15['Energy Supply per Capita'])['ESTPOP'])\\\n",
    ".loc[:,['CITPER', 'Energy Supply per Capita']].corr(method='pearson').iat[0,1]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to show the data in a graph ('Energy Supply per Capita' vs. 'Citable Documents per Capita'):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d8ed68>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15['PopEst'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita']\n",
    "Top15['Citable docs per Capita'] = Top15['Citable documents'] / Top15['PopEst']\n",
    "Top15.plot(x='Citable docs per Capita', y='Energy Supply per Capita', kind='scatter', xlim=[0, 0.0006])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to create a series with a 0 if the country's '% Renewable' value is below the median for all countries, and a 1 if the country's '% Renewable' value is at or above the madian."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "Australia             0\n",
       "Brazil                1\n",
       "Canada                1\n",
       "China                 1\n",
       "France                1\n",
       "Germany               1\n",
       "India                 0\n",
       "Iran                  0\n",
       "Italy                 1\n",
       "Japan                 0\n",
       "Russian Federation    1\n",
       "South Korea           0\n",
       "Spain                 1\n",
       "United Kingdom        0\n",
       "United States         0\n",
       "Name: HighRenew, dtype: int32"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15.assign(HighRenew=np.where(Top15['% Renewable']>=Top15['% Renewable'].median(), 1, 0))['HighRenew'].sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to use a dictionary to group the countries by continent, and then show details about each continent (number of countries, sum, mean and std deviation for the estimated population):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ContinentDict  = {'China':'Asia', \n",
    "                  'United States':'North America', \n",
    "                  'Japan':'Asia', \n",
    "                  'United Kingdom':'Europe', \n",
    "                  'Russian Federation':'Europe', \n",
    "                  'Canada':'North America', \n",
    "                  'Germany':'Europe', \n",
    "                  'India':'Asia',\n",
    "                  'France':'Europe', \n",
    "                  'South Korea':'Asia', \n",
    "                  'Italy':'Europe', \n",
    "                  'Spain':'Europe', \n",
    "                  'Iran':'Asia',\n",
    "                  'Australia':'Australia', \n",
    "                  'Brazil':'South America'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>5</td>\n",
       "      <td>2.898666e+09</td>\n",
       "      <td>5.797333e+08</td>\n",
       "      <td>6.790979e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1</td>\n",
       "      <td>2.331602e+07</td>\n",
       "      <td>2.331602e+07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>6</td>\n",
       "      <td>4.579297e+08</td>\n",
       "      <td>7.632161e+07</td>\n",
       "      <td>3.464767e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>2</td>\n",
       "      <td>3.528552e+08</td>\n",
       "      <td>1.764276e+08</td>\n",
       "      <td>1.996696e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>1</td>\n",
       "      <td>2.059153e+08</td>\n",
       "      <td>2.059153e+08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               size           sum          mean           std\n",
       "Continent                                                    \n",
       "Asia              5  2.898666e+09  5.797333e+08  6.790979e+08\n",
       "Australia         1  2.331602e+07  2.331602e+07           NaN\n",
       "Europe            6  4.579297e+08  7.632161e+07  3.464767e+07\n",
       "North America     2  3.528552e+08  1.764276e+08  1.996696e+08\n",
       "South America     1  2.059153e+08  2.059153e+08           NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15.assign(ESTPOP=Top15['Energy Supply']/Top15['Energy Supply per Capita'], Continent=pd.Series(ContinentDict))\\\n",
    ".groupby('Continent')['ESTPOP'].agg([ 'size', 'sum', 'mean', 'std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to convert the 'Population Estimate' number to a string with a comma separator (every three digits):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China                 1,367,645,161.2903225\n",
       "United States          317,615,384.61538464\n",
       "Japan                  127,409,395.97315437\n",
       "United Kingdom         63,870,967.741935484\n",
       "Russian Federation            143,500,000.0\n",
       "Canada                  35,239,864.86486486\n",
       "Germany                 80,369,696.96969697\n",
       "India                 1,276,730,769.2307692\n",
       "France                  63,837,349.39759036\n",
       "South Korea            49,805,429.864253394\n",
       "Italy                  59,908,256.880733944\n",
       "Spain                    46,443,396.2264151\n",
       "Iran                    77,075,630.25210084\n",
       "Australia              23,316,017.316017315\n",
       "Brazil                 205,915,254.23728815\n",
       "Name: PopEst, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "Top15.assign(PopEst=Top15['Energy Supply']/Top15['Energy Supply per Capita'])['PopEst'].\\\n",
    "apply(lambda x: \"{:,}\".format(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we want to show the data in a bubble chart showing '% Renewable' vs. 'Rank'. The color of the bubble corresponds to the continent, and the size corresponds to the countries' 2014 GDP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Top15 = connected_table()\n",
    "ax = Top15.plot(x='Rank', y='% Renewable', kind='scatter', \n",
    "                c=['#e41a1c','#377eb8','#e41a1c','#4daf4a','#4daf4a','#377eb8','#4daf4a','#e41a1c',\n",
    "                   '#4daf4a','#e41a1c','#4daf4a','#4daf4a','#e41a1c','#dede00','#ff7f00'], \n",
    "                xticks=range(1,16), s=6*Top15['2014']/10**10, alpha=.75, figsize=[16,6]);\n",
    "\n",
    "for i, txt in enumerate(Top15.index):\n",
    "    ax.annotate(txt, [Top15['Rank'][i], Top15['% Renewable'][i]], ha='center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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