chart_bli.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from radar_chart import radar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read csv with related columns\n",
    "cols = ['Country', 'Indicator', 'Inequality', 'Unit', 'Value']\n",
    "df = pd.read_csv('BLI_05052017143301063.csv', usecols=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Inequality</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>6.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>6.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Dwellings without basic facilities</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>6.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Housing expenditure</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Housing expenditure</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Housing expenditure</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Rooms per person</td>\n",
       "      <td>Total</td>\n",
       "      <td>Ratio</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Rooms per person</td>\n",
       "      <td>Men</td>\n",
       "      <td>Ratio</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Rooms per person</td>\n",
       "      <td>Women</td>\n",
       "      <td>Ratio</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net adjusted disposable income</td>\n",
       "      <td>Total</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>27323.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net adjusted disposable income</td>\n",
       "      <td>Men</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>27323.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net adjusted disposable income</td>\n",
       "      <td>Women</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>27323.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net adjusted disposable income</td>\n",
       "      <td>High</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>54666.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net adjusted disposable income</td>\n",
       "      <td>Low</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>8834.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net financial wealth</td>\n",
       "      <td>Total</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>92818.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net financial wealth</td>\n",
       "      <td>Men</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>92818.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Household net financial wealth</td>\n",
       "      <td>Women</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>92818.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employment rate</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employment rate</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employment rate</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employment rate</td>\n",
       "      <td>High</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employment rate</td>\n",
       "      <td>Low</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Long-term unemployment rate</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>1.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Long-term unemployment rate</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Long-term unemployment rate</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0.787414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Long-term unemployment rate</td>\n",
       "      <td>High</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>1.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>966</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Long-term unemployment rate</td>\n",
       "      <td>Low</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>1.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Personal earnings</td>\n",
       "      <td>Total</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>35672.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Personal earnings</td>\n",
       "      <td>Men</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>39327.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Personal earnings</td>\n",
       "      <td>Women</td>\n",
       "      <td>US Dollar</td>\n",
       "      <td>28060.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Self-reported health</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Self-reported health</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2353</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Self-reported health</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2390</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Self-reported health</td>\n",
       "      <td>High</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Self-reported health</td>\n",
       "      <td>Low</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Life satisfaction</td>\n",
       "      <td>Total</td>\n",
       "      <td>Average score</td>\n",
       "      <td>5.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2501</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Life satisfaction</td>\n",
       "      <td>Men</td>\n",
       "      <td>Average score</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Life satisfaction</td>\n",
       "      <td>Women</td>\n",
       "      <td>Average score</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2574</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Life satisfaction</td>\n",
       "      <td>High</td>\n",
       "      <td>Average score</td>\n",
       "      <td>6.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Life satisfaction</td>\n",
       "      <td>Low</td>\n",
       "      <td>Average score</td>\n",
       "      <td>5.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2630</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Homicide rate</td>\n",
       "      <td>Total</td>\n",
       "      <td>Ratio</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2667</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Homicide rate</td>\n",
       "      <td>Men</td>\n",
       "      <td>Ratio</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Homicide rate</td>\n",
       "      <td>Women</td>\n",
       "      <td>Ratio</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2741</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employees working very long hours</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>21.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2778</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employees working very long hours</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>28.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2815</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Employees working very long hours</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>13.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2852</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>Total</td>\n",
       "      <td>Hours</td>\n",
       "      <td>14.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2889</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>Men</td>\n",
       "      <td>Hours</td>\n",
       "      <td>14.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Time devoted to leisure and personal care</td>\n",
       "      <td>Women</td>\n",
       "      <td>Hours</td>\n",
       "      <td>14.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3125</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Labour market insecurity</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>2.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3164</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Labour market insecurity</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3203</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Labour market insecurity</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3242</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Labour market insecurity</td>\n",
       "      <td>High</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3281</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Labour market insecurity</td>\n",
       "      <td>Low</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>5.870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Stakeholder engagement for developing regulations</td>\n",
       "      <td>Total</td>\n",
       "      <td>Average score</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Stakeholder engagement for developing regulations</td>\n",
       "      <td>Men</td>\n",
       "      <td>Average score</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3397</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Stakeholder engagement for developing regulations</td>\n",
       "      <td>Women</td>\n",
       "      <td>Average score</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3435</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Feeling safe walking alone at night</td>\n",
       "      <td>Total</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>70.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3474</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Feeling safe walking alone at night</td>\n",
       "      <td>Men</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>79.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3513</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Feeling safe walking alone at night</td>\n",
       "      <td>Women</td>\n",
       "      <td>Percentage</td>\n",
       "      <td>61.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country                                          Indicator Inequality  \\\n",
       "14     Japan                 Dwellings without basic facilities      Total   \n",
       "51     Japan                 Dwellings without basic facilities        Men   \n",
       "87     Japan                 Dwellings without basic facilities      Women   \n",
       "123    Japan                                Housing expenditure      Total   \n",
       "160    Japan                                Housing expenditure        Men   \n",
       "196    Japan                                Housing expenditure      Women   \n",
       "232    Japan                                   Rooms per person      Total   \n",
       "269    Japan                                   Rooms per person        Men   \n",
       "305    Japan                                   Rooms per person      Women   \n",
       "341    Japan           Household net adjusted disposable income      Total   \n",
       "378    Japan           Household net adjusted disposable income        Men   \n",
       "414    Japan           Household net adjusted disposable income      Women   \n",
       "450    Japan           Household net adjusted disposable income       High   \n",
       "487    Japan           Household net adjusted disposable income        Low   \n",
       "524    Japan                     Household net financial wealth      Total   \n",
       "561    Japan                     Household net financial wealth        Men   \n",
       "597    Japan                     Household net financial wealth      Women   \n",
       "633    Japan                                    Employment rate      Total   \n",
       "670    Japan                                    Employment rate        Men   \n",
       "707    Japan                                    Employment rate      Women   \n",
       "744    Japan                                    Employment rate       High   \n",
       "781    Japan                                    Employment rate        Low   \n",
       "818    Japan                        Long-term unemployment rate      Total   \n",
       "855    Japan                        Long-term unemployment rate        Men   \n",
       "892    Japan                        Long-term unemployment rate      Women   \n",
       "929    Japan                        Long-term unemployment rate       High   \n",
       "966    Japan                        Long-term unemployment rate        Low   \n",
       "1003   Japan                                  Personal earnings      Total   \n",
       "1040   Japan                                  Personal earnings        Men   \n",
       "1077   Japan                                  Personal earnings      Women   \n",
       "...      ...                                                ...        ...   \n",
       "2279   Japan                               Self-reported health      Total   \n",
       "2316   Japan                               Self-reported health        Men   \n",
       "2353   Japan                               Self-reported health      Women   \n",
       "2390   Japan                               Self-reported health       High   \n",
       "2427   Japan                               Self-reported health        Low   \n",
       "2464   Japan                                  Life satisfaction      Total   \n",
       "2501   Japan                                  Life satisfaction        Men   \n",
       "2538   Japan                                  Life satisfaction      Women   \n",
       "2574   Japan                                  Life satisfaction       High   \n",
       "2599   Japan                                  Life satisfaction        Low   \n",
       "2630   Japan                                      Homicide rate      Total   \n",
       "2667   Japan                                      Homicide rate        Men   \n",
       "2704   Japan                                      Homicide rate      Women   \n",
       "2741   Japan                  Employees working very long hours      Total   \n",
       "2778   Japan                  Employees working very long hours        Men   \n",
       "2815   Japan                  Employees working very long hours      Women   \n",
       "2852   Japan          Time devoted to leisure and personal care      Total   \n",
       "2889   Japan          Time devoted to leisure and personal care        Men   \n",
       "2926   Japan          Time devoted to leisure and personal care      Women   \n",
       "3125   Japan                           Labour market insecurity      Total   \n",
       "3164   Japan                           Labour market insecurity        Men   \n",
       "3203   Japan                           Labour market insecurity      Women   \n",
       "3242   Japan                           Labour market insecurity       High   \n",
       "3281   Japan                           Labour market insecurity        Low   \n",
       "3320   Japan  Stakeholder engagement for developing regulations      Total   \n",
       "3359   Japan  Stakeholder engagement for developing regulations        Men   \n",
       "3397   Japan  Stakeholder engagement for developing regulations      Women   \n",
       "3435   Japan                Feeling safe walking alone at night      Total   \n",
       "3474   Japan                Feeling safe walking alone at night        Men   \n",
       "3513   Japan                Feeling safe walking alone at night      Women   \n",
       "\n",
       "               Unit         Value  \n",
       "14       Percentage      6.400000  \n",
       "51       Percentage      6.400000  \n",
       "87       Percentage      6.400000  \n",
       "123      Percentage     22.000000  \n",
       "160      Percentage     22.000000  \n",
       "196      Percentage     22.000000  \n",
       "232           Ratio      1.900000  \n",
       "269           Ratio      1.900000  \n",
       "305           Ratio      1.900000  \n",
       "341       US Dollar  27323.000000  \n",
       "378       US Dollar  27323.000000  \n",
       "414       US Dollar  27323.000000  \n",
       "450       US Dollar  54666.000000  \n",
       "487       US Dollar   8834.000000  \n",
       "524       US Dollar  92818.000000  \n",
       "561       US Dollar  92818.000000  \n",
       "597       US Dollar  92818.000000  \n",
       "633      Percentage     73.000000  \n",
       "670      Percentage     82.000000  \n",
       "707      Percentage     64.000000  \n",
       "744      Percentage     82.000000  \n",
       "781      Percentage     71.000000  \n",
       "818      Percentage      1.360000  \n",
       "855      Percentage      1.800000  \n",
       "892      Percentage      0.787414  \n",
       "929      Percentage      1.070000  \n",
       "966      Percentage      1.700000  \n",
       "1003      US Dollar  35672.000000  \n",
       "1040      US Dollar  39327.000000  \n",
       "1077      US Dollar  28060.000000  \n",
       "...             ...           ...  \n",
       "2279     Percentage     35.000000  \n",
       "2316     Percentage     37.000000  \n",
       "2353     Percentage     34.000000  \n",
       "2390     Percentage     40.000000  \n",
       "2427     Percentage     28.000000  \n",
       "2464  Average score      5.900000  \n",
       "2501  Average score      5.800000  \n",
       "2538  Average score      6.000000  \n",
       "2574  Average score      6.400000  \n",
       "2599  Average score      5.300000  \n",
       "2630          Ratio      0.300000  \n",
       "2667          Ratio      0.300000  \n",
       "2704          Ratio      0.200000  \n",
       "2741     Percentage     21.890000  \n",
       "2778     Percentage     28.180000  \n",
       "2815     Percentage     13.450000  \n",
       "2852          Hours     14.850000  \n",
       "2889          Hours     14.850000  \n",
       "2926          Hours     14.530000  \n",
       "3125     Percentage      2.400000  \n",
       "3164     Percentage      1.400000  \n",
       "3203     Percentage      0.600000  \n",
       "3242     Percentage      0.510000  \n",
       "3281     Percentage      5.870000  \n",
       "3320  Average score      1.300000  \n",
       "3359  Average score      1.300000  \n",
       "3397  Average score      1.300000  \n",
       "3435     Percentage     70.200000  \n",
       "3474     Percentage     79.900000  \n",
       "3513     Percentage     61.600000  \n",
       "\n",
       "[92 rows x 5 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataframe consist of a lot of information\n",
    "df[df['Country'] == 'Japan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use total values only to make it simpler\n",
    "df = df[df['Inequality'] == 'Total']\n",
    "\n",
    "# Reduce the number of statistics to make it simpler, \n",
    "# uses a similar formula and categorization from OECD presentation\n",
    "\n",
    "# Normalize values\n",
    "df['Value'] = df.groupby('Indicator')['Value'].apply(\n",
    "    lambda x: (x - x.min()) / (x.max() - x.min()))\n",
    "\n",
    "# since properties like homicide rate are better the lower, will process those first\n",
    "neg_vals = ['Dwellings without basic facilities', \n",
    "            'Housing expenditure', \n",
    "            'Long-term unemployment rate'\n",
    "            'Air pollution', \n",
    "            'Homicide rate', \n",
    "            'Employees working very long hours',\n",
    "            'Labour market insecurity', \n",
    "            'Stakeholder engagement for developing regulations']\n",
    "df.loc[df['Indicator'].isin(neg_vals),'Value'] = 1 - df.loc[df['Indicator'].isin(neg_vals),'Value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "# means of related values to create sub-groups\n",
    "household = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Dwellings without basic facilities', \n",
    "                                             'Housing expenditure', \n",
    "                                             'Rooms per person']), 'Value'].mean())\n",
    "satisfaction = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Life satisfaction']), \n",
    "                                                           'Value'].mean())                                             \n",
    "employement = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Employment rate', \n",
    "                                                'Long-term unemployment rate', \n",
    "                                                'Employees working very long hours', \n",
    "                                                'Time devoted to leisure and personal care', \n",
    "                                                'Labour market insecurity']), 'Value'].mean())\n",
    "wealth = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Household net adjusted disposable income', \n",
    "                                           'Household net financial wealth',\n",
    "                                           'Personal earnings']), 'Value'].mean())\n",
    "education = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Educational attainment', \n",
    "                                              'Student skills',\n",
    "                                              'Years in education']), 'Value'].mean())\n",
    "health = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Life expectancy', \n",
    "                                           'Self-reported health']), 'Value'].mean())\n",
    "government = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Quality of support network', \n",
    "                                               'Air pollution', \n",
    "                                               'Water quality', \n",
    "                                               'Voter turnout', \n",
    "                                               'Stakeholder engagement for developing regulations']), 'Value'].mean())\n",
    "safety = df.groupby('Country').apply(lambda x: x.loc[x['Indicator'].isin(['Homicide rate', \n",
    "                                           'Feeling safe walking alone at night']), 'Value'].mean())\n",
    "\n",
    "df = pd.concat([household, satisfaction, employement, wealth, education, government, safety], axis=1)\n",
    "df.columns = ['Household', 'Satisfaction', 'Employement', 'Wealth', 'Education', 'Government', 'Safety']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_country(ax, theta, v, c, l):\n",
    "    ax.plot(theta, v, color='k', label='')\n",
    "    ax.fill(theta, v, color=c, alpha=.5, label=l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = df.columns\n",
    "theta = radar(len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(subplot_kw={'projection': 'radar'})\n",
    "\n",
    "ax.set_varlabels(labels)\n",
    "ax.get_yaxis().set_ticks([])\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "plot_country(ax, theta, df.loc['Japan'],\n",
    "             np.random.rand(3, ), 'Japan')\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.9, box.height * 0.9])\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20a041f07b8>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can compare with another country, or OECD total\n",
    "plot_country(ax, theta, df.loc['OECD - Total'],\n",
    "             np.random.rand(3, ), 'OECD - Total')\n",
    "ax.legend(bbox_to_anchor=(1.1, 1.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}