FP4-storytelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FP4 European Funding Data\n",
    "\n",
    "Data from https://open-data.europa.eu/en/data/dataset/cordisfp4projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp4 = pd.read_csv('FP4-data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>acronym</th>\n",
       "      <th>subject</th>\n",
       "      <th>area</th>\n",
       "      <th>subject.1</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>status</th>\n",
       "      <th>cost</th>\n",
       "      <th>framework</th>\n",
       "      <th>country</th>\n",
       "      <th>contractor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Spot IV-VΘgΘtation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Environmental Protection; Forecasting; Meteoro...</td>\n",
       "      <td>Research and development work for potential fu...</td>\n",
       "      <td>Environmental Protection; Forecasting; Meteoro...</td>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>1997-07-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fourth Framework Programme</td>\n",
       "      <td>FR</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Formation and occurrence of nitrous acd in the...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Environmental Protection; Forecasting; Measure...</td>\n",
       "      <td>Tropospheric physics and chemistry</td>\n",
       "      <td>Environmental Protection; Forecasting; Measure...</td>\n",
       "      <td>1996-02-01</td>\n",
       "      <td>1998-07-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fourth Framework Programme</td>\n",
       "      <td>IT</td>\n",
       "      <td>CH; DE; DE; DE; DE; DK; FR; GB; GR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Process for Production of Light Olefins by Deh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Industrial Manufacture; Materials Technology</td>\n",
       "      <td>Materials engineering</td>\n",
       "      <td>Industrial Manufacture; Materials Technology</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fourth Framework Programme</td>\n",
       "      <td>FI</td>\n",
       "      <td>DE; FI; FR; FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>High resolution diode laser carbon dioxide env...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Environmental Protection; Measurement Methods;...</td>\n",
       "      <td>Troposphere</td>\n",
       "      <td>Environmental Protection; Measurement Methods;...</td>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>1999-03-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fourth Framework Programme</td>\n",
       "      <td>IT</td>\n",
       "      <td>DE; IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Subsurface Radar as a Tool for Non-destructive...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Industrial Manufacture; Materials Technology; ...</td>\n",
       "      <td>Reliability and quality of materials and products</td>\n",
       "      <td>Industrial Manufacture; Materials Technology; ...</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Completed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fourth Framework Programme</td>\n",
       "      <td>DE</td>\n",
       "      <td>DE; GB; GB; GB; IT; IT; NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                              title acronym  \\\n",
       "0   0                                 Spot IV-VΘgΘtation     NaN   \n",
       "1   1  Formation and occurrence of nitrous acd in the...     NaN   \n",
       "2   2  Process for Production of Light Olefins by Deh...     NaN   \n",
       "3   3  High resolution diode laser carbon dioxide env...     NaN   \n",
       "4   4  Subsurface Radar as a Tool for Non-destructive...     NaN   \n",
       "\n",
       "                                             subject  \\\n",
       "0  Environmental Protection; Forecasting; Meteoro...   \n",
       "1  Environmental Protection; Forecasting; Measure...   \n",
       "2       Industrial Manufacture; Materials Technology   \n",
       "3  Environmental Protection; Measurement Methods;...   \n",
       "4  Industrial Manufacture; Materials Technology; ...   \n",
       "\n",
       "                                                area  \\\n",
       "0  Research and development work for potential fu...   \n",
       "1                 Tropospheric physics and chemistry   \n",
       "2                              Materials engineering   \n",
       "3                                        Troposphere   \n",
       "4  Reliability and quality of materials and products   \n",
       "\n",
       "                                           subject.1       start         end  \\\n",
       "0  Environmental Protection; Forecasting; Meteoro...  1995-04-01  1997-07-31   \n",
       "1  Environmental Protection; Forecasting; Measure...  1996-02-01  1998-07-31   \n",
       "2       Industrial Manufacture; Materials Technology  1996-01-01  1998-12-31   \n",
       "3  Environmental Protection; Measurement Methods;...  1996-04-01  1999-03-31   \n",
       "4  Industrial Manufacture; Materials Technology; ...  1996-01-01  1998-12-31   \n",
       "\n",
       "   duration     status  cost                   framework country  \\\n",
       "0       NaN  Completed   NaN  Fourth Framework Programme      FR   \n",
       "1       NaN  Completed   NaN  Fourth Framework Programme      IT   \n",
       "2       NaN  Completed   NaN  Fourth Framework Programme      FI   \n",
       "3       NaN  Completed   NaN  Fourth Framework Programme      IT   \n",
       "4       NaN  Completed   NaN  Fourth Framework Programme      DE   \n",
       "\n",
       "                           contractor  \n",
       "0                                 NaN  \n",
       "1  CH; DE; DE; DE; DE; DK; FR; GB; GR  \n",
       "2                      DE; FI; FR; FR  \n",
       "3                              DE; IT  \n",
       "4          DE; GB; GB; GB; IT; IT; NO  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id            14567\n",
       "title         14567\n",
       "acronym        5351\n",
       "subject       14303\n",
       "area          14297\n",
       "subject.1     14303\n",
       "start         13740\n",
       "end           13676\n",
       "duration       3846\n",
       "status        14564\n",
       "cost           3931\n",
       "framework     14567\n",
       "country       14389\n",
       "contractor    10967\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> 14k records, but only ~4k have a cost -- let's focus on these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp4sel = fp4[(fp4['cost'] > 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEaCAYAAADqqhd6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRIjhTkQkQv8HEXdK6xW6GXcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b06668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ProjectsByCountry = fp4sel.groupby('country').size().order(ascending=False)\n",
    "ProjectsByCountry.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> top 3 could have been guessed, decaying pattern\n",
    "compare population: DE > FR > GB > IT > ES [wiki](http://en.wikipedia.org/wiki/List_of_European_countries_by_population)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about project duration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEHCAYAAABWecpSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4QEAMEZ4AACMER4AAGOEBwDA2P8DLah2aqIvN+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114be5860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp4sel['duration'].plot(kind='hist',log=True,bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> looks pretty random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What about cost per project?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAERCAYAAABRpiGMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ1b2cEYAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c5a2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp4sel['cost'].plot(kind='hist');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> mostly \"cheap\" projects\n",
    "We will have a look at the cumulated costs later..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEUCAYAAAA4DAFkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11501c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp4sel['cost'].plot(kind='hist',log=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEUCAYAAAA4DAFkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11504bbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp4sel['cost'].plot(kind='hist',log=True,bins=30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEUCAYAAAA4DAFkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAj/w/rIyv4TPUV7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114bd7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp4sel['cost'].plot(kind='hist',log=True,bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> as usual: details of shape depend on binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAERCAYAAABhKjCtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnXYbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ebb438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fp4sel['cost'].plot(kind='hist',bins=50,cumulative=True,normed=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Can we see Pareto's 80/20 principle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.9357798165\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for cumcost in fp4sel['cost'].order().cumsum():\n",
    "    i += 1\n",
    "    if ( cumcost >= 0.8 * fp4sel['cost'].sum() ):\n",
    "        print(i * 100 / fp4sel['cost'].count())\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.480000000000004"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.02 * fp4sel['cost'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.873098714596797"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4sel['cost'].order(ascending=False)[1:88].sum() * 100 / fp4sel['cost'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> 2 % of the projects account for 80% of the budget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How much total funding is given to each country?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>83</td>\n",
       "      <td>139652145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>197</td>\n",
       "      <td>426269187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>1</td>\n",
       "      <td>517000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH</th>\n",
       "      <td>8</td>\n",
       "      <td>27389500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CY</th>\n",
       "      <td>2</td>\n",
       "      <td>1310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CZ</th>\n",
       "      <td>2</td>\n",
       "      <td>202000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>605</td>\n",
       "      <td>1317023306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK</th>\n",
       "      <td>94</td>\n",
       "      <td>176785035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES</th>\n",
       "      <td>318</td>\n",
       "      <td>538240065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FI</th>\n",
       "      <td>73</td>\n",
       "      <td>154647404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR</th>\n",
       "      <td>610</td>\n",
       "      <td>1457214214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB</th>\n",
       "      <td>682</td>\n",
       "      <td>1236691486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR</th>\n",
       "      <td>165</td>\n",
       "      <td>234650044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HU</th>\n",
       "      <td>3</td>\n",
       "      <td>1441650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IE</th>\n",
       "      <td>97</td>\n",
       "      <td>155967754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IL</th>\n",
       "      <td>19</td>\n",
       "      <td>17430985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>18</td>\n",
       "      <td>23983712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>424</td>\n",
       "      <td>820471083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LU</th>\n",
       "      <td>12</td>\n",
       "      <td>22144971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NL</th>\n",
       "      <td>282</td>\n",
       "      <td>566523880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>70</td>\n",
       "      <td>143962183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PT</th>\n",
       "      <td>48</td>\n",
       "      <td>103724285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RO</th>\n",
       "      <td>1</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE</th>\n",
       "      <td>94</td>\n",
       "      <td>160906697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SI</th>\n",
       "      <td>3</td>\n",
       "      <td>1257000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TR</th>\n",
       "      <td>1</td>\n",
       "      <td>1084900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cost            \n",
       "        size         sum\n",
       "country                 \n",
       "AT        83   139652145\n",
       "BE       197   426269187\n",
       "BG         1      517000\n",
       "CH         8    27389500\n",
       "CY         2     1310000\n",
       "CZ         2      202000\n",
       "DE       605  1317023306\n",
       "DK        94   176785035\n",
       "ES       318   538240065\n",
       "FI        73   154647404\n",
       "FR       610  1457214214\n",
       "GB       682  1236691486\n",
       "GR       165   234650044\n",
       "HU         3     1441650\n",
       "IE        97   155967754\n",
       "IL        19    17430985\n",
       "IS        18    23983712\n",
       "IT       424   820471083\n",
       "LU        12    22144971\n",
       "NL       282   566523880\n",
       "NO        70   143962183\n",
       "PT        48   103724285\n",
       "RO         1      550000\n",
       "SE        94   160906697\n",
       "SI         3     1257000\n",
       "TR         1     1084900"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CostByCountry = fp4sel.groupby(['country']).agg({'cost': [np.size, np.sum]})\n",
    "CostByCountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEgCAYAAABGoJPLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nExHJAMOciEgG/h9Q/eUZN80kXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f3cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CostByCountry['cost','sum'].order(ascending=False).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> again the largest countries receive most total funding\n",
    "But they also had the most projects...so what about ratio?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "CostByProjectAndCountry = CostByCountry['cost','sum']/CostByCountry['cost','size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEaCAYAAAA/lAFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152fe198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CostByProjectAndCountry.order(ascending=False).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> Switzerland = No 1 ! (most funding per project)\n",
    "GB with most funding and most projects only in the middle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4CH = fp4sel[fp4sel['country'] == 'CH']\n",
    "fp4CH['cost'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> only 8 projects in that period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>acronym</th>\n",
       "      <th>subject</th>\n",
       "      <th>area</th>\n",
       "      <th>subject.1</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>duration</th>\n",
       "      <th>status</th>\n",
       "      <th>cost</th>\n",
       "      <th>framework</th>\n",
       "      <th>country</th>\n",
       "      <th>contractor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>183</td>\n",
       "      <td>Platform for engineering research and trials</td>\n",
       "      <td>EXPERT</td>\n",
       "      <td>Electronics, Microelectronics; Information Pro...</td>\n",
       "      <td>High speed networking</td>\n",
       "      <td>Electronics, Microelectronics; Information Pro...</td>\n",
       "      <td>1995-10-01</td>\n",
       "      <td>1998-03-31</td>\n",
       "      <td>30</td>\n",
       "      <td>Completed</td>\n",
       "      <td>15973000</td>\n",
       "      <td>Fourth Framework Programme</td>\n",
       "      <td>CH</td>\n",
       "      <td>BE; BE; BE; CH; CH; CH; CH; DE; DE; DE; DE; DK...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                         title acronym  \\\n",
       "183  183  Platform for engineering research and trials  EXPERT   \n",
       "\n",
       "                                               subject                   area  \\\n",
       "183  Electronics, Microelectronics; Information Pro...  High speed networking   \n",
       "\n",
       "                                             subject.1       start  \\\n",
       "183  Electronics, Microelectronics; Information Pro...  1995-10-01   \n",
       "\n",
       "            end  duration     status      cost                   framework  \\\n",
       "183  1998-03-31        30  Completed  15973000  Fourth Framework Programme   \n",
       "\n",
       "    country                                         contractor  \n",
       "183      CH  BE; BE; BE; CH; CH; CH; CH; DE; DE; DE; DE; DK...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4sel.sort(['cost'], ascending=[0]).head(31).tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## --> 31th most expensive project (of the 4k considered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27389500.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp4CH['cost'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.35388208639733626"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100/fp4sel['cost'].sum() * fp4CH['cost'].sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --> only ~0.3% of the total funding went to CH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion: Black or White\n",
    "\n",
    "The same data set can be (mis-)used to tell opposite stories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEaCAYAAAA/lAFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ffefd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CostByProjectAndCountry.order(ascending=False).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEgCAYAAABGoJPLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nExHJAMOciEgG/h9Q/eUZN80kXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114e6a240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CostByCountry['cost','sum'].order(ascending=False).plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}