Euro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eurovision Song Contest:  Strategic Voting Analysis\n",
    "\n",
    "### Data Source: https://data.world/datagraver/eurovision-song-contest-scores-1975-2018\n",
    "\n",
    "\n",
    "# Research Questions\n",
    "\n",
    "    1)Do countries vote tactically? \n",
    "        Detect strategic voting patterns between countries in Eurovision Song Contest.\n",
    "        \n",
    "    2) Is tactical voting getting worse? \n",
    "        Detect changes in volume / density of strategic behavour over time.\n",
    "\n",
    "# Approaches explored\n",
    "\n",
    "### 1. Network analysis and Jaccard Similarity\n",
    "\n",
    "#### Bi-Direcitonal, Weighted Network of Countries and Votes\n",
    "Original approach for a given time windows (e.g. 5 years), map semi-final and finalist countries as nodes in a network. Edges were bi-directional and wieghted according to the number of votes given and recieved between thos countried in that time period.\n",
    "\n",
    "#### Community detection: Louvain Modularity \n",
    "Community detection using Louvain Modularity applied to the networks for given time span\n",
    "\n",
    "#### Detect changes in communities: Jaccard Similarity\n",
    "A rolling comparison between detected communities for each time window. Each community for the current year is martched to its closest community from the last year (closeness being measured as the one with the highest jaccard index). The overall average similarity score is then a wieghed Jaccard score: sum (Jaccard of best match * size of community) / sum(size of all communities)\n",
    "\n",
    "### 2. Montecarlo Simulations\n",
    "\n",
    "##### Montecarlo simulation of expected votes based on non-biased sampling of possible votes distributinos each year\n",
    "Approach adatped from: Derek Gatherer(2006) Comparison of Eurovision Song Contest Simulation with Actual Results Reveals Shifting Patterns of Collusive Voting Alliances. Journal of Artificial Societies and Social Simulation vol. 9, no. 2 http://jasss.soc.surrey.ac.uk/9/2/1.html\n",
    "\n",
    "####  Compare non-biased expectation value to actual results\n",
    "Where actual votes given/recieved falls within the 90% centile, the vote is consider suspceouse. \n",
    "Where suspicouse votes are given and recieved between two countries an 'alliance' is considered have been detected\n",
    "\n",
    "#### Visualise alliances as networks\n",
    "For all detected alliances, map the alliances.\n",
    "\n",
    "#### Dectect changes over time\n",
    "Measure changes in volume of alliances and countries in alliances over time.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "data_folder = 'data/'\n",
    "df_votes = pd.read_csv(data_folder+'votes.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Year', '(semi-) final', 'Edition', 'Jury or Televoting',\n",
      "       'From country', 'To country', 'Points      ', 'Duplicate'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/garethwalker/anaconda3/envs/eurovis/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if __name__ == '__main__':\n",
      "/Users/garethwalker/anaconda3/envs/eurovis/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Netherlands miss-spelled\n",
    "print(df_votes.columns)\n",
    "df_votes.rename(columns={'Points      ': 'Points'},inplace=True)\n",
    "df_votes['From country'] = df_votes['From country'].str.replace('The Netherands','The Netherlands')\n",
    "\n",
    "\n",
    "#for 2006: sad story regarding serbia and mont: https://en.wikipedia.org/wiki/Serbia_and_Montenegro_in_the_Eurovision_Song_Contest_2006\n",
    "\n",
    "to_drop = df_votes[df_votes['Year']==2006][df_votes['From country'] == 'Serbia & Montenegro'].index\n",
    "df_votes = df_votes.drop(to_drop)\n",
    "\n",
    "\n",
    "# 2015: F R MAC\n",
    "to_drop = df_votes[df_votes['Year']==2015][df_votes['From country'] == 'F.Y.R. Macedonia'].index\n",
    "df_votes = df_votes.drop(to_drop)\n",
    "\n",
    "\n",
    "# I am only going to work with voting in the finals for now...\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>(semi-) final</th>\n",
       "      <th>Edition</th>\n",
       "      <th>Jury or Televoting</th>\n",
       "      <th>From country</th>\n",
       "      <th>To country</th>\n",
       "      <th>Points</th>\n",
       "      <th>Duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975</td>\n",
       "      <td>f</td>\n",
       "      <td>1975f</td>\n",
       "      <td>J</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975</td>\n",
       "      <td>f</td>\n",
       "      <td>1975f</td>\n",
       "      <td>J</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Finland</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975</td>\n",
       "      <td>f</td>\n",
       "      <td>1975f</td>\n",
       "      <td>J</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975</td>\n",
       "      <td>f</td>\n",
       "      <td>1975f</td>\n",
       "      <td>J</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975</td>\n",
       "      <td>f</td>\n",
       "      <td>1975f</td>\n",
       "      <td>J</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year (semi-) final Edition Jury or Televoting From country To country  \\\n",
       "0  1975             f   1975f                  J      Belgium    Belgium   \n",
       "1  1975             f   1975f                  J      Belgium    Finland   \n",
       "2  1975             f   1975f                  J      Belgium     France   \n",
       "3  1975             f   1975f                  J      Belgium    Germany   \n",
       "4  1975             f   1975f                  J      Belgium    Ireland   \n",
       "\n",
       "   Points Duplicate  \n",
       "0       0         x  \n",
       "1       0       NaN  \n",
       "2       2       NaN  \n",
       "3       0       NaN  \n",
       "4      12       NaN  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['f', 'sf', 'sf1', 'sf2'], dtype=object)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes['(semi-) final'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2, 12,  1,  6,  7,  4,  8,  3, 10,  5])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes['Points'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "       2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes['Year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Belgium', 'Finland', 'France', 'Germany', 'Ireland', 'Israel',\n",
       "       'Italy', 'Luxembourg', 'Malta', 'Monaco', 'Norway', 'Portugal',\n",
       "       'Spain', 'Sweden', 'Switzerland', 'The Netherlands', 'Turkey',\n",
       "       'United Kingdom', 'Yugoslavia', 'Austria', 'Greece', 'Denmark',\n",
       "       'Morocco', 'Cyprus', 'Iceland', 'Bosnia & Herzegovina', 'Croatia',\n",
       "       'Slovenia', 'Estonia', 'Hungary', 'Lithuania', 'Poland', 'Romania',\n",
       "       'Russia', 'Slovakia', 'F.Y.R. Macedonia', 'Latvia', 'Ukraine',\n",
       "       'Albania', 'Andorra', 'Belarus', 'Serbia & Montenegro', 'Bulgaria',\n",
       "       'Moldova', 'Armenia', 'Czech Republic', 'Georgia', 'Montenegro',\n",
       "       'Serbia', 'Azerbaijan', 'San Marino', 'Australia', 'Macedonia'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes['From country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Belgium', 'Finland', 'France', 'Germany', 'Ireland', 'Israel',\n",
       "       'Italy', 'Luxembourg', 'Malta', 'Monaco', 'Norway', 'Portugal',\n",
       "       'Spain', 'Sweden', 'Switzerland', 'The Netherlands', 'Turkey',\n",
       "       'United Kingdom', 'Yugoslavia', 'Austria', 'Greece', 'Denmark',\n",
       "       'Morocco', 'Cyprus', 'Iceland', 'Bosnia & Herzegovina', 'Croatia',\n",
       "       'Slovenia', 'Estonia', 'Hungary', 'Lithuania', 'Poland', 'Romania',\n",
       "       'Russia', 'Slovakia', 'F.Y.R. Macedonia', 'Latvia', 'Ukraine',\n",
       "       'Albania', 'Serbia & Montenegro', 'Andorra', 'Belarus', 'Moldova',\n",
       "       'Bulgaria', 'Armenia', 'Georgia', 'Serbia', 'Czech Republic',\n",
       "       'Montenegro', 'Azerbaijan', 'San Marino', 'Australia', 'Macedonia'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes['To country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>From country</th>\n",
       "      <th>To country</th>\n",
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       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>39</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>43</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>43</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>39</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "      From country  To country\n",
       "Year                          \n",
       "1975            19          19\n",
       "1976            18          18\n",
       "1977            18          18\n",
       "1978            20          20\n",
       "1979            19          19\n",
       "1980            19          19\n",
       "1981            20          20\n",
       "1982            18          18\n",
       "1983            20          20\n",
       "1984            19          19\n",
       "1985            19          19\n",
       "1986            20          20\n",
       "1987            22          22\n",
       "1988            21          21\n",
       "1989            22          22\n",
       "1990            22          22\n",
       "1991            22          22\n",
       "1992            23          23\n",
       "1993            25          25\n",
       "1994            25          25\n",
       "1995            23          23\n",
       "1996            23          23\n",
       "1997            25          25\n",
       "1998            25          25\n",
       "1999            23          23\n",
       "2000            24          24\n",
       "2001            23          23\n",
       "2002            24          24\n",
       "2003            26          26\n",
       "2004            36          24\n",
       "2005            39          24\n",
       "2006            37          24\n",
       "2007            42          24\n",
       "2008            43          25\n",
       "2009            42          25\n",
       "2010            39          25\n",
       "2011            43          25\n",
       "2012            42          26\n",
       "2013            39          26\n",
       "2014            37          26\n",
       "2015            39          27\n",
       "2016            42          26\n",
       "2017            42          26\n",
       "2018            43          26"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes[df_votes['(semi-) final']=='f'].groupby('Year').nunique()[['From country','To country']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>(semi-) final</th>\n",
       "      <th>Edition</th>\n",
       "      <th>Jury or Televoting</th>\n",
       "      <th>From country</th>\n",
       "      <th>To country</th>\n",
       "      <th>Points</th>\n",
       "      <th>Duplicate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>(semi-) final</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2004</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2005</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2006</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2007</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2008</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>25</td>\n",
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       "      <td>1</td>\n",
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       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>21</td>\n",
       "      <td>19</td>\n",
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       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2009</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2010</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2011</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2012</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2013</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2014</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>sf1</th>\n",
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       "      <td>1</td>\n",
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       "      <td>16</td>\n",
       "      <td>11</td>\n",
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       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>27</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2017</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2018</th>\n",
       "      <th>f</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Year  (semi-) final  Edition  Jury or Televoting  \\\n",
       "Year (semi-) final                                                     \n",
       "2002 f                 1              1        1                   1   \n",
       "2003 f                 1              1        1                   1   \n",
       "2004 f                 1              1        1                   1   \n",
       "     sf                1              1        1                   1   \n",
       "2005 f                 1              1        1                   1   \n",
       "     sf                1              1        1                   1   \n",
       "2006 f                 1              1        1                   1   \n",
       "     sf                1              1        1                   1   \n",
       "2007 f                 1              1        1                   1   \n",
       "     sf                1              1        1                   1   \n",
       "2008 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2009 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2010 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2011 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2012 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2013 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2014 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2015 f                 1              1        1                   1   \n",
       "     sf1               1              1        1                   1   \n",
       "     sf2               1              1        1                   1   \n",
       "2016 f                 1              1        1                   2   \n",
       "     sf1               1              1        1                   2   \n",
       "     sf2               1              1        1                   2   \n",
       "2017 f                 1              1        1                   2   \n",
       "     sf1               1              1        1                   2   \n",
       "     sf2               1              1        1                   2   \n",
       "2018 f                 1              1        1                   2   \n",
       "     sf1               1              1        1                   2   \n",
       "     sf2               1              1        1                   2   \n",
       "\n",
       "                    From country  To country  Points  Duplicate  \n",
       "Year (semi-) final                                               \n",
       "2002 f                        24          24      11          1  \n",
       "2003 f                        26          26      11          1  \n",
       "2004 f                        36          24      11          1  \n",
       "     sf                       36          22      11          1  \n",
       "2005 f                        39          24      11          1  \n",
       "     sf                       39          25      11          1  \n",
       "2006 f                        37          24      11          1  \n",
       "     sf                       37          23      11          1  \n",
       "2007 f                        42          24      11          1  \n",
       "     sf                       42          28      11          1  \n",
       "2008 f                        43          25      11          1  \n",
       "     sf1                      21          19      11          1  \n",
       "     sf2                      22          19      11          1  \n",
       "2009 f                        42          25      11          1  \n",
       "     sf1                      20          18      11          1  \n",
       "     sf2                      22          19      11          1  \n",
       "2010 f                        39          25      11          1  \n",
       "     sf1                      20          17      11          1  \n",
       "     sf2                      19          17      11          1  \n",
       "2011 f                        43          25      11          1  \n",
       "     sf1                      21          19      11          1  \n",
       "     sf2                      22          19      11          1  \n",
       "2012 f                        42          26      11          1  \n",
       "     sf1                      21          18      11          1  \n",
       "     sf2                      21          18      11          1  \n",
       "2013 f                        39          26      11          1  \n",
       "     sf1                      19          16      11          1  \n",
       "     sf2                      20          17      11          1  \n",
       "2014 f                        37          26      11          1  \n",
       "     sf1                      19          16      11          1  \n",
       "     sf2                      18          15      11          1  \n",
       "2015 f                        39          27      11          0  \n",
       "     sf1                      20          16      11          1  \n",
       "     sf2                      21          17      11          1  \n",
       "2016 f                        42          26      11          0  \n",
       "     sf1                      21          18      11          0  \n",
       "     sf2                      21          18      11          0  \n",
       "2017 f                        42          26      11          1  \n",
       "     sf1                      21          18      11          1  \n",
       "     sf2                      21          18      11          1  \n",
       "2018 f                        43          26      11          0  \n",
       "     sf1                      22          19      11          0  \n",
       "     sf2                      21          18      11          0  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes[df_votes['Year'].between(2002,2018)].groupby(['Year','(semi-) final']).nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Restrict Analysis to Finals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_votes_finals = df_votes[df_votes['(semi-) final']=='f']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>(semi-) final</th>\n",
       "      <th>Edition</th>\n",
       "      <th>Jury or Televoting</th>\n",
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       "      <th>21264</th>\n",
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       "    <tr>\n",
       "      <th>21267</th>\n",
       "      <td>2008</td>\n",
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       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
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       "      <td>0</td>\n",
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       "      <th>21268</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21269</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>21270</th>\n",
       "      <td>2008</td>\n",
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       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Finland</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21271</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>France</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21272</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21273</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21274</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Greece</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21275</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21276</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Israel</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21277</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21278</th>\n",
       "      <td>2008</td>\n",
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       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21279</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Poland</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21280</th>\n",
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       "      <td>Albania</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>21281</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Albania</td>\n",
       "      <td>Romania</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21282</th>\n",
       "      <td>2008</td>\n",
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       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21283</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2008</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>2008</td>\n",
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       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21287</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21288</th>\n",
       "      <td>2008</td>\n",
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       "    <tr>\n",
       "      <th>21289</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
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       "      <td>J</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21290</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
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       "      <td>Andorra</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21291</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
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       "      <td>Andorra</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21292</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
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       "      <td>J</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Bosnia &amp; Herzegovina</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>21293</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
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       "      <td>Andorra</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>22309</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Ukraine</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22310</th>\n",
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       "      <td>Ukraine</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22311</th>\n",
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       "      <td>Ukraine</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22312</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Ukraine</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>22313</th>\n",
       "      <td>2008</td>\n",
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       "      <td>Ukraine</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>22314</th>\n",
       "      <td>2008</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22315</th>\n",
       "      <td>2008</td>\n",
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       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22316</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22317</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Bosnia &amp; Herzegovina</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22318</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22319</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22320</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Finland</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22321</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>France</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22322</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22323</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Germany</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22324</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Greece</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22325</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22326</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Israel</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22327</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22328</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Norway</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22329</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Poland</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22330</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22331</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Romania</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22332</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Russia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22333</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22334</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22335</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22336</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22337</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22338</th>\n",
       "      <td>2008</td>\n",
       "      <td>f</td>\n",
       "      <td>2008f</td>\n",
       "      <td>J</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1075 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year (semi-) final Edition Jury or Televoting    From country  \\\n",
       "21264  2008             f   2008f                  J         Albania   \n",
       "21265  2008             f   2008f                  J         Albania   \n",
       "21266  2008             f   2008f                  J         Albania   \n",
       "21267  2008             f   2008f                  J         Albania   \n",
       "21268  2008             f   2008f                  J         Albania   \n",
       "21269  2008             f   2008f                  J         Albania   \n",
       "21270  2008             f   2008f                  J         Albania   \n",
       "21271  2008             f   2008f                  J         Albania   \n",
       "21272  2008             f   2008f                  J         Albania   \n",
       "21273  2008             f   2008f                  J         Albania   \n",
       "21274  2008             f   2008f                  J         Albania   \n",
       "21275  2008             f   2008f                  J         Albania   \n",
       "21276  2008             f   2008f                  J         Albania   \n",
       "21277  2008             f   2008f                  J         Albania   \n",
       "21278  2008             f   2008f                  J         Albania   \n",
       "21279  2008             f   2008f                  J         Albania   \n",
       "21280  2008             f   2008f                  J         Albania   \n",
       "21281  2008             f   2008f                  J         Albania   \n",
       "21282  2008             f   2008f                  J         Albania   \n",
       "21283  2008             f   2008f                  J         Albania   \n",
       "21284  2008             f   2008f                  J         Albania   \n",
       "21285  2008             f   2008f                  J         Albania   \n",
       "21286  2008             f   2008f                  J         Albania   \n",
       "21287  2008             f   2008f                  J         Albania   \n",
       "21288  2008             f   2008f                  J         Albania   \n",
       "21289  2008             f   2008f                  J         Andorra   \n",
       "21290  2008             f   2008f                  J         Andorra   \n",
       "21291  2008             f   2008f                  J         Andorra   \n",
       "21292  2008             f   2008f                  J         Andorra   \n",
       "21293  2008             f   2008f                  J         Andorra   \n",
       "...     ...           ...     ...                ...             ...   \n",
       "22309  2008             f   2008f                  J         Ukraine   \n",
       "22310  2008             f   2008f                  J         Ukraine   \n",
       "22311  2008             f   2008f                  J         Ukraine   \n",
       "22312  2008             f   2008f                  J         Ukraine   \n",
       "22313  2008             f   2008f                  J         Ukraine   \n",
       "22314  2008             f   2008f                  J  United Kingdom   \n",
       "22315  2008             f   2008f                  J  United Kingdom   \n",
       "22316  2008             f   2008f                  J  United Kingdom   \n",
       "22317  2008             f   2008f                  J  United Kingdom   \n",
       "22318  2008             f   2008f                  J  United Kingdom   \n",
       "22319  2008             f   2008f                  J  United Kingdom   \n",
       "22320  2008             f   2008f                  J  United Kingdom   \n",
       "22321  2008             f   2008f                  J  United Kingdom   \n",
       "22322  2008             f   2008f                  J  United Kingdom   \n",
       "22323  2008             f   2008f                  J  United Kingdom   \n",
       "22324  2008             f   2008f                  J  United Kingdom   \n",
       "22325  2008             f   2008f                  J  United Kingdom   \n",
       "22326  2008             f   2008f                  J  United Kingdom   \n",
       "22327  2008             f   2008f                  J  United Kingdom   \n",
       "22328  2008             f   2008f                  J  United Kingdom   \n",
       "22329  2008             f   2008f                  J  United Kingdom   \n",
       "22330  2008             f   2008f                  J  United Kingdom   \n",
       "22331  2008             f   2008f                  J  United Kingdom   \n",
       "22332  2008             f   2008f                  J  United Kingdom   \n",
       "22333  2008             f   2008f                  J  United Kingdom   \n",
       "22334  2008             f   2008f                  J  United Kingdom   \n",
       "22335  2008             f   2008f                  J  United Kingdom   \n",
       "22336  2008             f   2008f                  J  United Kingdom   \n",
       "22337  2008             f   2008f                  J  United Kingdom   \n",
       "22338  2008             f   2008f                  J  United Kingdom   \n",
       "\n",
       "                 To country  Points Duplicate  \n",
       "21264               Albania       0         x  \n",
       "21265               Armenia       2       NaN  \n",
       "21266            Azerbaijan       0       NaN  \n",
       "21267  Bosnia & Herzegovina       0       NaN  \n",
       "21268               Croatia       0       NaN  \n",
       "21269               Denmark       0       NaN  \n",
       "21270               Finland       0       NaN  \n",
       "21271                France       0       NaN  \n",
       "21272               Georgia       0       NaN  \n",
       "21273               Germany       0       NaN  \n",
       "21274                Greece      12       NaN  \n",
       "21275               Iceland       0       NaN  \n",
       "21276                Israel       4       NaN  \n",
       "21277                Latvia       0       NaN  \n",
       "21278                Norway       7       NaN  \n",
       "21279                Poland       0       NaN  \n",
       "21280              Portugal       0       NaN  \n",
       "21281               Romania       0       NaN  \n",
       "21282                Russia       6       NaN  \n",
       "21283                Serbia       5       NaN  \n",
       "21284                 Spain       1       NaN  \n",
       "21285                Sweden       3       NaN  \n",
       "21286                Turkey      10       NaN  \n",
       "21287               Ukraine       8       NaN  \n",
       "21288        United Kingdom       0       NaN  \n",
       "21289               Albania       0       NaN  \n",
       "21290               Armenia       0       NaN  \n",
       "21291            Azerbaijan       7       NaN  \n",
       "21292  Bosnia & Herzegovina       0       NaN  \n",
       "21293               Croatia       0       NaN  \n",
       "...                     ...     ...       ...  \n",
       "22309                 Spain       0       NaN  \n",
       "22310                Sweden       0       NaN  \n",
       "22311                Turkey       4       NaN  \n",
       "22312               Ukraine       0         x  \n",
       "22313        United Kingdom       0       NaN  \n",
       "22314               Albania       0       NaN  \n",
       "22315               Armenia       0       NaN  \n",
       "22316            Azerbaijan       0       NaN  \n",
       "22317  Bosnia & Herzegovina       0       NaN  \n",
       "22318               Croatia       0       NaN  \n",
       "22319               Denmark       3       NaN  \n",
       "22320               Finland       0       NaN  \n",
       "22321                France       2       NaN  \n",
       "22322               Georgia       0       NaN  \n",
       "22323               Germany       0       NaN  \n",
       "22324                Greece      12       NaN  \n",
       "22325               Iceland       6       NaN  \n",
       "22326                Israel       0       NaN  \n",
       "22327                Latvia      10       NaN  \n",
       "22328                Norway       7       NaN  \n",
       "22329                Poland       4       NaN  \n",
       "22330              Portugal       0       NaN  \n",
       "22331               Romania       0       NaN  \n",
       "22332                Russia       0       NaN  \n",
       "22333                Serbia       0       NaN  \n",
       "22334                 Spain       1       NaN  \n",
       "22335                Sweden       0       NaN  \n",
       "22336                Turkey       8       NaN  \n",
       "22337               Ukraine       5       NaN  \n",
       "22338        United Kingdom       0         x  \n",
       "\n",
       "[1075 rows x 8 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_votes_finals[df_votes_finals['Year']==2008]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis using 'community detection'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import community\n",
    "\n",
    "def detect_community(df, year_from, year_to):\n",
    "    global selected_df\n",
    "    # compile vote matrix for selected years\n",
    "    selected_df = df[df['Year'].between(year_from,year_to)]\n",
    "    selected_df = selected_df.groupby(['From country','To country']).Points.sum()\n",
    "    selected_df = selected_df.unstack().fillna(0)\n",
    "    loosers = list(set(selected_df.index)-set(selected_df.columns))\n",
    "    for i in range(len(loosers)):\n",
    "        selected_df[loosers[i]] = 0\n",
    "        \n",
    "    # convert df to graph object\n",
    "    np_matrix = selected_df.values\n",
    "    names = selected_df.columns\n",
    "    graph = nx.from_numpy_matrix(np_matrix)\n",
    "    graph = nx.relabel_nodes(graph, dict(enumerate(names)))\n",
    "    \n",
    "    \n",
    "    # use Louvain Modularity to identify communities\n",
    "    part = community.best_partition(graph)\n",
    "    \n",
    "\n",
    "    \n",
    "    return graph, part\n",
    "\n",
    "def visualise(graph, part):\n",
    "    # visualise\n",
    "    values = [part.get(node) for node in graph.nodes()]\n",
    "    plt.figure(figsize=(20,20))\n",
    "    pos=nx.spring_layout(graph)\n",
    "    nx.draw_networkx_nodes(graph, pos, node_size=300,cmap = plt.get_cmap('jet'), node_color = values,alpha=0.5)\n",
    "    nx.draw_networkx_labels(graph, pos, font_size=18, font_family='sans-serif')\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "part,graph = detect_community(df_votes,2013,2018)\n",
    "visualise(part,graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anlaysis using Jaccard similarity between communities to detect change over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import scipy.stats\n",
    "\n",
    "\n",
    "\n",
    "def jaccard_similarity(list1, list2):\n",
    "    intersection = len(list(set(list1).intersection(list2)))\n",
    "    #print(list(set(list1).intersection(list2)))\n",
    "    union = (len(list1) + len(list2)) - intersection\n",
    "    return float(intersection / union)\n",
    "\n",
    "\n",
    "def mean_confidence_interval(data, confidence=0.95):\n",
    "    a = 1.0 * np.array(data)\n",
    "    n = len(a)\n",
    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
    "    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n",
    "    return h\n",
    "\n",
    "\n",
    "\n",
    "def av_jaccard(current_year, prev_year):\n",
    "    \n",
    "    graph_prev, part_prev = detect_community(df_votes,prev_year,prev_year)\n",
    "    graph_current, part_current = detect_community(df_votes,current_year,current_year)\n",
    "    \n",
    "    clusters_prev = pd.Series(part_prev, name='prev').unique()\n",
    "    clusters_current = pd.Series(part_current, name='current').unique()\n",
    "\n",
    "    matches =[]\n",
    "\n",
    "    for i in np.nditer(clusters_current):\n",
    "        A = pd.Series(part_current)\n",
    "        A = A[A==i].index.astype(list)\n",
    "\n",
    "        compare = []\n",
    "        for j in np.nditer(clusters_prev):\n",
    "            B = pd.Series(part_prev)\n",
    "            B = B[B==j].index.astype(list)\n",
    "            compare.append(jaccard_similarity(A,B))\n",
    "            best_match = (max(compare))\n",
    "\n",
    "        matches.append((best_match,len(A)))\n",
    "\n",
    "       \n",
    "    \n",
    "    wieghted_score = np.sum([x*y for x,y in matches])\n",
    "    denominator = np.sum([y for x,y in matches])\n",
    "    wieghted_av_jaccard = wieghted_score/denominator\n",
    "    \n",
    "    return wieghted_av_jaccard\n",
    "\n",
    "def av_jaccard_2(year,window):\n",
    "    \n",
    "    graph_prev, part_prev = detect_community(df_votes,year-window-1,year-1) #community composed of last 'window' years\n",
    "    graph_current, part_current = detect_community(df_votes,year,year)\n",
    "    \n",
    "    clusters_prev = pd.Series(part_prev, name='prev').unique()\n",
    "    clusters_current = pd.Series(part_current, name='current').unique()\n",
    "\n",
    "    matches =[]\n",
    "\n",
    "    for i in np.nditer(clusters_current):\n",
    "        A = pd.Series(part_current)\n",
    "        A = A[A==i].index.astype(list)\n",
    "\n",
    "        compare = []\n",
    "        for j in np.nditer(clusters_prev):\n",
    "            B = pd.Series(part_prev)\n",
    "            B = B[B==j].index.astype(list)\n",
    "            compare.append(jaccard_similarity(A,B))\n",
    "            best_match = (max(compare))\n",
    "\n",
    "        matches.append((best_match,len(A)))\n",
    "\n",
    "       \n",
    "    \n",
    "    wieghted_score = np.sum([x*y for x,y in matches])\n",
    "    denominator = np.sum([y for x,y in matches])\n",
    "    wieghted_av_jaccard = wieghted_score/denominator\n",
    "    \n",
    "    return wieghted_av_jaccard\n",
    " \n",
    "def rolling_jaccard(start,end):\n",
    "    df_jac = pd.DataFrame(columns=['distance','+95%CI','-95%CI'])\n",
    "    for year in np.arange(start,end+1,1):\n",
    "       \n",
    "        print(\"\\r Processing: \" + str(year), end = \"\")\n",
    "        jacs = []\n",
    "        for i in range(10):\n",
    "            jacs.append(av_jaccard(year-1,year))\n",
    "        av_jac = np.mean(jacs)\n",
    "        conf_jac = mean_confidence_interval(jacs)\n",
    "        df_jac.loc[year,'distance']=av_jac\n",
    "        df_jac.loc[year,'+95%CI']=av_jac+conf_jac\n",
    "        df_jac.loc[year,'-95%CI']=av_jac-conf_jac\n",
    "        \n",
    "\n",
    "    return df_jac\n",
    "\n",
    "def rolling_jaccard_2(start,end,n=30,window=5):\n",
    "    df_jac = pd.DataFrame(columns=['distance','+95%CI','-95%CI'])\n",
    "    for year in np.arange(start,end+1,1):\n",
    "        print(\"\\r Processing: \" + str(year), end = \"\")\n",
    "        jacs = []\n",
    "        for i in range(n):\n",
    "            jacs.append(av_jaccard_2(year,window))\n",
    "        av_jac = np.mean(jacs)\n",
    "        conf_jac = mean_confidence_interval(jacs)\n",
    "        df_jac.loc[year,'distance']=av_jac\n",
    "        df_jac.loc[year,'+95%CI']=av_jac+conf_jac\n",
    "        df_jac.loc[year,'-95%CI']=av_jac-conf_jac\n",
    "        \n",
    "\n",
    "    return df_jac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing 1990\n",
      "processing 1991\n",
      "processing 1992\n",
      "processing 1993\n",
      "processing 1994\n",
      "processing 1995\n",
      "processing 1996\n",
      "processing 1997\n",
      "processing 1998\n",
      "processing 1999\n",
      "processing 2000\n",
      "processing 2001\n",
      "processing 2002\n",
      "processing 2003\n",
      "processing 2004\n",
      "processing 2005\n",
      "processing 2006\n",
      "processing 2007\n",
      "processing 2008\n",
      "processing 2009\n",
      "processing 2010\n",
      "processing 2011\n",
      "processing 2012\n",
      "processing 2013\n",
      "processing 2014\n",
      "processing 2015\n",
      "processing 2016\n",
      "processing 2017\n",
      "processing 2018\n"
     ]
    }
   ],
   "source": [
    "df_jac_pre = rolling_jaccard(1990,2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20b82978>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_jac_pre.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Processing: 2018"
     ]
    }
   ],
   "source": [
    "df_jac_pre_2 = rolling_jaccard_2(1990,2018)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d9a1f60>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_jac_pre_2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing 1995\n",
      "processing 1996\n",
      "processing 1997\n",
      "processing 1998\n",
      "processing 1999\n",
      "processing 2000\n",
      "processing 2001\n",
      "processing 2002\n",
      "processing 2003\n",
      "processing 2004\n",
      "processing 2005\n",
      "processing 2006\n",
      "processing 2007\n",
      "processing 2008\n",
      "processing 2009\n",
      "processing 2010\n",
      "processing 2011\n",
      "processing 2012\n",
      "processing 2013\n",
      "processing 2014\n",
      "processing 2015\n",
      "processing 2016\n",
      "processing 2017\n",
      "processing 2018\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_jac_3 = rolling_jaccard_2(1995,2018,n=30,window=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d15c128>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_jac_3.plot(xticks=df_jac_3.index,figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_test,graph_test=detect_community(df_votes,1999,2000)\n",
    "part_test2,graph_test2=detect_community(df_votes,2005,2006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise(part_test,graph_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualise(part_test2,graph_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for a range of years:\n",
    "\n",
    "#creat and expected array\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A frequentist Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approach adapted from:  \n",
    "\n",
    "Derek Gatherer(2006) Comparison of Eurovision Song Contest Simulation with Actual Results Reveals Shifting Patterns of Collusive Voting Alliances. Journal of Artificial Societies and Social Simulation vol. 9, no. 2\n",
    "http://jasss.soc.surrey.ac.uk/9/2/1.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def get_vote_dist(year):  #construct distribution\n",
    "    # N.B: Effective for year 1975 to 2015\n",
    "    \n",
    "    # count number of voters in that year\n",
    "    voters = len(df_votes[df_votes['Year']==year].groupby('From country').sum().index)\n",
    "    # subtract 1 to account for no voting for self\n",
    "    available_votes = voters - 1 \n",
    "    # calcualte vote probabilities\n",
    "    p_non_zero = 1/available_votes\n",
    "    p_zero     = (available_votes-10)/available_votes\n",
    "    #creat distribution\n",
    "    vote_values = [0,1,2,3,4,5,6,7,8,10,12]\n",
    "    vote_proba  = np.append([p_zero],np.full((10), p_non_zero))\n",
    "    \n",
    "    return (vote_values,vote_proba)\n",
    "\n",
    "\n",
    "\n",
    "def simulate_voting(start_year,end_year,simulations): # monte-carlo simulation for n years\n",
    "    mean_dist = []\n",
    "    for i in range(simulations):\n",
    "        print(\"\\r Similation: \"+str(i)+\" of \"+str(simulations), end = \"\")\n",
    "        votes = []\n",
    "\n",
    "        for year in np.arange(start_year,end_year,1):\n",
    "\n",
    "            vote_values,vote_proba = get_vote_dist(year)    # get vote probability distribution for current year\n",
    "            vote_sample = np.random.choice(vote_values, 1,p=vote_proba) # sample from distribution\n",
    "            votes.append(vote_sample)   # append sample to list\n",
    "\n",
    "        mean_sample = np.mean(votes)    \n",
    "        mean_dist.append(mean_sample)\n",
    "        \n",
    "    return(mean_dist)\n",
    "\n",
    "def get_threshold(mean_dist,p_val):\n",
    "    #fig, ax = plt.subplots(1, 1)\n",
    "    #plt.hist(mean_dist,bins=200)\n",
    "    threshold = np.percentile(mean_dist, p_val)\n",
    "    mean = np.percentile(mean_dist, 50)\n",
    "    #ax.axvline(x=threshold, c='red', alpha=1, linestyle='--')\n",
    "    #ax.axvline(x=mean, c='blue', alpha=1, linestyle='--')\n",
    "    #plt.show()\n",
    "    return (threshold)\n",
    "\n",
    "\n",
    "def detect_coalitions(df,test_stat,start_year,end_year): \n",
    "\n",
    "    # generate real values for these years \n",
    "\n",
    "    selected_df = df[df['Year'].between(start_year,end_year)]\n",
    "    selected_df = selected_df.groupby(['From country','To country']).Points.mean()\n",
    "    selected_df = selected_df.unstack().fillna(0)\n",
    "    \n",
    "    loosers = list(set(selected_df.index)-set(selected_df.columns))\n",
    "    for i in range(len(loosers)):\n",
    "        selected_df[loosers[i]] = 0\n",
    "\n",
    "    #detect suspect preferential voting\n",
    "    suspect = lambda x : x > test_stat\n",
    "    suspect_df = selected_df.apply(suspect)\n",
    "\n",
    "    coalitions= pd.DataFrame(columns=['CountryA','CountryB','Start year','End year','Av_voters'])\n",
    "    coalition_count = 0\n",
    "\n",
    "    # detect reciprocity\n",
    "\n",
    "    for countryA in suspect_df.index:    \n",
    "        for countryB in suspect_df.index:\n",
    "            if suspect_df.loc[countryA,countryB] == True:\n",
    "                if suspect_df.loc[countryB,countryA] == True:\n",
    "                    coalition_count += 1\n",
    "                    coalitions.loc[coalition_count,'CountryA'] = countryA\n",
    "                    coalitions.loc[coalition_count,'CountryB'] = countryB\n",
    "\n",
    "    #remove directionality\n",
    "    dupes = coalitions[['CountryA','CountryB']].T.apply(sorted).T.duplicated()\n",
    "\n",
    "    coalitions = coalitions[~dupes]\n",
    "    coalitions.loc[:,'Start year'] = start_year\n",
    "    coalitions.loc[:,'End year'] = end_year\n",
    "    coalitions.loc[:,'Av_voters']  = df_votes[df_votes['Year'].between(start_year,end_year)].groupby('Year').nunique().loc[:,'From country'].mean()\n",
    "\n",
    "    \n",
    "    return (coalitions)\n",
    "\n",
    "def visualise_df(coalitions_df,size):\n",
    "    \n",
    "    #draw results\n",
    "    g_coalitions=nx.from_pandas_edgelist(coalitions_df,'CountryA','CountryB')\n",
    "\n",
    "    plt.figure(figsize=(size,size))\n",
    "    nx.draw(g_coalitions,with_labels=True,font_size=30)\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test monte-carlo simulation : Inspect resulting distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "vote_values,vote_proba = get_vote_dist(2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(vote_values,vote_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999999999"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(vote_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Similation: 1999 of 2000"
     ]
    }
   ],
   "source": [
    "test_dist = simulate_voting(2000,2005,2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([375., 389., 397., 359., 239., 145.,  53.,  28.,  11.,   4.]),\n",
       " array([0.  , 0.84, 1.68, 2.52, 3.36, 4.2 , 5.04, 5.88, 6.72, 7.56, 8.4 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(test_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3189"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(test_dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyse_period (df,start,end,simulations,threshold):\n",
    "    \"\"\" \n",
    "    1) simulates voting for specified period\n",
    "    2) compares simulated voting with actual voting to detect outliers (favouratism)\n",
    "    3) detects reciprocal favouratism (alliances)\n",
    "    \n",
    "    \n",
    "    Parameters:\n",
    "    \n",
    "    df: df of votes to be analysed\n",
    "    start: start year to be assessed\n",
    "    end: end year of period\n",
    "    simulations: number of runs on monte-carlo to be simulated\n",
    "    threshold : percentile to be considered favouratism\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    dist= simulate_voting(start,end,simulations) #simulate voting in period\n",
    "    test_stat = get_threshold(dist, threshold) #calculate test statitic for given percentile\n",
    "    coalitions= detect_coalitions(df,test_stat,start,end) #detect coalitions\n",
    "\n",
    "    \n",
    "    return(coalitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Similation: 1999 of 2000"
     ]
    }
   ],
   "source": [
    "\n",
    "# def analyse_data(df,start_year,end_year,period,simulations,threshold):\n",
    "\n",
    "simulations = 2000\n",
    "threshold = 90\n",
    "coalitions_1975_1980 = analyse_period(df_votes,1975,1980,simulations,threshold)\n",
    "coalitions_1980_1985 = analyse_period(df_votes,1980,1985,simulations,threshold)\n",
    "coalitions_1985_1990 = analyse_period(df_votes,1985,1990,simulations,threshold)\n",
    "coalitions_1990_1995 = analyse_period(df_votes,1990,1995,simulations,threshold)\n",
    "coalitions_1995_2000 = analyse_period(df_votes,1995,2000,simulations,threshold)\n",
    "coalitions_2000_2005 = analyse_period(df_votes,2000,2005,simulations,threshold)\n",
    "coalitions_2005_2010 = analyse_period(df_votes,2005,2010,simulations,threshold)\n",
    "coalitions_2010_2015 = analyse_period(df_votes,2010,2015,simulations,threshold)\n",
    "coalitions = coalitions_1980_1985.append([coalitions_1975_1980,coalitions_1985_1990 ,coalitions_1990_1995,coalitions_1995_2000,coalitions_2000_2005,coalitions_2005_2010,coalitions_2010_2015]).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Analyse finalists only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Similation: 199 of 200"
     ]
    }
   ],
   "source": [
    "end=2015\n",
    "div = 3\n",
    "start=1975\n",
    "simulations = 2000\n",
    "threshold = 90\n",
    "span =end-start\n",
    "test = int(span/div)\n",
    "a_nu_start = 2015 - (div*test)\n",
    "\n",
    "starts = np.arange(a_nu_start,end,div)\n",
    "coalitions= pd.DataFrame(columns=['CountryA','CountryB','Start year','End year','Av_voters'])\n",
    "initialise = 0\n",
    "\n",
    "for year in starts:\n",
    "    initialise += 1\n",
    "    coalition = analyse_period(df_votes_finals,year,year+div,simulations,threshold)\n",
    "    coalitions = coalitions.append(coalition).reset_index(drop=True)\n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryA</th>\n",
       "      <th>CountryB</th>\n",
       "      <th>Start year</th>\n",
       "      <th>End year</th>\n",
       "      <th>Av_voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1976</td>\n",
       "      <td>1979</td>\n",
       "      <td>18.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>France</td>\n",
       "      <td>Greece</td>\n",
       "      <td>1976</td>\n",
       "      <td>1979</td>\n",
       "      <td>18.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1976</td>\n",
       "      <td>1979</td>\n",
       "      <td>18.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>1979</td>\n",
       "      <td>1982</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>1979</td>\n",
       "      <td>1982</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>1979</td>\n",
       "      <td>1982</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1979</td>\n",
       "      <td>1982</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1979</td>\n",
       "      <td>1982</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1982</td>\n",
       "      <td>1985</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Israel</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>1982</td>\n",
       "      <td>1985</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1982</td>\n",
       "      <td>1985</td>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Greece</td>\n",
       "      <td>1985</td>\n",
       "      <td>1988</td>\n",
       "      <td>20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Yugoslavia</td>\n",
       "      <td>1985</td>\n",
       "      <td>1988</td>\n",
       "      <td>20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1985</td>\n",
       "      <td>1988</td>\n",
       "      <td>20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Greece</td>\n",
       "      <td>1988</td>\n",
       "      <td>1991</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Italy</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1988</td>\n",
       "      <td>1991</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Malta</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1988</td>\n",
       "      <td>1991</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Malta</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>1988</td>\n",
       "      <td>1991</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Malta</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1988</td>\n",
       "      <td>1991</td>\n",
       "      <td>21.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Greece</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>France</td>\n",
       "      <td>Italy</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>France</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>France</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Russia</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Greece</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Hungary</td>\n",
       "      <td>Poland</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>Malta</td>\n",
       "      <td>1991</td>\n",
       "      <td>1994</td>\n",
       "      <td>23.75</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>274</th>\n",
       "      <td>Bosnia &amp; Herzegovina</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Greece</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>F.Y.R. Macedonia</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>F.Y.R. Macedonia</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>F.Y.R. Macedonia</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Greece</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Norway</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>Romania</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Norway</td>\n",
       "      <td>The Netherlands</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>The Netherlands</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>2012</td>\n",
       "      <td>2015</td>\n",
       "      <td>39.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 CountryA         CountryB Start year End year  Av_voters\n",
       "0                 Belgium   United Kingdom       1976     1979      18.75\n",
       "1                  France           Greece       1976     1979      18.75\n",
       "2                 Ireland            Italy       1976     1979      18.75\n",
       "3                 Germany          Ireland       1979     1982      19.00\n",
       "4                 Ireland      Switzerland       1979     1982      19.00\n",
       "5                   Italy         Portugal       1979     1982      19.00\n",
       "6                   Italy            Spain       1979     1982      19.00\n",
       "7             Switzerland   United Kingdom       1979     1982      19.00\n",
       "8                 Ireland           Sweden       1982     1985      19.00\n",
       "9                  Israel       Luxembourg       1982     1985      19.00\n",
       "10                 Norway           Sweden       1982     1985      19.00\n",
       "11                 Cyprus           Greece       1985     1988      20.50\n",
       "12                 Cyprus       Yugoslavia       1985     1988      20.50\n",
       "13                 Norway           Sweden       1985     1988      20.50\n",
       "14                 Cyprus           Greece       1988     1991      21.75\n",
       "15                  Italy            Spain       1988     1991      21.75\n",
       "16                  Malta            Spain       1988     1991      21.75\n",
       "17                  Malta           Turkey       1988     1991      21.75\n",
       "18                  Malta   United Kingdom       1988     1991      21.75\n",
       "19                 Cyprus           Greece       1991     1994      23.75\n",
       "20                 France            Italy       1991     1994      23.75\n",
       "21                 France           Poland       1991     1994      23.75\n",
       "22                 France         Portugal       1991     1994      23.75\n",
       "23                Germany          Hungary       1991     1994      23.75\n",
       "24                Germany           Poland       1991     1994      23.75\n",
       "25                Germany           Russia       1991     1994      23.75\n",
       "26                 Greece          Romania       1991     1994      23.75\n",
       "27                Hungary          Ireland       1991     1994      23.75\n",
       "28                Hungary           Poland       1991     1994      23.75\n",
       "29                Ireland            Malta       1991     1994      23.75\n",
       "..                    ...              ...        ...      ...        ...\n",
       "274  Bosnia & Herzegovina           Serbia       2012     2015      39.50\n",
       "275                Cyprus           Greece       2012     2015      39.50\n",
       "276                Cyprus           Sweden       2012     2015      39.50\n",
       "277               Denmark           Sweden       2012     2015      39.50\n",
       "278               Estonia           Latvia       2012     2015      39.50\n",
       "279               Estonia           Russia       2012     2015      39.50\n",
       "280      F.Y.R. Macedonia       Montenegro       2012     2015      39.50\n",
       "281      F.Y.R. Macedonia           Serbia       2012     2015      39.50\n",
       "282      F.Y.R. Macedonia           Turkey       2012     2015      39.50\n",
       "283               Georgia        Lithuania       2012     2015      39.50\n",
       "284               Georgia           Russia       2012     2015      39.50\n",
       "285               Georgia          Ukraine       2012     2015      39.50\n",
       "286                Greece           Russia       2012     2015      39.50\n",
       "287               Iceland           Norway       2012     2015      39.50\n",
       "288               Ireland   United Kingdom       2012     2015      39.50\n",
       "289                Latvia           Sweden       2012     2015      39.50\n",
       "290               Moldova          Romania       2012     2015      39.50\n",
       "291               Moldova           Russia       2012     2015      39.50\n",
       "292               Moldova          Ukraine       2012     2015      39.50\n",
       "293            Montenegro           Serbia       2012     2015      39.50\n",
       "294            Montenegro         Slovenia       2012     2015      39.50\n",
       "295                Norway           Sweden       2012     2015      39.50\n",
       "296                Norway  The Netherlands       2012     2015      39.50\n",
       "297                Poland          Ukraine       2012     2015      39.50\n",
       "298                Russia           Sweden       2012     2015      39.50\n",
       "299                Russia          Ukraine       2012     2015      39.50\n",
       "300                Serbia         Slovenia       2012     2015      39.50\n",
       "301                Serbia           Sweden       2012     2015      39.50\n",
       "302                Sweden  The Netherlands       2012     2015      39.50\n",
       "303                Sweden           Turkey       2012     2015      39.50\n",
       "\n",
       "[304 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coalitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "connections = coalitions.groupby('Start year').count().loc[:,'CountryA'] # number of reciprocal relationships\n",
    "countries = coalitions.groupby('Start year').nunique().loc[:,'CountryA'] # number of countries in a coalition\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary=pd.DataFrame(columns=['Deals Made','Countries in Deals','Average Countries in Period','Total Possible Deals'],index=connections.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "voters = []\n",
    "for year in summary.index:\n",
    "    voters.append(coalitions[coalitions['Start year']==year].loc[:,'Av_voters'].unique()[0])\n",
    "voters = np.asarray(voters)\n",
    "possible_connect = (voters*(voters-1))/2\n",
    "possible_connect = possible_connect.astype(int) #average possible connections in year  \n",
    "    \n",
    "summary['Deals Made']=connections\n",
    "summary['Countries in Deals']=countries\n",
    "summary['Average Countries in Period']= voters\n",
    "summary['Total Possible Deals'] = possible_connect\n",
    "summary['Percentage countries in deal'] = summary.apply(lambda row: row['Countries in Deals'] / row['Average Countries in Period'], axis=1)\n",
    "summary['Normalised deal-making'] = summary.apply(lambda row: row['Deals Made'] / row['Total Possible Deals'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Deals Made</th>\n",
       "      <th>Countries in Deals</th>\n",
       "      <th>Average Countries in Period</th>\n",
       "      <th>Total Possible Deals</th>\n",
       "      <th>Percentage countries in deal</th>\n",
       "      <th>Normalised deal-making</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Start year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>18.75</td>\n",
       "      <td>166</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.018072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>19.00</td>\n",
       "      <td>171</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.029240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>19.00</td>\n",
       "      <td>171</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.017544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20.50</td>\n",
       "      <td>199</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.015075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>21.75</td>\n",
       "      <td>225</td>\n",
       "      <td>0.137931</td>\n",
       "      <td>0.022222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>23.75</td>\n",
       "      <td>270</td>\n",
       "      <td>0.378947</td>\n",
       "      <td>0.062963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>24.00</td>\n",
       "      <td>276</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>24.25</td>\n",
       "      <td>281</td>\n",
       "      <td>0.577320</td>\n",
       "      <td>0.081851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>24.25</td>\n",
       "      <td>281</td>\n",
       "      <td>0.494845</td>\n",
       "      <td>0.060498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>34.50</td>\n",
       "      <td>577</td>\n",
       "      <td>0.579710</td>\n",
       "      <td>0.069324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>41.00</td>\n",
       "      <td>820</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.067073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>41.50</td>\n",
       "      <td>840</td>\n",
       "      <td>0.530120</td>\n",
       "      <td>0.065476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>39.50</td>\n",
       "      <td>760</td>\n",
       "      <td>0.607595</td>\n",
       "      <td>0.078947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Deals Made  Countries in Deals  Average Countries in Period  \\\n",
       "Start year                                                                \n",
       "1976                 3                   3                        18.75   \n",
       "1979                 5                   4                        19.00   \n",
       "1982                 3                   3                        19.00   \n",
       "1985                 3                   2                        20.50   \n",
       "1988                 5                   3                        21.75   \n",
       "1991                17                   9                        23.75   \n",
       "1994                18                  11                        24.00   \n",
       "1997                23                  14                        24.25   \n",
       "2000                17                  12                        24.25   \n",
       "2003                40                  20                        34.50   \n",
       "2006                55                  22                        41.00   \n",
       "2009                55                  22                        41.50   \n",
       "2012                60                  24                        39.50   \n",
       "\n",
       "            Total Possible Deals  Percentage countries in deal  \\\n",
       "Start year                                                       \n",
       "1976                         166                      0.160000   \n",
       "1979                         171                      0.210526   \n",
       "1982                         171                      0.157895   \n",
       "1985                         199                      0.097561   \n",
       "1988                         225                      0.137931   \n",
       "1991                         270                      0.378947   \n",
       "1994                         276                      0.458333   \n",
       "1997                         281                      0.577320   \n",
       "2000                         281                      0.494845   \n",
       "2003                         577                      0.579710   \n",
       "2006                         820                      0.536585   \n",
       "2009                         840                      0.530120   \n",
       "2012                         760                      0.607595   \n",
       "\n",
       "            Normalised deal-making  \n",
       "Start year                          \n",
       "1976                      0.018072  \n",
       "1979                      0.029240  \n",
       "1982                      0.017544  \n",
       "1985                      0.015075  \n",
       "1988                      0.022222  \n",
       "1991                      0.062963  \n",
       "1994                      0.065217  \n",
       "1997                      0.081851  \n",
       "2000                      0.060498  \n",
       "2003                      0.069324  \n",
       "2006                      0.067073  \n",
       "2009                      0.065476  \n",
       "2012                      0.078947  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23efc390>"
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary[['Normalised deal-making','Percentage countries in deal']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a23a5e4a8>"
      ]
     },
     "execution_count": 704,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary[['Normalised deal-making']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary['Normalised deal-making'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "visualise_df(coalitions_1990_1995,30) #visualise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryA</th>\n",
       "      <th>CountryB</th>\n",
       "      <th>Start year</th>\n",
       "      <th>End year</th>\n",
       "      <th>Av_voters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>F.Y.R. Macedonia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>Malta</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Greece</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Finland</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F.Y.R. Macedonia</td>\n",
       "      <td>Romania</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>F.Y.R. Macedonia</td>\n",
       "      <td>Russia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>Norway</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Romania</td>\n",
       "      <td>Russia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Russia</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>1995</td>\n",
       "      <td>2000</td>\n",
       "      <td>23.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CountryA          CountryB  Start year  End year  Av_voters\n",
       "1            Croatia  F.Y.R. Macedonia        1995      2000  23.833333\n",
       "2            Croatia             Malta        1995      2000  23.833333\n",
       "3             Cyprus            Greece        1995      2000  23.833333\n",
       "4            Denmark           Iceland        1995      2000  23.833333\n",
       "5            Denmark            Latvia        1995      2000  23.833333\n",
       "6            Denmark            Sweden        1995      2000  23.833333\n",
       "7            Estonia           Finland        1995      2000  23.833333\n",
       "8            Estonia            Latvia        1995      2000  23.833333\n",
       "9            Estonia            Sweden        1995      2000  23.833333\n",
       "11  F.Y.R. Macedonia           Romania        1995      2000  23.833333\n",
       "12  F.Y.R. Macedonia            Russia        1995      2000  23.833333\n",
       "14           Germany            Latvia        1995      2000  23.833333\n",
       "17           Iceland            Latvia        1995      2000  23.833333\n",
       "22            Latvia            Norway        1995      2000  23.833333\n",
       "25            Norway            Sweden        1995      2000  23.833333\n",
       "27           Romania            Russia        1995      2000  23.833333\n",
       "30            Russia          Slovenia        1995      2000  23.833333"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coalitions_1995_2000"
   ]
  },
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