elections.ipynb

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    "<h1 style=\"text-align: center;\">Comparison of different methods for electoral seats allocation.</h1>\n",
    "\n",
    "##### <center>(Calculations are based on the result of [Polish Sejm elections](https://wybory.gov.pl/sejmsenat2019/pl/wyniki/sejm/pl), that took place in 2019.)</center>\n",
    "\n",
    "----\n",
    "*You can read more about the methods under the following links:*\n",
    "\n",
    "* [D'Hondt method](https://en.wikipedia.org/wiki/D%27Hondt_method).\n",
    "* [Hare-Niemeyer method](https://en.wikipedia.org/wiki/Largest_remainder_method).\n",
    "* [Sainte-Laguë method](https://en.wikipedia.org/wiki/Webster/Sainte-Lagu%C3%AB_method).\n",
    "* [The modified Sainte-Laguë method](https://en.wikipedia.org/wiki/Webster/Sainte-Lagu%C3%AB_method#Modified_Sainte-Lagu%C3%AB_method).\n",
    "\n",
    "##### The file is divided into three parts:\n",
    "1. [Short description of the data and brief introduction to Polish electoral system.](#Electoral-system-and-data-description.)\n",
    "2. [Implementation of 4 above methods.](#Implementation-of-seats-distribution-methods.)\n",
    "3. [Applying the methods and comparing the results.](#Applying-methods.-Comparing-results.)\n",
    "\n",
    "\n",
    "* Bonus Plots:\n",
    "    * [Does a position on an electoral list matter?](#Does-a-position-on-an-electoral-list-translate-into-a-candidate's-success?-Let's-take-a-look.)\n",
    "    * [Number of candidates on party lists.](#Sizes-of-party-lists-for-each-constituency.)\n",
    "    * [Results split by constituency.](#Results-split-by-constituency.)\n",
    "    * [Who got the most/the least votes?](#Let's-see-who-got-the-most-and-the-least-votes-among-those-who-won-a-seat-and-among-those-who-didn't.)\n",
    "    * [Votes distribution per party.](#Party-votes-distribution.)\n",
    "\n",
    "----\n",
    "However, let's import required modules and set some variables first.\n",
    "\n",
    "Committees names and their abbreviations are held in  `COMMITTEES` and `COMMITTEES_SHORT` lists, where\n",
    "**None** value at index 0 is a placeholder.<br>The remaining indices refer to values in the dataframe `Party` column. Thus, we can easily map committees names with their appropriate `Party` ids.<br>Additionally, there's also a `style1` function definition, which will be used to add colors while displaying dataframes."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import re\n",
    "from math import ceil, modf\n",
    "from operator import add, sub\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "\n",
    "matplotlib.rc('font', family='Arial')\n",
    "\n",
    "DATA_FILE = \"PKW_DATA.csv\"\n",
    "\n",
    "COMMITTEES = [None, 'POLSKIE STRONNICTWO LUDOWE', 'PRAWO I SPRAWIEDLIWOŚĆ', 'SOJUSZ LEWICY DEMOKRATYCZNEJ',\n",
    "              'KONFEDERACJA WOLNOŚĆ I NIEPODLEGŁOŚĆ', 'KOALICJA OBYWATELSKA PO .N IPL ZIELONI', 'PRAWICA',\n",
    "              'AKCJA ZAWIEDZIONYCH EMERYTÓW RENCISTÓW', 'KOALICJA BEZPARTYJNI I SAMORZĄDOWCY',\n",
    "              'SKUTECZNI PIOTRA LIROYA-MARCA', 'MNIEJSZOŚĆ NIEMIECKA']\n",
    "\n",
    "COMMITTEES_SHORT = [None, 'PSL', 'PiS', 'SLD', 'KWiN', 'KO', 'PR', 'AZER', 'BS', 'Skuteczni', 'MN']\n",
    "\n",
    "\n",
    "def style1(colorize_last_row=False, hide_index=False):\n",
    "    rules = [{'selector': 'thead > tr:nth-of-type(1)', 'props': [('background', '#e4e2e9')]},\n",
    "             {'selector': 'tbody > tr:nth-of-type(even)', 'props': [('background', '#f7f6df')]},\n",
    "             {'selector': 'tr', 'props': [('font-family', 'arial')]}]\n",
    "    if colorize_last_row:\n",
    "        rules.append({'selector': 'tbody > tr:last-child', 'props': [('background', '#e4e2e9')]})\n",
    "    if hide_index:\n",
    "        rules.append({'selector': 'th:nth-of-type(1)', 'props': [('display', 'None')]})\n",
    "    return rules"
   ]
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   "source": [
    "\n",
    "\n",
    "### Electoral system and data description.\n",
    "\n",
    "Polish Sejm (lower house) elections are held in compliance with the following rules:\n",
    "1. A committee takes a part in mandates distribution, only when a total number of won votes exceeds the electoral threshold,<br />which is set to 5% for a single party or 8% for coalitions.\n",
    "However, the threshold constraint does not apply for ethnic-minority parties.<br />In this example **'MN'** has no threshold applied, for **'KO'** it's 8% and the remaining committees start as single parties.\n",
    "\n",
    "2. There are 41 constituencies (state for 2019) and 460 seats to take. Seats are distributed on the constituency level among committees,<br />which crossed the electoral threshold. D'Hondt method is used.\n",
    "\n",
    "----\n",
    "\n",
    "Data has been collected from the official [Polish electoral website](https://wybory.gov.pl/sejmsenat2019/pl/wyniki/sejm/pl) and aggregated into **PKW_DATA.csv** file.<br />\n",
    "It consists of 5113 rows, where each row represents a candidate and holds the following information:\n",
    "- *Num.* - position on an electoral list.\n",
    "- *Candidate* - candidate name.\n",
    "- *Constituency* - constituency where a candidate runs for election.\n",
    "- *Votes* - votes won by a candidate.\n",
    "- *Percentage* - votes won by a candidate (in percent).\n",
    "- *Seat won* - 'tak' if seat won, otherwise 'nie'.\n",
    "- *Party* - party id, used as an index to get party name from `COMMITTEES` variables.\n",
    "\n",
    "##### Important note!\n",
    "For two constituencies there were inconsistencies between the sum of all candidates votes and total votes won by a party. It happened because a candidate was removed from an electoral list, after physical lists had already been printed out. If such a removed candidate gets any votes, these are added to a total result of a party (it matters for seats distribution). But there's a 0 votes displayed next to such a candidate's name.\n",
    "\n",
    "The file was manually corrected by adding an appropriate number of votes (3300 and 51) to two such names. It's worth noting, this \"ad hoc\" fix works fine in this case only, since it doesn't change election results. These numbers simply didn't win a seat for any particular candidate, but they contribute to the overall party result. Thus, no other actions preventing a removed candidate from wining a seat were necessary.\n",
    "\n",
    "----\n",
    "##### Let's load the data and see how it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "data": {
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       "\n",
       "        <style  type=\"text/css\" >\n",
       "        \n",
       "            #T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5 thead > tr:nth-of-type(1) {\n",
       "            \n",
       "                background: #e4e2e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5 tbody > tr:nth-of-type(even) {\n",
       "            \n",
       "                background: #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5 tr {\n",
       "            \n",
       "                font-family: arial;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Num.\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Candidate\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Constituency\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">Votes\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">Percentage\n",
       "                \n",
       "                <th class=\"col_heading level0 col5\">Seat won\n",
       "                \n",
       "                <th class=\"col_heading level0 col6\">Party\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row0\">\n",
       "                    4944\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col0\" class=\"data row0 col0\">\n",
       "                    12\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col1\" class=\"data row0 col1\">\n",
       "                    JASKÓLSKA Wioletta\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col2\" class=\"data row0 col2\">\n",
       "                    Nr 34 [Elbląg]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col3\" class=\"data row0 col3\">\n",
       "                    787\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col4\" class=\"data row0 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col5\" class=\"data row0 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row0_col6\" class=\"data row0 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row1\">\n",
       "                    842\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col0\" class=\"data row1 col0\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col1\" class=\"data row1 col1\">\n",
       "                    ŁOŻYŃSKI Krystian Maciej\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col2\" class=\"data row1 col2\">\n",
       "                    Nr 34 [Elbląg]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col3\" class=\"data row1 col3\">\n",
       "                    1071\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col4\" class=\"data row1 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col5\" class=\"data row1 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row1_col6\" class=\"data row1 col6\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row2\">\n",
       "                    2732\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col0\" class=\"data row2 col0\">\n",
       "                    10\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col1\" class=\"data row2 col1\">\n",
       "                    FELCYN Józef\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col2\" class=\"data row2 col2\">\n",
       "                    Nr 38 [Piła]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col3\" class=\"data row2 col3\">\n",
       "                    1050\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col4\" class=\"data row2 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col5\" class=\"data row2 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row2_col6\" class=\"data row2 col6\">\n",
       "                    2\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row3\">\n",
       "                    4249\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col0\" class=\"data row3 col0\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col1\" class=\"data row3 col1\">\n",
       "                    ANKIEWICZ-JASIŃSKA Ewa Teresa\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col2\" class=\"data row3 col2\">\n",
       "                    Nr 4 [Bydgoszcz]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col3\" class=\"data row3 col3\">\n",
       "                    468\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col4\" class=\"data row3 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col5\" class=\"data row3 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row3_col6\" class=\"data row3 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row4\">\n",
       "                    218\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col0\" class=\"data row4 col0\">\n",
       "                    11\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col1\" class=\"data row4 col1\">\n",
       "                    ZDUŃCZYK Piotr Marcin\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col2\" class=\"data row4 col2\">\n",
       "                    Nr 6 [Lublin]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col3\" class=\"data row4 col3\">\n",
       "                    399\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col4\" class=\"data row4 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col5\" class=\"data row4 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row4_col6\" class=\"data row4 col6\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row5\">\n",
       "                    3704\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col0\" class=\"data row5 col0\">\n",
       "                    9\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col1\" class=\"data row5 col1\">\n",
       "                    RZYMAN Krzysztof Adam\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col2\" class=\"data row5 col2\">\n",
       "                    Nr 20 [Warszawa]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col3\" class=\"data row5 col3\">\n",
       "                    1740\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col4\" class=\"data row5 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col5\" class=\"data row5 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row5_col6\" class=\"data row5 col6\">\n",
       "                    5\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row6\">\n",
       "                    4676\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col0\" class=\"data row6 col0\">\n",
       "                    15\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col1\" class=\"data row6 col1\">\n",
       "                    BARAŃSKI Janusz Jacek\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col2\" class=\"data row6 col2\">\n",
       "                    Nr 22 [Krosno]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col3\" class=\"data row6 col3\">\n",
       "                    373\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col4\" class=\"data row6 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col5\" class=\"data row6 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row6_col6\" class=\"data row6 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row7\">\n",
       "                    1247\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col0\" class=\"data row7 col0\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col1\" class=\"data row7 col1\">\n",
       "                    DROPEK Magdalena\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col2\" class=\"data row7 col2\">\n",
       "                    Nr 13 [Kraków]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col3\" class=\"data row7 col3\">\n",
       "                    2344\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col4\" class=\"data row7 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col5\" class=\"data row7 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row7_col6\" class=\"data row7 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row8\">\n",
       "                    3074\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col0\" class=\"data row8 col0\">\n",
       "                    12\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col1\" class=\"data row8 col1\">\n",
       "                    WOŹNIAK Dorota Krystyna\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col2\" class=\"data row8 col2\">\n",
       "                    Nr 20 [Warszawa]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col3\" class=\"data row8 col3\">\n",
       "                    353\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col4\" class=\"data row8 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col5\" class=\"data row8 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row8_col6\" class=\"data row8 col6\">\n",
       "                    8\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row9\">\n",
       "                    687\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col0\" class=\"data row9 col0\">\n",
       "                    16\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col1\" class=\"data row9 col1\">\n",
       "                    KOZŁOWSKI Krzysztof Wiktor\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col2\" class=\"data row9 col2\">\n",
       "                    Nr 26 [Słupsk]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col3\" class=\"data row9 col3\">\n",
       "                    578\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col4\" class=\"data row9 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col5\" class=\"data row9 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row9_col6\" class=\"data row9 col6\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row10\">\n",
       "                    1568\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col0\" class=\"data row10 col0\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col1\" class=\"data row10 col1\">\n",
       "                    KUTWA Marzena\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col2\" class=\"data row10 col2\">\n",
       "                    Nr 26 [Słupsk]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col3\" class=\"data row10 col3\">\n",
       "                    1898\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col4\" class=\"data row10 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col5\" class=\"data row10 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row10_col6\" class=\"data row10 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row11\">\n",
       "                    4135\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col0\" class=\"data row11 col0\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col1\" class=\"data row11 col1\">\n",
       "                    CZUCZAK Tomasz\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col2\" class=\"data row11 col2\">\n",
       "                    Nr 40 [Koszalin]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col3\" class=\"data row11 col3\">\n",
       "                    10121\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col4\" class=\"data row11 col4\">\n",
       "                    0.05\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col5\" class=\"data row11 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row11_col6\" class=\"data row11 col6\">\n",
       "                    5\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row12\">\n",
       "                    4358\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col0\" class=\"data row12 col0\">\n",
       "                    13\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col1\" class=\"data row12 col1\">\n",
       "                    KUMOR-PILARCZYK Izabela Ewa\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col2\" class=\"data row12 col2\">\n",
       "                    Nr 8 [Zielona Góra]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col3\" class=\"data row12 col3\">\n",
       "                    623\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col4\" class=\"data row12 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col5\" class=\"data row12 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row12_col6\" class=\"data row12 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row13\">\n",
       "                    3532\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col0\" class=\"data row13 col0\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col1\" class=\"data row13 col1\">\n",
       "                    MAREK Dorota\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col2\" class=\"data row13 col2\">\n",
       "                    Nr 13 [Kraków]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col3\" class=\"data row13 col3\">\n",
       "                    6113\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col4\" class=\"data row13 col4\">\n",
       "                    0.03\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col5\" class=\"data row13 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row13_col6\" class=\"data row13 col6\">\n",
       "                    5\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row14\">\n",
       "                    973\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col0\" class=\"data row14 col0\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col1\" class=\"data row14 col1\">\n",
       "                    OBAZ Robert Marcin\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col2\" class=\"data row14 col2\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col3\" class=\"data row14 col3\">\n",
       "                    4205\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col4\" class=\"data row14 col4\">\n",
       "                    0.02\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col5\" class=\"data row14 col5\">\n",
       "                    tak\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row14_col6\" class=\"data row14 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row15\">\n",
       "                    2972\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col0\" class=\"data row15 col0\">\n",
       "                    22\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col1\" class=\"data row15 col1\">\n",
       "                    ROBACZYŃSKI Adrian Piotr\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col2\" class=\"data row15 col2\">\n",
       "                    Nr 11 [Sieradz]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col3\" class=\"data row15 col3\">\n",
       "                    52\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col4\" class=\"data row15 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col5\" class=\"data row15 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row15_col6\" class=\"data row15 col6\">\n",
       "                    8\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row16\">\n",
       "                    4396\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col0\" class=\"data row16 col0\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col1\" class=\"data row16 col1\">\n",
       "                    FIRST Agnieszka Teresa\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col2\" class=\"data row16 col2\">\n",
       "                    Nr 10 [Piotrków Trybunalski]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col3\" class=\"data row16 col3\">\n",
       "                    1301\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col4\" class=\"data row16 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col5\" class=\"data row16 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row16_col6\" class=\"data row16 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row17\">\n",
       "                    245\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col0\" class=\"data row17 col0\">\n",
       "                    10\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col1\" class=\"data row17 col1\">\n",
       "                    MAZUREK Michał\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col2\" class=\"data row17 col2\">\n",
       "                    Nr 7 [Chełm]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col3\" class=\"data row17 col3\">\n",
       "                    698\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col4\" class=\"data row17 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col5\" class=\"data row17 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row17_col6\" class=\"data row17 col6\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row18\">\n",
       "                    3094\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col0\" class=\"data row18 col0\">\n",
       "                    9\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col1\" class=\"data row18 col1\">\n",
       "                    KONIUCH Izabela\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col2\" class=\"data row18 col2\">\n",
       "                    Nr 24 [Białystok]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col3\" class=\"data row18 col3\">\n",
       "                    127\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col4\" class=\"data row18 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col5\" class=\"data row18 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row18_col6\" class=\"data row18 col6\">\n",
       "                    8\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row19\">\n",
       "                    1546\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col0\" class=\"data row19 col0\">\n",
       "                    10\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col1\" class=\"data row19 col1\">\n",
       "                    NOWAK Karolina\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col2\" class=\"data row19 col2\">\n",
       "                    Nr 25 [Gdańsk]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col3\" class=\"data row19 col3\">\n",
       "                    1466\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col4\" class=\"data row19 col4\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col5\" class=\"data row19 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row19_col6\" class=\"data row19 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row20\">\n",
       "                    170\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col0\" class=\"data row20 col0\">\n",
       "                    13\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col1\" class=\"data row20 col1\">\n",
       "                    STANKIEWICZ Marek Paweł\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col2\" class=\"data row20 col2\">\n",
       "                    Nr 4 [Bydgoszcz]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col3\" class=\"data row20 col3\">\n",
       "                    509\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col4\" class=\"data row20 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col5\" class=\"data row20 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row20_col6\" class=\"data row20 col6\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row21\">\n",
       "                    1198\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col0\" class=\"data row21 col0\">\n",
       "                    14\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col1\" class=\"data row21 col1\">\n",
       "                    PODPORA Mariusz Łukasz\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col2\" class=\"data row21 col2\">\n",
       "                    Nr 10 [Piotrków Trybunalski]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col3\" class=\"data row21 col3\">\n",
       "                    353\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col4\" class=\"data row21 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col5\" class=\"data row21 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row21_col6\" class=\"data row21 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row22\">\n",
       "                    1927\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col0\" class=\"data row22 col0\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col1\" class=\"data row22 col1\">\n",
       "                    JAROCH Andrzej Stanisław\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col2\" class=\"data row22 col2\">\n",
       "                    Nr 3 [Wrocław]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col3\" class=\"data row22 col3\">\n",
       "                    3799\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col4\" class=\"data row22 col4\">\n",
       "                    0.02\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col5\" class=\"data row22 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row22_col6\" class=\"data row22 col6\">\n",
       "                    2\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row23\">\n",
       "                    1360\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col0\" class=\"data row23 col0\">\n",
       "                    15\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col1\" class=\"data row23 col1\">\n",
       "                    LIS Grzegorz\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col2\" class=\"data row23 col2\">\n",
       "                    Nr 18 [Siedlce]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col3\" class=\"data row23 col3\">\n",
       "                    512\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col4\" class=\"data row23 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col5\" class=\"data row23 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row23_col6\" class=\"data row23 col6\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level6 row24\">\n",
       "                    4699\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col0\" class=\"data row24 col0\">\n",
       "                    16\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col1\" class=\"data row24 col1\">\n",
       "                    GUMIŃSKA Celina Anna\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col2\" class=\"data row24 col2\">\n",
       "                    Nr 23 [Rzeszów]\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col3\" class=\"data row24 col3\">\n",
       "                    229\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col4\" class=\"data row24 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col5\" class=\"data row24 col5\">\n",
       "                    nie\n",
       "                \n",
       "                <td id=\"T_1bb8fc52_9092_11ea_b2ac_d037454ddaf5row24_col6\" class=\"data row24 col6\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.core.style.Styler at 0x7f08304aaa20>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(DATA_FILE, encoding='utf-8')\n",
    "df.sample(25).style.set_table_styles(style1())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### Let's group by *'Party'* and sum up votes to get general election results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <style  type=\"text/css\" >\n",
       "        \n",
       "            #T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5 thead > tr:nth-of-type(1) {\n",
       "            \n",
       "                background: #e4e2e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5 tbody > tr:nth-of-type(even) {\n",
       "            \n",
       "                background: #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5 tr {\n",
       "            \n",
       "                font-family: arial;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5 tbody > tr:last-child {\n",
       "            \n",
       "                background: #e4e2e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Committee\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Total votes\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Total seats\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">Votes [%]\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">Seats [%]\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row0\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row0_col0\" class=\"data row0 col0\">\n",
       "                    PiS\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row0_col1\" class=\"data row0 col1\">\n",
       "                    8051935\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row0_col2\" class=\"data row0 col2\">\n",
       "                    235\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row0_col3\" class=\"data row0 col3\">\n",
       "                    43.59\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row0_col4\" class=\"data row0 col4\">\n",
       "                    51.09\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row1_col0\" class=\"data row1 col0\">\n",
       "                    KO\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row1_col1\" class=\"data row1 col1\">\n",
       "                    5060355\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row1_col2\" class=\"data row1 col2\">\n",
       "                    134\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row1_col3\" class=\"data row1 col3\">\n",
       "                    27.4\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row1_col4\" class=\"data row1 col4\">\n",
       "                    29.13\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row2\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row2_col0\" class=\"data row2 col0\">\n",
       "                    SLD\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row2_col1\" class=\"data row2 col1\">\n",
       "                    2319946\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row2_col2\" class=\"data row2 col2\">\n",
       "                    49\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row2_col3\" class=\"data row2 col3\">\n",
       "                    12.56\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row2_col4\" class=\"data row2 col4\">\n",
       "                    10.65\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row3_col0\" class=\"data row3 col0\">\n",
       "                    PSL\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row3_col1\" class=\"data row3 col1\">\n",
       "                    1578523\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row3_col2\" class=\"data row3 col2\">\n",
       "                    30\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row3_col3\" class=\"data row3 col3\">\n",
       "                    8.55\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row3_col4\" class=\"data row3 col4\">\n",
       "                    6.52\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row4\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row4_col0\" class=\"data row4 col0\">\n",
       "                    KWiN\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row4_col1\" class=\"data row4 col1\">\n",
       "                    1256953\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row4_col2\" class=\"data row4 col2\">\n",
       "                    11\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row4_col3\" class=\"data row4 col3\">\n",
       "                    6.81\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row4_col4\" class=\"data row4 col4\">\n",
       "                    2.39\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row5\">\n",
       "                    7\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row5_col0\" class=\"data row5 col0\">\n",
       "                    BS\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row5_col1\" class=\"data row5 col1\">\n",
       "                    144773\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row5_col2\" class=\"data row5 col2\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row5_col3\" class=\"data row5 col3\">\n",
       "                    0.78\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row5_col4\" class=\"data row5 col4\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row6\">\n",
       "                    9\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row6_col0\" class=\"data row6 col0\">\n",
       "                    MN\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row6_col1\" class=\"data row6 col1\">\n",
       "                    32094\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row6_col2\" class=\"data row6 col2\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row6_col3\" class=\"data row6 col3\">\n",
       "                    0.17\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row6_col4\" class=\"data row6 col4\">\n",
       "                    0.22\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row7\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row7_col0\" class=\"data row7 col0\">\n",
       "                    Skuteczni\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row7_col1\" class=\"data row7 col1\">\n",
       "                    18918\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row7_col2\" class=\"data row7 col2\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row7_col3\" class=\"data row7 col3\">\n",
       "                    0.1\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row7_col4\" class=\"data row7 col4\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row8\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row8_col0\" class=\"data row8 col0\">\n",
       "                    AZER\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row8_col1\" class=\"data row8 col1\">\n",
       "                    5448\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row8_col2\" class=\"data row8 col2\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row8_col3\" class=\"data row8 col3\">\n",
       "                    0.03\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row8_col4\" class=\"data row8 col4\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row9\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row9_col0\" class=\"data row9 col0\">\n",
       "                    PR\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row9_col1\" class=\"data row9 col1\">\n",
       "                    1765\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row9_col2\" class=\"data row9 col2\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row9_col3\" class=\"data row9 col3\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row9_col4\" class=\"data row9 col4\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level4 row10\">\n",
       "                    Total\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row10_col0\" class=\"data row10 col0\">\n",
       "                    \n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row10_col1\" class=\"data row10 col1\">\n",
       "                    18470710\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row10_col2\" class=\"data row10 col2\">\n",
       "                    460\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row10_col3\" class=\"data row10 col3\">\n",
       "                    100\n",
       "                \n",
       "                <td id=\"T_1bcf40a2_9092_11ea_b2ac_d037454ddaf5row10_col4\" class=\"data row10 col4\">\n",
       "                    100\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.core.style.Styler at 0x7f0804698748>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "committees_total = df.groupby('Party').agg({'Votes': sum, 'Seat won': lambda x: x.eq('tak').sum()})[['Votes', 'Seat won']]\n",
    "committees_total.reset_index(inplace=True)\n",
    "committees_total.columns = [\"Committee\", \"Total votes\", \"Total seats\"]\n",
    "\n",
    "# Map committees id numbers with their short names. \n",
    "committees_total['Committee'] = committees_total['Committee'].apply(lambda x: COMMITTEES_SHORT[x])\n",
    "committees_total.sort_values('Total votes', ascending=False, inplace=True)\n",
    "\n",
    "# Add percentage columns.\n",
    "all_votes = committees_total['Total votes'].sum()\n",
    "all_seats = 460\n",
    "committees_total['Votes [%]'] = round(committees_total['Total votes'] * 100 / all_votes, 2)\n",
    "committees_total['Seats [%]'] = round(committees_total['Total seats'] * 100 / all_seats, 2)\n",
    "\n",
    "# Temporally, add total at the bottom of the dataframe and some CSS styling as well.\n",
    "committees_total.append(\n",
    "    pd.DataFrame([committees_total[['Total votes', 'Total seats', 'Votes [%]', 'Seats [%]']].sum().astype('int64')\n",
    "                  .rename('Total')])).fillna('')[committees_total.columns].style.set_table_styles(style1(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "##### Let's take a look how seats to be taken were distributed among all electoral districts.\n",
    "`seats_per_district` dataframe will also be used later, while applying mandates distribution methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Constituency\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Seats\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level1 row0\">\n",
       "                    10\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row0_col0\" class=\"data row0 col0\">\n",
       "                    Nr 19 [Warszawa]\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row0_col1\" class=\"data row0 col1\">\n",
       "                    20\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level1 row1\">\n",
       "                    26\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row1_col0\" class=\"data row1 col0\">\n",
       "                    Nr 33 [Kielce]\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row1_col1\" class=\"data row1 col1\">\n",
       "                    16\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level1 row2\">\n",
       "                    37\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row2_col0\" class=\"data row2 col0\">\n",
       "                    Nr 6 [Lublin]\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row2_col1\" class=\"data row2 col1\">\n",
       "                    15\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level1 row3\">\n",
       "                    15\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row3_col0\" class=\"data row3 col0\">\n",
       "                    Nr 23 [Rzeszów]\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row3_col1\" class=\"data row3 col1\">\n",
       "                    15\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level1 row4\">\n",
       "                    22\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row4_col0\" class=\"data row4 col0\">\n",
       "                    Nr 3 [Wrocław]\n",
       "                \n",
       "                <td id=\"T_1bdf2ab2_9092_11ea_b2ac_d037454ddaf5row4_col1\" class=\"data row4 col1\">\n",
       "                    14\n",
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "seats_per_district = df.groupby('Constituency').agg({'Seat won': lambda x: x.eq('tak').sum()}).reset_index()\n",
    "seats_per_district.columns = ['Constituency', 'Seats'] \n",
    "seats_per_district.sort_values('Seats', ascending=False, inplace=True)\n",
    "seats_per_district.head(5).style.set_table_styles(style1(hide_index=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "----\n",
    "##### We can also check how votes were split among parties for every single electoral district.\n",
    "\n",
    "In order to colorize groups of rows with the same `Constituency`, `style_groups` function is defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Constituency\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Party\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Votes\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">Seat won\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row0\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row0_col0\" class=\"data row0 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row0_col1\" class=\"data row0 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row0_col2\" class=\"data row0 col2\">\n",
       "                    31006\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row0_col3\" class=\"data row0 col3\">\n",
       "                    1\n",
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       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row1_col0\" class=\"data row1 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row1_col1\" class=\"data row1 col1\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row1_col2\" class=\"data row1 col2\">\n",
       "                    183364\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row1_col3\" class=\"data row1 col3\">\n",
       "                    6\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row2\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row2_col0\" class=\"data row2 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row2_col1\" class=\"data row2 col1\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row2_col2\" class=\"data row2 col2\">\n",
       "                    71061\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row2_col3\" class=\"data row2 col3\">\n",
       "                    2\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row3\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row3_col0\" class=\"data row3 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row3_col1\" class=\"data row3 col1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row3_col2\" class=\"data row3 col2\">\n",
       "                    25319\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row3_col3\" class=\"data row3 col3\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row4\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row4_col0\" class=\"data row4 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row4_col1\" class=\"data row4 col1\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row4_col2\" class=\"data row4 col2\">\n",
       "                    108191\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row4_col3\" class=\"data row4 col3\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row5\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row5_col0\" class=\"data row5 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row5_col1\" class=\"data row5 col1\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row5_col2\" class=\"data row5 col2\">\n",
       "                    13495\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row5_col3\" class=\"data row5 col3\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row6\">\n",
       "                    64\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row6_col0\" class=\"data row6 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row6_col1\" class=\"data row6 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row6_col2\" class=\"data row6 col2\">\n",
       "                    20528\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row6_col3\" class=\"data row6 col3\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row7\">\n",
       "                    65\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row7_col0\" class=\"data row7 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row7_col1\" class=\"data row7 col1\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row7_col2\" class=\"data row7 col2\">\n",
       "                    114728\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row7_col3\" class=\"data row7 col3\">\n",
       "                    4\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row8\">\n",
       "                    66\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row8_col0\" class=\"data row8 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row8_col1\" class=\"data row8 col1\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row8_col2\" class=\"data row8 col2\">\n",
       "                    34957\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row8_col3\" class=\"data row8 col3\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level3 row9\">\n",
       "                    67\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row9_col0\" class=\"data row9 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row9_col1\" class=\"data row9 col1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row9_col2\" class=\"data row9 col2\">\n",
       "                    15346\n",
       "                \n",
       "                <td id=\"T_1bfb9c4c_9092_11ea_b2ac_d037454ddaf5row9_col3\" class=\"data row9 col3\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.core.style.Styler at 0x7f080465ceb8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ad hoc solution to colorize groups.\n",
    "idx, color, last_value = 0, '', ''\n",
    "colors = ['white', '#f7f6df']\n",
    "def style_groups(row, col):\n",
    "    \"\"\"It works for sorted data where col refers to a grouping column.\"\"\"\n",
    "    global idx, color, last_value\n",
    "    if row[col] != last_value:\n",
    "        idx ^= 1\n",
    "        color = colors[idx]\n",
    "        last_value = row[col]\n",
    "\n",
    "    return ['background-color: %s' % color] * len(row)\n",
    "\n",
    "# ----------------------------------------------------\n",
    "district_votes = df.groupby(['Constituency', 'Party']).agg({\n",
    "        'Votes': sum, 'Seat won': lambda x: x.eq('tak').sum()}).reset_index()\n",
    "\n",
    "# Sort by \"Constituency\". Since it's a string, we have to extract numbers first and move them to a temp column.\n",
    "# Next we do the sorting using this temp column. Finally, the column is dropped.\n",
    "district_votes['temp_col'] = district_votes[\"Constituency\"].str.extract('(\\d+)', expand=False).astype(int)\n",
    "district_votes.sort_values(['temp_col', 'Party'], ascending=True, inplace=True)\n",
    "district_votes.drop('temp_col', axis=1, inplace=True)\n",
    "\n",
    "district_votes.head(10).style.set_table_styles(style1(hide_index=True)).apply(style_groups, col='Constituency', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### Implementation of seats distribution methods.\n",
    "\n",
    "D'Hondt, Sainte-Laguë and the modified Sainte-Laguë methods are very similar to each other, they only take different divisors.<br />Hare-Niemeyer approach stands out from them a bit. I recommend to check the links listed above, to get better insight into how these methods work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "divisors = {\n",
    "    'dhondt': lambda seats: range(1, seats + 1),\n",
    "    'sainte_lague': lambda seats: range(1, 2 * seats, 2),\n",
    "    'sainte_lague_mod': lambda seats: [1.4] + list(range(3, 2 * seats, 2))\n",
    "}\n",
    "\n",
    "\n",
    "def hare_niemeyer(data, total_seats):\n",
    "    \"\"\"Hare–Niemeyer the largest remainder method.\"\"\"\n",
    "    total_votes = sum(i[0] for i in data)\n",
    "    seats_per_party, decimals = {}, []\n",
    "    for p in data:\n",
    "        party = p[1]\n",
    "        quotient = (p[0] * total_seats) / total_votes\n",
    "        decimal_part, int_part = modf(quotient)\n",
    "\n",
    "        seats_per_party[party] = int(int_part)\n",
    "        decimals.append((decimal_part, party))\n",
    "\n",
    "    remaining_seats = total_seats - sum(seats_per_party.values())\n",
    "    for rem_seat in sorted(decimals, reverse=True)[:remaining_seats]:\n",
    "        party = rem_seat[1]\n",
    "        seats_per_party[party] += 1\n",
    "\n",
    "    return seats_per_party\n",
    "\n",
    "\n",
    "def share_seats(data, method, total_seats):\n",
    "    \"\"\"Handles D’Hondt, Sainte-Laguë and Hare–Niemeyer \n",
    "    electoral mandates distribution methods.\n",
    "    \n",
    "    :param data: Example: [(333, 'a'), (222, 'b'), (111, 'c')]\n",
    "    :param method: str. Name of seats distribution method to be used.\n",
    "    :param total_seats: int. Total seats to be distributed.\n",
    "    :return: dict. Key is a committee name, value is a number of won seats.\n",
    "    \"\"\"\n",
    "    if method == 'hare_niemeyer':\n",
    "        return hare_niemeyer(data, total_seats)\n",
    "\n",
    "    seats_per_party = {party_name[-1]: 0 for party_name in data}\n",
    "\n",
    "    quotients = []\n",
    "    for divisor in divisors[method](total_seats):\n",
    "        for p in data:\n",
    "            quotients.append((ceil(p[0] / divisor), p[0], p[1]))\n",
    "\n",
    "    quotients.sort(reverse=True)\n",
    "    for q in quotients[:total_seats]:\n",
    "        party_name = q[-1]\n",
    "        seats_per_party[party_name] += 1\n",
    "    return seats_per_party"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### Applying methods. Comparing results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col7 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col0 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col1 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col2 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col3 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col4 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col5 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col6 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col7 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col0 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col1 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col2 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col3 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col4 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col5 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col6 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col7 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col0 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col1 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col2 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col3 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col4 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col5 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col6 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col7 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col0 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col1 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col2 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col3 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col4 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col5 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col6 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col7 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col0 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col1 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col2 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col3 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col4 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col5 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col6 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col7 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col0 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col1 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col2 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col3 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col4 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col5 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col6 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col7 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col0 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col1 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col2 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col3 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col4 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col5 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col6 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col7 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col0 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col1 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col2 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col3 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col4 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col5 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col6 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col7 {\n",
       "            \n",
       "                background-color:  white;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col0 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col1 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col2 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col3 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col4 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col5 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col6 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col7 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col0 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col1 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col2 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col3 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col4 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col5 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col6 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col7 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col0 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col1 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col2 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col3 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col4 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col5 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col6 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col7 {\n",
       "            \n",
       "                background-color:  #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Constituency\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Party\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Votes\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">Seat won\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">dhondt\n",
       "                \n",
       "                <th class=\"col_heading level0 col5\">sainte_lague_mod\n",
       "                \n",
       "                <th class=\"col_heading level0 col6\">sainte_lague\n",
       "                \n",
       "                <th class=\"col_heading level0 col7\">hare_niemeyer\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row0\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col0\" class=\"data row0 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col1\" class=\"data row0 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col2\" class=\"data row0 col2\">\n",
       "                    31006\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col3\" class=\"data row0 col3\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col4\" class=\"data row0 col4\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col5\" class=\"data row0 col5\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col6\" class=\"data row0 col6\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row0_col7\" class=\"data row0 col7\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col0\" class=\"data row1 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col1\" class=\"data row1 col1\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col2\" class=\"data row1 col2\">\n",
       "                    183364\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col3\" class=\"data row1 col3\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col4\" class=\"data row1 col4\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col5\" class=\"data row1 col5\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col6\" class=\"data row1 col6\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row1_col7\" class=\"data row1 col7\">\n",
       "                    5\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row2\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col0\" class=\"data row2 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col1\" class=\"data row2 col1\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col2\" class=\"data row2 col2\">\n",
       "                    71061\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col3\" class=\"data row2 col3\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col4\" class=\"data row2 col4\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col5\" class=\"data row2 col5\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col6\" class=\"data row2 col6\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row2_col7\" class=\"data row2 col7\">\n",
       "                    2\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row3\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col0\" class=\"data row3 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col1\" class=\"data row3 col1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col2\" class=\"data row3 col2\">\n",
       "                    25319\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col3\" class=\"data row3 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col4\" class=\"data row3 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col5\" class=\"data row3 col5\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col6\" class=\"data row3 col6\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row3_col7\" class=\"data row3 col7\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row4\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col0\" class=\"data row4 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col1\" class=\"data row4 col1\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col2\" class=\"data row4 col2\">\n",
       "                    108191\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col3\" class=\"data row4 col3\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col4\" class=\"data row4 col4\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col5\" class=\"data row4 col5\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col6\" class=\"data row4 col6\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row4_col7\" class=\"data row4 col7\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row5\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col0\" class=\"data row5 col0\">\n",
       "                    Nr 1 [Legnica]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col1\" class=\"data row5 col1\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col2\" class=\"data row5 col2\">\n",
       "                    13495\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col3\" class=\"data row5 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col4\" class=\"data row5 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col5\" class=\"data row5 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col6\" class=\"data row5 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row5_col7\" class=\"data row5 col7\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row6\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col0\" class=\"data row6 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col1\" class=\"data row6 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col2\" class=\"data row6 col2\">\n",
       "                    20528\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col3\" class=\"data row6 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col4\" class=\"data row6 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col5\" class=\"data row6 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col6\" class=\"data row6 col6\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row6_col7\" class=\"data row6 col7\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row7\">\n",
       "                    7\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col0\" class=\"data row7 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col1\" class=\"data row7 col1\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col2\" class=\"data row7 col2\">\n",
       "                    114728\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col3\" class=\"data row7 col3\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col4\" class=\"data row7 col4\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col5\" class=\"data row7 col5\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col6\" class=\"data row7 col6\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row7_col7\" class=\"data row7 col7\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row8\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col0\" class=\"data row8 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col1\" class=\"data row8 col1\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col2\" class=\"data row8 col2\">\n",
       "                    34957\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col3\" class=\"data row8 col3\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col4\" class=\"data row8 col4\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col5\" class=\"data row8 col5\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col6\" class=\"data row8 col6\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row8_col7\" class=\"data row8 col7\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row9\">\n",
       "                    9\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col0\" class=\"data row9 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col1\" class=\"data row9 col1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col2\" class=\"data row9 col2\">\n",
       "                    15346\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col3\" class=\"data row9 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col4\" class=\"data row9 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col5\" class=\"data row9 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col6\" class=\"data row9 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row9_col7\" class=\"data row9 col7\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row10\">\n",
       "                    10\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col0\" class=\"data row10 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col1\" class=\"data row10 col1\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col2\" class=\"data row10 col2\">\n",
       "                    90812\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col3\" class=\"data row10 col3\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col4\" class=\"data row10 col4\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col5\" class=\"data row10 col5\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col6\" class=\"data row10 col6\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row10_col7\" class=\"data row10 col7\">\n",
       "                    3\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row11\">\n",
       "                    11\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col0\" class=\"data row11 col0\">\n",
       "                    Nr 2 [Wałbrzych]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col1\" class=\"data row11 col1\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col2\" class=\"data row11 col2\">\n",
       "                    6631\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col3\" class=\"data row11 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col4\" class=\"data row11 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col5\" class=\"data row11 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col6\" class=\"data row11 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row11_col7\" class=\"data row11 col7\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row12\">\n",
       "                    12\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col0\" class=\"data row12 col0\">\n",
       "                    Nr 3 [Wrocław]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col1\" class=\"data row12 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col2\" class=\"data row12 col2\">\n",
       "                    42269\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col3\" class=\"data row12 col3\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col4\" class=\"data row12 col4\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col5\" class=\"data row12 col5\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col6\" class=\"data row12 col6\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row12_col7\" class=\"data row12 col7\">\n",
       "                    1\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row13\">\n",
       "                    13\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col0\" class=\"data row13 col0\">\n",
       "                    Nr 3 [Wrocław]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col1\" class=\"data row13 col1\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col2\" class=\"data row13 col2\">\n",
       "                    226915\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col3\" class=\"data row13 col3\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col4\" class=\"data row13 col4\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col5\" class=\"data row13 col5\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col6\" class=\"data row13 col6\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row13_col7\" class=\"data row13 col7\">\n",
       "                    5\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level7 row14\">\n",
       "                    14\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col0\" class=\"data row14 col0\">\n",
       "                    Nr 3 [Wrocław]\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col1\" class=\"data row14 col1\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col2\" class=\"data row14 col2\">\n",
       "                    100843\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col3\" class=\"data row14 col3\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col4\" class=\"data row14 col4\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col5\" class=\"data row14 col5\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col6\" class=\"data row14 col6\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c3b3064_9092_11ea_b2ac_d037454ddaf5row14_col7\" class=\"data row14 col7\">\n",
       "                    2\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.core.style.Styler at 0x7f0804685208>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Threshold dictionary holds other than default (5%) threshold values.\n",
    "# MN - 0% for minority (means no threshold). KO - 8% for coalition.\n",
    "threshold = {COMMITTEES_SHORT.index('MN'): 0, COMMITTEES_SHORT.index('KO'): 8}\n",
    "\n",
    "# Generate set of parties ids, that crossed required threshold.\n",
    "above_threshold = set()\n",
    "for _, row in committees_total.iterrows():\n",
    "    party_id = COMMITTEES_SHORT.index(row['Committee'])\n",
    "    if row['Votes [%]'] >= threshold.get(party_id, 5):\n",
    "        above_threshold.add(party_id)\n",
    "\n",
    "# Add a column for each method and set seats number to 0. \n",
    "methods = ['dhondt', 'sainte_lague_mod', 'sainte_lague', 'hare_niemeyer']\n",
    "for col_name in methods:\n",
    "    district_votes[col_name] = 0\n",
    "\n",
    "# Create a dataframe with a subset of district-party data, that crossed required threshold.\n",
    "qualified_for_seats_sharing = district_votes[district_votes['Party'].isin(above_threshold)]\n",
    "qualified_for_seats_sharing.set_index('Constituency', inplace=True)\n",
    "\n",
    "# Temporally, set index for faster computation.\n",
    "district_votes.set_index(['Constituency', 'Party'], inplace=True)\n",
    "\n",
    "# Compute seats distribution for each method.\n",
    "for district, mandates_to_share in seats_per_district[['Constituency', 'Seats']].values:\n",
    "    district_data = qualified_for_seats_sharing.loc[district][['Votes', 'Party']].values.tolist()\n",
    "    for method in methods:\n",
    "        distributed = share_seats(district_data, method, mandates_to_share)\n",
    "        for party in distributed.items():\n",
    "            district_votes.loc[(district, party[0]), method] = party[1]\n",
    "\n",
    "district_votes.reset_index(inplace=True)\n",
    "district_votes.head(15).style.set_table_styles(style1(hide_index=True)).apply(style_groups, col='Constituency', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "##### Let's aggregate the data and see how the final election results would look like, depending on seats allocation method applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <style  type=\"text/css\" >\n",
       "        \n",
       "            #T_1c4ae784_9092_11ea_b2ac_d037454ddaf5 thead > tr:nth-of-type(1) {\n",
       "            \n",
       "                background: #e4e2e9;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c4ae784_9092_11ea_b2ac_d037454ddaf5 tbody > tr:nth-of-type(even) {\n",
       "            \n",
       "                background: #f7f6df;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c4ae784_9092_11ea_b2ac_d037454ddaf5 tr {\n",
       "            \n",
       "                font-family: arial;\n",
       "            \n",
       "            }\n",
       "        \n",
       "            #T_1c4ae784_9092_11ea_b2ac_d037454ddaf5 th:nth-of-type(1) {\n",
       "            \n",
       "                display: None;\n",
       "            \n",
       "            }\n",
       "        \n",
       "        \n",
       "        </style>\n",
       "\n",
       "        <table id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" None>\n",
       "        \n",
       "\n",
       "        <thead>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th class=\"blank\">\n",
       "                \n",
       "                <th class=\"col_heading level0 col0\">Party\n",
       "                \n",
       "                <th class=\"col_heading level0 col1\">Seat won\n",
       "                \n",
       "                <th class=\"col_heading level0 col2\">Votes\n",
       "                \n",
       "                <th class=\"col_heading level0 col3\">dhondt\n",
       "                \n",
       "                <th class=\"col_heading level0 col4\">sainte_lague_mod\n",
       "                \n",
       "                <th class=\"col_heading level0 col5\">sainte_lague\n",
       "                \n",
       "                <th class=\"col_heading level0 col6\">hare_niemeyer\n",
       "                \n",
       "                <th class=\"col_heading level0 col7\">Votes [%]\n",
       "                \n",
       "                <th class=\"col_heading level0 col8\">dh [%]\n",
       "                \n",
       "                <th class=\"col_heading level0 col9\">slm [%]\n",
       "                \n",
       "                <th class=\"col_heading level0 col10\">sl [%]\n",
       "                \n",
       "                <th class=\"col_heading level0 col11\">hn [%]\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </thead>\n",
       "        <tbody>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row0\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col0\" class=\"data row0 col0\">\n",
       "                    PiS\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col1\" class=\"data row0 col1\">\n",
       "                    235\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col2\" class=\"data row0 col2\">\n",
       "                    8051935\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col3\" class=\"data row0 col3\">\n",
       "                    235\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col4\" class=\"data row0 col4\">\n",
       "                    213\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col5\" class=\"data row0 col5\">\n",
       "                    201\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col6\" class=\"data row0 col6\">\n",
       "                    202\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col7\" class=\"data row0 col7\">\n",
       "                    43.59\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col8\" class=\"data row0 col8\">\n",
       "                    51.09\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col9\" class=\"data row0 col9\">\n",
       "                    46.3\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col10\" class=\"data row0 col10\">\n",
       "                    43.7\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row0_col11\" class=\"data row0 col11\">\n",
       "                    43.91\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row1\">\n",
       "                    4\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col0\" class=\"data row1 col0\">\n",
       "                    KO\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col1\" class=\"data row1 col1\">\n",
       "                    134\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col2\" class=\"data row1 col2\">\n",
       "                    5060355\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col3\" class=\"data row1 col3\">\n",
       "                    134\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col4\" class=\"data row1 col4\">\n",
       "                    129\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col5\" class=\"data row1 col5\">\n",
       "                    123\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col6\" class=\"data row1 col6\">\n",
       "                    125\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col7\" class=\"data row1 col7\">\n",
       "                    27.4\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col8\" class=\"data row1 col8\">\n",
       "                    29.13\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col9\" class=\"data row1 col9\">\n",
       "                    28.04\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col10\" class=\"data row1 col10\">\n",
       "                    26.74\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row1_col11\" class=\"data row1 col11\">\n",
       "                    27.17\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row2\">\n",
       "                    2\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col0\" class=\"data row2 col0\">\n",
       "                    SLD\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col1\" class=\"data row2 col1\">\n",
       "                    49\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col2\" class=\"data row2 col2\">\n",
       "                    2319946\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col3\" class=\"data row2 col3\">\n",
       "                    49\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col4\" class=\"data row2 col4\">\n",
       "                    56\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col5\" class=\"data row2 col5\">\n",
       "                    58\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col6\" class=\"data row2 col6\">\n",
       "                    57\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col7\" class=\"data row2 col7\">\n",
       "                    12.56\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col8\" class=\"data row2 col8\">\n",
       "                    10.65\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col9\" class=\"data row2 col9\">\n",
       "                    12.17\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col10\" class=\"data row2 col10\">\n",
       "                    12.61\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row2_col11\" class=\"data row2 col11\">\n",
       "                    12.39\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col0\" class=\"data row3 col0\">\n",
       "                    PSL\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col1\" class=\"data row3 col1\">\n",
       "                    30\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col2\" class=\"data row3 col2\">\n",
       "                    1578523\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col3\" class=\"data row3 col3\">\n",
       "                    30\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col4\" class=\"data row3 col4\">\n",
       "                    32\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col5\" class=\"data row3 col5\">\n",
       "                    40\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col6\" class=\"data row3 col6\">\n",
       "                    39\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col7\" class=\"data row3 col7\">\n",
       "                    8.55\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col8\" class=\"data row3 col8\">\n",
       "                    6.52\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col9\" class=\"data row3 col9\">\n",
       "                    6.96\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col10\" class=\"data row3 col10\">\n",
       "                    8.7\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row3_col11\" class=\"data row3 col11\">\n",
       "                    8.48\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row4\">\n",
       "                    3\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col0\" class=\"data row4 col0\">\n",
       "                    KWiN\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col1\" class=\"data row4 col1\">\n",
       "                    11\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col2\" class=\"data row4 col2\">\n",
       "                    1256953\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col3\" class=\"data row4 col3\">\n",
       "                    11\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col4\" class=\"data row4 col4\">\n",
       "                    29\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col5\" class=\"data row4 col5\">\n",
       "                    37\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col6\" class=\"data row4 col6\">\n",
       "                    36\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col7\" class=\"data row4 col7\">\n",
       "                    6.81\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col8\" class=\"data row4 col8\">\n",
       "                    2.39\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col9\" class=\"data row4 col9\">\n",
       "                    6.3\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col10\" class=\"data row4 col10\">\n",
       "                    8.04\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row4_col11\" class=\"data row4 col11\">\n",
       "                    7.83\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row5\">\n",
       "                    7\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col0\" class=\"data row5 col0\">\n",
       "                    BS\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col1\" class=\"data row5 col1\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col2\" class=\"data row5 col2\">\n",
       "                    144773\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col3\" class=\"data row5 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col4\" class=\"data row5 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col5\" class=\"data row5 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col6\" class=\"data row5 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col7\" class=\"data row5 col7\">\n",
       "                    0.78\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col8\" class=\"data row5 col8\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col9\" class=\"data row5 col9\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col10\" class=\"data row5 col10\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row5_col11\" class=\"data row5 col11\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row6\">\n",
       "                    9\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col0\" class=\"data row6 col0\">\n",
       "                    MN\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col1\" class=\"data row6 col1\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col2\" class=\"data row6 col2\">\n",
       "                    32094\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col3\" class=\"data row6 col3\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col4\" class=\"data row6 col4\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col5\" class=\"data row6 col5\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col6\" class=\"data row6 col6\">\n",
       "                    1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col7\" class=\"data row6 col7\">\n",
       "                    0.17\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col8\" class=\"data row6 col8\">\n",
       "                    0.22\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col9\" class=\"data row6 col9\">\n",
       "                    0.22\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col10\" class=\"data row6 col10\">\n",
       "                    0.22\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row6_col11\" class=\"data row6 col11\">\n",
       "                    0.22\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row7\">\n",
       "                    8\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col0\" class=\"data row7 col0\">\n",
       "                    Skuteczni\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col1\" class=\"data row7 col1\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col2\" class=\"data row7 col2\">\n",
       "                    18918\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col3\" class=\"data row7 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col4\" class=\"data row7 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col5\" class=\"data row7 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col6\" class=\"data row7 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col7\" class=\"data row7 col7\">\n",
       "                    0.1\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col8\" class=\"data row7 col8\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col9\" class=\"data row7 col9\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col10\" class=\"data row7 col10\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row7_col11\" class=\"data row7 col11\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row8\">\n",
       "                    6\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col0\" class=\"data row8 col0\">\n",
       "                    AZER\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col1\" class=\"data row8 col1\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col2\" class=\"data row8 col2\">\n",
       "                    5448\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col3\" class=\"data row8 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col4\" class=\"data row8 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col5\" class=\"data row8 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col6\" class=\"data row8 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col7\" class=\"data row8 col7\">\n",
       "                    0.03\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col8\" class=\"data row8 col8\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col9\" class=\"data row8 col9\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col10\" class=\"data row8 col10\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row8_col11\" class=\"data row8 col11\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "                \n",
       "                <th id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5\" class=\"row_heading level11 row9\">\n",
       "                    5\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col0\" class=\"data row9 col0\">\n",
       "                    PR\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col1\" class=\"data row9 col1\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col2\" class=\"data row9 col2\">\n",
       "                    1765\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col3\" class=\"data row9 col3\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col4\" class=\"data row9 col4\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col5\" class=\"data row9 col5\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col6\" class=\"data row9 col6\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col7\" class=\"data row9 col7\">\n",
       "                    0.01\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col8\" class=\"data row9 col8\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col9\" class=\"data row9 col9\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col10\" class=\"data row9 col10\">\n",
       "                    0\n",
       "                \n",
       "                <td id=\"T_1c4ae784_9092_11ea_b2ac_d037454ddaf5row9_col11\" class=\"data row9 col11\">\n",
       "                    0\n",
       "                \n",
       "            </tr>\n",
       "            \n",
       "        </tbody>\n",
       "        </table>\n",
       "        "
      ],
      "text/plain": [
       "<pandas.core.style.Styler at 0x7f0804686128>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_cols = ['Seat won', 'Votes', 'dhondt', 'sainte_lague_mod', 'sainte_lague', 'hare_niemeyer']\n",
    "final = district_votes.groupby('Party').agg({c: sum for c in final_cols})[final_cols].reset_index()\n",
    "final['Party'] = final['Party'].apply(lambda x: COMMITTEES_SHORT[x])\n",
    "final.sort_values('Votes', ascending=False, inplace=True)\n",
    "\n",
    "# Add percentage columns.\n",
    "final['Votes [%]'] = round(final['Votes'] * 100 / all_votes, 2)\n",
    "final['dh [%]'] = round(final['dhondt'] * 100 / all_seats, 2)\n",
    "final['slm [%]'] = round(final['sainte_lague_mod'] * 100 / all_seats, 2)\n",
    "final['sl [%]'] = round(final['sainte_lague'] * 100 / all_seats, 2)\n",
    "final['hn [%]'] = round(final['hare_niemeyer'] * 100 / all_seats, 2)\n",
    "\n",
    "final.style.set_table_styles(style1(hide_index=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "##### Let's present the results in a form of a bar chart.\n",
    "For readiness purposes, let's include only those parties, which won at least 1 seat (according to any method) or got more than 100k votes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "# Threshold line coordinates.\n",
    "line_coords = {'y': [], 'xmin': [], 'xmax': []}\n",
    "\n",
    "# Parties, which won at least 1 seat or got more than 100k votes.\n",
    "final_filtered = final.loc[(final['Votes'] >= 10**5) | (final[final[methods] > 0].any(axis=1))]\n",
    "num_of_parties = final_filtered.shape[0]\n",
    "\n",
    "plot = final_filtered.plot.bar('Party', ['Votes [%]', 'dh [%]', 'slm [%]', 'sl [%]', 'hn [%]'],\n",
    "                               color=['#c9f0c1', '#f07a94', '#867d97', '#bfb9ca', '#eff9c3'],\n",
    "                               figsize=(13, 7), width=0.8, fontsize=12)\n",
    "\n",
    "plot.set_xticklabels(final_filtered['Party'], rotation=10)\n",
    "plot.set_title('Comparison of electoral seats allocation methods (Sejm 2019).', fontsize=18, fontstyle='oblique')\n",
    "plot.set_ylabel(\"Percentage\", fontsize=16)\n",
    "plot.set_xlabel(\"Committee\", fontsize=16)\n",
    "\n",
    "# Order votes and seats numbers in a way patches are iterated.\n",
    "seats_nums = final_filtered[['Votes'] + methods].values.transpose().ravel()\n",
    "\n",
    "for bar_idx, p in enumerate(plot.patches):\n",
    "    # Text on top of bars.\n",
    "    plot.text(p.get_x() + 0.05, p.get_height() + 6, str(p.get_height()) + ' %', \n",
    "              rotation=85, fontsize=9, color='black')\n",
    "    \n",
    "    # Patches are ordered by columns, so we can get all parties names after 'num_of_parties' iterations.\n",
    "    # Parties names are needed to make sure we get right threshold values.\n",
    "    # Seats numbers are added to all methods bars as well. \n",
    "    # (\"if\" clause for threshold in votes bars, \"else\" clause for seats numbers.)\n",
    "    if bar_idx < num_of_parties:\n",
    "        party_name = plot.get_xticklabels(which='major')[bar_idx].get_text()\n",
    "        current_threshold = threshold.get(COMMITTEES_SHORT.index(party_name), 5)\n",
    "        line_coords['y'].append(current_threshold)\n",
    "        line_coords['xmin'].append(p.get_x())\n",
    "        line_coords['xmax'].append(p.get_x() + p.get_width())\n",
    "    else:\n",
    "        h = p.get_height() // 1.5\n",
    "        plot.text(p.get_x() + 0.05, h if h > 1 else 1, seats_nums[bar_idx], \n",
    "                  rotation=90, fontsize=9, color='blue')\n",
    "\n",
    "plot.hlines(line_coords['y'], xmin=line_coords['xmin'], xmax=line_coords['xmax'], \n",
    "            linestyle='-', color='red', linewidth=2, alpha=0.7)\n",
    "plot.legend(['threshold', 'Votes [%]', \"D'Hondt [%]\", 'Sainte-Laguë modified [%]', \n",
    "             'Sainte-Laguë [%]', 'Hare-Niemeyer [%]'])\n",
    "plot.grid(alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "### Bonus plots:\n",
    "##### Does a position on an electoral list translate into a candidate's success? Let's take a look.\n",
    "It probably won't tell us too much, since strong candidates should win a seat regardless of a position they start from.\n",
    "Also, such candidates usually occupy leading positions, which are considered to be the best ones. Therefore, it might be a kind of vicious circle example. The proof, however, is in the pudding, so let's check it anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select only those parties, that crossed electoral threshold.\n",
    "candidates = df[df['Party'].isin(above_threshold)]\n",
    "\n",
    "fig, axa = plt.subplots(2, 1, figsize=(12, 12))\n",
    "\n",
    "# Plot 1\n",
    "candidates_per_pos = candidates.groupby('Num.').agg({'Num.': 'count'})\n",
    "positions = candidates_per_pos.index\n",
    "candidates_total = candidates_per_pos['Num.']\n",
    "\n",
    "ax1 = axa[0]\n",
    "ax1.vlines(positions, ymin=0, ymax=candidates_total, color='#ffc94d', lw=2, alpha=0.9)\n",
    "ax1.scatter(x=positions, y=candidates_total, s=50, color='#f0a700', alpha=0.9)\n",
    "ax1.set_title('Total number of candidates per position on electoral lists.', \n",
    "              fontsize=15, fontstyle='oblique')\n",
    "\n",
    "ax1.set_xticks(positions)\n",
    "ax1.set_xticklabels(positions)\n",
    "ax1.set_xlim(0, 40.5)\n",
    "ax1.set_ylim(0, 250)\n",
    "ax1.set_ylabel('Number of candidates', fontsize=14)\n",
    "\n",
    "for xx, yy in candidates_total.iteritems():\n",
    "    ax1.text(xx, yy + 6 + (xx % 2 * 9), yy, horizontalalignment='center', \n",
    "             fontdict={'size': 9, 'color': '#155393', 'fontweight': 100})\n",
    "\n",
    "# Plot 2\n",
    "total_votes_per_list_pos = candidates.groupby('Num.').agg({\n",
    "        'Votes': sum, 'Seat won': lambda x: x.eq('tak').sum()}).reset_index()\n",
    "total_votes_per_list_pos['Votes [%]'] = round(total_votes_per_list_pos['Votes'] * 100 / all_votes, 2)\n",
    "\n",
    "# Convert values to string, since tick params would differ between plot 2 and plot 3 otherwise.\n",
    "total_votes_per_list_pos['Num.'] = total_votes_per_list_pos['Num.'].astype(str)\n",
    "\n",
    "ax2 = total_votes_per_list_pos.plot.bar('Num.', 'Votes [%]', ax=axa[1], width=0.6, color='#c9f0c1')\n",
    "ax2.set_title('Total votes & seats won per position on electoral lists.', \n",
    "              fontsize=15, fontstyle='oblique')\n",
    "\n",
    "ax2.set_ylabel(\"Total votes [%]\", color='#73b964', fontsize=14)\n",
    "ax2.tick_params(axis='y', labelcolor='#73b964')\n",
    "ax2.set_ylim(0, 45)\n",
    "\n",
    "# Plot 3\n",
    "ax3 = total_votes_per_list_pos.plot.line('Num.', 'Seat won', ax=ax2, secondary_y=True, alpha=0.9, \n",
    "                                         lw=2, ls='dotted', marker='.', color='#ff1a1a')\n",
    "\n",
    "ax3.set_ylabel(\"Seats won\", color='#ff1a1a', fontsize=14)\n",
    "ax3.tick_params(axis='y', labelcolor='#ff1a1a')\n",
    "ax3.set_ylim(0, 180)\n",
    "ax3.set_xlim(-0.6, 39.5)\n",
    "\n",
    "# Add last position indicator\n",
    "last_positions_numbers = candidates.groupby(['Constituency', 'Party']).agg({\"Num.\": max}).reset_index()\n",
    "all_last_positions = candidates.merge(last_positions_numbers, on=['Constituency', 'Party', 'Num.'])\n",
    "last_positions_seats_won = all_last_positions[all_last_positions['Seat won'] == 'tak'].groupby(\n",
    "    'Num.').agg({'Seat won': lambda x: x.eq('tak').sum()}).reset_index()\n",
    "\n",
    "lp_seats_won = {r[0]: r[1] for r in last_positions_seats_won.values}\n",
    "lp_seats_won_sum = sum(lp_seats_won.values())\n",
    "lp_votes_percent = round(all_last_positions['Votes'].sum() * 100 / all_votes, 2)\n",
    "\n",
    "# Last position bar\n",
    "bar_pos = 20\n",
    "ax2.bar(9.7, lp_votes_percent, 0.6, bar_pos, color='#c9f0c1')\n",
    "ax2.text(10, bar_pos + lp_votes_percent + 0.7, lp_seats_won_sum, ha='center', \n",
    "         fontdict={'size':12, 'color': '#155393', 'fontweight':400})\n",
    "ax2.text(10, bar_pos - 2, 'Last position', ha='center')\n",
    "\n",
    "# Text\n",
    "ax2.text(15, 25, \"The arrows point at positions that were also the last positions\\nfor a given number of seats won:\", \n",
    "         ha='left', fontdict={'size':12, 'color': '#155393', 'fontweight':400})\n",
    "\n",
    "for i, val in enumerate(total_votes_per_list_pos['Seat won'].values):\n",
    "    ax3.text(i, val + 5, val, horizontalalignment='center', \n",
    "             fontdict={'size':10, 'color': '#155393', 'fontweight':400})\n",
    "    if i + 1 in lp_seats_won:\n",
    "        ax3.arrow(i, 80, 0, -50, head_length=8, length_includes_head=True, head_width=0.3, ls='-', color='orange', alpha=0.7)\n",
    "        ax3.text(i, 85, lp_seats_won[i+1], horizontalalignment='center', fontdict={'size':12, 'color': '#155393', 'fontweight':400})\n",
    "\n",
    "for p in (ax1, ax2):\n",
    "    p.set_xlabel(\"Position on a list\", fontsize=14)\n",
    "    p.tick_params(axis='x', labelsize=11)\n",
    "    p.grid(alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, 50% of people voted on two leading positions, that won more than a half of total seats (238/460).<br /> 1<sup>st</sup> position stands out from the rest by far, gaining 37% of all votes and ~76% of candidates who occupied this position, finally won a seat (156/206).\n",
    "\n",
    "It's worth noting, that last positions got 15 mandates and together with first 10 positions, they won almost 98% of seats. <br />These positions, however, covered only 50% of candidates (2266/4567), so the other half had to fight for the remaining ~2% of seats.\n",
    "\n",
    "It becomes clear, that candidates occupying the latter half, had very limited chances to succeed, while those at first positions <br />could almost be certain to win a seat, especially if they were popular.\n",
    "\n",
    "----\n",
    "##### Number of candidates on party lists for each constituency.\n",
    "In order to register a list in a constituency, a party is required to provide at least as many candidates as there are seats to be taken. <br />Top limit is twice the required minimum number. The chart below shows number of candidates on party lists for each constituency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_colors(n, minv, maxv, prepend_color=None, append_color=None):\n",
    "    \"\"\"Saturation (minv related) and brightness (maxv related) are constant.\n",
    "    Hue changes according to a step length, which is calculated based on given\n",
    "    n, minv, maxv parameters.\n",
    "\n",
    "    :param n: number of colors to be generated.\n",
    "    :param minv: 0-255, bottom border of color mixing.\n",
    "    :param maxv: 0-255, top border of color mixing.\n",
    "    :param prepend_color:\n",
    "    :param append_color:\n",
    "    :return: List of generated colors.\n",
    "    \"\"\"\n",
    "    rgb = {'r': maxv, 'g': minv, 'b': minv}\n",
    "    colors = []\n",
    "    steps_num = (n + (-n % 6)) // 6\n",
    "    step = (maxv - minv) // steps_num\n",
    "    \n",
    "    oper = [add, sub]\n",
    "    for idx, i in enumerate('brgbrg'):\n",
    "        for _ in range(steps_num):\n",
    "            rgb[i] = oper[idx % 2](rgb[i], step)\n",
    "            col_hex = '#' + ''.join([format(rgb[x], '02x') for x in 'rgb'])\n",
    "            colors.append(col_hex)\n",
    "    \n",
    "    if prepend_color:\n",
    "        colors = [prepend_color] + colors[:n-1]\n",
    "    if append_color:\n",
    "        colors = colors[:n-1] + [append_color]\n",
    "    return colors[:n]\n",
    "\n",
    "# Data--------------------------------------------------------------------------\n",
    "# for ax4\n",
    "party_district_list_size = df.groupby(['Constituency', 'Party']).agg({\"Num.\": max}).reset_index()\n",
    "party_district_list_size['Party'] = party_district_list_size['Party'].apply(lambda x: COMMITTEES_SHORT[x])\n",
    "party_district_list_size = party_district_list_size.pivot('Constituency', 'Party', 'Num.').fillna(0).astype(int)\n",
    "party_district_list_size = party_district_list_size.reindex(sorted(\n",
    "        party_district_list_size.index, key=lambda x: int(re.search(r'\\d+', x).group(0))))\n",
    "\n",
    "# for ax5\n",
    "min_candi_num = seats_per_district.set_index('Constituency')\n",
    "min_candi_num = min_candi_num.reindex(sorted(\n",
    "        min_candi_num.index, key=lambda x: int(re.search(r'\\d+', x).group(0)))).values.flatten()\n",
    "\n",
    "# for ax6\n",
    "total_candi_per_party = party_district_list_size.sum(axis=0).values\n",
    "#--------------------------------------------------------------------------\n",
    "\n",
    "total_xlabels, total_ylabels = party_district_list_size.shape\n",
    "\n",
    "# Generate some colors.\n",
    "color_list = generate_colors(256, 115, 222, '#f0f0f5')\n",
    "color_map = ListedColormap(color_list)\n",
    "\n",
    "# Plot\n",
    "fig2, ax4 = plt.subplots(1, 1, figsize=(12, 7))\n",
    "ax4.pcolormesh(party_district_list_size.transpose(), edgecolors='w', linewidths=1, cmap=color_map)\n",
    "ax4.set_title(label='Number of candidates on party lists in each constituency.', fontsize=17, \n",
    "              fontstyle='oblique', fontweight=555, y=1.15)\n",
    "\n",
    "# Bottom ticks, labels.\n",
    "ax4.set_xlim(0, total_xlabels)\n",
    "ax4.set_xticks([i + 0.5 for i in range(total_xlabels)], minor=True)\n",
    "ax4.set_xticklabels(party_district_list_size.index.values, minor=True, rotation=90)\n",
    "\n",
    "# Left ticks, labels.\n",
    "ax4.set_yticks([i + 0.5 for i in range(total_ylabels)], minor=True)\n",
    "ax4.set_yticklabels(party_district_list_size.columns.values, minor=True)\n",
    "\n",
    "# Add text to chart squares.\n",
    "elect_lists_sizes = party_district_list_size.values\n",
    "for xs in range(total_xlabels):\n",
    "    for ys in range(total_ylabels):\n",
    "        val = elect_lists_sizes[xs, ys]\n",
    "        if val == 0:\n",
    "            continue\n",
    "        ax4.text(xs + 0.5, ys + 0.5, val, ha='center', va='center', fontsize=11, alpha=0.7, color='black')\n",
    "\n",
    "# top ticks, labels.\n",
    "ax5 = ax4.twiny()\n",
    "ax5.set_xlim(ax4.get_xlim())\n",
    "ax5.set_xticks([i + 0.5 for i in range(total_xlabels)], minor=True)\n",
    "ax5.set_xticklabels(min_candi_num, minor=True, color='#e18814', fontsize=11)\n",
    "ax5.set_xlabel('Min. number of candidates required to register a party list in a constituency.', \n",
    "               fontsize=14, fontstyle='oblique', color='#e18814')\n",
    "\n",
    "# Right ticks, labels.\n",
    "ax6 = ax4.twinx()\n",
    "ax6.set_ylim(ax4.get_ylim())\n",
    "ax6.set_yticks([i + 0.5 for i in range(total_ylabels)], minor=True)\n",
    "ax6.set_yticklabels(total_candi_per_party, minor=True, color='#ff1a1a', fontsize=11)\n",
    "ax6.set_ylabel(\"Candidates total\", color='#ff1a1a', fontsize=14, rotation=-90)\n",
    "ax6.yaxis.set_label_coords(1.048, 0.5)\n",
    "\n",
    "# Remove major ticks, labels.\n",
    "for axi in (ax4, ax5, ax6):\n",
    "    axi.tick_params(left=False, right=False, top=False, bottom=False, labelleft=False, \n",
    "                    labelright=False, labeltop=False, labelbottom=False, which='major')\n",
    "\n",
    "fig2.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "##### Results split by constituency. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
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       "        stop: function(event, ui) {\n",
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       "        this.waiting = true;\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
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       "    };\n",
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       "\n",
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       "    var height = Math.abs(y1 - y0);\n",
       "\n",
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       "\n",
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       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
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       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
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       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
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       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
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       "                    fig.imageObj.src);\n",
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       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_district_rank = district_votes[['Constituency', 'Party', 'Votes']].copy()\n",
    "party_district_rank['Rank'] = party_district_rank.groupby('Constituency')['Votes'].rank(ascending=False)\n",
    "party_district_rank['Party'] = party_district_rank['Party'].apply(lambda x: COMMITTEES_SHORT[x])\n",
    "party_district_rank = party_district_rank.pivot('Constituency', 'Party', 'Rank').fillna(0).astype(int)\n",
    "party_district_rank = party_district_rank.reindex(sorted(\n",
    "        party_district_rank.index, key=lambda x: int(re.search(r'\\d+', x).group(0))))\n",
    "\n",
    "# Plot\n",
    "col_list = ['#f0f0f5', '#82e878', '#eec068', '#c3cf20', '#5cb2e6', '#f1e098', \n",
    "            '#dcc2f9', '#d6d2db', '#be9cbf', '#e76f78', '#c7ca86']\n",
    "col_map = ListedColormap(col_list)\n",
    "\n",
    "fig3, ax7 = plt.subplots(1, 1, figsize=(12, 6))\n",
    "ax7.pcolormesh(party_district_rank.transpose(), edgecolors='w', linewidths=1, cmap=col_map)\n",
    "ax7.set_title(label='Party results in each constituency ordered by votes number.', fontsize=15, \n",
    "              fontstyle='oblique', fontweight=555)\n",
    "\n",
    "# Bottom ticks, labels.\n",
    "ax7.set_xlim(0, total_xlabels)\n",
    "ax7.set_xticks([i + 0.5 for i in range(total_xlabels)], minor=True)\n",
    "ax7.set_xticklabels(party_district_rank.index.values, minor=True, rotation=90)\n",
    "\n",
    "# Left ticks, labels.\n",
    "ax7.set_yticks([i + 0.5 for i in range(total_ylabels)], minor=True)\n",
    "ax7.set_yticklabels(party_district_rank.columns.values, minor=True)\n",
    "\n",
    "# Add text to chart squares.\n",
    "for xs in range(total_xlabels):\n",
    "    for ys in range(total_ylabels):\n",
    "        val = party_district_rank.values[xs, ys]\n",
    "        if val == 0:\n",
    "            continue\n",
    "        ax7.text(xs + 0.5, ys + 0.5, val, ha='center', va='center', fontsize=11, alpha=0.9, color='black')\n",
    "\n",
    "ax7.tick_params(left=False, right=False, top=False, bottom=False, labelleft=False, \n",
    "                labelright=False, labeltop=False, labelbottom=False, which='major')\n",
    "\n",
    "fig3.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "##### Let's see who got the most and the least votes among those who won a seat and among those who didn't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "palette = ['black', '#b1f6ba', '#edcc91', '#f2696b', '#8ec2a6', '#91bfed', 'brown', '#ede591', 'gray', 'pink', 'green']\n",
    "dfs_vars = (('top_seat', 'tak', False), ('bottom_seat', 'tak', True), \n",
    "            ('top_no_seat', 'nie', False), ('bottom_no_seat', 'nie', True))\n",
    "\n",
    "# Create 4 dataframes\n",
    "dfs = {}\n",
    "for v in dfs_vars:\n",
    "    tab = df[df['Seat won'] == v[1]].sort_values('Votes', ascending=v[2]).head(10)\n",
    "    tab['Candidate'] = tab['Candidate'].apply(lambda x: ' '.join(re.sub(\n",
    "                r'(?<= \\w)[\\w+]\\w*\\s?\\w*\\s?', '.', x.title()).split()[::-1]))\n",
    "    dfs[v[0]] = tab\n",
    "\n",
    "# Create plots\n",
    "fig4, axa5 = plt.subplots(2, 2, figsize=(13, 12))\n",
    "\n",
    "topseat = 'The most votes among those who won a mandate'\n",
    "botseat = 'The least votes among those who won a mandate'\n",
    "topnoseat = 'The most votes among those who didn\\'t win a mandate'\n",
    "botnoseat = 'The least votes among those who didn\\'t win a mandate'\n",
    "\n",
    "plt_vars = {'top_seat':(topseat, (0, 0)), 'bottom_seat': (botseat, (0, 1)), \n",
    "            'top_no_seat':(topnoseat, (1, 0)), 'bottom_no_seat':(botnoseat, (1, 1))}\n",
    "for df_name, d in dfs.items():\n",
    "    party_colors = [palette[i] for i in d.Party.values]\n",
    "    \n",
    "    r, c = plt_vars[df_name][1]\n",
    "    ax8 = d.plot('Candidate', 'Votes', kind='barh', ax=axa5[r, c], color=party_colors,\n",
    "                 width=0.5, legend=False, alpha=0.7, rot=35)\n",
    "    \n",
    "    ax8.invert_yaxis()\n",
    "    \n",
    "    ax8.tick_params(axis='y', labelcolor='black', labelsize=12, right=False)\n",
    "    ax8.tick_params(axis='x', top=False, bottom=False)\n",
    "    \n",
    "    ax8.get_xaxis().set_visible(False)\n",
    "    ax8.yaxis.get_label().set_visible(False)\n",
    "    ax8.spines['right'].set_visible(False)\n",
    "    ax8.spines['bottom'].set_visible(False)\n",
    "    ax8.grid(axis='y', alpha=0.5)\n",
    "    \n",
    "    ax8.set_title(plt_vars[df_name][0], fontsize=14, fontstyle='oblique', fontweight=777)\n",
    "    \n",
    "    # Vertical alignment must be one of ('top', 'bottom', 'center', 'baseline')\n",
    "    for text_obj in ax8.get_yticklabels():\n",
    "        text_obj.set_verticalalignment('top')\n",
    "    \n",
    "    for patch in ax8.patches:\n",
    "        # Text along bars\n",
    "        ax8.text(patch.get_width() // 3, patch.get_y() + (patch.get_height() * 0.75), \n",
    "                 int(patch.get_width()), fontsize=11, fontstyle='oblique', color='black')\n",
    "        \n",
    "    # Create legend\n",
    "    artists, labels = [], []\n",
    "    for party_id in pd.unique(d.Party):\n",
    "        artists.append(Line2D([0], [0], color=palette[party_id], lw=4))\n",
    "        labels.append(COMMITTEES_SHORT[party_id])\n",
    "    \n",
    "    ax8.legend(artists, labels, loc='best', prop={'size':11})\n",
    "\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.4, hspace=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "##### Party votes distribution.\n",
    "\n",
    "Box plot below shows distribution of all candidates votes per party with whiskers set from 0% to 99% and outliers covering top 1%. Since votes numbers differ significantly between candidates, log scale is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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       "\n",
       "    this.ws = websocket;\n",
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       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
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       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
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       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
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       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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z0Ga5yR/wtJz98zebZd2UwfXF6b++z0zeyONi8NjmyHnOPpxVQD+isayt6Q+H/+rG8ueS310Q3JwAE7qe9ku2vAfU/+T5OnZOxf1XA6/MnLb1gnAfy1724TFVaC/LHv/8NlN3R+L13xzBPhc6kOon5l6e/GuxnOL6wus6ygB+Itz4ehvcuEh0J+V+hZHizY1t5f3a9T+LUesEYAN9+9T72jdkLteYRW4q8XhrItpvyvx/rPs7cg1lz+XOlQ8dmn531ha7lXz+Q9OfYuYVev5cC4MB38/eyO4uyuW/6k12veQ1DvDy+u4NXvhZ51DoJu3X1mu5SO58N6sj2+sY/H4N3LhVaAvNgAvzq9tTmdTB+Km/72i1g+mvvr2quC2399rv+D28NTnfa76O55L/Q+LpsMC8PLo6wsb85u/d5W/1lj3k1c8/3GpDyvd7+93LvUVx5dfw0n3/bgqAF+S+p8cq+o9l+Q7GsseFoA/I/U9kPdr9w2pX5fHqXlhrYNG/f9FVr//zyV5dy68v24zAL8p9d9o+WcOO0R8lXUD8CWpj2ppjv4u/Jf5/Pdn7yr4y7U0z+V/3EXUCcCGekLqwxST+uqfTzxgWaD2oNTBc7Gzfc0Byz4qyY+kDpRnUp9P9x+z+nZEj0l9C5YzqUdgXtZ47gGpLyr0x6kPW/3L1Oclrrqi8qNSj+Iult1OPZL7zBx8UaOmT5iv4z2pw8B/S32rpUXIaF6R+Svn887mwgB8Sep79r52Xu+H548/ndXB6d/O1/vB1KNtj04dNBb9vOqKvc0LDjXD/QsbNT0k9ZV9b0ndH69PfUuoZfdL/be5LXWfvXJew6nGuprBbb+/V/N3LwfR+6Q+HP33Uo+ovy/1qP4X5q5uaKxnOQAv2ty8CvRBv3eVP58v/7IDlvni1K+zW1MHq/ekbvOzc9dz2xe67sfF1cWXrwr+UanPr74xdd++N/VI9vL7c7/XTNMjUh8t8MbUAex06r/Zt2Xvll7HqVnzYVf+fnTqfz69LXvbnB/I3q3SFpYvgvVZqfvrQ6lfG89LfTTAUf237P3dDgrAT50vt+pCZPdJ8n2pX3fvSn3kzPLrbXEF/ndk/e0aAOTFqe8d+JrUH1rL90MEYJyelzpA/F7fhdCLw64CXbrFkQbf2XchAAzLD2fvXp9fleTf9VgLAMfnE1OPyp/N6iMSGLchB+D7px6pvjP1Fa4B2GCPTX2IWtOjUt9+4nTq83ye1Hju32Xv6rWPSD0SDMBmWFyR/N/0XQjHbsgBeHH484/0XQgA/XpakjtSXzRi4V6pr/46TX0u3RNTn8/0SfPnvyjJT8y//sdJXn4chQJQhPsnuT31eZQf3XMtHK9HZu+84uV7fpfsktTn85/OheeSA7BhXpT6wh/TXBiAH5/knUvLvjJ79wi9JPXFMX4zyS/l8FtnAAAAQK8W93F8XC4MwM9Kfc/Sppdkb9R3XZckeVjqi2SZTCaTyWQymUym/qeHpd5Ph421HICfm717Ki68IMnPHnG9D0tSmUwmk8lkMplMpqKmhwU22ONyYQD+hiS/vLTMS1Lf3/MoTiSptra2qu3t7WKmyWTSew1DmfSVftJP+qrkST/pK/2kn0qfSuurra2tRQA+cdHJAUZgOQA/Psnbl5Z5ZZJnH3G9J5JU29vbVUmm02nfJQyGvlqPflqPflqfvlqPflqfvlqPflqPflpfaX21vb0tAEPuGoDvleRtSb41yT1T3wLpdOpbHh2FADxw+mo9+mk9+ml9+mo9+ml9+mo9+mk9+ml9pfWVAAy15QCcJFcm+bUk20n+KMkXXMR6iwzAs9ms7xIGQ1+tRz+tRz+tT1+tRz+tT1+tRz+tRz+tr7S+EoChW0UGYAAA2EQCMHRLAAYAgEIIwNCtE0mqyWRS3OEfAACwSWazWTWZTARg6JARYAAAKIQRYOiWAAwAAIUQgKFbAjAAABRCAIZuCcAAAFAIARi6JQADAEAhBGDolgAMAACFEIChWwIwAAAUQgCGbrkPMAAAFMB9gKF7RoABAKAQRoChWwIwAAAUQgCGbgnAAABQCAEYuiUAAwBAIQRg6JYADAAAhRCAoVsCMAAAFEIAhm4JwAAAUAgBGLolAAMAQCEEYOjWiSTVZDKpZrNZ3+93AADYWLPZrJpMJgIwdMgIMAAAFMIIMHRLAAYAgEIIwNAtARgAAAohAEO3BGAAACiEAAzdEoABAKAQAjB0SwAGAIBCCMDQLQEYAAAKIQBDtwRgAAAohAAM3RKAAQCgEAIwdOtEkmoymVSz2azv9zsAAGys2WxWTSYTARg6ZAQYAAAKYQQYuiUAAwBAIQRg6JYADAAAhRCAoVsCMAAAFEIAhm4JwAAAUAgBGLolAAMAQCEEYOiWAAwAAIUQgKFbAjAAABRCAIZuCcAAAFAIARi6JQADAEAhBGDolgAMAACFEIChWyeSVJPJpJrNZn2/3wEAYGPNZrNqMpkIwNAhI8AAAFAII8DQLQEYAAAKIQBDtwRgAAAohAAM3RKAAQCgEAIwdEsABgCAQgjA0C0BGAAACiEAQ7cEYAAAKIQADN0SgAEAoBACMHRLAAYAgEIIwNAtAZiNMJvN+i4BAOBQAjB0SwBmI0yn075LAAA4lAAM3RKA2QgCMAAwBAIwdEsAZrRms1k1nU6r6XRaJTn/tcOhAYBSCcDQLQGYjWAEGAAYAgEYunUiSTWZTIyKMWoCMABQutlsVk0mEwEYOmQEmI3gHzwAwBAYAYZuCcAAAFAIARi6JQADAEAhBGDolgAMAACFEIChWwIwAAAUQgCGbgnAAABQCAEYuiUAAwBAIQRg6JYADAAAhRCAoVsCMAAAFEIAhm4JwAAAUAgBGLolAAMAQCEEYOiWAAwAAIUQgKFbAjAAABRCAIZuCcAAAFAIARi6JQADAEAhBGDolgAMAACFEIChWwIwAAAUQgCGbgnAAABQCAEYunUiSTWZTKrZbNb3+x0AADbWbDarJpOJAAwdMgIMAACFMAIM3RKAAQCgEAIwdEsABgCAQgjA0C0BGAAACiEAQ7cEYAAAKIQADN0SgAEAoBACMHRLAAYAgEIIwNAtARgAAAohAEO3BGAAACiEAAzdEoABgMGYzWZ9lwCdEoChWwIwADAY0+m07xKgUwIwdEsABgAGQwBm7ARg6JYADAAUbTabVdPptJpOp1WS8187HJoxEoChWwIwADAYRoAZOwEYuiUAAwCDIQAzdgIwdEsABgAGw2HPjJ0ADN0SgAEAoBACMHRLAAYAgEIIwNAtARgAAAohAEO3BGAAACiEAAzdEoABAKAQAjB0SwAGAIBCCMDQLQEYAAAKIQBDtwRgAAAohAAM3RKAAQCgEAIwdEsABgCAQgjA0K0TSarJZFLNZrO+3+8AALCxZrNZNZlMBGDokBFgAAAohBFg6JYADAAAhRCAoVsCMAAAFEIAhm4JwAAAUAgBGLolAAMAQCEEYOiWAAwAAIUQgKFbAjAAABRCAIZuCcAAAFAIARi6JQADAEAhBGDolgAMAACFEIChWwIwAAAUQgCGbgnAAABQCAEYuiUAAwBAIQRg6JYADAAAhRCAoVsCMAAAFEIAhm4JwAAAUAgBGLolAAMAQCEEYOiWAAwAAIUQgKFbAjAAABRCAIZuCcAAAFAIARi6JQADAIMxm836LmEQ9NNwCcDQLQEYABiM6XTadwmDoJ+GSwCGbgnAAMBgXH311X2XMAgC8HAJwNAtARgAKNpsNqum02k1nU6rJOe/dpjvhfTTOAjA0C0BGAAYjMsuu6zvEgbBCPBwCcDQLQEYACiakc2jE4CHSwCGbgnAAEDRBOCj0zfDJQBDtwRgAGAwTp482XcJ0CkBGLolAAMAg+Eq0IydAAzdEoABgMFwaC9jJwBDtwRgAAAohAAM3RKAAQCgEAIwdEsABgCAQgjA0C0BGAAYDOcAM3YCMHRLAAYABmM6nfZdAnRKAIZuCcAAwGAIwIydAAzdEoABgKLNZrNqOp1W0+m0SnL+a4dDM0YCMFy8P0zyK/Ppq/ZZRgAGAAbDCDBjJwDDxblvkl9YYzkBGAAYDAGYsROA4eJ8TvZGgH86yWX7LCcAAwCD4bBnxk4AhovzaUmeNv/6HyX5sX2WE4ABAKAQAjDUHpvkPUvzHpXkdUlOJ3lTkic1nvvo+ZQklya5cZ/1CsAAAFAIARjqkdw7kry/Me9eSd6aZJrkHkmemOQDST5p/vzXJPmO+df/IMkP77NuARgAAAohALPpXpR69HaaCwPw45O8c2nZVyb5tvnX90597u9r5/MfvM/6BWA2gnPGAIAhEIDZdJ8wf3xcLgzAz0p9gaumlyT5iSOuXwBmI7hqKAAwBAIw1JYD8HOTvGppmRck+dkjrlcAZiMIwADAEAjAUFsOwN+Q5JeXlnlJkh8/4npPJKmuueaaajqdVtPptHr1q19dzWaz6s477zz/RrzpppuqW2655fz3p0+frq6//voL3qw33nhjtbW1df7722+/vbrhhhsuWOaGG26obr/99vPfb21tVTfeeOMFy1x//fXV6dOnz39/yy23VDfddNP57++88071qW+t+l760peef10nqZ761KdWz3jGM84fDt13faX3n/rUpz71qU996jue+mazWTWdTqtnPOMZ1dVXXy0AQ+4agB+f5O1Ly7wyybOPuF4jwGwEI8AAwBAYAYbacgC+V5K3JfnWJPdMfQuk00keccT1CsBsBAEYABgCARhqywE4Sa5M8mtJtpP8UZIvuIj1CsBsBFeBBhiHa6+9tu8SoFMCMHRLAGYjCMAA43DFFVf0XQJ0SgCGbgnAbASHQAOMw2WXXdZ3CdApARi6JQCzEQRggOG69tprq5MnT1YnT56skpz/2uHQjJEADN06kaSaTCYOEWV0FrcUWNwGafG11zrAsNiesylms1k1mUwEYOiQEWA2ghFggHG4xz3u0XcJ0CkjwNAtAZiNIAADDJcRYDaJAAzdEoDZCHaSAIZLAGaTCMDQLQEYABiMkydP9l0CdEoAhm4JwADAYFx99dV9lwCdEoChWwIwG8FhcgDjYHvO2AnA0C23QWIjGDEAAErnNkjQPSPAbATnjAEAQ2AEGLolADNarhoKAAyNAAzdEoAZLQEYYDh2d3er7e3t1qbd3d2+mwQXRQCGbgnAbASHQAOUrbHT38pk34ahEoChWwIwG8FFsADKts4I8G23bVfPfnb9aASYsRKAoVsCMBvBYc8Aw7e9XVVJ/QhjJQBDtwRgAGAQBGA2gQAM3RKAAYBBEIDZBAIwdOtEkmoymThElFG79tpr+y4BgLtJAGbsZrNZNZlMBGDokBFgNoKrQAMMnwDMJjACDN0SgNkIAjDA8AnAbAIBGLolADNa1157bXXy5Mnq5MmTVZLzXzscGmCYzp6tqptvrh9hrARg6JYAzEa47LLL+i4BAOBQAjB0SwBmIwjAAMAQCMDQLQGY0ZrNZtV0Oq2m02mV5PzXrngOAJRKAIZuCcBsBBfBWo9/DgBAvwRg6JYAzEYQgNcznU77LgEANpoADN0SgBkth0AfnQAMlMxVoNkEAjB0SwBmI1x99dV9l1As/ygAhsJ9gNkEAjB060SSajKZ2Nll1BwCvR4jwEDJBGDGbjabVZPJRACGDhkBZiMYAV6PAAyUTABmExgBhm4JwIyWQ3uPTt8AJROA2QQCMHRLAGYjGNkEGD4BmE0gAEO3BGA2ggAMMHwCMJtAAIZuCcBsBIf2Agzfzk5VnTpVP8JYCcDQLQEYAAAKIQBDtwRgAAAohAAM3RKAGbTd3d1qe3u7tWl3d7fvJgEAG0wAhm4JwAxa40Oilcl7AQDokwAM3RKAGbR1RoC3trarpH40AgwAlEwAhm4JwIye22YAAEMhAEO3TiSpJpOJ28QwWgIwwDicPl1VV11VP8IYzWazajKZCMDQISPAjJ4ADDAOtudsAiPA0C0BmNHb2amqU6fqRwCGSwBmEwjA0C0BGAAYBAGYTSAAQ7cEYABgEARgNqrhRNIAACAASURBVIEADN0SgAGAQRCA2QQCMHRLAAYABsFVoNfn7h7DJQBDtwRgAICRmU6nfZfARRKAoVsCMADAyAjAwyUAQ7cEYEbPIXMAbILZbFZNp9NqOp1WSc5/7XDoYRGAoVsCMKPnoikAbJorrrii7xK4SAIwdEsAZvQEYAA2zWWXXdZ3CVwkARi6JQAzegIwAJvAIdDjIABDtwRgRk8ABhiHnZ2qOnWqfuSuBOBxEIChWwIwoycAA4yD7fn6nAM8XAIwdOtEkmoymfjvIKPlKtAA4yAAr+/yyy/vuwQuwmw2qyaTiQAMHTICDAAMggC8vquvvrrvErhIRoChWwIwADAIAvDBnAM8DgIwdEsABs6zkwSUTABenxHg4RKAoVsCMHDedDrtuwSAfQnA6zt58mTfJXCRBGDolgAMnCcAAyU7e7aqbr65fuRgn/u5n9t3CVwkARi6JQDDhnPOGMA42J6PgwAM3RKAGb2dnao6dap+5GBGgAHGwSHQwyUAQ7cEYEbPOWPrE4ChP0bpuLuMAI+DAAzdEoAZPQF4fXaSoD/+AUWbnAM8XAIwdEsAZvQEYGAIBGDa5BDo4RKAoVsCMKMnAAOlcsjq0bgK9PrcB3i4BGDolgDM6AnAwBAYAT6c7fnB/ENlHARg6JYAzOjZYVqfnSTojwB8ONvz9RkBHi4BGLolADN6Dplbnx1w6I9/QB1OAF7f5Zdf3ncJXCQBGLolAAPnCcDQHwH4cALw+q644oq+S+AiCcDQLQEYNpxzxqAM/gF1OAH4YLbn4yAAQ7cEYOA8O+DQH++/wwnAVbW7u1ttb2+vnK677rpqMplUk8mkSnL+6+uuu27fn9ne3q52d3f7bhYNAjB0SwAGzrMDDsfLiN3R7OxU1alT9eOmaoSj1ib7gWURgKFbJzL/D6EPW8B2APrjH1Cs46AR4Ob0pCc9aa3ljACXZTabnR/BjwAMnTACzOi5CjQwBAIwbfIPzeEyAgzdEoAZPeeMAUMgsABVJQBD1wRgRk8ABgCGQgCGbgnAjJ4ADMAmOX26qq66qn5keARg6JYAzOgJwABsEp97wyYAQ7cEYEbPjgDAOBjZXI/PvWETgKFbAjCjZ0cAYBxsz9ejn4ZNAIZuCcCM3s5OVZ06VT8CMFyC3Xr007AJwNAtARgAGATBbj36adgEYOiWAAwADIJgtx7nSg+bAAzdEoCB82azWd8lAOxLAGYTCMDQLQEYOG86nfZdAsC+jGyyCQRg6JYADJwnAEN/HIEBVJUADF0TgGHDzWazajqdVtPptEpy/ms743C8/AMKqCoBGLomADN6Dplbnx1w6I/3H1BVAjB0TQBm9Fw0ZX1XX3113yXARnEEBrBMAIZuCcCMngC8viuvvLLvEmBjGQGmLTs7VXXqVP3I8AjA0C0BmNETgNd32WWX9V0CbCwBmLb43Bs2ARi6JQAzenYEDuYQTCiD99zhjGyux+fesAnA0C0BmNGzI3Cwa6+9tjp58mR18uTJKsn5r6+99tq+S4ONIgAfzvZ8Pfpp2ARg6JYAzOi5CvT6HAIN/XERusMJduvRT8MmAEO3BGDgvMsvv7zvEmBjnTx5su8SiifYrUc/DZsADN0SgIHzHPYMx8s5+Ecj2K1HPw2bAAzdEoABoCcC8NEIdus5e7aqbr65fmR4BGDolgAMnGenG/rjEOjDCcBsAgEYuiUAA+e5CA/058orr+y7hOIZ2WQTCMDQrRNJqslkYuQHMAIFPXIROmA2m1WTyUQAhg4ZAWb0dnaq6tSp+pG7cg4ilEEABqrKCDB0TQBm9JwzdjABGPpz7bXXVidPnqxOnjxZJTn/tSuyw+YSgKFbAjCjJwCvzyHQ0J/LLrus7xIYCedKD5sADN0SgBk9AXh9LoIFx8sRGHTB596wCcDQLQGY0bMjsD473dCfhz/84X2XUDwjm+vxuTdsAjB0SwBm9OwIVNXu7m61vb3d6rS7u9t3s2BUHAJ9ONvz9einYROAoVsCMKNnR+CCD9PWJtsNaNcVV1zRdwnFsz1fj34aNgEYuiUAM3oOmVtvBPjWW7erRz6yfjQCDMfDVaCPRrBbj34aNgEYuiUAA1VV2WGCLhzl9IPHPOYx/vl0CNup9einYROAoVsCMFBVlR0m6ILTD9plO7WenZ2qOnWqfmR4BGDolgAMVFVlxxK6sO4I8NbWdpV8e7W1ZQT4ILZTbAIBGLolAANVVdmxhD4ZsVuPfmITCMDQLQEYqKpKAAaAEgjA0C0BmNFzFej1nD5dVVddVT8CAP0QgKFbAjCjZ2QTABgKARi6JQAzegIwADAUAjB0SwBm9ARgADaJU1qGTQCGbgnAjJ4ADMAm8bk3bAIwdEsAZvTsCAClM2K3Hv20Hp97wyYAQ7cEYEbPjgBQOtup9ein9einYROAoVsCMKO3s1NVp07Vj+xPPx3NbDbruwRGRGBZj35aj34aNgEYuiUAA1VV2WE6qul02ncJjIj333r003r007AJwNAtARioqsoO01EJwLTJ+289+mk9zpUeNgEYuiUAA1VV2bFcx2w2q6bTaTWdTqsk5792ODR3l/ffevQTm0AAZmguTfL3k7w4ySuSvCrJjyd5XpK/k+Se/ZW2kgAMVFVlx/KojADTJiN269FPbAIBmKF4aJKXJHlfknPzabcxLea9M8mLkjy4nzLvQgAGqqoSgI9KAAagCwIwQ/CM1MF3EXTfnOR/JPne1GH3+5Ncl+RPsheM35vk6/oodokADFRVJQAflcOeAeiCAMwQ7Cb5/SSTJA87ZNmHJPnKJL+eOgz3TQBm9Bwyt56zZ6vq5pvrRwCgHwIwQ/D5F/lzf73VKi6OAMzoGdkEAIZCAGao7pXks5I8Zf79fXus5SACMKMnAAOwSXZ2qurUqfqR4RGAGaKnJ7kt9SHOZ+fz/jDJd/RW0f4EYEZPAAZgk/jcGzYBmKG5Jne9+vM9Uwfhc0m+vr/SVhKAGT07AkDpjNitRz+tx+fesAnADM3vpg66T0iyNf/6kiTPSh2I39hfaSsJwIyeHQGgdLZT69FP69FPwyYAMzRnUh/+nOwF4IX3JPnQsVd0MAGY0XMV6PW4CjT0R2BZj35aj34aNgGYoXlXkg8neWAuDMCfkXoE+Nae6tqPAAxUVWWHCfrk/bce/bQe/TRsAjBD80Opg+7NST6QOgC/Isn751//UH+lrSQAA1VV2WGCPnn/rUc/rUc/DZsAzNA8IPV5wLsrpjcmeXB/pa0kAANVVdlhgj55/61HP63HKS3DJgAzRPdO8jVJfirJLyf5ySRfneSje6jlyiR/cMDzAjBQVZUdS+iT99969BObQACGu+enc/CVpwVgoKoqO5bQJyN269FPbAIBmCH4niNM332MdT0lyTQCMLAGARgA+icAMwS7qS9wddi0WO44XJrk55PcI8mbDlhOAGb0dnaq6tSp+pH96ScA6J8AzBC8LskNR5guxmNT30e46VHz3306dch9UuO55yd5wvxrAZiNZmQTABgKARiSpyW5I/WtlBbuleStqQ9xvkeSJ6a+7dInzZ9/bZJfSfKrST6Y5GX7rFsAHrjZbNZ3CcUTgAHYJM6VHjYBmLH5jCMu/6IkN6YOus0A/Pgk71xa9pVJvm3FOpwDPGLT6bTvEoonAAOwSXzuDZsAzNDcL/WFrm5M8uYkfzyf/iT1Icxnj7i+T5g/Pi4XBuBnpR7hbXpJkp844voF4IETgA9nRwAonRG79ein9fjcGzYBmKH53tQXu9pv+shFrnc5AD83yauWlnlBkp894npPJKmuueaaajqdVtPptHr1q19dzWaz6s477zz/RrzpppuqW2655fz3p0+frq6//voL3qw33nhjtbW1df7722+/vbrhhhsuWOaGG26obr/99vPfb21tVTfeeOMFy1x//fXV6dOnz39/yy23VDfddNP57++8886Nr282m1XT6bT6qq/6qirJ+b/dbDYror6FUvpvsSMwm5VZ30Kp/ac+9amv+/rq7dSd1XXXlVlfVZXRf7feevp8sCuxvlL6741v3KqSGy8IwCXVV3r/9VHfYt/uGc94RnX11VcLwAzK21Jf6fllSX4nyW8neWaS30gdYB95ketdDsDfkOSXl5Z5SZIfP+J6jQAPnBHgw/lPOFA626n16Kf16KdhMwLM0Owkeff8629NffhzkjwoyYeT/PeLXO9yAH58krcvLfPKJM8+4noF4IETgA/nkLn1nD5dVVddVT8Cx0tgWY9+Wo9+GjYBmKG5PfXVmO+d5P9LPRr80NRXbd7OhSH2KJYD8L1SjzZ/a5J7pr4F0ukkjzjiegXggXMVaNpihwn64/23Hv20Hv00bAIwQ/MLqUPvq5LcJ8mZJG9I8mupzwF+x0WudzkAJ8mV8/VuJ/mjJF9wEesVgIGqquwwQZ+8/9ajn9azs1NVp07VjwyPAMzQfGaS92XvAlU/mgsvgvVdPdW1HwEYqKrKjiX0aezvvzNn6rbd3Wlrq+6nra121nfmTN89A3clADNED03y+fOv75P64lSzJM9L8tF9FbUPARioqmr8O+BQsjGP2J05U1WXXlpvX0qbLr1UCKY8AjBj8FF9F3CAE0mqyWTiXFLYcAIw0IXFtqWtUdu2psVosm0eJZnNZtVkMhGAGZwvS/K6xvefmeTPk3x5L9UczAgwo+cq0OtxFWigC3fcsVvl3tvV1m3b1fZOOdPWbdtV7r1d3XHHbt9dBBcwAszQXJP6XN9zST5+Pu8rGvO+pJ+y9iUAM3pGNgH6s3XbdpXnp9hp6zYfDpRFAGZo3pA66H5fkvvO5z04yctTh+DX91TXfgRgRk8ABuiPEWA4GgGYoTmd+irQq7wv9S2LSiIAM3oCMEB/St0Gl1pXG5zSMmwCMEPz3iQfSfLApfkPTXI2+4fjvgjAjN6Yd3IASlfqNrjUutow5rZtAgGYofmfqQ+B/oMk/zL1OcHflOTN8/k/119pKwnAjJ4dAaB0Yx6xK3UbXGpdbRhz2zaBAMzQXJn6MOfFRa8W0+58/l/rr7SVBGBGz44AULoxb6dKbVupdbVhzG3bBAIwQ/TIJD+V5PbU4ff2+fef2mdR+3AfYEZvZ6eqTp2qH9mffoL+jDmwlNq2Uutqw5jbNnbuAwzdMwIMVFVlhwn6NOb3X6ltK7WuNoy5bZvACDBDdZ/G11+U5OmpL4RVGgEYqKrKDhP0aczvv1LbVmpdbRjzOeVdKO0oSAGYoXlAkl9M8r3z71+evfOA35Hkk3uqaz8CMFBV1bh3BqF0Y37/ldq2Uuvi+E2n075LuIAAzND8h9QXvHptkvsn2UlyZ5J3zue/orfKVhOAgaqq7AxCn8Y8YlfqtqXUujh+AjDcPW9Lfb/fz0x96PNuku9OfV/gj8yfL4kADFRVZWcQ6Eap25ZS6+J4zGazajqdVtPptEpy/usSDocWgBmaM6lHe5PkZakPfX7K/Pt3JflQH0UdQAAGqqqyMwh0o9RtS6l1cfyMAMPd8/bUIfh+Sd6UejT445N8Suow/Nb+SltJAGb0xnxoYZvOnq2qm2+uHwHaUmrQLLUujp8ADHfPz6Q+7Hk7deD99SQfm+S98++/v7/SVhKAGT07OQD9KXUbXGpdHL8SDntuEoAZmk9JPQq8m+T9ST5nPv8DSd6c5CE91bWfE0mqyWRS3Jsf2mInB6A/pW6DS62rDTs7VXXqVP3IsMxms2oymQjADM59knx2kgc15j0tZb6IjQAzemPeyQEoXanb4FLrasOY27YJjABDtwRgRs+OAFC6MY/YlboNLrWuNoy5bZtAAIZuCcCMnh0BoHRj3k6V2rZS62rDmNu2CQRg6JYAzOi5CvR6XAUa+jPmwFJq20qtqw1jbtsmEIChWwIwUFWVHSbo05jff6W2rdS62jDmtm0CARi6JQADVVXZYYI+jfn9V2rbSq2rDWNu2yYQgBmazz1g+pwkj0nygN6quysBGKiqyg4T9GnM779S21ZqXW1wSsuwCcAMzW6Sc2tMP5vkfj3V2CQAA1VVjXtnEEo35vdfqW0rtS4QgBmam5O8O3UQ3k1ypvH1uSRnG1+/rKcamwRgoKoqO4PQpzGP2JW6bSm1LhCAGZq/meQDSX4kyYPn8x6c5MeT3Jbk0Uk+P3UI/rM+ClxyIkk1mUyq2WzW9/sd6JGdQaALpW5bSq2LzTabzarJZCIAMyhvSB2A77E0/6OTfCjJDfPvb0uyc4x17ccIMKO3s1NVp07Vj+xPPwFdKDVolloXGAFmaM4k+XCS+y/Nf8B8/ofm32/Pl+2bAMzo2ckB6E+p2+BS6wIBmKG5OfX5vb+V5B+lvvLzl8y/P5fkTUkmqQ+BfktPNTYJwIyenRyA/pS6DS61rjaM+ZzyTSAAMzRfnNVXgl7Me1qSF8+///c91dgkADN6Y97JAShdqdvgUutqw5jbtgkEYIbo6iR/mAuv/vz6JF84f/55Sb4ryb16qe5CAjBF2t3drbZ3tluZtm7brnLv+rGtde7u7vbdRcCIjHnErtQwVmpdbRhz2zaBAMyQnUjy8NQXwCqVAEyRtne2qzw/xU7bO94zQHvGHFhKbVupdbVhzG3bBAIwQ/VpqUd6vy/Jc5Jc2W85+xKAKZIRYGCTjDmwlNq2Uutqw5jbtgkEYIboRUnO5sJzgM8m+dd9FrUPAZjRG/OhhW06fbqqrrqqfgSO15gDS6ltK7WuNoy5bZtAAGZo/mn2zv3dSvK6+WPzIlglEYCBqqrsMEGfxvz+K7VtpdbVhjG3bRMIwAzNG1IH3ecluWQ+75Ikp1KH4N/uqa79CMBAVVV2mKBPY37/ldq2Uutqw85OVZ06VT8yPAIwQ/OhJO9fMf+SJKfnz5dEAAaqqhr3ziCUbszvv1LbVmpdIAAzNLcl+UiSj1ua/6Akd86fL8mJJNVkMqlms1nf73egR3YGoT9jHrErddtSal1sttlsVk0mEwGYQbku9SHQNyT5vCSfmORvzr8/l+Rn+ittJSPAQFVVdgaBbpS6bSm1LjACzNA8OvVhzueWpt0kO0k+vb/SVhKAGT1XgV6Pq0ADXSg1aJZaFwjADNHnJbkpe1eD3k3y5iR/u8+i9iEAM3p2cgD6U+o2uNS6QABmyK5IHYYv77uQAwjAjJ6dHID+lLoNLrUuEIChWwIwo2cnB6A/pW6DS62rDU5pGTYBmCF4/xGm7Z5q3I8APHCu3n24Me/kAJSu1G1wqXW1Ycxt2wQCMEOwe4TpXE817kcAHrjpdNp3CcWzIwCUbswjdqVug0utqw1jbtsmEIAZgq884lQSAXjgxhyAz5ypP7zv7rS1Ve8IbG21s74zZ/ruGWBsxhxYSm1bqXW1Ycxt2wQCMHRLAB6g2WxWTafTajqdVknOfz2mw6HPnKmqSy+tP8BLmy69dJwheGenqk6dqh+B4zXmwFJq20qtqw1jbtsmEIAZgh9IcuURf+avJHlJB7UclQA8cGMdAV58eLc1atvWtBhNHuNbxg4T9GfM779S21ZqXW0Yc9s2gQDMEJxOfW7v65JMkzwmyT2Xlrlnkk9P8s1JfiHJnUk+cIw17kcAHrixB+DSXpql1tWGMbcNSjfm91+pbSu1rjaM+ZzyTSAAMwSPSPKq7F3k6lySjyR5V5I/TXLr/PvFc7tJfm7+c30TgAduTIc9N5W6Y1JqXW0Yc9ugdGN+/5XatlLrAgGYIfnsJD+Z5D1ZfQXo25P8aJLP6KvAFQRgilTqjkmpdbVhzG2D0o15xK7UbUupdYEAzBDdI3XIfWKSfzp/fFSvFe1PAKZIpe6YlFpXG8bcNqA/pW5bSq0LBGDo1okk1WQyGe2htAxTqTsmpdbVhjG3DehPqduWUutis81ms2oymQjA0CEjwBSp1B2TUutqw9mzVXXzzfUjQFtK3W6WWhcYAYZuCcAUqdQdk1LrAihVqdvNUusCARi6JQBTpFJ3TEqtC6BUpW43S62rDTs7VXXqVP3I8AjADM0n9F3AEQnAFKnUHZNS6wIoVanbzVLrasOY27YJBGCG5m1Jfj/Jx/ddyJoEYIpU6od3qXUBwzbmEbtSt5ul1tWGMbdtEwjADM0HUt8HeCgE4IEb69W7S/3wLrUuYNjGvG0ptW2l1tWGMbdtEwjADM0LkpxL8q+SPLjnWtYhAA/cdDrtu4ROlPrhXWpdbXAVaOjPmLctpbat1LraMOa2bQIBmKH5zSRnUofgc0nuTHI6yfvn03Z/pa0kAA+cAHy8Sq2rDWNuG5RuzO+/UttWal1tGHPbNoEAzNDsHjKd66+0lQTgAZrNZtV0Oq2m02mV5PzXYzocutQP71LrasOY2walG/P7r9S2lVpXG8bctk0gADM0X7nGVBIBeOCMAB+vUutqw5jbBqUb8/uv1LaVWlcbnNIybAIwdEsAHjgB+HiVWlcbxtw2KN2Y33+ltq3UukAAZojum2Sa5DVJ/mA+7+uS/JXeKtqfADxwYzrsuanUHZNS62rDmNsGpRvziF2p25ZS6wIBmKF5SJK3pD7Xt3nO73uSvCPJp/RU134EYIpU6o5JqXW1YcxtA/pT6ral1LpAAGZofjR18P2lJHekDsD3TvKn86+v66+0lQRgilTqjkmpdbVhZ6eqTp2qHwHaUup2s9S6QABmaN6V5ENJPibJVvZGgD9uPv/2nurajwBMkUrdMSm1Ltqzu7tbbW9vtzbt7u723STo1fYdu9XHZrva3tquN56FTNtb23Vdd3iPUhYBmKE5k/pw5+TCAHzPJB9M8oE+ijqAAEyRSg2apdZFexo7Hq1Mtq9suu2t+Yaz0Gl7a3zv0TGfU74JBGCG5ndSh96vSfKX868/OcmPpT40+rd6q2w1AZgilRo0S62L9hgBhnYZAT5+PquGTQBmaJ6cOvTuN31Jf6WtJAAPnKtAH69S6wKGbcwjdqVuN0utqw1jbtsmEIAZomuSvD31iO9ieluSf9ZnUfsQgAfOfYCPV6l1AcM25m1LqW0rta42jLltm0AAZqguSXJlks9PckWSe/Rbzr5OJKkmk8loRxLHTgA+XqXWxfEa82gd/RjztqXUtpVaVxvG3Laxm81m1WQyEYAZpMcn+ddJvjPJc5J8Xr/l7MsI8ADNZrNqOp1W0+m0SnL+6zH9E6PUD+9S62rD6dNVddVV9SMHG/PrgH6M+TVVattKrasNY27bJjACzNA8IMn/yerzf2dJ7ttfaSsJwANnBPh4lVpXG8bctrbpK9o25tdUqW0rta42jLltm0AAZmj+c/bO+701yW+nvh3SbuoQ/AP9lbaSADxwAvDxKrWuNoy5bW3TV7RtzK+pUttWal1t2NmpqlOn6keGRwBmaN6bvdsgNX116hD8nrv8RL8E4IEb02HPTaXumJRaVxvG3La26SvaNubXVKltK7UuEIAZmvcluWOf57aT3H6MtaxDAKZIpe6YlFpXG8bctrbpK9o25hG7Ut8vpdYFAjBD832pR4AfuTT/salHgL/z2Cs6mABMkUrdMSm1rjaMuW1t01ewvlLfL6XWBQIwQ/CNjek5ST6QeqT35UmeneQ/ph4VfmuSJ/dU434EYIpU6o5JqXW1wVWg1zfm0TpoW6nbzVLrAgGYIVhc4Ko5Lc9bXBjrbE817kcApkil7piUWhdAqUrdbpZaFwjADMHbU1/ped2pJAIwRSp1x6TUugBKVep2s8S6dnd3q+2d7SKn3d3dvrtnYwjA0C0BmCKVuGNSVeXWBVCqUrebJda1vbNd5fkpctreKaijRk4Ahm4JwBSpxB2Tqiq3LoBSlbrdLLGutkaAt27brnLv+tEI8PAIwAzNxyR5aZIbk7w5yR8vTW/pr7SVBGCKVOKOSVWVWxcwbGO+CF2p281S62rDmNu2CQRghuY/5cKLXi1P5/orbSUBmCKV+uFdal3AsI1521Jq20qtqw1jbtsmEIAZmvelDrnXJ3lhkueumEoiAFOkUj+8S62rDW7ts74xj9bRjzFvW0ptW6l1tWHMbdsEAjBD8+7U9/wdCgF44GazWd8ldKLUD+9S62rDmNvWNn1F28b8miq1baXW1YYxt20TCMAMzcuT3Jnk0X0XsiYBeOCm02nfJXSi1A/vUutqw5jb1jZ9RdvG/JoqtW2l1tUGR6kMmwDM0Nw7yRuTfDDJq5L8SJIfakw/2F9pKwnAAycAH69S62rDmNvWNn1F28b8miq1baXWBQIwQ/Oc7F3sanlyESxaMZvNqul0Wk2n0yrJ+a/HdDh0qTsmpdbVhjG3rW36iraNecSu1PdLqXWBAMzQvCt1yL0tya+lvhjW8lQSAXjgjAAfr1LrasOY29Y2fQXrK/X9UmpdIAAzNHekPvz5/n0XsiYBeOAE4ONVal1tGHPb2jbm0TpoW6nbllLrAgGYofmhJGeSPLDvQtYkABdod3e32t7eXmu67rrr1lpud3e372YdSak7JqXW1YazZ6vq5pvrR4C2lLrdLLUuEIAZmi9K8s4ktyR5cZJ/leQbl6aSCMAFamz4WpuG9jcudcek1LoASlXqdrPUukAAZmgWF7ra70JYLoLFoY4yArzuZAS4HaXWBVCqUrebpdbVhp2dqjp1qn5keARghubtSbYOmUoiAFOkUndMSq0LoFSlbjdLrasNY27bJhCAoVsCMEUq9cO71LqAYRvziF2p281S62rDmNu2CQRg6JYAPGBjvhJtqR/epdYFDNuYty2ltq3Uutow5rZtAgGYofnIIdOH+yttpRNJqslkUs1ms77f7xzRmD/gSm1bqXW1wVWg1zfm0Tr6MeZtS6ltK7WuNoy5bWM3m82qyWQiADMou2tMJTECPGBj/oArtW2l1tWGMbetbfqKto35NVVq20qtqw1jbtsmMALM0Hzp0vTlSb4+yW8k+fMkn99bZasJwAM25g+4UttWal1tGHPb2qavaNuYX1Oltq3Uutow5rZtAgGYsbhPktuSfH/fhSwRgAdsspCNsgAAIABJREFUzB9wpbat1LraMOa2tU1f0bYxv6ZKbVupdbXBKS3DJgAzJm9L8u6+i1giAA/YmD+8S21bqXW1Ycxta5u+om1jfk2V2rZS6wIBmKH5xqXpm5I8J8lrUp//+77+SltJAB4wV4E+fqXW1YYxt61t+oq2jXnErtT3S6l1gQDM0OwmObdiWlwA64f7K20lAZgilbpjUmpdbRhz29qmr2B9pb5fSq0LBGCG5u1JtpamtyX5/SQvTXLf/kpbSQCmSKXumJRaVxvc2md9Yx6tg7aVut0stS4QgKFbAjBFKnXHpNS6gOO3u7tbbe9sFznt7u723T3nlbrdLLUuEIChWwIwRSp1x6TUuoDjt72zXeX5KXLa3ilnI1XqdrPUutrgKJVhE4AZmkuTvCDJjUnenOSPl6a39FfaSgIwRSp1x6TUuoDjZwR4PaVuN0utqw1jbtsmEIAZmh/KXS981ZzO9VfaSgIwRSr1w7vUuoBhG/OIXanbzVLrasOY27YJBGCG5n2pQ+7vJvm3Sb49yXOXppIIwAM25osWlfrhXWpdwLCNedtSattKrasNY27bJhCAGZr3Jflg6kOhh0AAHrAxf8CV2rZS6+J4jXm0jn6MedtSattKrasNY27bJhCAGZr/lGQnyQP7LmRNAvCAjfkDrtS2lVpXG06frqqrrqofOdiYXwf0Y8yvqVLbVmpdbRhz2zaBAMzQfFaSP03yB0m+LslTkzxlaSqJADxgY/6AK7VtpdbVhjG3rW36iraN+TVVattKrevMmbqmuzNtbdVt29q6++va3q5r4vgIwAzNuUOms/2VtpIAPGClfni3odS2lVpXG8bctrbpK9o25tdUqW0rsa4zZ6rq0kvrukqaLr1UCD5OAjBDs+rKz8tTSQTgASvxw7stpbat1LraMOa2tU1fsdDGaF3bI3alBZVS3y8l1rWoqa2R2zZfmyX109gJwAzNPdaYSiIAD1iJH95tKbVtpdbVhjG3rW36iqoyWreuUt8vJdalJqpKAIauCcADNuYr0Zb6gVtqXW0Yc9vapq+oKqN16yr1/VJiXWpq1+7ubrW9vd3atLu7eyx1C8DQLQGYIpX6gVtqXW1wFej1jfke3KyvxO2BmtZXYl1qalcjSLYyHdf+sgAM3RKAKVKpH7gl1rW7u1tt72wXOR3Xf8uhDyVuD9S0vhLrUlO71hkBvvXW7eqRj6wfjQDDZhCAKVKpH7gl1rW9s13l+Sly2t4pqKOgZUVuD9S0thLrUtPxK7F9AjB0SwCmSCV+IFVVmXUZAYZ+lLg9UNP6SqxLTcevxPYJwAzVfRpff1GSpyd5aE+1HEQApkglfiBVVbl1AcevxO2BmtZXYl1qOn4ltk8AZmgekOQXk3zv/PuXJzk3n96R5JN7qms/AvCAuQr08Su1Lg5npJy2lbg9UNP6SqxLTcevxPYJwAzNf0iym+S1Se6fZCfJnUneOZ//it4qW00AHrASN9ptKbVtpdbF4ZwrTdtK3B6oaX0l1qWm41di+wRghuZtSc4m+czUhz7vJvnuJA9M8pH58yURgAesxI12W0ptW6l1tWHst/ZpcwT41ndvV498dP1oBHhzlbg9UNP6SqxLTcevxFsACsAMzZnUo71J8rLUhz4/Zf79u5J8qI+iDiAAD9iYP5RKbVupdbVhzG1rm76iqsp8HahpfSXWpSaqSgBmeN6eOgTfL8mbUo8Gf3yST0kdht/aX2krCcADNuYPpVLbVmpdbRhz29qmr6iqMl8HJdZ0xx11TVtbdV2lTFtb5fVViX+/EmsaOwGYofmZ1Ic9b6cOvL+e5GOTvHf+/ff3V9pKAvCAjflDqdS2lVpXG8bctrbpK6qqzNdBiTUtgmaJ06WXVtWZM3330J4S/34l1jR2AjBD8ympR4F3k7w/yefM538gyZuTPKSnuvYjAA/YmD+Utu/YrT4229X2VgHDBI1pe2u7ruuO8Z2zOebXU9v0FVVV5uugxJrOnatD8B13tDdq29Zocknht6rK/PuVWNPYCcAM0X2SfHaSBzXmPS1lvogF4AEb80WLtre2+x8aOGDa3hrfe8ZOzvr0FVVV5uugxJrapH3Hr8Saxk4AZqjuleSzsncBrPv2WMtBBGCKZAT4+NnJWV+JVw3l+JX4nimxpjZp3/ErsaaxE4AZoqcnuS31Ob9n5/P+MMl39FbR/gRgilTqB26pdbXh7Nmquvnm+hE4XInbgxJrapP2Hb8Sa2pTiUfTCcAMzTWpz/9dTOeS3DN1ED6X5Ov7K20lAZgilfqBW2pdwPErcXtQYk1t0r7jV2JNbSqxfQIwQ/O7qYPuE5Jszb++JMmzUgfiN/ZX2koCMEUq8QOpqsqtCzh+JW4PSqypTdp3/EqsqU0ltk8AZmjOpD78OdkLwAvvSfKhY6rjnkl+KvVtmF6R+pzkVQRgilTiB1JVlVsXcPxK3B6UWFObxn6qRol/vxJralOJ7ROAGZp3JflwkgfmwgD8Gfm/7d17uGRXXebxN4QkHSAtVyUJSBhHhTY4jIowjyIqPGEiFwecRwODiDJqsBzhOMrNwQZvjIQHHW8gM1zE2zAazQhIMS0EaDBKO3G0Q6K2acAiGrppOJUDnGO6u9b8sXb12af6nD7rdK+1692rvp/n2U/1qVOn+rdW7b32fmvt2hVngO/oqI5rtP6Z41dIet4WjyMAw5LjDikE37oAdM9xPHCsCekcXz/HmnJybB8BGH3zRsWge4vid/+eVJyBvav59xs7rOW8ZnmDpKdu8RgCcI/VfCVaxx1SCL51Aeie43jgWBPSOb5+jjXl5Ng+AjD65r6KnwOebLLcKulBHdfzQUl/J+mLt/g9AbjHHAftXFzb5lpXDrWfWpiT41VD0T3H8cCxJqRzfP0ca8rJsX0EYPTRRZJ+UPEzuO+V9FuSvl/ShefwnI9T/Axx25WSPiRpRdJtkp62xd8+XdJvbvE7AnCPOQ7aubi2zbWuHGpuW270FULwXA8ca0I6x9fPsaacHN/8JQCjb54t6ds3uf98SS+Q9D1n8ZzPkrSseBr11AWSDktaap77qYqnXF/R/P75iqFbkr5V0lu3eG4CcI/VvFNybZtrXTnU3Lbc6CuE4LkeONaEdI6vn2NNtSMAo28mkj6xxe+WFS+StROvlnRAMei2A/BVku6ceew7JL2s+fduSe+UdKOkd0u6fIvnJwD3WM07Jde2udaVQ81ty42+Qgie64FjTTk5ztbl5Pj6OdZUOwIw3J0n6T2Kn7P9O8UAfHfr5+nyyeZ3yzt8/kub2ydoYwB+oaT3zTz2Om19qvNWCMA9VvNOybVtrnXlUHPbcqOvEILneuBYU060r3uONdWOAIw+eKbWL3R1slk2uwjWRNIfnOX/MRuAf0JxhrftVWfx/ATgHuMq0N1zrSuHmtuWG32FEDzXA8eacqJ93XOsqXYEYPTFf1IMpRNJn23+PV1eLunFkv69pF1n+fyzAfhFihfYartO0tt2+Ly7JYVrrrkmLC0thaWlpfCud70rDIfDcPz48VMb4sGDB8OhQ4dO/byyshL27du3YWM9cOBAGI1Gp34+evRo2L9//4bH7N+/Pxw9evTUz6PRKBw4cGDDY/bt2xdWWonu0KFD4eDBg6d+Pn78OPUtQH3Hjh0/tcN1qi/WFeubcqhvdTX21bFjx8P118f6xuN43003HQw333zo1M933LESbrhh36mfx+MQbrzxQPjIR0ZBCmE0CuHw4aNhONy/4THD4f5w+PDRUz/feuso3HjjgQ2PueGGfeGOO1bCeBzC6mp/17/t6rvhhn0bDgjd6nPvv1rqawcDl/piTXH7bXOpL4Rze33vuGPlVJ871neu/Rfbt29D2Jx3fTfffOhUn7v3X5/rGw6HYWlpKVx77bXhGc94BgEYvfLTkn6swPPOBuCrFE+rbnuHpJfu8HmZAYYl13ecHetaXQ1h165Yl9Oya1esrUa1fw4RaRzHA8eacqJ93XOsKSfH8ZwZYPTVoyS9QtKvKM4C7znH55sNwBcoXmzrJZLuqfgVSCuSHrbD5yUAw5LrDtexrmlNo9H6TOy8l9HIr5+A3JzHA6eacqJ93XOsKSfH9hGA0Uc/J+mE1j8PfLL5+SfP4TlnA7AkPVLSBySNJX1U0tVn8bwEYFhy3CGF4FkXNQHz4bieO9aUE+3rnmNNOTm2jwCMvvlurV/waiTpQ83t9AJZz5pfaZsiAMOS4w4pBM+6qAmYD8f13LGmnNbWQti7N97WyPH1c6wpJ8f2EYDRN3+hGHRfofgVSWpu9yqG4I/Mqa6tEIBhyXGHFIJnXdQEzIfjeu5YE9I5vn6ONeXk2D4CMPrmCzr9VGUphuCV5vdOCMA9VvM74Y47pBA866ImYD4c13PHmpDO8fVzrCknx/YRgNE3RyTdLel+M/c/QNLx5vdOdksKg8EgDIfDeW/v2CHHQTsX17Y51kVN3XO8aii657ieO9aEdI6vn2NNObm1bzgchsFgQABGr1yveAr0fknfIOkySd/Y/HxS0u/Pr7RNMQPcY26Ddk6ubXOsi5q6V3v7kMZxPXCsCekcXz/HmnJyPJuOGWD0zVcrnuZ8cmaZSFqT9Oj5lbYpAnCP1bxTcm2bY13U1L3a24c0juuBY01I5/j6OdZUOwIw+uC8mZ+/QdJBrV8NeiLpbyR9a8d1pSAA91jNOyXXtjnWRU3dq719SOO4HjjWhHSOr59jTbUjAKMP7pD0WklfM3P/v1QMw1/WeUXpCMA9VvNOybVtjnVRU/dqbx/SOK4HjjXltLISwp498bZGjq+fY021IwCjD6bf8XtS0m2SfkLSw+daUToCcI/VvFNybZtjXdTUvdrbhzSO64FjTTnRvu451lQ7AjD64M2SPq31052nYfhPJf2QpAfOr7RtEYB7rOYr0brucB3roqZ0q6uxpnNdRqPYvtHo3J9rdXXevYKz5bieO9aUE+3rnmNNtSMAoy/Ol3SVpDcqftVROwzfLeldkp4t6V7zKnALBGBYct3hOtZFTWlWV0PYtSvW5bTs2kUI7ivH9dyxppxoX/cca6odARh9dL6kJ0p6g6Q7tXFW+K451rUZvgcYllx3uI51UVOaaU05Zm1zLdOZZKd+Qjrn9dypppxoX/cca8rJ7XPlfA8wavBISe/VxhlhJ8wAw5LrDtexLmpKQ03Ibbw8CZdoHMYjg3dTmmU8Gsealifz7p4iat9mHNvnWFNOju1jBhh9dIWkl0i6WRu/B3gi6Z/mV9amCMCw5LhDCsGzLmpKQ03IbTwaz/8c+i2W8ajOlcptti43xzHBsaacHNtHAEZfPETSj0r6c50eeu+S9DZJT1Y8PdoJARiWHHdIIXjWRU1pqAm5MQOM3BzHBMeacnJsHwEYffBhSSe0MfTeLekdkq6RdPH8StsWAbjHuAp09xzroqY01ITcHF8/x5qQjjdVuue4zRCA0QeT1vIhSS+Q9IC5VpSOANxjjoN2Lq5tc6yLmtJQE3JzfP0ca0I6TqvvnuM2QwBGH3xU0ssVP/vbNwTgHnMctHNxbZtjXdSUhpqQm+Pr51gT0jED3D3Hz5UTgIGyCMA9VvOBjmvbHOuipjTUhNwcXz/HmpDO8fVzrKl2BGCgLMsAzHcSp6l5p+TaNse6qCkNNSE3x9fPsSakc3z9HGuqHQEYKGu3pDAYDKxC59LS0rxL6IWad0qubXOsi5rSUBNyc3z9HGvKaW0thL17422NHF8/x5pqNhwOw2AwIAADBVnOABOA09S8U3Jtm2Nd1JSGmpCb4+vnWFNOtK97jjXVjhlgoCybADwcDsPS0lJYWloKkk7922lm2k3N74S77nAd66KmNNSE3BxfP8eacqJ93XOsqXYEYKAsmwDcxgwwXHe4jnVRUxpqQm6Or59jTTnRvu451lQ7AjBQFgEYllx3uI51UVMaakJujq+fY0050b7uOdaUk+PZdARgoCzLAMxpz3Dd4TrWRU1pqAm5Ob5+jjXlRPu651hTTo7tIwADZVkGYMBxhxSCZ13UlIaakJvj6+dYU060r3uONeXk2D4CMFAWARiWHHdIIXjWRU1pqAm5Ob5+jjXldOJECLfcEm9r5Pj6OdaUk2P7CMBAWQRgWHLcIYXgWRc1paEm5Ob4+jnWhHSOr59jTTk5to8ADJRFAO6xlZUQ9uyJt7Vx3CGF4FkXNaWhJuTm+Po51oR0jq+fY005ObaPAAyUtVtSGAwGXHiqhxwH7Vxc2+ZYFzWloSbk5vj6OdaEdI6vn2NNObm1bzgchsFgQAAGCmIGuMfcBu2cXNvmWBc1paEm5Ob4+jnWhHSOr59jTTk5fq6cGWCgLAJwj9W8U3Jtm2Nd1JSGmpCb4+vnWBPSOb5+jjXVjgAMlEUA7rGad0qubXOsi5rSUBNyc3z9HGvKyXG2LifH18+xptoRgIGyCMA9VvNOybVtjnVRUxpqQm6Or59jTTnRvu451lQ7AjBQFgG4x7gKdPcc66KmNNSE3BxfP8eacqJ93XOsqXYEYKAsAjAsue5wHeuipjTj5Um4ROMwHo1jYQbLeDSONS1P5t09OAuW67lhTTnRvu451lQ7AjBQFgEYllx3uI51EezSjEfNi2e4jEdGKxSSWY4HhjXlRPu651hTTo6fKycAA2URgGHJdYfrWBfBLg1vFCA3y/HAsKacaF/3HGvKybF9BGCgLAIwLC0vxx3SaDT3jLJhGY0Md5QEuySeBzl+NSGd4+vnWFNOtK97jjXl5Ng+AjBQFgEYlqZB03HZtSuE1dV599A6z503NaVwrAnpHF8/x5pyWlsLYe/eeFsjx9fPsaacHNtHAAbKIgDD0smTMQQvL+ebtc01m+wUfkNw3XlTUwrHmpDO8fVzrAnpHF8/x5pycmwfARgoa7ekMBgMwnA4nPf2jh2q/Z3wXBx3bjk5to+a0jjWhHSOr59jTUjn+Po51pSTW/uGw2EYDAYEYKAgZoB7zG3QdlV7Pzm2j5rSONaEdI6vn2NNSOf4+jnWlJNj+5gBBsoiAPeY46DtqPZ+cmwfNaVxrAnpHF8/x5qQzvH1c6wpJ8ez6QjAQFkE4B6rfaeUS+395Ng+akrjWBPSOb5+jjUhnePr51hT7QjAQFkE4B5jp5Sm9n5ybB81pXGsCekcXz/HmpDO8fVzrKl2BGCgLAJwj7FTSnPiRAi33BJva+S4HlBTGseakM7x9XOsKaeVlRD27Im3NXJ8/Rxrqh0BGCiLANxj7JQQgud6QE1pHGtCOsfXz7GmnGhf9xxrqh0BGCiLANxjtc9sIo3jwQk1pXGsCekcXz/HmnKifd1zrKl2BGCgLAIw0HOOByfUlMaxJqRzfP0ca8qJ9nXPsabaEYCBsgjAQM85HpxQUxrHmpBueTm+fqNRfA0dltGo7nWq9m3GsX2ONeXk+LlyAjBQFgEY6DnHgxNqSuNYE9JNw6bbsmtXCKur8+6dMmrfZhzb51hTTo7tIwADZRGAUb3aPyvtufOmphSONSHdyZMxBC8v55u5zTGbXGv4DcFzti4nxzHBsaacHNtHAAbKIgCjeo47t5wc20dNaRxrwnywLiAEz/XAsaacHNtHAAbKIgD3WO0zm7k47txycmwfNaVxrCm34XA47xJ6YRHWBWzPcT1wrCknx/YRgIGyOg3Ak8kkjMfjrMtkMumkdkeOg7aj2vvJsX3UlMaxptyWlpbmXUIvLMK6gO1xYbXuOW57BGCgrN2SwmAw6ORd+tYGnW1Z5Nlrx0HbUe395Ng+akrjWFNuBOA0i7AuYHtcWK17bp8rHw6HYTAYEICBgpgB7jEOmNLU3k+O7aOmNI415TAcDsPS0lJYWloKkk79m9Oht1bruoCdyXVhtZwXVRuP6w2/rpgBBsriM8A9xgFTmtr7ybF91JTGsabcmAFOwzUdkNMijC01IwADZRGAe4wdXJq1tRD27o23NXJcD6gpjWNNuRGAkVPt43kuizC21IwADJRFAJ6D1dW8F6bgeyMXm+OBDjWlcawpVepHWq6//vqkxy36R1qQps/bTJfop34jAANlEYA7troaLyYx7wtaLNIFLmrneKBDTWkca0rFRQ0xD33eZrpEP/UbARgoiwDcseXlSdBF43Dr7eMwOuKx3Hr7OOiicVheZvaljxwPdKgpjWNNqbioIeahz9tMl+infiMAA2XZBWC3y9HnNjoyDnqlLJfREZ/1AOkcD3SoKY1jTTlxYSfkVvs2kwvbXjrHz5UTgIGy7AJw7Tu36Qzw6Mg4jNc8ltERZoD7zHGboaY0jjXlVHv7ciKwpGGdQm6O6xQBGCiLANwxx/Y51oR0jq8fNaVxrCmn2tuXE32Vhn5Cbo7rFAEYKIsA3DHH9jnWlFPtp9U7vn7UlMaxppxqb19O9FUaZsqRm+O2RwAGyiIAd8yxfY415UT7uudY0/Jyvq8Ny7VMv8rMqZ9yclwPXNFXwHw4bnsEYKAsAnDHHNvnWFNOtK97jjVNw6bbUvNXkDmuB67oK2A+HLc9AjBQll0A5nTV7jnWlBPt655jTSdPxhC8vJxv5jbHbHKt4TcEz/XAFX2FnDhVPJ3jtkcABsqyC8C18xxo/WrKifZ1z7GmnGpvXy6OXy/iinUKObE+pXN8s4AADJRFAO6Y407JsaacaF/3HGvKqfb2oXu8WYCcGKP6jQAMlEUA7pjjTsmxppxqP62eizt1r/ZtBnDlOFvniDGq3wjAQFkE4I457pQca0I6Lu7UPbYZYD7Y9tLQT/1GAAbKIgB3zHGn5FgT0uW6uFPOCzuNx/WG3xDYZoB5YdtLQz/1GwEYKGu3pDAYDMJwOJz39r4QHHdKjjWhe6wH6TgNE5gPxqk09FN/DYfDMBgMCMBAQXYzwLVfCMRxp+RYE7rHegDAHeNUmtqPpXJyfEOTGWCgLLsAXPvOzbF9jjWhe6wHyK32C9Che4xTyM1xnSIAA2URgDvm2D7HmtA91gPkxjqVjjcL0rBOITfHdYoADJRFAO6YY/sca8qJU8HS1L4eoHusU+noqzSM58jNcdsjAANlEYA75tg+x5pyqr19uTh+Dgr9xraXjr4C5sNx2yMAA2URgDvm2D7HmnKqvX3oHm8WpGHbS0dfAfPhuO0RgIGyCMAdc2yfY0051d4+dI91Kg39lI6+AubDcdsjAANl2QXg2mdWPAdav5pyqr196B7rVBou7JSOdQo5se2lc/xcOQEYKMsuANfO8SDHsaacam8fusc6hdwILMiJMarfCMBAWQTgjjnulBxryqn29iGvyWQSxuPxGZfRaBykeLvdYyeTybybBGDB1L7fm0wmYbw2tlxyjPkEYKAsAnDHHHdKjjXlVPtp9bnQT1HrwCPLwvgK5MNMeZra9+vjtXHQK2W5jNfOvdMJwEBZBOCOOe6UHGtC91gPopQZ4J0szAAD+TBOpam9n5gBBnAuCMAdc9wpOdaE7rEeAHDHOJWGfuo3AjBQll0Arv00TMedkmNN6B7rAQB3jFNpOFW83wjAQFl2Abj2nZtj+xxrQvdYD5Cb49eLoN8Yp5Cb45sFBGCgLAJwxxzb51gTusd6gNxYp9LxZkEa1ink5rhOEYCBsgjAHXNsn2NNOdV+Wn0uta8H6B7rVDr6Ko3jbB36zXHbIwADZRGAO+bYPseacqq9fbkwA4Xc2PbS0VfAfDhuewRgoCwCcMcc2+dYU061tw9wxbaXjr4C5sNx2yMAA2URgDvm2D7HmnKqvX2AK7a9dPQVMB+O2x4BGCjLLgDXfhqm50DrV1NOtbcPcMW2l46+Qk61H0vl5Pi5cgIwUJZdAK7deHkSLtE4jEfjeKRjsIxH41jT8mTe3VMEB5bAfHABunSMU8iJ9anfCMBAWQTgjo1HzV7JcBmP6lwPOBAA4I43C5AT+71+IwADZRGAO8YMcPc4FQwA6sB4noYA3G8EYKAsAnDHHHdKjjWhe46fgwKANvZXaeinfiMAA2URgDvmuFNyrAndYz0A4I5xKg391G8EYKCs3ZLCYDAIw+Fw3tv7QnDcKTnWhO6xHgBwxziVhn7qr+FwGAaDAQEYKMhuBrj20zAdd0qONaF7rAfIjQs7ITfGqTRse+kcP1fODDBQll0Arn3n5tg+x5rQPdYD5MY6lY7AkoZ1Crk5rlMEYKAsAnDHHNvnWBO6x3qA3Fin0tFXaegn5Oa4ThGAgbIIwB1zbJ9jTTnVflp9LvQTcqt9bMmJvkrDTDlyc9z2CMBAWQTgjjm2z7GmnGpvH+CKbS8dfQXMh+O2RwAGyiIAd8yxfY415VR7+wBXbHvp6CtgPhy3PQIwUJZdAK79NEzPgdavppxqbx/gim0vHX2FnDhVPJ3jtkcABsqyC8C18xxo/WrKqfb2Aa4cv17EFeMUcmJ9Suf4ZgEBGCiLANwxx52SY0051d4+AP3HmwXIif1evxGAgbIIwB1z3Ck51pRT7afVA8CicJytc1T7fr12BGCgLAJwxxx3So41oXvMQAFwx/4qDf3UbwRgoCwCcMccd0qONaF7rAcA3DFOpaGf+o0ADJRFAO6Y407JsSZ0j/UAgDvGqTT0U78RgIGy7AJw7adhOu6UHGtC91gPAMzTZDIJ4/H4jMtoNA5SvN3usZPJZN5Nmpvaj6VycvxcOQEYKMsuANd+EO7YPsea0D3WA+TGBeiwE62D/iyL07ENfDnu+wjAQFkE4I45ts+xJnSP9QC5sU6l482CtBngnSyLPAOMdI7jFAEYKIsA3DHH9jnWlBOngqWpfT1A91in0tFXwHw4bnsEYKAsAnDHHNvnWFNOtbcvF8fPQaHf2PbS0VfAfDhuewRgoCwCcMcc2+dYU061tw9wxbaXjr4C5sNx2yMAA2URgDvm2D7HmnKqvX2AK7a9dPQVsDOrq3F7OddlNIrb3mh07s+1upqnbQRgoCy7AFz7aZiOBzmONeVUe/sAV1zYKR3jFHKqfdtbXQ1h1664zTi/lM9YAAAgAElEQVQtu3blCcEEYKAsuwBcO8eDHMeacqq9fQD6r/bAgm7Vvt+bti/HrG2uZTqTnKPPCcBAWQTgjjnulBxryqn29gEA0Fb7fs+xfTlrIgADZRGAO1b7oO2o9tPqc6GfAKAOte/XHdtHAAb6gwDcsdoHbfQX6wEA1KH28dyxfQRgoD8IwB2rfdBGf7EeAEAdah/PHdtHAAb6wy4A134aZu2DNvqL9QAA6lD7RdUc91cEYKA/7AKw46CWk2P7HGtC91gPkNvaWgh798ZbAMjFcX9FAAb6gwDcMcf2OdaE7rEeIDfWqXS8WQCkcxxbCMBAfxCAO+bYPseacqr9tPpcal8P0D3WqXT0FZDOcXshAAP9QQDumGP7HGvKqfb25cIMFHJj20tHXwHpHLcXAjDQHwTgjjm2z7GmnGpvH+CKbS8dfQWkc9xeCMBAf2QNwKurccM/l2U0igPIaHTuzzUex5qcLC/nbV+OZdrnTjuSnBx3lMAiYNtLR18B6Ry3FwIw0B/ZAvDqagi7dsWN32nZtcsrBE/Dptvi1k85Oe4ogUXAtpeOvkJOtX+kxXF7IQAD/ZEtAE83fGY2z+zkyVjX8rLXbHmt4TcEzx0lsAi4AF06xinkVPv65Ng+AjDQH9kDcK2DkaPa25cL/QTAHW8WIKfa93uO7SMAA/1BAO6x2tuXS+2nggEA0Fb78YFj+wjAwPxdKOl/SXq/pBslPXyLxxGAe6z29qFbKysh7NkTbwEA/VX78YFj+wjAwPw9X9J/af79JElv3uJxBOAeq7196BbrEwDUofbx3LF9BGBg/u4l6eLm31dLev0WjyMA91jt7UO3WJ8AoA61j+eO7SMAAz4eIumvJF2xxe8JwD3GRVOQU+3bC7rHGAXMR+3bnuP+igAM5Pc4Scdm7rtS0ockrUi6TdLTZn7/lZL+UtJjz/C82QLw8vIk6KJxGB0Zh/GaxzI6Mg66aByWlyfnPhoBlXM8oEC/sU6lqz2wADk5ji0EYCCvZ0lalnRX674LJB2WtCTpfElPlfQ5rc/0Xqo483vlNs+dLQCPjoyDXinLZXTEaIQETDkeUKDfWKfS0VdAOsfthQAM5PNqSQcUg247AF8l6c6Zx75D0suaf/83SR+X9L5muW6L52cGGNXj6sZp6Cfk5niQ6oq+AtKNlyfhEo3DeDSOG43BMh6NY00ZjjkJwFh0lza3T9DGAPxCxWDbdp2k39zh8/MZYFSP9QCYD7a9dPQVkG48ajYYw2U8ynFMTQAGpNMD8E9IeufMY14l6Q92+Ly7JYVrrrkmLC0thaWlpfCud70rDIfDcPz48VMb4sGDB8OhQ4dO/byyshL27du3YWO98cYDQRqd2nkfPXo07N+/f8Nj9u/fH44ePXrq59FoFA4cOLDhMfv27QsrrSmoQ4cOhYMHD576+fjx48n1tQ8oDhw4EEaj0anHONTXRn3l6puuB8OhZ31Trv1HfdR3tvXFbe9guPlmz/pC8Om/2FfHw/XXe9YXgnf/Ud9i1TedAb5p303h0M03n5qFXbnjjrDvhhs2zMweuPHGMLr11lM/Hz18OOwfDjc8Zv9wGI4ePnzq59Gtt4YDN9644TH7brghrNxxx6mfD918czh4002nfj72sWPhYl0fjn367rPqv+FwGJaWlsK1114bnvGMZxCAAZ0egF8k6b0zj7lO0tt2+LzMAPcYF01JU/t6AOQ2mUzCp5bjx0jOZbn19vgxlFtvP/fnGh0Zh08tj8NkUudHWhinkFPtxweO2wufAQbymw3AV0n65Mxj3iHppTt8XgJwj9XevlzoJ2BnPrXse1HDTy3XuSEzTiGn2tcnx/YRgIH8ZgPwBZI+Ieklku6p+BVIK5IetsPnJQD3WO3ty4V+AnZmelHDXDO3OZbpbHKtFzVcWwth7954C5yr2vd7ju0jAAP5zQZgSXqkpA9IGkv6qKSrz+J5CcA9Vnv7cuHqxsDOOI4tjjUBrmrfXhzbRwAG+oMA3GO1tw/dYgYKU45ji2NNgKvatxfH9hGAgf4gAPdY7e1Dt1ifMOW4LjjWBLiqfXtxbB8BGOiP3ZLCYDAIw+HQZsPPxbGmnGpvH7rF+oQpx3XBsSbAVe3bi2P7ctU0HA7DYDAgAAMFMQPcY5yyipxq316QznFdcKwJcFX78YHjeMAMMNAfBGAAIQS2F6xzXBcca0oxmUzCeDzOutT6XchAKsfxgAAM9AcBGEAIge0F6xzXBceaUrQOZLMtOfbZQJ85jgcEYKA/CMCoXu2nguXC9oIpx3XBsaYUzAAD+TmOBwRgoD8IwKge60GaEydCuOWWeIvF5rjNONYEYD4cxwMCMNAfBGBUj/UA2BnHbcaxJgDz4TgeEICB/iAAo3qsB8DOOG4zjjUBmA/H8YAADPQH3wPcYysrIezZE2+xtdrXAyA3x23GsSbAVe3HB47jQa6a+B5goDxmgHus9vblQj8BO+O4zTjWBLiqfXtxbB8zwEB/EIB7rPb25UI/ATvjuM041gS4qn17cWwfARjoDwJwj9Xevly4unEa+glTjmOLY02Aq9q3F8f2EYCB/iAA91jt7UO3WJ8w5bguONYEuKp9e3FsHwEY6A8CcI/V3j50i/UJU47rgmNNgKvatxfH9hGAgf4gAPdY7Vd5RLdq316QznFdcKwJcFX78YHjeEAABvqDAAwghMD2gnWO64JjTQDmw3E8IAAD/UEARq9NJpMwHo+zLZPJZN5Nmhu2F0w5rguONQGYD8fxgAAM9MduSWEwGIThcGiz4efiWBPyau0ksiw53gzqK7YXTDmuC441AfOQ+43fPr756zge5KppOByGwWBAAAYKyjcDvDwJl2gcxqNx3PoNlvFoHGta7tfAjnTMAOezthbC3r3xFout5oNLoO9yv/Gb6ziwS47jATPAQH/kC8CjZss3XMYjoxESAMzVfnAJ9BkzwJ7jAQEY6A9mgAEAG9R+cAmg3xzHAwIw0B9cBKvHOGUVQAmOY6djTQDmw3E8IAAD/UEA7rHa2wdgPhzHFseaAMyH43hAAAb6gwDcY7W3D8B8OI4tjjUBmA/H8YAADPQHAbjHam8fgPlwHFscawIwH47jAQEY6A8CcI/V3j50a2UlhD174i0Wm+PY4lgTgPlwHA8IwEB/EIB7rPb2oVusT5hyXBccawIwH47jAQEY6I/dksJgMAjD4dBmw8/Fsaacam8fusX6hCnHdcGxJgDz4Tge5KppOByGwWBAAAYKYga4x06cCOGWW+ItcK5q316QznFdcKwJwHw4jgfMAAP9QQAGEEJge8E6x3XBsSYA8+E4HhCAgf4gAAMIIbC9YJ3juuBYE4D5cBwPCMBAfxCAAYQQuAo01jmOnY41AZgPx/GAAAz0BwEYALCB49jpWBOA+XAcDwjAQH8QgAEAGziOnY41AZiP5eU4HoxGcUxwWEYjAjDQFwTgHuMq0ABKcBw7HWsCMB/TsOm27NoVwurqubePAAyURQDusdrbh3wmk0kYj8dZl8lkMu9moRDHscWxJgDzcfJkDMHLy/lmbnPMJucIvyEQgIHSCMA9Vnv7kE9rZ5ptyTFuwJPj2OJYE4D+cxxbCMBAWQTgHqu9fciHGWDsxHh5Ei7ROIxHBh+sa5bxaBxrWma9A5CP47EUARgoiwDcY7W3D8B8jEfj+X+YbotlPGLAA5CP47EUARgoK1sArv2KfI4cB20A/ccMMIBF4XgsRQAGytotKQwGgzAcDs9pY639inyOHAdtAP3nOLY41gSg/9zGluFwGAaDAQEYKCjbDHCuK/LlvBrfeFxv+A0hhLW1EPbujbcAkIvbAWEInjUB6D/Hr5RkBhgoK1sAzoWDHACYL8dx2LEmACiBAAyURQAGAGzgOA471gQAJRCAgbIIwACADRzHYceaAKAEAjBQFgEYALCB4zjsWBMAlEAABsoiAAMANnAchx1rAoASCMBAWXYB2PFqfACwSBzDpmNNAPrP8biTAAyUZReAkW5lJYQ9e+ItAOTiGDYdawLQf45jCwEYKIsA3GOOgzaA/nMcWxxrAtB/jmMLARgoiwDcY46DNoD+cxxbHGsC0H+OYwsBGCiLANxjjoM2gP5zHFscawLQf45jCwEYKIsA3GOOgzaA/nMcWxxrAtB/jmMLARgoyy4AO16Nz5XjoA2g/xzHFseaAPSf49hCAAbKsgvAjgORK64CDaAEx3HYsSYA/be2FsLevfHWBQEYKGu3pDAYDMJwOJz39h5C4CAHAObNcRx2rAkAchsOh2EwGBCAgYKYAQYAbOA4DjvWBAAlMAMMlEUABgBs4DgOO9YEACUQgIGyCMAAgA0cx2HHmgCgBAIwUBYBGACwgeM47FgTAJRAAAbKsgvAjlfjA4BF4hg2HWsCgBIIwEBZdgEY6XizAEAJy8sxbI5GMXA6LKMRARhAfo5fKUkABsoiAPcYMyIASpiGTbdl164QVlfn3TsAauJ4LEUABsoiAPeY46ANoP9OnowheHk5z6xtrplkwi+A3ByPpQjAQFkE4B5zHLQBYIoxCoA7x3GKAAyURQDuMcdBGwCmGKMAuHMcpwjAQFkE4B5zHLQBYIoxCoA7x3GKAAyUZReAHa/G58px0AaAKcYoAO4cjzsJwEBZdgGYA6Z0J06EcMst8RYA3DCeA8DOEYCBsgjAAIAieJMOAHaOAAyURQAGAAAATBCAgbIIwAAAAIAJAjBQFgEYAAAAMEEABsqyC8COV+MDAAAAukAABsraLSkMBoMwHA7nvb1jh7jADAAAwNlbWwth795462A4HIbBYEAABgqymwFGOk4XB+CMN+kAuHM8lmIGGCiLANxjjoM2AEwxRgFw5zhOEYCBsgjAPeY4aAPAFGMUAHeO4xQBGCiLANxjjoM2AEwxRgFw5zhOEYCBsgjAPeY4aAPAFGMUAHeO4xQBGCjLLgC7XY3PmeOgDQBTjFEA3DmOUwRgoCy7AOw4ELnizQIAzhjPAbhzvFo9ARgoiwAMACiCN+kAYOcIwEBZBGAAAADABAEYKIsADAAAAJggAANlEYABAAAAEwRgoCwCMAAAAGCCAAyUZReAHa/GBwAAgPo4HncSgIGy7AIw0q2shLBnT7wFAADAzjieeUgABsoiAPeY46ANAFO8SQfAneOxFAEYKIsA3GOOgzYATDFGAXDnOE4RgIGyCMA95jhoA8AUYxQAd47jFAEYKIsA3GOOgzYATDFGAXDnOE4RgIGy7AKw49X4XDkO2gAwxRgFwJ3jOEUABsqyC8COA5ErLjADwBnjOQB3a2sh7N0bb10QgIGyCMAAgCJ4kw4Ado4ADJRFAAYAAABMEICBsgjAAIAdm0wmYTweZ10mk8m8mwWgIrnHqa7GKAIwUBYBGACwY60DtGyL074IQP/lHqe6GqMIwEBZBGAAwI4xAwzAHTPAADZjF4Adr8YHAAAAdIEADJS1W1IYDAZhOBzOe3vHDvFmAQAAQD2Gw2EYDAYEYKAguxlgpON0cQAAgLowAwyURQDuMQIwAABAXQjAQFkE4B4jAAMAANSFAAyURQDuMQIwAABAXQjAQFkE4B4jAAMAANSFAAyUZReAV1ZC2LMn3uLMCMAAAAB1IQADZdkFYEJduhMnQrjllngLAACA/iMAA2URgAEAAAATBGCgLAIwAAAAYIIADJRFAAYAAABMEICBsgjAAAAAgAkCMFCWXQDmKtAAAABYVARgoCy7AIx0XAUaAACgLgRgoCwCcI9xujgAAEBdCMBAWQTgHiMAAwAA1IUADJRFAO4xAjAAAEBdCMBAWQTgHiMAAwAA1IUADJRFADY0mUzCeDzedhmNxkGKt9s9djKZzLtZAAAA2AYBGCjLLgCvrYWwd2+8XVStgS/b4vQaAwAAYHMEYKAsuwDMab3pM8A7WZgBBgAA8EcABsoiAAMAAAAmCMBAWQRgAAAAwAQBGCiLAAwAAACYIAADZRGAAQAAABMEYKAsAjAAAABgggAMlGUXgE+cCOGWW+ItAAAAsEgIwEBZnQZgvt4HAAAA2BoBGCir0wDc2qCzLU6z1wAAAMC5IAADZTEDDAAAAJggAANl2X0GGAAAAFhUBGCgLAIwAAAAYIIADJRFAAYAAABMEICBsgjAAAAAgAkCMFAWARgAAAAwQQAGyiIAAwAAACYIwEBZBGAAAADABAEYKIsADAAAAJggAAN5vFDSize5nwAMAAAAmCAAA+fu9ZIOiQAMAAAAWCMAA+fmPEn/TtJzRQAGAAAArBGAgTy+RwRgAAAAwBoBGNjocZKOzdx3paQPSVqRdJukp23yd70KwMPhcN4l9AZ9lYZ+SkM/paOv0tBP6eirNPRTGvopnVtfEYCBdc+StCzprtZ9F0g6LGlJ0vmSnirpc5KumPnbXgXgpaWleZfQG/RVGvopDf2Ujr5KQz+lo6/S0E9p6Kd0bn1FAAaiV0s6oBh02wH4Kkl3zjz2HZJeNnMfAbhS9FUa+ikN/ZSOvkpDP6Wjr9LQT2nop3RufUUABqJLm9snaGMAfqGk98089jpJv5n4vLslhdFoFMbjsc0yGAzmXkNfFvqKfqKf6CvnhX6ir+gn+sl9ceur0WhEAAZaZgPwT0h658xjXiXpDxKf73LFDYyFhYWFhYWFhYWFxWe5XABOC8AvkvTemcdcJ+ltic93nuLGtZuFhYWFhYWFhYWFxWK5XPE4HVh4swH4KkmfnHnMOyS9tLOKAAAAAAAoYDYAXyDpE5JeIumeil+BtCLpYd2XBgAAAFh7+LwLALAzswFYkh4p6QOSxpI+Kunqros6g1+UNJH0mNZ9z1YM6Xe1lpXmca9oHjNR/Dqn2cccaH7/hOYx09+Ntf49yM8t1pr5aPfFiqQ7JL1O0j1aj7lC0v+UdLR53G2SXj7zmJpNJH3NzH2/JOmfJD26dd/TJL1fcX05Jmm/pGs6qK9L2/XFQcWrybf9cfN3l7bue5Ckk5IeIen1ih+tkKRXSlqV9FUzz/G1zXPUYCLpnyU9YJPfHWh+f//m549L+uAmj/tlSW8uUZyh9hg1Vvy6vvdL+obm9w/TxvH6rubxh7X5txPUZLu+kbYfv6f9d38thu367Bsl/WPr8VdosfZ/D5D0RsU+WJF0u6TXSNrV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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAYO//A21VZHdekb66AAAAAElFTkSuQmCC\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_candi_votes = df[['Party', 'Votes']].copy()\n",
    "party_candi_votes['Party'] = party_candi_votes['Party'].apply(lambda x: COMMITTEES_SHORT[x])\n",
    "party_candi_votes = party_candi_votes.pivot(columns='Party', values='Votes')\n",
    "\n",
    "# Box plot below shows distribution of all candidates votes per party with whiskers set from 0% to 99% \n",
    "# and outliers covering top 1%. Since votes numbers differ significantly between candidates, log scale is applied.\n",
    "ax9 = party_candi_votes.plot.box(whis=[0, 99], figsize=(12, 12), meanline=True, showmeans=True, \n",
    "                                 meanprops=dict(linestyle='-', color='green'))\n",
    "ax9.grid(axis='y', alpha=0.5)\n",
    "ax9.set_ylabel('Votes number (log scale)', fontweight=555, fontsize=15)\n",
    "ax9.set_xlabel('Committee', fontweight=555, fontsize=15)\n",
    "ax9.set_title('Votes distribution. (Outliers = Top 1%)', fontweight=555, fontsize=18)\n",
    "plt.yscale('log')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}