scrape-and-compute-all.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from bs4 import BeautifulSoup\n",
    "import urllib\n",
    "from collections import OrderedDict\n",
    "from math import ceil, floor\n",
    "from operator import itemgetter\n",
    "import matplotlib.pyplot as plt\n",
    "from os.path import isfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scrape Wikipedia Page"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, I could have also scraped the number of electors per state from the election pages. I decided to type them in, instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list of state abbreviations\n",
    "states = ['AL','AK','AZ','AR','CA','CO','CT','DC','DE','FL',\n",
    "          'GA','HI','ID','IL','IN','IA','KS','KY','LA','ME',\n",
    "          'MD','MA','MI','MN','MS','MO','MT','NE','NV','NH',\n",
    "          'NJ','NM','NY','NC','ND','OH','OK','OR','PA','RI',\n",
    "          'SC','SD','TN','TX','UT','VT','VA','WA','WV','WI','WY', 'US']\n",
    "\n",
    "def append_sum(ec, ignore_senate_ec_votes=False):\n",
    "    subtract = 100 if ignore_senate_ec_votes else 0\n",
    "    ec.append(sum(ec)-subtract)\n",
    "\n",
    "def build_ec_votes_df(ignore_senate_ec_votes=False):\n",
    "    ev = pd.DataFrame.from_dict(\n",
    "         {1992: [9,  3,  8,  6,  54, 8,  8,  3,  3,  25,\n",
    "                 13, 4,  4,  22, 12, 7,  6,  8,  9,  4,\n",
    "                 10, 12, 18, 10, 7,  11, 3,  5,  4,  4,\n",
    "                 15, 5,  33, 14, 3,  21, 8,  7,  23, 4,\n",
    "                 8,  3,  11, 32, 5,  3,  13, 11, 5,  11, 3, 538],\n",
    "          2004: [9,  3,  10, 6,  55, 9,  7,  3,  3,  27,\n",
    "                 15, 4,  4,  21, 11, 7,  6,  8,  9,  4,\n",
    "                 10, 12, 17, 10, 6,  11, 3,  5,  5,  4,\n",
    "                 15, 5,  31, 15, 3,  20, 7,  7,  21, 4,\n",
    "                 8,  3,  11, 34, 5,  3,  13, 11, 5,  10, 3, 538],\n",
    "          2012: [9,  3,  11, 6,  55, 9,  7,  3,  3,  29,\n",
    "                 16, 4,  4,  20, 11, 6,  6,  8,  8,  4,\n",
    "                 10, 11, 16, 10, 6,  10, 3,  5,  6,  4,\n",
    "                 14, 5,  29, 15, 3,  18, 7,  7,  20, 4,\n",
    "                 9,  3,  11, 38, 6,  3,  13, 12, 5,  10, 3, 538],\n",
    "          'states': states})\n",
    "    ev.set_index(keys='states', inplace=True)\n",
    "    if ignore_senate_ec_votes:\n",
    "        for series in ev.columns:\n",
    "            ev[series] -= 2\n",
    "            ev[series][-1] -= 100\n",
    "    ev[1996]=ev[1992]\n",
    "    ev[2000]=ev[1992]\n",
    "    ev[2016]=ev[2012]\n",
    "    ev[2008]=ev[2004]\n",
    "    ev.sort_index(axis=1, inplace=True)\n",
    "    return ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1992</th>\n",
       "      <th>1996</th>\n",
       "      <th>2000</th>\n",
       "      <th>2004</th>\n",
       "      <th>2008</th>\n",
       "      <th>2012</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>states</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>538</td>\n",
       "      <td>538</td>\n",
       "      <td>538</td>\n",
       "      <td>538</td>\n",
       "      <td>538</td>\n",
       "      <td>538</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        1992  1996  2000  2004  2008  2012  2016\n",
       "states                                          \n",
       "WA        11    11    11    11    11    12    12\n",
       "WV         5     5     5     5     5     5     5\n",
       "WI        11    11    11    10    10    10    10\n",
       "WY         3     3     3     3     3     3     3\n",
       "US       538   538   538   538   538   538   538"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_votes = build_ec_votes_df()\n",
    "ec_votes.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_headings(head_row, num_candidates):\n",
    "    \"\"\"\n",
    "    Parse the table header row for the candidate names.\n",
    "    head_row - the header row element containing cells with the candidate names\n",
    "    num_candidates - the number of candidate columns in the \"Results_by_state\" table, including \"Other\"\n",
    "    returns: list of length num_candidates with the candidate names\n",
    "    \"\"\"\n",
    "    headings = []\n",
    "    for cell in head_row.find_all('th', attrs={'colspan': '3'}):\n",
    "        children = list(cell.children)\n",
    "        if len(children) == 3:\n",
    "            headings.append((children[0].strip(), children[2].strip()))\n",
    "        else:\n",
    "            headings.append((children[0], None))\n",
    "    headings = headings[:num_candidates]\n",
    "    headings.append(('Total', None))\n",
    "    return headings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_table_rows(year, num_candidates):\n",
    "    \"\"\"\n",
    "    Scrape the U.S. Presidential election Wikipedia page for the results by state. header rows of the \n",
    "\n",
    "    year - string containing 4-digit election year\n",
    "    num_candidates - the number of candidate columns in the \"Results_by_state\" table, including \"Other\"\n",
    "    returns: a tuple; first element is a list of length num_candidates with the candidate names; second\n",
    "             element is a list of the table row elements needing to be parsed for the vote counts\n",
    "    \"\"\"\n",
    "    page = BeautifulSoup(urllib.request.urlopen(\n",
    "            'https://en.wikipedia.org/wiki/United_States_presidential_election,_'+str(year)).read(), \"html.parser\")\n",
    "    header = page.find('span', id='Results_by_state').parent\n",
    "    table_container = header.find_next_sibling('div')\n",
    "    table = table_container.table\n",
    "    if table is None:\n",
    "        table_container = table_container.find_next_sibling('div')\n",
    "        table = table_container.table\n",
    "    head_row = table.find('tr')\n",
    "    all_rows = list(table.find_all('tr'))\n",
    "    vote_rows = all_rows[2:]\n",
    "    return get_headings(head_row, num_candidates), vote_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_and_convert(count_text):\n",
    "    \"\"\"\n",
    "    Clean up vote count text, and return an actual number.\n",
    "    count_text - the text from a vote count table cell\n",
    "    returns: int representing number of votes (N/A or blank imply zero)\n",
    "    \"\"\"\n",
    "    clean = count_text.strip().replace(',','').replace('.', '').replace('-','0').replace(\n",
    "                'N/A', '0').replace('★', '').replace('–', '').replace('−', '')\n",
    "    return int(clean) if len(clean) else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_for_year(year):\n",
    "    return 'state-vote-data-{}.csv'.format(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_in_data(year, num_candidates, total_column):\n",
    "    \"\"\"\n",
    "    Scrape the U.S. Presidential election Wikipedia for the given year. Requires a couple of numbers describing the\n",
    "    shape of the table.\n",
    "\n",
    "    year - string containing 4-digit election year\n",
    "    num_candidates - the number of candidate columns in the \"Results_by_state\" table, including \"Other\"\n",
    "    total_column - the column number that contains the total votes for each state/district, counting from zero\n",
    "    returns: a Pandas DataFrame, indexed by state/district, with columns for each candidate, total vote, and\n",
    "             state total electoral college votes\n",
    "    \"\"\"\n",
    "    filename = file_for_year(year)\n",
    "    if isfile(filename):\n",
    "        return pd.read_csv(filename, index_col=0)\n",
    "    headings, vote_rows = get_table_rows(year, num_candidates)\n",
    "    data = OrderedDict()\n",
    "    columns_with_vote_counts = list(range(2, 3*num_candidates, 3))\n",
    "    columns_with_vote_counts.append(total_column)\n",
    "    abbr_column = total_column + 1\n",
    "    vote_rows = list(zip(vote_rows, ['td'] * len(vote_rows)))\n",
    "    vote_rows[-1] = (vote_rows[-1][0], 'th') # last row uses <th> instead of <td>\n",
    "    for row, cell_type in vote_rows:\n",
    "        cells = list(row.find_all(cell_type))\n",
    "        try:\n",
    "            abbr = cells[abbr_column].string.strip()\n",
    "        except IndexError:\n",
    "            print('{}, {}, {}'.format(year, abbr_column, cells))\n",
    "            return None\n",
    "        if len(abbr)==2 or len(abbr)==6:\n",
    "            abbr = abbr[:2]\n",
    "            data[abbr] = [clean_and_convert(cells[i].text) for i in columns_with_vote_counts]\n",
    "    abbreviations  = []\n",
    "    for h in headings:\n",
    "        abbreviations.append(''.join([s[0] for s in h[0].split()]))\n",
    "    vote_data = pd.DataFrame.from_dict(data, orient='index')\n",
    "    vote_data.columns=abbreviations\n",
    "    return vote_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_totals(data, year):\n",
    "    state_totals_valid = True\n",
    "    for idx, state in data.iterrows():\n",
    "        calculated_sum = state.iloc[:-1].sum()\n",
    "        reported_sum = state['T']\n",
    "        if calculated_sum != reported_sum:\n",
    "            state_totals_valid = False\n",
    "            print('Uh Oh! {} in year {} data had reported vote total {}, but I calculated {}!'.format(\n",
    "                idx, year, reported_sum, calculated_sum))\n",
    "            print(state)\n",
    "            print()\n",
    "    candidate_totals_valid = True\n",
    "    trans = data.transpose()\n",
    "    for idx, candidate in trans.iterrows():\n",
    "        calculated_sum = candidate.iloc[:-1].sum()\n",
    "        reported_sum = candidate['US']\n",
    "        if calculated_sum != reported_sum:\n",
    "            if candidate_totals_valid:\n",
    "                print(\"For our purposes, errors in candidates' national totals can't affect the \")\n",
    "                print(\"EC allocation calculations. Only errors in total votes cast in each state \")\n",
    "                print(\"can affect the results.\")\n",
    "                print()\n",
    "            candidate_totals_valid = False\n",
    "            print('Uh Oh! {} in year {} data had reported vote total {}, but I calculated {}!'.format(\n",
    "                idx, year, reported_sum, calculated_sum))\n",
    "    if not candidate_totals_valid:\n",
    "        print()\n",
    "    return state_totals_valid and candidate_totals_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vote_data = OrderedDict()\n",
    "shapes = [(1996, 6, 22), (2000, 8, 28), (2004, 7, 25), (2008, 7, 25), (2012, 5, 19), (2016, 6, 22)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1996 Validated\n",
      "--------------\n",
      "BC    47400125\n",
      "BD    39198755\n",
      "RP     8085402\n",
      "RN      685297\n",
      "HB      485798\n",
      "O       420024\n",
      "T     96275401\n",
      "Name: US, dtype: int64\n",
      "\n",
      "2000 Validated\n",
      "--------------\n",
      "GWB     50456002\n",
      "AG      50999897\n",
      "RN       2882955\n",
      "PB        448895\n",
      "HB        384431\n",
      "HP         98020\n",
      "JH         83714\n",
      "O          51186\n",
      "T      105405100\n",
      "Name: US, dtype: int64\n",
      "\n",
      "2004 Validated\n",
      "--------------\n",
      "GWB     62040610\n",
      "JK      59028444\n",
      "RN        465151\n",
      "MB        397265\n",
      "MP        143630\n",
      "DC        119859\n",
      "O          99887\n",
      "T      122294846\n",
      "Name: US, dtype: int64\n",
      "\n",
      "2008 Validated\n",
      "--------------\n",
      "BO     69498516\n",
      "JM     59948323\n",
      "RN       739034\n",
      "BB       523715\n",
      "CB       199750\n",
      "CM       161797\n",
      "O        242685\n",
      "T     131313820\n",
      "Name: US, dtype: int64\n",
      "\n",
      "Uh Oh! US in year 2012 data had reported vote total 129085410, but I calculated 129085407!\n",
      "BO     65915795\n",
      "MR     60933504\n",
      "GJ      1275971\n",
      "JS       469627\n",
      "O        490510\n",
      "T     129085410\n",
      "Name: US, dtype: int64\n",
      "\n",
      "For our purposes, errors in candidates' national totals can't affect the \n",
      "EC allocation calculations. Only errors in total votes cast in each state \n",
      "can affect the results.\n",
      "\n",
      "Uh Oh! BO in year 2012 data had reported vote total 65915795, but I calculated 65915794!\n",
      "Uh Oh! T in year 2012 data had reported vote total 129085410, but I calculated 129085406!\n",
      "\n",
      "Uh Oh! US in year 2016 data had reported vote total 136669276, but I calculated 136670976!\n",
      "HC     65853514\n",
      "DT     62984828\n",
      "GJ      4489341\n",
      "JS      1457218\n",
      "EM       731991\n",
      "O       1154084\n",
      "T     136669276\n",
      "Name: US, dtype: int64\n",
      "\n",
      "For our purposes, errors in candidates' national totals can't affect the \n",
      "EC allocation calculations. Only errors in total votes cast in each state \n",
      "can affect the results.\n",
      "\n",
      "Uh Oh! HC in year 2016 data had reported vote total 65853514, but I calculated 65853516!\n",
      "Uh Oh! DT in year 2016 data had reported vote total 62984828, but I calculated 62984825!\n",
      "Uh Oh! GJ in year 2016 data had reported vote total 4489341, but I calculated 4489221!\n",
      "Uh Oh! JS in year 2016 data had reported vote total 1457218, but I calculated 1457216!\n",
      "Uh Oh! EM in year 2016 data had reported vote total 731991, but I calculated 731788!\n",
      "Uh Oh! O in year 2016 data had reported vote total 1154084, but I calculated 1152671!\n",
      "Uh Oh! T in year 2016 data had reported vote total 136669276, but I calculated 136669237!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for year, num_candidates, total_column in shapes:\n",
    "    vote_data[year] = read_in_data(year, num_candidates, total_column)\n",
    "    if vote_data and check_totals(vote_data[year], year):\n",
    "        print('{} Validated'.format(year))\n",
    "        print('--------------')\n",
    "        print(vote_data[year].loc['US'])\n",
    "        print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_data(year, df):\n",
    "    filename = 'state-vote-data-{}.csv'.format(year)\n",
    "    df.to_csv(filename, sep=',')\n",
    "\n",
    "for year, data in vote_data.items():\n",
    "    export_data(year, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def partial_result_from_dict(data, category):\n",
    "    \"\"\"\n",
    "    Prepare part of the final result from the given data, adding a category column to the index.\n",
    "    data - a dict-like object mapping states/districts to counts with the partial result, \n",
    "           e.g., {'state abbr': int_count, ... }\n",
    "    category - a string labelling the type of data, which is placed in a MultiIndex\n",
    "    \"\"\"\n",
    "    result = pd.DataFrame.from_dict(data, orient='index')\n",
    "    result.index.name = 'State'\n",
    "    result['Category'] = category\n",
    "    result.set_index('Category', append=True, inplace=True)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fair_efficient(df, ev):\n",
    "    \"\"\"\n",
    "    Compute a DataFrame representing fair and efficient allocation of Electoral College votes, and the\n",
    "    number of wasted popular votes if fair and efficient allocation is used.\n",
    "\n",
    "    df - a Pandas DataFrame, indexed by state/district, with columns for each candidate, total vote, and\n",
    "         state total electoral college votes\n",
    "    ev - a Pandas Series, indexed by state/district, containing the number of electoral votes each state gets to allocate\n",
    "    returns: a DataFrame multi-indexed by state/district and data category with candidate columns; data category = 'EC'\n",
    "             gives Electoral College allocations ; data category = 'Wasted' gives wasted popular votes, i.e., votes that\n",
    "             didn't end up counting towards an Elector\n",
    "    \"\"\"\n",
    "    ec_votes = OrderedDict()\n",
    "    wasted = OrderedDict()\n",
    "    for idx, st in df.iterrows():\n",
    "        if idx == 'US':\n",
    "            continue\n",
    "        c = OrderedDict(st.loc[:'O'])\n",
    "        e = OrderedDict()\n",
    "        r = {}\n",
    "        E = ev[idx]\n",
    "        V = st.loc['T']\n",
    "        if V == 0:\n",
    "            V = st.iloc[:-1].sum()\n",
    "        for candidate, pop_votes in c.items():\n",
    "            e[candidate] = floor(E * pop_votes / V)\n",
    "            r[candidate] = ceil(pop_votes - V * e[candidate] / E)\n",
    "        r = OrderedDict(sorted(r.items(), key=itemgetter(1), reverse=True))\n",
    "        remainder = E - sum(e.values())\n",
    "        for candidate in r.keys():\n",
    "            if candidate != 'O': # not mappable to a single candidate\n",
    "                e[candidate] += 1\n",
    "                remainder -= 1\n",
    "                r[candidate] = 0\n",
    "                if remainder == 0:\n",
    "                    break\n",
    "        ec_votes[st.name] = e\n",
    "        wasted[st.name] = r\n",
    "    ec_votes = partial_result_from_dict(ec_votes, 'EC')\n",
    "    wasted = partial_result_from_dict(wasted, 'Wasted')\n",
    "    result = pd.concat([ec_votes, wasted], sort=True)\n",
    "    result.sort_index(level=['State', 'Category'], inplace=True)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wta(df, ev):\n",
    "    \"\"\"\n",
    "    Compute a DataFrame representing winner-take-all allocation (WTA) of Electoral College votes, and the\n",
    "    number of wasted popular votes if WTA allocation is used.\n",
    "\n",
    "    df - a Pandas DataFrame, indexed by state/district, with columns for each candidate, total vote, and\n",
    "         state total electoral college votes\n",
    "    ev - a Pandas Series, indexed by state/district, containing the number of electoral votes each state gets to allocate\n",
    "    returns: a DataFrame multi-indexed by state/district and data category with candidate columns; data category = 'EC'\n",
    "             gives Electoral College allocations ; data category = 'Wasted' gives wasted popular votes, i.e., votes that\n",
    "             didn't end up counting towards an Elector\n",
    "    \"\"\"\n",
    "    ec_votes = OrderedDict()\n",
    "    wasted = OrderedDict()\n",
    "    for idx, st in df.iterrows():\n",
    "        if idx == 'US':\n",
    "            continue\n",
    "        c = st.loc[:'O']\n",
    "        e = pd.Series(0, c.index)\n",
    "        r = c.copy()\n",
    "        E = ev[idx]\n",
    "        V = st.loc['T']\n",
    "        if V == 0:\n",
    "            V = st.iloc[:-1].sum()\n",
    "        idxmax = c.idxmax()\n",
    "        e[idxmax] = E\n",
    "        r[idxmax] = 0\n",
    "        r[idxmax] = c.max() - r.max() - 1\n",
    "        ec_votes[st.name] = e\n",
    "        wasted[st.name] = r\n",
    "    ec_votes = partial_result_from_dict(ec_votes, 'EC')\n",
    "    wasted = partial_result_from_dict(wasted, 'Wasted')\n",
    "    result = pd.concat([ec_votes, wasted], sort=True)\n",
    "    result.sort_index(level=['State', 'Category'], inplace=True)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_ec_allocations(methods, year, data, elec_votes):\n",
    "    \"\"\"\n",
    "    Calculate a set of Electoral College allocations for comparison purposes.\n",
    "    \n",
    "    methods - an OrderedDict of functions for calculating allocations, with string keys used for labelling results\n",
    "    year - 4-digit election year (int)\n",
    "    data - a Pandas DataFrame, indexed by state/district, with columns for each candidate, total vote, and\n",
    "         state total electoral college votes\n",
    "    elec_votes - a Pandas DataFrame, indexed by state/district, with columns for each presidential election year,\n",
    "         containing each state's number of electoral college votes to allocate\n",
    "    returns: a 2-tuple; the first element is an OrderedDict using the same keys as methods, with DataFrames containing\n",
    "         the full state-by-state allocation results; the second elment is a summary DataFrame distilling all methods\n",
    "         for the nation as a whole, i.e., tells you what the final EC vote counts come to\n",
    "    \"\"\"\n",
    "    full_results = OrderedDict()\n",
    "    cols = []\n",
    "    total = data['T']['US']\n",
    "    for key, fn in methods.items():\n",
    "        result = fn(data, elec_votes[year])\n",
    "        full_results[key] = result\n",
    "        ec = result.loc[(slice(None), ['EC']),:].sum()\n",
    "        cols.append(ec)\n",
    "        wasted = result.loc[(slice(None), ['Wasted']),:].sum()\n",
    "        cols.append(wasted)\n",
    "        effgap = (wasted.sum() - 2 * wasted)/total\n",
    "#         effgap.loc[ec == 0] = np.nan\n",
    "        cols.append(effgap)\n",
    "        summary = pd.concat(cols, axis=1)\n",
    "    data_labels = ['Electors', 'Wasted Votes', 'Eff. Gap']\n",
    "    col_labels = [(m, dl) for m in methods.keys() for dl in data_labels]\n",
    "    summary.columns = pd.MultiIndex.from_tuples(col_labels)\n",
    "    return full_results, summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_ec_summary(year, data, elec_votes, show_state_data=False):\n",
    "    \"\"\"\n",
    "    Print a useful summary of the calculations. By default, suppresses outputing the full dataset.\n",
    "\n",
    "    year - 4-digit election year (int)\n",
    "    data - a Pandas DataFrame, indexed by state/district, with columns for each candidate, total vote, and\n",
    "         state total electoral college votes\n",
    "    elec_votes - a Pandas DataFrame, indexed by state/district, with columns for each presidential election year, containing\n",
    "         each state's number of electoral college votes to allocate\n",
    "    show_state_data - whether to show the full computed results by state/district; defaults to False\n",
    "    \"\"\"\n",
    "    pd.options.display.max_rows = 102\n",
    "    print(year)\n",
    "    print('====')\n",
    "    methods = OrderedDict([('WTA', wta), ('FnE', fair_efficient)])\n",
    "    full_results, summary = calc_ec_allocations(methods, year, data, elec_votes)\n",
    "    print(summary)\n",
    "    print()\n",
    "    for key in methods.keys():\n",
    "        print('{} Total Wasted Votes: {}'.format(key, summary[key,'Wasted Votes'].sum()))\n",
    "    print()\n",
    "    if show_state_data:\n",
    "        for key, result in full_results.items():\n",
    "            print('{} Results by State:'.format(key))\n",
    "            print(result)\n",
    "            print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wasted votes are defined as votes for a candidate that didn't count towards allocating any electors.\n",
    "Efficiency gap is a measure of unfair skew for a candidate. I invert the usual sign, and calculate it\n",
    "as all other wasted votes minus a particular candates vote, all divided by the total votes cast. A\n",
    "positive number, and indicates that the candidate had fewer wasted votes than the sum of the\n",
    "other candidates' wasted votes. Negative means unfavorable. Magnitude matters. A fair allocation\n",
    "of Electors should keep all efficiency gaps under 7% to 10%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1996\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "BC      379     20694429  0.179459      267       808881  0.027152\n",
      "BD      159     28295406  0.021558      224       905165  0.025152\n",
      "HB        0       485798  0.599268        0       485798  0.033863\n",
      "O         0       420024  0.600634        0       420024  0.035230\n",
      "RN        0       685297  0.595124        1       499751  0.033573\n",
      "RP        0      8085402  0.441396       46      1112185  0.020851\n",
      "\n",
      "WTA Total Wasted Votes: 58666356\n",
      "FnE Total Wasted Votes: 4231804\n",
      "\n",
      "2000\n",
      "====\n",
      "         WTA                             FnE                       \n",
      "    Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "AG       267     28317499  0.040316      262       863577  0.020549\n",
      "GWB      271     28617825  0.034618      263       765498  0.022410\n",
      "HB         0       384431  0.570330        0       384431  0.029641\n",
      "HP         0        98020  0.575764        0        98020  0.035075\n",
      "JH         0        83714  0.576036        0        83714  0.035347\n",
      "O          0        51186  0.576653        0        51186  0.035964\n",
      "PB         0       448895  0.569107        0       448895  0.028418\n",
      "RN         0      2882955  0.522922       13      1197844  0.014207\n",
      "\n",
      "WTA Total Wasted Votes: 60884525\n",
      "FnE Total Wasted Votes: 3893165\n",
      "\n",
      "2004\n",
      "====\n",
      "         WTA                             FnE                       \n",
      "    Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "DC         0       119859  0.562456        0       119859  0.024696\n",
      "GWB      286     35491836 -0.016015      280       858574  0.012615\n",
      "JK       252     32307540  0.036061      258      1175528  0.007432\n",
      "MB         0       397265  0.557919        0       397265  0.020159\n",
      "MP         0       143630  0.562067        0       143630  0.024307\n",
      "O          0        99887  0.562782        0        99887  0.025022\n",
      "RN         0       465151  0.556809        0       465151  0.019049\n",
      "\n",
      "WTA Total Wasted Votes: 69025168\n",
      "FnE Total Wasted Votes: 3259894\n",
      "\n",
      "2008\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "BB        0       523715  0.573062        0       523715  0.024099\n",
      "BO      364     30576671  0.115335      289       979528  0.017156\n",
      "CB        0       199750  0.577996        0       199750  0.029033\n",
      "CM        0       161797  0.578574        0       161797  0.029611\n",
      "JM      174     43854738 -0.086899      248      1473774  0.009628\n",
      "O         0       242685  0.577342        0       242685  0.028379\n",
      "RN        0       739034  0.569783        1       630653  0.022470\n",
      "\n",
      "WTA Total Wasted Votes: 76298390\n",
      "FnE Total Wasted Votes: 4211902\n",
      "\n",
      "2012\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "BO      332     30066196  0.114808      276      1044675  0.015143\n",
      "GJ        0      1275971  0.560873        1      1132750  0.013778\n",
      "JS        0       469627  0.573366        0       469627  0.024052\n",
      "MR      206     42650154 -0.080163      261       906515  0.017283\n",
      "O         0       490510  0.573043        0       490510  0.023729\n",
      "\n",
      "WTA Total Wasted Votes: 74952458\n",
      "FnE Total Wasted Votes: 4044077\n",
      "\n",
      "2016\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "DT      305     30530954  0.153510      261       437117  0.039812\n",
      "EM        0       731788  0.589587        1       543217  0.038259\n",
      "GJ        0      4489221  0.534601       14      2089651  0.015629\n",
      "HC      233     43680181 -0.038914      261       893253  0.033137\n",
      "JS        0      1457216  0.578971        1      1199369  0.028657\n",
      "O         0      1152671  0.583428        0      1152671  0.029340\n",
      "\n",
      "WTA Total Wasted Votes: 82042031\n",
      "FnE Total Wasted Votes: 6315278\n",
      "\n",
      "WTA Results by State:\n",
      "                     DT      EM      GJ       HC      JS       O\n",
      "State Category                                                  \n",
      "AK    EC              3       0       0        0       0       0\n",
      "      Wasted      46932       0   18725   116454    5735   14307\n",
      "AL    EC              9       0       0        0       0       0\n",
      "      Wasted     588707       0   44467   729547    9391   21712\n",
      "AR    EC              6       0       0        0       0       0\n",
      "      Wasted     304377   13255   29829   380494    9473   12712\n",
      "AZ    EC             11       0       0        0       0       0\n",
      "      Wasted      91233   17449  106327  1161167   34345    1476\n",
      "CA    EC              0       0       0       55       0       0\n",
      "      Wasted    4483810   39596  478500  4269977  278657  147244\n",
      "CO    EC              0       0       0        9       0       0\n",
      "      Wasted    1202484   28917  144121   136385   38437   27418\n",
      "CT    EC              0       0       0        7       0       0\n",
      "      Wasted     673215    2108   48676   224356   22841     508\n",
      "DC    EC              0       0       0        3       0       0\n",
      "      Wasted      12723       0    4906   270106    4258    6551\n",
      "DE    EC              0       0       0        3       0       0\n",
      "      Wasted     185127     706   14757    50475    6103    1518\n",
      "FL    EC             29       0       0        0       0       0\n",
      "      Wasted     112910       0  207043  4504975   64399   25736\n",
      "GA    EC             16       0       0        0       0       0\n",
      "      Wasted     211140   13017  125306  1877963    7674    1668\n",
      "HI    EC              0       0       0        4       0       0\n",
      "      Wasted     128847       0   15954   138043   12737    4508\n",
      "IA    EC              6       0       0        0       0       0\n",
      "      Wasted     147313   12366   59186   653669   11479   28348\n",
      "ID    EC              4       0       0        0       0       0\n",
      "      Wasted     219289   46476   28331   189765    8496    8132\n",
      "IL    EC              0       0       0       20       0       0\n",
      "      Wasted    2146015   11655  209596   944713   76802    1627\n",
      "IN    EC             11       0       0        0       0       0\n",
      "      Wasted     524159       0  133993  1033126    7841    2712\n",
      "KS    EC              6       0       0        0       0       0\n",
      "      Wasted     244012    6520   55406   427005   23506     947\n",
      "KY    EC              8       0       0        0       0       0\n",
      "      Wasted     574116   22780   53752   628854   13913    1879\n",
      "LA    EC              8       0       0        0       0       0\n",
      "      Wasted     398483    8547   37978   780154   14031    9684\n",
      "MA    EC              0       0       0       11       0       0\n",
      "      Wasted    1090893    2719  138018   904302   47661   50559\n",
      "MD    EC              0       0       0       10       0       0\n",
      "      Wasted     943169    9630   79605   734758   35945   35169\n",
      "ME    EC              0       0       0        4       0       0\n",
      "      Wasted     335593    1887   38105    22141   14251     356\n",
      "MI    EC             16       0       0        0       0       0\n",
      "      Wasted      10703    8177  172136  2268839   51463   19126\n",
      "MN    EC              0       0       0       10       0       0\n",
      "      Wasted    1322951   53076  112972    44764   36985   51113\n",
      "MO    EC             10       0       0        0       0       0\n",
      "      Wasted     523442    7071   97359  1071068   25419   13177\n",
      "MS    EC              6       0       0        0       0       0\n",
      "      Wasted     215582       0   14435   485131    3731    5346\n",
      "MT    EC              3       0       0        0       0       0\n",
      "      Wasted     101530    2297   28037   177709    7970    1894\n",
      "NC    EC             15       0       0        0       0       0\n",
      "      Wasted     173314       0  130126  2189316   12105   47386\n",
      "ND    EC              3       0       0        0       0       0\n",
      "      Wasted     123035       0   21434    93758    3780    8594\n",
      "NE    EC              5       0       0        0       0       0\n",
      "      Wasted     211466       0   38946   284494    8775   16051\n",
      "NH    EC              0       0       0        4       0       0\n",
      "      Wasted     345790    1064   30777     2735    6496   11643\n",
      "NJ    EC              0       0       0       14       0       0\n",
      "      Wasted    1601933       0   72477   546344   37772   13586\n",
      "NM    EC              0       0       0        5       0       0\n",
      "      Wasted     319667    5825   74541    65566    9879    3173\n",
      "NV    EC              0       0       0        6       0       0\n",
      "      Wasted     512058       0   37384    27201       0   36683\n",
      "NY    EC              0       0       0       29       0       0\n",
      "      Wasted    2819534   10373  176598  1736589  107934   50890\n",
      "OH    EC             18       0       0        0       0       0\n",
      "      Wasted     446840   12574  174498  2394164   46271   27975\n",
      "OK    EC              7       0       0        0       0       0\n",
      "      Wasted     528760       0   83481   420375       0       0\n",
      "OR    EC              0       0       0        7       0       0\n",
      "      Wasted     782403       0   94231   219702   50002   72594\n",
      "PA    EC             20       0       0        0       0       0\n",
      "      Wasted      44291    6472  146715  2926441   49941   65176\n",
      "RI    EC              0       0       0        4       0       0\n",
      "      Wasted     180543     516   14746    71981    6220    9594\n",
      "SC    EC              9       0       0        0       0       0\n",
      "      Wasted     300015   21016   49204   855373   13034    9011\n",
      "SD    EC              3       0       0        0       0       0\n",
      "      Wasted     110262       0   20850   117458       0    4064\n",
      "TN    EC             11       0       0        0       0       0\n",
      "      Wasted     652229   11991   70397   870695   15993   16026\n",
      "TX    EC             38       0       0        0       0       0\n",
      "      Wasted     807178   42366  283492  3877868   71558    8895\n",
      "UT    EC              6       0       0        0       0       0\n",
      "      Wasted     204554  243690   39608   310676    9438   12787\n",
      "VA    EC              0       0       0       13       0       0\n",
      "      Wasted    1769443   54054  118274   212029   27638   33749\n",
      "VT    EC              0       0       0        3       0       0\n",
      "      Wasted      95369     639   10078    83203    6758   23650\n",
      "WA    EC              0       0       0       12       0       0\n",
      "      Wasted    1221747       0  160879   520970   58417  133258\n",
      "WI    EC             10       0       0        0       0       0\n",
      "      Wasted      22747   11855  106674  1382536   31072   38729\n",
      "WV    EC              5       0       0        0       0       0\n",
      "      Wasted     300576    1104   23004   188794    8075    4075\n",
      "WY    EC              3       0       0        0       0       0\n",
      "      Wasted     118445       0   13287    55973    2515    9655\n",
      "\n",
      "FnE Results by State:\n",
      "                   DT     EM      GJ      HC      JS       O\n",
      "State Category                                              \n",
      "AK    EC            2      0       0       1       0       0\n",
      "      Wasted        0      0   18725   10252    5735   14307\n",
      "AL    EC            6      0       0       3       0       0\n",
      "      Wasted        0      0   44467   21757    9391   21712\n",
      "AR    EC            4      0       0       2       0       0\n",
      "      Wasted        0  13255   29829    3616    9473   12712\n",
      "AZ    EC            5      0       1       5       0       0\n",
      "      Wasted    82781  17449       0       0   34345    1476\n",
      "CA    EC           18      0       2      34       1       0\n",
      "      Wasted        0  39596       0       0   20810  147244\n",
      "CO    EC            4      0       1       4       0       0\n",
      "      Wasted        0  28917       0  103205   38437   27418\n",
      "CT    EC            3      0       0       4       0       0\n",
      "      Wasted        0   2108   48676       0   22841     508\n",
      "DC    EC            0      0       0       3       0       0\n",
      "      Wasted    12723      0    4906       0    4258    6551\n",
      "DE    EC            1      0       0       2       0       0\n",
      "      Wasted    37189    706   14757       0    6103    1518\n",
      "FL    EC           14      0       1      14       0       0\n",
      "      Wasted    70281      0       0       0   64399   25736\n",
      "GA    EC            8      0       1       7       0       0\n",
      "      Wasted    31738  13017       0   77768    7674    1668\n",
      "HI    EC            1      0       0       3       0       0\n",
      "      Wasted    21613      0   15954       0   12737    4508\n",
      "IA    EC            3      0       0       3       0       0\n",
      "      Wasted    17968  12366   59186       0   11479   28348\n",
      "ID    EC            3      0       0       1       0       0\n",
      "      Wasted        0  46476   28331   17202    8496    8132\n",
      "IL    EC            8      0       1      11       0       0\n",
      "      Wasted        0  11655       0   45696   76802    1627\n",
      "IN    EC            6      0       1       4       0       0\n",
      "      Wasted    65491      0       0   38596    7841    2712\n",
      "KS    EC            4      0       0       2       0       0\n",
      "      Wasted        0   6520   55406   32205   23506     947\n",
      "KY    EC            5      0       0       3       0       0\n",
      "      Wasted      378  22780   53752       0   13913    1879\n",
      "LA    EC            5      0       0       3       0       0\n",
      "      Wasted        0   8547   37978   19267   14031    9684\n",
      "MA    EC            4      0       0       7       0       0\n",
      "      Wasted        0   2719  138018       0   47661   50559\n",
      "MD    EC            4      0       0       6       0       0\n",
      "      Wasted        0   9630   79605    9061   35945   35169\n",
      "ME    EC            2      0       0       2       0       0\n",
      "      Wasted        0   1887   38105       0   14251     356\n",
      "MI    EC            8      0       1       7       0       0\n",
      "      Wasted        0   8177       0  169153   51463   19126\n",
      "MN    EC            5      0       0       5       0       0\n",
      "      Wasted        0  53076  112972       0   36985   51113\n",
      "MO    EC            6      0       0       4       0       0\n",
      "      Wasted        0   7071   97359       0   25419   13177\n",
      "MS    EC            4      0       0       2       0       0\n",
      "      Wasted        0      0   14435   82012    3731    5346\n",
      "MT    EC            2      0       0       1       0       0\n",
      "      Wasted        0   2297   28037   11994    7970    1894\n",
      "NC    EC            8      0       0       7       0       0\n",
      "      Wasted        0      0  130126       0   12105   47386\n",
      "ND    EC            2      0       0       1       0       0\n",
      "      Wasted        0      0   21434       0    3780    8594\n",
      "NE    EC            3      0       0       2       0       0\n",
      "      Wasted        0      0   38946       0    8775   16051\n",
      "NH    EC            2      0       0       2       0       0\n",
      "      Wasted        0   1064   30777       0    6496   11643\n",
      "NJ    EC            6      0       0       8       0       0\n",
      "      Wasted        0      0   72477       0   37772   13586\n",
      "NM    EC            2      0       1       2       0       0\n",
      "      Wasted      340   5825       0   65907    9879    3173\n",
      "NV    EC            3      0       0       3       0       0\n",
      "      Wasted        0      0   37384       0       0   36683\n",
      "NY    EC           11      0       1      17       0       0\n",
      "      Wasted        0  10373       0   29755  107934   50890\n",
      "OH    EC            9      0       1       8       0       0\n",
      "      Wasted    92762  12574       0       0   46271   27975\n",
      "OK    EC            5      0       0       2       0       0\n",
      "      Wasted        0      0   83481    5235       0       0\n",
      "OR    EC            3      0       0       4       0       0\n",
      "      Wasted        0      0   94231       0   50002   72594\n",
      "PA    EC           10      0       0      10       0       0\n",
      "      Wasted        0   6472  146715       0   49941   65176\n",
      "RI    EC            2      0       0       2       0       0\n",
      "      Wasted        0    516   14746   20453    6220    9594\n",
      "SC    EC            5      0       0       4       0       0\n",
      "      Wasted        0  21016   49204       0   13034    9011\n",
      "SD    EC            2      0       0       1       0       0\n",
      "      Wasted        0      0   20850       0       0    4064\n",
      "TN    EC            7      0       0       4       0       0\n",
      "      Wasted        0  11991   70397       0   15993   16026\n",
      "TX    EC           20      0       1      17       0       0\n",
      "      Wasted        0  42366   47460       0   71558    8895\n",
      "UT    EC            3      1       0       2       0       0\n",
      "      Wasted        0  55119   39608       0    9438   12787\n",
      "VA    EC            6      0       0       7       0       0\n",
      "      Wasted        0  54054  118274       0   27638   33749\n",
      "VT    EC            1      0       0       2       0       0\n",
      "      Wasted        0    639   10078       0    6758   23650\n",
      "WA    EC            5      0       1       6       0       0\n",
      "      Wasted        0      0       0   84209   58417  133258\n",
      "WI    EC            5      0       0       5       0       0\n",
      "      Wasted        0  11855  106674       0   31072   38729\n",
      "WV    EC            4      0       0       1       0       0\n",
      "      Wasted        0   1104   23004   45910    8075    4075\n",
      "WY    EC            2      0       0       1       0       0\n",
      "      Wasted     3853      0   13287       0    2515    9655\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for year, data in vote_data.items():\n",
    "    print_ec_summary(year, data, ec_votes, show_state_data = (year == 2016))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following computes assuming the extra 2 \"Senate\" Electors are eliminated.\n",
      "from each state, i.e., it's thought experiment on the effect of the\n",
      "\"Constitutional Gerrymander\". In this case, 219 would be the requisite number\n",
      "of electors to win.\n",
      "\n",
      "1996\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "BC      315     20694429  0.179459      214      1685777  0.022455\n",
      "BD      121     28295406  0.021558      187       658137  0.043803\n",
      "HB        0       485798  0.599268        0       485798  0.047383\n",
      "O         0       420024  0.600634        0       420024  0.048750\n",
      "RN        0       685297  0.595124        1       492615  0.047242\n",
      "RP        0      8085402  0.441396       34      1791095  0.020267\n",
      "\n",
      "WTA Total Wasted Votes: 58666356\n",
      "FnE Total Wasted Votes: 5533446\n",
      "\n",
      "2000\n",
      "====\n",
      "         WTA                             FnE                       \n",
      "    Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "AG       225     28317499  0.040316      216      1289541  0.026633\n",
      "GWB      211     28617825  0.034618      212      1384086  0.024840\n",
      "HB         0       384431  0.570330        0       384431  0.043807\n",
      "HP         0        98020  0.575764        0        98020  0.049242\n",
      "JH         0        83714  0.576036        0        83714  0.049513\n",
      "O          0        51186  0.576653        0        51186  0.050131\n",
      "PB         0       448895  0.569107        0       448895  0.042584\n",
      "RN         0      2882955  0.522922        8      1646514  0.019860\n",
      "\n",
      "WTA Total Wasted Votes: 60884525\n",
      "FnE Total Wasted Votes: 5386387\n",
      "\n",
      "2004\n",
      "====\n",
      "         WTA                             FnE                       \n",
      "    Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "DC         0       119859  0.562456        0       119859  0.038045\n",
      "GWB      224     35491836 -0.016015      225      1648889  0.013039\n",
      "JK       212     32307540  0.036061      211      2017699  0.007008\n",
      "MB         0       397265  0.557919        0       397265  0.033508\n",
      "MP         0       143630  0.562067        0       143630  0.037656\n",
      "O          0        99887  0.562782        0        99887  0.038371\n",
      "RN         0       465151  0.556809        0       465151  0.032398\n",
      "\n",
      "WTA Total Wasted Votes: 69025168\n",
      "FnE Total Wasted Votes: 4892380\n",
      "\n",
      "2008\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "BB        0       523715  0.573062        0       523715  0.035268\n",
      "BO      306     30576671  0.115335      234      1905783  0.014218\n",
      "CB        0       199750  0.577996        0       199750  0.040202\n",
      "CM        0       161797  0.578574        0       161797  0.040780\n",
      "JM      130     43854738 -0.086899      201      2014234  0.012566\n",
      "O         0       242685  0.577342        0       242685  0.039548\n",
      "RN        0       739034  0.569783        1       630653  0.033639\n",
      "\n",
      "WTA Total Wasted Votes: 76298390\n",
      "FnE Total Wasted Votes: 5678617\n",
      "\n",
      "2012\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "BO      278     30066196  0.114808      225      1773681  0.016018\n",
      "GJ        0      1275971  0.560873        1      1132750  0.025948\n",
      "JS        0       469627  0.573366        0       469627  0.036223\n",
      "MR      158     42650154 -0.080163      210      1748488  0.016408\n",
      "O         0       490510  0.573043        0       490510  0.035899\n",
      "\n",
      "WTA Total Wasted Votes: 74952458\n",
      "FnE Total Wasted Votes: 5615056\n",
      "\n",
      "2016\n",
      "====\n",
      "        WTA                             FnE                       \n",
      "   Electors Wasted Votes  Eff. Gap Electors Wasted Votes  Eff. Gap\n",
      "DT      245     30530954  0.153510      207      1069733  0.041028\n",
      "EM        0       731788  0.589587        1       488098  0.049539\n",
      "GJ        0      4489221  0.534601       10      2573663  0.019019\n",
      "HC      191     43680181 -0.038914      217      1272875  0.038055\n",
      "JS        0      1457216  0.578971        1      1189639  0.039273\n",
      "O         0      1152671  0.583428        0      1152671  0.039814\n",
      "\n",
      "WTA Total Wasted Votes: 82042031\n",
      "FnE Total Wasted Votes: 7746679\n",
      "\n",
      "WTA Results by State:\n",
      "                     DT      EM      GJ       HC      JS       O\n",
      "State Category                                                  \n",
      "AK    EC              1       0       0        0       0       0\n",
      "      Wasted      46932       0   18725   116454    5735   14307\n",
      "AL    EC              7       0       0        0       0       0\n",
      "      Wasted     588707       0   44467   729547    9391   21712\n",
      "AR    EC              4       0       0        0       0       0\n",
      "      Wasted     304377   13255   29829   380494    9473   12712\n",
      "AZ    EC              9       0       0        0       0       0\n",
      "      Wasted      91233   17449  106327  1161167   34345    1476\n",
      "CA    EC              0       0       0       53       0       0\n",
      "      Wasted    4483810   39596  478500  4269977  278657  147244\n",
      "CO    EC              0       0       0        7       0       0\n",
      "      Wasted    1202484   28917  144121   136385   38437   27418\n",
      "CT    EC              0       0       0        5       0       0\n",
      "      Wasted     673215    2108   48676   224356   22841     508\n",
      "DC    EC              0       0       0        1       0       0\n",
      "      Wasted      12723       0    4906   270106    4258    6551\n",
      "DE    EC              0       0       0        1       0       0\n",
      "      Wasted     185127     706   14757    50475    6103    1518\n",
      "FL    EC             27       0       0        0       0       0\n",
      "      Wasted     112910       0  207043  4504975   64399   25736\n",
      "GA    EC             14       0       0        0       0       0\n",
      "      Wasted     211140   13017  125306  1877963    7674    1668\n",
      "HI    EC              0       0       0        2       0       0\n",
      "      Wasted     128847       0   15954   138043   12737    4508\n",
      "IA    EC              4       0       0        0       0       0\n",
      "      Wasted     147313   12366   59186   653669   11479   28348\n",
      "ID    EC              2       0       0        0       0       0\n",
      "      Wasted     219289   46476   28331   189765    8496    8132\n",
      "IL    EC              0       0       0       18       0       0\n",
      "      Wasted    2146015   11655  209596   944713   76802    1627\n",
      "IN    EC              9       0       0        0       0       0\n",
      "      Wasted     524159       0  133993  1033126    7841    2712\n",
      "KS    EC              4       0       0        0       0       0\n",
      "      Wasted     244012    6520   55406   427005   23506     947\n",
      "KY    EC              6       0       0        0       0       0\n",
      "      Wasted     574116   22780   53752   628854   13913    1879\n",
      "LA    EC              6       0       0        0       0       0\n",
      "      Wasted     398483    8547   37978   780154   14031    9684\n",
      "MA    EC              0       0       0        9       0       0\n",
      "      Wasted    1090893    2719  138018   904302   47661   50559\n",
      "MD    EC              0       0       0        8       0       0\n",
      "      Wasted     943169    9630   79605   734758   35945   35169\n",
      "ME    EC              0       0       0        2       0       0\n",
      "      Wasted     335593    1887   38105    22141   14251     356\n",
      "MI    EC             14       0       0        0       0       0\n",
      "      Wasted      10703    8177  172136  2268839   51463   19126\n",
      "MN    EC              0       0       0        8       0       0\n",
      "      Wasted    1322951   53076  112972    44764   36985   51113\n",
      "MO    EC              8       0       0        0       0       0\n",
      "      Wasted     523442    7071   97359  1071068   25419   13177\n",
      "MS    EC              4       0       0        0       0       0\n",
      "      Wasted     215582       0   14435   485131    3731    5346\n",
      "MT    EC              1       0       0        0       0       0\n",
      "      Wasted     101530    2297   28037   177709    7970    1894\n",
      "NC    EC             13       0       0        0       0       0\n",
      "      Wasted     173314       0  130126  2189316   12105   47386\n",
      "ND    EC              1       0       0        0       0       0\n",
      "      Wasted     123035       0   21434    93758    3780    8594\n",
      "NE    EC              3       0       0        0       0       0\n",
      "      Wasted     211466       0   38946   284494    8775   16051\n",
      "NH    EC              0       0       0        2       0       0\n",
      "      Wasted     345790    1064   30777     2735    6496   11643\n",
      "NJ    EC              0       0       0       12       0       0\n",
      "      Wasted    1601933       0   72477   546344   37772   13586\n",
      "NM    EC              0       0       0        3       0       0\n",
      "      Wasted     319667    5825   74541    65566    9879    3173\n",
      "NV    EC              0       0       0        4       0       0\n",
      "      Wasted     512058       0   37384    27201       0   36683\n",
      "NY    EC              0       0       0       27       0       0\n",
      "      Wasted    2819534   10373  176598  1736589  107934   50890\n",
      "OH    EC             16       0       0        0       0       0\n",
      "      Wasted     446840   12574  174498  2394164   46271   27975\n",
      "OK    EC              5       0       0        0       0       0\n",
      "      Wasted     528760       0   83481   420375       0       0\n",
      "OR    EC              0       0       0        5       0       0\n",
      "      Wasted     782403       0   94231   219702   50002   72594\n",
      "PA    EC             18       0       0        0       0       0\n",
      "      Wasted      44291    6472  146715  2926441   49941   65176\n",
      "RI    EC              0       0       0        2       0       0\n",
      "      Wasted     180543     516   14746    71981    6220    9594\n",
      "SC    EC              7       0       0        0       0       0\n",
      "      Wasted     300015   21016   49204   855373   13034    9011\n",
      "SD    EC              1       0       0        0       0       0\n",
      "      Wasted     110262       0   20850   117458       0    4064\n",
      "TN    EC              9       0       0        0       0       0\n",
      "      Wasted     652229   11991   70397   870695   15993   16026\n",
      "TX    EC             36       0       0        0       0       0\n",
      "      Wasted     807178   42366  283492  3877868   71558    8895\n",
      "UT    EC              4       0       0        0       0       0\n",
      "      Wasted     204554  243690   39608   310676    9438   12787\n",
      "VA    EC              0       0       0       11       0       0\n",
      "      Wasted    1769443   54054  118274   212029   27638   33749\n",
      "VT    EC              0       0       0        1       0       0\n",
      "      Wasted      95369     639   10078    83203    6758   23650\n",
      "WA    EC              0       0       0       10       0       0\n",
      "      Wasted    1221747       0  160879   520970   58417  133258\n",
      "WI    EC              8       0       0        0       0       0\n",
      "      Wasted      22747   11855  106674  1382536   31072   38729\n",
      "WV    EC              3       0       0        0       0       0\n",
      "      Wasted     300576    1104   23004   188794    8075    4075\n",
      "WY    EC              1       0       0        0       0       0\n",
      "      Wasted     118445       0   13287    55973    2515    9655\n",
      "\n",
      "FnE Results by State:\n",
      "                    DT     EM      GJ      HC      JS       O\n",
      "State Category                                               \n",
      "AK    EC             1      0       0       0       0       0\n",
      "      Wasted         0      0   18725  116454    5735   14307\n",
      "AL    EC             4      0       0       3       0       0\n",
      "      Wasted    104900      0   44467       0    9391   21712\n",
      "AR    EC             3      0       0       1       0       0\n",
      "      Wasted         0  13255   29829   97836    9473   12712\n",
      "AZ    EC             5      0       0       4       0       0\n",
      "      Wasted         0  17449  106327   17539   34345    1476\n",
      "CA    EC            17      0       2      33       1       0\n",
      "      Wasted         0  39596       0       0   11080  147244\n",
      "CO    EC             3      0       0       4       0       0\n",
      "      Wasted     10950  28917  144121       0   38437   27418\n",
      "CT    EC             2      0       0       3       0       0\n",
      "      Wasted     15247   2108   48676       0   22841     508\n",
      "DC    EC             0      0       0       1       0       0\n",
      "      Wasted     12723      0    4906       0    4258    6551\n",
      "DE    EC             0      0       0       1       0       0\n",
      "      Wasted    185127    706   14757       0    6103    1518\n",
      "FL    EC            13      0       1      13       0       0\n",
      "      Wasted     82312      0       0       0   64399   25736\n",
      "GA    EC             7      0       1       6       0       0\n",
      "      Wasted     31738  13017       0  114507    7674    1668\n",
      "HI    EC             1      0       0       1       0       0\n",
      "      Wasted         0      0   15954   52423   12737    4508\n",
      "IA    EC             2      0       0       2       0       0\n",
      "      Wasted     17968  12366   59186       0   11479   28348\n",
      "ID    EC             1      0       0       1       0       0\n",
      "      Wasted     63928  46476   28331       0    8496    8132\n",
      "IL    EC             7      0       1      10       0       0\n",
      "      Wasted         0  11655       0   14938   76802    1627\n",
      "IN    EC             5      0       1       3       0       0\n",
      "      Wasted     37865      0       0  121474    7841    2712\n",
      "KS    EC             2      0       0       2       0       0\n",
      "      Wasted     78817   6520   55406       0   23506     947\n",
      "KY    EC             4      0       0       2       0       0\n",
      "      Wasted         0  22780   53752       0   13913    1879\n",
      "LA    EC             4      0       0       2       0       0\n",
      "      Wasted         0   8547   37978  103810   14031    9684\n",
      "MA    EC             3      0       0       6       0       0\n",
      "      Wasted         0   2719  138018       0   47661   50559\n",
      "MD    EC             3      0       0       5       0       0\n",
      "      Wasted         0   9630   79605       0   35945   35169\n",
      "ME    EC             1      0       0       1       0       0\n",
      "      Wasted         0   1887   38105       0   14251     356\n",
      "MI    EC             7      0       0       7       0       0\n",
      "      Wasted         0   8177  172136       0   51463   19126\n",
      "MN    EC             4      0       0       4       0       0\n",
      "      Wasted         0  53076  112972       0   36985   51113\n",
      "MO    EC             5      0       0       3       0       0\n",
      "      Wasted         0   7071   97359   17842   25419   13177\n",
      "MS    EC             2      0       0       2       0       0\n",
      "      Wasted     96036      0   14435       0    3731    5346\n",
      "MT    EC             1      0       0       0       0       0\n",
      "      Wasted         0   2297   28037  177709    7970    1894\n",
      "NC    EC             7      0       0       6       0       0\n",
      "      Wasted         0      0  130126     902   12105   47386\n",
      "ND    EC             1      0       0       0       0       0\n",
      "      Wasted         0      0   21434   93758    3780    8594\n",
      "NE    EC             2      0       0       1       0       0\n",
      "      Wasted         0      0   38946    3085    8775   16051\n",
      "NH    EC             1      0       0       1       0       0\n",
      "      Wasted         0   1064   30777       0    6496   11643\n",
      "NJ    EC             5      0       0       7       0       0\n",
      "      Wasted         0      0   72477       0   37772   13586\n",
      "NM    EC             1      0       0       2       0       0\n",
      "      Wasted     53561   5825   74541       0    9879    3173\n",
      "NV    EC             2      0       0       2       0       0\n",
      "      Wasted         0      0   37384       0       0   36683\n",
      "NY    EC            10      0       1      16       0       0\n",
      "      Wasted         0  10373       0       0  107934   50890\n",
      "OH    EC             8      0       1       7       0       0\n",
      "      Wasted     92762  12574       0       0   46271   27975\n",
      "OK    EC             3      0       0       2       0       0\n",
      "      Wasted     77341      0   83481       0       0       0\n",
      "OR    EC             2      0       0       3       0       0\n",
      "      Wasted         0      0   94231       0   50002   72594\n",
      "PA    EC             9      0       0       9       0       0\n",
      "      Wasted         0   6472  146715       0   49941   65176\n",
      "RI    EC             1      0       0       1       0       0\n",
      "      Wasted         0    516   14746   20453    6220    9594\n",
      "SC    EC             4      0       0       3       0       0\n",
      "      Wasted         0  21016   49204       0   13034    9011\n",
      "SD    EC             1      0       0       0       0       0\n",
      "      Wasted         0      0   20850  117458       0    4064\n",
      "TN    EC             6      0       0       3       0       0\n",
      "      Wasted         0  11991   70397   34686   15993   16026\n",
      "TX    EC            19      0       1      16       0       0\n",
      "      Wasted         0  42366   34347       0   71558    8895\n",
      "UT    EC             2      1       0       1       0       0\n",
      "      Wasted         0      0   39608   27819    9438   12787\n",
      "VA    EC             5      0       0       6       0       0\n",
      "      Wasted         0  54054  118274       0   27638   33749\n",
      "VT    EC             0      0       0       1       0       0\n",
      "      Wasted     95369    639   10078       0    6758   23650\n",
      "WA    EC             4      0       1       5       0       0\n",
      "      Wasted         0      0       0   84209   58417  133258\n",
      "WI    EC             4      0       0       4       0       0\n",
      "      Wasted         0  11855  106674       0   31072   38729\n",
      "WV    EC             2      0       0       1       0       0\n",
      "      Wasted     13089   1104   23004       0    8075    4075\n",
      "WY    EC             1      0       0       0       0       0\n",
      "      Wasted         0      0   13287   55973    2515    9655\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_without_senate_ec_slots():\n",
    "    ev = build_ec_votes_df(ignore_senate_ec_votes=True)\n",
    "    for year, data in vote_data.items():\n",
    "        print_ec_summary(year, data, ev, show_state_data = (year == 2016))\n",
    "\n",
    "print('The following computes assuming the extra 2 \"Senate\" Electors are eliminated.')\n",
    "print(\"from each state, i.e., it's thought experiment on the effect of the\")\n",
    "print('\"Constitutional Gerrymander\". In this case, 219 would be the requisite number')\n",
    "print('of electors to win.')\n",
    "print()\n",
    "print_without_senate_ec_slots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_wasted():\n",
    "    colors = {1996: 'yellow', 2000: 'red', 2004:'magenta', 2008:'blue', 2012:'cyan', 2016:'black'}\n",
    "    plt.title('Wasted Votes')\n",
    "    plt.xlabel(\"Fair, Efficient\")\n",
    "    plt.ylabel(\"Winner-Take-All\")\n",
    "    for year, data in vote_data.items():\n",
    "        result_wta = wta(data, ec_votes[year])\n",
    "        fe = fair_efficient(data, ec_votes[year])\n",
    "        few = fe.loc[(slice(None), ['Wasted']),:].sum(axis=1)\n",
    "        wtaw = result_wta.loc[(slice(None), ['Wasted']),:].sum(axis=1)\n",
    "        plt.scatter(x=few, y=wtaw, color=colors[year], label=str(year), alpha=0.7)\n",
    "    plt.legend()\n",
    "    plt.ticklabel_format(style='plain')\n",
    "plot_wasted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bHHDAAZx00km8/PLL7Lzzztx8882MHDkSgIsuuoirr76akpISvv/973PMMccA0NDQwKc+9Sn++te/cswxx/Af//Ef7d7C8yjhZtZdnY0S7qTUC3mZuqK+PnuG1NiYXSHV1vYqIfUXJyUz6y5PXVHMqqsHZBIyMysEj31nZmZFw0nJzMyKhpOSmZkVDSclMzMrGk5KZmZWNJyUtkD5nLqi1fHHH88+++zTZ+dgZoOTk9IWKJ9TVwD87Gc/o6ztcEhmZgXgpNTf6oEaoCot8zBAeL6mroBsUNfvfe97fOUrX+l9w8zMuuCk1J/qgTqycdLHpmUdeUlMrXozdQXA+eefT11dHdttt13+GmVm1gEnpf40FygFysk+ifL0Pj8zV/R66oonn3ySxYsXc+KJJ+anQWZmXXBS6k+NQNtHNWUp3kv5mLrioYceYsGCBUyYMIFDDz2UF198kcMPP7z3jTMz64CTUn+qBNrMXEFzivdCvqauOOOMM3j11Vd56aWXeOCBB9hjjz24//77e9c4M7NOeEDW/lRL9gwJsiukZqAlxXvhwQcf5IYbbmDKlClMnToVyKauOPfccznppJO46qqrNk5dAbD33ntz0kknMXnyZEpKSrjsssvYaqutetcIM7Me8NQVvZCfqSvIniE1kl0h1QIDcNBwT11hZt3lqSuKWTUDMgmZmRWCnymZmVnRcFIyM7Oi4aRkZmZFw0nJzMyKhpOSmZkVDSelLVA+p6746U9/ypQpU9h3332ZMWMGr732Wp+fj5kNHk5KW6B8TV2xbt06vvCFL3Dffffx9NNPs++++zJ3bp4G5usH9fVQUwNVVdmyPo8D35pZfjgp9bNCfFHma+qKiCAiaGlpISJYs2YNO+64Y+8b2A/q66GuDpYvh7Fjs2VdnROTWbFxUupHffFF2ZupK7beemsuv/xypkyZwo477sjChQs5/fTT89e4PjR3LpSWQnk5DBmSLUtLs7iZFQ8npX5U6C/K3k5d8c4773D55ZfzxBNP8Oqrr7LvvvvyrW99Kz+N62ONjdB28tyysixuZsXDSakfFfKLMh9TVzz55JMA7LbbbkjipJNO4ve//33vG9cPKiuhuc2I7M3NWdzMioeTUj8q1BdlvqauGD9+PAsXLmTFihUAzJ8/f8AOulpbCy0tsGYNbNiQLVtasriZFQ8PyNqPamuzZ0iQXSE1N+fnizJfU1fsuOOOXHDBBRx22GFsvfXW7LLLLlx77bW9a1w/qa6GOXOyW6ONjVnir63N4mZWPDx1RS/kY+qK+vot44vSU1eYWXd56ooiVl09MJOQmVkh+JmSmZkVDSclMzMrGgVLSpL2lPRkzmuNpLMkfVXSKznxY3PKnCdpsaQXJB2dE99P0jNp2w8lKcWHSroxxR+RNCGnzCxJi9JrVk68Mu27KJXdJsWV6l4s6WlJ0wr1t7GeqQdqgKq09GAMZluegiWliHghIqZGxFRgP+At4Odp86Wt2yLiDgBJk4GZwN7ADOBHkrZK+18OzAYmpteMFD8dWBURuwOXApekukYCFwAHAPsDF0gakcpcko4/EViV6gA4Jqf+2emYViTqgTpgOTA2LetwYjLb0vTV7bvpwB8j4s+d7HMCMC8i3o6IRmAxsL+kcUB5RDwUWVfB64GP5pS5Lq3fAkxPV1FHA/MjYmVErALmAzPStg+nfUllc+u6PjIPA8PTsa0IzAVKgXKyf7Tl6b1HCTLbsvRVUpoJ/DTnfW26RXZ1zhXMeGBJzj5NKTY+rbeNb1ImItYBbwCjOqlrFLA67dthXe1s20jSbEkNkhpaf1RabDZ36orXX3+dI444grKyMmpzfiT11ltv8fd///dMmjSJvffem3PPPbdfzgegEWgz+AVlKW5mW46CJ6X0zOZ44OYUuhzYDZgKLAXmtO7aTvHoJN6TMj2pa9NAxBURURURVaNHj26nSP/b3Kkrhg0bxje+8Q2++93vvqeuc845h+eff54nnniCBx98kDvvvLOvTweASqDN4Bc0p7iZbTn64krpGODxiFgGEBHLImJ9RGwAriR75gPZlclOOeUqgFdTvKKd+CZlJJUA7wNWdlLXa2S35Uo6q6udbQVTiIf3mzt1RWlpKYceeijDhg3bpJ7tttuOI444AoBtttmGadOm0dTURH+oBVqANcCGtGxJcTPbcvRFUjqZnFt3bZ7TnAj8Ia3fDsxMPeoqyTocPBoRS4E3JR2YngmdCtyWU6a1Z90ngN+k5053A0dJGpFuDx4F3J223Zf2JZXNrevU1AvvQOCNdOyC6YuH992ZuqI7Vq9ezS9/+UumT5+ex9Z1XzXZJfUYYFlazklxM9tyFHREB0nbAUcC/5wT/rakqWS3xl5q3RYRz0q6CVgIrAM+HxHrU5kzgGuBbYE70wvgKuAGSYvJrpBmprpWSvoG8Fja7+sRsTKtfxmYJ+lC4IlUB8AdwLFkHSzeAk7r/V+gc7kP78lZziU/X7bdnbqiK+vWrePkk0/mzDPPZNddd81Dy3qmGichsy1dp0lJ0i9p57lKq4g4vrPyEfEWWeeC3Ngpnex/EXBRO/EGYJ924n8ju+vVXl1XA1e3E/8T794yzI0H8PmO2lYIjWRXSLny9fC+s6krxo0bt8nUFV2ZPXs2EydO5KyzzspDy8zMOtbVldJ7n3xb3lSS3bLLvYbJx8P7rqauOPfcczeZuqIzX/nKV3jjjTf48Y9/3MtWmZl1zaOE90JvRwlvfaZUSnaF1Ez28L63z0oeeOABPvShDzFlyhSGDMkeG37zm9/kgAMO4KSTTuLll1/eOHXFyJEjAZgwYQJr1qxh7dq1DB8+nHvuuYfy8nJ22mknJk2axNChQwGora3lM5/5zHuO6VHCzay7ejxKuKRnaP/2ncjueO2bh/YNWq0P7+eS3bKrJOtN1tvnJoceemi7U5wD3Hvvve3GX3rppXbj/p8WM+tLXd2++0iftGIQ88N7M7N3dZqUOhoWSNIhwD/Sxx0DzMxsy9btLuGpG/c/AieR3W36WYHaZGZmg1RXz5T2IPvtz8nA68CNZJ0jjuiDtpmZ2SDT1ZXS88DvgOMiYjGApC8WvFVmZjYodTXM0MeBvwD3SbpS0nTaH7jUzMys1zpNShHx84j4B2AScD/wRWCspMslHdUH7bMeyNfUFQBr165l9uzZ7LHHHkyaNIlbb721z8/HzAaPbg3IGhEtEfGTiPgI2ejZTwL9N7mOdSqfU1dcdNFFjBkzhhdffJGFCxdSXe0O7GZWOD0ZJfziiPiviPhw3lszCNXX11NTU0NVVRU1NTXU1/d+jPB8TV0BcPXVV3PeeecBMGTIEHbYYYdet8/MrCM9SUrtDg1hm6++vp66ujqWL1/O2LFjWb58OXV1dXlJTK16M3XF6tWrATj//POZNm0aNTU1LFu2LG9tMzNrqydJqfuT8Fin5s6dS2lpKeXl5QwZMoTy8nJKS0uZO3duXurv7dQV69ato6mpiUMOOYTHH3+cgw46iHPOOScvbTMza89mJSVJpRExo1CNGWwaGxspKyvbJFZWVkZjY+8nr+hs6gqgW1NXjBo1iu22244TTzwRgJqaGh5//PFet83MrCPdSkqSDpa0EHguvX+/pB8VtGWDQGVlJc3NzZvEmpubqazs3eQVXU1dAXRr6gpJHHfccdx///1ANpjr5MmTe9U2M7POdGvqCkmPkE0hfntEfCDF/hAR75l4bzDp9dQV6ZlSaWkpZWVlNDc309LSwpw5c3rVyy1fU1dMnjyZP//5z5xyyimsXr2a0aNHc80117Dzzju/55ieusLMuqvHU1fkiogl0ia/m13f0b7WPdXV1cyZM4e5c+fS2NhIZWUltbW1ve52nc+pK3bZZRd++9vf9qo9Zmbd1d2ktETSwUBI2gY4k3Qrz3qnurrav/0xM0u629Hhs2TTVIwHmoCpwOcK1CYzMxukunul9MGI+GRuQNJngf/Mf5MGvoigza3OLZpnpzWzfOnuldL5kjaO4CDpS0DnXbcGqWHDhvH6668Pmi/qiOD1119vdzQIM7PN1d0rpeOBX0n6V2AG2QCtxxesVQNYRUUFTU1NrFixor+b0meGDRtGRUVFfzfDzLYA3UpKEfGapOOB/wUWAJ+IwXIpsJm23nrrXv/OyMxssOpq5tk3gdzksw2wK/AJSRERmz92jZmZWQc6TUoRsX1fNcTMzKzbP56VNAKYCGx8oh0R/lWlmZnlTbeSkqTPAF/g3Qn+DgQeAjynkpmZ5U13u4R/Afgg8OeIOAL4ADB4upeZmVmf6G5S+ltE/A1A0tCIeB7Ys3DNMjOzwajTpCTpm2m1SdJw4BfAfEm3Aa8WtmlmZjbYdPVMaQbwbxFxYnr/VUn3Ae8D7ipoy8zMbNDpKiltlXrd5Q7k9kxalgErC9IqMzMblLp6pjSJbASH3FdDzrJDkvaU9GTOa42ksySNlDRf0qK0HJFT5jxJiyW9IOnonPh+kp5J236oNNqppKGSbkzxRyRNyCkzKx1jkaRZOfHKtO+iVHabFFeqe7GkpyVN6+bf0MzM8qSrpLQwInaNiMqcV+v7XTsrGBEvRMTUiJgK7Ae8BfwcOBe4NyImAvem90iaDMwE9ia7bfgjSVul6i4HZpP9Tmpi2g5wOrAqInYHLgUuSXWNBC4ADgD2By7ISX6XAJem469KdQAck1P/7HRMMzPrQ93tfddb04E/RsSfyUYXvy7FrwM+mtZPAOZFxNsR0QgsBvaXNA4oj4iH0nh717cp01rXLcD0dBV1NDA/IlZGxCpgPjAjbftw2re9418fmYeB4enYZmbWR7pKSj9oG5D0eA+OMxP4aVofGxFLAdJyTIqPB5bklGlKsdaJBdvGNykTEeuAN4BRndQ1Clid9u2wrna2bSRptqQGSQ2DaSRwM7O+0GlSiohr2wlv1ux16ZnN8cDNXe3aXhM6ifekTE/q2jQQcUVEVEVE1ejRo9spYmZmPdXl7TtJW0n6Yk7o15t5jGOAxyNiWXq/rPW2WFouT/EmYKecchVkv4VqSutt45uUkVRC1lV9ZSd1vUZ2W66ks7ra2WZmZn2gy6QUEevJmWU2Ir6ymcc4mXdv3QHcDrT2hpsF3JYTn5l61FWSdTh4NN3ie1PSgemZ0KltyrTW9QngN+m5093AUZJGpA4ORwF3p233pX3bO/6pqRfegcAbrbcZzcysb3R3lPAHJc0FbgRaWoMR0enzJUnbAUcC/5wTvhi4SdLpwMtATarrWUk3AQuBdcDnU0IEOAO4FtgWuDO9AK4CbpC0mOwKaWaqa6WkbwCPpf2+HhGtv6n6MjBP0oXAE6kOgDuAY8k6WLwFnNb1n8XMzPJJ3ZlANo3i0FZExKAeJbyqqioaGjr9udZ71QNzgUagEqgFqvPeNDOzoiVpQURUtbetu9OhH5HfJg1S9UAdUAqMJXuaVgfMwYnJzIxu/k5J0lhJV0m6M72fnG6/2eaYS5aQysn+8uXp/dz+bJSZWfHo7o9nryXrPLBjev8icFYB2rNlayQbMTBXWYqbmVm3k9IOEXETsAE2/lB1fedF7D0qgeY2seYUNzOzbielFkmjSD8mbe0yXbBWbalqyfouriFL72vS+9r+bJSZWfHoblI6m+x3PLtJepBs/Ll/KVirtlTVZJ0axgDL0tKdHMzMNupu77vHJVWTTYEu4IWIeKegLdtSVeMkZGbWge7+eBayKSAmpDLTJBER1xekVWZmNih1KylJugHYDXiSdzs4tE4jYWZmlhfdvVKqAiZHd4Z/MDMz66HudnT4A/B/CtkQMzOz7l4p7QAslPQo8HZrMCKOL0irzMxsUOpuUvpqIRthZmYG3e8SXl/ohpiZmXV3QNaPSVok6Q1JayS9KWlNoRtnZmaDS3dv330bOC4initkY8zMbHDrbu+7ZU5IZmZWaN29UmqQdCPwCzbtffezQjTKzMwGp+4mpXLgLeConFgATkpmZpY33e19d1qhG2JmZtZpUpL0pYj4tqT/IM2llCsizixYy8zMbNDp6kppqKQPAk8Ba8mmrTAzMyuIrpLS+4AfAHuRJabfAw8CD0XEygK3zczMBplOk1JEnAMgaRuykcIPBj4NXClpdURMLnwTzcxssOhu77ttyXrgvS+9XgWeKVSjzMxscOqqo8MVwN7Am8AjZLfvvhcRq/qgbWZmNsh0NaLDzsBQ4C/AK0ATsLrAbTIzs0Gqq2dKMySJ7GrpYKAO2EfSSrLODhf0QRvNzGyQ6PKZUpoC/Q+SVgNvpNdHgP0BJyUzM8ubrp4pnUl2hXQI8A6pOzhwNe7oYGZmedbVldIE4BbgixGxtPDNMTOzwayrZ0pn91VDzMzMujufkpmZWcEVNClJGi7pFknPS3pO0kGSvirpFUlPptexOfufJ2mxpBckHZ0T30/SM2nbD1OPQCQNlXRjij8iaUJOmVlpCvdFkmblxCvTvotS2W1SXKnuxZKeljStkH8bMzN7r0JfKf0AuCsiJgHvB1pnr700Iqam1x0AkiYDM8m6n88AfiRpq7T/5cBsYGJ6zUjx04FVEbE7cClwSaprJFnPwANIvQQljUhlLknHnwisSnUAHJNT/+x0TDMz60MFS0qSyoHDgKsAImJtRKzupMgJwLyIeDsiGoHFwP6SxgHlEfFQ6p5+PfDRnDLXpfVbgOnpKupoYH5ErEyjT8wHWn9z9eG0L6lsbl3XR+ZhYHg6tpmZ9ZFCXintCqwArpH0hKQfSypN22rTLbKrc65gxgNLcso3pdj4tN42vkmZiFhH9huqUZ3UNQpYnfbtsK52tm0kabakBkkNK1as6MafwczMuquQSakEmAZcHhEfAFqAc8lui+0GTAWWAnPS/u3N1RSdxHtSpid1bRqIuCIiqiKiavTo0e0UMTOznipkUmoCmiLikfT+FmBaRCyLiPURsQG4kuyZT+v+O+WUryAbjbwprbeNb1JGUgnZCOYrO6nrNbLbciWd1dXONjMz6wMFS0oR8RdgiaQ9U2g6sLDNc5oTgT+k9duBmalHXSVZh4NH049235R0YHomdCpwW06Z1p51nwB+k5473Q0cJWlEuj14FHB32nZf2pdUNreuU1MvvAOBN/yDYTOzvtXd+ZR66l+An6Ru138CTgN+KGkq2a2xl4B/BoiIZyXdBCwE1gGfj4j1qZ4zgGvJ5nW6M70g60Rxg6TFZFdIM1NdKyV9A3gs7ff1nJlyvwzMk3Qh8ESqA+AO4FiyDhZvpbaamVkfUnbxYD1RVVUVDQ0N/d0MM7MBRdKCiKhqb5tHdDAzs6LhpGRmZkXDScnMzIqGk5KZmRUNJyUzMysaTkpmZlY0nJTMzKxoOCmZmVnRcFIyM7Oi4aRkZmZFw0nJzMyKhpOSmZkVDSclMzMrGk5KZmZWNJyUzMysaDgpmZlZ0XBSMjOzouGkZGZmRcNJyczMioaTkpmZFQ0nJTMzKxpOSmZmVjSclMzMrGg4KZmZWdFwUjIzs6LhpGRmZkXDScn6Vn091NRAVVW2rK/v7xaZWRFxUrK+U18PdXWwfDmMHZst6+qcmMxsIycl6ztz50JpKZSXw5Ah2bK0NIubmeGkZH2psRHKyjaNlZVlcTMznJSsL1VWQnPzprHm5ixuZoaTkvWl2lpoaYE1a2DDhmzZ0pLFzcxwUrK+VF0Nc+bAmDGwbFm2nDMni5uZUeCkJGm4pFskPS/pOUkHSRopab6kRWk5Imf/8yQtlvSCpKNz4vtJeiZt+6EkpfhQSTem+COSJuSUmZWOsUjSrJx4Zdp3USq7TYor1b1Y0tOSphXybzNoVVfDzTdDQ0O2dEIysxyFvlL6AXBXREwC3g88B5wL3BsRE4F703skTQZmAnsDM4AfSdoq1XM5MBuYmF4zUvx0YFVE7A5cClyS6hoJXAAcAOwPXJCT/C4BLk3HX5XqADgmp/7Z6ZhmZtaHCpaUJJUDhwFXAUTE2ohYDZwAXJd2uw74aFo/AZgXEW9HRCOwGNhf0jigPCIeiogArm9TprWuW4Dp6SrqaGB+RKyMiFXAfGBG2vbhtG97x78+Mg8Dw9OxzcysjxTySmlXYAVwjaQnJP1YUikwNiKWAqTlmLT/eGBJTvmmFBuf1tvGNykTEeuAN4BRndQ1Clid9u2wrna2mZlZHyhkUioBpgGXR8QHgBbSrboOqJ1YdBLvSZme1LUJSbMlNUhqWLFiRTtFzMyspwqZlJqApoh4JL2/hSxJLWu9LZaWy3P23ymnfAXwaopXtBPfpIykEuB9wMpO6nqN7LZcSWd1tbNto4i4IiKqIqJq9OjRXfwJzMxscxQsKUXEX4AlkvZMoenAQuB2oLU33CzgtrR+OzAz9airJOtw8Gi6xfempAPTM6FT25RpresTwG/Sc6e7gaMkjUgdHI4C7k7b7kv7tnf8U1MvvAOBN1pvM5qZWd8o6XqXXvkX4Cep2/WfgNPIEuFNkk4HXgZqACLiWUk3kSWudcDnI2J9qucM4FpgW+DO9IKsE8UNkhaTXSHNTHWtlPQN4LG039cjYmVa/zIwT9KFwBOpDoA7gGPJOli8ldpqZmZ9SNnFg/VEVVVVNDQ09HczzMwGFEkLIqKqvW0e0cHMzIqGk5KZmRUNJyUzMysaTko2CNWT9a+pSkvPfGtWLJyUbJCpB+rIfh43Ni3rcGIyKw5OSjbIzAVKgXKyf/7l6b2nZDcrBk5KNsg0Am2mZKcsxc2svzkp2SBTCbSZkp3mFDez/uakZINMLdnYwGuADWnZkuJm1t+clGyQqQbmkM2Ysiwt56S4mfW3Qo99Z1aEqnESMitOvlIyM7Oi4aRkZmZFw0nJzMyKhpOSmZkVDSclMzMrGp7krxckrQD+3N/t6MAOwGv93Yg88HkUF59H8RjI57BLRIxub4OT0hZKUkNHMzsOJD6P4uLzKB5bwjm0x7fvzMysaDgpmZlZ0XBS2nJd0d8NyBOfR3HxeRSPLeEc3sPPlMzMrGj4SsnMzIqGk5KZmRUNJ6UiIGmYpEclPSXpWUlfS/GvSnpF0pPpdWxOmfMkLZb0gqSjc+L7SXombfuhJKX4UEk3pvgjkibklJklaVF6zcqJV6Z9F6Wy23TjXLaS9ISkX6X3IyXNT3XMlzSi2M+hg/MYiJ/FS+n4T0pqGKifRwfnMRA/j+GSbpH0vKTnJB00ED+PgosIv/r5BQgoS+tbA48ABwJfBc5pZ//JwFPAULIpU/8IbJW2PQoclOq8EzgmxT8H/GdanwncmNZHAn9KyxFpfUTadhMwM63/J3BGN87lbOB/gF+l998Gzk3r5wKXFPs5dHAeA/GzeAnYoU1swH0eHZzHQPw8rgM+k9a3AYYPxM+j0K9+/0L2q80HAtsBjwMHdPIf3nnAeTnv707/SMcBz+fETwb+K3eftF5C9ktw5e6Ttv1XiintU5LiBwF3d9H2CuBe4MO8+2X+AjAurY8DXijmc+jkPAbUZ5H2e4n3fpkPxM+jvfMYUJ8HUA40kjqXDeTPo9Av374rEspuFz0JLAfmR8QjaVOtpKclXZ1zaT8eWJJTvCnFxqf1tvFNykTEOuANYFQndY0CVqd929bVke8DXyKbZ7zV2IhYmo67lGyq12I+h47OAwbWZwEQwD2SFkianWID8fNo7zxgYH0euwIrgGuU3Rb+saRSBubnUVBOSkUiItZHxFSy/0vfX9I+wOXAbsBUYCnZvN2Q/R/Oe6roJN6TMp3V9R6SPgIsj4gFHe3TtkgPjlvQc4BOz2PAfBY5DomIacAxwOclHdbJvgPtPAba51ECTAMuj4gPAC1kt+s6UqznUXBOSkUmIlYD9wMzImJZSlYbgCuB/dNuTcBOOcUqgFdTvKKd+CZlJJUA7wNWdlLXa8DwtG/butpzCHC8pJeAecCHJf03sEzSuHTccWRXgsV6Dh2exwD7LACIiFfTcjnw89TmgfZ5tHseA/DzaAKacu6A3EKWpAbc51Fw/X3/0K8AGA0MT+vbAr8DPkK615ziXwTmpfW92fQh6J949yHoY2SdJFofgh6b4p9n04egN6X1kWT3ukekVyMwMm27mU0fgn6um+dzOO8+i/kOmz7I/fZAOId2zmNAfRZAKbB9zvrvgRkD7fPo5DwG1OeR9vsdsGda/2r6LAbU59EXr37/QvYrAPYFngCeBv4A/L8UvwF4JsVvb/Mf4r+T9ch5gdT7JsWrUh1/BOby7qgdw9I/wMVkvXd2zSnz6RRfDJyWE9817bs4lR3azfM5nHe/zEeRdRpYlJYjB8I5tHMeA+qzSPs/lV7PAv8+ED+PTs5jQH0eqcxUoCG1+RdkCWJAfR598fIwQ2ZmVjT8TMnMzIqGk5KZmRUNJyUzMysaTkpmZlY0nJTMzKxoOCmZFYik9TmjWD+ZO2pzO/v+vgf1359GkG6t/5YUH51Gfn5C0ock1aRRqe+TVCXph13Ue4ek4ZvbnlT2o5Im96SsGWRDX5hZYfw1sqGjuhQRB7eNSdoqItZ3UfSTEdHQJjadbNDOWameu8h+FHlf2t52/7ZtObaz7V34KPArYGEv6rBBzFdKZn1EUpmkeyU9nubDOSFnW3NaHp6uaP6H7Mehm3uMqWTTIRybrp4uAA4F/lPSd1L9rXNElUm6JrXlaUkfT/GXJO2Q1v9J2VxfT0r6L0lbtbZX0kXK5gB7WNJYSQcDxwPfSfvv1pu/lw1OvlIyK5xt08jvkA3tUgOcGBFr0pf+w5Juj/f+gn1/YJ+IaOzGMX4i6a9pfX5E/Kuk/wdURUQtgKQjyKZ5aJB0eE7Z84E3ImJK2m9EzjYk7QX8A9mAqO9I+hHwSeB6siF/Ho6If5f0beD/RsSFkm4nGwXjlm603ew9nJTMCmeT23eStga+mUa53kA2TcBY4C9tyj3azYQE7d++666/IxsjDYCIWNVm+3RgP+AxZZObbsu7A4auJbtNB7AAOLKHbTDbhJOSWd/5JNngu/ulK4+XyMYra6ulj9ojOp+qQMB1EXFeO9veybnCW4+/SyxP/EzJrO+8j2yupnfSLbVdulMoPYcqxORr9wC1OccZ0Wb7vcAnJI1J20dK6qrNbwLb57WVNqg4KZn1nZ8AVZIayK6anu+qgKQhwO5k8+K0W2dOl/D/3cz2XAiMkPQHSU8BR+RujIiFwFfIZn19GphPNh13Z+YB/5q6o7ujg202jxJuVsTSDMSfjoiz+7stZn3BScnMzIqGb9+ZmVnRcFIyM7Oi4aRkZmZFw0nJzMyKhpOSmZkVDSclMzMrGv8fnntHWqSioMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_wasted_sum():\n",
    "    colors = {1996: 'yellow', 2000: 'red', 2004:'magenta', 2008:'blue', 2012:'cyan', 2016:'black'}\n",
    "    plt.title('Wasted Votes')\n",
    "    plt.xlabel(\"Fair, Efficient\")\n",
    "    plt.ylabel(\"Winner-Take-All\")\n",
    "    for year, data in vote_data.items():\n",
    "        result_wta = wta(data, ec_votes[year])\n",
    "        fe = fair_efficient(data, ec_votes[year])\n",
    "        few = fe.loc[(slice(None), ['Wasted']),:].sum(axis=1).sum()\n",
    "        wtaw = result_wta.loc[(slice(None), ['Wasted']),:].sum(axis=1).sum()\n",
    "        plt.scatter(x=few, y=wtaw, color=colors[year], label=str(year), alpha=0.7)\n",
    "    plt.legend()\n",
    "    plt.ticklabel_format(style='plain')\n",
    "plot_wasted_sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_waste_reduction_versus_votes_cast(include_years=None):\n",
    "    colors = {1996: 'yellow', 2000: 'red', 2004:'magenta', 2008:'blue', 2012:'cyan', 2016:'black'}\n",
    "    plt.title('Waste Reduction versus Votes Cast by State')\n",
    "    plt.xlabel(\"Votes Cast\")\n",
    "    plt.ylabel(\"Waste Reduction [%]\")\n",
    "    plt.xscale('log')\n",
    "    plt.yscale('log')\n",
    "    for year, data in vote_data.items():\n",
    "        if include_years is None or year in include_years:\n",
    "            result_wta = wta(data, ec_votes[year])\n",
    "            fe = fair_efficient(data, ec_votes[year])\n",
    "            few = fe.loc[(slice(None), ['Wasted']),:].sum(axis=1)\n",
    "            wtaw = result_wta.loc[(slice(None), ['Wasted']),:].sum(axis=1)\n",
    "            wr = (1.0 - few/wtaw) * 100.0\n",
    "            votes = data['T'].drop(data.index[[51]])\n",
    "            plt.scatter(x=votes, y=wr, color=colors[year], label=str(year), alpha=0.7)\n",
    "    plt.legend()\n",
    "#     plt.ticklabel_format(style='sci')\n",
    "plot_waste_reduction_versus_votes_cast()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_waste_reduction_versus_votes_cast([2000, 2016])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_waste_reduction_versus_votes_cast([2008, 2012])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_waste_reduction_versus_votes_cast([2000, 2004])"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}