oakland_2022.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "139c2851",
   "metadata": {},
   "source": [
    "# 2022 general election for Oakland Unified Board of Education District 4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cd66ae37",
   "metadata": {},
   "source": [
    "This notebook creates a pref_voting ProfileWithTies object for the 2022 general election for Oakland Unified Board of Education District 4 (see https://ballotpedia.org/Oakland_Unified_School_District,_California,_elections\\_(2022)), using the CSV file available at https://dataverse.harvard.edu/file.xhtml?fileId=6938408&version=9.0.\n",
    "\n",
    "This election is of interest because it involved a majority cycle. It also involved a tabulation error for IRV: initially Nick Resnick was declared the winner, but after the tabulation error was discovered, Mike Hutchinson was declared the winner (see https://oaklandside.org/2023/03/07/judge-declares-mike-hutchinson-winner-of-ousd-school-board-race/).\n",
    "\n",
    "We note that only two voters submitted rankings with overvotes. We allow these ballots in the profile below, ignoring the overvotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75fb971c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pref_voting.profiles_with_ties import *\n",
    "from pref_voting.voting_methods import *\n",
    "import csv\n",
    "import pandas as pd\n",
    "from collections import Counter, defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f42b6f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'NICK RESNICK', 1: 'MIKE HUTCHINSON', 2: 'PECOLIA MANIGO', 3: 'Write-in'}\n",
      "+------+-----+------+------+------+------+------+------+-----+------+------+------+-----+------+------+----+----+-----+----+----+----+---+-----+----+---+---+----+---+---+---+----+---+---+---+----+----+---+---+---+----+----+---+---+---+----+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+\n",
      "| 2286 | 908 | 3708 | 2161 | 4342 | 1796 | 1844 | 2223 | 720 | 2228 | 2428 | 1217 | 917 | 1413 | 1712 | 15 | 53 | 526 | 26 | 38 | 12 | 2 | 139 | 19 | 6 | 6 | 27 | 5 | 3 | 3 | 35 | 3 | 7 | 5 | 10 | 14 | 2 | 6 | 6 | 12 | 10 | 5 | 9 | 2 | 18 | 7 | 4 | 3 | 6 | 8 | 1 | 2 | 1 | 4 | 6 | 1 | 6 | 2 | 2 | 1 | 1 | 1 |\n",
      "+------+-----+------+------+------+------+------+------+-----+------+------+------+-----+------+------+----+----+-----+----+----+----+---+-----+----+---+---+----+---+---+---+----+---+---+---+----+----+---+---+---+----+----+---+---+---+----+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+\n",
      "|  1   |  0  |  0   |  0   |      |  1   |  2   |  0   |  2  |  1   |  2   |  1   |  0  |  2   |  2   | 2  | 3  |  1  | 0  | 1  | 2  | 3 |  3  | 1  | 2 | 2 | 1  | 3 | 3 | 3 | 2  | 2 | 3 | 0 | 1  | 0  | 0 | 3 | 1 | 3  | 3  | 0 | 1 | 1 | 3  | 2 | 0 | 3 | 0 | 3 | 3 | 1 | 0 | 2 | 1 | 3 | 0 | 0 | 1 | 2 | 2 | 1 |\n",
      "|      |  1  |      |  1   |      |  0   |      |  2   |  0  |  2   |  1   |  2   |  2  |  0   |  1   | 0  | 1  |  0  | 3  | 2  | 1  | 2 |     | 2  | 1 | 3 | 3  | 1 | 2 | 1 | 1  | 0 | 0 | 1 | 2  | 2  | 3 | 0 | 0 | 0  | 0  | 2 | 3 | 3 | 1  | 3 | 1 | 0 | 3 | 2 | 1 | 0 | 3 | 0 | 0 | 2 | 2 | 3 | 3 | 3 | 3 | 3 |\n",
      "|      |     |      |  2   |      |  2   |      |  1   |     |  0   |      |      |     |  1   |  0   | 3  | 2  |     |    | 0  | 0  | 0 |     | 3  | 3 |   |    | 2 | 1 | 0 | 3  | 1 | 2 | 2 | 3  | 3  | 1 | 2 | 2 |    | 1  | 3 | 2 | 2 |    | 1 | 3 | 1 | 2 | 1 | 0 | 3 | 2 | 3 | 3 |   | 1 | 1 | 0 | 1 | 0 | 0 |\n",
      "|      |     |      |      |      |      |      |      |     |      |      |      |     |      |      |    |    |     |    | 3  | 3  | 1 |     |    | 0 |   |    | 0 | 0 | 2 |    | 3 |   | 3 | 0  |    |   | 1 | 3 |    |    | 1 |   | 0 |    |   |   | 2 | 1 |   |   | 2 |   | 1 |   |   | 3 | 2 |   | 0 | 1 | 2 |\n",
      "+------+-----+------+------+------+------+------+------+-----+------+------+------+-----+------+------+----+----+-----+----+----+----+---+-----+----+---+---+----+---+---+---+----+---+---+---+----+----+---+---+---+----+----+---+---+---+----+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Borda (for Truncated Profiles) winner is {1}\n",
      "Instant Runoff (Truncated Linear Orders) winner is {1}\n",
      "Minimax winner is {1}\n",
      "Plurality winner is {0}\n"
     ]
    }
   ],
   "source": [
    "# Load the CSV file\n",
    "file_path = 'edata/Oakland_11082022_Schoolboarddistrict4.csv'  # Replace with your CSV file path\n",
    "election_data = pd.read_csv(file_path)\n",
    "\n",
    "# Function to process each voter's rankings, ignoring \"overvote\"\n",
    "def process_rankings(row):\n",
    "    seen_candidates = set()\n",
    "    ranking = defaultdict(list)\n",
    "    rank = 1\n",
    "    for i in range(1, 6):\n",
    "        candidate = row[f'rank{i}']\n",
    "        if candidate != 'overvote' and candidate != 'skipped' and candidate not in seen_candidates:\n",
    "            ranking[rank].append(candidate)\n",
    "            seen_candidates.add(candidate)\n",
    "            rank += 1\n",
    "    return {c: r for r, cs in ranking.items() for c in cs}\n",
    "\n",
    "# Apply the function and count occurrences of each ranking\n",
    "election_data['processed_rankings'] = election_data.apply(process_rankings, axis=1)\n",
    "ranking_counts = Counter(election_data['processed_rankings'].apply(tuple))\n",
    "\n",
    "# Create candidate list and set \"Write-in\" index last\n",
    "candidates = [c for c in set().union(*[r for r in ranking_counts]) if c != 'Write-in']\n",
    "candidates.append('Write-in')\n",
    "\n",
    "# Convert rankings to indices format\n",
    "candidate_to_index = {candidate: index for index, candidate in enumerate(candidates)}\n",
    "\n",
    "# Display the candidates and their indices for reference\n",
    "candidates_with_indices = {index: candidate for candidate, index in candidate_to_index.items()}\n",
    "print(candidates_with_indices)\n",
    "\n",
    "# Function to convert rankings (in tuple format) to indices and create dictionaries as keys\n",
    "def ranking_to_indices(ranking):\n",
    "    ranking_dict = {}\n",
    "    for rank, candidate in enumerate(ranking, start=1):\n",
    "        ranking_dict[candidate_to_index.get(candidate, -1)] = rank\n",
    "    return ranking_dict\n",
    "\n",
    "# Creating rankings with indices, ignoring ballots with \"overvote\"\n",
    "rankings = [ranking_to_indices(ranking) for ranking, count in ranking_counts.items()]\n",
    "rcounts = [count for ranking, count in ranking_counts.items()]\n",
    "\n",
    "# Create ProfileWithTies object\n",
    "profile = ProfileWithTies(rankings, rcounts)\n",
    "profile.display()\n",
    "\n",
    "# Treat unranked candidates as tied for last place below ranked candidates for the purposes of the margin graph\n",
    "profile.use_extended_strict_preference()\n",
    "\n",
    "# Display the margin graph\n",
    "profile.display_margin_graph()\n",
    "\n",
    "borda_for_profile_with_ties.display(profile)\n",
    "instant_runoff_for_truncated_linear_orders.display(profile)\n",
    "minimax.display(profile)\n",
    "plurality.display(profile)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77192c86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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