NaimKabir/data-sourcing.ipynb

## data-sourcing.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting + Formatting Data\n",
    "\n",
    "I downloaded Oakland County, MI election data from [**this link.**](https://results.enr.clarityelections.com//MI/Oakland/105840/269402/reports/detailxml.zip)\n",
    "\n",
    "You can navigate to it from the Oakland county [**gov site**](https://www.oakgov.com/clerkrod/elections/Pages/default.aspx), which will redirect you to that results site if you for to \"Unofficial Results\" for November's elections.\n",
    "\n",
    "I have to use XML rather than their XLS offering because I don't want to pay to use Excel and get spreadsheets. Would it kill people to just use generic CSVs? Godddamn. Anyway, such is life."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext blackcellmagic\n",
    "\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as et\n",
    "tree = et.parse('detail.xml') \n",
    "root = tree.getroot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I'll just grab the contests I care about: straight ticket votes and votes for presidential candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "straight_ticket_xml = None\n",
    "presidential_vote_xml = None\n",
    "for child in root:\n",
    "    if child.attrib.get(\"text\", \"\") == \"Straight Party Ticket\":\n",
    "        straight_ticket_xml = child\n",
    "    if (\n",
    "        child.attrib.get(\"text\", \"\")\n",
    "        == \"Electors of President and Vice-President of the United States\"\n",
    "    ):\n",
    "        presidential_vote_xml = child\n",
    "assert straight_ticket_xml is not None\n",
    "assert presidential_vote_xml is not None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what kind of info we're working with here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VoteType Undervotes\n",
      "VoteType Overvotes\n",
      "Choice Democratic Party\n",
      "Choice Republican Party\n",
      "Choice Libertarian Party\n",
      "Choice U.S. Taxpayers Party\n",
      "Choice Working Class Party\n",
      "Choice Green Party\n",
      "Choice Natural Law Party\n"
     ]
    }
   ],
   "source": [
    "for child in straight_ticket_xml:\n",
    "    print(child.tag, child.attrib.get(\"name\", child.attrib.get(\"text\", \"\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have undervotes and overvotes, which we don't super care about so I'll just ignore.\n",
    "\n",
    "We also have data for each choice voter can make, which will be useful for grabbing vote totals at the precinct level and structuring our dataset. Ok, let's start to get some structure so we can work with dataframes instead of XML (god I hate XML)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vote_dataframe(vote_xml):\n",
    "    \"\"\"Let's deal with tables instead of XML.\"\"\"\n",
    "\n",
    "    choice_data = {}  # store precinct votes for each choice voters can make\n",
    "    for child in vote_xml:\n",
    "        if child.tag == \"Choice\":\n",
    "            precinct_data = {}  # store votes for each precint\n",
    "\n",
    "            # we don't care about the Absentee vs. In-person distinction, so we'll\n",
    "            # just pool votes from both types\n",
    "            for vote_type_node in child:\n",
    "                for precinct_node in vote_type_node:\n",
    "                    precinct_name = precinct_node.attrib[\"name\"]\n",
    "                    precinct_data[precinct_name] = precinct_data.get(\n",
    "                        precinct_name, 0\n",
    "                    ) + int(precinct_node.attrib[\"votes\"])\n",
    "\n",
    "            choice_data[child.attrib[\"text\"]] = precinct_data\n",
    "    return pd.DataFrame(choice_data)\n",
    "\n",
    "\n",
    "straight_ticket_data = get_vote_dataframe(straight_ticket_xml)\n",
    "presidential_vote_data = get_vote_dataframe(presidential_vote_xml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Democratic Party</th>\n",
       "      <th>Republican Party</th>\n",
       "      <th>Libertarian Party</th>\n",
       "      <th>U.S. Taxpayers Party</th>\n",
       "      <th>Working Class Party</th>\n",
       "      <th>Green Party</th>\n",
       "      <th>Natural Law Party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Addison Township, Precinct 1</th>\n",
       "      <td>226</td>\n",
       "      <td>673</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addison Township, Precinct 2</th>\n",
       "      <td>236</td>\n",
       "      <td>716</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addison Township, Precinct 3</th>\n",
       "      <td>155</td>\n",
       "      <td>435</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloomfield Township, Precinct 1</th>\n",
       "      <td>387</td>\n",
       "      <td>392</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloomfield Township, Precinct 2</th>\n",
       "      <td>610</td>\n",
       "      <td>436</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walled Lake, Precinct 3</th>\n",
       "      <td>287</td>\n",
       "      <td>359</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 1</th>\n",
       "      <td>452</td>\n",
       "      <td>722</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 2</th>\n",
       "      <td>380</td>\n",
       "      <td>537</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 3</th>\n",
       "      <td>986</td>\n",
       "      <td>372</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 4</th>\n",
       "      <td>627</td>\n",
       "      <td>660</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Democratic Party  Republican Party  \\\n",
       "Addison Township, Precinct 1                  226               673   \n",
       "Addison Township, Precinct 2                  236               716   \n",
       "Addison Township, Precinct 3                  155               435   \n",
       "Bloomfield Township, Precinct 1               387               392   \n",
       "Bloomfield Township, Precinct 2               610               436   \n",
       "...                                           ...               ...   \n",
       "Walled Lake, Precinct 3                       287               359   \n",
       "Wixom, Precinct 1                             452               722   \n",
       "Wixom, Precinct 2                             380               537   \n",
       "Wixom, Precinct 3                             986               372   \n",
       "Wixom, Precinct 4                             627               660   \n",
       "\n",
       "                                 Libertarian Party  U.S. Taxpayers Party  \\\n",
       "Addison Township, Precinct 1                     4                     0   \n",
       "Addison Township, Precinct 2                     4                     1   \n",
       "Addison Township, Precinct 3                     2                     0   \n",
       "Bloomfield Township, Precinct 1                  5                     1   \n",
       "Bloomfield Township, Precinct 2                  5                     0   \n",
       "...                                            ...                   ...   \n",
       "Walled Lake, Precinct 3                          4                     0   \n",
       "Wixom, Precinct 1                               10                     1   \n",
       "Wixom, Precinct 2                                4                     0   \n",
       "Wixom, Precinct 3                                7                     8   \n",
       "Wixom, Precinct 4                                7                     2   \n",
       "\n",
       "                                 Working Class Party  Green Party  \\\n",
       "Addison Township, Precinct 1                       0            2   \n",
       "Addison Township, Precinct 2                       1            2   \n",
       "Addison Township, Precinct 3                       1            1   \n",
       "Bloomfield Township, Precinct 1                    0            1   \n",
       "Bloomfield Township, Precinct 2                    0            2   \n",
       "...                                              ...          ...   \n",
       "Walled Lake, Precinct 3                            2            1   \n",
       "Wixom, Precinct 1                                  0            1   \n",
       "Wixom, Precinct 2                                  2            0   \n",
       "Wixom, Precinct 3                                  7            5   \n",
       "Wixom, Precinct 4                                  5            2   \n",
       "\n",
       "                                 Natural Law Party  \n",
       "Addison Township, Precinct 1                     0  \n",
       "Addison Township, Precinct 2                     0  \n",
       "Addison Township, Precinct 3                     0  \n",
       "Bloomfield Township, Precinct 1                  0  \n",
       "Bloomfield Township, Precinct 2                  0  \n",
       "...                                            ...  \n",
       "Walled Lake, Precinct 3                          1  \n",
       "Wixom, Precinct 1                                0  \n",
       "Wixom, Precinct 2                                0  \n",
       "Wixom, Precinct 3                                1  \n",
       "Wixom, Precinct 4                                1  \n",
       "\n",
       "[506 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "straight_ticket_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make presidential header names a bit more wieldy, I'm going to abbreviate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rename_mapper = {\n",
    "    \"Joseph R. Biden/Kamala D. Harris\": \"biden\",\n",
    "    \"Donald J. Trump/Michael R. Pence\": \"trump\",\n",
    "    \"Jo Jorgensen/Jeremy Cohen\": \"jorgensen\",\n",
    "    \"Don Blankenship/William Mohr\": \"blankenship\",\n",
    "    \"Howie Hawkins/Angela Walker\": \"hawkins\",\n",
    "    \"Rocky De La Fuente/Darcy Richardson\": \"rocky\",\n",
    "}\n",
    "presidential_vote_data = presidential_vote_data.rename(columns=rename_mapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>biden</th>\n",
       "      <th>trump</th>\n",
       "      <th>jorgensen</th>\n",
       "      <th>blankenship</th>\n",
       "      <th>hawkins</th>\n",
       "      <th>rocky</th>\n",
       "      <th>Rejected write-ins</th>\n",
       "      <th>Unassigned write-ins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Addison Township, Precinct 1</th>\n",
       "      <td>439</td>\n",
       "      <td>1123</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addison Township, Precinct 2</th>\n",
       "      <td>484</td>\n",
       "      <td>1096</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Addison Township, Precinct 3</th>\n",
       "      <td>336</td>\n",
       "      <td>713</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloomfield Township, Precinct 1</th>\n",
       "      <td>685</td>\n",
       "      <td>617</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bloomfield Township, Precinct 2</th>\n",
       "      <td>1099</td>\n",
       "      <td>710</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walled Lake, Precinct 3</th>\n",
       "      <td>520</td>\n",
       "      <td>574</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 1</th>\n",
       "      <td>1011</td>\n",
       "      <td>1206</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 2</th>\n",
       "      <td>815</td>\n",
       "      <td>996</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 3</th>\n",
       "      <td>1379</td>\n",
       "      <td>540</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wixom, Precinct 4</th>\n",
       "      <td>1103</td>\n",
       "      <td>1006</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 biden  trump  jorgensen  blankenship  \\\n",
       "Addison Township, Precinct 1       439   1123         19            1   \n",
       "Addison Township, Precinct 2       484   1096         17            0   \n",
       "Addison Township, Precinct 3       336    713          8            0   \n",
       "Bloomfield Township, Precinct 1    685    617          6            0   \n",
       "Bloomfield Township, Precinct 2   1099    710         14            0   \n",
       "...                                ...    ...        ...          ...   \n",
       "Walled Lake, Precinct 3            520    574         18            0   \n",
       "Wixom, Precinct 1                 1011   1206         15            4   \n",
       "Wixom, Precinct 2                  815    996         33            1   \n",
       "Wixom, Precinct 3                 1379    540         27            4   \n",
       "Wixom, Precinct 4                 1103   1006         28            3   \n",
       "\n",
       "                                 hawkins  rocky  Rejected write-ins  \\\n",
       "Addison Township, Precinct 1           2      1                   0   \n",
       "Addison Township, Precinct 2           4      1                   0   \n",
       "Addison Township, Precinct 3           0      0                   0   \n",
       "Bloomfield Township, Precinct 1        2      1                   0   \n",
       "Bloomfield Township, Precinct 2        7      2                   0   \n",
       "...                                  ...    ...                 ...   \n",
       "Walled Lake, Precinct 3                2      0                   0   \n",
       "Wixom, Precinct 1                      6      0                   0   \n",
       "Wixom, Precinct 2                      4      0                   0   \n",
       "Wixom, Precinct 3                      9      0                   0   \n",
       "Wixom, Precinct 4                      8      2                   0   \n",
       "\n",
       "                                 Unassigned write-ins  \n",
       "Addison Township, Precinct 1                        1  \n",
       "Addison Township, Precinct 2                        4  \n",
       "Addison Township, Precinct 3                        4  \n",
       "Bloomfield Township, Precinct 1                     3  \n",
       "Bloomfield Township, Precinct 2                     3  \n",
       "...                                               ...  \n",
       "Walled Lake, Precinct 3                             1  \n",
       "Wixom, Precinct 1                                   7  \n",
       "Wixom, Precinct 2                                   2  \n",
       "Wixom, Precinct 3                                   7  \n",
       "Wixom, Precinct 4                                   3  \n",
       "\n",
       "[506 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "presidential_vote_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Ayyadurai's Analysis, This Time On Democrats\n",
    "\n",
    "We'll do the same operations as Ayyadurai. We'll take fractions of the straight-ticket vote for Democrats, and fraction of votes for Biden among split-ticket voters to conduct our analysis.\n",
    "\n",
    "To get split-ticket voter percentages alone, we need to remove straight-ticket votes we know go towards candidates. This is to get as close to Ayyadurai's quantities as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_ticket_data = presidential_vote_data.copy()\n",
    "split_ticket_data[\"biden\"] -= straight_ticket_data[\"Democratic Party\"]\n",
    "split_ticket_data[\"trump\"] -= straight_ticket_data[\"Republican Party\"]\n",
    "split_ticket_data[\"jorgensen\"] -= straight_ticket_data[\"Libertarian Party\"]\n",
    "split_ticket_data[\"blankenship\"] -= straight_ticket_data[\"U.S. Taxpayers Party\"]\n",
    "split_ticket_data[\"hawkins\"] -= straight_ticket_data[\"Green Party\"]\n",
    "split_ticket_data[\"rocky\"] -= straight_ticket_data[\"Natural Law Party\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get totals so we can frame our numbers as percentages\n",
    "\n",
    "straight_ticket_data[\"totals\"] = straight_ticket_data.sum(axis=1)\n",
    "split_ticket_data[\"totals\"] = split_ticket_data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get percentages\n",
    "straight_ticket_data[\"%_democrat\"] = (\n",
    "    straight_ticket_data[\"Democratic Party\"] / straight_ticket_data[\"totals\"]\n",
    ")\n",
    "split_ticket_data[\"%_biden\"] = (\n",
    "    split_ticket_data[\"biden\"] / split_ticket_data[\"totals\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot precinct percentages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Biden Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "seaborn.set()\n",
    "\n",
    "diffs = split_ticket_data[\"%_biden\"] - straight_ticket_data[\"%_democrat\"]\n",
    "plt.scatter(straight_ticket_data[\"%_democrat\"], diffs)\n",
    "plt.xlabel(\"% straight ticket democrats\")\n",
    "plt.ylabel(\"% individual biden votes - % straight ticket democrats\")\n",
    "\n",
    "plt.savefig('biden')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trump Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get percentages\n",
    "straight_ticket_data[\"%_republican\"] = (\n",
    "    straight_ticket_data[\"Republican Party\"] / straight_ticket_data[\"totals\"]\n",
    ")\n",
    "split_ticket_data[\"%_trump\"] = (\n",
    "    split_ticket_data[\"trump\"] / split_ticket_data[\"totals\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffs = split_ticket_data[\"%_trump\"] - straight_ticket_data[\"%_republican\"]\n",
    "plt.scatter(straight_ticket_data[\"%_republican\"], diffs)\n",
    "plt.xlabel(\"% straight ticket republicans\")\n",
    "plt.ylabel(\"% individual trump votes - % straight ticket republicans\")\n",
    "\n",
    "plt.savefig('trump')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They both slope down."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## True Correlation Between Split Ticket Votes & Straight Ticket\n",
    "\n",
    "While we're here, may as well demonstrate the actual correlation between the % of split-ticket votes for Trump and the % of straight-ticket Republican votes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.5985107836180562, intercept=0.11211390676525906, rvalue=0.9003374902755328, pvalue=3.0993297588564935e-184, stderr=0.01288643105960907)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import linregress\n",
    "import numpy as np\n",
    "\n",
    "linear_regression = linregress(\n",
    "    straight_ticket_data[\"%_republican\"], split_ticket_data[\"%_trump\"]\n",
    ")\n",
    "print(linear_regression)\n",
    "\n",
    "# plot the trendline\n",
    "lin_x = np.linspace(\n",
    "    min(straight_ticket_data[\"%_republican\"]),\n",
    "    max(straight_ticket_data[\"%_republican\"]),\n",
    "    1000,\n",
    ")\n",
    "y_hat = lin_x * linear_regression.slope + linear_regression.intercept\n",
    "ax = plt.plot(lin_x, y_hat, color=\"k\", linestyle=\"--\", linewidth=4)\n",
    "\n",
    "plt.annotate(\n",
    "    f\"y = {round(linear_regression.slope, 2)}*x + {round(linear_regression.intercept, 2)}\",\n",
    "    xy=(150, 90),\n",
    "    xycoords=\"figure points\",\n",
    "    fontsize= 16\n",
    ")\n",
    "\n",
    "# plot scatter plot\n",
    "plt.scatter(straight_ticket_data[\"%_republican\"], split_ticket_data[\"%_trump\"], alpha=0.4)\n",
    "plt.xlabel(\"% straight ticket republicans\")\n",
    "plt.ylabel(\"% individual trump votes\")\n",
    "\n",
    "plt.title(\"Correlation from Oakland County, MI data\")\n",
    "\n",
    "plt.savefig(\"trump-corr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}