{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Margin Matters\n",
    "\n",
    "Allen Downey\n",
    "\n",
    "[MIT License](https://en.wikipedia.org/wiki/MIT_License)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracking Congress\n",
    "\n",
    "If you Google \"Republican Congress backbone\", you will get a lot of answers to the same question: \"Why do Republicans in Congress vote with Donald Trump, even when his policies conflict with long-standing Republican principles?\n",
    "\n",
    "[This example from *The New York Times*](https://www.nytimes.com/2020/02/05/opinion/trump-senate-acquittal-impeachment.html) suggests it comes down to fear of reprisal from Trump, the party leadership, the conservative media, and social media.\n",
    "\n",
    "At the same time, when pundits are asked to explain a vote from a particular member of Congress, they often start with something like, \"So-and-so represents a district that voted for Trump by a 20% margin...\"\n",
    "\n",
    "So that makes me wonder: if members of Congress are voting out of fear, to what degree are they afraid of their constituents?\n",
    "\n",
    "It seems like our friends at FiveThirtyEight have thought about the same question, because they publish \"An updating tally of how often every member of the House and the Senate votes with or against the president\": \n",
    "\n",
    "* [Tracking Congress In The Age Of Trump](https://projects.fivethirtyeight.com/congress-trump-score/?ex_cid=rrpromo)\n",
    "\n",
    "The table there includes every member of the 115th and 116th Congresses who cast a vote; it reports Trump's margin of victory in the region they represent and the fraction of votes they cast that agree with Trump (they explain how they determined that).\n",
    "\n",
    "I'll use their data to explore the relationship between these voting patterns and Trump's margin victory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data file\n",
    "\n",
    "import os\n",
    "\n",
    "if not os.path.exists('averages.csv'):\n",
    "    !wget https://projects.fivethirtyeight.com/congress-tracker-data/csv/averages.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what the dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>congress</th>\n",
       "      <th>chamber</th>\n",
       "      <th>bioguide</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "      <th>agree_pct</th>\n",
       "      <th>predicted_agree</th>\n",
       "      <th>net_trump_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000055</td>\n",
       "      <td>Aderholt</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Republican</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.957037</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>115</td>\n",
       "      <td>house</td>\n",
       "      <td>A000055</td>\n",
       "      <td>Aderholt</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>R</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.968421</td>\n",
       "      <td>0.946349</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116</td>\n",
       "      <td>house</td>\n",
       "      <td>A000055</td>\n",
       "      <td>Aderholt</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>R</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.985507</td>\n",
       "      <td>0.971751</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000367</td>\n",
       "      <td>Amash</td>\n",
       "      <td>MI</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Independent</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.638037</td>\n",
       "      <td>0.753716</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115</td>\n",
       "      <td>house</td>\n",
       "      <td>A000367</td>\n",
       "      <td>Amash</td>\n",
       "      <td>MI</td>\n",
       "      <td>3.0</td>\n",
       "      <td>R</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.847386</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   congress chamber bioguide last_name state  district        party  votes  \\\n",
       "0         0   house  A000055  Aderholt    AL       4.0   Republican  164.0   \n",
       "1       115   house  A000055  Aderholt    AL       4.0            R   95.0   \n",
       "2       116   house  A000055  Aderholt    AL       4.0            R   69.0   \n",
       "3         0   house  A000367     Amash    MI       3.0  Independent  163.0   \n",
       "4       115   house  A000367     Amash    MI       3.0            R   96.0   \n",
       "\n",
       "   agree_pct  predicted_agree  net_trump_vote  \n",
       "0   0.975610         0.957037            63.0  \n",
       "1   0.968421         0.946349            63.0  \n",
       "2   0.985507         0.971751            63.0  \n",
       "3   0.638037         0.753716             9.4  \n",
       "4   0.541667         0.847386             9.4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('averages.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll select the rows with `congress==0`, which is the sum of votes from the 115th and 116th Congresses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>congress</th>\n",
       "      <th>chamber</th>\n",
       "      <th>bioguide</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "      <th>agree_pct</th>\n",
       "      <th>predicted_agree</th>\n",
       "      <th>net_trump_vote</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000055</td>\n",
       "      <td>Aderholt</td>\n",
       "      <td>AL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Republican</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0.975610</td>\n",
       "      <td>0.957037</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000367</td>\n",
       "      <td>Amash</td>\n",
       "      <td>MI</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Independent</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.638037</td>\n",
       "      <td>0.753716</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000369</td>\n",
       "      <td>Amodei</td>\n",
       "      <td>NV</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Republican</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0.969512</td>\n",
       "      <td>0.808592</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000370</td>\n",
       "      <td>Adams</td>\n",
       "      <td>NC</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>163.0</td>\n",
       "      <td>0.116564</td>\n",
       "      <td>0.135310</td>\n",
       "      <td>-40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>A000371</td>\n",
       "      <td>Aguilar</td>\n",
       "      <td>CA</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>164.0</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.206453</td>\n",
       "      <td>-21.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    congress chamber bioguide last_name state  district        party  votes  \\\n",
       "0          0   house  A000055  Aderholt    AL       4.0   Republican  164.0   \n",
       "3          0   house  A000367     Amash    MI       3.0  Independent  163.0   \n",
       "7          0   house  A000369    Amodei    NV       2.0   Republican  164.0   \n",
       "10         0   house  A000370     Adams    NC      12.0     Democrat  163.0   \n",
       "13         0   house  A000371   Aguilar    CA      31.0     Democrat  164.0   \n",
       "\n",
       "    agree_pct  predicted_agree  net_trump_vote  \n",
       "0    0.975610         0.957037            63.0  \n",
       "3    0.638037         0.753716             9.4  \n",
       "7    0.969512         0.808592            12.3  \n",
       "10   0.116564         0.135310           -40.0  \n",
       "13   0.182927         0.206453           -21.1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both_sessions = (df['congress'] == 0)\n",
    "df_all = df.loc[both_sessions].copy()\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Log odds\n",
    "\n",
    "Since the dependent variable is a percentage, I will transform it to a log-odds scale.\n",
    "\n",
    "Also, to deal with cases where we have a small number of votes, I will use the Bayesian posterior mean for each member of congress, [a little trick that is explained nicely in this video](https://youtu.be/8idr1WZ1A7Q?t=106)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     160.0\n",
       "3     104.0\n",
       "7     159.0\n",
       "10     19.0\n",
       "13     30.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yes = np.round(df_all['votes'] * df_all['agree_pct'])\n",
    "yes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "no = df_all['votes'] - yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    648.000000\n",
       "mean       0.549302\n",
       "std        2.279818\n",
       "min       -3.164068\n",
       "25%       -1.744286\n",
       "50%        1.283486\n",
       "75%        2.708050\n",
       "max        4.276666\n",
       "Name: agree_log_odds, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['agree_log_odds'] = np.log((yes+1) / (no+1))\n",
    "df_all['agree_log_odds'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter\n",
    "\n",
    "Here's a scatter plot of the fraction of votes in agreement with Trump and the margin of victory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def scatter(df, **options):\n",
    "    agree = df['agree_pct']\n",
    "    margin = df['net_trump_vote']\n",
    "    plt.plot(margin, agree, 'o', alpha=0.1, **options)\n",
    "\n",
    "    plt.xlabel(\"Trump's margin of victory\")\n",
    "    plt.ylabel(\"% of votes in agreement with Trump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "scatter(df_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two clusters are apparent: it will come as no surprise that they represent the two parties.\n",
    "\n",
    "I'll generate that plot again with the data points color-coded by party."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Republican     339\n",
       "Democrat       306\n",
       "Independent      3\n",
       "Name: party, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all['party'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "306"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem = (df_all['party'] == 'Democrat')\n",
    "dem_all = df_all[dem]\n",
    "len(dem_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "339"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rep = (df_all['party'] == 'Republican')\n",
    "rep_all = df_all[rep]\n",
    "len(rep_all)"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(dem_all, color='C0', label='Democrat')\n",
    "scatter(rep_all, color='C3', label='Republican')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter log odds\n",
    "\n",
    "Here's what the scatter plots look like on a log-odds scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def scatter2(df, **options):\n",
    "    agree = df['agree_log_odds']\n",
    "    margin = df['net_trump_vote']\n",
    "    plt.plot(margin, agree, 'o', alpha=0.1, **options)\n",
    "\n",
    "    plt.xlabel(\"Trump's margin of victory\")\n",
    "    plt.ylabel(\"Log odds in agreement with Trump\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "scatter2(dem_all, color='C0', label='Democrat')\n",
    "scatter2(rep_all, color='C3', label='Republican')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression\n",
    "\n",
    "Now let's see, within each party, what the relationship looks like between the margin of victory and the voting record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>agree_log_odds</td>  <th>  R-squared:         </th> <td>   0.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   27.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 03 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>2.73e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:27:04</td>     <th>  Log-Likelihood:    </th> <td> -285.88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   306</td>      <th>  AIC:               </th> <td>   575.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   304</td>      <th>  BIC:               </th> <td>   583.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>   -1.5457</td> <td>    0.050</td> <td>  -30.947</td> <td> 0.000</td> <td>   -1.644</td> <td>   -1.447</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>net_trump_vote</th> <td>    0.0074</td> <td>    0.001</td> <td>    5.260</td> <td> 0.000</td> <td>    0.005</td> <td>    0.010</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.367</td> <th>  Durbin-Watson:     </th> <td>   1.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.306</td> <th>  Jarque-Bera (JB):  </th> <td>   2.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.016</td> <th>  Prob(JB):          </th> <td>   0.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.434</td> <th>  Cond. No.          </th> <td>    49.9</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         agree_log_odds   R-squared:                       0.083\n",
       "Model:                            OLS   Adj. R-squared:                  0.080\n",
       "Method:                 Least Squares   F-statistic:                     27.66\n",
       "Date:                Wed, 03 Jun 2020   Prob (F-statistic):           2.73e-07\n",
       "Time:                        22:27:04   Log-Likelihood:                -285.88\n",
       "No. Observations:                 306   AIC:                             575.8\n",
       "Df Residuals:                     304   BIC:                             583.2\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept         -1.5457      0.050    -30.947      0.000      -1.644      -1.447\n",
       "net_trump_vote     0.0074      0.001      5.260      0.000       0.005       0.010\n",
       "==============================================================================\n",
       "Omnibus:                        2.367   Durbin-Watson:                   1.122\n",
       "Prob(Omnibus):                  0.306   Jarque-Bera (JB):                2.410\n",
       "Skew:                           0.016   Prob(JB):                        0.300\n",
       "Kurtosis:                       3.434   Cond. No.                         49.9\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols('agree_log_odds ~ net_trump_vote', data=dem_all)\n",
    "results_dem = model.fit()\n",
    "results_dem.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>     <td>agree_log_odds</td>  <th>  R-squared:         </th> <td>   0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   24.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 03 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>1.23e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:27:04</td>     <th>  Log-Likelihood:    </th> <td> -369.23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   339</td>      <th>  AIC:               </th> <td>   742.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   337</td>      <th>  BIC:               </th> <td>   750.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>      <td>    2.3525</td> <td>    0.066</td> <td>   35.410</td> <td> 0.000</td> <td>    2.222</td> <td>    2.483</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>net_trump_vote</th> <td>    0.0126</td> <td>    0.003</td> <td>    4.941</td> <td> 0.000</td> <td>    0.008</td> <td>    0.018</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>58.343</td> <th>  Durbin-Watson:     </th> <td>   1.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 140.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.841</td> <th>  Prob(JB):          </th> <td>2.82e-31</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.670</td> <th>  Cond. No.          </th> <td>    44.3</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:         agree_log_odds   R-squared:                       0.068\n",
       "Model:                            OLS   Adj. R-squared:                  0.065\n",
       "Method:                 Least Squares   F-statistic:                     24.41\n",
       "Date:                Wed, 03 Jun 2020   Prob (F-statistic):           1.23e-06\n",
       "Time:                        22:27:04   Log-Likelihood:                -369.23\n",
       "No. Observations:                 339   AIC:                             742.5\n",
       "Df Residuals:                     337   BIC:                             750.1\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "Intercept          2.3525      0.066     35.410      0.000       2.222       2.483\n",
       "net_trump_vote     0.0126      0.003      4.941      0.000       0.008       0.018\n",
       "==============================================================================\n",
       "Omnibus:                       58.343   Durbin-Watson:                   1.867\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              140.684\n",
       "Skew:                          -0.841   Prob(JB):                     2.82e-31\n",
       "Kurtosis:                       5.670   Cond. No.                         44.3\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.ols('agree_log_odds ~ net_trump_vote', data=rep_all)\n",
    "results_rep = model.fit()\n",
    "results_rep.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "To visualize the results of the models, I'll plot their predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "hypo = pd.DataFrame()\n",
    "hypo['net_trump_vote'] = np.linspace(-80, 20)\n",
    "\n",
    "pred_dem = results_dem.predict(hypo)\n",
    "pred_dem.index = hypo['net_trump_vote']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "hypo = pd.DataFrame()\n",
    "hypo['net_trump_vote'] = np.linspace(-10, 70)\n",
    "\n",
    "pred_rep = results_rep.predict(hypo)\n",
    "pred_rep.index = hypo['net_trump_vote']"
   ]
  },
  {
   "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": [
    "pred_rep.plot(color='C3', label='Republicans')\n",
    "pred_dem.plot(color='C0', label='Democrats')\n",
    "\n",
    "scatter2(dem_all, color='C0')\n",
    "scatter2(rep_all, color='C3')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the margin matters.\n",
    "\n",
    "The slope for democrats is lower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept        -1.545690\n",
       "net_trump_vote    0.007447\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dem.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept         2.352523\n",
       "net_trump_vote    0.012576\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_rep.params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like it gets dragged down by a cluster of representatives from swing districts who vote against Trump with remarkable consistency.  Let's see who they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "swing = (np.abs(dem_all['net_trump_vote']) < 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "anti_trump = (dem_all['agree_log_odds'] < -2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>congress</th>\n",
       "      <th>chamber</th>\n",
       "      <th>bioguide</th>\n",
       "      <th>last_name</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "      <th>agree_pct</th>\n",
       "      <th>predicted_agree</th>\n",
       "      <th>net_trump_vote</th>\n",
       "      <th>agree_log_odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>C001117</td>\n",
       "      <td>Casten</td>\n",
       "      <td>IL</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.120612</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>C001119</td>\n",
       "      <td>Craig</td>\n",
       "      <td>MN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.285124</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>D000629</td>\n",
       "      <td>Davids</td>\n",
       "      <td>KS</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.217135</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>D000630</td>\n",
       "      <td>Delgado</td>\n",
       "      <td>NY</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.503497</td>\n",
       "      <td>6.8</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>H001066</td>\n",
       "      <td>Horsford</td>\n",
       "      <td>NV</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.146273</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>H001081</td>\n",
       "      <td>Hayes</td>\n",
       "      <td>CT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.158511</td>\n",
       "      <td>-4.1</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>H001087</td>\n",
       "      <td>Hill</td>\n",
       "      <td>CA</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.101966</td>\n",
       "      <td>-6.7</td>\n",
       "      <td>-2.813411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>H001090</td>\n",
       "      <td>Harder</td>\n",
       "      <td>CA</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.177954</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.862201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>K000368</td>\n",
       "      <td>Kirkpatrick</td>\n",
       "      <td>AZ</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.140082</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>-3.044522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>K000394</td>\n",
       "      <td>Kim</td>\n",
       "      <td>NJ</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.477804</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>L000592</td>\n",
       "      <td>Levin</td>\n",
       "      <td>MI</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.112899</td>\n",
       "      <td>-7.8</td>\n",
       "      <td>-3.164068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>L000593</td>\n",
       "      <td>Levin</td>\n",
       "      <td>CA</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.115675</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-2.862201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>M001203</td>\n",
       "      <td>Malinowski</td>\n",
       "      <td>NJ</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.219601</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1027</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>P000618</td>\n",
       "      <td>Porter</td>\n",
       "      <td>CA</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.140677</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>-2.610070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>R000616</td>\n",
       "      <td>Rouda</td>\n",
       "      <td>CA</td>\n",
       "      <td>48.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.206456</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-2.610070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>S001207</td>\n",
       "      <td>Sherrill</td>\n",
       "      <td>NJ</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.276188</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-2.610070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>S001208</td>\n",
       "      <td>Slotkin</td>\n",
       "      <td>MI</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.499208</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>S001215</td>\n",
       "      <td>Stevens</td>\n",
       "      <td>MI</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.402540</td>\n",
       "      <td>4.4</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>S001216</td>\n",
       "      <td>Schrier</td>\n",
       "      <td>WA</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.179072</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.610070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>0</td>\n",
       "      <td>house</td>\n",
       "      <td>U000040</td>\n",
       "      <td>Underwood</td>\n",
       "      <td>IL</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.382518</td>\n",
       "      <td>3.9</td>\n",
       "      <td>-2.624669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      congress chamber bioguide    last_name state  district     party  votes  \\\n",
       "286          0   house  C001117       Casten    IL       6.0  Democrat   72.0   \n",
       "290          0   house  C001119        Craig    MN       2.0  Democrat   72.0   \n",
       "371          0   house  D000629       Davids    KS       3.0  Democrat   72.0   \n",
       "373          0   house  D000630      Delgado    NY      19.0  Democrat   72.0   \n",
       "570          0   house  H001066     Horsford    NV       4.0  Democrat   72.0   \n",
       "597          0   house  H001081        Hayes    CT       5.0  Democrat   72.0   \n",
       "606          0   house  H001087         Hill    CA      25.0  Democrat   51.0   \n",
       "610          0   house  H001090       Harder    CA      10.0  Democrat   72.0   \n",
       "667          0   house  K000368  Kirkpatrick    AZ       2.0  Democrat   64.0   \n",
       "712          0   house  K000394          Kim    NJ       3.0  Democrat   72.0   \n",
       "809          0   house  L000592        Levin    MI       9.0  Democrat   72.0   \n",
       "811          0   house  L000593        Levin    CA      49.0  Democrat   72.0   \n",
       "906          0   house  M001203   Malinowski    NJ       7.0  Democrat   72.0   \n",
       "1027         0   house  P000618       Porter    CA      45.0  Democrat   71.0   \n",
       "1121         0   house  R000616        Rouda    CA      48.0  Democrat   71.0   \n",
       "1231         0   house  S001207     Sherrill    NJ      11.0  Democrat   71.0   \n",
       "1233         0   house  S001208      Slotkin    MI       8.0  Democrat   72.0   \n",
       "1247         0   house  S001215      Stevens    MI      11.0  Democrat   72.0   \n",
       "1249         0   house  S001216      Schrier    WA       8.0  Democrat   71.0   \n",
       "1306         0   house  U000040    Underwood    IL      14.0  Democrat   72.0   \n",
       "\n",
       "      agree_pct  predicted_agree  net_trump_vote  agree_log_odds  \n",
       "286    0.055556         0.120612            -7.0       -2.624669  \n",
       "290    0.055556         0.285124             1.2       -2.624669  \n",
       "371    0.055556         0.217135            -1.2       -2.624669  \n",
       "373    0.055556         0.503497             6.8       -2.624669  \n",
       "570    0.055556         0.146273            -4.9       -2.624669  \n",
       "597    0.055556         0.158511            -4.1       -2.624669  \n",
       "606    0.039216         0.101966            -6.7       -2.813411  \n",
       "610    0.041667         0.177954            -3.0       -2.862201  \n",
       "667    0.031250         0.140082            -4.9       -3.044522  \n",
       "712    0.055556         0.477804             6.2       -2.624669  \n",
       "809    0.027778         0.112899            -7.8       -3.164068  \n",
       "811    0.041667         0.115675            -7.5       -2.862201  \n",
       "906    0.055556         0.219601            -1.1       -2.624669  \n",
       "1027   0.056338         0.140677            -5.4       -2.610070  \n",
       "1121   0.056338         0.206456            -1.7       -2.610070  \n",
       "1231   0.056338         0.276188             0.9       -2.610070  \n",
       "1233   0.055556         0.499208             6.7       -2.624669  \n",
       "1247   0.055556         0.402540             4.4       -2.624669  \n",
       "1249   0.056338         0.179072            -3.0       -2.610070  \n",
       "1306   0.055556         0.382518             3.9       -2.624669  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dem_all[swing & anti_trump]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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