examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4/11/24\n",
    "\n",
    "Boolean indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "test = np.arange(20)\n",
    "test[test>6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test>6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.44398415, 9.36420893, 9.00317465, 7.63789732, 9.37600861])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_test = np.random.rand(10)*10\n",
    "new_test[new_test>7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.96026508 2.4103536  8.04444795]\n",
      " [3.2156973  4.59750843 1.79751238]\n",
      " [8.66740412 8.90578376 9.04371427]\n",
      " [8.20323187 8.98261525 7.88502701]\n",
      " [9.5821283  8.7368732  8.55561514]\n",
      " [8.68181363 4.46928926 5.25214926]\n",
      " [4.37933932 0.63781719 5.60018471]\n",
      " [2.17679609 5.38520742 8.56754552]\n",
      " [5.4914045  4.83645937 7.13848968]\n",
      " [5.63276287 8.29734102 7.56031025]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[8.66740412, 8.90578376, 9.04371427],\n",
       "       [8.20323187, 8.98261525, 7.88502701],\n",
       "       [9.5821283 , 8.7368732 , 8.55561514],\n",
       "       [8.68181363, 4.46928926, 5.25214926]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test3 = np.random.rand(10,3)*10\n",
    "print(test3)\n",
    "#test3[test3>7]\n",
    "test3[test3[:,0]>6, :] #rows of array where the first column entry is greater than 6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.66740412, 8.90578376, 9.04371427],\n",
       "       [8.20323187, 8.98261525, 7.88502701],\n",
       "       [9.5821283 , 8.7368732 , 8.55561514],\n",
       "       [5.63276287, 8.29734102, 7.56031025]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test3[test3[:,1]>6, :] #rows of array where the second column entry is greater than 6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>CD</th>\n",
       "      <th>Incumbent</th>\n",
       "      <th>Party</th>\n",
       "      <th>Clinton\\n2016</th>\n",
       "      <th>Trump\\n2016</th>\n",
       "      <th>Obama\\n2012</th>\n",
       "      <th>Romney\\n2012</th>\n",
       "      <th>Obama\\n2008</th>\n",
       "      <th>McCain\\n2008</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK-AL</td>\n",
       "      <td>Young, Don</td>\n",
       "      <td>(R)</td>\n",
       "      <td>37.6</td>\n",
       "      <td>52.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>55.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL-01</td>\n",
       "      <td>Byrne, Bradley</td>\n",
       "      <td>(R)</td>\n",
       "      <td>34.1</td>\n",
       "      <td>63.5</td>\n",
       "      <td>37.4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>38.5</td>\n",
       "      <td>60.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL-02</td>\n",
       "      <td>Roby, Martha</td>\n",
       "      <td>(R)</td>\n",
       "      <td>33.0</td>\n",
       "      <td>64.9</td>\n",
       "      <td>36.4</td>\n",
       "      <td>62.9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL-03</td>\n",
       "      <td>Rogers, Mike</td>\n",
       "      <td>(R)</td>\n",
       "      <td>32.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>36.8</td>\n",
       "      <td>62.3</td>\n",
       "      <td>36.6</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AL-04</td>\n",
       "      <td>Aderholt, Rob</td>\n",
       "      <td>(R)</td>\n",
       "      <td>17.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>74.8</td>\n",
       "      <td>25.5</td>\n",
       "      <td>73.3</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>WI-08</td>\n",
       "      <td>Gallagher, Mike</td>\n",
       "      <td>(R)</td>\n",
       "      <td>38.6</td>\n",
       "      <td>56.2</td>\n",
       "      <td>47.6</td>\n",
       "      <td>51.3</td>\n",
       "      <td>53.7</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>WV-01</td>\n",
       "      <td>McKinley, David</td>\n",
       "      <td>(R)</td>\n",
       "      <td>26.4</td>\n",
       "      <td>68.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>62.2</td>\n",
       "      <td>41.5</td>\n",
       "      <td>56.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>WV-02</td>\n",
       "      <td>Mooney, Alex</td>\n",
       "      <td>(R)</td>\n",
       "      <td>29.4</td>\n",
       "      <td>65.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>43.9</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>WV-03</td>\n",
       "      <td>Jenkins, Evan</td>\n",
       "      <td>(R)</td>\n",
       "      <td>23.3</td>\n",
       "      <td>72.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>65.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>WY-AL</td>\n",
       "      <td>Cheney, Liz</td>\n",
       "      <td>(R)</td>\n",
       "      <td>22.5</td>\n",
       "      <td>70.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>32.7</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CD        Incumbent Party  Clinton\\n2016  Trump\\n2016  Obama\\n2012  \\\n",
       "0    AK-AL       Young, Don   (R)           37.6         52.8         41.2   \n",
       "1    AL-01   Byrne, Bradley   (R)           34.1         63.5         37.4   \n",
       "2    AL-02     Roby, Martha   (R)           33.0         64.9         36.4   \n",
       "3    AL-03     Rogers, Mike   (R)           32.3         65.3         36.8   \n",
       "4    AL-04    Aderholt, Rob   (R)           17.4         80.4         24.0   \n",
       "..     ...              ...   ...            ...          ...          ...   \n",
       "430  WI-08  Gallagher, Mike   (R)           38.6         56.2         47.6   \n",
       "431  WV-01  McKinley, David   (R)           26.4         68.0         35.5   \n",
       "432  WV-02     Mooney, Alex   (R)           29.4         65.8         38.0   \n",
       "433  WV-03    Jenkins, Evan   (R)           23.3         72.5         32.8   \n",
       "434  WY-AL      Cheney, Liz   (R)           22.5         70.1         27.6   \n",
       "\n",
       "     Romney\\n2012  Obama\\n2008  McCain\\n2008  \n",
       "0            55.3         38.1          59.7  \n",
       "1            61.8         38.5          60.9  \n",
       "2            62.9         35.0          64.5  \n",
       "3            62.3         36.6          62.6  \n",
       "4            74.8         25.5          73.3  \n",
       "..            ...          ...           ...  \n",
       "430          51.3         53.7          45.0  \n",
       "431          62.2         41.5          56.7  \n",
       "432          60.0         43.9          54.7  \n",
       "433          65.0         42.3          55.7  \n",
       "434          68.2         32.7          65.2  \n",
       "\n",
       "[435 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "elections = pd.read_csv('Daily_Kos_Elections_08_12_16_congress_districts.csv')\n",
    "elections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chaging the indexing from the leftmost column 0, 1, 2, ... to the congressional district \n",
    "elections = elections.set_index(elections['CD'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CD                    AK-AL\n",
       "Incumbent        Young, Don\n",
       "Party                   (R)\n",
       "Clinton\\n2016          37.6\n",
       "Trump\\n2016            52.8\n",
       "Obama\\n2012            41.2\n",
       "Romney\\n2012           55.3\n",
       "Obama\\n2008            38.1\n",
       "McCain\\n2008           59.7\n",
       "Name: AK-AL, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To access specific rows, use 'loc' and 'iloc'\n",
    "elections.loc['AK-AL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Incumbent    Byrne, Bradley\n",
       "Party                   (R)\n",
       "Name: AL-01, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elections.loc['AL-01', ['Incumbent', 'Party']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CD                      AL-02\n",
       "Incumbent        Roby, Martha\n",
       "Party                     (R)\n",
       "Clinton\\n2016            33.0\n",
       "Trump\\n2016              64.9\n",
       "Obama\\n2012              36.4\n",
       "Romney\\n2012             62.9\n",
       "Obama\\n2008              35.0\n",
       "McCain\\n2008             64.5\n",
       "Name: AL-02, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elections.iloc[2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>CD</th>\n",
       "      <th>Incumbent</th>\n",
       "      <th>Party</th>\n",
       "      <th>Clinton\\n2016</th>\n",
       "      <th>Trump\\n2016</th>\n",
       "      <th>Obama\\n2012</th>\n",
       "      <th>Romney\\n2012</th>\n",
       "      <th>Obama\\n2008</th>\n",
       "      <th>McCain\\n2008</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD</th>\n",
       "      <th></th>\n",
       "      <th></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>AK-AL</th>\n",
       "      <td>AK-AL</td>\n",
       "      <td>Young, Don</td>\n",
       "      <td>(R)</td>\n",
       "      <td>37.6</td>\n",
       "      <td>52.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>55.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL-06</th>\n",
       "      <td>AL-06</td>\n",
       "      <td>Palmer, Gary</td>\n",
       "      <td>(R)</td>\n",
       "      <td>26.1</td>\n",
       "      <td>70.8</td>\n",
       "      <td>24.7</td>\n",
       "      <td>74.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>74.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR-02</th>\n",
       "      <td>AR-02</td>\n",
       "      <td>Hill, French</td>\n",
       "      <td>(R)</td>\n",
       "      <td>41.7</td>\n",
       "      <td>52.4</td>\n",
       "      <td>42.9</td>\n",
       "      <td>54.7</td>\n",
       "      <td>44.3</td>\n",
       "      <td>53.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR-03</th>\n",
       "      <td>AR-03</td>\n",
       "      <td>Womack, Steve</td>\n",
       "      <td>(R)</td>\n",
       "      <td>30.5</td>\n",
       "      <td>61.9</td>\n",
       "      <td>31.6</td>\n",
       "      <td>65.5</td>\n",
       "      <td>33.9</td>\n",
       "      <td>63.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ-01</th>\n",
       "      <td>AZ-01</td>\n",
       "      <td>O'Halleran, Tom</td>\n",
       "      <td>(D)</td>\n",
       "      <td>46.6</td>\n",
       "      <td>47.7</td>\n",
       "      <td>47.9</td>\n",
       "      <td>50.4</td>\n",
       "      <td>47.8</td>\n",
       "      <td>51.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA-09</th>\n",
       "      <td>WA-09</td>\n",
       "      <td>Smith, Adam</td>\n",
       "      <td>(D)</td>\n",
       "      <td>70.5</td>\n",
       "      <td>23.3</td>\n",
       "      <td>68.3</td>\n",
       "      <td>29.6</td>\n",
       "      <td>68.6</td>\n",
       "      <td>29.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA-10</th>\n",
       "      <td>WA-10</td>\n",
       "      <td>Heck, Denny</td>\n",
       "      <td>(D)</td>\n",
       "      <td>51.3</td>\n",
       "      <td>39.9</td>\n",
       "      <td>56.3</td>\n",
       "      <td>41.1</td>\n",
       "      <td>57.2</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI-02</th>\n",
       "      <td>WI-02</td>\n",
       "      <td>Pocan, Mark</td>\n",
       "      <td>(D)</td>\n",
       "      <td>65.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>68.3</td>\n",
       "      <td>30.5</td>\n",
       "      <td>70.1</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI-04</th>\n",
       "      <td>WI-04</td>\n",
       "      <td>Moore, Gwen</td>\n",
       "      <td>(D)</td>\n",
       "      <td>74.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>75.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>74.4</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI-05</th>\n",
       "      <td>WI-05</td>\n",
       "      <td>Sensenbrenner, Jim</td>\n",
       "      <td>(R)</td>\n",
       "      <td>37.3</td>\n",
       "      <td>57.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>61.3</td>\n",
       "      <td>41.8</td>\n",
       "      <td>57.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          CD           Incumbent Party  Clinton\\n2016  Trump\\n2016  \\\n",
       "CD                                                                   \n",
       "AK-AL  AK-AL          Young, Don   (R)           37.6         52.8   \n",
       "AL-06  AL-06        Palmer, Gary   (R)           26.1         70.8   \n",
       "AR-02  AR-02        Hill, French   (R)           41.7         52.4   \n",
       "AR-03  AR-03       Womack, Steve   (R)           30.5         61.9   \n",
       "AZ-01  AZ-01     O'Halleran, Tom   (D)           46.6         47.7   \n",
       "...      ...                 ...   ...            ...          ...   \n",
       "WA-09  WA-09         Smith, Adam   (D)           70.5         23.3   \n",
       "WA-10  WA-10         Heck, Denny   (D)           51.3         39.9   \n",
       "WI-02  WI-02         Pocan, Mark   (D)           65.8         29.0   \n",
       "WI-04  WI-04         Moore, Gwen   (D)           74.0         21.8   \n",
       "WI-05  WI-05  Sensenbrenner, Jim   (R)           37.3         57.4   \n",
       "\n",
       "       Obama\\n2012  Romney\\n2012  Obama\\n2008  McCain\\n2008  \n",
       "CD                                                           \n",
       "AK-AL         41.2          55.3         38.1          59.7  \n",
       "AL-06         24.7          74.3         25.0          74.1  \n",
       "AR-02         42.9          54.7         44.3          53.8  \n",
       "AR-03         31.6          65.5         33.9          63.7  \n",
       "AZ-01         47.9          50.4         47.8          51.0  \n",
       "...            ...           ...          ...           ...  \n",
       "WA-09         68.3          29.6         68.6          29.9  \n",
       "WA-10         56.3          41.1         57.2          41.0  \n",
       "WI-02         68.3          30.5         70.1          28.6  \n",
       "WI-04         75.3          23.8         74.4          24.6  \n",
       "WI-05         37.7          61.3         41.8          57.2  \n",
       "\n",
       "[249 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean Indxing in a Pandas DataFrame\n",
    "elections[elections['Trump\\n2016'] < elections['Romney\\n2012']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CD</th>\n",
       "      <th>Incumbent</th>\n",
       "      <th>Party</th>\n",
       "      <th>Clinton\\n2016</th>\n",
       "      <th>Trump\\n2016</th>\n",
       "      <th>Obama\\n2012</th>\n",
       "      <th>Romney\\n2012</th>\n",
       "      <th>Obama\\n2008</th>\n",
       "      <th>McCain\\n2008</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD</th>\n",
       "      <th></th>\n",
       "      <th></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>ME-02</th>\n",
       "      <td>ME-02</td>\n",
       "      <td>Poliquin, Bruce</td>\n",
       "      <td>(R)</td>\n",
       "      <td>41.1</td>\n",
       "      <td>51.4</td>\n",
       "      <td>52.9</td>\n",
       "      <td>44.4</td>\n",
       "      <td>54.6</td>\n",
       "      <td>43.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MN-08</th>\n",
       "      <td>MN-08</td>\n",
       "      <td>Nolan, Rick</td>\n",
       "      <td>(D)</td>\n",
       "      <td>38.6</td>\n",
       "      <td>54.2</td>\n",
       "      <td>51.7</td>\n",
       "      <td>46.2</td>\n",
       "      <td>53.1</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ-02</th>\n",
       "      <td>NJ-02</td>\n",
       "      <td>LoBiondo, Frank</td>\n",
       "      <td>(R)</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.6</td>\n",
       "      <td>53.5</td>\n",
       "      <td>45.4</td>\n",
       "      <td>53.2</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ-03</th>\n",
       "      <td>NJ-03</td>\n",
       "      <td>MacArthur, Tom</td>\n",
       "      <td>(R)</td>\n",
       "      <td>45.2</td>\n",
       "      <td>51.4</td>\n",
       "      <td>51.8</td>\n",
       "      <td>47.2</td>\n",
       "      <td>51.1</td>\n",
       "      <td>47.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY-02</th>\n",
       "      <td>NY-02</td>\n",
       "      <td>King, Peter</td>\n",
       "      <td>(R)</td>\n",
       "      <td>43.9</td>\n",
       "      <td>53.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>47.2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY-11</th>\n",
       "      <td>NY-11</td>\n",
       "      <td>Donovan, Dan</td>\n",
       "      <td>(R)</td>\n",
       "      <td>43.8</td>\n",
       "      <td>53.6</td>\n",
       "      <td>51.6</td>\n",
       "      <td>47.3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY-19</th>\n",
       "      <td>NY-19</td>\n",
       "      <td>Faso, John</td>\n",
       "      <td>(R)</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.8</td>\n",
       "      <td>52.1</td>\n",
       "      <td>45.9</td>\n",
       "      <td>53.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NY-21</th>\n",
       "      <td>NY-21</td>\n",
       "      <td>Stefanik, Elise</td>\n",
       "      <td>(R)</td>\n",
       "      <td>40.0</td>\n",
       "      <td>53.9</td>\n",
       "      <td>52.2</td>\n",
       "      <td>46.1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PA-17</th>\n",
       "      <td>PA-17</td>\n",
       "      <td>Cartwright, Matt</td>\n",
       "      <td>(D)</td>\n",
       "      <td>43.3</td>\n",
       "      <td>53.4</td>\n",
       "      <td>55.4</td>\n",
       "      <td>43.3</td>\n",
       "      <td>56.9</td>\n",
       "      <td>41.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          CD         Incumbent Party  Clinton\\n2016  Trump\\n2016  Obama\\n2012  \\\n",
       "CD                                                                              \n",
       "ME-02  ME-02   Poliquin, Bruce   (R)           41.1         51.4         52.9   \n",
       "MN-08  MN-08       Nolan, Rick   (D)           38.6         54.2         51.7   \n",
       "NJ-02  NJ-02   LoBiondo, Frank   (R)           46.0         50.6         53.5   \n",
       "NJ-03  NJ-03    MacArthur, Tom   (R)           45.2         51.4         51.8   \n",
       "NY-02  NY-02       King, Peter   (R)           43.9         53.0         51.6   \n",
       "NY-11  NY-11      Donovan, Dan   (R)           43.8         53.6         51.6   \n",
       "NY-19  NY-19        Faso, John   (R)           44.0         50.8         52.1   \n",
       "NY-21  NY-21   Stefanik, Elise   (R)           40.0         53.9         52.2   \n",
       "PA-17  PA-17  Cartwright, Matt   (D)           43.3         53.4         55.4   \n",
       "\n",
       "       Romney\\n2012  Obama\\n2008  McCain\\n2008  \n",
       "CD                                              \n",
       "ME-02          44.4         54.6          43.4  \n",
       "MN-08          46.2         53.1          44.5  \n",
       "NJ-02          45.4         53.2          45.5  \n",
       "NJ-03          47.2         51.1          47.7  \n",
       "NY-02          47.2         51.0          48.0  \n",
       "NY-11          47.3         48.0          51.0  \n",
       "NY-19          45.9         53.0          45.0  \n",
       "NY-21          46.1         52.0          47.0  \n",
       "PA-17          43.3         56.9          41.9  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elections[np.logical_and(elections['Trump\\n2016'] > 50, elections['Obama\\n2012'] >50)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clinton\\n2016</th>\n",
       "      <th>Trump\\n2016</th>\n",
       "      <th>Obama\\n2012</th>\n",
       "      <th>Romney\\n2012</th>\n",
       "      <th>Obama\\n2008</th>\n",
       "      <th>McCain\\n2008</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD</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>AK-AL</th>\n",
       "      <td>37.6</td>\n",
       "      <td>52.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>55.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>59.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL-01</th>\n",
       "      <td>34.1</td>\n",
       "      <td>63.5</td>\n",
       "      <td>37.4</td>\n",
       "      <td>61.8</td>\n",
       "      <td>38.5</td>\n",
       "      <td>60.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL-02</th>\n",
       "      <td>33.0</td>\n",
       "      <td>64.9</td>\n",
       "      <td>36.4</td>\n",
       "      <td>62.9</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL-03</th>\n",
       "      <td>32.3</td>\n",
       "      <td>65.3</td>\n",
       "      <td>36.8</td>\n",
       "      <td>62.3</td>\n",
       "      <td>36.6</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL-04</th>\n",
       "      <td>17.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>74.8</td>\n",
       "      <td>25.5</td>\n",
       "      <td>73.3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WI-08</th>\n",
       "      <td>38.6</td>\n",
       "      <td>56.2</td>\n",
       "      <td>47.6</td>\n",
       "      <td>51.3</td>\n",
       "      <td>53.7</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV-01</th>\n",
       "      <td>26.4</td>\n",
       "      <td>68.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>62.2</td>\n",
       "      <td>41.5</td>\n",
       "      <td>56.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV-02</th>\n",
       "      <td>29.4</td>\n",
       "      <td>65.8</td>\n",
       "      <td>38.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>43.9</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WV-03</th>\n",
       "      <td>23.3</td>\n",
       "      <td>72.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>65.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WY-AL</th>\n",
       "      <td>22.5</td>\n",
       "      <td>70.1</td>\n",
       "      <td>27.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>32.7</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clinton\\n2016  Trump\\n2016  Obama\\n2012  Romney\\n2012  Obama\\n2008  \\\n",
       "CD                                                                          \n",
       "AK-AL           37.6         52.8         41.2          55.3         38.1   \n",
       "AL-01           34.1         63.5         37.4          61.8         38.5   \n",
       "AL-02           33.0         64.9         36.4          62.9         35.0   \n",
       "AL-03           32.3         65.3         36.8          62.3         36.6   \n",
       "AL-04           17.4         80.4         24.0          74.8         25.5   \n",
       "...              ...          ...          ...           ...          ...   \n",
       "WI-08           38.6         56.2         47.6          51.3         53.7   \n",
       "WV-01           26.4         68.0         35.5          62.2         41.5   \n",
       "WV-02           29.4         65.8         38.0          60.0         43.9   \n",
       "WV-03           23.3         72.5         32.8          65.0         42.3   \n",
       "WY-AL           22.5         70.1         27.6          68.2         32.7   \n",
       "\n",
       "       McCain\\n2008  \n",
       "CD                   \n",
       "AK-AL          59.7  \n",
       "AL-01          60.9  \n",
       "AL-02          64.5  \n",
       "AL-03          62.6  \n",
       "AL-04          73.3  \n",
       "...             ...  \n",
       "WI-08          45.0  \n",
       "WV-01          56.7  \n",
       "WV-02          54.7  \n",
       "WV-03          55.7  \n",
       "WY-AL          65.2  \n",
       "\n",
       "[435 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols = elections.select_dtypes(include='number')\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CD\n",
       "ME-02     Poliquin, Bruce\n",
       "MN-08         Nolan, Rick\n",
       "NJ-02     LoBiondo, Frank\n",
       "NJ-03      MacArthur, Tom\n",
       "NY-02         King, Peter\n",
       "NY-11        Donovan, Dan\n",
       "NY-19          Faso, John\n",
       "NY-21     Stefanik, Elise\n",
       "PA-17    Cartwright, Matt\n",
       "Name: Incumbent, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df = elections.loc[np.logical_and(elections['Trump\\n2016'] > 50, elections['Obama\\n2012'] > 50), 'Incumbent']\n",
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = elections[elections['Party'] == \"(D)\"]\n",
    "df.to_excel(\"democratic_incumbent.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clinton\\n2016</th>\n",
       "      <th>Trump\\n2016</th>\n",
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       "      <th>CD</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TN-01</th>\n",
       "      <td>19.7</td>\n",
       "      <td>76.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>72.7</td>\n",
       "      <td>28.6</td>\n",
       "      <td>69.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-02</th>\n",
       "      <td>29.7</td>\n",
       "      <td>65.1</td>\n",
       "      <td>30.9</td>\n",
       "      <td>67.3</td>\n",
       "      <td>34.5</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-03</th>\n",
       "      <td>30.2</td>\n",
       "      <td>65.4</td>\n",
       "      <td>35.1</td>\n",
       "      <td>63.3</td>\n",
       "      <td>37.3</td>\n",
       "      <td>61.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-04</th>\n",
       "      <td>27.4</td>\n",
       "      <td>68.6</td>\n",
       "      <td>33.1</td>\n",
       "      <td>65.3</td>\n",
       "      <td>35.8</td>\n",
       "      <td>62.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-05</th>\n",
       "      <td>56.6</td>\n",
       "      <td>38.2</td>\n",
       "      <td>55.9</td>\n",
       "      <td>42.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>41.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-06</th>\n",
       "      <td>23.7</td>\n",
       "      <td>72.6</td>\n",
       "      <td>29.5</td>\n",
       "      <td>69.1</td>\n",
       "      <td>33.5</td>\n",
       "      <td>64.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-07</th>\n",
       "      <td>28.2</td>\n",
       "      <td>67.5</td>\n",
       "      <td>32.9</td>\n",
       "      <td>65.7</td>\n",
       "      <td>36.3</td>\n",
       "      <td>62.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-08</th>\n",
       "      <td>30.7</td>\n",
       "      <td>66.3</td>\n",
       "      <td>32.8</td>\n",
       "      <td>66.1</td>\n",
       "      <td>34.9</td>\n",
       "      <td>64.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-09</th>\n",
       "      <td>77.5</td>\n",
       "      <td>19.8</td>\n",
       "      <td>78.3</td>\n",
       "      <td>20.9</td>\n",
       "      <td>77.0</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clinton\\n2016  Trump\\n2016  Obama\\n2012  Romney\\n2012  Obama\\n2008  \\\n",
       "CD                                                                          \n",
       "TN-01           19.7         76.7         25.7          72.7         28.6   \n",
       "TN-02           29.7         65.1         30.9          67.3         34.5   \n",
       "TN-03           30.2         65.4         35.1          63.3         37.3   \n",
       "TN-04           27.4         68.6         33.1          65.3         35.8   \n",
       "TN-05           56.6         38.2         55.9          42.5         57.5   \n",
       "TN-06           23.7         72.6         29.5          69.1         33.5   \n",
       "TN-07           28.2         67.5         32.9          65.7         36.3   \n",
       "TN-08           30.7         66.3         32.8          66.1         34.9   \n",
       "TN-09           77.5         19.8         78.3          20.9         77.0   \n",
       "\n",
       "       McCain\\n2008  \n",
       "CD                   \n",
       "TN-01          69.9  \n",
       "TN-02          64.0  \n",
       "TN-03          61.3  \n",
       "TN-04          62.6  \n",
       "TN-05          41.3  \n",
       "TN-06          64.9  \n",
       "TN-07          62.4  \n",
       "TN-08          64.1  \n",
       "TN-09          22.5  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tennessee = elections[elections['CD'].str.contains('TN')]\n",
    "tennessee.select_dtypes(include='number')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Clinton\\n2016</th>\n",
       "      <th>Obama\\n2012</th>\n",
       "      <th>Obama\\n2008</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TN-01</th>\n",
       "      <td>19.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-02</th>\n",
       "      <td>29.7</td>\n",
       "      <td>30.9</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-03</th>\n",
       "      <td>30.2</td>\n",
       "      <td>35.1</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-04</th>\n",
       "      <td>27.4</td>\n",
       "      <td>33.1</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-05</th>\n",
       "      <td>56.6</td>\n",
       "      <td>55.9</td>\n",
       "      <td>57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-06</th>\n",
       "      <td>23.7</td>\n",
       "      <td>29.5</td>\n",
       "      <td>33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-07</th>\n",
       "      <td>28.2</td>\n",
       "      <td>32.9</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-08</th>\n",
       "      <td>30.7</td>\n",
       "      <td>32.8</td>\n",
       "      <td>34.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TN-09</th>\n",
       "      <td>77.5</td>\n",
       "      <td>78.3</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Clinton\\n2016  Obama\\n2012  Obama\\n2008\n",
       "CD                                            \n",
       "TN-01           19.7         25.7         28.6\n",
       "TN-02           29.7         30.9         34.5\n",
       "TN-03           30.2         35.1         37.3\n",
       "TN-04           27.4         33.1         35.8\n",
       "TN-05           56.6         55.9         57.5\n",
       "TN-06           23.7         29.5         33.5\n",
       "TN-07           28.2         32.9         36.3\n",
       "TN-08           30.7         32.8         34.9\n",
       "TN-09           77.5         78.3         77.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tennessee.iloc[:, 3::2] #starating at column index 3 and skippng evey other columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='CD'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tennessee.iloc[:, 3::2].plot(kind = 'bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4/23/24 Parameter Estimation\n",
    "Fitting the parameters in the SVIR.py model to the data provided in \"epi_data\\vaccated_data.csv\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccinated_csv = pd.read_csv('./epi_data/vaccated_data.csv')\n",
    "vaccinated_csv"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}