Anes_2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepared by Susan Li\n",
    "\n",
    "2021-08-07"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# American National Election Studies 2019\n",
    "\n",
    "The data was downloaded from [American National Election Studies 2019 Pilot Study](https://electionstudies.org/data-center/2019-pilot-study/). The ANES 2019 Pilot Study was conducted for the purpose of testing new questions and conducting methodological research to provide public opinion data in December 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 1000)\n",
    "df = pd.read_csv('data/anes_pilot_2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 677,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3165 entries, 0 to 3164\n",
      "Columns: 900 entries, version to flag_state\n",
      "dtypes: float64(223), int64(600), object(77)\n",
      "memory usage: 21.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "Cleaning up the data, that including rename columns, re-code category features, remove columns that will not be used, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 678,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycols = ['caseid', 'liveurban', 'vote16', 'particip_3', 'vote20jb', 'mip', 'confecon', 'ideo5', 'pid7', 'guarinc', 'famsep', \n",
    "          'freecol', 'loans', 'race', 'birthyr', 'gender', 'educ', 'inputstate', 'weight']\n",
    "ftcols = [x for x in df.columns if x.startswith('ft') and not x.endswith('timing') and not x.endswith('skp')]\n",
    "df_clean = df[mycols + ftcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 679,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/pandas/core/frame.py:4174: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/opt/tljh/user/lib/python3.6/site-packages/pandas/core/frame.py:4308: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_clean.drop('inputstate', axis=1, inplace=True)\n",
    "\n",
    "df_clean.rename({'particip_3': 'protest', \n",
    "                 'vote20jb': 'vote', \n",
    "                 'mip': 'most_important_issue', \n",
    "                 'ideo5': 'ideology', \n",
    "                 'pid7': 'partyID', \n",
    "                 'guarinc': 'universal_income', \n",
    "                 'famsep': 'family_separation', \n",
    "                 'freecol': 'free_college', \n",
    "                 'loans': 'forgive_loans', \n",
    "                 'gender': 'sex', \n",
    "                 'educ': 'education'}, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/pandas/core/generic.py:5170: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "replace_map = {1: 'Donald Trump',\n",
    "               2: 'Joe Biden', \n",
    "               3: 'Someone else',\n",
    "               4: 'Probably will not vote'}\n",
    "df_clean.vote = df_clean.vote.map(replace_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_map = {-7: np.nan,\n",
    "               1: 'Liberal',\n",
    "               2: 'Liberal',\n",
    "               3: 'Moderate',\n",
    "               4: 'Conservative', \n",
    "               5: 'Conservative',\n",
    "               6: np.nan}\n",
    "df_clean.ideology = df_clean.ideology.map(replace_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_map = {'liveurban': {1: 'Rural', 2: 'Town', 3: 'Suburb', 4: 'City'},\n",
    "               'vote16': {-1: 'Did not vote', 1: 'Donald Trump', 2: 'Hillary Clinton', 3: 'Someone else'},\n",
    "               'protest': {1: True, 2: False},\n",
    "               'confecon': {1: 'Not at all worried', 2: 'A little worried', 3: 'Moderately worried', \n",
    "                            4: 'Very worried', 5: 'Extremely worried'},\n",
    "               'partyID': {8: np.nan, 1: 'Democrat', 2: 'Democrat', 3: 'Democrat', 4: 'Independent', 5: 'Republican',\n",
    "                           6: 'Republican', 7: 'Republican'},\n",
    "               'universal_income': {1: 'Favor a great deal', 2: 'Favor a moderate amount', 3: 'Favor a little', \n",
    "                                    4: 'Neither favor nor oppose', 5: 'oppose a little', 6: 'Oppose a moderate amount',\n",
    "                                    7: 'Oppse a great deal'},\n",
    "               'family_separation': {-7: np.nan, 1: 'Favor strongly', 2: 'Favor somewhat', 3: 'Neither favor nor disagree',\n",
    "                                     4: 'Oppose somewhat', 5: 'Oppose strongly'},\n",
    "               'free_college': {-7: np.nan, 1: 'Favor a great deal', 2: 'Favor a moderate amount', 3: 'Favor a little',\n",
    "                                4: 'Neither favor nor oppose', 5: 'Oppose a little', 6: 'Oppose a moderate amount', \n",
    "                                7: 'Oppose a great deal'},\n",
    "               'forgive_loans': {-7: np.nan, 1: 'Favor a great deal', 2: 'Favor a moderate amount', 3: 'Favor a little', \n",
    "                                 4: 'Neither favor nor oppose', 5: 'Oppose a little', 6: 'Oppose a moderate amount', \n",
    "                                 7: 'Oppose a great deal'},\n",
    "               'race': {1: 'White', 2: 'Black', 3: 'Hispanic', 4: 'Other', 5: 'Other', 6: 'Other', 7: 'Other', 8: 'Other'},\n",
    "               'sex': {1: 'Male', 2: 'Female'},\n",
    "               'education': {1: 'No HS diploma', 2: 'High school graduate', 3: 'Some college', 4: '2-year degree', \n",
    "                             5: '4-year degree', 6: 'Post-graduate'}}\n",
    "df_clean = df_clean.replace(replace_map)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "metadata": {},
   "outputs": [],
   "source": [
    "ftcols = [x for x in df_clean.columns if x.startswith('ft')]\n",
    "df_clean[ftcols] = df_clean[ftcols].replace([-1, -7, 997], np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_clean['ftimmig'] = df_clean['ftimmig1']\n",
    "df_clean.ftimmig[df_clean['ftimmig1'].isnull()] = df_clean.ftimmig2[df_clean['ftimmig1'].isnull()]\n",
    "df_clean = df_clean.drop(['ftimmig1', 'ftimmig2'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vote Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bar = df_clean.vote.value_counts().reset_index()\n",
    "df_bar['pct'] = round(100*df_bar['vote'] / df_bar['vote'].sum(),2)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "myplot = sns.barplot(x='index', y='pct', data=df_bar, palette=['blue','red','brown','black'])\n",
    "plt.xlabel('Candidate')\n",
    "plt.ylabel('Percent')\n",
    "plt.title('Vote choice as of December 2019')\n",
    "sns.despine()\n",
    "for rect in myplot.patches:\n",
    "    xcoor = rect.get_x() + .5*rect.get_width()\n",
    "    ycoor = rect.get_height()   \n",
    "    plt.text(xcoor, ycoor, str(int(ycoor)),\n",
    "             horizontalalignment = 'center', \n",
    "             verticalalignment = 'bottom',\n",
    "             fontsize = 14, color = 'white');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the data, Donald Trump and Joe Biden were statistical tied as of December 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_bar = df_clean.groupby(['partyID', 'vote']).size().reset_index().rename(columns = {0:'count'})\n",
    "df_bar['sum'] = df_bar.groupby('partyID')['count'].transform('sum')\n",
    "df_bar['pct'] = (df_bar['count'] / df_bar['sum'])*100\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "myplot = sns.barplot(x='vote', y='pct', hue='partyID', data=df_bar, palette=['blue','yellow','red'])\n",
    "plt.xlabel('Candidate')\n",
    "plt.ylabel('Percent')\n",
    "plt.title('Vote Choice as of December 2019')\n",
    "sns.despine()\n",
    "for rect in myplot.patches:\n",
    "    xcoor = rect.get_x() + .5*rect.get_width()\n",
    "    ycoor = rect.get_height()\n",
    "    plt.text(xcoor, ycoor, str(int(ycoor)),\n",
    "             horizontalalignment='center',\n",
    "             verticalalignment='bottom',\n",
    "             fontsize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "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>caseid</th>\n",
       "      <th>liveurban</th>\n",
       "      <th>vote16</th>\n",
       "      <th>protest</th>\n",
       "      <th>vote</th>\n",
       "      <th>most_important_issue</th>\n",
       "      <th>confecon</th>\n",
       "      <th>ideology</th>\n",
       "      <th>partyID</th>\n",
       "      <th>universal_income</th>\n",
       "      <th>family_separation</th>\n",
       "      <th>free_college</th>\n",
       "      <th>forgive_loans</th>\n",
       "      <th>race</th>\n",
       "      <th>birthyr</th>\n",
       "      <th>sex</th>\n",
       "      <th>education</th>\n",
       "      <th>weight</th>\n",
       "      <th>fttrump</th>\n",
       "      <th>ftobama</th>\n",
       "      <th>ftbiden</th>\n",
       "      <th>ftwarren</th>\n",
       "      <th>ftsanders</th>\n",
       "      <th>ftbuttigieg</th>\n",
       "      <th>ftharris</th>\n",
       "      <th>ftblack</th>\n",
       "      <th>ftwhite</th>\n",
       "      <th>fthisp</th>\n",
       "      <th>ftasian</th>\n",
       "      <th>ftmuslim</th>\n",
       "      <th>ftillegal</th>\n",
       "      <th>ftjournal</th>\n",
       "      <th>ftnato</th>\n",
       "      <th>ftun</th>\n",
       "      <th>ftice</th>\n",
       "      <th>ftnra</th>\n",
       "      <th>ftchina</th>\n",
       "      <th>ftnkorea</th>\n",
       "      <th>ftmexico</th>\n",
       "      <th>ftsaudi</th>\n",
       "      <th>ftukraine</th>\n",
       "      <th>ftiran</th>\n",
       "      <th>ftbritain</th>\n",
       "      <th>ftgermany</th>\n",
       "      <th>ftjapan</th>\n",
       "      <th>ftisrael</th>\n",
       "      <th>ftfrance</th>\n",
       "      <th>ftcanada</th>\n",
       "      <th>ftturkey</th>\n",
       "      <th>ftrussia</th>\n",
       "      <th>ftpales</th>\n",
       "      <th>ftimmig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>679</td>\n",
       "      <td>Town</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>False</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Poor health care need to take private for prof...</td>\n",
       "      <td>Moderately worried</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Independent</td>\n",
       "      <td>Oppose a moderate amount</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>Other</td>\n",
       "      <td>1968</td>\n",
       "      <td>Male</td>\n",
       "      <td>No HS diploma</td>\n",
       "      <td>.836432294131514</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98</td>\n",
       "      <td>53</td>\n",
       "      <td>99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>98</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3045</th>\n",
       "      <td>3046</td>\n",
       "      <td>Suburb</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>True</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>There are millions that are without healthcare...</td>\n",
       "      <td>Not at all worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1978</td>\n",
       "      <td>Male</td>\n",
       "      <td>4-year degree</td>\n",
       "      <td>.608038345981773</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>80</td>\n",
       "      <td>96</td>\n",
       "      <td>68</td>\n",
       "      <td>1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>90</td>\n",
       "      <td>20.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      caseid liveurban        vote16  protest          vote  \\\n",
       "678      679      Town  Someone else    False  Donald Trump   \n",
       "3045    3046    Suburb  Someone else     True  Donald Trump   \n",
       "\n",
       "                                   most_important_issue            confecon  \\\n",
       "678   Poor health care need to take private for prof...  Moderately worried   \n",
       "3045  There are millions that are without healthcare...  Not at all worried   \n",
       "\n",
       "     ideology      partyID          universal_income family_separation  \\\n",
       "678       NaN  Independent  Oppose a moderate amount   Oppose strongly   \n",
       "3045  Liberal     Democrat        Favor a great deal   Oppose strongly   \n",
       "\n",
       "                 free_college            forgive_loans   race  birthyr   sex  \\\n",
       "678   Favor a moderate amount  Favor a moderate amount  Other     1968  Male   \n",
       "3045       Favor a great deal       Favor a great deal  White     1978  Male   \n",
       "\n",
       "          education            weight  fttrump  ftobama  ftbiden  ftwarren  \\\n",
       "678   No HS diploma  .836432294131514      2.0     49.0      0.0       2.0   \n",
       "3045  4-year degree  .608038345981773      1.0      1.0      0.0       0.0   \n",
       "\n",
       "      ftsanders  ftbuttigieg  ftharris  ftblack  ftwhite  fthisp  ftasian  \\\n",
       "678         3.0         68.0       0.0     98.0     99.0    99.0    100.0   \n",
       "3045      100.0          0.0       0.0     86.0     75.0    85.0     74.0   \n",
       "\n",
       "      ftmuslim  ftillegal  ftjournal  ftnato  ftun  ftice  ftnra  ftchina  \\\n",
       "678       67.0        8.0         98     1.0   0.0  100.0   99.0        1   \n",
       "3045      78.0       88.0         52     0.0   2.0    0.0    0.0        3   \n",
       "\n",
       "      ftnkorea  ftmexico  ftsaudi  ftukraine  ftiran  ftbritain  ftgermany  \\\n",
       "678        1.0      99.0      2.0       49.0     0.0         98         53   \n",
       "3045       0.0      83.0      0.0       15.0     2.0         80         96   \n",
       "\n",
       "      ftjapan  ftisrael  ftfrance  ftcanada  ftturkey  ftrussia  ftpales  \\\n",
       "678        99       1.0      97.0        98      15.0       2.0      6.0   \n",
       "3045       68       1.0      65.0        90      20.0      60.0     99.0   \n",
       "\n",
       "      ftimmig  \n",
       "678      46.0  \n",
       "3045     88.0  "
      ]
     },
     "execution_count": 694,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.query(\"vote=='Donald Trump' & fttrump < 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These 2 people gave Trump thermometer rating < 5 but still voted for him. It looks like they dislike Biden even more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "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>caseid</th>\n",
       "      <th>liveurban</th>\n",
       "      <th>vote16</th>\n",
       "      <th>protest</th>\n",
       "      <th>vote</th>\n",
       "      <th>most_important_issue</th>\n",
       "      <th>confecon</th>\n",
       "      <th>ideology</th>\n",
       "      <th>partyID</th>\n",
       "      <th>universal_income</th>\n",
       "      <th>family_separation</th>\n",
       "      <th>free_college</th>\n",
       "      <th>forgive_loans</th>\n",
       "      <th>race</th>\n",
       "      <th>birthyr</th>\n",
       "      <th>sex</th>\n",
       "      <th>education</th>\n",
       "      <th>weight</th>\n",
       "      <th>fttrump</th>\n",
       "      <th>ftobama</th>\n",
       "      <th>ftbiden</th>\n",
       "      <th>ftwarren</th>\n",
       "      <th>ftsanders</th>\n",
       "      <th>ftbuttigieg</th>\n",
       "      <th>ftharris</th>\n",
       "      <th>ftblack</th>\n",
       "      <th>ftwhite</th>\n",
       "      <th>fthisp</th>\n",
       "      <th>ftasian</th>\n",
       "      <th>ftmuslim</th>\n",
       "      <th>ftillegal</th>\n",
       "      <th>ftjournal</th>\n",
       "      <th>ftnato</th>\n",
       "      <th>ftun</th>\n",
       "      <th>ftice</th>\n",
       "      <th>ftnra</th>\n",
       "      <th>ftchina</th>\n",
       "      <th>ftnkorea</th>\n",
       "      <th>ftmexico</th>\n",
       "      <th>ftsaudi</th>\n",
       "      <th>ftukraine</th>\n",
       "      <th>ftiran</th>\n",
       "      <th>ftbritain</th>\n",
       "      <th>ftgermany</th>\n",
       "      <th>ftjapan</th>\n",
       "      <th>ftisrael</th>\n",
       "      <th>ftfrance</th>\n",
       "      <th>ftcanada</th>\n",
       "      <th>ftturkey</th>\n",
       "      <th>ftrussia</th>\n",
       "      <th>ftpales</th>\n",
       "      <th>ftimmig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>782</td>\n",
       "      <td>City</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Crime high cost of renting gets higher ever ye...</td>\n",
       "      <td>A little worried</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Favor a little</td>\n",
       "      <td>Neither favor nor disagree</td>\n",
       "      <td>Oppose a little</td>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>White</td>\n",
       "      <td>1955</td>\n",
       "      <td>Female</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>1.07978658850445</td>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72</td>\n",
       "      <td>53</td>\n",
       "      <td>57</td>\n",
       "      <td>79.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>58</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>2062</td>\n",
       "      <td>City</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>healthcare</td>\n",
       "      <td>A little worried</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>Favor somewhat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>White</td>\n",
       "      <td>1940</td>\n",
       "      <td>Female</td>\n",
       "      <td>No HS diploma</td>\n",
       "      <td>1.5074583391068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2321</th>\n",
       "      <td>2322</td>\n",
       "      <td>Suburb</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Climate change</td>\n",
       "      <td>Moderately worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1990</td>\n",
       "      <td>Male</td>\n",
       "      <td>2-year degree</td>\n",
       "      <td>1.72607310797372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2599</th>\n",
       "      <td>2600</td>\n",
       "      <td>Suburb</td>\n",
       "      <td>Did not vote</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>capitalism</td>\n",
       "      <td>Very worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1992</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>7.03646496881757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50</td>\n",
       "      <td>62.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48</td>\n",
       "      <td>50.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>50</td>\n",
       "      <td>69</td>\n",
       "      <td>87</td>\n",
       "      <td>50.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>49</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2974</th>\n",
       "      <td>2975</td>\n",
       "      <td>City</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>True</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>the climate crisis</td>\n",
       "      <td>Very worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Oppse a great deal</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>4-year degree</td>\n",
       "      <td>1.08737874806145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>90</td>\n",
       "      <td>69.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40</td>\n",
       "      <td>75</td>\n",
       "      <td>70</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>55</td>\n",
       "      <td>35.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      caseid liveurban           vote16  protest       vote  \\\n",
       "781      782      City     Someone else    False  Joe Biden   \n",
       "2061    2062      City  Hillary Clinton    False  Joe Biden   \n",
       "2321    2322    Suburb     Someone else    False  Joe Biden   \n",
       "2599    2600    Suburb     Did not vote    False  Joe Biden   \n",
       "2974    2975      City  Hillary Clinton     True  Joe Biden   \n",
       "\n",
       "                                   most_important_issue            confecon  \\\n",
       "781   Crime high cost of renting gets higher ever ye...    A little worried   \n",
       "2061                                         healthcare    A little worried   \n",
       "2321                                     Climate change  Moderately worried   \n",
       "2599                                         capitalism        Very worried   \n",
       "2974                                 the climate crisis        Very worried   \n",
       "\n",
       "          ideology     partyID          universal_income  \\\n",
       "781   Conservative  Republican            Favor a little   \n",
       "2061      Moderate    Democrat  Neither favor nor oppose   \n",
       "2321       Liberal    Democrat        Favor a great deal   \n",
       "2599       Liberal    Democrat   Favor a moderate amount   \n",
       "2974       Liberal    Democrat        Oppse a great deal   \n",
       "\n",
       "               family_separation        free_college  \\\n",
       "781   Neither favor nor disagree     Oppose a little   \n",
       "2061              Favor somewhat  Favor a great deal   \n",
       "2321             Oppose strongly  Favor a great deal   \n",
       "2599             Oppose strongly  Favor a great deal   \n",
       "2974             Oppose strongly  Favor a great deal   \n",
       "\n",
       "                 forgive_loans   race  birthyr     sex             education  \\\n",
       "781   Neither favor nor oppose  White     1955  Female  High school graduate   \n",
       "2061   Favor a moderate amount  White     1940  Female         No HS diploma   \n",
       "2321        Favor a great deal  White     1990    Male         2-year degree   \n",
       "2599        Favor a great deal  White     1992    Male  High school graduate   \n",
       "2974        Favor a great deal  White     1991    Male         4-year degree   \n",
       "\n",
       "                weight  fttrump  ftobama  ftbiden  ftwarren  ftsanders  \\\n",
       "781   1.07978658850445     65.0     50.0      3.0       1.0        6.0   \n",
       "2061   1.5074583391068      0.0    100.0      0.0     100.0      100.0   \n",
       "2321  1.72607310797372      0.0     70.0      1.0      90.0       90.0   \n",
       "2599  7.03646496881757      0.0     59.0      0.0      61.0       86.0   \n",
       "2974  1.08737874806145      0.0     55.0      1.0      75.0       98.0   \n",
       "\n",
       "      ftbuttigieg  ftharris  ftblack  ftwhite  fthisp  ftasian  ftmuslim  \\\n",
       "781           0.0       3.0     71.0     56.0    62.0     66.0      53.0   \n",
       "2061         50.0      99.0     70.0     90.0    50.0     50.0       1.0   \n",
       "2321         50.0      50.0     50.0     50.0    50.0     50.0      50.0   \n",
       "2599         29.0       0.0    100.0     25.0   100.0    100.0     100.0   \n",
       "2974          1.0      30.0     85.0      1.0    85.0     85.0      85.0   \n",
       "\n",
       "      ftillegal  ftjournal  ftnato  ftun  ftice  ftnra  ftchina  ftnkorea  \\\n",
       "781        53.0          1    20.0   9.0   47.0   25.0        7       1.0   \n",
       "2061       50.0         50    51.0  51.0   50.0   91.0       50      50.0   \n",
       "2321       50.0         60    50.0  50.0    0.0    0.0        5       0.0   \n",
       "2599      100.0         50    62.0  52.0    0.0    0.0       48      50.0   \n",
       "2974       95.0         90    69.0  80.0    0.0    0.0       25       1.0   \n",
       "\n",
       "      ftmexico  ftsaudi  ftukraine  ftiran  ftbritain  ftgermany  ftjapan  \\\n",
       "781       57.0     46.0       50.0     0.0         72         53       57   \n",
       "2061      50.0     50.0       50.0    50.0         50         50       50   \n",
       "2321      50.0      0.0       50.0    50.0         50         50       50   \n",
       "2599      65.0     21.0       50.0    38.0         50         69       87   \n",
       "2974      55.0      0.0       45.0    20.0         40         75       70   \n",
       "\n",
       "      ftisrael  ftfrance  ftcanada  ftturkey  ftrussia  ftpales  ftimmig  \n",
       "781       79.0      49.0        58      47.0      15.0     51.0     56.0  \n",
       "2061      50.0      50.0        50      50.0      50.0     50.0     50.0  \n",
       "2321       5.0      50.0        50      50.0       5.0     50.0     50.0  \n",
       "2599      50.0      38.0        49      50.0      50.0     88.0    100.0  \n",
       "2974      10.0      40.0        55      35.0      40.0     90.0     95.0  "
      ]
     },
     "execution_count": 695,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.query(\"vote == 'Joe Biden'\").query(\"ftbiden < 5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These 5 people gave Biden rating < 5 but still voted for him. It looks like they all liked Obama."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "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>caseid</th>\n",
       "      <th>liveurban</th>\n",
       "      <th>vote16</th>\n",
       "      <th>protest</th>\n",
       "      <th>vote</th>\n",
       "      <th>most_important_issue</th>\n",
       "      <th>confecon</th>\n",
       "      <th>ideology</th>\n",
       "      <th>partyID</th>\n",
       "      <th>universal_income</th>\n",
       "      <th>family_separation</th>\n",
       "      <th>free_college</th>\n",
       "      <th>forgive_loans</th>\n",
       "      <th>race</th>\n",
       "      <th>birthyr</th>\n",
       "      <th>sex</th>\n",
       "      <th>education</th>\n",
       "      <th>weight</th>\n",
       "      <th>fttrump</th>\n",
       "      <th>ftobama</th>\n",
       "      <th>ftbiden</th>\n",
       "      <th>ftwarren</th>\n",
       "      <th>ftsanders</th>\n",
       "      <th>ftbuttigieg</th>\n",
       "      <th>ftharris</th>\n",
       "      <th>ftblack</th>\n",
       "      <th>ftwhite</th>\n",
       "      <th>fthisp</th>\n",
       "      <th>ftasian</th>\n",
       "      <th>ftmuslim</th>\n",
       "      <th>ftillegal</th>\n",
       "      <th>ftjournal</th>\n",
       "      <th>ftnato</th>\n",
       "      <th>ftun</th>\n",
       "      <th>ftice</th>\n",
       "      <th>ftnra</th>\n",
       "      <th>ftchina</th>\n",
       "      <th>ftnkorea</th>\n",
       "      <th>ftmexico</th>\n",
       "      <th>ftsaudi</th>\n",
       "      <th>ftukraine</th>\n",
       "      <th>ftiran</th>\n",
       "      <th>ftbritain</th>\n",
       "      <th>ftgermany</th>\n",
       "      <th>ftjapan</th>\n",
       "      <th>ftisrael</th>\n",
       "      <th>ftfrance</th>\n",
       "      <th>ftcanada</th>\n",
       "      <th>ftturkey</th>\n",
       "      <th>ftrussia</th>\n",
       "      <th>ftpales</th>\n",
       "      <th>ftimmig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>268</td>\n",
       "      <td>City</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>False</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Education.</td>\n",
       "      <td>A little worried</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Favor a little</td>\n",
       "      <td>Oppose somewhat</td>\n",
       "      <td>Favor a little</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1959</td>\n",
       "      <td>Female</td>\n",
       "      <td>Post-graduate</td>\n",
       "      <td>.746904935636401</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21</td>\n",
       "      <td>48.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50</td>\n",
       "      <td>50.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>58</td>\n",
       "      <td>40</td>\n",
       "      <td>63</td>\n",
       "      <td>90.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>53</td>\n",
       "      <td>53.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>1527</td>\n",
       "      <td>City</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>False</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>EVERYTHING</td>\n",
       "      <td>Not at all worried</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Neither favor nor disagree</td>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>White</td>\n",
       "      <td>1956</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>.847649185791497</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>98</td>\n",
       "      <td>94.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>71</td>\n",
       "      <td>70.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>85</td>\n",
       "      <td>77</td>\n",
       "      <td>83</td>\n",
       "      <td>57.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>91.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>1972</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Did not vote</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Proverty</td>\n",
       "      <td>Moderately worried</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1993</td>\n",
       "      <td>Female</td>\n",
       "      <td>2-year degree</td>\n",
       "      <td>.872100489544827</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>53</td>\n",
       "      <td>100.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>98</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>2272</td>\n",
       "      <td>Town</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>False</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>jobs and homeless and Police just shoting norm...</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male</td>\n",
       "      <td>Some college</td>\n",
       "      <td>1.05546596271457</td>\n",
       "      <td>100.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>86</td>\n",
       "      <td>86.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>68</td>\n",
       "      <td>27.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>68</td>\n",
       "      <td>10</td>\n",
       "      <td>77</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>62</td>\n",
       "      <td>95.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2408</th>\n",
       "      <td>2409</td>\n",
       "      <td>City</td>\n",
       "      <td>Did not vote</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Not being the best number one country.</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Independent</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor strongly</td>\n",
       "      <td>Oppose a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Black</td>\n",
       "      <td>1995</td>\n",
       "      <td>Male</td>\n",
       "      <td>Some college</td>\n",
       "      <td>3.47027368867645</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2721</th>\n",
       "      <td>2722</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>True</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Racism</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Black</td>\n",
       "      <td>1990</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>2.13773400773957</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>90</td>\n",
       "      <td>91.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>88</td>\n",
       "      <td>92.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>86.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>88</td>\n",
       "      <td>93.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      caseid liveurban           vote16  protest          vote  \\\n",
       "267      268      City     Donald Trump    False  Donald Trump   \n",
       "1526    1527      City     Donald Trump    False  Donald Trump   \n",
       "1971    1972     Rural     Did not vote    False     Joe Biden   \n",
       "2271    2272      Town  Hillary Clinton    False  Someone else   \n",
       "2408    2409      City     Did not vote    False     Joe Biden   \n",
       "2721    2722     Rural  Hillary Clinton     True     Joe Biden   \n",
       "\n",
       "                                   most_important_issue            confecon  \\\n",
       "267                                          Education.    A little worried   \n",
       "1526                                         EVERYTHING  Not at all worried   \n",
       "1971                                           Proverty  Moderately worried   \n",
       "2271  jobs and homeless and Police just shoting norm...   Extremely worried   \n",
       "2408             Not being the best number one country.   Extremely worried   \n",
       "2721                                             Racism   Extremely worried   \n",
       "\n",
       "          ideology      partyID          universal_income  \\\n",
       "267   Conservative   Republican            Favor a little   \n",
       "1526  Conservative   Republican        Favor a great deal   \n",
       "1971           NaN   Republican  Neither favor nor oppose   \n",
       "2271  Conservative     Democrat        Favor a great deal   \n",
       "2408      Moderate  Independent        Favor a great deal   \n",
       "2721       Liberal     Democrat        Favor a great deal   \n",
       "\n",
       "               family_separation              free_college  \\\n",
       "267              Oppose somewhat            Favor a little   \n",
       "1526  Neither favor nor disagree  Neither favor nor oppose   \n",
       "1971             Oppose strongly        Favor a great deal   \n",
       "2271              Favor strongly        Favor a great deal   \n",
       "2408              Favor strongly       Oppose a great deal   \n",
       "2721              Favor strongly        Favor a great deal   \n",
       "\n",
       "                 forgive_loans   race  birthyr     sex             education  \\\n",
       "267         Favor a great deal  White     1959  Female         Post-graduate   \n",
       "1526  Neither favor nor oppose  White     1956    Male  High school graduate   \n",
       "1971        Favor a great deal  White     1993  Female         2-year degree   \n",
       "2271        Favor a great deal  White     1980    Male          Some college   \n",
       "2408        Favor a great deal  Black     1995    Male          Some college   \n",
       "2721        Favor a great deal  Black     1990    Male  High school graduate   \n",
       "\n",
       "                weight  fttrump  ftobama  ftbiden  ftwarren  ftsanders  \\\n",
       "267   .746904935636401    100.0      0.0    100.0       1.0        4.0   \n",
       "1526  .847649185791497    100.0     99.0    100.0      71.0       81.0   \n",
       "1971  .872100489544827    100.0     99.0    100.0      66.0      100.0   \n",
       "2271  1.05546596271457    100.0     78.0    100.0      58.0       29.0   \n",
       "2408  3.47027368867645    100.0    100.0    100.0      99.0      100.0   \n",
       "2721  2.13773400773957    100.0     98.0    100.0      98.0       99.0   \n",
       "\n",
       "      ftbuttigieg  ftharris  ftblack  ftwhite  fthisp  ftasian  ftmuslim  \\\n",
       "267           NaN       NaN     70.0     90.0    61.0     63.0      15.0   \n",
       "1526         80.0      90.0     99.0    100.0    95.0     91.0      82.0   \n",
       "1971         60.0      57.0    100.0    100.0   100.0    100.0     100.0   \n",
       "2271          5.0     100.0     90.0     94.0    89.0      9.0      88.0   \n",
       "2408         99.0     100.0    100.0    100.0   100.0     99.0      75.0   \n",
       "2721         79.0      99.0    100.0     98.0    85.0     82.0      84.0   \n",
       "\n",
       "      ftillegal  ftjournal  ftnato   ftun  ftice  ftnra  ftchina  ftnkorea  \\\n",
       "267         6.0         21    48.0   36.0   49.0   43.0       50      50.0   \n",
       "1526       50.0         98    94.0   89.0   96.0   99.0       71      70.0   \n",
       "1971      100.0         53   100.0   67.0  100.0  100.0      100     100.0   \n",
       "2271       89.0         86    86.0   79.0   79.0   69.0       68      27.0   \n",
       "2408        2.0        100   100.0  100.0   99.0   99.0      100     100.0   \n",
       "2721       94.0         90    91.0   97.0   97.0   92.0       88      92.0   \n",
       "\n",
       "      ftmexico  ftsaudi  ftukraine  ftiran  ftbritain  ftgermany  ftjapan  \\\n",
       "267       43.0     39.0       60.0    43.0         58         40       63   \n",
       "1526      78.0     68.0       69.0    47.0         85         77       83   \n",
       "1971     100.0     99.0      100.0   100.0        100        100       98   \n",
       "2271      57.0      7.0        9.0     8.0         68         10       77   \n",
       "2408     100.0    100.0      100.0   100.0        100        100      100   \n",
       "2721      97.0     94.0       94.0    88.0         92         93       94   \n",
       "\n",
       "      ftisrael  ftfrance  ftcanada  ftturkey  ftrussia  ftpales  ftimmig  \n",
       "267       90.0      69.0        53      53.0      56.0     54.0     16.0  \n",
       "1526      57.0     100.0       100      91.0      88.0     76.0     99.0  \n",
       "1971     100.0     100.0       100     100.0      98.0    100.0    100.0  \n",
       "2271       5.0       8.0        62      95.0       9.0     78.0     88.0  \n",
       "2408     100.0     100.0       100     100.0     100.0    100.0    100.0  \n",
       "2721      86.0      96.0        88      93.0      94.0     89.0     93.0  "
      ]
     },
     "execution_count": 696,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.query('ftbiden == 100 & fttrump == 100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting, these 6 people rated Joe Biden 100 and Trump 100 at the same time. 2 of them voted for Trump while 3 of them voted for Biden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>caseid</th>\n",
       "      <th>liveurban</th>\n",
       "      <th>vote16</th>\n",
       "      <th>protest</th>\n",
       "      <th>vote</th>\n",
       "      <th>most_important_issue</th>\n",
       "      <th>confecon</th>\n",
       "      <th>ideology</th>\n",
       "      <th>partyID</th>\n",
       "      <th>universal_income</th>\n",
       "      <th>family_separation</th>\n",
       "      <th>free_college</th>\n",
       "      <th>forgive_loans</th>\n",
       "      <th>race</th>\n",
       "      <th>birthyr</th>\n",
       "      <th>sex</th>\n",
       "      <th>education</th>\n",
       "      <th>weight</th>\n",
       "      <th>fttrump</th>\n",
       "      <th>ftobama</th>\n",
       "      <th>ftbiden</th>\n",
       "      <th>ftwarren</th>\n",
       "      <th>ftsanders</th>\n",
       "      <th>ftbuttigieg</th>\n",
       "      <th>ftharris</th>\n",
       "      <th>ftblack</th>\n",
       "      <th>ftwhite</th>\n",
       "      <th>fthisp</th>\n",
       "      <th>ftasian</th>\n",
       "      <th>ftmuslim</th>\n",
       "      <th>ftillegal</th>\n",
       "      <th>ftjournal</th>\n",
       "      <th>ftnato</th>\n",
       "      <th>ftun</th>\n",
       "      <th>ftice</th>\n",
       "      <th>ftnra</th>\n",
       "      <th>ftchina</th>\n",
       "      <th>ftnkorea</th>\n",
       "      <th>ftmexico</th>\n",
       "      <th>ftsaudi</th>\n",
       "      <th>ftukraine</th>\n",
       "      <th>ftiran</th>\n",
       "      <th>ftbritain</th>\n",
       "      <th>ftgermany</th>\n",
       "      <th>ftjapan</th>\n",
       "      <th>ftisrael</th>\n",
       "      <th>ftfrance</th>\n",
       "      <th>ftcanada</th>\n",
       "      <th>ftturkey</th>\n",
       "      <th>ftrussia</th>\n",
       "      <th>ftpales</th>\n",
       "      <th>ftimmig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>118</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Did not vote</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Holding on to our Democracy is our most pressi...</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Black</td>\n",
       "      <td>1949</td>\n",
       "      <td>Male</td>\n",
       "      <td>2-year degree</td>\n",
       "      <td>1.04281941711299</td>\n",
       "      <td>50.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>95</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>77</td>\n",
       "      <td>50.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>93</td>\n",
       "      <td>85</td>\n",
       "      <td>83</td>\n",
       "      <td>81.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>91</td>\n",
       "      <td>71.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Trumpism</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1942</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>.56925888192293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>95</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>60</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>90</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>301</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Women's equality</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Oppse a great deal</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1943</td>\n",
       "      <td>Male</td>\n",
       "      <td>4-year degree</td>\n",
       "      <td>1.02441840964603</td>\n",
       "      <td>3.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>70</td>\n",
       "      <td>83.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>48</td>\n",
       "      <td>7.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>99</td>\n",
       "      <td>81</td>\n",
       "      <td>67</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>96</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>1873</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DONALD TRUMP!</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Conservative</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>Oppse a great deal</td>\n",
       "      <td>Oppose somewhat</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Other</td>\n",
       "      <td>1949</td>\n",
       "      <td>Male</td>\n",
       "      <td>4-year degree</td>\n",
       "      <td>1.62469079796581</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>99</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>100</td>\n",
       "      <td>60</td>\n",
       "      <td>53</td>\n",
       "      <td>21.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>96</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057</th>\n",
       "      <td>2058</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Did not vote</td>\n",
       "      <td>False</td>\n",
       "      <td>Someone else</td>\n",
       "      <td>The destruction of the planet</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Liberal</td>\n",
       "      <td>Independent</td>\n",
       "      <td>oppose a little</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1943</td>\n",
       "      <td>Male</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>.827305943187592</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71</td>\n",
       "      <td>11.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>49.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>97</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>2094</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>Guns and meds</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>oppose a little</td>\n",
       "      <td>Oppose somewhat</td>\n",
       "      <td>Oppose a great deal</td>\n",
       "      <td>Oppose a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1948</td>\n",
       "      <td>Female</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>.984520079262263</td>\n",
       "      <td>41.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>43</td>\n",
       "      <td>56.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>26</td>\n",
       "      <td>25.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>32</td>\n",
       "      <td>32.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>66</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2106</th>\n",
       "      <td>2107</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>False</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>Dem-os</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Republican</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Oppose a great deal</td>\n",
       "      <td>Oppose a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1948</td>\n",
       "      <td>Female</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>.954396916008379</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>2137</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Hillary Clinton</td>\n",
       "      <td>False</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>wealthy not paying a fair share of taxes so bu...</td>\n",
       "      <td>Extremely worried</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>oppose a little</td>\n",
       "      <td>Oppose strongly</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>White</td>\n",
       "      <td>1949</td>\n",
       "      <td>Female</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>1.02515128827396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      caseid liveurban           vote16  protest          vote  \\\n",
       "117      118     Rural     Did not vote    False     Joe Biden   \n",
       "167      168     Rural     Someone else    False     Joe Biden   \n",
       "300      301     Rural  Hillary Clinton    False     Joe Biden   \n",
       "1872    1873     Rural  Hillary Clinton    False     Joe Biden   \n",
       "2057    2058     Rural     Did not vote    False  Someone else   \n",
       "2093    2094     Rural  Hillary Clinton    False     Joe Biden   \n",
       "2106    2107     Rural     Donald Trump    False  Donald Trump   \n",
       "2136    2137     Rural  Hillary Clinton    False     Joe Biden   \n",
       "\n",
       "                                   most_important_issue           confecon  \\\n",
       "117   Holding on to our Democracy is our most pressi...  Extremely worried   \n",
       "167                                            Trumpism  Extremely worried   \n",
       "300                                    Women's equality  Extremely worried   \n",
       "1872                                      DONALD TRUMP!  Extremely worried   \n",
       "2057                      The destruction of the planet  Extremely worried   \n",
       "2093                                      Guns and meds  Extremely worried   \n",
       "2106                                             Dem-os  Extremely worried   \n",
       "2136  wealthy not paying a fair share of taxes so bu...  Extremely worried   \n",
       "\n",
       "          ideology      partyID         universal_income family_separation  \\\n",
       "117       Moderate     Democrat       Favor a great deal   Oppose strongly   \n",
       "167        Liberal     Democrat  Favor a moderate amount   Oppose strongly   \n",
       "300        Liberal     Democrat       Oppse a great deal   Oppose strongly   \n",
       "1872  Conservative     Democrat       Oppse a great deal   Oppose somewhat   \n",
       "2057       Liberal  Independent          oppose a little   Oppose strongly   \n",
       "2093      Moderate     Democrat          oppose a little   Oppose somewhat   \n",
       "2106      Moderate   Republican       Favor a great deal   Oppose strongly   \n",
       "2136      Moderate     Democrat          oppose a little   Oppose strongly   \n",
       "\n",
       "                  free_college        forgive_loans   race  birthyr     sex  \\\n",
       "117    Favor a moderate amount   Favor a great deal  Black     1949    Male   \n",
       "167         Favor a great deal   Favor a great deal  White     1942    Male   \n",
       "300   Neither favor nor oppose   Favor a great deal  White     1943    Male   \n",
       "1872        Favor a great deal   Favor a great deal  Other     1949    Male   \n",
       "2057        Favor a great deal   Favor a great deal  White     1943    Male   \n",
       "2093       Oppose a great deal  Oppose a great deal  White     1948  Female   \n",
       "2106       Oppose a great deal  Oppose a great deal  White     1948  Female   \n",
       "2136        Favor a great deal   Favor a great deal  White     1949  Female   \n",
       "\n",
       "                 education            weight  fttrump  ftobama  ftbiden  \\\n",
       "117          2-year degree  1.04281941711299     50.0     98.0     96.0   \n",
       "167   High school graduate   .56925888192293      0.0     70.0     60.0   \n",
       "300          4-year degree  1.02441840964603      3.0     98.0     70.0   \n",
       "1872         4-year degree  1.62469079796581      3.0    100.0     71.0   \n",
       "2057  High school graduate  .827305943187592      0.0     50.0      0.0   \n",
       "2093  High school graduate  .984520079262263     41.0     52.0     90.0   \n",
       "2106  High school graduate  .954396916008379    100.0      0.0      0.0   \n",
       "2136  High school graduate  1.02515128827396      0.0    100.0     89.0   \n",
       "\n",
       "      ftwarren  ftsanders  ftbuttigieg  ftharris  ftblack  ftwhite  fthisp  \\\n",
       "117       91.0       90.0         88.0      86.0     91.0     90.0    90.0   \n",
       "167       70.0       70.0         60.0      60.0     60.0     40.0    70.0   \n",
       "300       59.0       63.0         76.0      84.0     65.0     63.0    64.0   \n",
       "1872      69.0       51.0         89.0      41.0     51.0     50.0    40.0   \n",
       "2057      28.0      100.0         30.0      30.0    100.0     20.0   100.0   \n",
       "2093      59.0       58.0         45.0      51.0     31.0     85.0    28.0   \n",
       "2106       0.0        0.0          0.0       0.0      0.0     51.0     0.0   \n",
       "2136      50.0       91.0         51.0      49.0     57.0     58.0     7.0   \n",
       "\n",
       "      ftasian  ftmuslim  ftillegal  ftjournal  ftnato   ftun  ftice  ftnra  \\\n",
       "117      90.0      87.0       89.0         95   100.0  100.0   60.0   50.0   \n",
       "167      60.0      60.0       60.0         95    70.0   80.0   20.0   10.0   \n",
       "300      60.0      64.0       62.0         70    83.0   50.0   11.0    2.0   \n",
       "1872     71.0      61.0       81.0         99    90.0   90.0   50.0    5.0   \n",
       "2057    100.0      90.0       30.0        100     0.0    1.0    0.0    0.0   \n",
       "2093     31.0      23.0       13.0         43    56.0   73.0   54.0   42.0   \n",
       "2106      0.0       0.0        0.0         20     0.0   52.0   21.0   72.0   \n",
       "2136      6.0       5.0        0.0         34     1.0    1.0    1.0    2.0   \n",
       "\n",
       "      ftchina  ftnkorea  ftmexico  ftsaudi  ftukraine  ftiran  ftbritain  \\\n",
       "117        77      50.0      91.0     50.0       91.0    72.0         93   \n",
       "167        30      10.0      90.0     10.0       85.0    10.0         70   \n",
       "300        48       7.0      71.0      3.0       80.0    20.0         99   \n",
       "1872       50       5.0       3.0     11.0       22.0     6.0        100   \n",
       "2057       71      11.0      89.0      0.0       51.0    22.0        100   \n",
       "2093       26      25.0       9.0     36.0       22.0    19.0         40   \n",
       "2106        0       0.0      11.0      0.0        0.0     0.0          5   \n",
       "2136        0       0.0       1.0      0.0        1.0     0.0          0   \n",
       "\n",
       "      ftgermany  ftjapan  ftisrael  ftfrance  ftcanada  ftturkey  ftrussia  \\\n",
       "117          85       83      81.0      94.0        91      71.0      40.0   \n",
       "167          70       60      20.0      80.0        90      20.0      10.0   \n",
       "300          81       67      38.0      71.0        96      13.0       3.0   \n",
       "1872         60       53      21.0      91.0        96      20.0      33.0   \n",
       "2057        100      100      49.0      81.0        97      20.0      50.0   \n",
       "2093         47       32      32.0      43.0        66      33.0      18.0   \n",
       "2106         11        0       0.0      11.0        21       6.0       0.0   \n",
       "2136          0        0       0.0       0.0         3       1.0       0.0   \n",
       "\n",
       "      ftpales  ftimmig  \n",
       "117      83.0     91.0  \n",
       "167      80.0     85.0  \n",
       "300      79.0     64.0  \n",
       "1872     52.0     62.0  \n",
       "2057    100.0    100.0  \n",
       "2093     33.0     41.0  \n",
       "2106      0.0      0.0  \n",
       "2136      0.0      1.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.query(\"confecon == 'Extremely worried' & liveurban == 'Rural' & birthyr < 1950\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Want to see all of the voters that were extremely worried about the economy and live in rural areas and were born prior to 1950. There were 8 voters in this category and only 1 voted for Trump, and 6 voted for Biden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Likability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "like       0.535881\n",
       "neutral    0.361934\n",
       "dislike    0.102184\n",
       "Name: ftbiden_level, dtype: float64"
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['ftbiden_level'] = pd.cut(df_clean['ftbiden'], bins=[-0.1, 40, 70, 100], labels=('dislike', 'neutral', 'like'))\n",
    "df_clean.loc[df_clean['vote'] == 'Joe Biden']['ftbiden_level'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among voters who voted for Biden, 54% liked him, roughly 10% disliked him."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "like       0.846336\n",
       "neutral    0.136328\n",
       "dislike    0.017336\n",
       "Name: fttrump_level, dtype: float64"
      ]
     },
     "execution_count": 704,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['fttrump_level'] = pd.cut(df_clean['fttrump'], bins=[-0.1, 40, 70, 100], labels=('dislike', 'neutral', 'like'))\n",
    "df_clean.loc[df_clean['vote'] == 'Donald Trump']['fttrump_level'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among voters who voted for Trump, 85% liked him, only 1.7% disliked him."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean = df_clean.assign(age = 2021 - df_clean['birthyr'])\n",
    "df_clean.loc[df_clean['vote'] == 'Donald Trump']['age'].hist(bins=20)\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Around 60-70 year old were Trump's largest voting age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clean.loc[df_clean['vote'] == 'Joe Biden']['age'].hist(bins=20)\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were more young people voted for Biden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [],
   "source": [
    "catcolumns = ['liveurban', 'vote16', 'protest', 'vote', 'confecon', 'ideology', 'partyID', 'universal_income', \n",
    "              'family_separation', 'free_college', 'forgive_loans', 'race', 'sex', 'education']\n",
    "df_clean[catcolumns] = df_clean[catcolumns].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     0.713509\n",
       "False    0.286491\n",
       "Name: worried_econ, dtype: float64"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['worried_econ'] = df_clean['confecon'].apply(lambda x: x in \n",
    "                                                      ['Moderately worried', 'Very worried', 'Extremely worried'])\n",
    "df_clean.loc[df_clean['vote'] == 'Joe Biden']['worried_econ'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among Joe Biden's voters, over 71% worried about economy. 29% did not worry about economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.813826\n",
       "True     0.186174\n",
       "Name: worried_econ, dtype: float64"
      ]
     },
     "execution_count": 720,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.loc[df_clean['vote'] == 'Donald Trump']['worried_econ'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among Trump's voters, only 19% worried about economy, and 81% did not worry about economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean['confecon'] = df_clean['confecon'].cat.reorder_categories(['Not at all worried',\n",
    "                                                                    'A little worried', \n",
    "                                                                    'Moderately worried',\n",
    "                                                                    'Very worried',\n",
    "                                                                    'Extremely worried'])\n",
    "mybars = df_clean['confecon'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "myplot = plt.barh(mybars_df['index'], mybars_df['confecon'])\n",
    "plt.ylabel('Level of worry')\n",
    "plt.xlabel('Number of people')\n",
    "plt.title('Amount of Worry About the Current State of the Economy')\n",
    "for rect in myplot.patches:\n",
    "    xcoor = rect.get_y() + .5*rect.get_height()\n",
    "    ycoor = rect.get_width()\n",
    "    pcent = round(100*ycoor / sum(mybars_df['confecon']), 2)\n",
    "    plt.text(ycoor, xcoor, str(pcent) + '%',\n",
    "             horizontalalignment = 'right', \n",
    "             verticalalignment = 'center',\n",
    "             fontsize = 14, color = 'white');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most voters did not very worry about the state of the economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAGkCAYAAADzO6nVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABZrklEQVR4nO3deXxU1f3/8dcnAUF2UfCLBAUV2UmACK4I8pNFqYo7ooIbFutWa5HWfWltqxYVV9SKIoKKC2jRgiyuVQgaFkUEFAWkgiiCCArh8/vj3sQhTEISMrkzyfv5eMwjc8/dPnfmZO5nzpx7rrk7IiIiIiJSemlRByAiIiIikqqUTIuIiIiIlJGSaRERERGRMlIyLSIiIiJSRkqmRURERETKSMm0iIiIiEgZKZkWkRKxwBNm9r2ZzY46nvJmZm5mB0cdB4CZNQ/jqRZ1LFJ6ZjbAzFaY2Y9m1qkEy/cws5UVEZuIlD8l0yLlxMxmhYlmjahjicfMhpjZO7uxiaOA44AMd+9aaNvVwsShW0zZoDAhLFz26W7EsFvMrIWZbTezhypwnzeb2dO7WGa5mf2/BMZQz8zuMbOvwvdpWTi9T6L2WRrh/85Fu1jmQjP71Mw2mtk3ZjbFzOqG88aY2e2l2N/u/i/syl3AZe5ex90/irP/hH1xC48tL3yfYx/7JWJ/IqJkWqRcmFlz4GjAgROjjSZhDgCWu/umwjPcfRvwX6B7THF34NM4ZW+VZqfxWmd3o8X2POB74Mxk/dJT3sxsD2A60A7oC9QDDgfWAV2LWbWo7ZXn+1HSfR4D/BUY6O51gTbAs4nc5246APg4wv3/N0zkYx9fRxiPSOXm7nroocduPoAbgXeBfwKvFpo3BngQeA34MVzu/4B7CBK7T4FOMcu3AWYB6wlOyCfGzJsFXBQzPQR4J2bagd8CS8L1HwAs3OYWIC+MYX0Rx7EfMBn4DlgKXByWX1ho/VvirHsD8ErM9CdhfIXLzgmfXxzu47twn/sVOo7fhcfxBdADWAlcC/wPGAssBH4Ts0514NvY17JQfAYsA4YB3wCnFZrvwBXA5+F27gTSwnlpwPXAl8Aa4CmgfjivB7Cy0LaWA/+PIHn9Bdgavm7z4sQ1FtgObA6XGQ40D+MZDHwVxnNdzDppwIjweNYBzwENizjui8LjrVNM/XXg4EJ19vbY4yv02t8MTASeBjaE+6gPPA6sBlYBtwPpsfWUoMX2+/A97RfO+wtBvdoSHv/9ceK7Bni5iNiHhq/vL+H6r4Tl+a/PRoJ6NyDm/2un/wWgRhjfV+Hr9TCwZxH7jFsfwm38GL6em4BlcdZ9K2b+j8CZMa/xH8LtrQbOj1mnNLENIeYzIc78ZsCLwNqw7txfgjrenOLrYw2Cz7Ovw8c9QI1C9Wd4zLGdDBwPfEbw///ncNn/A34C9o7Zducw1url+Zmthx7l+Yg8AD30qAwPgqTwUqBLeGLfN2bemPDk0wWoCcwgSCbOA9IJko6Z4bLVw239GdgDODZMBlqF82ex62T6VaABsH94Euobb9kijuMtgsS/JpAVrn9sSdYHjglPjGnAPuFJuVZ48s8v8zCuY8PXpHN4Ih4FvFXoOKYBDYE9wxPyNuDv4fJ7hifnZ2PWOQlYUEx8RwM/A3uF+3ul0HwHZob73D880V8UzrsgfF8OBOoQJCNjw3k9KCKZDp/fDDy9i9e9YPlwunkYz6PhsWaGsbcJ518JvA9khK/HI8D4IrY9AXhyF/vfVTJd+LW/maCenxy+t3sCL4Vx1AYaA7OBS2LqzlaCL1DpBF9ovgYsXr0u4r3bDNwCHEmYqMWLN6bsdIIvh2kECesmoElRdRkYSfClriFQF3gFuKOIeIqsD/FezxK83vmv8a0EnwHHEySVe5Uhtp2OLWZeOjAv3F5tgv/zo0pQx5tTfH28laA+NgYaAe8BtxU6thvDY7uY4HPlmfBY2oXvbYtw+SnAsELvy6iSfA7roUdUj8gD0EOPVH8Q9CXeCuwTTn8K/D5m/hjg0Zjpy4FFMdMd+LV17GiC1r+0mPnjgZvD57PYdTJ9VMz0c8CIeMvGOY5mBK11dWPK7gDGlHD9mgQtfpnAAGBcWP5+TNkXYdnjwD9i1q0TvobNY47j2Jj5PQhaHmvGlO1H8EWjXjg9ERheTHyPEbZuEnRz2Ao0LvTa9Y2ZvhSYHj6fDlwaM69VuH41EptMZ8SUzQbOCp8vAnrFzGuSH0+cbU8D/raL/e8qmS782t/Mjl9+9iVIrvaMKRvIr18ShwBLY+bVCvf5f/HqdREx9iNIItcTtOj+k19bvgviLWb9XOCkIv5vjCDZPiim7HDC+hpnW0XWh3ivZwle7x4ECWW1mLI1wGFliG0IQfK6PuaxLGa9tUXUk+Lq+K7q4zLg+Jh5fQi6hMUeW/57VTfcVreY5ecCJ4fPzwTeDZ+nE3wedi3uvdVDj6gfulJcZPcNBqa6+7fh9DNh2ciYZb6Jeb45znSd8Pl+wAp33x4z/0ugaSni+V/M859itr0r+wHfufvGQvvOLsnK7r4lHOWjO0Hr1tvhrHdiyvL7S+8HfBiz7o9mto7gOJeHxSsK7WKtu2+JWedrM3sXONXMXiJItq6MF5uZ7UnQUnlRuO5/zewr4GyCn6Tzxe7zyzDO/Hi/LDSvGkESmUhFvZcHAC+ZWWw9yQvjWVVoG+sIku3dscNrH4p9rQ4gaHVcbWb5ZWmFlik4Fnf/KVyupHUTd38NeM3M0oCewPPAYoLW8J2Y2XnA1QSJYP6+irrgshFBgj83Jn4jSObiKa4+FH79S2qdB9ce5Mt/v0sbG8D77n5UnPJmwJeF9pOvJHW8qPoYb93YCx7XuXte+Hxz+Leoz8BJwMNm1oIgof/B3Svd6EFSuSiZFtkNYZJ2BpBuZvknmhpAAzPLdPd5pdzk10AzM0uLSajzuxxA0EJVK2b5/yvFtr0E+25oZnVjEur9KV1y8BZB4tyCoCUYgqT6nLAsfxSNrwkSMADMrDawd6F9FY43XvxPEiTI1Qguuioq1gEEF949aGajwrIGBF967olZrhm/Xji2fxjnTvGG87YRJAT7EfOemFk6QQJUXNyFlWSZWCuAC9z93RIs+wZwu5nV9jgXj4Z+Yud6FTtUW7z4YstWELRM71NEorYrJT7+8P9iupnNANrHW9/MDiDoktCLoF7kmVkuQRIab3/fEiR07YqpQ7GKqw/lrbSxFWcFsL+ZVYvzPhV3TBm72G7+uvH+d0ol/FL+HMFnRmuCPvoiSU2jeYjsnpMJWgTbEvQxziK4wOltgj7RpfUBQWIz3Myqm1kP4DcE/V4h+Kn6FDOrFQ6tdWEptv0NkBGO7rATd19B0NfxDjOraWYdw+0XO6xbIW8RtBo2I7joC4ILLnsQvDb5LdPjgfPNLCscVeOvwAfuvrwU+wJ4maDf9ZUEF0wVZTDwL4IuNVnh40gg08w6xCz3RzPby8yahdvMHzFiPPD7cGi9OmG8z4YJyWdATTM7wcyqE1zEFTtSyDdA87BFtSjfELTcl9TDwF/CpBEza2RmJxWx7FiCJOoFM2ttZmlmtreZ/dnMjg+XyQXONrN0M+tL0P+9xNx9NTAVuDschi/NzA4KR+EoiWKP38xOMrOzwvfGzKxrGOP7RaxfmyBhXhuufz6/Jt75yxf8L4QJ+qPASDNrHK7T1Mz6FBFScfVht483VhliK85sggsA/2ZmtcP/8yPDebtzTOOB68N6uA9B/+jSfG4U9hRBd5UTUTItKUDJtMjuGQw84e5fufv/8h/A/cCg0g4Z5u6/ECTP/QhapB4EznP3/LGZRxL0X/2GoFV2XCk2P4Og5eh/ZvZtEcsMJPhZ/GuCC8pucvc3SrGP9whGNfjA3T08pm8Jkpo17r4kLHuDYPSPFwhO7gcBZ5ViP4Tb2RxuowXBBVM7MbOmBC2U98S+R+4+F3id4D3MN4mg/2Yu8G+Cvt0QJOJjCb4MfEHQN/zyMIYfCPpXP0bQsr6JHVt1nw//rjOzD4nvDoJkZL2ZXVOCQ7+X4IK0qWa2kSCp7BZvQXf/mWBkkU8J+k9vIEiq9iH48gbBF4ffEPSvHUTwJaW0ziO4aPYTghE7JlLy7iX3AqdZME77fXHmf09w4dqSMP6ngTvdPb/+Pw60DV+/l939E+BuguEavyH4EhXbih/vf+Faggvw3jezDQQt+q2KiLfI+lBCNwNPhvGeUYLlSxMbwOFxxpk+NOxq8RvgYIJROVYS9FHe3WO6HcgB5gMLCLpwlXjc78LCX1y2Ax+6+5e7Wl4kavlXUouIpCQzuxE4xN3PiToWESkfYTeeZ9z9sV0uLBIx9ZkWkZRlZg0JuqKcG3UsIlI+zOxQgu5bRXVdEkkq6uYhIinJzC4m6Av8mruX6q6KIpKczOxJgm4sVxUaWUgkaambh4iIiIhIGallWkRERESkjJRMi4iIiIiUUUpfgLjPPvt48+bNow5DRERERCq5uXPnfuvujQqXp3Qy3bx5c3JycqIOQ0REREQqOTOLO+65unmIiIiIiJSRkmmhefPmdOjQgaysLLKzswG4+eabadq0KVlZWWRlZTFlyhQAfvnlF84//3w6dOhAZmYms2bNijByERERkWildDcPKT8zZ85kn3322aHs97//Pddcs+OdjR999FEAFixYwJo1a+jXrx9z5swhLU3fy0RERKTqUTItpfLJJ59w7LHHAtC4cWMaNGhATk4OXbt2jTgyERGR1LV161ZWrlzJli1bog6lyqtZsyYZGRlUr169RMsrmRbMjN69e2NmXHLJJQwdOhSA+++/n6eeeors7Gzuvvtu9tprLzIzM5k8eTIDBw5kxYoVzJ07lxUrViiZFhER2Q0rV66kbt26NG/eHDOLOpwqy91Zt24dK1eupEWLFiVaR7/NC++88w4ffvghr732Gg888ABvvfUWw4YNY9myZeTm5tKkSRP+8Ic/AHDBBReQkZFBdnY2V111FUcccQTp6ekRH4GIiEhq27JlC3vvvbcS6YiZGXvvvXepfiFQy7TQtGlTIOi2MWDAAGbPnk337t0L5l988cX0798fgGrVqjFy5MiCeUcccQSHHHJIxQYsIiJSCSmRTg6lfR/UMl3Fbdq0iY0bNxY8nzp1Ku3bt2f16tUFy7z00ku0b98egJ9++olNmzYBMG3aNKpVq0bbtm0rPnARERFJGi+//DKffPLJLpfr0aNHwT1C8kcT69ChA23btuX6669PyT7japmu4r755hsGDBgAwLZt2zj77LPp27cv5557Lrm5uZgZzZs355FHHgFgzZo19OnTh7S0NJo2bcrYsWOjDF9EREQitm3bNl5++WX69+9f6ga2/NHEfvzxR4YOHcoll1zCk08+maBIE0PJdBV34IEHMm/evJ3Ki0qSmzdvzuLFixMdloiIiFSg5cuX07dvX7p06cKHH35Iu3bteOqpp7jrrrt45ZVX2Lx5M0cccQSPPPIIZkaPHj3IysrinXfeYcCAAUyePJk333yT22+/nRdeeIHTTz+dDz/8EIAlS5Zw5plnFkzHU6dOHR5++GGaNWvGd999R8OGDSvq0HebunmIiIiICIsXL+bSSy9l0aJF1KtXjwcffJDLLruMOXPmsHDhQjZv3syrr75asPwvv/xCTk4O1113HSeeeCJ33nknubm5HHTQQdSvX5/c3FwAnnjiCc4///xd7r9evXq0aNGCJUuWJOoQE0LJdFVklriHiIiIpKRmzZpx5JFHAnDOOefwzjvvMHPmTLp160aHDh2YMWMGH3/8ccHyZ555ZpHbuuiii3jiiSfIy8vj2Wef5eyzzy5RDO6+ewcRASXTIiIiIrLTKBZmxqWXXsrEiRNZsGABF1988Q4XCNauXbvIbZ166qm89tprvPrqq3Tp0oW99957l/vfuHEjy5cvT7lRwpRMi4iIiAhfffUV//3vfwF45plnOOqoowAKLhCcOHFikevWrVu3YHQwCO4i2KdPH4YNG1aiLh4//vgjl156KSeffDJ77bXXbh5JxVIyLSIiIiK0atWKBx54gDZt2vD9998zbNgwLr74Ytq3b0+fPn049NBDi1z3rLPO4s4776RTp04sW7YMgEGDBpGWlkbv3r2LXK9nz560b9+erl27sv/++xeMHpZKLBX7puTLzs72/LEKpRQS2bc5heuTiIhIVBYtWkSbNm0i2//y5cvp378/CxcuLLdt3nXXXfzwww/cdttt5bbNihLv/TCzue6eXXhZDY0nIiIiIuVqwIABLFu2jBkzZkQdSsIpmRYRERGp4po3b16urdIvvfRSuW0r2anPtIiIiIhIGSmZFhEREREpIyXTIiIiIiJlpGRaRERERKSMlEyLiIiICOnp6WRlZdGuXTsyMzO5++672b59e9RhFeuee+7hp59+ijQGJdMiIiIiScasfB8lseeee5Kbm8vHH3/MtGnTeO2117jlllsSe6C74O7FJvRKpkVEREQk6TRu3JjRo0dz//334+7k5eXxxz/+kUMPPZSOHTsW3Klw1qxZHHPMMZx00kkceOCBjBgxgnHjxtG1a1c6dOhQcDfE5cuXc+yxx9KxY0d69erFV199BcA333zDgAEDyMzMJDMzk/fee4/ly5fTqlUrzjvvPNq3b8+KFSsYNmwY2dnZtGvXjptuugmA++67j6+//pqePXvSs2fPaF4olEyLiIiISBwHHnggeXl5rFmzhscff5z69eszZ84c5syZw6OPPsoXX3wBwLx583j44YdZtGgRY8eO5bPPPmP27NlcdNFFjBo1CoDLL7+cwYMHM3/+fAYNGsQVV1wBwBVXXMExxxzDvHnz+PDDD2nXrh0AS5Ys4dJLL+Xjjz/mgAMO4C9/+Qs5OTnMnz+fN998k/nz53PFFVew3377MXPmTGbOnBnNi4SSaRERERHZhalTp/LUU0+RlZVFt27dWLduHUuWLAHg0EMPpUmTJtSoUYODDjqI3r17A9ChQweWL18OwH//+1/OPvtsAM4991zeeecdAGbMmMGwYcOAoM92/fr1ATjggAM47LDDCvb/3HPP0blzZzp16sTHH3/MJ598UiHHXRK6A6KIiIiI7OTzzz8nPT2dxo0b4+6MGjWKPn367LDMrFmzqFGjRsF0WlpawXRaWhrbtm0r075r165d8PyLL77grrvuYs6cOey1114MGTKELVu2lGm7iaCWaRERERHZwdq1a/ntb3/LZZddhpnRp08fHnroIbZu3QrAZ599xqZNm0q8vSOOOIIJEyYAMG7cOI4++mgAevXqxUMPPQRAXl4eP/zww07rbtiwgdq1a1O/fn2++eYbXnvttYJ5devWZePGjWU+zvKglmkRERERYfPmzWRlZbF161aqVavGueeey9VXXw3ARRddxPLly+ncuTPuTqNGjXj55ZdLvO1Ro0Zx/vnnc+edd9KoUSOeeOIJAO69916GDh3K448/Tnp6Og899BBNmjTZYd3MzEw6depE69atadasGUceeWTBvKFDh9K3b9+CvtNRMHePZMflITs723NycqIOI/WUdIycskjh+iQiIhKVRYsW0aZNm6jDkFC898PM5rp7duFl1c1DRERERKSMlEyLiIiIiJSRkmkRERERkTJKaDJtZsvNbIGZ5ZpZTljW0MymmdmS8O9eYbmZ2X1mttTM5ptZ50TGJiIiIiKyuyqiZbqnu2fFdNgeAUx395bA9HAaoB/QMnwMBR6qgNhERERERMosim4eJwFPhs+fBE6OKX/KA+8DDcysSZz1RURERESSQqKTaQemmtlcMxsalu3r7qvD5/8D9g2fNwVWxKy7MiwTEam08vLy6NSpE/379wdg+vTpdO7cmaysLI466iiWLl0KwFtvvUXnzp2pVq0aEydOjDJkEamk6tSpU6rlZ82aVfDZFYXSxhtrzJgxfP311+USR6Jv2nKUu68ys8bANDP7NHamu7uZlWpg4jApHwqw//77l1+kIiIRuPfee2nTpg0bNmwAYNiwYUyaNIk2bdrw4IMPcvvttzNmzBj2339/xowZw1133RVxxCJSEWbNKt97QvTooftAxBozZgzt27dnv/322+1tJbRl2t1XhX/XAC8BXYFv8rtvhH/XhIuvAprFrJ4RlhXe5mh3z3b37EaNGiUyfBGRhFq5ciX//ve/ueiiiwrKzKwgsf7hhx8KPuibN29Ox44dSUvTIEwiklizZs2iR48enHbaabRu3ZpBgwaRf5O/119/ndatW9O5c2defPHFgnU2bdrEBRdcQNeuXenUqROTJk0CgqT1pJNOokePHrRs2ZJbbrmlYJ2nn36arl27kpWVxSWXXEJeXh4QtDhfd911ZGZmcthhh/HNN98A8MUXX3D44YfToUMHrr/++h1ivvPOOzn00EPp2LEjN910EwDLly+nTZs2XHzxxbRr147evXuzefNmJk6cSE5ODoMGDSIrK4vNmzfv1uuVsE9lM6ttZnXznwO9gYXAZGBwuNhgYFL4fDJwXjiqx2HADzHdQUREKp2rrrqKf/zjHzskyI899hjHH388GRkZjB07lhEjRhSzBRGRxPjoo4+45557+OSTT/j8889599132bJlCxdffDGvvPIKc+fO5X//+1/B8n/5y1849thjmT17NjNnzuSPf/wjmzZtAmD27Nm88MILzJ8/n+eff56cnBwWLVrEs88+y7vvvktubi7p6emMGzcOCBLzww47jHnz5tG9e3ceffRRAK688kqGDRvGggULdrjl+NSpU1myZAmzZ88mNzeXuXPn8tZbbwGwZMkSfve73/Hxxx/ToEEDXnjhBU477TSys7MZN24cubm57Lnnnrv1WiWyiWNf4B0zmwfMBv7t7q8DfwOOM7MlwP8LpwGmAJ8DS4FHgUsTGJuISKReffVVGjduTJcuXXYoHzlyJFOmTGHlypWcf/75XH311RFFKCJVWdeuXcnIyCAtLY2srCyWL1/Op59+SosWLWjZsiVmxjnnnFOw/NSpU/nb3/5GVlYWPXr0YMuWLXz11VcAHHfccey9997sueeenHLKKbzzzjtMnz6duXPncuihh5KVlcX06dP5/PPPAdhjjz0K+mJ36dKF5cuXA/Duu+8ycOBAAM4999wd9j116lQ6depE586d+fTTT1myZAkALVq0ICsra6dtlaeE9Zl298+BzDjl64Beccod+F2i4hERSSbvvvsukydPZsqUKWzZsoUNGzZwwgkn8Omnn9KtWzcAzjzzTPr27RtxpCJSFdWoUaPgeXp6Otu2bSt2eXfnhRdeoFWrVjuUf/DBB5jt2P/bzHB3Bg8ezB133LHTtqpXr16wTuF9F95W/r7/9Kc/cckll+xQvnz58p2OY3e7dMSjznciIhG44447WLlyJcuXL2fChAkce+yxTJo0iR9++IHPPvsMgGnTptGmTZuIIxURCbRu3Zrly5ezbNkyAMaPH18wr0+fPowaNaqgb/VHH31UMG/atGl89913bN68mZdffpkjjzySXr16MXHiRNasCS6d++677/jyyy+L3f+RRx7JhAkTAAq6hOTv+1//+hc//vgjAKtWrSrYblHq1q3Lxo0bS3roxVIyLSKSJKpVq8ajjz7KqaeeSmZmJmPHjuXOO+8EYM6cOWRkZPD8889zySWX0K5du4ijFZGqpmbNmowePZoTTjiBzp0707hx44J5N9xwA1u3bqVjx460a9eOG264oWBe165dOfXUU+nYsSOnnnoq2dnZtG3blttvv53evXvTsWNHjjvuOFavLv5SuXvvvZcHHniADh06sGrVr2NU9O7dm7PPPrvg4sTTTjttl4nykCFD+O1vf1suFyBa/jeIVJSdne05OTlRh5F64vxEUm5SuD6JiIhEZdGiRZXyl6gxY8aQk5PD/fffH3UopRLv/TCzuTF39C6glmkRERERkTJK9E1bREQkpB+FRKSqGTJkCEOGDIk6jIRSy7SIiIiISBkpmRYRERERKSMl0yIiIiIiZaRkWkRERESkjJRMi4iIiAjp6elkZWXRvn17fvOb37B+/fpy30edOnXilg8ZMoSJEycCcNFFF/HJJ5+U+74TRcm0iIiISLIxK99HCey5557k5uaycOFCGjZsyAMPPJDgg4zvscceo23btpHsuyyUTIuIiIjIDg4//PCCuwwuW7aMvn370qVLF44++mg+/fRT4Ne7CGZnZ3PIIYfw6quvAsGNWi677LKCbfXv359Zs2YVTP/+97+nXbt29OrVi7Vr1+607x49epB/U77XX3+dzp07k5mZSa9evQCYPXs2hx9+OJ06deKII45g8eLFBfs95ZRT6Nu3Ly1btmT48OHl/8LEoWRaRERERArk5eUxffp0TjzxRACGDh3KqFGjmDt3LnfddReXXnppwbLLly9n9uzZ/Pvf/+a3v/0tW7ZsKXbbmzZtIjs7m48//phjjjmGW265pchl165dy8UXX8wLL7zAvHnzeP755wFo3bo1b7/9Nh999BG33norf/7znwvWyc3N5dlnn2XBggU8++yzrFixYndeihLRTVtEREREhM2bN5OVlcWqVato06YNxx13HD/++CPvvfcep59+esFyP//8c8HzM844g7S0NFq2bMmBBx5Y0GpdlLS0NM4880wAzjnnHE455ZQil33//ffp3r07LVq0AKBhw4YA/PDDDwwePJglS5ZgZmzdurVgnV69elG/fn0A2rZty5dffkmzZs1K+UqUjlqmRURERKSgz/SXX36Ju/PAAw+wfft2GjRoQG5ubsFj0aJFBetYof7YZka1atXYvn17QVlxrdWF1y+JG264gZ49e7Jw4UJeeeWVHbZfo0aNgufp6els27at1NsvLSXTIiIiIlKgVq1a3Hfffdx9993UqlWLFi1aFHSxcHfmzZtXsOzzzz/P9u3bWbZsGZ9//jmtWrWiefPm5Obmsn37dlasWMHs2bMLlt++fXvBqB3PPPMMRx11VJFxHHbYYbz11lt88cUXAHz33XdA0DLdtGlTIOgnHTUl0yIiIiKyg06dOtGxY0fGjx/PuHHjePzxx8nMzKRdu3ZMmjSpYLn999+frl270q9fPx5++GFq1qzJkUceSYsWLWjbti1XXHEFnTt3Lli+du3azJ49m/bt2zNjxgxuvPHGImNo1KgRo0eP5pRTTiEzM7Oge8jw4cP505/+RKdOnSqk5XlXzN2jjqHMsrOzPf9qTymFMvykUmIpXJ9EEk3/eiJSlEWLFtGmTZuowyiVIUOG0L9/f0477bSoQyl38d4PM5vr7tmFl1XLtIiIiIhIGWk0DxEREREptWTor5wM1DItIiIiIlJGSqZFREREkkAqX8dWmZT2fVAyLSIiIhKxmjVrsm7dOiXUEXN31q1bR82aNUu8jvpMi4iIiEQsIyODlStXsnbt2qhDqfJq1qxJRkZGiZdXMi0iIiISserVqxfcNltSi7p5iIiIiIiUkZJpEREREZEyUjItIiIiIlJGSqZFRERERMpIybSIiIiISBkpmRYRERERKSMl0yIiIiIiZaRkWkRERESkjJRMi4iIiIiUkZJpEREREZEyUjItIiIiIlJGSqZFRERERMpIybSIiIiISBklPJk2s3Qz+8jMXg2nW5jZB2a21MyeNbM9wvIa4fTScH7zRMcmIiIiIrI7KqJl+kpgUcz034GR7n4w8D1wYVh+IfB9WD4yXE5EREREJGklNJk2swzgBOCxcNqAY4GJ4SJPAieHz08Kpwnn9wqXFxERERFJSolumb4HGA5sD6f3Bta7+7ZweiXQNHzeFFgBEM7/IVxeRERERCQpJSyZNrP+wBp3n1vO2x1qZjlmlrN27dry3LSIiIiISKkksmX6SOBEM1sOTCDo3nEv0MDMqoXLZACrwuergGYA4fz6wLrCG3X30e6e7e7ZjRo1SmD4IiIiIiLFS1gy7e5/cvcMd28OnAXMcPdBwEzgtHCxwcCk8PnkcJpw/gx390TFJyIiIiKyu6IYZ/pa4GozW0rQJ/rxsPxxYO+w/GpgRASxiYiIiIiUWLVdL7L73H0WMCt8/jnQNc4yW4DTKyIeESneli1b6N69Oz///DPbtm3jtNNO45ZbbuHoo49m48aNAKxZs4auXbvy8ssv4+5ceeWVTJkyhVq1ajFmzBg6d+4c8VGIiIgkXoUk0yKSWmrUqMGMGTOoU6cOW7du5aijjqJfv368/fbbBcuceuqpnHTSSQC89tprLFmyhCVLlvDBBx8wbNgwPvjgg6jCFxERqTC77OZhZoeY2XQzWxhOdzSz6xMfmohExcyoU6cOAFu3bmXr1q3EDvu+YcMGZsyYwcknnwzApEmTOO+88zAzDjvsMNavX8/q1aujCF1ERKRClaTP9KPAn4CtAO4+n+CCQhGpxPLy8sjKyqJx48Ycd9xxdOvWrWDeyy+/TK9evahXrx4Aq1atolmzZgXzMzIyWLVq1U7bFBERqWxKkkzXcvfZhcq2xV1SRCqN9PR0cnNzWblyJbNnz2bhwoUF88aPH8/AgQMjjE5ERCQ5lCSZ/tbMDgIcwMxOA/T7rUgV0aBBA3r27Mnrr78OwLfffsvs2bM54YQTCpZp2rQpK1asKJheuXIlTZs23WlbIiIilU1JkunfAY8Arc1sFXAVMCyRQYlItNauXcv69esB2Lx5M9OmTaN169YATJw4kf79+1OzZs2C5U888USeeuop3J3333+f+vXr06RJkyhCFxERqVC7HM0jHMru/5lZbSDN3TcmPiwRidLq1asZPHgweXl5bN++nTPOOIP+/fsDMGHCBEaM2HEY+OOPP54pU6Zw8MEHU6tWLZ544okowhYREalwVtRNBs3s6uJWdPd/JiSiUsjOzvacnJyow0g9MaMylDvdtFKkSPrXExFJXWY2192zC5cX1zJdN4HxiIiIiIikvCKTaXe/pSIDEZGIJarZVE2mIiJSiZXkpi0HmtkrZrbWzNaY2SQzO7AighMRERERSWYlGc3jGeA5oAmwH/A8MD6RQYmIiIiIpIKS3rRlrLtvCx9PAzV3uZaIiIiISCW3y6HxgNfMbAQwgeDGLWcCU8ysIYC7f5fA+EREREREklZJkukzwr+XFCo/iyC5Vv9pEREREamSSnLTlhYVEYiIiIiISKrZZTJtZtUJbh/ePSyaBTzi7lsTGJeIiIiISNIrSTePh4DqwIPh9Llh2UWJCkpEREREJBWUJJk+1N0zY6ZnmNm8RAUkIiIiIpIqSjI0Xp6ZHZQ/Ed6wJS9xIYmIiIiIpIaStEz/EZhpZp8DBhwAnJ/QqEREREREUkBJRvOYbmYtgVZh0WJ3/zmxYYmIiIiIJL9ddvMws1oErdOXu/t8YH8z65/wyEQqiS1bttC1a1cyMzNp164dN910EwCDBg2iVatWtG/fngsuuICtW4MBctydK664goMPPpiOHTvy4YcfRhm+iIiIFKMkfaafAH4BDg+nVwG3JywikUqmRo0azJgxg3nz5pGbm8vrr7/O+++/z6BBg/j0009ZsGABmzdv5rHHHgPgtddeY8mSJSxZsoTRo0czbNiwiI9AREREilKSZPogd/8HsBXA3X8i6DstIiVgZtSpUweArVu3snXrVsyM448/HjPDzOjatSsrV64EYNKkSZx33nmYGYcddhjr169n9erVUR6CiIiIFKEkyfQvZrYnwa3DCUf2UJ9pkVLIy8sjKyuLxo0bc9xxx9GtW7eCeVu3bmXs2LH07dsXgFWrVtGsWbOC+RkZGaxatarCYxYREZFdK0kyfRPwOtDMzMYB04HhCY1KpJJJT08nNzeXlStXMnv2bBYuXFgw79JLL6V79+4cffTREUYoIiIiZVGS0TymmdmHwGEE3TuudPdvEx6ZSCXUoEEDevbsyeuvv0779u255ZZbWLt2LY888kjBMk2bNmXFihUF0ytXrqRp06ZRhCsiIiK7UJKWaYBjgF5AT0DNZyKlsHbtWtavXw/A5s2bmTZtGq1bt+axxx7jP//5D+PHjyct7dd/xRNPPJGnnnoKd+f999+nfv36NGnSJKLoRUREpDi7bJk2sweBg4HxYdElZvb/3P13CY1MpJJYvXo1gwcPJi8vj+3bt3PGGWfQv39/qlWrxgEHHMDhhwcD5ZxyyinceOONHH/88UyZMoWDDz6YWrVq8cQTT0R8BCIiIlIUc/fiFzD7FGjj4YJmlgZ87O5tKiC+YmVnZ3tOTk7UYaQeS+BgLLuoT5LEElUvVCcK6F9PRCR1mdlcd88uXF6Sbh5Lgf1jppuFZSIiIiIiVdouu3kAdYFFZjabYHi8rkCOmU0GcPcTExifSEpSC6SIiEjVUJJk+saERyEiIiIikoJKMjTemxURiIiIiIhIqinp0HgiIiIiIlKIkmkRERERkTIqMpk2s+nh37+XZcNmVtPMZpvZPDP72MxuCctbmNkHZrbUzJ41sz3C8hrh9NJwfvOy7FdEREREpKIU1zLdxMyOAE40s05m1jn2UYJt/wwc6+6ZQBbQ18wOA/4OjHT3g4HvgQvD5S8Evg/LR4bLiYiIVBkrVqygZ8+etG3blnbt2nHvvfcCkJuby2GHHUZWVhbZ2dnMnj17h/XmzJlDtWrVmDhxYhRhi1RpxV2AeCNwA5AB/LPQPAeOLW7D4U1efgwnq4eP/PXODsufBG4GHgJOCp8DTATuNzPzXd1VRkREpJKoVq0ad999N507d2bjxo106dKF4447juHDh3PTTTfRr18/pkyZwvDhw5k1axYAeXl5XHvttfTu3Tva4EWqqCKTaXefCEw0sxvc/baybNzM0oG5BLcjfwBYBqx3923hIiuBpuHzpsCKcN/bzOwHYG/g27LsW0REJNU0adKEJk2aAFC3bl3atGnDqlWrMDM2bNgAwA8//MB+++1XsM6oUaM49dRTmTNnTiQxi1R1JRka7zYzOxHoHhbNcvdXS7Jxd88DssysAfAS0LqsgeYzs6HAUID9999/F0uLiIikpuXLl/PRRx/RrVs37rnnHvr06cM111zD9u3bee+99wBYtWoVL730EjNnzlQyLRKRXY7mYWZ3AFcCn4SPK83sr6XZibuvB2YChwMNzCw/ic8AVoXPVxHcqpxwfn1gXZxtjXb3bHfPbtSoUWnCEBERSQk//vgjp556Kvfccw/16tXjoYceYuTIkaxYsYKRI0dy4YXB5UZXXXUVf//730lL0+BcIlGxXXVJNrP5QJa7bw+n04GP3L3jLtZrBGx19/VmticwleCiwsHAC+4+wcweBua7+4Nm9jugg7v/1szOAk5x9zOK20d2drbn5OSU8FClgO51nXAp+RInKmjViQIpWS+kwm3dupX+/fvTp08frr76agDq16/P+vXrMTPcnfr167NhwwZatGhB/nn822+/pVatWowePZqTTz45wiMQqZzMbK67ZxcuL8ntxAEaAN+Fz+uXcJ0mwJNh8p0GPOfur5rZJ8AEM7sd+Ah4PFz+cWCsmS0N93VWCfcjIiJSKbg7F154IW3atClIpAH2228/3nzzTXr06MGMGTNo2bIlAF988UXBMkOGDKF///5KpEUqWEmS6TuAj8xsJmAEfadH7Gold58PdIpT/jnQNU75FuD0EsQjIiJSKb377ruMHTuWDh06kJWVBcBf//pXHn30Ua688kq2bdtGzZo1GT16dLSBikiBXXbzADCzJsCh4eRsd/9fQqMqIXXzKCP91pxwKfkSq5tHwqVkvRAREWA3u3m4+2pgcrlHJSIiIiKSwkraZ1pEREQSQL9YiKQ2jaUjIiIiIlJGxSbTZpZuZp9WVDAiIiIiIqmk2GQ6vIPhYjPTrQZFRERERAopSZ/pvYCPzWw2sCm/0N1PTFhUIiIiIiIpoCTJ9A0Jj0JEREREJAXtMpl29zfN7ACgpbu/YWa1gPTEhyYiIiIiktx2OZqHmV0MTAQeCYuaAi8nMCYRERERkZRQkqHxfgccCWwAcPclQONEBiUiIiIikgpKkkz/7O6/5E+YWTVAw8CLiIiISJVXkmT6TTP7M7CnmR0HPA+8ktiwRERERESSX0mS6RHAWmABcAkwBbg+kUGJiIiIiKSCkozmsd3MngQ+IOjesdjd1c1DRERERKq8XSbTZnYC8DCwDDCghZld4u6vJTo4EREREZFkVpKbttwN9HT3pQBmdhDwb0DJtIiIiIhUaSXpM70xP5EOfQ5sTFA8IiIiIiIpo8iWaTM7JXyaY2ZTgOcI+kyfDsypgNhERERERJJacd08fhPz/BvgmPD5WmDPhEUkIiIiIpIiikym3f38igxERERERCTVlGQ0jxbA5UDz2OXd/cTEhSUiIiIikvxKMprHy8DjBHc93J7QaEREREREUkhJkukt7n5fwiMREREREUkxJUmm7zWzm4CpwM/5he7+YcKiEhERERFJASVJpjsA5wLH8ms3Dw+nRURERESqrJIk06cDB7r7L4kORkREREQklZTkDogLgQYJjkNEREREJOWUpGW6AfCpmc1hxz7TGhpPRERERKq0kiTTNyU8ChERERGRFLTLZNrd36yIQEREREREUk1J7oC4kWD0DoA9gOrAJnevl8jARERERESSXUlapuvmPzczA04CDktkUCIiIiIiqaAko3kU8MDLQJ/EhCMiIiIikjpK0s3jlJjJNCAb2JKwiEREREREUkRJRvP4TczzbcBygq4eIiIiIiJVWkn6TJ9fEYGIiIiIiKSaIpNpM7uxmPXc3W8rbsNm1gx4CtiXYDSQ0e5+r5k1BJ4FmhO0cp/h7t+HFzfeCxwP/AQMcfcPS3EsIiIiIiIVqrgLEDfFeQBcCFxbgm1vA/7g7m0JRv/4nZm1BUYA0929JTA9nAboB7QMH0OBh0p3KCIiIiIiFavIlml3vzv/uZnVBa4EzgcmAHcXtV7M+quB1eHzjWa2CGhK0N+6R7jYk8AsguT8JOApd3fgfTNrYGZNwu2IiIiIiCSdYofGM7OGZnY7MJ8g8e7s7te6+5rS7MTMmgOdgA+AfWMS5P8RdAOBINFeEbPayrBMRERERCQpFZlMm9mdwBxgI9DB3W929+9LuwMzqwO8AFzl7hti54Wt0B53xaK3N9TMcswsZ+3ataUNR0RERESk3BTXMv0HYD/geuBrM9sQPjaa2YZi1itgZtUJEulx7v5iWPyNmTUJ5zcB8lu5VwHNYlbPCMt24O6j3T3b3bMbNWpUkjBERKQcXHDBBTRu3Jj27dsXlJ155plkZWWRlZVF8+bNycrKAmDcuHEF5VlZWaSlpZGbmxtN4CIiCWRB43ACNhyMzvEk8J27XxVTfiewzt3/ZmYjgIbuPtzMTgAuIxjNoxtwn7t3LW4f2dnZnpOTk5D4KzWzxG07QfUp1aTkS5yooFUnCqRkvYjx1ltvUadOHc477zwWLly40/w//OEP1K9fnxtv3HEwqAULFnDyySezbNmyxAeZglK9XohUFWY2192zC5eX5KYtZXUkcC6wwMxyw7I/A38DnjOzC4EvgTPCeVMIEumlBEPjaXxrEZEk0r17d5YvXx53nrvz3HPPMWPGjJ3mjR8/nrPOOivB0YmIRCNhybS7vwMU9X27V5zlHfhdouIREZHEefvtt9l3331p2bLlTvOeffZZJk2aFEFUIiKJV+xoHiIiIiUxfvx4Bg4cuFP5Bx98QK1atXboZy0iUpkomRYRkd2ybds2XnzxRc4888yd5k2YMCFuki0iJRfv4l+AUaNG0bp1a9q1a8fw4cMLyufPn8/hhx9Ou3bt6NChA1u2bKnokKuURPaZFhGRKuCNN96gdevWZGRk7FC+fft2nnvuOd5+++2IIhOpHIYMGcJll13GeeedV1A2c+ZMJk2axLx586hRowZr1gSDo23bto1zzjmHsWPHkpmZybp166hevXpUoVcJapkWEalEStOCtW7dOnr27EmdOnW47LLLdrntgQMHcvjhh7N48WIyMjJ4/PHHgaJbn9966y2aNWvGgQceWA5HJlJ1de/enYYNG+5Q9tBDDzFixAhq1KgBQOPGjQGYOnUqHTt2JDMzE4C9996b9PT0ig24ilHLtIhIJVKaFqyaNWty2223sXDhwrhD3RU2fvz4uOVjxoyJW96jRw/ef//90h+EiOzSZ599xttvv811111HzZo1ueuuuzj00EP57LPPMDP69OnD2rVrOeuss3boAiLlT8m0iEglEm/4uqJasGrXrs1RRx3F0qVLKzpMEdlN27Zt47vvvuP9999nzpw5nHHGGXz++eds27aNd955hzlz5lCrVi169epFly5d6NVrp4HUpJyom4eISCWX34LVrVs3jjnmGObMmVPylc0S8xCR3ZKRkcEpp5yCmdG1a1fS0tL49ttvycjIoHv37uyzzz7UqlWL448/ng8//DDqcCs1JdMiIpVcbAvWnXfeyRlnnEGi7n4rIhXj5JNPZubMmUDwhfmXX35hn332oU+fPixYsICffvqJbdu28eabb9K2bduIo63c1M1DRKSSK6oFq1GjRlGHJiIlMHDgQGbNmlXQ8nzLLbdwwQUXcMEFF9C+fXv22GMPnnzyScyMvfbai6uvvppDDz0UM+P444/nhBNOiPoQKjUl0yIilVx+C1bPnj13aMESkdRQ1MW/Tz/9dNzyc845h3POOSeRIUkMJdMiIpVIaVqwAJo3b86GDRv45ZdfePnll5k6dap+EhYRKQVL5X5z2dnZnpOTE3UYqSeRF/+kcH0qTyn5EicqaNWJAqoXMVQvCqRkvZCEU71IPmY2192zC5frAkQRERERkTJSNw8RkUpg1qzENGP1SMhWRUQqD7VMi4iIiIiUkZJpEREREZEyUjItksIuuOACGjduTPv27QvKbrjhBjp27EhWVha9e/fm66+/3mGdOXPmUK1aNSZOnFjR4YqIiFQ6SqZFUtiQIUN4/fXXdyj74x//yPz588nNzaV///7ceuutBfPy8vK49tpr6d27d0WHKiIiUikpmRZJYd27d6dhw4Y7lNWrV6/g+aZNmwrGEwYYNWoUp556Ko0bN66wGEVERCozjeYhUgldd911PPXUU9SvX5+ZM2cCsGrVKl566SVmzpzJnDlzIo5QRESkclDLtEgl9Je//IUVK1YwaNAg7r//fgCuuuoq/v73v5OWpn97ERGR8qKWaZFKbNCgQRx//PHccsst5OTkcNZZZwHw7bffMmXKFKpVq8bJJ58cbZAiIiIpTMm0SCWzZMkSWrZsCcCkSZNo3bo1AF988UXBMkOGDKF///5KpEVERHaTfu9NEfGGQHv++edp164daWlp5OTkFJT/8ssvnH/++XTo0IHMzExmzZoVQcRSEQYOHMjhhx/O4sWLycjI4PHHH2fEiBG0b9+ejh07MnXqVO69996owxQREam01DKdIoYMGcJll13GeeedV1DWvn17XnzxRS655JIdln300UcBWLBgAWvWrKFfv37MmTNHfWUrofHjx+9UduGFF+5yvTFjxiQgGhERkapH2VWKiDcEWps2bWjVqtVOy37yyScce+yxADRu3JgGDRrs0HItIiIiIuVDLdOVUGZmJpMnT2bgwIGsWLGCuXPnsmLFCrp27Rp1aFIOZs2yXS9UBj0SslUREZHKTcl0JXTBBRewaNEisrOzOeCAAzjiiCNIT0+POiwRERGRSkfJdCVUrVo1Ro4cWTB9xBFHcMghh0QYkYiIiEjlpD7TldBPP/3Epk2bAJg2bRrVqlWjbdu2EUclIiIiUvmoZTpFDBw4kFmzZvHtt9+SkZHBLbfcQsOGDbn88stZu3YtJ5xwAllZWfznP/9hzZo19OnTh7S0NJo2bcrYsWOjDl9ERESkUlIynSLiDYEGMGDAgJ3KmjdvzuLFixMdkoiIiEiVp24eIiIiIiJlpJbpJKYh0ERERESSm1qmRURERETKSMm0iIiIiEgZKZkWERERESkjJdMiIiIiImWUsGTazP5lZmvMbGFMWUMzm2ZmS8K/e4XlZmb3mdlSM5tvZp0TFZeIiIiISHlJZMv0GKBvobIRwHR3bwlMD6cB+gEtw8dQ4KEExiUiIiIiUi4Slky7+1vAd4WKTwKeDJ8/CZwcU/6UB94HGphZk0TFJiIiIiJSHiq6z/S+7r46fP4/YN/weVNgRcxyK8MyEREREZGkFdkFiO7ugJd2PTMbamY5Zpazdu3aBEQmIiIiIlIyFZ1Mf5PffSP8uyYsXwU0i1kuIyzbibuPdvdsd89u1KhRQoMVERGpbBYvXkxWVlbBo169etxzzz3cfPPNNG3atKB8ypQpUYcqkhIq+nbik4HBwN/Cv5Niyi8zswlAN+CHmO4gKWnx4sWceeaZBdOff/45t956K+vWrWPSpEmkpaXRuHFjxowZw3777RdhpCIiUpW0atWK3NxcAPLy8mjatCkDBgzgiSee4Pe//z3XXHNNtAGKpJiEJdNmNh7oAexjZiuBmwiS6OfM7ELgS+CMcPEpwPHAUuAn4PxExVVRivqw2muvvbjtttsAuO+++7j11lt5+OGHI4xURESqqunTp3PQQQdxwAEHRB2KSMpKWDLt7gOLmNUrzrIO/C5RsUStqA+rTZs2YWYRRSUiIlXdhAkTGDjw19P1/fffz1NPPUV2djZ33303e+21V4TRiaQG3QGxAhT+sLruuuto1qwZ48aN49Zbb40wMhERqap++eUXJk+ezOmnnw7AsGHDWLZsGbm5uTRp0oQ//OEPEUcokhqUTCdY4Q8rgL/85S+sWLGCQYMGcf/990cYnYiIVFWvvfYanTt3Zt99g1Fq9913X9LT00lLS+Piiy9m9uzZEUcokhqUTCdY4Q+rWIMGDeKFF16IICoREanqxo8fv8OvpqtX/3rd/0svvUT79u2jCEsk5VT0aB5VTuEPqyVLltCyZUsAJk2aROvWraMKTUREqqhNmzYxbdo0HnnkkYKy4cOHk5ubi5nRvHnzHeaJSNGUTCdQvA+rESNGsHjxYtLS0jjggAM0koeIiFS42rVrs27duh3Kxo4dWy7bbt68OXXr1iU9PZ1q1aqRk5NDbm4uv/3tb9myZQvVqlXjwQcfpGvXruWyP5GoKZlOoHgfVurWISIild3MmTPZZ599CqaHDx/OTTfdRL9+/ZgyZQrDhw9n1qxZ0QUoUo6UTIuIiFRSs2YlZvjVHj28VMubGRs2bADghx9+0M3KpFJRMl0OEjVU9MyZidmuiIhIopgZvXv3xsy45JJLGDp0KPfccw99+vThmmuuYfv27bz33ntRhylSbpRMi4iISLl55513aNq0KWvWrOG4446jdevWTJw4kZEjR3Lqqafy3HPPceGFF/LGG29EHapIudDQeCIiIlJumjZtCkDjxo0ZMGAAs2fP5sknn+SUU04B4PTTT9cY1lKpKJkWERGRcrFp0yY2btxY8Hzq1Km0b9+e/fbbjzfffBOAGTNmFAwRK1IZqJuHiIiIlItvvvmGAQMGALBt2zbOPvts+vbtS506dbjyyivZtm0bNWvWZPTo0RFHKlJ+lEyLiIhIuTjwwAOZN2/eTuVHHXUUc+fOjSAiKa28vDyys7Np2rQpr776Kl988QVnnXUW69ato0uXLowdO5Y99tgj6jCTirp5iIiIiAgA9957L23atCmYvvbaa/n973/P0qVL2WuvvXj88ccjjC45KZkWERGR0jFLzEMitXLlSv79739z0UUXAeDuzJgxg9NOOw2AwYMH8/LLL0cYYXJSMi0iIiIiXHXVVfzjH/8gLS1ID9etW0eDBg2oVi3oFZyRkcGqVauiDDEpKZkWERERqeJeffVVGjduTJcuXcp921u2bKFr165kZmbSrl07brrpJgAuvPBCMjMz6dixI6eddho//vhjue+7IugCRBEREZEq7t1332Xy5MlMmTKFLVu2sGHDBq688krWr1/Ptm3bqFatGitXriwYR7w0atSowYwZM6hTpw5bt27lqKOOol+/fowcOZJ69eoBcPXVV3P//fczYsSI8j60hFPLtIiIiEgVd8cdd7By5UqWL1/OhAkTOPbYYxk3bhw9e/Zk4sSJADz55JOcdNJJpd62mVGnTh0Atm7dytatWzGzgkTa3dm8eTOWov3mlUyLiIiISFx///vf+ec//8nBBx/MunXruPDCC8u0nby8PLKysmjcuDHHHXcc3bp1A+D888/n//7v//j000+5/PLLyzP0CqNkWkREREQK9OjRg1dffRUIxg6fPXs2S5cu5fnnn6dGjRpl2mZ6ejq5ubmsXLmS2bNns3DhQgCeeOIJvv76a9q0acOzzz5bbsdQkZRMi4iIiEiFaNCgAT179uT1118vKEtPT+ess87ihRdeiDCystMFiCIiIiJVyKxZiemb3KOHxy1fu3Yt1atXp0GDBmzevJlp06YxfPhwli5dysEHH4y7M3nyZFq3bp2QuBJNybSIiIiIJMzq1asZPHgweXl5bN++nTPOOIMTTjiBo48+mg0bNuDuZGZm8tBDD0UdapkomRYRERGRhOnYsSMfffTRTuXvvvtuBNGUP/WZFhEREREpI7VMi4iIiMjuS9Q40R6/L3ayUMu0iIiIiEgZKZkWERERESkjJdMiIiIiImWkZFpEREREpIyUTIuIiIiIlJGSaRERERGRMlIyLSIiIiJSRkqmRURERETKSMm0iIiIiEgZKZkWERERESmjpEqmzayvmS02s6VmNiLqeEREREREipM0ybSZpQMPAP2AtsBAM2sbbVQiIiIiIkVLmmQa6AosdffP3f0XYAJwUsQxiYiIiIgUKZmS6abAipjplWGZiIiIiEhSMnePOgYAzOw0oK+7XxROnwt0c/fLCi03FBgaTrYCFldooMlrH+DbqIOQpKN6IfGoXkg8qhcSj+rFrw5w90aFC6tFEUkRVgHNYqYzwrIduPtoYHRFBZUqzCzH3bOjjkOSi+qFxKN6IfGoXkg8qhe7lkzdPOYALc2shZntAZwFTI44JhERERGRIiVNy7S7bzOzy4D/AOnAv9z944jDEhEREREpUtIk0wDuPgWYEnUcKUpdXyQe1QuJR/VC4lG9kHhUL3YhaS5AFBERERFJNcnUZ1pEREREJKUomRYRERERKaOk6jMtu2ZmpxQ3391frKhYJHmoXohISZlZw+Lmu/t3FRWLJA+dR8pOyXTq+U34tzFwBDAjnO4JvAeosldNqheyEzPbCBR5YYy716vAcCR5zCWoFwbsD3wfPm8AfAW0iCwyiZLOI2WkZDrFuPv5AGY2FWjr7qvD6SbAmAhDkwipXkg87l4XwMxuA1YDYwmSpkFAkwhDkwi5ewsAM3sUeCkcSQsz6wecHGFoEiGdR8pOo3mkKDNb5O5tYqbTgI9jy6TqUb2QeMxsnrtn7qpMqhYzW+DuHXZVJlWLziOlp5bp1DXdzP4DjA+nzwTeiDAeSQ6qFxLPJjMbBEwg+Hl/ILAp2pAkCXxtZtcDT4fTg4CvI4xHkoPOI6WklukUZmYDgO7h5Fvu/lKU8UhyUL2QwsysOXAvcCRBMv0ucJW7L48wLIlYeCHiTQSfFw68BdyqCxBF55HSUTKdwszsAKClu79hZrWAdHffGHVcEi3VCxEpDTOr7e76pUIK6DxSOhpnOkWZ2cXAROCRsKgp8HJkAUlSUL2QeMzsEDObbmYLw+mO4c/7UoWZ2RFm9gmwKJzONLMHIw5LIqbzSOkpmU5dvyP4yXYDgLsvIRjORqo21QuJ51HgT8BWAHefD5wVaUSSDEYCfYB1AO4+j19/2peqS+eRUlIynbp+dvdf8ifMrBrFjCcrVYbqhcRTy91nFyrbFkkkklTcfUWhorxIApFkovNIKSmZTl1vmtmfgT3N7DjgeeCViGOS6KleSDzfmtlBhCdEMzuNYNxpqdpWmNkRgJtZdTO7hrDLh1RpOo+Uki5ATFHhuI8XAr0JbsLwH+Ax1xtapaleSDxmdiAwmuCuZt8DXwDnaDSPqs3M9iEY5eX/EXxeTAWudPd1kQYmkdJ5pPSUTIuIVBFmVhtI01X5IiLlR8l0ijGz59z9DDNbQJw+TO7eMYKwJGKqFxKPmZ3j7k+b2dXx5rv7Pys6JomemQ1393+Y2Sjif15cEUFYEjGdR8pOd0BMPVeGf/tHGoUkG9ULiad2+LdupFFIssnvF50TaRSSbHQeKSO1TKcgM0sH3nD3nlHHIslD9ULiCevFFe4+MupYJHmE9eLv7n5N1LFI8tB5pGw0mkcKcvc8YLuZ1Y86FkkeqhcST1gvBkYdhySXsF4cGXUcklx0HikbdfNIXT8CC8xsGlBwG1j1davyVC8knnfN7H7gWXasFx9GF5IkgVwzm0ww9FlsvXgxupAkCeg8UkpKplPXi+FDJJbqhcSTFf69NabMgWMrPhRJIjUJ7n4YWw8cfYZUdTqPlJKS6RQU9mkaoj5NEkv1QuIJ68Vk9ZmWWGG9WKc+0xJL55GyUZ/pFKQ+TRKP6oXEoz7TEo/6TEs8Oo+UjVqmU5f6NEk8qhcSj/pMSzzqMy3x6DxSSkqmU5f6NEk8qhcST1b4V32mJZb6TEs8Oo+UksaZTmFmtgdwSDi52N23RhmPJAfVCxER2R06j5SO+kynKDPrASwBHgAeBD4zs+5RxiTRU72QeMysvpn908xywsfd6hMpZpZhZi+Z2Zrw8YKZZUQdl0RL55HSU8t0ijKzucDZ7r44nD4EGO/uXaKNTKKkeiHxmNkLwELgybDoXCDT3U+JLiqJWtgn9hlgbFh0DjDI3Y+LLiqJms4jpac+06mren5FB3D3z8ysepQBSVJQvZB4DnL3U2OmbzGz3KiCkaTRyN2fiJkeY2ZXRRWMJA2dR0pJyXTqyjGzx4Cnw+lBQE6E8UhyUL2QeDab2VHu/g6AmR0JbI44JoneOjM7BxgfTg8kuCBRqjadR0pJ3TxSlJnVAH4HHBUWvQ086O4/RxeVRE31QuIxsyyCLh71AQO+Awa7+/wo45JomdkBwCjgcIJRPN4DrnD3ryINTCKl80jpKZlOUWbWC3jP3dW6JAVUL6Q4ZlYPwN03RB2LRM/Marr7lqjjkOSi80jpKZlOUWb2JEFrwncE3xrfAt5x9+8jDUwipXoh8ZjZMuB9gjrxtrt/HHFIkgTMbCnwDWG9IPis+CHaqCRqOo+UnpLpFGdm+wGnAdcA+7m7+sGL6oXsIPzZthtwNMEtpFsB8919QKSBSeTMbH9+rRfHA+vdPSvSoCQp6DxScnphUlR40cjRQAfgW+B+gm+QUoWpXkgR8oCt4d/twJrwIVVYOKb0kQSfGZnAx8A7kQYlkdN5pPTUMp2izOxbYBnwMDDT3ZdHG5EkA9ULicfMfgIWAP8E3nB3jdggmNl2YA7wV3efFHU8khx0Hik9JdMpzMzaAd0JrrhtSXDLz3OjjUqipnohhZnZSQT1oSvwC8GoDW+5+/RIA5NImVkmQb3oDuxPcNe7N9398UgDk8jpPFI66uaRosKr8vcHDgCaEwx5tT3KmCR6qhcST9jqOMnMWgP9gKuA4cCeUcYl0XL3eeHFqcsIftY/BzgGUDJdhek8UnpqmU5RZjafoG/bOwQtTCsjDkmSgOqFxBPeTjyTIGl6i6B+fKBh0ao2M8sBahD8UpE/0suX0UYlUdN5pPSUTIuIVHJmlg185O55UcciycPMGrn72qjjEEl1SqZFRERERMooLeoARERERERSlZJpEREREQHAzPaOOoZUo24eKcbMRgFFvmnufkUFhiNJxswOAR4C9nX39mbWETjR3W+PODSJkJm9SDBCw2vurqvyBQAzmwv8C3hGt4qWfGa2BMgFniD4zFCiuAtqmU49OcDcYh5StT0K/Ingbne4+3zgrEgjkmTwIHA2sMTM/mZmraIOSJLCmcB+wBwzm2BmfczMog5KIncIMBo4l+Az469hQ40UQS3TIpWImc1x90PN7CN37xSW5bp7VsShSRIws/rAQOA6YAXBl6+n3X1rpIFJpMwsDehP8KtWHkGL5L3u/l2kgUnkzKwn8DRQG5gHjHD3/0YbVfLRTVtSjJm9QvHdPE6swHAk+XxrZgcR1hEzOw1YHW1IkgzCfpDnELQ2fQSMI7i72WCgR3SRSZTCrmDnA8cDL/BrvZgBZEUXmUSl0GfFN8DlwGSC+vA80CKy4JKUkunUc1fUAUhS+x3Bz3OtzWwV8AXBh6JUYWb2EtAKGAv8xt3zv2A9G964Q6qgsM/0eoL+9CPc/edw1gdmdmRkgUnU/kvwWXFyoRu25JjZwxHFlNTUzUOkEjKz2kCau2+MOhaJnpn1dPeZUcchySPs2jHC3f8adSySXMzMdNFh6SiZTlFm1hK4A2gL1Mwvd/cDIwtKImNmVxc3393/WVGxSPIws1OKm+/uL1ZULJJ8zCzH3bOjjkOSg7qRlp26eaSuJ4CbgJFAT4I+bxqdpeqqW8w8fWOuun5TzDwHlExXbW+Y2TXAs8Cm/EJdeFhlqRtpGallOkWZ2Vx372JmC9y9Q2xZ1LFJdMzsSHd/d1dlIiJm9kWcYtcvnGJmewL7u/viqGNJBWrJTF0/h33elpjZZWY2AKgTdVASuVElLJMqxMz2NbPHzey1cLqtmV0YdVwSLXdvEeehRLqKM7PfENy05fVwOsvMJkcaVJJTN4/UdSVQC7gCuI2gq8fgSCOSyJjZ4cARQKNC/afrAenRRCVJZAxB17DrwunPCH7afzyqgCR6ZlYLuJqgBXJoeC1OK3d/NeLQJFo3A12BWQDunmtmGg6vGGqZTlHuPsfdf3T3le5+vruf6u7vRx2XRGYPgl8mqhH0n85/bABOizAuSQ77uPtzwHYAd99GcHMOqdqeAH4h+CIOsAq4PbpwJElsdfcfCpWpT3Ax1DItUgm4+5vAm2a22d3/ETvPzE4HlkQTmSSJTeGNGPJv5nMYUPhkKVXPQe5+ppkNBHD3n3Q7cQE+NrOzgfTw14orgPcijimpqWVapHI5K07Znyo8Ckk2VxPcwewgM3sXeIrgBClV2y/hhWb5X7IOAn4ufhWpAi4H2hHUhfEEv3BeFWVAyU6jeaQojdogscysH8HtgM8g6Aubrx7Q1t27RhKYJAUzq0HQraMVYMBigpv6KHGqwsysN0E/+rbAVOBIYIi7z4oyLpFUo2Q6RZnZh+7eeVdlUjWYWSaQBdwK3BgzayMw092/jyIuSQ76vJBYZvYA8Iy7vxt2/zmM4EvW++7+bbTRSdTMLBv4M9CcmO7A7t4xqpiSnfpMpxiN2iDxuPs8YJ6ZjQsvLhPBzP4PaArsaWadCBImCD4vakUWmETtM+AuM2sCPAeMd/ePIo5Jksc44I/AAsKLlqV4SqZTT+FRG/Jp1IYqzMyec/czgI/MbKefm9SiUGX1AYYAGUDsLeU3ErQ8SRXk7vcC95rZAQTXWfwr7Ds9niCx/izSACVqa91d40qXgrp5pCgzO8Ddv4w6DkkOZtbE3VeHJ8edqK5UbWZ2qru/EHUckrzCXy7+BXR0d/3KWYWZWS9gIDCdmAtS3f3FyIJKckqmU5SZNQKGE1xxWzO/3N2PjSwoEUlaZnYCO39e3BpdRBI1M6sG9CNone5FcJOO8e4+Kcq4JFpm9jTQGviYX7t5uLtfEF1UyU3dPFLXOIJRG/oDvyW4++HaSCOSyJjZRuIPqm8EH4L1KjgkSSJm9jBBH+mewGMEXcJmRxqURMbMjiNoeTyeoB5MAIa6+6ZIA5Nkcai7t4o6iFSilukUZWZz3b2Lmc3P7w9rZnPc/dCoYxOR5JL/ORHztw7wmrsfHXVsUvHMbAbwDPCCRvqRwszsCeBOd/8k6lhShVqmU9fW8O/q8Ofbr4GGEcYjIslrS/j3JzPbD1gHNIkwHomQugPKLhwG5JrZFwR9pvN/4dSF7EVQMp26bjez+sAfgFEEQ139PtqQRCRJvWJmDYA7gQ8JugQ9GmlEIpKs+kYdQKpRNw8RkUrMzNKAw9z9vXC6BlDT3X+INjIRSVZmdhTQ0t2fCAc8qOPuX0QdV7JKizoAEUkcMzsqvNuZVFHuvh14IGb6ZyXSIlIUM7sJuBb4U1hUHXg6uoiSn5JpkUrGzDqZ2Z1mthy4Dfg04pAketPN7FQzs10vKiJV3ADgRGATgLt/zY43iZNC1Gc6RZlZi8I/ucQrk6rBzA4hGOpqIPAtwbCJ5u49Iw1MksUlwNVAnpltRkMmikjRfnF3z7+brpnVjjqgZKeW6dQV725mEys8CkkWnwLHAv3d/Sh3HwXkRRyTJAl3r+vuae5e3d3rhdNKpEUknufM7BGggZldDLyBLlgullqmU4yZtSa4i1l9MzslZlY9Yu5sJlXOKQR3MZtpZq8T3IRBP+kLAGH3jkFAC3e/zcyaAU3cXTduEZEC4WfFswR3QNwAtAJudPdpkQaW5DSaR4oxs5OAkwn6M02OmbURmJB/xb5UTeHPcScRdPc4FngKeMndp0YamETKzB4iuC3wse7exsz2AqbqJk8iUpiZLXD3DlHHkUqUTKcoMzvc3f8bdRySvMKE6XTgTHfvFXU8Eh0z+9DdO5vZR+7eKSyb5+6ZUccmIsnFzJ4E7nf3OVHHkirUZzp1rTCzl8xsTfh4wcwyog5Kkoe7f+/uo5VIC7DVzNIJbtZCOG7s9mhDEpEk1Q34r5ktM7P5ZrbAzOZHHVQyU5/p1PUE8AxByyPAOWHZcZFFJCLJ6j7gJaCxmf0FOA24PtqQRCRJ9Yk6gFSjbh4pKt5PtGaW6+5ZEYUkIkksvHi5F8GFqdPdfVHEIYlIEjKzse5+7q7K5FdqmU5d35rZOcD4cHogsC7CeEQkyZhZw5jJNfz6eYGZNXT37yo+KhFJcu1iJ8IuYl0iiiUlKJlOXRcAo4CRBP0g3wPOjzQiEUk2cwk+HwzYH/g+fN4A+ApoEVlkIpJUzOxPwJ+BPc1sQ34x8AsaZ7pY6uYhIlLJmdmjBEMkTgmn+wEnu/sl0UYmIsnGzO5w9z9FHUcqUct0ijGzG4uZ7e5+W4UFIyKp4jB3vzh/wt1fM7N/RBmQiCStpbETYTeP6939lojiSXoaGi/1bIrzALgQuDaqoEQkqX1tZtebWfPwcR3wddRBiUhS6mVmU8ysiZm1B94H6kYdVDJTN48UZmZ1gSsJEunngLvdfU20UYlIsgkvRLwJ6B4WvQXcogsQRSQeMzsTeICgwe5sd3834pCSmpLpFBSeGK8GBgFPAve6+/fRRiUiyS78Au7u/mPUsYhIcjKzlgS5xQKgDfAJcLW7/xRpYElM3TxSjJndCcwBNgId3P1mJdIiUhwz62BmHwELgY/NbG74862ISGGvADeEFygfAywhyDukCGqZTjFmth34GdhGeGvg/FkELU71IglMRJKWmb0HXOfuM8PpHsBf3f2IKOMSkeRjZvXcfUOhskPc/bOoYkp2aplOMe6e5u57untdd68X86irRFpEilA7P5EGcPdZQO3owhGRZGNmwwHcfYOZnV5o9pCKjyh1KJkWEan8PjezG2JG87ge+DzqoEQkqZwV87zwONN9KzKQVKNkWkSk8rsAaAS8GD4ahWUiIvmsiOfxpiWGbtoiIlLJhRcpXxF1HCKS1LyI5/GmJYYuQBQRqaTMbHJx8939xIqKRUSSm5nlEYwrbcCeQP5QeAbUdPfqUcWW7NQyLSJSeR0OrADGAx+gn2pFpAjunh51DKlKLdMiIpWUmaUDxwEDgY7Av4Hx7v5xpIGJiFQiugBRRKSScvc8d3/d3QcDhwFLgVlmdlnEoYmIVBrq5iEiUomZWQ3gBILW6ebAfcBLUcYkIlKZqJuHiEglZWZPAe2BKcAEd18YcUgiIpWOkmkRkUrKzLYTXJ0POw5tZYDrrqkiIrtPybSIiIiISBnpAkQRERERkTJSMi0iIiIiUkZKpkVEREREykjJtIhIBTGzHxO47ZvN7JpSLN/IzD4ws4/M7OhExSUiUtkpmRYRqZp6AQvcvZO7v13eGzezasVNi4hUFkqmRUQiZGYHmdnrZjbXzN42s9ZmVt/MvjSztHCZ2ma2wsyqx1t+F9tvbmYzzGy+mU03s/3NLAv4B3CSmeWa2Z4xyx9qZi+Gz08ys81mtoeZ1TSzz8PyLDN7P9zmS2a2V1g+y8zuMbMc4MpC09eZ2RdmVj1ctl7stIhIqlIyLSISrdHA5e7eBbgGeNDdfwBygWPCZfoD/3H3rfGW38X2RwFPuntHYBxwn7vnAjcCz7p7lrtvjln+IyArfH40sBA4FOgGfBCWPwVcG25zAXBTzPp7uHu2u99daPoWYBbB3RgBzgJeDI9JRCRl6Wc3EZGImFkd4AjgeTPLL64R/n0WOBOYSZB4PriL5YtyOHBK+HwsQYt0kdx9m5ktM7M2QFfgn0B3IB1428zqAw3c/c1wlSeB52M28WyhTcZOPwYMB14Gzgcu3kXsIiJJT8m0iEh00oD17p4VZ95k4K9m1hDoAswAahezfHl6C+gHbAXeAMYQJNN/LMG6m4qadvd3w24nPYB03d5cRCoDdfMQEYmIu28AvjCz0wEskBnO+xGYA9wLvOruecUtX4z3CFq2AQYBJbnY8G3gKuC/7r4W2BtoBSwMu6B8HzMCyLnAm3G3Et9TwDPAE6VYR0QkaSmZFhGpOLXMbGXM42qCBPdCM5sHfAycFLP8s8A57NhVorjl47kcON/M5hMkvleWIM4PgH0JWqgB5hOM/OHh9GDgznCbWcCtJdhmvnHAXsD4UqwjIpK07NfPRhERkcQys9OAk9z93KhjEREpD+ozLSIiFcLMRhH0xT4+6lhERMqLWqZFRERERMpIfaZFRERERMpIybSIiIiISBkpmRYRERERKSMl0yIiIiIiZaRkWkRERESkjJRMi4iIiIiU0f8HOa5auVaq6+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xtab = pd.crosstab(df_clean['confecon'], df_clean['partyID'])\n",
    "myplot = xtab.plot(kind='bar', figsize=(12, 5), color=['b', 'y', 'r'])\n",
    "plt.xlabel('Level of worry')\n",
    "plt.ylabel('Number of people')\n",
    "plt.title('Amount of Worry About the Current State of the Economy')\n",
    "for rect in myplot.patches:\n",
    "    xcoor = rect.get_x() + .5*rect.get_width()\n",
    "    ycoor = rect.get_height()\n",
    "    plt.text(xcoor, ycoor, str(int(ycoor)),\n",
    "             horizontalalignment = 'center',\n",
    "             verticalalignment = 'bottom',\n",
    "             fontsize=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df_plot, col='partyID', hue='partyID',  hue_kws={'color': ['b', 'y', 'r']},\n",
    "                  height=4, aspect=1)\n",
    "g.map(plt.barh, 'confecon', 'count')\n",
    "g.set_titles('{col_name}')\n",
    "g.set_axis_labels('Number of people', 'Level of worry') \n",
    "g.fig.subplots_adjust(top=.8)\n",
    "g.fig.suptitle('Amount of Worry About the Current State of the Economy, by Party', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Republicans were least worry about the state of the economy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = df_clean.groupby(['partyID', 'sex', 'confecon']).size().reset_index()\n",
    "df_plot = df_plot.rename(columns={0: 'count'})\n",
    "g = sns.FacetGrid(df_plot, row='sex', col='partyID', hue_kws={'color': ['b', 'y', 'r']}, hue='partyID')\n",
    "g.map(plt.barh, 'confecon', 'count')\n",
    "g.set_titles('{row_name}, {col_name}')\n",
    "g.set_axis_labels('Number of people', 'Level of worry')\n",
    "g.fig.subplots_adjust(top=0.8)\n",
    "g.fig.suptitle('Amount of Worry About the Current State of the Economy, by Party & Gender', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This applies to both male and female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/seaborn/axisgrid.py:643: UserWarning:\n",
      "\n",
      "Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_plot = df_clean\n",
    "df_plot['age_cat'] = pd.cut(df_clean.age, [17,25,35,45,50,55,65,75,85,100])\n",
    "df_plot = df_plot.groupby(['confecon','age_cat']).size().reset_index()\n",
    "df_plot = df_plot.rename({0:'count'}, axis=1)\n",
    "\n",
    "g = sns.FacetGrid(df_plot, col = 'age_cat', col_wrap=3, height=3, aspect=1)\n",
    "g.map(sns.barplot, 'count', 'confecon')\n",
    "g.set_titles('{col_name}')\n",
    "g.set_axis_labels('Number of people', 'Level of worry') \n",
    "g.fig.subplots_adjust(top=.9)\n",
    "g.fig.suptitle('Amount of Worry About the Current State of the Economy, by Age Group', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal basic income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "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>universal_income</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "      <th>cumulative_count</th>\n",
       "      <th>cumulative_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Oppse a great deal</td>\n",
       "      <td>820</td>\n",
       "      <td>64.414768</td>\n",
       "      <td>820</td>\n",
       "      <td>64.414768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>135</td>\n",
       "      <td>10.604870</td>\n",
       "      <td>955</td>\n",
       "      <td>75.019639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oppose a moderate amount</td>\n",
       "      <td>85</td>\n",
       "      <td>6.677141</td>\n",
       "      <td>1040</td>\n",
       "      <td>81.696779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Favor a little</td>\n",
       "      <td>72</td>\n",
       "      <td>5.655931</td>\n",
       "      <td>1112</td>\n",
       "      <td>87.352710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>60</td>\n",
       "      <td>4.713276</td>\n",
       "      <td>1172</td>\n",
       "      <td>92.065986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>55</td>\n",
       "      <td>4.320503</td>\n",
       "      <td>1227</td>\n",
       "      <td>96.386489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>oppose a little</td>\n",
       "      <td>46</td>\n",
       "      <td>3.613511</td>\n",
       "      <td>1273</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           universal_income  count    percent  cumulative_count  \\\n",
       "0        Oppse a great deal    820  64.414768               820   \n",
       "1  Neither favor nor oppose    135  10.604870               955   \n",
       "2  Oppose a moderate amount     85   6.677141              1040   \n",
       "3            Favor a little     72   5.655931              1112   \n",
       "4        Favor a great deal     60   4.713276              1172   \n",
       "5   Favor a moderate amount     55   4.320503              1227   \n",
       "6           oppose a little     46   3.613511              1273   \n",
       "\n",
       "   cumulative_percent  \n",
       "0           64.414768  \n",
       "1           75.019639  \n",
       "2           81.696779  \n",
       "3           87.352710  \n",
       "4           92.065986  \n",
       "5           96.386489  \n",
       "6          100.000000  "
      ]
     },
     "execution_count": 749,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.loc[df_clean['vote'] == 'Donald Trump'].stb.freq(['universal_income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trump's voters tend to strongly oppose universal basic income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "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>universal_income</th>\n",
       "      <th>count</th>\n",
       "      <th>percent</th>\n",
       "      <th>cumulative_count</th>\n",
       "      <th>cumulative_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neither favor nor oppose</td>\n",
       "      <td>333</td>\n",
       "      <td>25.854037</td>\n",
       "      <td>333</td>\n",
       "      <td>25.854037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Favor a great deal</td>\n",
       "      <td>239</td>\n",
       "      <td>18.555901</td>\n",
       "      <td>572</td>\n",
       "      <td>44.409938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Favor a moderate amount</td>\n",
       "      <td>212</td>\n",
       "      <td>16.459627</td>\n",
       "      <td>784</td>\n",
       "      <td>60.869565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Favor a little</td>\n",
       "      <td>199</td>\n",
       "      <td>15.450311</td>\n",
       "      <td>983</td>\n",
       "      <td>76.319876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oppse a great deal</td>\n",
       "      <td>106</td>\n",
       "      <td>8.229814</td>\n",
       "      <td>1089</td>\n",
       "      <td>84.549689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>oppose a little</td>\n",
       "      <td>105</td>\n",
       "      <td>8.152174</td>\n",
       "      <td>1194</td>\n",
       "      <td>92.701863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oppose a moderate amount</td>\n",
       "      <td>94</td>\n",
       "      <td>7.298137</td>\n",
       "      <td>1288</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           universal_income  count    percent  cumulative_count  \\\n",
       "0  Neither favor nor oppose    333  25.854037               333   \n",
       "1        Favor a great deal    239  18.555901               572   \n",
       "2   Favor a moderate amount    212  16.459627               784   \n",
       "3            Favor a little    199  15.450311               983   \n",
       "4        Oppse a great deal    106   8.229814              1089   \n",
       "5           oppose a little    105   8.152174              1194   \n",
       "6  Oppose a moderate amount     94   7.298137              1288   \n",
       "\n",
       "   cumulative_percent  \n",
       "0           25.854037  \n",
       "1           44.409938  \n",
       "2           60.869565  \n",
       "3           76.319876  \n",
       "4           84.549689  \n",
       "5           92.701863  \n",
       "6          100.000000  "
      ]
     },
     "execution_count": 750,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.loc[df_clean['vote'] == 'Joe Biden'].stb.freq(['universal_income'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biden's voters tend to not oppose universal basic income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal basic income vs. political ideology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "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>ideology</th>\n",
       "      <th>Liberal</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Conservative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>universal_income</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oppse a great deal</th>\n",
       "      <td>6.71</td>\n",
       "      <td>23.62</td>\n",
       "      <td>62.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oppose a moderate amount</th>\n",
       "      <td>5.61</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oppose a little</th>\n",
       "      <td>6.71</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neither favor nor oppose</th>\n",
       "      <td>22.07</td>\n",
       "      <td>26.00</td>\n",
       "      <td>11.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favor a little</th>\n",
       "      <td>15.73</td>\n",
       "      <td>12.30</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favor a moderate amount</th>\n",
       "      <td>18.78</td>\n",
       "      <td>11.00</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favor a great deal</th>\n",
       "      <td>24.39</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ideology                  Liberal  Moderate  Conservative\n",
       "universal_income                                         \n",
       "Oppse a great deal           6.71     23.62         62.19\n",
       "Oppose a moderate amount     5.61      7.87          7.11\n",
       "oppose a little              6.71      8.20          3.99\n",
       "Neither favor nor oppose    22.07     26.00         11.71\n",
       "Favor a little              15.73     12.30          6.24\n",
       "Favor a moderate amount     18.78     11.00          3.99\n",
       "Favor a great deal          24.39     11.00          4.77"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['universal_income'] = df_clean['universal_income'].cat.reorder_categories(['Oppse a great deal', 'Oppose a moderate amount', \n",
    "                                                                                    'oppose a little', 'Neither favor nor oppose',\n",
    "                                                                                    'Favor a little', 'Favor a moderate amount', \n",
    "                                                                                    'Favor a great deal'])\n",
    "df_clean['ideology'] = df_clean['ideology'].cat.reorder_categories(['Liberal', 'Moderate', 'Conservative'])\n",
    "\n",
    "(pd.crosstab(df_clean['universal_income'], df_clean['ideology'], normalize='columns')*100).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conservative tend to strongly oppose universal basic income, and Liberal tend to not oppose universal basic income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Universal basic income and free college"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.610248\n",
       "True     0.389752\n",
       "Name: favor_both, dtype: float64"
      ]
     },
     "execution_count": 723,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['favor_both'] = df_clean.apply(lambda x: \n",
    "                                        x['universal_income'] in ['Favor a great deal', \n",
    "                                                                  'Favor a little', 'Favor a moderate amount'] \n",
    "                                        and x['free_college'] in ['Favor a great deal', \n",
    "                                                                  'Favor a moderate amount', 'Favor a little'], axis=1)\n",
    "df_clean.loc[df_clean['vote'] == 'Joe Biden']['favor_both'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among Biden's voters, 39% favor both universal basic income and free college."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    0.924588\n",
       "True     0.075412\n",
       "Name: favor_both, dtype: float64"
      ]
     },
     "execution_count": 724,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.loc[df_clean['vote'] == 'Donald Trump']['favor_both'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among Trump's voters, only 7.5% favor both universal basic income and free college."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Journalists & Immigration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "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>fttrump</th>\n",
       "      <th>ftbiden</th>\n",
       "      <th>ftimmig</th>\n",
       "      <th>ftjournal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Donald Trump</th>\n",
       "      <td>87.835303</td>\n",
       "      <td>15.743286</td>\n",
       "      <td>68.801887</td>\n",
       "      <td>26.502749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joe Biden</th>\n",
       "      <td>8.450549</td>\n",
       "      <td>70.724649</td>\n",
       "      <td>75.315217</td>\n",
       "      <td>72.365683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Probably will not vote</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>39.595238</td>\n",
       "      <td>61.734043</td>\n",
       "      <td>49.706714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Someone else</th>\n",
       "      <td>23.126582</td>\n",
       "      <td>34.022293</td>\n",
       "      <td>67.503125</td>\n",
       "      <td>52.386293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          fttrump    ftbiden    ftimmig  ftjournal\n",
       "vote                                                              \n",
       "Donald Trump            87.835303  15.743286  68.801887  26.502749\n",
       "Joe Biden                8.450549  70.724649  75.315217  72.365683\n",
       "Probably will not vote  28.750000  39.595238  61.734043  49.706714\n",
       "Someone else            23.126582  34.022293  67.503125  52.386293"
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby('vote').agg({'fttrump': 'mean',\n",
    "                             'ftbiden': 'mean',\n",
    "                             'ftimmig': 'mean',\n",
    "                             'ftjournal': 'mean'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voters who favor Trump had the lowest favorability on journalists. Voters who favor Biden had the highest favorability on immigration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Urban vs. Rural"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "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>liveurban</th>\n",
       "      <th>City</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Town</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Donald Trump</th>\n",
       "      <td>235</td>\n",
       "      <td>314</td>\n",
       "      <td>465</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joe Biden</th>\n",
       "      <td>401</td>\n",
       "      <td>204</td>\n",
       "      <td>462</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Probably will not vote</th>\n",
       "      <td>88</td>\n",
       "      <td>61</td>\n",
       "      <td>83</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Someone else</th>\n",
       "      <td>92</td>\n",
       "      <td>64</td>\n",
       "      <td>107</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "liveurban               City  Rural  Suburb  Town\n",
       "vote                                             \n",
       "Donald Trump             235    314     465   259\n",
       "Joe Biden                401    204     462   221\n",
       "Probably will not vote    88     61      83    51\n",
       "Someone else              92     64     107    58"
      ]
     },
     "execution_count": 731,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_clean.groupby(['vote', 'liveurban']).size().unstack())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biden had more voters than Trump in cities, and Trump had more voters in rural areas. Trump had a slight lead among people in towns, and Trump and Biden were within three votes among people in suburbs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important Issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vote\n",
       "Joe Biden                 325\n",
       "Donald Trump               59\n",
       "Someone else               25\n",
       "Probably will not vote     10\n",
       "Name: most_important_issue, dtype: int64"
      ]
     },
     "execution_count": 732,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby('vote').most_important_issue.apply(lambda x: \n",
    "                                                    x.str.contains('trump', case=False).sum()).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biden's voters mentioned \"Trump\" 325 times when talking about the most important issue, while Trump's voters only mentioned \"Trump\" 59 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thermometer rating for the candidates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "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>ftbiden</th>\n",
       "      <th>fttrump</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ftbiden</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.646357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fttrump</th>\n",
       "      <td>-0.646357</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ftbiden   fttrump\n",
       "ftbiden  1.000000 -0.646357\n",
       "fttrump -0.646357  1.000000"
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[['ftbiden', 'fttrump']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation between Biden and Trump’s thermometer ratings is negative, which is not surprising."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df_clean, col = 'partyID', height=4, aspect=1)\n",
    "g.map(sns.scatterplot, 'ftbiden', 'fttrump', alpha=.1, color='black')\n",
    "g.set_titles('{col_name}')\n",
    "g.set_axis_labels('Joe Biden thermometer rating', 'Donald Trump thermometer rating') \n",
    "g.fig.subplots_adjust(top=.8)\n",
    "g.fig.suptitle('Thermometer Ratings of Biden vs. Trump, by Party', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Democrats were mostly united in rating Trump very low, but they had a wide range of mixed feelings towards Biden. Republicans in contrast were more clustered around higher ratings for Trump. Independents were spread out and were more common in the middle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df_clean, col = 'sex', height=4, aspect=1)\n",
    "g.map(sns.scatterplot, 'ftbiden', 'fttrump', alpha=.1, color='black')\n",
    "g.set_titles('{col_name}')\n",
    "g.set_axis_labels('Joe Biden thermometer rating', 'Donald Trump thermometer rating') \n",
    "g.fig.subplots_adjust(top=.8)\n",
    "g.fig.suptitle('Thermometer Ratings of Biden vs. Trump, by Gender', fontsize=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There isn't obvious difference between how male vs. female rated on both candidates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAGDCAYAAACFuAwbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAClRklEQVR4nO2ddZwU9RvHP881cEeX0tLdpTRIiqKCiIWo2N1iYGC3PwNRKYMQUEBQkBAUkK47ukPgiAPuDu64+P7+eGZu42Z2Z+t29+55v1772t3Zie/Ozs585klSSkEQBEEQBEEIHhHBHoAgCIIgCEJRRwSZIAiCIAhCkBFBJgiCIAiCEGREkAmCIAiCIAQZEWSCIAiCIAhBRgSZIAiCIAhCkBFBJghhBBEpIqqjvR5LRC8HYQx/EdE9Bb1dIT/BOgYEQfA/IsgEIUAQ0QEiukhEaXaPy/21fqXU/UqpN/y1PgAgos52Y03XBKD9+Kv7c3uhjPb79QrAOvVj4jgRTSSieIvL3klE/9hPC8QxYGEcfxFRhvYdzhHRciJq6uM679SOtaH+GqcghBsiyAQhsAxUSsXbPf4L9oBcoZT6Wx8rgMba5NJ24z/kr20RUyjPQW6+20Bt/7YA0BLACwU2MP/xsPYdygL4C8D3Pq5vOIAzAO7wcT2CELYUypOhIIQyRFSKiL4jomNEdJSIxhBRpN3ndxHRdiJKIaIFRFTDZD0TiWiM9robER0hoqeIKFlb9wi7ecsR0VwiOk9Ea7Vt/mO0XovUIKIVRJRKRAuJqLzdtjoQ0UoiOktEm4mom91nfxHRm0S0AsAFAFdolpEHiWi3tr43iKi2to7zRDSdiGLs1jGSiPYQ0RkimmNvdfRiXdcQ0SZtrCuJqJk2/XsA1QHM1SxBz3rz3VztQKXUcQALwMJMX8fzRLRXG/s2Irpem94QwFgAHbXxnNWm++UY0ATkx9py54loKxE1cTV+7TvkAJgKoJG2nspEdIGIytlttxURnSSiaKN1aMd3VwD3AuhDRJWdPn9W+y7/EdE95Oi2jyWiD4joEBGdIHbhFnM3bkEIRUSQCULBMxFANoA6YAtJbwD3AAARXQdgFIAbAFQA8DeAKRbXWxlAKQBVANwN4AsiKqN99gWAdG2e4drDF24BMAJARQAxAJ7Wxl8FwDwAY8DWk6cBzCSiCnbL3g6++CYAOKhN6wOgNYAOAJ4FMA7AbQCqAWgCYJi2/h4A3gZwE4DLtOWnOo3N6rpaAhgP4D4A5QB8DWAOEcUqpW4HcAg2C+d7Pnw3Q4ioKoB+APbYTd4LoDP4d3wNwA9EdJlSajuA+wGs0sZT2mS13h4DvQF0AVBPW/4mAKddjV/7DjEAbgXwL5AnMv/Slte5HcBUpVSWyWruALBOKTUTwHZtffr6+wJ4EkAv8P+lm9Oy72hjbqF9XgXAK+7GLQghiVJKHvKQRwAeAA4ASANwVnv8CqASgEwAxezmGwZgqfb6dwB3230WAba21NDeKwB1tNcTAYzRXncDcBFAlN2yyWBREgkgC0B9u8/GAPjHzfhratuLcpr+F4CX7N4/COAP7fVzAL53mn8BgOF2y77u9LkCcJXd+/UAnrN7/yGAT7TX3wF4z+6zeO271fRiXV8BeMNpLDsBdLX7/XrZfebxd3NxTKRqY10Mdgmbzb8JwHXa6zudfzN/HQMAegDYpc0b4eY7/AU+Js+Cj+VzAHrafT4UwArtdSSA4wDauVjfbgCPa69fALDZ7rPxAN62e19H2291ABBYYNa2+7wjgP3+/i/LQx4F8RALmSAElkFKqdLaYxCAGgCiARzT3F5nwZaZitr8NQB8avfZGfCFp4qFbZ1WSmXbvb8AFiwVAEQBOGz3mf1rbzhusB2Axz9EH7/2HTqBrVmutn3C7vVFg/f6+i+HneVJKZUGtuTY7x+r66oB4CmnsVbTtmGEt9/NmUFKqQSwgGoAwN7de4edC/Us2KJX3nAtxnh1DCillgD4HGxFSyaicURU0sV2HlVspSsG4BoAM3R3L4DZABoRUS0AVwM4p5RaY7QSIroKQC3YrJw/AWhKRC2095ebjVn7TsUBrLfbX39o0wUh7IgK9gAEoYhxGGxVKO904bT//E2l1I9+3OZJsIu0KtgKArDwCASHwVakkS7mUT6s/z+wMAIAEFEJsLvxqBfr0vf1myafO4/Tr99NKbWMiCYC+ADAIC2W6hsAPcGuyRwi2gQW5B6t2wC3x4BS6jMAnxFRRQDTATwDwGVJDaVULoC/iWgP2O25RSmVQUTTwW7iBnAd8D8c/P02EZHz9E0Ajmlj1rEf8ymwwG6slPLm9xeEkEIsZIJQgCiljgFYCOBDIipJRBFa0HlXbZaxAF4gosZAXgLAEB+3mQNgFoBXiag4ETVA4LLZfgAwkIj6EFEkEcVpweZV3S5pjSkARhBRCyKKBfAWgNVKqQNerOsbAPcTUXstqL0EEQ0gogTt8xNwDMwPxHf7BMDVRNQcQAmw6DoJAFpAvn1g/QkAVckuKcEq7o4BImqr7YdosBswA0CulXUTUUdwUH+S3eTJYBfrtTARZEQUB441uxccA6Y/HgFwCxFFgYXhCCJqSETFYScQNTH4DYCPNREJIqpCRH2sjFsQQg0RZIJQ8NwBDoTfBiAFwAxobi+l1C8A3gUwlYjOA0gEB377ysPgYO3j4AvkFLClzq8opQ4D0BMTToKtSs/AT+capdQi8EV5Jth6UhvAzV6uax2AkWBXXQo4uP5Ou1neBvCS5g57OhDfTSl1EixeXlFKbQPHuK0Ci6+mAFbYzb4ELHqOE9EpLzbn6hgoCRY3KWCX8GkA77tY1+ek1abT1vWSUup3u++1AizoNiilzJIbBoEtXJOVUsf1BzhuLApAX22dnwFYCv59/tWW1cf9nD5d+78sAlDfwr4QhJCDlPLFCi4IQjhCRO8CqKyU8jXbUghTAn0MENESAD8ppb714zobgm9SYk1c/oIQtoiFTBCKAETUgIiaaa65duCSCL8Ee1xCwVGQxwARtQXQCsA0P6zreuJ6Y2XA1uO5IsaEwkjABBkRjScuMphoN60sEf1JXLTxT70+jnaC+Iy42OMWImoVqHEJQhElARxDlA6+SH4IzoYTig4FcgwQ0SSw6/BxpVSqH1Z5H7h8x14AOQAe8MM6BSHkCJjLkoi6gOvtTFZKNdGmvQfgjFLqHSJ6HkAZpdRzRNQfHMjZH0B7AJ8qpdoHZGCCIAiCIAghRsAsZEqp5eAaSvZcB2CS9noSOKhTnz5ZMf8CKE1El0EQBEEQBKEIUNAxZJW0tH+AM30qaa+rwLHg3xFYK4QpCIIgCIIQ9gStMKxSShGRx/5SIroXXLcGJUqUaN2gQQO/j00QBEEQBMHfrF+//pRSyrCbREELshPEjXKPaS7JZG36UThWYK4Kk8rbSqlx4GbBaNOmjVq3bl0gxysIgiAIguAXiMisLl+BuyzngFtiQHuebTf9Di3bsgO499kxoxUIgiAIgiAUNgJmISOiKeDmueWJ6AiA0QDeATCdiO4GV4O+SZt9PjjDcg+4Ge6IQI1LEARBEAQh1AiYIFNKDTP5qKfBvArAQ4EaiyAIgiAIQigTtKD+QJGVlYUjR44gIyMj2EMRTIiLi0PVqlURHR0d7KEIgiAIRYUpU4A6dYC2bYM9EkMKnSA7cuQIEhISULNmTRBRsIcjOKGUwunTp3HkyBHUqlUr2MMRBEEQigq33MLPublACOqDQtfLMiMjA+XKlRMxFqIQEcqVKycWTEEQBCE4rFwZ7BEYUugEGQARYyGO/D6CIAhCgZKba3udlha8cbigUAqycOfVV1/FBx98AAB45ZVXsGjRonzz/PXXX7jmmmtcrmfTpk2YP39+QMYoCIIgCGGDUsCnnwKrVgF9+gR7NIYUuhiywsbrr7/u9bKbNm3CunXr0L9/fz+OSBAEQRDCjMhI4NFH+fXu3cD+/UDv3sEdkxNiIfMDkydPRrNmzdC8eXPcfvvtmDt3Ltq3b4+WLVuiV69eOHHiBAC2fN11113o1q0brrjiCnz22Wd563jzzTdRr149dOrUCTt37sybfuedd2LGjBkAgD/++AMNGjRAq1atMGvWrLx51qxZg44dO6Jly5a48sorsXPnTly6dAmvvPIKpk2bhhYtWmDatGlIT0/HXXfdhXbt2qFly5aYPXs2BEEQBKHQk5oKzJwJHD0KPPIIMGIEkJUV7FE5IBYyH0lKSsKYMWOwcuVKlC9fHmfOnAER4d9//wUR4dtvv8V7772HDz/8EACwY8cOLF26FKmpqahfvz4eeOABbNmyBVOnTsWmTZuQnZ2NVq1aoXXr1g7bycjIwMiRI7FkyRLUqVMHQ4cOzfusQYMG+PvvvxEVFYVFixZh1KhRmDlzJl5//XWsW7cOn3/+OQBg1KhR6NGjB8aPH4+zZ8+iXbt26NWrF0qUKFFwO0wQBEEQCppDh4DBg4Hp04GHHwYGDgR+/RUYMiTYI8tDBJmPLFmyBEOGDEH58uUBAGXLlsXWrVsxdOhQHDt2DJcuXXIo7zBgwADExsYiNjYWFStWxIkTJ/D333/j+uuvR/HixQEA1157bb7t7NixA7Vq1ULdunUBALfddhvGjRsHADh37hyGDx+O3bt3g4iQZaL6Fy5ciDlz5uTFp2VkZODQoUNo2LCh/3aIIAiCIIQa6en8XLw40LcvUKsW8PnnISXIxGUZAB555BE8/PDD2Lp1K77++muHEg+xsbF5ryMjI5Gdne3z9l5++WV0794diYmJmDt3rmlJCaUUZs6ciU2bNmHTpk0ixgRBEISigS7ISpTgeLIHHgCWLwe2bg3uuOwQQeYjPXr0wM8//4zTp08DAM6cOYNz586hSpUqAIBJkya5XUeXLl3w66+/4uLFi0hNTcXcuXPzzdOgQQMcOHAAe/fuBQBMmTIl7zP77U2cODFvekJCAlJTU/Pe9+nTB//73//AnaqAjRs3evhtBUEQBCEMsRdkAHDXXUBcHFvJQgQRZD7SuHFjvPjii+jatSuaN2+OJ598Eq+++iqGDBmC1q1b57kyXdGqVSsMHToUzZs3R79+/dDWoK1DXFwcxo0bhwEDBqBVq1aoWLFi3mfPPvssXnjhBbRs2dLB4ta9e3ds27YtL6j/5ZdfRlZWFpo1a4bGjRvj5Zdf9s9OEARBEIRQxlmQlSsHTJ4MvPBC8MbkBOnWknCkTZs2at26dQ7Ttm/fLm64MEB+J0EQBKHAWLYM+OQTYOxYoFKloA2DiNYrpdoYfSYWMkEQBEEQCjdduwK//JJfjE2fDvTr51jJP0iIIBMEQRAEoXCTlgakpHDFfnuysoA//gAMOuIUNCLIBEEQBEEo3LzzDseNOTN4MFCxYkgE94sgEwRBEAShcJOezgH9RI7TY2OBe+8FfvuN2ykFESkMKwiCIAhC4UYXZEbcdx+7NO3qhAYDEWSCIAiCIBRuXAmyqlWBjz8u2PEYIC7LABEfH+/zOmrWrImmTZuiRYsWaNq0qUMz8CuvvNJwGftm5IIgCIIgwLUgCxHEQhbiLF26FOXLl8fOnTvRu3dvXHfddQCAlStXBnlkgiAIghAmxMUFtf6YFcRCFkCUUnjmmWfQpEkTNG3aFNOmTcv77P3330fbtm3RrFkzjB492u26zp8/jzJlyuS91y1wSik8/PDDqF+/Pnr16oXk5OS8edavX4+uXbuidevW6NOnD44dOwYA6NatG5577jm0a9cO9erVw99//+2vrywIgiAIocfUqcCffwZ7FC4p1Bayxx8HNm3y7zpbtOBiv1aYNWsWNm3ahM2bN+PUqVNo27YtunTpgq1bt2L37t1Ys2YNlFK49tprsXz5cnTp0iXfOrp37w6lFPbt24fp06fn+/yXX37Bzp07sW3bNpw4cQKNGjXCXXfdhaysLDzyyCOYPXs2KlSogGnTpuHFF1/E+PHjAQDZ2dlYs2YN5s+fj9deew2LQqAGiyAIgiAUVQq1IAs2//zzD4YNG4bIyEhUqlQJXbt2xdq1a7F8+XIsXLgQLVu2BACkpaVh9+7dhoJMd1nu3bsXPXv2RLdu3Rzi05YvX563jcsvvxw9evQAAOzcuROJiYm4+uqrAQA5OTm47LLL8pa74YYbAACtW7fGgQMHArULBEEQBCH49O4NtGkDvPVWsEdiSqEWZFYtWQWNUgovvPAC7rvvPsvL1K5dG5UqVcK2bdvQrl07S9to3LgxVq1aZfh5rJbeGxkZ6dCQXBAEQRAKHYmJQI0awR6FSySGLIB07twZ06ZNQ05ODk6ePInly5ejXbt26NOnD8aPH4+0tDQAwNGjRx1iv4xITk7G/v37UcPpgOrSpUveNo4dO4alS5cCAOrXr4+TJ0/mCbKsrCwkJSUF4FsKgiAIQogjWZZFk+zsbMTGxuL666/HqlWr0Lx5cxAR3nvvPVSuXBmVK1fG9u3b0bFjRwAcoP/DDz+gYsWK+dbVvXt3REZGIisrC++88w4qOWWJXH/99ViyZAkaNWqE6tWr560zJiYGM2bMwKOPPopz584hOzsbjz/+OBo3bhz4HSAIgiAIoYJSYSHISDk32gwj2rRpo9atW+cwbfv27WjYsGGQRsRs3rwZI0eOxJo1a4I6jlAmFH4nQRAEoQiQmcllL958Exg1KqhDIaL1Sqk2Rp+Jy9LPjB07FsOGDcOYMWOCPRRBEARBENLT+TnELWTisvQz999/P+6///5gD0MQBEEQBAAoWRLYvh0oXz7YI3GJCDJBEARBEAovUVFAgwbBHoVbxGUpCIIgCELhZf9+4P77gRCvNCCCTBAEQRCEwsvhw8DXXwPHjwd7JC4RQSYIgiAIQuElTIL6RZAFgMjISLRo0QKNGzdG8+bN8eGHHyI3N9fv2+nWrRucy34AwMSJE/Hwww87TJswYQJatGiBFi1aICYmBk2bNkWLFi3w/PPP+31cgiAIghAyhIkgk6D+AFCsWDFs0rqaJycn45ZbbsH58+fx2muvBW1MI0aMwIgRIwAANWvWzOuRaU9OTg4iIyODMTxBEARBCAxhIsjEQhZgKlasiHHjxuHzzz+HUgoZGRkYMWIEmjZtipYtW+a1Opo4cSJuuOEG9O3bF3Xr1sWzzz6bt44HHngAbdq0QePGjTF69GjD7UyYMAH16tVDu3btsGLFCsvji4+Px1NPPYXmzZtj1apVqFmzJk6dOgUAWLduHbp16wYAePXVVzF8+HB07twZNWrUwKxZs/Dss8+iadOm6Nu3L7KysgCw2NOnt2vXDnv27PFmtwmCIAiCfwgTQVb4LWSaoMjHX3/x8+OPA5o1y4FPPgFatAAmTuSH83IecMUVVyAnJwfJycn44YcfQETYunUrduzYgd69e2PXrl0AgE2bNmHjxo2IjY1F/fr18cgjj6BatWp48803UbZsWeTk5KBnz57YsmULmjVrlrf+Y8eOYfTo0Vi/fj1KlSqF7t27o2XLlpbGlp6ejvbt2+PDDz90O+/evXuxdOlSbNu2DR07dsTMmTPx3nvv4frrr8e8efMwaNAgAECpUqWwdetWTJ48GY8//jh+++03j/eZIAiCIPiFLl2Azz8HSpcO9khcIhayAuaff/7BbbfdBgBo0KABatSokSfIevbsiVKlSiEuLg6NGjXCwYMHAQDTp09Hq1at0LJlSyQlJWHbtm0O61y9ejW6deuGChUqICYmBkOHDrU8nsjISNx4442W5u3Xrx+io6PRtGlT5OTkoG/fvgCApk2b4sCBA3nzDRs2LO9Zb24uCIIgCEGhSRPgoYeA2Nhgj8Qlhd9C5s6i9cknrj+/805++MC+ffsQGRlp2Dzcnli7gyUyMhLZ2dnYv38/PvjgA6xduxZlypTBnXfeiYyMDJ/GY09cXJxD3FhUVFReAoLzdvTxRUREIDo6GkSU9z47OztvPn2682tBEARBKHDWrePSF9dfH+yRuEQsZAHm5MmTuP/++/Hwww+DiNC5c2f8+OOPAIBdu3bh0KFDqF+/vuny58+fR4kSJVCqVCmcOHECv//+e7552rdvj2XLluH06dPIysrCzz//7PV4a9asifXr1wMAZs6c6dU6pk2blvfcsWNHr8ciCIIgCD4zfjxw773BHoVbCr+FLAhcvHgRLVq0QFZWFqKionD77bfjySefBAA8+OCDeOCBB9C0aVNERUVh4sSJDpYxZ5o3b46WLVuiQYMGqFatGq666qp881x22WV49dVX0bFjR5QuXRotWrTweuyjR4/G3XffjZdffjkvoN9TUlJS0KxZM8TGxmLKlClej0UQBEEQfCY9PeQD+gGAlFLBHoPXtGnTRjnX4dq+fTsaNmwYpBEJNWvWxLp16/KV1HBGfidBEAShQBg8mJuLh0DrJCJar5RqY/SZuCwFQRAEQSi8pKcDxYsHexRuEZel4Ffssy0FQRAEIeiEictSBJkgCIIgCIWX7t1DvuQFUEgFmVJKyi2EMOEctygIgiCEGUFsW+gJhS6GLC4uDqdPn5aLfoiilMLp06cRFxcX7KEIgiAIRYGjR4HU1GCPwi2FzkJWtWpVHDlyBCdPngz2UAQT4uLiULVq1WAPQxAEQSgKNG0K3HILt08KYQqdIIuOjkatWrWCPQxBEARBEEKBMAnqL3QuS0EQBEEQBABAdjZw6ZIIMkEQBEEQhKCRns7PIsgEQRAEQRCChAgyQRAEQRCEIJOVBVStCpQtG+yRuKXQBfULgiAIgiAAAGrUAA4fDvYoLCEWMkEQBEEQhCATFEFGRE8QURIRJRLRFCKKI6JaRLSaiPYQ0TQiignG2ARBEAQhIHz+ObBgQbBHUbRYvhyoXRvYuDHYI3FLgQsyIqoC4FEAbZRSTQBEArgZwLsAPlZK1QGQAuDugh6bIAiCIASMRx4B+vYN9iiKFmfOAPv2BXsUlgiWyzIKQDEiigJQHMAxAD0AzNA+nwRgUHCGJgiCIAhCoUCyLM1RSh0F8AGAQ2Ahdg7AegBnlVLZ2mxHAFQp6LEJgiAIQkDIyODnkSODO46ihggyc4ioDIDrANQCcDmAEgAs23CJ6F4iWkdE66RfpSAIghAWpKdzLNOVVwZ7JOHBmjUsXpXybT0iyFzSC8B+pdRJpVQWgFkArgJQWnNhAkBVAEeNFlZKjVNKtVFKtalQoULBjFgQBEEQfKFcOeDrr4F//wVycoI9mtBn2DDg22+Bc+d8W48IMpccAtCBiIoTEQHoCWAbgKUABmvzDAcwOwhjEwRBEITAsG0bi7LTp4M9ktDniSf4OSvLt/U88giwaxcQHe37mAJMMGLIVoOD9zcA2KqNYRyA5wA8SUR7AJQD8F1Bj00QBEEQAsKffwKPPsqvJdzGPbGx/JyZ6dt6SpUC6tb1fTwFQFAq9SulRgMY7TR5H4B2QRiOIAiCIASW5GTb61OngjeOcOHee/n50iXf1jNuHLB3L/Duu76PKcBIpX5BEARBCDRnz9pei4XMOr5ayBYvBn791S9DCTTSy1IQBEEQAk1Kiu21CDL3FCsG3HYbUL++b+tJTw+LgH5ALGSCIAiCEHhSUjgu6rPPgM6dgz2a0CYjA7h4EahZE4jwUaaIIBMEQRAEIY+UFKB8ec76a9Ik2KMJbfR4uxdfBLZs8W1dYSTIxGUpCIIgCIHm00+B8+c525II6NUr2CMKXewTIOxfe0N6OlCtmm/rKCBEkAmCIAhCoElI4MdNNwFxcSLIXNGyJTBnDnDttb4H9b/5JlCypH/GFWBEkAmCIAhCoHnySaBWLaBCBWDfvmCPJrSJjASqV+fXvgqyQYN8Hk5BITFkgiAIghBoZs4E1q9nQSZZlq6ZNg24805+7asgmzgR2LjR1xEVCCLIBEEQBCHQpKQApUuzIDt1yvem2YWZtWuBTZv4tS+CLDcXGDEibOqQiSATBEEQhECSnQ2kpgJlyrAgy852LBQrOJKcDFSqBPz2m2+xdhcv8rNkWQqCIAiCgHPn+LlMGaBZM3bH5eQEdUghTXIyUKMGMGCAb+tJT+fnMBFkYiETBEEQhECiV+kvXRro1g2YMIFrkgnGJCezeH3/feDff71fjwgyQRAEQRDyqFgR+PlnoGtXtowdOFC4XJbZ2VxVf8YM/6wvOZkF67PPAkuWeL8eEWSCIAiCIORRsiQweDC74f77j8tf+Eu8hAJ79gAHDwJPP+2f9Y0fDzzxBL/2Jag/IYGD+mvX9s+4AozEkAmCIAhCINm+nS09t93GQf2A7xXoQ4lt2/jZXyKzd29+jonxTZDVqMHiLkwQC5kgCIIgBJK//wYefhhIS+Mq/fHxhasWWVISPzds6Pu6Tpzg2LF9+1iQXbrk/bpSU3k9WVm+j6sAEEEmCIIgCIHEPqgfKHzFYXVB9t57vq9r1y6OHdu7F4iN9c1CNm8euyv37PF9XAWACDJBEARBCCRnzwLR0UDx4vy+fPnCJcj69OHnDRt8X5fuyq1YEXjsMd/qkIVZUL/EkAmCIAhCIElJ4TIORPy+YUMgIyO4YzJj507OBG3UyPoyI0YAkyaxi9BXdEFWqRLw8su+rUsEmSAIgiAIeehtk3QmTQraUNzSoAE/W23tdOoUkJjIYtOfgqx8eV5vdDRQv7536wozQSYuS0EQBEEIJL1725plhzL2AfR62yF3LF0KdO/O5TzS0vJ9fOECcPPNwKFDFseQnAyUKwdERXFW6nPPWVzQgPR0ICKCY9HCABFkgiAIghBI7r4beOEF2/tvvmHRYSBggsrOnbbXW7ZYWyYpia1jbdsaWsi2bwemTQMWL7Y4hp49gSef5Ne+lr1ISGD3sO4qDnFEkAmCIAhCINm8mS1IOlFRwJkzoRfY37QpC6zHH7fe2ikpiTMZX3wRmDkz38e6RrNcdu2GG4BRo/i1r1mWzz3Hbs8wQQSZIAiCIASSnj2BN9+0vdeLw4aaIAM4mP/jj61Xt09KAho3ZktUhw75PtaNgJYF2cqV3FoKYEHmSx2yMEMEmSAIgiAECqW47IV9UH+oCrIhQ4B33+W6XQbWrnxcugTs3s2CbPVq4KWX8mWP6hayEycsjuGaa4APPuDXvros77wT6NfP++ULGBFkgiAIghAoUlO5jESZMrZpoSjIMjKA2bNZPE6cCAwd6j6w//x5YNAg4KqrgPXr2Qp4/rzDLB5ZyC5d4ozUihX5faNGvlX/P3oUOHfO++ULGCl7IQiCIAiB4uxZfg51QbZ1K7cYatOGMxNzcjiwv31782XKlwd+/plff/89P6em2gQVPIwhO3WKn/XldUuZt6Snh03JC0AEmSAIgiAEDr1tkr0gi4/nIH+rgfMFwbp1/NymjS0rcf1614Ls6FHOZCxZkr8TkC9z1CMLmX2Vfn+Qnm4Tv2GAuCwFQRAEIZC0agVcdpntPRG/j44O3picWbeOBWL16kC1avxaF2lmPPool7sAbILMqfSF/vbkSSA3180YnAXZww/zeLxFLGSCIAiCIAAAmjdnS5MzL77IrsE33ij4MRmxbp2jdax1a+Nx26NnWAJsKQPyWch0QZadzd7bsmVdrC8uDujcGaha1TZNr7bvDSLIBEEQBEEAwFmWRoVJ167lgPNQEWSLF9vi3QAur79nj/n4MzL48yFD+H2NGsBrrwFXXOEwm70+O3HCjSDr0gVYvtz23tc6ZJs3A5GR3i9fwIjLUhAEQRACxSefsPXIKfsQFSqEVlB/+fJAnTq293feCYwZY17lXm9CrlvILrsMeOUVoF49h9nsPZhu48gyMhz9mr4KsooVuSNCmCCCTBAEQRACRUoKu870GCudUBJkU6YAw4c71hBTijMvd+wwXiYpiZ91QZadzX0t9+93mC0tDShVil+7FWQPPuhYkDYmhtfrNvjMgMxMLt3x22+eLxskRJAJgiAIQqBISWFFEuF0ua1QgdWKUyHVoPDHH8DChRzDZU+PHsB77xkvk50N1K1rs4jl5PD8U6Y4zJaaatNYbgWZs09TbwruTbX+tDRg+nRg717Plw0SIsgEQRAEIVCcPetY8kInlGqR6QH99hDxNLPA/jvuAHbtsomm2FjOGjUI6q9Rg1fnVpAlJzuWvHjqKbZ06dvwBD0ZIIyC+kWQCYIgCEKgSEkxFmR9+rA7zWWUewGQlgZs355fkAGcaZmUZFyx3yi2Kz7esA5Z6dIcoua2fVJyMlCpku19TAw/zOLYXCGCTBAEQRCEPJz7WOrUqAEMGBA4wXDmDLcyOnjQ9XwbN3K8mJkg0yv225ORwYkKH3/sOD0+3rAOWXw8G75cWsiUym8hW7gQ6N3bg87kdoggEwRBEAQhj2XLgDlz8k9PTeXWQBs3Bma7qamc9ThunOv59OKvrVvn/0wXac4FYnfs4DZLVao4Tk9IcLCQKcVvExLY8OVSV124AERFOQqy48eBP//MJ/IsIYJMEARBEIQ8IiONRUFWFvDMMyzYAsHffwO1agHffuu6dMTtt3NQf+XK+T+rWhW47rr87YcSE/lZz7DU6dzZoRl4RgYb2BISLFjISpRg4fXkk7ZpeuyYN6UvGjQAfvoJaNLE82WDhBSGFQRBEIRA0acPcOutHARvT+nSLNYCEdSfkcF1xFq14izDmTOBW24xnrd8eR6jEUTAr7/mn56UxNasunUdp48d6/BWN5ZZclnq2Gej+pJlWakSMGyY58sFEbGQCYIgCEIgyMjgOKijR/N/FhHBYigQgmzbNjZNPf00F3v98kvj+c6dA+66y3WLpJwcDvq3F0VJSVzuIibGcV6lHMp46J5G3UJ27pyLKh9//gm0a+dYpkJfvzcWsqQkFohOSQahjAgyQRAEQQgEKSn8bBTUDwSuOOymTfzcogXwwAPAihVcWd+ZDRuACROAU6fM1zV3LtCokWOs2+nT+d2VAFsB7abrgky3kAEuvu6BA9xOyr4Wmi8uy2XL+LtfuOD5skFCXJaCIAiCEAj03pBGZS+AwFnINm8GihfniqwjRgDduwP16+efz1VAv47+2bp1QPv2/HrFCo6Bc8ap7IX+MiGBS5QB7LasVs1gO3pNDPt4tRYtOL7NmzgwPai/eHHPlw0SIsgEQRAEIRDoFjIzQXbHHYGp1L95M9CsGceolSlj235urmOM1rp1QM2aLAzNqFqVRZKzW1NXWPY4CTJ7l2XJkvzaNI4sOZktifZu0HLlzOPb3BGGgkxcloIgCIIQCNwJshEj2K3mbwYO5N6UOufOAW3bAl995TifUYV+Z4jYSqYLsilT2GJ17Fj+eePj2UWYkwMgf1A/4EaQ2Ze8ANiV+vbbHMPmKenpQLFi+VtWhTBuR0pEqUR03ulxmIh+IaIrCmKQgiAIghB2tG8PLFjAJRiMOH4cWLIkT8D4jaeeAu6/3/a+VCkWVl98wYH3ABeO3bfPvSADHCv2b9oE7N6dvxQGwKYwIM865RzUD3goyE6fBkaNssXEeUJ6eljVIAOsuSw/AXAEwE8ACMDNAGoD2ABgPIBuARqbIAiCIIQv5ctzpXkzZs4EHn6YrU1GdcC84cABFnpt2nBpCp0HH2SL3LJlQLdubD2aPZsD9t1x5ZVAx44c75aUxPFoUQbyIT6eg/LT04GSJR2C+kuU4E2atk8aPz5/iyZfsiyvusomEMMEK7a8a5VSXyulUpVS55VS4wD0UUpNA2BihxUEQRCEIs6iRcBbb9msUs4EosH45MksoJxj04YOZdepXgKjWDHg2mu5LIY7+vfnQrPVq7MgM8qwBICRI1lUXXYZAMegfiI3tchq1nQoKgvAtzpkt94KvPuu58sFESuC7AIR3UREEdrjJgD6L21ylAmCIAhCEef331mQmTXH1gWZq7ITnrJ5M4us+HjH6cWKcc2xX34B/vuP48mmT7e+3txctr4dOGAuyJy+Z2oqG9J0Q5dp+6TsbOC++4DFix2n+1L2Yt8+4PBhz5cLIlYE2a0AbgeQDOCE9vo2IioG4OEAjk0QBEEQwpeUFPOAfiAwFrLNm4HmzY0/u/9+bmp+4ADwzjsszqwyeDC3YgLMBdmmTRw3t3YtAFsfS12nmVrITp/mnps7djhO98VlOXx4/u4IIY5bQaaU2qeUGqiUKq+UqqC93qOUuqiU+qcgBikIgiAIYUdKinlRWMD/guz8ea5036KF8ed16nBAfp06wKFD1gL6dRo14jIa+/aZx8VlZQFr1uQFiqWmOoZxmQoyfaJzUH+xYsDzz3MFf08pjEH9RFQBwEgANe3nV0rdFbhhCYIgCEKYc/asawtZuXIcfF6unH+2t3UrP5tZyAA2V82axa89EWRt2nA26IkTNkuZM7qbVAseS0119JzqgkwpJ++mmSCLiuKyF95QGAUZgNkA/gawCICfc3MFQRAEoZCSksIuQjOiooB//OhoiooCBgwAWrY0n0cpW+2zVq2sr1uv2D9woLlFTzeHaemVustSp2JFNqLl06m6IKtUKf86V69mS+IVHlbZKqSCrLhS6rmAj0QQBEEQChP33QeULet6Hr0hd7Fivm+vfXvgt99cz0ME/PADsHKlZ2UhqlblZ1fLuLGQ6XorOdlEkDlbyADg6quBu+8GPv7Y+liBQivIfiOi/kqp+QEfjSAIgiAUFqxU4e/enUXS0qW+b2/HDi4fYd+g24hbb+WHJxBx/JmrmDgnQZaW5mj0si8O69Bas3t34NNPjdcdG+tdUH/t2lymI4ywIsgeAzCKiDIBZIGLwyqlVMmAjkwQBEEQwpWcHC7j0LgxUKWK+XxlyrDQ8cf2WrViq5yn1iSruKtZFhXFlreaNQEYB/UDBoH9zZrxwwhvBZneOD2MsJJlmaCUilBKFVNKldTeixgTBEEQBDPOnuXG2DNnup6vQgX/ZFnu3s1FWc0yLAuKjh0dCsM6B/UDBtX6FywwtxDGxHgnyMIQU0FGRA2051ZGD182SkSliWgGEe0gou1E1JGIyhLRn0S0W3uWLgCCIBRdkpOBzz4zr/IuhDbuGovrVKjAdbhyc33b3ubN/Owqw7Ig+OAD4PvvAeS3kJUvz8/5LGSjR5tnU8bGel6p/9gxjt374QfPlgsyrixkT2rPHxo8PvBxu58C+EMp1QBAcwDbATwPYLFSqi6Axdp7QRCEosnIkcBjjwEbNgR7JII36ILMVcwVwIIsJ8c2v7ds3swuQ+f2QwXN5MnArFnIymLDlr2FLCqKK3zkE2RGjcV12rSx1t7JnrQ03/dnEDCNIVNK3au97KeUcmiKRURuIgbNIaJSALoAuFPbziUAl4joOtgalU8C8BcAye4UBKFool+1/JF9JxQ8Z8/ysxULWbFiLCB8qUe2aRMXb9XbDQWL+HggLc2hj6U9hu2TXAkyzdpmz7//Atu3c690Q9LT+TnMsiyttE5aaXGaVWoBOAlgAhFtJKJviagEgEpKqWPaPMcBGBQkAYjoXiJaR0TrTvqz3YQgCEIokZHBMUiNGgV7JII3WHVZDhsGXLjguRXImfLlgR49fFuHP4iPB1JT9VJk+Vpq5qvWn57ODzNBZsBXXwFPP+1ihsImyIioMhG1BlCMiFraxY91A1Dch21GAWgF4CulVEsA6XByTyqlFEwalyulximl2iil2lTQ204IgiAUJnJy2ARw+eVhmS0mgF2V3bvbAqfMiLBiF7HA5MmBy670hIQElxayfIJMN6yYCbL+/YFOnRwmnTvHetc07K6wCTIAfcCxYlUBfARb/NiTAEb5sM0jAI4opVZr72eABdoJIroMALRno45XgiAIhZ+0NGDIEODPP4FevSSwPxzp3RtYssS4+rw9p09zQddp07zf1oULXAI/FNBclrqFzEiQOWRZRkUBd94JNGlivD4ithbbce4c/yXOnzcZQ2ETZEqpSUqp7gDuVEp1t3tcq5Sa5e0GlVLHARwmIr0sXE8A2wDMATBcmzYc3LJJEASh6FGqFMfOvPwyX3327w/2iEKfSZO48XWocPEiWzrdER/PDbn37PF+W199xevR49aCybBhwAsvuHRZnj1rlzhZtSowYYJ5A3GDOmTnzvHzmTMmY7jmGra8NW7szTcIGm4LwyqlZhLRAACNAcTZTX/dh+0+AuBHIooBsA/ACLA4nE5EdwM4COAmH9YvCIIQvhw6xNXW9Z6EGzd63suvKHHgAFtZWrcOHRfvE08Av/4KHD/uer7YWDYj+RITvXkzu0bdZXQWBH37AgDSfuW3RkH9AH/dKlUAnDrFFq1q1YzdtwZlL3TL2JkzJn+L6Gj3ruIQxK3zmojGAhgKFlEEYAgAF91S3aOU2qTFgTVTSg1SSqUopU4rpXoqpeoqpXoppcy0ryAIQuHmuefYjdW0KRAZyYJMMKdGDb6Ye1qvKpCkpFgXSL4Wh920KfgFYXX27wd+/RVpZ7MBGFvIALs4sm+/5cr+Tm7JPLyxkP3yCzB4MFspwwgr0YRXKqXuAJCilHoNQEcA9QI7LEEQhCJMYiLH1MTFcZalCDLXEAG3387xWKFCSor7DEsdXwRZZiYngAS7IKzOnDnA9dfj0ik2YxnFkAF2giw5mVVbcZNcQadK/UpZEGSJidwhITrau+8QJKwIMl22XiCiy8H9LC8L3JAEQRCKMFlZwM6dtviXgQPFXemKlBTOZjxzBvjvv9CIowIKzkK2fTuQnR06FjLNJJZ5mtMszQRZXmD/iROuS1588QVw5Eje24wMW/6CqSBLT2chF2WlXXfoYEWQzSWi0gDeB7ABwAEAPwVwTIIgCEWXPXv4iqMLsjffBP73v+COKZT5/Xfgr79sFqJt24I6nDzOnrVuIfvoI+Dnn73bzvHjvJ1QsZBpgiw7JRVE+Q1fhhYyV4IsOprd9hr2mZUuBVmYZVgCbgQZEUWA2xmdVUrNBMeONVBKvVIgoxMEQShqJCbys30ZgLNnXVx9ijizZ3Ok+HPPAUePcnPrUCAnh/spWqFuXe8Lw/bty67aeiESSaSZxLLPpiE+nr3Jzh/HxnogyCZNAq6+Ou+t7q4EipggU0rlAvjC7n2mUuqci0UEQRAEX1CK48YaNOD3qal8YR87NrjjCkUuXWIL2cCBbJm5/PL8CiBY7Ntn3bK5YgXwwAPIq6bqCUrxdw6V761ZyHLOp+UL6Ad4mA7tkypUcC0mDxwAFi3KKyFSZAWZxmIiupEoVH5tQRCEQsxNNwFJSbYelgkJQK1aEthvxF9/sWC97jp+P2YMN2QPFaxeNnfvZsHtUDHVAkoB1asDb73l+dgCxWWXAQMH4kx2qXzxYzoO1foXLQLef998fXpvTi2D1pLL8umnw9LNb0WQ3QfgZwCZRHSeiFKJyKw+riAIguALepVxe1q2FEFmxKJFHKTUsye/372bs+uCzdGjQKtWwPz51ubX2wB6Gth/5Ag/QqH+mE7dusCcOdgc3cbQQgYYVOt3hS7ItExL3UJWrpwLQda2rYObM1xwK8iUUglKqQilVIxSqqT2vmRBDE4QBKFIkZnJF9d33nGc3rIlsHevo78mFNi3D+jXz6k5YQHyzjtcg0u3JjZqxGIo2Pvp1CkW0Ga1tZzxVpDNmcPPegHhUEApIDUVWecuuLeQ7djByuq338zXFxPDz5qFTP9pa9VyIci+/971OkMUP3U1FQRBEHxm504uYVCzpuN0/YK7eXOBD8klTz4J/PGH/xpke0pEBFtkdBo14uft24MzHp2UFH72pA4Z4JkgO3AAeP55oEcPLiIcKqSnAyVLov/+L9wKMnX8BKsqsxpkgKmFzKUge+cdYOJEr4YfTESQCYIghApJSfzs3IOvZUuuRaY3CAwFjhxhl9zDDwenTc2bb3Lj9exs2zR9v+n7MRCkpACffw4sXQocPGg+D+BZHTLAes/S3Fzg7rv59fjxwRPERmjiKvKicVA/wEH9ly4BFw5qllVXWZb9+rFrWjvG9BiyGjVYkCllsEwhDuoXBEEQCoKkJC5mWb++4/TLLmOX5YABwRmXEZ98wsKgaVPglSBUQpo5k12C9sU/a9Rg92Uga5F98w3wyCNsmfr2W+N59OK0Vi1k8fHAq69aT0hQimOkPv2Uv3MoEREBlCiBmMxUlxYyAEjfZ0GQVanCMYKaW/rcOdZaFSpwuT6jkMtCKciIKJKIdhTUYARBEIo0iYnsgtPjZpwJld58KSnA118DQ4cCx45xduP5Asz1OnSIY7T07EqdyEi22j35ZGC2e+kS8NlnLMa6duUaaEZ46rIEgNGj2Qp04gSwdav5fLm5/D2ffx646y7r6y9IEhIQc8ncQqbrr4zDySzgypUzX9fu3cAbb3AXBrAgK1XKVuJN39UOFEZBppTKAbCTiKoX0HgEQRBCj4wM4KefbEHUgSI1lS1ORnz4IbvArAaKB5I5c7hm1rPPciFWpYA1awpu+3Pn8vO11+b/rFs3tqoEgp9/5qSBJ5/kbW/dauxmvP12YPXq/H2DrHDTTUCfPsDhw/k/y83lzz74wPP1FiAqPh6x2WluLWTZR5NZhNpV4s/Hnj1sgT10CEB+QZYvjiw3l29cCpsg0ygDIImIFhPRHP0R6IEJgiAEncRE4PHH+QJ/663Ad98FdnuLFwNTphh/VqMGW2j0Sv7BZPhwdgs2b84B5UTAqlUFt/3Zs9mt6+zaBYD167nIqr/j7ZRiUdygAcc16dY5XRzaU6EC0K6dd7Fdn3/OFp4BA/JbHb/4guOprHYACBI55SshB5FuBdmiaz9zH+/nFNR//jxQsqQLQZaTw4L5yiu9G3wQsXK0vAzgGgCvA/jQ7iEIglA4SU5my0/TpsBXX3G8zqJF3Mblzz8Da6Uyu4jrmZbBrkd28iSLk4YN+X2pUpzd6Ikg27QJGDUKmDrVxOfkgqwsFoNG1jGAkw3GjvV/puW///K+f+IJ/o1q1+bvbWQ1/e474L33vNtO06bAjBk8/iFDbJ209+zh9lD9+gEjRnj/PQqAk7P+wR343tRlqecwHD8d7T4hxKDshUsLWXQ0C+fevb0bfBCxUodsGbiheLT2ei24ybggCELhpEIFjmv58EN2UU2dyoHFixfziX7LFv9v86efOH7s6FHjz2vVYtNAMAVZdjbQoQPw4IOO0zt2ZMGSm2ttPaNHA2+/DQwbxuUbAGDCBK6wPmUKx6WZER3N2Y0vv2z8eaAyLTt0YFF+++22aU88AVxzTf55f/kFmDbN+21dfTWLyoULeV/n5AB33sni5JtvQqdNkgm6cdLMQhYdzYKq95Q73VfUNyh74VKQXbwIrFtnS6wII6LczUBEIwHcC6AsgNoAqgAYC6BnYIcmCIJQgCjFF7++fdkdZVRYsnVrfl6/nl1S/mTLFo6TMcs4i4gAWrQIriCbOZOLwTpbH0aOBPr3Z0Hmzk2XmcnC5t572bWoC6jNm1mEZGZyRt3zzwPPPGMr+qpz6RILE7Orfa1afBH3Z6ZlTg7HOfV0uuzdc4/x/CkpngX0G3H33byvIyLYjbliBVtoAxUf50cS3hmFediErHjzTgUVKwJN9/4K7HZTZ95TQbZnD1fqnz6dLYxhhBWX5UMArgJwHgCUUrsBuMhRFQRBCEM++IAFgasMtxo1+EqwIQBOgsREjomKjjafp2VLviIZFl8KMEoB777LY3TObmzXDrj+escSFGYsWwZcuMDraNHC9n0/+YRNK+vXs9Vp9Gh2i9oXS83KAqpVY8ulGZGRHOflTwvZww/zeI32+9at7GK0JyXFP+2MxozhDMMbbuBne+tcKHPiBJphi8uchirlM1Ei6xwXJXNF5crAiy/mxQvqMWTFirFWyyfI9DoYhTSoP1MpdUl/Q0RRAIJwNhAEQQgQ8+ZxfM7QoXzyN4OIexSuX+//MSQl5S8I68yHH7LlJxguq0WL2Dr3zDPGVrBp07hljTs2buSrabdu+T+Ljub9O306sGQJCxE94OjQIWD5co7vq13b9TYaNfKfhezUKbZMVahgvN8/+YQtZZcu2aadPeu7hQywba9aNeCll0LeVamTERmPBJjXIQOA2iU1oe2qBpn++ZgxQJMmyM5mvVWqFO+KsmWLniBbRkSjABQjoqvBjcYN0koEQRDCkO3bOZapRQuueu7uote6NVuzNBeKX0hL41gqd4JMLw8QDAvZO+8Al18O3Hab8ecTJlgLZH/uOY6Tc9UuBwC6dwc++ohfL1jAnQoeegiIi3PfOPqBB/xXGmLsWI5LeuIJ48+vu46tln//bZvmD5dlGHMhMgHxSEN8CfPjtFZxrbu4O0GWlcX79ujRvKTTUqX4uSgKsucBnASwFcB9AOYrpVzcQgresmULcPx4sEchCEWI3Fyu+1SsGJdScCcSABYKN95ouRCqUqwrXPbf3rmTn5s0cb+y1q05vqogyc3lGLHXXrPF9DjTsSNb+Vw19s7J4WdPxUrr1myF2rWLY/zcXWw7dwYGD/ZsG0ZkZnL8Vt++5mK5Vy8WiXq2pV4eY9Ag37cfpqRTPCKRi4Ro82zk6nH8h8gu58Zlef480KULMHNm3qFVlAXZI0qpb5RSQ5RSg5VS3xCRxf4OglWU4njRZ58N9kgEoQgREcElCn79ld1CVujThzMBdVeaG7ZuBZ56yrzLDgAWHGfO8LpdQcRjXrfO2lj9BRF/CbMgdsBagdjPP+dMUleizYjy5dlStWePtVpwmZm8w30tVjtlClfOf+op83mKF2eL3ezZ/P2JODkkDOtg+YtUsK8yAea14C41a4P+mIdTFRq6XpldUL9+D1RSywMwFGTx8Rxrqau2MMKKIBtuMO1OP4+jyHPgAIcqrF0b7JEIQhFh9mwu49CuHYsJTzh7lq01FtBzBNz+t8uUyZ9RaETLlhyHVRBuy4MHOdvvySeB06ddz2ulQOy8eRwn5u3F8oorrBVFjYzkQPyZM73bjs6lS9wmyTm70plrr+V9tXUrK4Tp04u0u2Nj3ZvQGIkoUdXcElqydgX8jv44ftHNsaALskuXrFnIBg7kpJvLL/du8EHEVJAR0TAimgugln2FfiJaCsB5Fwg+omey79zJ4SSCEPYkJ3Mw7vz5bGUIJaZPZ5eSt5X3b7yRK/dbwJIge/BB18kE9rRsyTFKWiuZgPDffyxo6tYFfviBXZZxca6XcVcgNi2NMywLokG63qDd20zLrCwWoPfey7Xn3MUVDhzIddUqVAB27ODkkM2bvdt2IeBEbgXsi2uMqGLmGcP198zDw/ifa1c+YMvczcy0JsjCGFcWspXgivw74Fih/ykAbuzqgqfoWfRKFen/sVAYuO8+bvGyeTMX7xwwgFPXq1ZlS4J98HOw+OYbFht33+3d8q1bc9CnfWadCbogO3rURb3TuXONexcaEeiK/aNHcxbj119z8+o9e4BPP7UWkzN6NPDII8afLV7M+6t/f/+O1wxvMi1zc7kIcOPGfBxbpVIljuu77DLvGosXMood2Y0v8JBLK3LVVdPxFD50L8iI2EpmIMjKlOEKKg75NaNHs1ILRuKLj5gKMqXUQaXUX0qpjnCs1L8dgAW7uuAJGzbYyrEEIqNeEAqEnBwufbBnD8fVnDvHVpGPPuJg+PnzgYkTgzvG8+d5TIMGWaubZUSrViwuLFzwExO5VilgYiU7e5bb/bjLsNRp1ozjyHbsyP+ZUrbAeW/JyWELz86dHLdlNbYO4EKcZoJr/nwu5tqpk2/js0qjRtz4Ww/ydoVSwO+/s9AeNoytgcONonVccOwY8OabtmOiCAuyyDMncVfGl8aN1zWKp55AMiq6F2QAB/VXr24YQwY4dd86f56P4TApEWKP2xgyrVL/DABfa5OqAvg1gGMqkmzcyPG8lSsHpuakIBQI+/dziYCmTfl9yZJ8Mn3iCRZqmZmBb9DtjgUL2CU1cKD367Cv2O+Cc+fYs3jbbRzWZCjI9Au4VUFWvDi7g597joXcr79yjap+/biEwE8/Wf4aeRw7xmUiTpzgAqQTJ3K8lqdkZfGyK1bk/+zwYT7JuSp860/0/WkkXO1RikVk//78g/3wA5+QPT0+UlL4d5g0id/7ozBsmHImSytA5qLBe9SZZJwii4Js4ULgoYcMXZaAk9syPT0sMywBqdQfEhw7xvGfrVrxo6gJMis3sKHK4cNcvH358mCPxP8o5UVcsu6fMyvfEBnJbqFgxpT99htbLzwN5Lendm229rgRZImJ/Ny+PesDw6Q/fSZ3JS/sKVeO3cHVqnGF/Hfe4RPJtdeyOS4ri4WxVebN44KvJ0/6ZlmIjGTxbWQFnT8f+PFH79ftKW3asBvRnaWKiAP3v/iCxdutt9rqvXlCw4Z8XOhxa0VYkJ3K0LqKuwiIpuRkpBazKMgAQCmcO8dds/RwxqIoyKRSf4DRQ0FatmRBtm2bZ+fScGb3bj5fhqugWbGCLSAjR/q3TmiwUQp49FEO+zLrdW2ILi4aNTKfZ+DA/K13CpLbbwfef997dyXALsPrrnN7sbfXWm3bcrWKfKEtSUl8Aale3bMxDBjAjZlXrWJLxKZNbH2sU4eDyydMsL6u33/nH9uqlc6MiAhuwu0c2K9fmGNifFu/J1SvzoH2Zpa+jAzeX9nZLEYffNC38RGxIAa49VNBWQJDkJMX3QgypYDkZFxMqGjt3qx+feD22/P6WOoURUEmlfoDjG4Ra9GCBVlOjut2eoWJlSv5Zn7hwmCPxDv0m+Fdu7jNX2Hhrbe4ZFROjoex44mJfAGMjzef58orgdWrOZMvGPTq5X0wvz3ff88xQy7YupW9ttWrc3WNM2e4X7QDL73EbYLcNeV2pmNHzoTs0MGxXEalSly3a948a+u5dAn480922fkj7qZjR76rtK811q+ffwq1esq2bcDSpcaf6S2PVq703/b0G40RI/y3zjAk+aIbl2V2NvDyy9hRvbc1C1lkZF4dMj1+DCiagixfpX4ALwVyUOHAsmUcv5qd7fu6Nm7km9qSJVmQAUXHbbllCz+7Kl0UyiQlcR/joUNZxOzeHewR+c6337JGuPFGfq9beSwxZoz7oH29gvns2V6MzkemTeOHv0hNdelz37qVrWNEbCEDDOLIKlRgteYviNh6tmQJp6C5Y8UK/h7+yn7UC8SuXs3vz5xh0dPQTQHQQPDaa2y+dub4cRbT117LMY7+4qqr+PmXX/y3zjDkdFosfmz/GdC7t/EM0dHAyy/jWMMe1gSZXZalWwvZvHkcJxqGuBVkSqlc50r9SoVhPqmfeeUVYMYM//Sv3bDBJsSqV+eDrKgJsjVrfE8OCwZJSeyd+/hjPmc8+GBYZlvnMWcOZ/v37ctFyqtX91CQ1a3LbWtc0agRz/frr74M1TveeIPLOfiDvXv56vDzz4YfK8X7Tg8Na9KEY18cBNmZM5zN5++K0AMGsEvur7/czzt/Pl8ge/Twz7adC8QuXMhxgwVRf8yZRo3YJOksTF98kWMM/NXvUicqim9K6tb173rDjLR0wup2j9iSX5w5eRJYsgTVy6QiOdnCOTMmJq8wrL0gK1mSjWcOgiwmxrWFPoSxkmV5DRFtJKIzRHSeiFKJyFoTt0JKUpIt5km/CfSWM2e4Sr8uyIiCF9ivFFv+brmFvUpPPMGi07R2kh+2t3kzx76mpXlfwzFYZGZydYfGjbn80NtvA4sWsZDxFKXYdZuWxseEPyyvnrJiBVv62rRhjREdzSLCsiDbu5crurtIdQfAB/mgQWzBOXvWx1F7wP79fJD5kl1pT61a7BoxCew/dox/Sz3hNDqawxIctNfWrcDkye6r4HtKly6cjWnFbXnbbVzeIiHBP9suWZL/DLrAmzePXai6ibAgadyY/1x6r1CAT64TJgCPPRYY4fTii3zHXkRRig2urZN/N862BbgWYc+eqBOxDxkZFoqhm1jIiDiM00GQDR/Od8hhiBWX5Sfg9knllFIllVIJSqmSbpYp1Hz1FR8fJUv63ipt0yZ+1ms9AizItm61VHPSL6SkcN3HRo2Abt04vjcigs/RQ4ZwB4orruDz9ldfeRjk7YITJ/hG6Y47+H24uS137uQbfz0O+r77+JrzxBNOdXGcuHSJe5ZWrMjXwNhY3t8xMfy+XDn+LQoyxCopieOQq1fn66d+g9mkCbB9u0WBuHo1nwituMluuIEtKQXZXua33/j5mmv8s76ICFZYJoJMjwPVBRnAx8f69Xb7U+9JaX8C8Adxcfw9rZxEmjfnArD+5Lnn2FKakwP88QebXL3JXPQVPbnE/m5v4kQWiC8V+cibgJCRwT/7gCVP8oXFCM1PWbwmF2xwG9gfGwtcupQvhgwwqNa/YAGftMIQK4LsMIBEcVMyaWl8Q3vTTRwq4auFTLeEOQsyizUnvUbvAXzXXUCVKsDjj7OlauJEFgL//MMxuf/+C3z4IY9v0SJ2yTVtah4n6wm6u3LQIA6j+fdf39fpC8ePW9MSOvo5XhdkkZHsDTt1Chg1yniZ3bvZ+vj++2zEGDmSBdzLL7On4733ODng2DEOvyiItiCHDnF5qGLF+FxWvrztsyZN+Fjcs8fCirZuZTOQFatDhw58kDVo4PW43ZGZ6eSxmzuXs7X8aRVp3Zrvqgz87UbVLNq25WMs73qxbh2XrtCrQvuTqVO5I4Er5s9nEZ2V5d9tp6TwiXLdOi6uWFDV+Z2pU4fdiPYn008/5ZNNGDafDgd0a1dOsQTzoH5NkJW8orz9W3N+/x345598FjLAQJCFcVA/lFIuHwDaAvgDwAsAntQf7pYriEfr1q1VQTN2rFKAUitXKvXKK0pFRCiVmur9+m65RamqVR2n7drF2/juO/fLHz2qVIsWSq1e7dl2P/6YtxEfr9T99yu1caP7ZXJzldqyRalGjZSKilLq668926Yz773HYzh1SqmBA5WqX9+39flCVpZSlSop9cQT1pd58UWlIiOVyshwnP7440oRKbVqleP0yZN5f5cpo9SsWa7XvXixUjExSnXooFRamvUxecr580o1bKhUqVJKbd6c//MNG/g3+vlnCyu75hqlmjSxvvHcXN7AxYvWl/GAL77gse/erfiLRkcr9fTT/t3I5Mm8kcTEfB8NH67UZZc5Ttu+3em/Xbu2Ujfc4N8x2ZOVpdR//5l/fu21StWsyb+FP9G/6Lff8nt/r98T7rmHd/jFi0qtXRu8cRQR9u7ln/6/Bt2V6tTJeKYHH1SqbFm1fj3P+8sv7tebm8vX25decpzev79SeVIgN5dPvi++6MtXCCgA1ikTTWPFQvYmgAsA4gAk2D2KHEqxy65FC77Bb9eOXVa+xHvZB/Tr6DUnrax3yhS+QX/6aevB5OfOAa+/ztn///1n+07uIGLr2KpV3BXnvvvYsuZtvNOWLWydK1eOrY07dwavUeyqVWw296Qe2rZtbGyJjXWc/vrr/L3uv5/3TWoqu2XvuIMtjZs3cy1PV/TowQaONWs42zFQ7utp09haM306d+RxpkED9sxZiiNLTHT0z7ljyRI++Bctsr6MB+jhBBs3gv3B06b53zXXqhX7eQ1u8bduzb876tVjl8vatWAr0t69gY2t6tGDWwEZkZnJ/SX9Ve7Cnnr1OLhn1ix+H8w2Nt98w7/7Rx/xvrZzZ82YwbkGp075b3MHD/LPWlTRLWQqPt61haxixTzDsFsL2dtvI3vorcjNdWMhy8jgC2GYWsisCLLLlVI3KKVGK6Ve0x8BH1kI8u+/fDF94AE+v+iZ6t66LdPTWYQ4h49ERPA0K4JMD77++28uJWSFTz/la8E773gXx1uyJHt/Hn+c1zVwoGPJIats2cLhK4CtaLqvLmBvmT/fNiarBV6TkozraCYkAJ99xsfKo4/yNfvHH4FXX2UNYrU14PXX87VkwQIWc4HIQp0yha+dV19t/HmxYuz1cSvIUlM5O8WTavOdO/PB5Cbb8tgx4NFBh7B9m2dRE/r/Z/NmsGq+/nr/l15o3JivwN27O0zOyWHB7rw7IiI4aWLtWvDOnT+fAzUDRadO7Bo2Sp74+28+CQXCnRgRAdSsyd/vq6/8v35PyMpi4fnaa3nHQEoKx8QOGcJD/P13/21u5EjP22AWJnQNRgkJ5tH6DRoAPXqgQgV+61aQ7dwJWvkPADcxZHoJmkIsyOYTkUkxkaLFl1/yxfaWW/h9hQqcaOVtYP/mzSzmnS1kAE8zCU3J49AhFjAvvsjte156yb2VLCWFbxQHDTLPSLZCZCSHnowbxwaOjh09uyu8dIlvVHWrTJs2fA4PVmD//Pl8zc7KssW2uSIjw5ZhacSgQRxT/dVXPO/SpcDo0Z4Xh7/rLo4rmzaNa4D6M5Lz2DEe1803uzZgWMq0jIjgmCFPKvDHxLB5Ys4c0wP9xAng9Xa/ofPspzBpsnUry8WLtrChxM05bM4N5MHlZMLcu5d/dyODYdu2mvCnOC6YWrt24MY1YADvW6PKy/pB7yQm/YYeH9ihQ2DWb5VffmF3wKVLwPvv488/+XeZOpWTIRMS/FsbNjGRW4wWVXQNdqF1Jz6+jXjjDeCLLxATw7HLVoL6VSb/x4wsZOfOaZ6ahAROItE7JoQZVgTZAwD+IKIMreRFkSx7ceoUu3WGD3cscdKunfdWHb0Cupkgu3jRdV/cGTP4+dZb+cSydi1brlzx4Yd88L7mJxvnyJFsmTtxgpPmrJZT2rGDxY8uyOLj+XUwAvuPHuUL5D338Hsr30HPsDTrEETE1q0xY1hY+1J78plnOCtz7Fj/ZtPPmMECb+hQ1/M1acLJCBkZLmYqUYJbEnnaemfQIE61NbginjwJ3HPVdrx75BZUij2LDSsz+AC/6y63PqYtW1iHlC4N0No1fOdw8KBnY7PKmDF8d2YnKo0yLHXatuVj//iL//OuRoondOjAVyzdBGzP/PmcVl28eGC2/cUXfCfh7wxST9HM8NmDbsQjn9RG7958vlm1is+D7dv7T6unpvKNzsmT/llfOKJbyC7c8QC39jLi3Lm8u8uKFS1YyGJjQZrrwkiQAZoRODaWM5Rq1vRm6EHHSmHYBKVUhFIqTntdJMtejB/PN1j33+84vX17bjDtTa2uDRs4o61KlfyfWanYP306n+vq1GGXVt26nK2Xm2s8/6lT7GK86SbjeCFv6daNRWlUFN/4WEG3QukuS4CvHatXm48/UOjuivvv5+uqFUGmW19c6Y/Kldl6Wa6c72N85x3u9jNmDFtq/cHUqXwcuGo7CbAgy811fXOAuXNtdwie0LcvW8qcKpufOgUM6paCj/Zdh7gyxfDnzeOxamMccnfu5pZF9euz4jU5WPT/zS23AG1PzIWKjORtBYKqVYHz5x3aNCQmsig38pDqIWNlx7/v/g7KV/Tv/fvvjvtKKT6pBbJeVpkyfLIJNvXrY9vY5WieNAWff87lxzZutP0OV17JAtos3MkTdu3i5wsXXDZwKNTo+zEh8gKbCp3N+llZfKc0ZgwAi4IsJga4xILM2WWpt5NNSQHfXb/zjvtaiCGKpeZpRHQtEX2gPfxUxCd8yM3lcgZdu+a/AOtxZN64LfWAfiN3Uf36HGJiJsh0d6UefhIVxXd7W7aYFg7He+/xieLVVz0fqzvq1GFv1bJl1oL8N2/m/1i9erZpHTvydS2Q5T6MmD+f47IbN+aTtBVBlpTE1zr78QcSIj4G+/Vji5mvLpGDB9kodfPN7ufV46Bcui0//phreXhKyZLAI484KJczZ4A+vXIwescw1I48gJg5M1GvZzWkpQHb+z3JJscmTYB77+WrqUGzzQ0bWAhfcw0wEHNxrmlnvggEAt33b1ePbOtW/k8YGZ+qVQMalz+BhJTDBVMsddAgvvOxLz5LxPvuyisDtlmlgpekY8+hQ0DLRzsjNSMaixdzC0v71p8dO/I53h/NEnRBBhRdK5nusqzw85d8sDvHkenWbS2AzKqFLCLLtYXszBlwHMkLLxReQUZE7wB4DMA27fEYEb0d6IGFEgsXcveNBx7I/1mrVnxh9lSQZWbyRd3Mmh8VxedQM0GmGyPs44GHDuXr1OjR+UXRiRPcLPqWWwLXUq5nTxZUJnUyHdiyhQWQfUyVHtjvi9tSKe7F+N571ubX+yr362frN7h9u/vK0UlJbJGMifF+rJ4SGcnWsdxcdmH6wvTp/OzOXQmwsIiJcSPI7HsEecoHH+T1Gzx7luuv9U78CL1zFyDiyy+ATp0cE2gaN+YCY5Mn84n3ppvyxaCtX8//zVZlD6ApErG1pp+q8xvRsCEXYnUSZGYJp0TAkFpaQdg2bQI3Lp0hQ/gkpkdQA3yS+PHHgG7222/ZeOivQtIAGxT1WrpWmTeP/+cLFhh3h2rfnp/9EUcmgsxmIYspp2WMOZ9MdfVVkYvCVqpkQZDddRfmPbEYgHItyIpAUH9/AFcrpcYrpcYD6AsgCE3JgseXX/JBY1SqoFgxdvt4GkeWlMSWW6P4MZ3Wrfnm38gr8/PPNnelTkQEl1zYuRP44QfH+d95h09Ko0d7Nk5P0GODFy92P699hqVOnTps1fA2niM7m40tI0cCzz9vrZjpP//w+UJPNGvThve3gdHFAbMMy0BTsyaLsSlTOEnOW6ZOZevuFVe4nzc6muOzTQVZcjJffbwVZACQmIi0+cvRpw8fG92n3sdxX5pQq1uXDVx5Nz5EHLO2Ywf/GSIj2ew3eTIyMxQSE/n/U3ENV+f/IyqAhn2nu6eLF/nYc7U7upVYixxEILVOAcVX5eTY2oJcvAj1/vvIWrEaJ0/ybtu+nY95f5ZX+f573hczZ/pnfRcusEX3scc8W27BAk56MqtBXKYMu+39EUdm36HJUtPsQkhaGv8lokprwdbOvmAnQVaxIhtvXXpWatfGzspdAVCRF2QAUNrudZEqb3zwIN9h3XOPuTVED2b3JPbJVUC/TqtWfCw7Zy8eOsRWJKNseT178rXXbCfXo0c522/4cEcB528qVGBx6k6QJSdzVXznODYijiPzxkKWmsqJNV98wQl1kZHWsu3nz+ffVb9z1j1IrtwXGRn8mwRDkAHclaZaNRaf3pTC2LWLtYMVd6WOy0xL/QNPapA5cemu+3F0yGPIXr8Z8748iN6DS+aJMYBvNtq2NbBElyljK6L39dfA8OHI6tgFDbK2cDjA8DvwYrO5+PNggH3LrVtD75K8fTufC1ztjobpa7EdDbF+ZwE1QdbabTx4w3FcV3oZ6OJFDPyqPypWZJHfqBGfb/zVTej4cb7ZAbwLLTRi/nwWZatWWa8blpXFpWb69HGdSdyxI593fI1f3bXL1giiKFvIEhIAStCObTcWsooV2bPh8jddswat54xGcVzIp7WKmiB7G8BGIppIRJMArAcXiy0SjBvHz/feaz5Pu3bsqrM3V7tjwwY+aF1ZKMwC+43clTpEHCt54ADw3Xc87a23+ML98svWx+ctPXtyP1lXGXl6QL9RYkGHDhxD5knP6cOHudzSwoWciTh2LDB4MMcsuwusnT+fYwP1zNlKlVjsuBJkzj0sC5rixfn6unmz+844RkybxseJJ/HWTZrwzcl5o/xqPaXQSwvZqlXA2zuuR/0Lm7Aivjeu/nqwYX2Pdu342DFtbzVmDPDdd4jcswMb0ApXz38cUAoXe16DxMTA1HHL45NP+MAlcplhqRP7/JN4GW/4JW7JEr25ctHFX37HfdV/x6WoYrj6ja743//4PDFlClu4J0/2T2P7X37hn/Cmm1iYeZP05Mz06XzzpBRXNrDCqlUsEPr0cT1fx458QffkHO6MUrz8VVfx+4IQZFlZbO0OpcaGqana+VQvculsIbtwgV38doIMcGNRXLcO3f9+HZcnpCLCSbXooaGFQZBZalEE4DIA12qPylaWKYhHoFsnZWYqVbEit/VxRVISt3+YONH6ujt2VKpLF/fbj4lR6tlnHad36MDtkszIzVXqqquUuvxypXbs4I4x991nfWy+8NtvvC8WLzaf58MPeZ7k5PyfLVrEn/3xh7XtrV/P7WkSEhyX+ecfXs+4cebL7t/P83z8seP0669Xqk4d8+V+/JGX27rV2hgDQW6uUl27KlWunFKnT3u2XMOG7o89Z+bM4e/s3A5KKaXU8uV8kHrYHic3V6kvv+Tjs3v1PbyBYsVM+3jpY/jnH9frffLO0+qbmAd45nr11JTPTymAu/kUBE89pVRsLHctckXNmkoNGVIwY8rNyVXHo6uoecVvVLm163C/GSdmzeJd9vvvvm+vZ09uhbZtG6/zf//zbX2pqXxo3H8/tzgbOtTacnp7s5QU1/Pp5/Dx470f43//2b5rbKz/u3QZMXWqtf9EQXLjjdxeT61bxyfnJUvyz5Sbm3e+WLaMv8Off7pY6bffKgWoDpcfNPy4VCmlHn1UKbVihVLPPBOwdmz+AD62TgIAPRo0CsCVRHSD35VhCPLLL6zaH3zQ9Xz16/PNgNXAfj2cw115npgYvsu2t5AdPmzurtTRrWT//ceuOCIuv1AQdO7M7kJXbsvNm4HLLnOMMdZp147Ha8VtOWcOby86mgNy7e+Cr7ySPVmff25+96iXu3AuVN62LccApaQYL5eUxDESBZVhaQQRdwNISfEsLjAxkeOFPHFXAm4yLTt35o7oHrTHycjgMh4PPshdAmZuqs0H7a+/mvbx0t3J7v5n/2wri5+u+pLNnPfdh/odOC/eSsFfr1GKzSyPPYbERHYBmhYBXr8e+OordGqZXmAWsnnzCbOz+qP/hZmgvXsMq/P3788e4MmTfdvWqVOcc3HjjZzv0KiReea3VebN43i0m2/mWrcLFljrh75gAYeUuEuwbdCA5/Eljky3rtWvz1afgrCQ6f9H+9i1YKO7LNG6NV+EjAoPE+WdLyy1T9LihcrFG7dRyavWf+WVnNEVF+f9FwgiVrIsxwMYD+BGAAO1R5EofdGvH5vze7vpUxAZycHgVgP7d+7kk4ur+DGdVq1YkOmiwpW70p5u3Wy9Ku+7z3q7Hl8pWZJFlStBtmWLeR20hAS++Ls7MU6bxvFyjRrxfnf2lhFxZfstW2yxLM7Mn88uYz3mQ0e/8Jtlc+k9LAsyw9KIZs048/fLL21eQ3dMncrH6403eratGjXYC5BPkCnFUdse+KQOHWIX84QJ7EafO1erJfTiiy7/bJUrc3kSV/+zrCwW/K1agf+UTz6JRk0iEBWltVAKFEQ8uB9/xI4tl1yH082YATz2GFq2jcKBA4G/cCvF5cbWV9JysV5/nX36TsTGsuD59VffanLNns03nfomhgzhBJTjx71f5/TpfBPXqROXMjl71n1W5KlTrH3duSsBjlHs0ME3QaaLonr1+GazIASZXiLIF1erv0lLcyyeng+n3lKWXJZaw+AyJYyzTvIE2Z49nqfhhhBWLGQdlFJtlFLDlVIjtIefO/SGJiVLclFwZ5+1Ee3b8wnfZTVzDSsB/TqtWvGBdugQv58+nQ0IziLCiPffZ2E2apT7ef3G8ePo2ZONE0b9LbOy+CTinGFpT8eOrgvEnjjBQqR9e657Vrmy8XzDhvGF/vPP83+WkWHeV1kvK2X2vw5WhqURr7/Od/aPPeY+jkQpFmQ9e9pOglaJiODvnE+QHTzIV16LBU7//pv37+7dfOF+/XVr/y+d9u1dW8i2b+eSMvb/rdhYtoAEVJABfJE5fRotjs13HU63di3QtClaXxmb9zaQ/Porn3O6vt6TBW+XLjazhBO33+57ZuSMGdxSTjd0DhnCx57eZ9xTUlP55mnwYL6Z6NWLreK//eZ6uT//5O1aEWQAn3eSkrzrywuwKIqL45vfChUKJstSF2R2NYmDTp6FLDWV1em33zrOsHmzQ6+k0qXZmuyyfZIuyIq7sZB9+GFgerMWEFZOhauIyE0tb6FdOw6GdVcuAWCLV1yceRq2PfaB/Vbclfa0aMG9Cs0Ei1/ZtYtTtGrWRO/255CbCyxfnn+2nTs5+zPPQrZvX75I8Y4d+Q7YzAz/+OMcuzl+vOuuL8WLs1ts1qz8tZCWLeMLj9F/t0wZzkY1ulDqGZbuqtsXFGXLsqdv6VJg3rijLiLeWWDu2+e5u1LHMNNSn2AxoP+hh2zufW/azbVrx6XHzKwPunvfuU9rs2YBdlkCQO/euFS2EoZjkrmFTCn+Idq0ySsKHUhBlpvL1rF69YCb7opnH17Xrqbzd+jAx763bsuUFL7RGTzYdqPTuDG7Lr11W86dy/87PQklIYFvNOfNc73cggX8/7Ba6u3KK/nn8bYVnp5hGRFRMC7LzEybEAs1QRYfD77I7d6d3zSanOxwR0hkoThsw4b4rMxo5JQ3vpHIE2Tp6eEb0A9rgmwyWJTtJKItRLSViAJ9ags79OKCVuLINm7kC4SVRtNNm/Jd4YYN1t2VQeGee4A33wQyM9Hhv1mIizN2W+bLsHzgAf43vv12nolH70Vs5D747Te28rz0kkmBW62BsG6qfOABdp/o2bI68+fz+aJbN+OvY1axf8eO4GZYGnHvvUDzZgrX3F8VOdeYN/eeOpUtC0b19KzQpAnfxTpcaDwQZDk5vP+GDOE4G29w1xljwwa+GDhbkJs35xsaK5XjMzO9rDAfFYVtLW7FAMxD8yomOfx79rAJpm1bJCTwMRxIQTZjBv9Er75q7Xyjl3f76y/eX54ydy5bwZ1d4oMH8w2a2ybSBkyfzu3l7JsKDBjA1tB9+4yXUYqzrnv14vOnFfT4VW/dljt32uJKC8JCtns3/6cuv5xfF3TLOTPS0jQLWXQ0W7bs/d9K5RNkgAVBVq8exkS9itzLDPoMomgJsu8A3A4uCKvHjwWw7HV4cvnlfNJwJ8iUYkFmtd9usWJsjdmwge8wrborC5Q9e9gX9dZbQO3aiP75J3TqZC7I9EKjOHoUWLSIG3qOGpUXFFKvHlupnAP7U1NZYDVuzLW4DHn5Za6cqpnnrriCT95ff+1Y9HL+fE54sG+hYk/bttyeyPnmzkoPy4ImMhL4+kX2aUcuXWSYEJGby3F3/fp530FI11xJSXYTt27l2CnnBnMGHDzIF2tfkiFat2YLhNn/bP16/o84u0F1F7kVK9nw4VyguEkTrvU2c6b1uldzyw7HsYgqqJy+13gG3Q+umW104R+IsgU5OSzEGjXyrMTJbbfxeLwp5D9jBrvsdOGsM2QIH4Oeui3Pn+fkmyFDHH/Ta7QoZjMrWWIihzVadVcCfAhbiV81IiuLxaF+o1GxIlvgA9nPUj8XXXcd33/6syOCL+RZyAB+YV+HLD2dd4ynguz8ebQ8uxSVo4z/iLogU2mFX5CdVErNUUrtV0od1B8BH1kY0r69e3P3gQPsjrMSP6bTqhXrnVWrQtQ6NnEiny3vuIN7My1ZgmvbHkNiYv474s2b+QIRHQ0+4+fm8m11iRLsgwSvqn37/CfGUaP4pPPttyYB9UuWsHXsvvv4tloLnnv4YR6HHhezezdrSFehBrqbY9068NXp5ZeB3bvzMixDTRS3H3QZUirWwwUUQ8eOLDYXLbJd6Fes4H03bJj32zDMtPSgZZIeeOyLICtRgjdnJMj07GWj/5ZukXUXR3b+PGdXd+7MbX8mTGDrjl70+NFHbQXvjVhwrBnuuHIvqEN74xmaNGHzrqbo27blC5E31ih3TJ3KVqTXXrNuJQL4Juaqq9ht6YlQPH+erVI33JA/LrNJExYrnhaJnTOHb6ScBWXt2rw+sziyBQv42V1CljNXXuldgdgDBzhkxd5CBgTWbbltG58rdXEaCoH9WVlsYdZLkCEhwdFC5lQUVsdd+6RLibuwIKsHGp41Vstly/JvlpNa+AXZRiL6iYiGEdEN+iPgIwtD2rXj+CL7Hr7O6DEungoy/ZgOOUGWkwNMmsRnvipVWJDl5uK6jGkAOLbJnrwMS6V4uY4d2Vw4dCibcLS7KecA25UruQr/I4/YXJoOnDnDgrBuXQ7sTEnh9efm4uqrebIe3D9/Pj/362f+tVq14pPd2rXgs+2YMUCfPkHpYWmJmBiUefAWFKMMfPpuBnbu5HIS7dqxwPjpJ46pG+iDbbtyZT7xOQiybt0sB4P5Q5AB/J3WrMkvFnbt4hA65/gxgMdeoYJ7C5kuAN59l4uPpqTwsffmm7yO777ji/acOfmXVUrTp80ijM2rAMcgvPGGdkdiC3XwR9see7KzWYg1a8YCyVPuuIPFnFkvXSPmzeOLsUECJ/fvHMKuUE9cedOmscWtvYG+veYaXp9R39kFC1jzVq1qfVsAn3fOnePv7gn2GZaATZAF0m2ZlMTiWb/ZCIU4Mv23yBNkzhayatV45zqdMypW5JtmsxuA1Esc1F8y1jyoHwAuVm/gOmMsxLEiyIoByATQG0Ws7IWnWIkj27CB71Y9KWqui7fmzUPPMoPFi/niM2IEv2/QALjhBlStXwKlSjm6LU+d4jIczZuDd8S2bXzmBzidNT097xa6Y0f+c65dyyf5e+7h//KYMQZjUIqtYidOsPIoUYKvCv/9B/z7LyIiOJh85Ure7O+/8921qy4JJUqwJW/tWrCvDQBGjgypDEsHPvsMWL4c1L8/Hh1+Dvv2cdxcSgpfkMeOZTHmy80jkUFg/6ef8r63wK5d7BbyNMPTmfbtWX87txTTe3sb3ewQ8YXLnYVs+nRHARAdzcfiqFFs/dm/n/fB9dfn75Jw+DBfzFvVPscmnI8/dpwhJ4cXOmhzMLRowb/JihXuv7cn/PADX6A9zWLVGTKEbzq+/976MjNnsmi1j/WyZ/BgtmL88ou19Z09y8LK2V2pc801LJ4XLXKcfuECexQ8tY4B/FsDngtk+xpkgO0YD7SFrFEjDpcpViy0BFmey3LuXD756OjxKrqC0nDn4k3N5Dvg+BjzshcAsPvZb/L/78IIl39VIooEcNqu3IXfyl4QUSQRbSSi37T3tYhoNRHtIaJpRBRqNgi3tG7NJ34zQXbuHB+fjRp5VreuRQu+49C1S0hRqRKLKfs7npkzEXHfSHTr5ijIHAL64+KAW29lyxjAZ/F69fLclvYBtu+8wzdVY8fa3XnZc+kSi7IxY2zmkQEDOKBUE3jDh/OF7733+K7aSmZ0mzZafM++/QCAjGtvCmoPS5dMnsxK/7ffgEqVEBvL5X527GCNevXVwBNP+L4ZXZApBRa8+/db9mvt2sUXLA/qxxpiFtjvLnu5eXMeu1lroLNn2SpmJgAAvnDovRHvvZetUPrX14Vq/XalWA388INjv6YdO3ihZcvyJkVFscXXl0bxzmRlsRBr1cq7TFaAYzgHDuSWSlYKsKans+X5hhvM912zZnxDaTXbcvZs3rZ+inDmqquAUqXyuy2XLeObOE/ix3Tq1uX4QXc1zpzZtYuX04VBoF2WWVm8zcaNeX/XrRsaLkvdk5N3nq5Z0/EObNEirgbtVOjOXS0y3UJWIsrYQlaGaz/jzOkQ6iHlBS4FmVIqB8BVAdr2YwDsDcPvAvhYKVUHQAqAuwO03YCRkMB/EKM4spMnOa5Hj+nwhPh49po9/rg/RulnmjdnP46zwjxyBLfWWon9+/maDTgJssaN+YKl/5OIODhs4kQAfKJt1IhdFm+9xZ5QUxdjbCyf5Z95xjatZEm+KM6YASiF0qU5e2zaND5ZWxFkbduyVe/c5gMAgNMT5qCd+jdkSl7kkZvLt8tNmnB0r53PPCqK48YWLjR2+3hKkyZ8Y3HkCFgh163LO9QCu3b5p7tBo0bsfnX+n23YwIejWTZh8+aOpQKc0QWAuwD4+Hie9847OWj+vvtY5Dm09Bw+nAWr/R2JHtCvVx7W6NSJ/xve1r9yZsIE/s+9/rpv4veOO/gCuXCh+3n/+IMtHK4KDtu7La0IlenTuSCx0+7KIzqaRdf8+Y4xXwsW8OmoSxf32zAaY8eOnlvI7DMsgcC7LPfs4WNOPxfVrRsaFjJdZ+VZyL7+Gnj+edsMq1cDX32V57LXcSfIzmW4FmS6EO58Q3ngqae8GXpIYMWYvYmI5hDR7f6KISOiqgAGAPhWe08AegDQQz4nARjkyzaChVF8y6FDHCS8bRufyL0pO1C2rHeuh4Dy++8shIwiYIcPx7W/3AlAYckSnrRlCxvUKh1Yzf4NZ1NF584OfsQOHThOIiGBezfnIyuLU4wWLOAzqfMOGjyYz/x79gBgtyXAlrLOnd1/Pf1CsDezKnDddaj0+Uu4FT+GnoVs/36+GjZuzGfmp58O2KYcAvu3buXtWTD3XrzI/wN/CLKoKLZe2lvIcnM5e9kofkzHXWD/9OmcMOqcIWhEdDQbc198kb2QN97I46laVctiveYafjFpkm2htWv5SuW0Ezp14vFbaRfmjlWr2BLaqZPv9TH79mWrjxW35YwZnCztTgQNGcJGw19/dT3fmTMsBG+6ybWoHDCAsynt6z8uWMDjMMugdkfHjmzM9KT0iW791YmP579FoCxkeqazvSDbu9c/jeF9IV8M2cqVfBesk5zMN8tO5wx37ZNSMotjGboguqp5HTIAiLqYGoIBvtaxcomPA3AaLJj8FUP2CYBnAehX8nIAziql9MPpCADDgiNEdC8RrSOidScLojeFh7RrxwYKvT7Orl18cjx2jE8wYVxEOD+jR3OAstEZ85ZbEHtwN/qUW59nJNi8Wbsovv8+168wcnXNmsW9z3Jy0KkTT/r4Y+O+l3jjDY6udiosm8eQIfwP1wLv9NifYcPyCj+7pFkzvvBOLzUS+PVX7Lu8E7pjaejF8eln5yZNuKpnABvb6WI0MREeZVju3cs/t7/6f7ZrxxdhvZTJ3r18GLhKlmnYEKYtlFJSrAkAe/SesV98waEIs2bBVhA2Lo4r8P7yi+34XLuWFaNTymP79jzJrMWXVZKSWKBcdhkLJF9dwzEx/BVmz3ZtvcvIYLfhoEHua501b87hde7clr/+yuLCnbWyXz/+nnr5i0OHWEx5467U0WPgrBaITU1lY6j9sU0U2PZJ27bxNnT3fL16vL/swhODQj6XpXNQv0ENMsC9hex0dil0wzJTa0aZMkAUshCZk1W4sywN4sd8iiEjomsAJCul1nuzvFJqnNbKqU0Fw6t0cLEP7N+4kcVYRgab6a1YZcKGpCS+wIwYYXzmv/FGICYGj5X/EUuWsDErKQloX/cMX71uvTWf2RoAr+uvv4CFC3HrrfzyttsMtv/XX5z6dued5qmnxYrxmSErK0/8zZqVPxjbjNhYFmX7VhwDcnLwb1w3NEESYs4WQE8UT9CDlxo14jNzAINJypblIOJdG9NZBRVgyQt72rVj96PuBreSvRwby6LMKNNSFwBm8UquePBBFkCxsci7iQDAbSLuvZdPAJcusRI08L8lJHCcqC+C7OBBFiGxsSwsTTojecwdd/DwXZWrWLiQr7lG2ZXO6G7LJUtc13abPp2N5a4sngCLnvbtbXFkunvVF0HWti0LZKtxZLqr0PnYDmRx2G3buD2V3qlEv0kMttsyX1B/fHz+shcGgky/lJsVDuZ7GoWSJXIMP4+LAyoU0zICCrMgI6J6RLSYiBK1982I6CUftnkVgGuJ6ACAqWDL26cAShORfn9VFUCIlLnzjCZNWAd88w1XBIiL4xOt1UKwYcOECXw7bKiWwO6aAQPQ9fhUnDyRg9mz+QI64MJ0vjiZZSgMGMC+j/HjER3NXV7y6b3VqzlauW5dzi50xfLlfHWy0tPKgA6tLmHqiqpQr72OuandeaJdUHZIcMMNHNSfkMB+k9OnXdde8ZEmTYCLG7azyHXZRduGLsj8ZV10zmjesIEtOu7cyWaZltOn8wXOnQAw44Yb+GLy7LN2E9u0YV97xYqsap58ko9vAzp14sPavnixVU6e5HDJtDR217nKHvaUtm1ZaLhyW86cyX/37t2trVN3W86ebfz56dMc+23VWnnNNXxvePw4f/8qVXxrbVaiBB8nVuPInDMsdQLZPknPsNTRxWCwA/vzWcgSEvjEr2eGnDhheLcQF8eeTNMYsnNAFqJR+lPzAOwqpYuAIAPwDYAXAGQBgFJqCwAvu+EBSqkXlFJVlVI1tfUsUUrdCmApAP0eazgAk79raBMVxSf1pUvZdbBihf+sAiFDVhafoQcONPElatxyC4qfO47uWJqXidx0wyS+ouudh52JieHo+9mzjW+hL1zguLGKFTlg2jDt0o7Gjfn2ytOKlBpdrjiCSOTicGRNzD7SGpkx8fmLqwWbBg14nwG2gy2At8pNmgDH9mdAtWlr1wPLNbt28f/B3c9llWrV+LyuC7L161kbugsfad6cC+Ta61VPBYAZpUoZuOzOn+cg5pQUzk4x6dXVuTPH2Xl635CaymEQhw6x4dniz2EZvZXSsmX8NaZO5SiBP//kG8116/ivet111kN3WrZk0fjFF/yYMIHDjObO5d/hs89YsFntLqAXRtWX79PHd3dtx44skHOMDTIO7NzJ26td23F6oFyW2dm8TXtBVqECC5pgW8jyBfXrL/R6Fm+8YQvmdcJVtX5dkEVmmycQVS55gV+EsSCDUsrlA8Ba7Xmj3bRN7paz8gDQDcBv2usrAKwBsAfAzwBi3S3funVrFYp8951SAwYolZwc7JEEiDlzlAL42RUXLij19NOqR7VdClCqYeROXu79910vt3Urz/fJJ8afL16s1MGD1sd79dVK1amjVG6u9WU09oxbrBSgvhyyRAFKrX5wglIrV3q8noCRlaXU668rtWULv9+1S6nLLlPq998Dtsnx4/nn2bXL+jJXXaVU167+HcfAgUo1aMA/a5kySo0c6X6ZBQt47EuW2KZ9+y1PW7/ev+NTSvFxSqRU795KJSWZznbsGI/hgw+srzojQ6mePZWKjHT/V/SFAweUio3l8Zk95s71bJ1vveV6ffXqWf+75uYqVbWqUrVq8bJTp3r+HZ354Qde16ZN7ue95RalatTIP/3JJ5UqVsz3sTizYwePbeJEx+mtW/NhFkxefpkP95wcbUJSEg/0wgW3y155pVI9ehh/ds89Sp2lUko99pjp8t26KdW1UzafE0MYAOuUiaaxYiE7RUS1ASgAIKLBAI75SQz+pZS6Rnu9TynVTilVRyk1RCllLZc+BLnrLo5pCMEQN/+gl5Po29f1fMWKAe+/j9p92U9VvEF1vhU2c3PqNGnCvhL7tLPERG45oxTXD6le3fp4Bw/mTEu9LoEH1FRcs2PC0poAgPiH7rRVjwwF9uwBXnnFZlqpW5cjjN39Nj7QpAlwOY4icav1mj/+KnlhT/v2HMC9ZQsboKx0vzDKtJw+na0bAQkrqF6dfXkLF7qsW1O5MudjWI0jy8lhy9XixVx1xpcODO6oUYMtF/v3s6ts3Tqum7ZwIVvH5s0z9cSa8sILbE05cYJL+ujrXb6cS2joidNWIOLt79/Pr3v18vgr5sOTArHOGZY6gepnadZPt1690LCQlShhl/DeqBGXgClWjH3q33xj2hHeVfuk8+eB7IgYlyV2ypYFTqVEus8sCWGsCLKHAHwNoAERHQXwOIAHAjkoIcSJjeWgfaOgfGcyM3Ffzpfoir/QoEUc+yEqV3a/3MKFXNEU4Ktuz57s2zCL+nTFoEF8hvDCbRl5+ACyEYkNp6ohKgqoUzWD/Sz+Lq3uLXpAv/PZORDdqjUaXZaCw6iG+HEfWZo/JYVdN/4WZHp5iq+/5mcr8V+VK/OFUhdkp06xqPHVXemSW27h5zw/jjGdOrEgs/LTvf8+Zyp+8AFf7wJNyZJc47NhQ97PnTpxseFrr2WXqTf7Lj6ef4saNWzr7dyZXY41a3q2Ll0Qtm3LpTp8pVYtHps7QaZU/hpkOoEqDqsLMucCyHXrcnKHxbKAASEtzSks4dgxrg9z/Dgr73vvtdXjc0Jvn2TEuXNAdmSsyy/XOns1xu7u4XnfqxDCSpblPqVULwAVADRQSnVSSh0I+MiE0OSrrzhD0uq/PioKLee+ju9wD17aeTv/Qa1QujSf5VeuZIsYEadmWRFzzlSsyFcPTwoL6eTkILlcI+QgCvXqATHFo7iPjic9ZQJJUhLvm4YNbdMeeshaMS0vKbHxH0RAYUVmG0vzm2Wh+YqesPj995wVZzG/AM2b2zItf/nFs3glr7j5Zr6BefVVl7N16sQC0V1gdk4O3xP07h3WNTD9Ss+ebCHxpm+nEURc/sKdIDtxgq1CrgSZ1UzL3FxrYnzbNhaxzvq+bl1eh4kBqkBITXUSZLt3c7bxtm2mjcV1Klbk498obi9PkLkotFY14ig6XVoKlRG2zjVLWZaxRHQLuLL+E0T0ChG9EvihCSHJhAl8xbBSyAsAIiMRccvNqI29qL9zNkc+W+Wll7g/SkYGmzGM/AJWmT/f1l3cE956C0s/5at348Zgc3iXLqET2K93GNbz3wF2D2zdalyw1x8sX46siBjMPm6t9L9z42V/Ubo0HxJpafzbWG1H1qwZ77bsbHZX1q0b4H7EJUqwddZNxL1eMsOd2/LPP7lTwsiRfhpfIaB4cRYi/qyJfNVVrCdcCRz92DZzWQLWLGTZ2ezd/uor9/MmJRlnkRZAPo9b0tKchKL+Ji3NJshMarJUrMiC1ChB/Nw54In+uzib3ISyMewbzogM36B+Ky7L2QCuA5ANIN3uIRQ1Tp1ic7OnDfJuvRUAQDfd5Cgc3HHjjXxWXLrU9waSERF863X4sMeL6paYvJNgt24sSv/7z7cx+YPERONgksxMr76rJZYvx9Eq7bF1d5yl+Jhdu3j3+7Mcg45uCPSkXIXeQumff9joGlB3pQfUq8cVX9z1tfz2W57P2z6VhZVSpfLV3PWJYcP4/ut//zOfx1V9PU9clocPc/bvuHGu58vJ4QgOo9OhXlImmKUv8lnIdEGWmmrJQgYYWxTPnwcSSrmWK6Wj+WSUcqlwC7KqSqmhSqn3lFIf6o+Aj6ywsmcP56iHI4sX8y1M796eLdemDfDll1zZ3xNatuSrpr/MF4MG2XLkrXDpElCpEuouHouvvuLwBwC2gkt//eV6+dOn+ZY3UJYqAHjkES7Oa49+ux6Iiv2pqcD69ci5qgtycqy1+9m1i2NyAtHRRBdkVgL6dfTDafRo/mm8KQYbCIhscWRmnDzJZSfuuCOsO8SEBVWqsFj/7jvzZiC6s6BatfyfeeKy1K1wmze7/tvu3883E0YWsjJlWKgH0kJ24YLrZvOpqU4WMl2dpaWxfzcy0ta/2AlX7ZPOnQNGrH+I+4KZUDKSBdmZzMItyFYSkcXoDMElr77KsT6hcgXwlAUL2E/UxlrsUB5E3CrJ6KxVkPTqxcFDVm8hDx8GkpNBxeJw//1coR4AX9FLl3bvtuzVi8u4e9qp2BMeeICFpj2BrBJ57BjQvDkq39wNERHWauQGIsNSp08frm/mSWZdgwacj7J8Ob+22GygQOjUiRsgmIVafv89XxDvvrtgx1VUeeIJFhnjxxt/vnMnW6aMLHOe9LPcv9/22r71ozN6QL9Z4du6dQNrIevQgesbm5EvqN/eZdmyJXD//aZNmXULmXNgf04OL171bKLLQn0JESzITmcUQkFGRIlEtAVAJwAbiGgnEW0hoq3adMFTMjP5qr5uHedDhxvLlvGVz59+gYJEj/idOdPa/AcO8LNzyldkJPDpp9y2yYz584FNm/h1oPqnbN/O23Eu7165Mp8V7c/y/qJePWD9epS4rhdatWJR4wqlAivI9CofzhlnroiJseVAhIq7UkePIzNK4lWK3ZUdO/pWiV6wTps2/JvoxWqdcXVse9LPct8+do9edRUX3zUL7tfb1trn8NhTt27gLGQnTnBo6owZ5kb/fC7LEiXYnNugAZcfchHHa+ay1K2TFBvjspXFxZuGoxf+xJnzhbPsRRVwI/F+AOoA6A1bY/EAVr0pRFy4wLcTenuft97i19nZXrfyCSpbtiCv5H44Uq0an2F//93a/LqgMcrBv+MOPnsakZ7OlrEGDTghwaQhrs/89BMHEjmfHYk4//2DD/y/Tbvb1y5d2GWZkWE++3//8d8g1LpV6G7LgGZXekHLlpyTYeS2/Pdf1uBiHStYHn+cTwVz5jhOz85ma6arXCOr/Sz37ePTzC238G+sV7NxZts2Po2VLGn8eb16HIvm79pnALenAriChX6v6Uy+oP6ICGDSJK5LsmOHy7JFZcrwva6pIItzXfYivnENLEYvr5LpQwVXgmy/Uuqg2aPARhiuLF3Kefgff2yLGSOyNeGzEnwTapQoAVStGuxR+Ea7dnw2sRLXdeAAnyGMvnNmJsfFGZmI/vqLz4pff80BJmlp1nqweEpiIlcTNUovLFPG/6afixc5Fez11wFwn9HMTFv7IiP83VTcX9x7L/D8877nivibmBh2CxkJsm+/5YtduEY8hCuDBrFYcr4X3b+fRZmrY9tqP8t9+zjO8sYbWcOYuS2de1g6owf279njfpuesnYtj42IDfPO5OYauCwBTgn+7z+O33VRpyUiwljAnjvHz5HFXAuyCn/PxMP4X6EVZBWJ6EmzR4GNMBwZN45rZ0VE8MXZ3lJRuTLw7rt8NQsn7rgDeO65YI/Cdzp35iueWZSuPQcO8O2oUeXn6Gjg5Zf57s+ZAQP4DNulCxe4LV2au19bZeFC7mbgrihRUpJ5ANSsWSz+XZmvPEXvfq1F0HfqxCdnV27LUBVknToBb78d7FEY06kTG9D1voAAv542jcWYm/qygp+JjOTcmb//5p6pOlaObU8sZFdcwYHt3bvzb+3898/NZeuZK0EWyNIXa9bwttu0MRZkulUu3/HZvTvwzDNsHTPJsNQxqtafJ8iKuxZkcXOm4yF8WWgFWSSAeAAJJg/BjG+/Zd/D5s3GwuvZZz3L0w82mZkcOBCOcW/O3HyzTSS5Y8IE84D8iAj+be0D+3NzgSlT2BqmJzA0a8bvrUS/6xw4APz4o81HYMTFi3wbbGbi0U1X/rxVXrbMlgoILsTZtKnrr7ZrFxvwwt2wWpB06sSH0urVtmnTpvEFT9yVweHuu1lofPKJbZqrGmQ6VmLIzp/nhGy9LMzQofy3dY5qOXCA//auBFmdOvzs78B+pfh01K4dd2ZYvTp/vbC0NH7OZyGLj2eVlZbmVpAZNRjX752T73uZz68mUHo6MqNKFFpBdkwp9bpS6jWjR4GNMNzIyeF/4dCh5jW3jh8Hxo71f0+NQLFiBZ8JPC13EapkZVnb99HRrjsDdOvGfouDmgd/3DgOApk71zZP5cp82+ou+l3n9GnuKlCihK0nkBE7dvBZ0sxCFohMy+XLbRmmGl27cjMFs1T4XbvYjWKSWCUY0KED7y97t+V33/GFuEOH4I2rKFOqFIuyqVNt5Qd37eKbEletmqz0s9RDVXVBdsMNbJR3dlua9bC0Jz6es479bSHbv59PTbogy83l+1p7dItuPgtZfLytrocFQeYcZqZbyGJbNHSd4Z+ejksxhVeQhVDuURgRGcnddl259w4c4HIFVjsJB5uFC1mcdOsW7JH4hw4dXGdIAmxhuvpqR3HljH09suPHOSipRw/guusc5+vShf0dVuLIxo5lBdO3L5/99bORM7Gx7EY2K8ClCzJ/1SK7dImthU4W3y5dOGjf3pVjj1njZcGckiVZ9+qnh6QkDjm9++7QyggtajzyCP+Fv/yS31vJHrZSi0zXKrogK1eOk9mnT3d0W+qCzCzDUicQTcZ1Y33btqyJypfP77Y0tZAlJHgkyMxclhXW/wG88Yb5wunpyI4tvIKsZ4GNojBx6JDLflsA2J0ZExM+gf0LFnBjt8ISvNKokXmakM6hQ8CiRdwZ24zGjfnsuXQpFyy6eJELwTpfNbt2Bc6e5Zxxd8ybxyLr+edZ6egN1o2+w6RJQO3axp8nJPCtsr8sZMeOsbJyEuVduvCzkQEwK4vPw6EWPxYOdOrEp4esLLaORUcDt98e7FEVbWrX5nutsWP5r75zp/ubDSvV+nWtUquWbdrQoXzfbp8ws20bV01yF20RiFpka9bwPWDTpmy97dsX+OMPx9wo3UJm6LIE+HxZpYrL7VSsyNZEe4uiLsgS1i52HfiZno7cuEIqyJRSYfy1gki/fpwq44rYWBZl4SDIzp7lM0FhcVcCvO//+8/1batZDTJ7IiK4Hlm1amzNGjXKWH106cLRqu5aLZ06xcdE//4cY9iyJbstjYL7N292Hy1cv77/zsw1anBQi1MR2ooVubqHURzZgQPus9AEYzp14ovSmjXcvu+662wXdyF4PPEEu+7GjuW/s7tj20o/y/37WWTZF7AfNIjv2e3dlmY9LJ2pV4+3d/as+3mtsnYtn46io/l9//62Tno6uoUs3317jRrsTUhM5JW4wGh/nT/PLtyo4rEu65DhwQexsd7QwinIBC84dozFi1l9Kns6dOCj2Z01LdiULs1noAcfDPZI/Id+UnBlJbMiyADu06mbgZ5/3nie6tX52Ojf3/W6Fixg8TVgAFvZPvrIPI7s+uuBxx5zvb7PP+fkAH9w6pTpR127snvN2SMbqhmW4YB+CnnuOf77STB/aNC5M58+Xn2V3/vLZenc57V0ae5CMX06W6H0DEsrZVr00hf+cltmZ3NIgt6mDOD784gIR7elqYXs/fe57Z4FjNonnTvHMXwUF8snGbPQj4cewr5Wg0WQCRp6xl2PHu7nbd+eXVJmFQBDifh4a1mJ4YJeFdSdIIuKsuuX5ILvv+cu1bGx5vMQ8S2kq1IW8+bxLaKegdutGx8nzi7QtDS+rXZ3dm7c2L2gtEJ2Nl8xTARnly58F7t5s+N0EWTeU6UKu7BWrGAD7NVXB3tEAsB/xSeesGX+uXNZWrGQGQkygBPCjx7lpJnDh9liasVC5m9Btm0bX6ratrVNK1eOT01GgswwsuWZZyydi4zaJ+mCLK95q1npi8WLUYsOIC3NtSEtlBFB5k8WL2bh4sYsC4BvtV58MbSFjlJAixYu212EJWXL8vdyUdPGZQ0yZyIi3MZG4Ndf+bfescN8nipVgOHDHVMSV63iq7F93bTt2/nZXRPGffuA++6z9Vvxls2b+Wxr0uRdjyNzdltayUITzNHbKI0YEb7dygojQ4fakq/1MhNmlCjhup9lbi7fW9nHj+kMHMjLTpvmvoelPbVrs3D0lyDTA/rtLWQAG/zXrrWJJ9Og/rFjgQ8/tKSSjNon5Qky/YbXaD05OUCvXuiwYyIA16G/oYwIMn+hFAuy7t2tnT2rVgXGjPGPBSNQJCbyxdisfEc4s3EjF3Y145VXjIu+ekuTJu7rkb3/PvDee47ToqI4ucDe9ahbVd1ZyLKzuRSHfaCHN+hj1pWXE1Wr8h2+c2B/IHtYFgX69eM2SiNGBHskgj0xMdwF77bb+Pdxhd7P0sxl+d9/rC+MLGQJCRy98PPPtnwgdxmWAIu46tX9Fz66Zg3fSzqLTz0CY8ECfja1kOlhORauI0Yu3vPntVZRV17JWZa6pcyeCxcAANGlubF4uLotRZD5i/R0tl+7ixOyZ+9e8yy6UEAvNFOYAvrtcRWPUL8+WzH9Re3a7P40E2RbtxqfRdq0YWuefXB/UhKfdY3O4vbUqsWCztcz8/LlPH4XVsCuXXk2+6wrEWS+cfPNXE0llO/ZiiojRnCkghVctU9yrkHmzNChbIH65huOr7JqbfZn6Yu1a/k05FxLsEULHpPeGjgtjU9L+ZwKukJzp17Bmk2vI6uTZyFr1w546SVjYaelZcaWZUEmFrKiTnw83yrcc4/1ZaZN46DwUJXzCxeyjbwwlllftoxvu4yq4WdkcMyDq0r5nkLEFqbly43jyG6/nStCGi13771sqdQtXeXLc1Nxd5bY6Gg+07sSZNnZwOzZ5pUrc3O5hpqbVl9duvBhrLtW0tO5hZ0IMu8hMm8iLYQPrqr1O9cgc2bAAHZ7umrKYYRe+sJd9zV3XLwIbNmS310JsEDr148ve9nZbCHL564EbBMtelqc2yflCbLkZPYW6L5Re7TzV1w5sZAVblautFVid8XevZ5nTOplt111Z9bZuDHPLFsgXLzI4qGwWsdq1uT9aRTYf+gQ9x91Fe/lDV27so9i717H6UePsuAys67eeiufzPSMy+efN+8+7Ey9eq6Lw37wAefYt2tni02z5+RJjqVzUxRY12u6AVDv2CSCTCjquHJZ7tvHwqZ6dePPixfnWDLAWvyYTt26LGRcJEdbYtMmdiLYB/Tb078/W6NWr2ZBZhjQX4JFklVB5lytP0+QLVvG8bR6Brw9miArXkEEWeHl9GnOmHzrLdfzKcX+7Xvv9Wz9bdrwbbB90zojzp7lYqG9evl+y6Nz5Ahf4M36U27dygKzsAqy6tW58I9zwzjAeskLT+nShc82zgJfT1UyE2QlSwLDhnFtiYwMz+zxuu/C3peos3cv8NprXGPh5EkWZceOOc5TqRKfld1UJa1Zk3WbLsgkw1IQGFcuy3372AFhFBalM3QoP3siyPzVZFy3FRhZyADWR5GRfApLSzOxkOlmXndlejTsq/UrZRdDpgf1GyVjxcYCPXqgeF0OqxBBVhgpV46DBSZOdF3UMymJjyBPY45KlmQ7tLsCsaVLA6+/zhl3P//s2TbMeOgh4P77ufn1ypX5P2/Xjo/qnoW0YQMRB0EYWcj0wA5/C7KGDTkoyHmfzp/PAtGVT+KDDziYf/16Tl3UI2ndcdttwA8/GAuy779nt+a0abwfPvqIq/srZTvpWRR/zh5ZXZC5y0IThMJOhQrm/SzNSl7YM2AA8O67HFNoFb30ha/ho2vXcuioWfWf0qXZFjF/vguXZevWfP20GF9tL8guXGALnUPZC6Msy/r1gcWLUaJXRxCJICu8PPMMHxEffWQ+j170zkr9MWc6dGALmdEFE+AL+IkTwAsvsEXt4Yd9t0Nv3AjMmcNWl6gojsR0Rin+d7m6dQt3WrbkAAlnV/OBAyxUrNQg8wQifti7njMzgT//5JOVq0aFpUvzb/XXX/zeaoPIli25c4RR+Y7Ro/lY0M+4I0fy9G++YUG+cyeLyEcftbSprl35UN21ixetWtXmrRCEooqr4rD797sXZNHRwLPPOlbyd0fNmmy58oeFzMxdqdO/P9/P7d5t4rKMjeUbPYvXEt2imJtra5vkUPbCyEKWnQ3k5CAigveTCLLCyhVX8K3J2LHswjRiyRLOQqtRw/P133AD14rKyDD+/NVXuTdNZiYwfjy7Lx9/3PPt2DNmDB/hX37J1r1WrVh09uvHjfP++4//QK4aaxcGWrRgoXL4sOP0AwfYYhWI4k9TpvC+P3SI358/zz6JwYPdL/v775xlBJgHnTiTnc3Hrn0j+9OnOTiWyLgXZvXqHNfWogUrLHf1zjTs+1pKhqUgMGbFYS9c4AgBd4LMG6zk87gjJYVFlpm7Ukc3fB0+bGIh85CKFVmMnTnjgSD7+Wc+l+/cibJlRZAVbp5/nu3NX36Z/7PsbLZaeOva69ePG6YaBTz+9x8wYQJfsEuU4M6uo0ZxRXd3fRHNUIrdlC+9xFYXPZc5JYW/4z33cAnmEycKZ3alPTffzP9456qMt91mEz7+pmFDPmb0ol0VKrAItnL8XHml7bVzDroZkZFs5Z0+3TbtmWf4uHMWojp9+/Itb+vWvLxFy2+9ehxytmwZW8hEkAmCeYNxPVQ1EIIMYLelLxYyPanbnYWsaVNbRRxDC5mH2LdP0uthlywJDtXo0UNTZ07o/uDixUWQFXqaNOELmpHr5vRpFjB9+3q//u3buUeKMx9/zBfvZ56xTRs1imsLeOtOI2JX1dNPO04vX56F5bff8sFdtappZfZCQ3S0sbAZMAC4887AbLNpUxbCevT73LnWuwCXKsXxYJ5YLolYGem3ykuXssh/+mmOwjejalU+HvbssRwIpseRzZ/PX0kEmSCYuyz1khdGVfr9gZ7P46040QP627RxPR8R398B/rOQAWwTcLCQNWjA4UF6dQJ7dEFWooQIsiLBkCF8VDjHelWqxPW6rr/e+3U/8kh+sZeSwq6mm292dCvFxLA78fx5z4vKbt/OlY71ksrORERwF+O9eznRwKoVJpy57z7H+l8ZGbxfzaxHvhIZyT1xli9nsXPttdYrTAJcAuOaazzbZv36LMgyMvj7XnEFdyJwR1SUx4kNXbva8gBEkAmCucvSXVFYX+nTh5MJrriCCwWYlRo0Y+1a/g9b6e6nuy39YSGzb5/kIMiAvFixfIggK4JMnszWMnsf9o4dntcfc6ZDB65DZR/s/fnnnEds0tAZn3zCF+clS6xvZ8wYTtdx1cMR4KhId70ZCwu5uY7FWg8c4P3q3AfIn3TtygJp/Hh+70l3B2+oV4+/1+jRfMs8dqylqtneYN9dSQSZIJj3s9y3jz/TLWj+pm9fzlnq1o3bJteuDXzxhfXG22vWuI8f0+nViy8b/hCXpoLs4EH2ahi1tEtP55vdmBgRZEWGKlXYyqQfEBkZnMU2apRv6+3QgVX/+vW2addfz6UOmjY1Xubpp9mVdM891m59du4Epk7lchfly/s23sJEixbsdj5yhN8HqgaZPV278lnnf/9j65VRYL0/qVePBed773F83NVXB2xTjRtzqIcXxjVBKJSY9bPUS164Sq72lSZNgF9/5cpGDRpwkn6DBtwa1yyxH+CcnmPHrAuyhAR2Ktx1l+9jLluWnTP5Yshclb3IyuJBEKFsWQ6ZMOuKF8qIIPOEHj34CH33XbaKrVrFosyk6bJl2rfnZ/t6ZE2aAE89Zb5M8eIcDL5/v7UA9Lfe4iwVV+ssirRsyc96PbKCEGRt2rBbOCuL49UCTbt2fIucmAh8+mlANxURwbWEmzThm1lBEIyLw+7bF7j4MWc6duTw0T/+YBfkbbfxNLOCtXr8mLuAfntKlPBPlEtkpE3AnjvHgjUhAa6zLN97L88sVrYs33/q1rVwQgSZJxCxNWzfPg7yX7yYjx5fBVmFCmwlWb2a1X+/ftZckV26AA88wBdZva2OEXv28C3RAw/Y7MEC06wZ/656xX69BtlllwVum0R8dszMDLy7EmBL6pgxNvNVgPn6a1tfekEQ8lvIlLJWFNafEHFc2bp1HLa6dStfQo4ezT/v2rVs5W7RouDGZ4/ePuncORZjERFwLciAPFOjfooLR7elCDJPGTiQe1i8/TbXcmrb1j8dgO+6iy0Z33/PtzFZWdaWe/ddduAb/at0Zs9mkWGfrSkw8fGcH751K78/cIDryQU6oeHCBT7TeNrdIQwoWTJwcTGCEI44Nxg/eZJPAQUpyHQiIthC9scffNno3NmWYKCzZg3fqxrVDC8I9Gr9587ZXV5dCbKnn+aENIggK1pERLCVrGxZtmj5q7XQqFHsTnz3XXajWe0hmZDAbXT0rLkPP+T1pKXZ5nnqKY4hq1zZP2MtbPz1F8fXAew+9qRHibcMHcoBEoW5E4IgCABsLks9d0gveREMQabTpQs7ec6eZVG2YwdPz81lK5rV+LFAoAuy8+ftMiyjovhhFBy2aVPeF9AFmSctf0MFg34qgltuvZUd8Pfd57/m27m57FbavZvdoZ5EehLZWuMcOcIZmD//zEHjDRpwULfVyu5FEXv35BNPBG8cgiAUSuz7WcbHh4YgA9jB89dfnOfTpQt3cYuLY8uUJ/Fj/sbeQuZQB/bSJeNrY3p6XhE0sZAVRa64go9eX+PHdLKzuU0S4FgXy1M+/pjb5JQqBQwaxILsgw/8McLCy86dnLCxbBkX3TVrYyUIguAFztX6dUEWCpnIzZoBf//NHsFu3bg0BhB8C1lqKseROQgyM0NFenpe41wRZILvxMQAEydyxX5feyhedRWwYQO7P9u25SbigjmlS3OQ/U8/ceD7rFnBHpEgCIUI5+Kw+/ezYT5A5QA9pl49FmXlyrFjpUQJ7vIWLPT2SXv2OIVoDxrE1zVn7ASZ3oQ9HAWZuCxDieHD/beu6Gjg2Wf5IbimUiWOr/vlF34fCretgiAUGpzbJxV0hqUVatbketh9+3Kek692AV/QBWxGhpOFbMMG40xxO0EWFcUiTgSZIIQrLVsCv//OrwuqOJAgCEUCI5dl167BG48Zl1/OTWOCXVTVvjqTgyCLiTHOspw3z2HGMmXCU5CJy1IQAMeCO7q9XBAEwQ/YuywvXeKq9qFmIdOxzxELFqaCLDbWWJC1bs31FjXCtX2SCDJBABwFWVFoqi4IQoGh97NMTuaWjEqJId4V9oLMIYYsNjZ/66RLlzg0559/8iaJIBOEcKZ3by5lIgkQgiD4Gb2f5cmTtiKsoWohCwVKlMgLCXNvITt/Hnj/fVu3FYSvIJMYMkEAONNy5cpgj0IQhEKKXhw2VGqQhToVK7J4dRBkY8fmb5Kbns7PuoKDCDJBEARBEEzQ+1nu28eGnkC2yy0MGAqy5s3zz+hCkCnlWY31YCMuS0EQBEEIMLrLct8+LjEhoaqu0ePIHGLIfvoJ+OwzxxlNBFl2tmMHwXBADglBEARBCDD2LktxV7pHF2QOFrJZs4Cvv3ac0USQAeHnthRBJgiCIAgBRu9nuX27CDIrGAoyoyzLmjWBt9/marYa4SrIJIZMEARBEAKMffV5EWTu6dWLEyf1VkgAjAvD1qwJPP+8w6RwFWRiIRMEQRCEAKNX6wdEkFmhRw9unuLQwsmo7MXBg8Aff7DS1dAFWUpK4MfpT0SQCYIgCEKAsRdkUhTWS4xclnPnAv36cT0yjXC1kInLUhAEQRACjH31eRFkXnLNNUD16o7TDIL6dTenCDJBEARBEBzQLWTlyzuVchCsc/XV/LBHF2TFiuVNKlaMH+EmyMRlKQiCIAgBRu9nKfFjPnDgAMeL5ebapqWnA8WL5yvsFo7V+kWQCYIgCEKAIWJvW/36wR5JGDN9OseL2QXwIz3dwV2pE46CTFyWgiAIglAAzJ3rVFdL8IzYWH7OzGSrGAA0aAD07Ztv1jJlRJC5hYiqAZgMoBIABWCcUupTIioLYBqAmgAOALhJKRVmSauCIAiCYEy9esEeQZhjL8h0Hn/ccNayZYG9ewM/JH8SDJdlNoCnlFKNAHQA8BARNQLwPIDFSqm6ABZr7wVBEARBELgwLOAoyNLSgKysfLOGo8uywAWZUuqYUmqD9joVwHYAVQBcB2CSNtskAIMKemyCIAiCIIQoRhayfv0MXZYiyDyEiGoCaAlgNYBKSqlj2kfHwS5NQRAEQRAEoGpVoHdvmzADXAb1X7zIj3AhaEH9RBQPYCaAx5VS54ko7zOllCIiZbLcvQDuBYDqzgXiBEEQBEEonHTtyg97XAgygNsn2ZUoC2mCYiEjomiwGPtRKTVLm3yCiC7TPr8MQLLRskqpcUqpNkqpNhXse1EIgiAIglB4UYrdlTk5tmluBFk4uS0LXJARm8K+A7BdKfWR3UdzAAzXXg8HMLugxyYIgiAIQoiybBlX112+3DatEAmyYLgsrwJwO4CtRLRJmzYKwDsAphPR3QAOArgpCGMTBEEQBCEU0bMs7RuMR0UZ9qISQWYBpdQ/AMjk454FORZBEARBEMIEoyzLkycNZ7WPIQsXpHWSIAiCIAihj5EgMyEcLWQiyARBEARBCH2cBdnx40DLlsCcOflmjY9nb6YIMkEQBEEQBH8SGwtER9uyLM+dAzZtAlJT881KFH7FYaW5uCAIgiAIoU/16o4B/enp/GyQZQmEnyATC5kgCIIgCOGHCDJBEARBEIQCJjMTuPJKYJLW9loEmSAIgiAIQgETFQWsWgUcOsTv3QiyMmVEkAmCIAiCIPiXyEggIsKWZdmzJ7BuHVCvnuHs4WYhk6B+QRAEQRDCg9hYmyArXRpo3dp01rJlgfPngawsTs4MdcRCJgiCIAhCeGAvyBYtAp56yjHz0g69OOzZswUzNF8RQSYIgiAIQngQE2MTYCtXAh99xK5MA8KtWr+4LAVBEARBCA/mzQPKlePX6elsMRNBJgiCIAiCUIC0aWN7nZ5ummEJhJ8gE5elIAiCIAjhwWefOdYhsyDIUlIKYFx+QASZIAiCIAjhwaRJwIwZ/LqQWcjEZSkIgiAIQnhgn2U5fDhw+rTprKVKcZNxEWSCIAiCIAj+JCbGJsgGDHA5a2QklyoLF0EmLktBEARBEMIDewvZn38Ca9e6nD2cqvWLIBMEQRAEITyIjbXVIXvsMeC991zOHk6CTFyWgiAIgiCEB8OHcz8kwG1QPyCCTBAEQRAEwf/ceKPttUVBtm9fgMfkJ8RlKQiCIAhCeJCUxD0sgUJnIRNBJgiCIAhCePC//wG33grk5AAZGW4FWZkyXBg2N7eAxucDIsgEQRAEQQgP9CzL7Gx2XzZp4nL2smVZjOlhZ6GMxJAJgiAIghAe6IIsNtZWsd8F9tX6S5cO7NB8RSxkgiAIgiCEB7ogy8oCTp3iZxeEU/skEWSCIAiCIIQHsbGAUsDWrUCFCsCcOS5nF0EmCIIgCILgb+rVA/r1swWFWciyBESQCYIgCIIg+I+bbgLmz+egfsCyIEtJCfC4/IAE9QuCIAiCEB7o5S5SU/m9hbIXgFjIBEEQBEEQ/MfkyUB8PLBjB793I8hiYnh2EWSCIAiCIAj+IiaGnzMz2fwVH+92kXCp1i+CTBAEQRCE8CA2lp9vvJFVVpUqbhcRQSYIgiAIguBPdEGWmWl5ERFkgiAIgiAI/kQXZG+/DbRqZWkREWSCIAiCIAj+JC4OKFYM2L8f2LvX0iIiyARBEARBEPxJly7AhQtsHXOTYamjCzKlAjw2HxFBJgiCIAhCeJGe7pEgu3SJdVwoI4JMEARBEITwYP9+oF074JdfLAuycCkOK4JMEARBEITwICcHWLuWsyw9sJABoS/IpHWSIAiCIAjhgV4YdvRo4PbbLS0SLoJMLGSCIAiCIIQHetmLihWB2rUtLSKCTBAEQRAEwZ/oguypp4AffrC0iBVBNn++rV95sBBBJgiCIAhCeKC7LDMygH/+sbSILshSUow/P3gQGDgQeO89P4zPB0SQCYIgCIIQHsTFAatX82uLQf3FirFhzcxCNnYsP48c6Yfx+YAIMkEQBEEQwoOICKBtW4DIsiAjMq/Wn5EBfPstcN11QPXqfh6rh4ggEwRBEAQhfHjmGS67b1GQAeaCbPp04NQp4KGH/Dg+LxFBJgiCIAhC+PDhh/zsB0H2+edAgwZAjx5+GpsPiCATBEEQBCF8SEjg527dLC9iJMjWrOEasw8/zG7NYCOCTBAEQRCE8KFMGWD4cKBJE8uLGAmyL74A4uMt15cNOCLIBEEQBEEIH86fByZNAo4ft7yIsyA7eRKYOpV1XcmSARijF4ggEwRBEAQhfDh7lp+3b7e8SNmyQHo6t8AEgO++Ay5dAh580P/D8xYRZIIgCIIghA833sjPHgb1A1wcNjsb+OorDuRv1CgA4/MSEWSCIAiCIIQPQ4bwsxeC7MwZ4LffgEOHOJg/lBBBJgiCIAhC+LB0KT97IMjKlOHnM2e41EW1atwuKZQQQSYIgiAIQvjw9df87IWFbMUKYPFi4P77gaioAIzNB0JKkBFRXyLaSUR7iOj5YI9HEARBEIQQQ1dSej0yC+iC7KOPuD/5PfcEYFw+EjKCjIgiAXwBoB+ARgCGEVEIhdsJgiAIghB0Bg0CGjbkRuMW0QVZcjJw001AxYqBGZovhIwgA9AOwB6l1D6l1CUAUwFcF+QxCYIgCIIQSqSnA0eOeLRIyZJAZCS/DrVgfp1Q8qBWAXDY7v0RAO2DNBZBEARBEEKR33/3eBEitpJVrw60axeAMfmBUBJkliCiewHcq71NI6Kdflp1eQCn/LSuwoDsj/zIPnFE9kd+ZJ84IvsjP7JPHPF+f3jRgPLkSSAiuL7BGmYfhJIgOwqgmt37qto0B5RS4wCM8/fGiWidUqqNv9cbrsj+yI/sE0dkf+RH9okjsj/yI/vEEdkfNkIphmwtgLpEVIuIYgDcDGBOkMckCIIgCIIQcELGQqaUyiaihwEsABAJYLxSKinIwxIEQRAEQQg4ISPIAEApNR/A/CBt3u9u0DBH9kd+ZJ84IvsjP7JPHJH9kR/ZJ47I/tAgpVSwxyAIgiAIglCkCaUYMkEQBEEQhCJJkRNkRFSNiJYS0TYiSiKix7TpZYnoTyLarT2XCfZYCwoiiiOiNUS0Wdsnr2nTaxHRaq2V1TQt2aLIQESRRLSRiH7T3hf1/XGAiLYS0SYiWqdNK8r/m9JENIOIdhDRdiLqWMT3R33t2NAf54no8SK+T57QzqmJRDRFO9cW9fPIY9r+SCKix7VpRfYYsafICTIA2QCeUko1AtABwENai6bnASxWStUFsFh7X1TIBNBDKdUcQAsAfYmoA4B3AXyslKoDIAXA3cEbYlB4DMB2u/dFfX8AQHelVAu7NPWi/L/5FMAfSqkGAJqDj5Uiuz+UUju1Y6MFgNYALgD4BUV0nxBRFQCPAmijlGoCTla7GUX4PEJETQCMBHfmaQ7gGiKqgyJ6jDhT5ASZUuqYUmqD9joVfBKtAm7TNEmbbRKAQUEZYBBQTJr2Nlp7KAA9AMzQphepfUJEVQEMAPCt9p5QhPeHC4rk/4aISgHoAuA7AFBKXVJKnUUR3R8G9ASwVyl1EEV7n0QBKEZEUQCKAziGon0eaQhgtVLqglIqG8AyADegaB8jeRQ5QWYPEdUE0BLAagCVlFLHtI+OA6gUrHEFA809twlAMoA/AewFcFb70wDcyqpKkIYXDD4B8CyAXO19ORTt/QGwSF9IROu1jhlA0f3f1AJwEsAEza39LRGVQNHdH87cDGCK9rpI7hOl1FEAHwA4BBZi5wCsR9E+jyQC6ExE5YioOID+4ILwRfIYcabICjIiigcwE8DjSqnz9p8pTj0tUumnSqkczdVQFWxObhDcEQUPIroGQLJSan2wxxJidFJKtQLQD+zq72L/YRH730QBaAXgK6VUSwDpcHKzFLH9kYcWE3UtgJ+dPytK+0SLg7oOLN4vB1ACQN+gDirIKKW2g122CwH8AWATgByneYrMMeJMkRRkRBQNFmM/KqVmaZNPENFl2ueXgS1FRQ7N7bIUQEcApTVTO2DSyqqQchWAa4noAICpYBfDpyi6+wNA3h0/lFLJ4Nigdii6/5sjAI4opVZr72eABVpR3R/29AOwQSl1QntfVPdJLwD7lVInlVJZAGaBzy1F/TzynVKqtVKqCziGbheK7jHiQJETZFos0HcAtiulPrL7aA6A4drr4QBmF/TYggURVSCi0trrYgCuBsfWLQUwWJutyOwTpdQLSqmqSqmaYNfLEqXUrSii+wMAiKgEESXorwH0BrsfiuT/Ril1HMBhIqqvTeoJYBuK6P5wYhhs7kqg6O6TQwA6EFFx7bqjHyNF9jwCAERUUXuuDo4f+wlF9xhxoMgVhiWiTgD+BrAVtvigUeA4sukAqgM4COAmpdSZoAyygCGiZuBAykiwSJ+ulHqdiK4AW4jKAtgI4DalVGbwRlrwEFE3AE8rpa4pyvtD++6/aG+jAPyklHqTiMqh6P5vWoCTPmIA7AMwAtr/B0VwfwB5Yv0QgCuUUue0aUX5GHkNwFBwdv9GAPeAY8aK5HkEAIjob3BMbhaAJ5VSi4vyMWJPkRNkgiAIgiAIoUaRc1kKgiAIgiCEGiLIBEEQBEEQgowIMkEQBEEQhCAjgkwQBEEQBCHIiCATBEEQBEEIMiLIBEEISYhoEBEpIgp41witFt9qrQ1SZx/XNcpf4xIEoegggkwQhFBlGIB/tOdA0xPAVqVUS6XU3z6uy2NBRkSRPm5TEIQwRwSZIAghh9ZrthOAu8HdEvTpEUT0JRHtIKI/iWg+EQ3WPmtNRMu05ucL9FYsTuutSURLiGgLES0moupagdf3AFxHRJu0bhX6/H2J6Ge7992I6Dft9TAi2kpEiUT0rjbtHQDFtPX8qE27jYjWaNO+1sUXEaUR0YdEtBlARyJ6h4i2aWP7wN/7VBCE0EYEmSAIoch1AP5QSu0CcJqIWmvTbwBQE0AjALeDe67q/Wn/B2CwUqo1gPEA3jRY7/8ATFJKNQPwI4DPlFKbALwCYJpSqoVS6qLd/IsAtNcq0ANcdX0qEV0ObpLcA0ALAG2JaJBS6nkAF7X13EpEDbVlrlJKtQA3Ur5VW1cJAKuVUs3BrcquB9BYG9sYb3aaIAjhiwgyQRBCkWHg9jLQnnW3ZScAPyulcrV+kku16fUBNAHwJxFtAvASuHGzMx3BvfMA4HttfaYopbIB/AFgoNYQegC4z15bAH9pjaOzweKui8EqegJoDWCtNq6eAK7QPssBMFN7fQ5ABoDviOgGABdcjUsQhMJHlPtZBEEQCg4iKgu2PDUlIgXusaqI6BlXiwFIUkp1DMCQpgJ4GMAZAOuUUqncK9oSBLbIvWDwWYZSKgdg4UdE7cCCbbC2vR4+j1wQhLBBLGSCIIQagwF8r5SqoZSqqZSqBmA/gM4AVgC4UYslqwSgm7bMTgAViCjPhUlEjQ3WvRK2mLRbAVgJ4F8GoBWAkbBZ7dYA6EpE5bWYsGHafACQpblQAWAxgMFEVFEbV1kiquG8AS1mrpRSaj6AJwA0tzAuQRAKEWIhEwQh1BgGjs+yZ6Y2/SGwFWkbgMMANgA4p5S6pAX3f0ZEpcDntk8AJDmt5xEAEzRr20kAI9wNRimVowXy3wlguDbtGBE9D3aZEoB5SqnZ2iLjAGwhog1aHNlLABYSUQSALO07HHTaTAKA2UQUp63vSXfjEgShcEFKqWCPQRAEwTJEFK+USiOicmBL1VVaPJkgCELYIhYyQRDCjd+IqDSAGABviBgTBKEwIBYyQRAEQRCEICNB/YIgCIIgCEFGBJkgCIIgCEKQEUEmCIIgCIIQZESQCYIgCIIgBBkRZIIgCIIgCEFGBJkgCIIgCEKQ+T/YVV9V8mxUpgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_line = df_clean.groupby('age').agg({'ftbiden':'mean',\n",
    "                                       'fttrump':'mean',\n",
    "                                       'ftobama':'mean',\n",
    "                                       'ftsanders':'mean'})\n",
    "df_line = df_line.reset_index()\n",
    "df_line = pd.melt(df_line, id_vars='age', \n",
    "                    value_vars = ['ftbiden', 'fttrump', 'ftobama', 'ftsanders'])\n",
    "df_line = df_line.rename({'variable':'candidate', 'value':'thermometer'}, axis=1)\n",
    "df_line['candidate'] = df_line['candidate'].map({'ftbiden':'Joe Biden',\n",
    "                                                 'fttrump':'Donald Trump',\n",
    "                                                 'ftobama':'Barack Obama',\n",
    "                                                 'ftsanders':'Bernie Sanders'})\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(x='age', y='thermometer', hue='candidate', palette=['blue','red'], style='candidate',\n",
    "             data=df_line.query(\"candidate=='Donald Trump' | candidate=='Joe Biden'\"))\n",
    "plt.xlabel('Age of voters')\n",
    "plt.ylabel('Thermometer rating')\n",
    "plt.title('Feeling Thermometer Ratings By Age')\n",
    "plt.ylim(0,100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently, Biden was more popular among younger voters and Trump was more popular among older voters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.kdeplot(df_clean.ftbiden, shade=True, label='Biden')\n",
    "sns.kdeplot(df_clean.fttrump, shade=True, label='Trump')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Thermometer rating')\n",
    "plt.title('Distribution of Thermometer Ratings')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More people had extreme opinions of Trump, and more people had mediocre opinions of Biden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_vio = pd.melt(df_clean, id_vars = ['caseid', 'sex'], \n",
    "                    value_vars = ['ftbiden', 'fttrump'])\n",
    "df_vio = df_vio.rename(columns=({'variable':'candidate',\n",
    "                        'value':'thermometer'}))\n",
    "df_vio['candidate'] = df_vio['candidate'].map({'ftbiden':'Joe Biden',\n",
    "                                                'fttrump':'Donald Trump'})\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.violinplot(x = 'candidate', y = 'thermometer', data = df_vio,\n",
    "              hue = 'sex')\n",
    "plt.xlabel(\"Political Figure\")\n",
    "plt.ylabel(\"Feeling Thermometer Ratings\")\n",
    "plt.title(\"Distributions of Ratings for Political Figures\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this applies to both male and female voters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Analysis\n",
    "\n",
    "### Weighted average of candidates’ thermometer ratings\n",
    "\n",
    "We can recalculate the average Biden rating & average Trump rating while using these weights to count every row in a way that is more representative of the U.S. adult population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.31193635270897"
      ]
     },
     "execution_count": 759,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.weight = pd.to_numeric(df_clean.weight.str.strip().replace({'':1}))\n",
    "biden_temp = df_clean.loc[~df_clean.ftbiden.isna()]\n",
    "np.average(biden_temp.ftbiden, weights = biden_temp.weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.25284206257595"
      ]
     },
     "execution_count": 760,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trump_temp = df_clean.loc[~df_clean.fttrump.isna()]\n",
    "np.average(trump_temp.fttrump, weights = trump_temp.weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The weighted average of Biden’s thermometer ratings is similar with Trump's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted median of candidates' thermometer ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.0"
      ]
     },
     "execution_count": 761,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import weighted\n",
    "\n",
    "weighted.median(df_clean['ftbiden'], df_clean.weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.0"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weighted.median(df_clean['fttrump'], df_clean.weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the weighted median of Biden’s thermometer ratings is much higher (10 points) than Trump's."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measures of variability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1118.0106501193227, 33.436666253071984]"
      ]
     },
     "execution_count": 765,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[df_clean.ftbiden.var(), df_clean.ftbiden.std()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variability and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 100.0]"
      ]
     },
     "execution_count": 766,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[df_clean.ftbiden.min(), df_clean.ftbiden.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min and Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[70.0, 7.0, 63.0]"
      ]
     },
     "execution_count": 763,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[df_clean['ftbiden'].quantile(.75), \n",
    "df_clean['ftbiden'].quantile(.25),\n",
    "df_clean['ftbiden'].quantile(.75) - df_clean['ftbiden'].quantile(.25)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biden's thermometer ratings at 75th and 25th percentiles, and interquartile range (IQR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[91.0, 2.0, 89.0]"
      ]
     },
     "execution_count": 764,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[df_clean['fttrump'].quantile(.75),\n",
    "df_clean['fttrump'].quantile(.25),\n",
    "df_clean['fttrump'].quantile(.75) - df_clean['fttrump'].quantile(.25)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trump's thermometer ratings at 75th and 25th percentiles, and interquartile range (IQR)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean, median & quantile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 779,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">ftbiden</th>\n",
       "      <th colspan=\"4\" halign=\"left\">fttrump</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>q25</th>\n",
       "      <th>q75</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>q25</th>\n",
       "      <th>q75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partyID</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>Democrat</th>\n",
       "      <td>66.38</td>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>11.21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Independent</th>\n",
       "      <td>35.26</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>40.89</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republican</th>\n",
       "      <td>18.82</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>82.36</td>\n",
       "      <td>94.0</td>\n",
       "      <td>74.75</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ftbiden                    fttrump                     \n",
       "               mean median   q25   q75    mean median    q25    q75\n",
       "partyID                                                            \n",
       "Democrat      66.38   70.0  50.0  88.0   11.21    2.0   0.00    7.0\n",
       "Independent   35.26   33.0   6.0  53.0   40.89   37.0   2.50   73.5\n",
       "Republican    18.82    8.0   1.0  31.0   82.36   94.0  74.75  100.0"
      ]
     },
     "execution_count": 779,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def q25(x):\n",
    "    return x.quantile(.25)\n",
    "\n",
    "def q75(x):\n",
    "    return x.quantile(.75)\n",
    "\n",
    "df_clean.groupby('partyID').agg({'ftbiden': ['mean', 'median', q25, q75],\n",
    "                                 'fttrump': ['mean', 'median', q25, q75]}).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear relationship between party affiliation and candidates' thermometer ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 781,
   "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>ideology</th>\n",
       "      <th>Liberal</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Conservative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liveurban</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>56.66</td>\n",
       "      <td>21.66</td>\n",
       "      <td>-45.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rural</th>\n",
       "      <td>46.16</td>\n",
       "      <td>6.45</td>\n",
       "      <td>-64.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suburb</th>\n",
       "      <td>55.42</td>\n",
       "      <td>21.39</td>\n",
       "      <td>-63.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town</th>\n",
       "      <td>52.12</td>\n",
       "      <td>19.71</td>\n",
       "      <td>-63.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ideology   Liberal  Moderate  Conservative\n",
       "liveurban                                 \n",
       "City         56.66     21.66        -45.38\n",
       "Rural        46.16      6.45        -64.23\n",
       "Suburb       55.42     21.39        -63.63\n",
       "Town         52.12     19.71        -63.97"
      ]
     },
     "execution_count": 781,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean['partisanship'] = df_clean['ftbiden'] - df_clean['fttrump']\n",
    "df_clean[['ftbiden', 'fttrump', 'partisanship']]\n",
    "(pd.crosstab(df_clean['liveurban'], df_clean['ideology'], values=df_clean['partisanship'], aggfunc='mean')).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People who live in cities and are liberal rated Biden 56.66 points higher than Trump on average, and people who are conservative and live in suburbs rated Trump 63.63 points higher than Biden on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis tests\n",
    "\n",
    "For example, we want to know where there is significant difference between men and women on how they rate Trump."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=5.22707643132577, pvalue=1.8362222726982473e-07)"
      ]
     },
     "execution_count": 782,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fttrump_men = df_clean.query(\"sex=='Male'\").fttrump.dropna()\n",
    "fttrump_women = df_clean.query(\"sex=='Female'\").fttrump.dropna()\n",
    "stats.ttest_ind(fttrump_men, fttrump_women, equal_var=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reject the null hypothesis and conclude that there is a statisitically significant difference between men and women in terms of how highly they rate Trump."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to compare the average rating of Trump to the average rating of Biden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'trump': 43.87000958160332,\n",
       " 'biden': 42.15189466923571,\n",
       " 'difference': 1.7181149123676107}"
      ]
     },
     "execution_count": 783,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{'trump': df_clean['fttrump'].mean(),\n",
    " 'biden': df_clean['ftbiden'].mean(), \n",
    " 'difference': df_clean['fttrump'].mean() - df_clean['ftbiden'].mean()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_relResult(statistic=1.6327284676310017, pvalue=0.10262803725374475)"
      ]
     },
     "execution_count": 784,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ttest = df_clean[['fttrump', 'ftbiden']].dropna()\n",
    "stats.ttest_rel(df_ttest['fttrump'], df_ttest['ftbiden'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fail to reject the null hypothesis that the two candidates have the same average thermometer rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test of multiple comparisons\n",
    "\n",
    "We want to know whether there is a significant difference between the average age of Democrats, independents, and Republicans:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "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>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partyID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Democrat</th>\n",
       "      <td>51.734894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Independent</th>\n",
       "      <td>49.705104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Republican</th>\n",
       "      <td>57.350877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   age\n",
       "partyID               \n",
       "Democrat     51.734894\n",
       "Independent  49.705104\n",
       "Republican   57.350877"
      ]
     },
     "execution_count": 786,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.groupby('partyID').agg({'age': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=52.588970634465824, pvalue=3.517577203359592e-23)"
      ]
     },
     "execution_count": 787,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(df_clean.query(\"partyID == 'Democrat'\").age.dropna(),\n",
    "               df_clean.query(\"partyID == 'Independent'\").age.dropna(),\n",
    "               df_clean.query(\"partyID == 'Republican'\").age.dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is much smaller than .05, so we reject the null hypothesis that the three groups have the same average age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tests of association\n",
    "\n",
    "Conservatives overwhelmingly oppose a universal basic income, with 62.2% of conservatives stating that they oppose a universal basic income a great deal. Only 6.7% of liberals, in contrast, oppose a universal basic income a great deal.\n",
    "\n",
    "Whether these differences are strong enough for us to conclude that there are ideological differences in support for a universal basic income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "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>ideology</th>\n",
       "      <th>Liberal</th>\n",
       "      <th>Moderate</th>\n",
       "      <th>Conservative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>universal_income</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Oppse a great deal</th>\n",
       "      <td>6.71</td>\n",
       "      <td>23.62</td>\n",
       "      <td>62.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oppose a moderate amount</th>\n",
       "      <td>5.61</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oppose a little</th>\n",
       "      <td>6.71</td>\n",
       "      <td>8.20</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neither favor nor oppose</th>\n",
       "      <td>22.07</td>\n",
       "      <td>26.00</td>\n",
       "      <td>11.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favor a little</th>\n",
       "      <td>15.73</td>\n",
       "      <td>12.30</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favor a moderate amount</th>\n",
       "      <td>18.78</td>\n",
       "      <td>11.00</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Favor a great deal</th>\n",
       "      <td>24.39</td>\n",
       "      <td>11.00</td>\n",
       "      <td>4.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ideology                  Liberal  Moderate  Conservative\n",
       "universal_income                                         \n",
       "Oppse a great deal           6.71     23.62         62.19\n",
       "Oppose a moderate amount     5.61      7.87          7.11\n",
       "oppose a little              6.71      8.20          3.99\n",
       "Neither favor nor oppose    22.07     26.00         11.71\n",
       "Favor a little              15.73     12.30          6.24\n",
       "Favor a moderate amount     18.78     11.00          3.99\n",
       "Favor a great deal          24.39     11.00          4.77"
      ]
     },
     "execution_count": 788,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(df_clean['universal_income'], df_clean['ideology'], normalize='columns')*100).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(849.5464372904162,\n",
       " 3.8910750579483107e-174,\n",
       " 12,\n",
       " array([[280.2137931 , 316.77827586, 394.00793103],\n",
       "        [ 56.83448276,  64.25068966,  79.91482759],\n",
       "        [ 50.04827586,  56.57896552,  70.37275862],\n",
       "        [157.49655172, 178.04793103, 221.45551724],\n",
       "        [ 89.06896552, 100.69137931, 125.23965517],\n",
       "        [ 85.39310345,  96.53586207, 120.07103448],\n",
       "        [100.94482759, 114.11689655, 141.93827586]]))"
      ]
     },
     "execution_count": 789,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chi2_contingency(pd.crosstab(df_clean['universal_income'], df_clean['ideology']).values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value at 3.89e-174 is much less than .05. Therefore, we reject this null hypothesis and conclude that there is a statistically significant relationship between ideology and support for a universal basic income."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multidimensional Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import euclidean_distances\n",
    "\n",
    "df_ft = df_clean.loc[:, 'fttrump': 'ftimmig'].dropna()\n",
    "names = df_ft.columns.str[2:]\n",
    "ft_distance = euclidean_distances(np.transpose(df_ft))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import manifold\n",
    "\n",
    "mds = manifold.MDS(n_components=2, dissimilarity='precomputed')\n",
    "mds_fit = mds.fit(ft_distance)\n",
    "pos = mds_fit.embedding_\n",
    "\n",
    "plt.rcParams['figure.figsize'] = 12,8\n",
    "plt.scatter(pos[:, 0], pos[:, 1], color='navy', s=0)\n",
    "\n",
    "for i in enumerate(names):\n",
    "    plt.annotate(i[1], (pos[i[0], 0], pos[i[0], 1]))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph represents a map of politics according to a representative sample of adults in the data in the United States as of December 2019. Entities that are closer together have smaller Euclidean distances, which indicates that they are similar in some ways. \n",
    "\n",
    "Democratic candidates for president, Biden, Sanders, Warren, Buttigieg, and Harris are all very close together. Obama is also close to that cluster of Democrats. Trump is about as far from them as possible. Trump is closest to the NRA and ICE, The Democrats are close to the UN, the NATO, journalists, and illegal immigrants. The racial groups, white, black, Asian, and Hispanic are close to each other and to immigrants. Among countries, there are two main groupings: one includes Canada, France, Britain, Germany, France, and Mexico, and the other includes China, Turkey, Iran, Saudi Arabia, Russia, and North Korea. Israel and Palestine are more distant from other countries and very far from each other. Ukraine is in the middle.\n",
    "\n",
    "This graph helps us understand similarities between features, helps us understand the viewpoints towards different topics in the American electorate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Component Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prince import PCA\n",
    "\n",
    "pca = PCA(n_components=2, \n",
    "          rescale_with_mean=True,\n",
    "          rescale_with_std=True\n",
    ")\n",
    "pca = pca.fit(df_ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "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>dim1</th>\n",
       "      <th>dim2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ftjournal</th>\n",
       "      <td>-0.830682</td>\n",
       "      <td>0.225883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftun</th>\n",
       "      <td>-0.817509</td>\n",
       "      <td>0.169747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftillegal</th>\n",
       "      <td>-0.811237</td>\n",
       "      <td>0.137007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftobama</th>\n",
       "      <td>-0.804786</td>\n",
       "      <td>0.376682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftwarren</th>\n",
       "      <td>-0.804091</td>\n",
       "      <td>0.360561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftharris</th>\n",
       "      <td>-0.774592</td>\n",
       "      <td>0.292580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftmuslim</th>\n",
       "      <td>-0.769883</td>\n",
       "      <td>-0.099410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftbiden</th>\n",
       "      <td>-0.752664</td>\n",
       "      <td>0.319838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftbuttigieg</th>\n",
       "      <td>-0.750555</td>\n",
       "      <td>0.254972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftsanders</th>\n",
       "      <td>-0.746146</td>\n",
       "      <td>0.355578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftfrance</th>\n",
       "      <td>-0.743863</td>\n",
       "      <td>-0.204568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftmexico</th>\n",
       "      <td>-0.716031</td>\n",
       "      <td>-0.262071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftpales</th>\n",
       "      <td>-0.708715</td>\n",
       "      <td>-0.070890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftnato</th>\n",
       "      <td>-0.702495</td>\n",
       "      <td>-0.065117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftgermany</th>\n",
       "      <td>-0.661523</td>\n",
       "      <td>-0.292994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftcanada</th>\n",
       "      <td>-0.616064</td>\n",
       "      <td>-0.262214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftiran</th>\n",
       "      <td>-0.576525</td>\n",
       "      <td>-0.108440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftukraine</th>\n",
       "      <td>-0.537361</td>\n",
       "      <td>-0.395648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftchina</th>\n",
       "      <td>-0.524933</td>\n",
       "      <td>-0.310720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fthisp</th>\n",
       "      <td>-0.495543</td>\n",
       "      <td>-0.480103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftblack</th>\n",
       "      <td>-0.437558</td>\n",
       "      <td>-0.442102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftimmig</th>\n",
       "      <td>-0.433261</td>\n",
       "      <td>-0.465348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftbritain</th>\n",
       "      <td>-0.429749</td>\n",
       "      <td>-0.493661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftturkey</th>\n",
       "      <td>-0.417361</td>\n",
       "      <td>-0.384296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftjapan</th>\n",
       "      <td>-0.415978</td>\n",
       "      <td>-0.542368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftasian</th>\n",
       "      <td>-0.410437</td>\n",
       "      <td>-0.565739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftnkorea</th>\n",
       "      <td>-0.270753</td>\n",
       "      <td>-0.282780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftsaudi</th>\n",
       "      <td>-0.262063</td>\n",
       "      <td>-0.475873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftwhite</th>\n",
       "      <td>-0.104892</td>\n",
       "      <td>-0.582802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftrussia</th>\n",
       "      <td>-0.071980</td>\n",
       "      <td>-0.494301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftisrael</th>\n",
       "      <td>0.124992</td>\n",
       "      <td>-0.695917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftice</th>\n",
       "      <td>0.478175</td>\n",
       "      <td>-0.632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftnra</th>\n",
       "      <td>0.550674</td>\n",
       "      <td>-0.613404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fttrump</th>\n",
       "      <td>0.685936</td>\n",
       "      <td>-0.561953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dim1      dim2\n",
       "ftjournal   -0.830682  0.225883\n",
       "ftun        -0.817509  0.169747\n",
       "ftillegal   -0.811237  0.137007\n",
       "ftobama     -0.804786  0.376682\n",
       "ftwarren    -0.804091  0.360561\n",
       "ftharris    -0.774592  0.292580\n",
       "ftmuslim    -0.769883 -0.099410\n",
       "ftbiden     -0.752664  0.319838\n",
       "ftbuttigieg -0.750555  0.254972\n",
       "ftsanders   -0.746146  0.355578\n",
       "ftfrance    -0.743863 -0.204568\n",
       "ftmexico    -0.716031 -0.262071\n",
       "ftpales     -0.708715 -0.070890\n",
       "ftnato      -0.702495 -0.065117\n",
       "ftgermany   -0.661523 -0.292994\n",
       "ftcanada    -0.616064 -0.262214\n",
       "ftiran      -0.576525 -0.108440\n",
       "ftukraine   -0.537361 -0.395648\n",
       "ftchina     -0.524933 -0.310720\n",
       "fthisp      -0.495543 -0.480103\n",
       "ftblack     -0.437558 -0.442102\n",
       "ftimmig     -0.433261 -0.465348\n",
       "ftbritain   -0.429749 -0.493661\n",
       "ftturkey    -0.417361 -0.384296\n",
       "ftjapan     -0.415978 -0.542368\n",
       "ftasian     -0.410437 -0.565739\n",
       "ftnkorea    -0.270753 -0.282780\n",
       "ftsaudi     -0.262063 -0.475873\n",
       "ftwhite     -0.104892 -0.582802\n",
       "ftrussia    -0.071980 -0.494301\n",
       "ftisrael     0.124992 -0.695917\n",
       "ftice        0.478175 -0.632289\n",
       "ftnra        0.550674 -0.613404\n",
       "fttrump      0.685936 -0.561953"
      ]
     },
     "execution_count": 793,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadings = pca.column_correlations(df_ft)\n",
    "loadings.columns = ['dim1', 'dim2']\n",
    "loadings.sort_values('dim1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first feature (dim1) seems to represent the traditional left-right ideological spectrum. The Democrats, along with the U.N., illegal immigrants, and journalists load most strongly on one side of this index, and Trump, the NRA, and ICE load most strongly on the other side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "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>dim1</th>\n",
       "      <th>dim2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ftisrael</th>\n",
       "      <td>0.124992</td>\n",
       "      <td>-0.695920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftice</th>\n",
       "      <td>0.478175</td>\n",
       "      <td>-0.632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftnra</th>\n",
       "      <td>0.550674</td>\n",
       "      <td>-0.613404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftwhite</th>\n",
       "      <td>-0.104892</td>\n",
       "      <td>-0.582802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftasian</th>\n",
       "      <td>-0.410437</td>\n",
       "      <td>-0.565740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fttrump</th>\n",
       "      <td>0.685936</td>\n",
       "      <td>-0.561953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftjapan</th>\n",
       "      <td>-0.415978</td>\n",
       "      <td>-0.542368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftrussia</th>\n",
       "      <td>-0.071980</td>\n",
       "      <td>-0.494301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftbritain</th>\n",
       "      <td>-0.429749</td>\n",
       "      <td>-0.493660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fthisp</th>\n",
       "      <td>-0.495543</td>\n",
       "      <td>-0.480104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftsaudi</th>\n",
       "      <td>-0.262063</td>\n",
       "      <td>-0.475874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftimmig</th>\n",
       "      <td>-0.433261</td>\n",
       "      <td>-0.465341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftblack</th>\n",
       "      <td>-0.437558</td>\n",
       "      <td>-0.442105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftukraine</th>\n",
       "      <td>-0.537361</td>\n",
       "      <td>-0.395656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftturkey</th>\n",
       "      <td>-0.417361</td>\n",
       "      <td>-0.384297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftchina</th>\n",
       "      <td>-0.524933</td>\n",
       "      <td>-0.310716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftgermany</th>\n",
       "      <td>-0.661523</td>\n",
       "      <td>-0.292994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftnkorea</th>\n",
       "      <td>-0.270753</td>\n",
       "      <td>-0.282779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftcanada</th>\n",
       "      <td>-0.616064</td>\n",
       "      <td>-0.262212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftmexico</th>\n",
       "      <td>-0.716031</td>\n",
       "      <td>-0.262069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftfrance</th>\n",
       "      <td>-0.743863</td>\n",
       "      <td>-0.204568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftiran</th>\n",
       "      <td>-0.576525</td>\n",
       "      <td>-0.108441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftmuslim</th>\n",
       "      <td>-0.769883</td>\n",
       "      <td>-0.099410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftpales</th>\n",
       "      <td>-0.708715</td>\n",
       "      <td>-0.070891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftnato</th>\n",
       "      <td>-0.702494</td>\n",
       "      <td>-0.065116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftillegal</th>\n",
       "      <td>-0.811237</td>\n",
       "      <td>0.137009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftun</th>\n",
       "      <td>-0.817509</td>\n",
       "      <td>0.169748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftjournal</th>\n",
       "      <td>-0.830682</td>\n",
       "      <td>0.225884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftbuttigieg</th>\n",
       "      <td>-0.750555</td>\n",
       "      <td>0.254972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftharris</th>\n",
       "      <td>-0.774592</td>\n",
       "      <td>0.292578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftbiden</th>\n",
       "      <td>-0.752664</td>\n",
       "      <td>0.319839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftsanders</th>\n",
       "      <td>-0.746146</td>\n",
       "      <td>0.355575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftwarren</th>\n",
       "      <td>-0.804091</td>\n",
       "      <td>0.360558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ftobama</th>\n",
       "      <td>-0.804786</td>\n",
       "      <td>0.376683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 dim1      dim2\n",
       "ftisrael     0.124992 -0.695920\n",
       "ftice        0.478175 -0.632289\n",
       "ftnra        0.550674 -0.613404\n",
       "ftwhite     -0.104892 -0.582802\n",
       "ftasian     -0.410437 -0.565740\n",
       "fttrump      0.685936 -0.561953\n",
       "ftjapan     -0.415978 -0.542368\n",
       "ftrussia    -0.071980 -0.494301\n",
       "ftbritain   -0.429749 -0.493660\n",
       "fthisp      -0.495543 -0.480104\n",
       "ftsaudi     -0.262063 -0.475874\n",
       "ftimmig     -0.433261 -0.465341\n",
       "ftblack     -0.437558 -0.442105\n",
       "ftukraine   -0.537361 -0.395656\n",
       "ftturkey    -0.417361 -0.384297\n",
       "ftchina     -0.524933 -0.310716\n",
       "ftgermany   -0.661523 -0.292994\n",
       "ftnkorea    -0.270753 -0.282779\n",
       "ftcanada    -0.616064 -0.262212\n",
       "ftmexico    -0.716031 -0.262069\n",
       "ftfrance    -0.743863 -0.204568\n",
       "ftiran      -0.576525 -0.108441\n",
       "ftmuslim    -0.769883 -0.099410\n",
       "ftpales     -0.708715 -0.070891\n",
       "ftnato      -0.702494 -0.065116\n",
       "ftillegal   -0.811237  0.137009\n",
       "ftun        -0.817509  0.169748\n",
       "ftjournal   -0.830682  0.225884\n",
       "ftbuttigieg -0.750555  0.254972\n",
       "ftharris    -0.774592  0.292578\n",
       "ftbiden     -0.752664  0.319839\n",
       "ftsanders   -0.746146  0.355575\n",
       "ftwarren    -0.804091  0.360558\n",
       "ftobama     -0.804786  0.376683"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadings.sort_values('dim2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The secone feature(dim2) seems associated with left-right political ideology as well, but it appears to partly identify the countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Correspondence Analysis (MCA)\n",
    "\n",
    "Similar to PCA, but MCA works with categorical observed features instead of continuous-valued ones. Like multidimensional scaling, MCA can be used to generate a map that is an excellent tool for EDA. In this case, the map will show would closely categories of different features correspond to one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from prince import MCA\n",
    "\n",
    "df_cat = df_clean[['liveurban', 'vote16', 'vote', 'confecon', 'ideology', 'partyID', \n",
    "                   'universal_income', 'family_separation', 'free_college', \n",
    "                   'forgive_loans', 'race', 'sex', 'education']].dropna()\n",
    "mca = MCA(n_components=2)\n",
    "mca = mca.fit(df_cat)\n",
    "\n",
    "mca.plot_coordinates(X=df_cat, \n",
    "                     figsize=(12, 12), \n",
    "                     show_row_points=False, \n",
    "                     row_points_size=10, \n",
    "                     show_row_labels=False, \n",
    "                     show_column_points=True,\n",
    "                     column_points_size=30,\n",
    "                     show_column_labels=True,\n",
    "                     legend_n_cols=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>forgive_loans_Favor a great deal</th>\n",
       "      <td>-1.016875</td>\n",
       "      <td>-0.948425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free_college_Favor a great deal</th>\n",
       "      <td>-1.014847</td>\n",
       "      <td>-0.968513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ideology_Liberal</th>\n",
       "      <td>-0.976584</td>\n",
       "      <td>-0.538839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote16_Hillary Clinton</th>\n",
       "      <td>-0.920940</td>\n",
       "      <td>-0.321139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>partyID_Democrat</th>\n",
       "      <td>-0.886805</td>\n",
       "      <td>-0.179322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote16_Donald Trump</th>\n",
       "      <td>1.015532</td>\n",
       "      <td>-0.148591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>universal_income_Oppse a great deal</th>\n",
       "      <td>1.030309</td>\n",
       "      <td>-0.274405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forgive_loans_Oppose a great deal</th>\n",
       "      <td>1.031305</td>\n",
       "      <td>-0.277152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confecon_Not at all worried</th>\n",
       "      <td>1.038713</td>\n",
       "      <td>-0.219533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free_college_Oppose a great deal</th>\n",
       "      <td>1.063889</td>\n",
       "      <td>-0.278377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            0         1\n",
       "forgive_loans_Favor a great deal    -1.016875 -0.948425\n",
       "free_college_Favor a great deal     -1.014847 -0.968513\n",
       "ideology_Liberal                    -0.976584 -0.538839\n",
       "vote16_Hillary Clinton              -0.920940 -0.321139\n",
       "partyID_Democrat                    -0.886805 -0.179322\n",
       "...                                       ...       ...\n",
       "vote16_Donald Trump                  1.015532 -0.148591\n",
       "universal_income_Oppse a great deal  1.030309 -0.274405\n",
       "forgive_loans_Oppose a great deal    1.031305 -0.277152\n",
       "confecon_Not at all worried          1.038713 -0.219533\n",
       "free_college_Oppose a great deal     1.063889 -0.278377\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 795,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mca.column_coordinates(df_cat).sort_values(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first latent feature we have categories is for liberal ideology, voting for Hillary Clinton in 2016, identifying as a Democrat, and supporting free college and educational loan forgiveness a great deal. On the other end we have categories for voting for Trump in 2016, no worry about the economy, and a great deal of opposition to free college, loan forgiveness, and universal basic income. This first latent feature appears to be the left-right ideological dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>free_college_Favor a great deal</th>\n",
       "      <td>-1.014847</td>\n",
       "      <td>-0.968513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forgive_loans_Favor a great deal</th>\n",
       "      <td>-1.016875</td>\n",
       "      <td>-0.948425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>universal_income_Favor a great deal</th>\n",
       "      <td>-0.844009</td>\n",
       "      <td>-0.786407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>confecon_Extremely worried</th>\n",
       "      <td>-0.801452</td>\n",
       "      <td>-0.561779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ideology_Liberal</th>\n",
       "      <td>-0.976584</td>\n",
       "      <td>-0.538839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>universal_income_Neither favor nor oppose</th>\n",
       "      <td>-0.468033</td>\n",
       "      <td>0.943663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote16_Did not vote</th>\n",
       "      <td>-0.305585</td>\n",
       "      <td>1.122188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forgive_loans_Neither favor nor oppose</th>\n",
       "      <td>-0.357096</td>\n",
       "      <td>1.485270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>free_college_Neither favor nor oppose</th>\n",
       "      <td>-0.369833</td>\n",
       "      <td>1.552549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_Probably will not vote</th>\n",
       "      <td>-0.414181</td>\n",
       "      <td>1.903382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  0         1\n",
       "free_college_Favor a great deal           -1.014847 -0.968513\n",
       "forgive_loans_Favor a great deal          -1.016875 -0.948425\n",
       "universal_income_Favor a great deal       -0.844009 -0.786407\n",
       "confecon_Extremely worried                -0.801452 -0.561779\n",
       "ideology_Liberal                          -0.976584 -0.538839\n",
       "...                                             ...       ...\n",
       "universal_income_Neither favor nor oppose -0.468033  0.943663\n",
       "vote16_Did not vote                       -0.305585  1.122188\n",
       "forgive_loans_Neither favor nor oppose    -0.357096  1.485270\n",
       "free_college_Neither favor nor oppose     -0.369833  1.552549\n",
       "vote_Probably will not vote               -0.414181  1.903382\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 796,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mca.column_coordinates(df_cat).sort_values(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On one end, we have positions that do not take a position: did not vote in 2016, probably will not vote in 2020, and neither favor nor oppose universal basic income, loan forgiveness, and free college. This appears to measure voter apathy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The end"
   ]
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}