Multilevel regression with post-stratification_election2020.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting the 2020 US election\n",
    "\n",
    "Given the structure of the US electorate, they use a two-stage modelling approach. we model how likely a respondent is to vote for Trump, given various information such as state, education, age, gender, race etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 300)\n",
    "\n",
    "import pymc3 as pm\n",
    "import scipy as sp\n",
    "\n",
    "from theano import shared\n",
    "import arviz as az\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survey data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_stata('data/ns20200625.dta', \n",
    "                    columns=['vote_2020', 'race_ethnicity', 'education', 'state', 'gender', 'age', 'census_region'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vote_2020         4\n",
       "race_ethnicity    0\n",
       "education         0\n",
       "state             0\n",
       "gender            0\n",
       "age               0\n",
       "census_region     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6475, 7)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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     "data": {
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       "<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>vote_2020</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>education</th>\n",
       "      <th>state</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>census_region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>White</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>WI</td>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Midwest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I am not sure/don't know</td>\n",
       "      <td>White</td>\n",
       "      <td>College Degree (such as B.A., B.S.)</td>\n",
       "      <td>VA</td>\n",
       "      <td>Female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>White</td>\n",
       "      <td>College Degree (such as B.A., B.S.)</td>\n",
       "      <td>VA</td>\n",
       "      <td>Female</td>\n",
       "      <td>46.0</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>White</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>TX</td>\n",
       "      <td>Female</td>\n",
       "      <td>75.0</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>White</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>WA</td>\n",
       "      <td>Female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  vote_2020 race_ethnicity  \\\n",
       "0              Donald Trump          White   \n",
       "1  I am not sure/don't know          White   \n",
       "2              Donald Trump          White   \n",
       "3              Donald Trump          White   \n",
       "4              Donald Trump          White   \n",
       "\n",
       "                             education state  gender   age census_region  \n",
       "0                     Associate Degree    WI  Female  49.0       Midwest  \n",
       "1  College Degree (such as B.A., B.S.)    VA  Female  39.0         South  \n",
       "2  College Degree (such as B.A., B.S.)    VA  Female  46.0         South  \n",
       "3                 High school graduate    TX  Female  75.0         South  \n",
       "4                 High school graduate    WA  Female  52.0          West  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Joe Biden                   0.419923\n",
       "Donald Trump                0.383166\n",
       "I am not sure/don't know    0.100541\n",
       "I would not vote            0.057761\n",
       "Someone else                0.038610\n",
       "Name: vote_2020, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.vote_2020.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    717\n",
       "NY    519\n",
       "FL    507\n",
       "TX    480\n",
       "OH    293\n",
       "IL    291\n",
       "PA    274\n",
       "NC    214\n",
       "NJ    206\n",
       "VA    205\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                                 4815\n",
       "Black, or African American             772\n",
       "Some other race                        460\n",
       "Asian (Asian Indian)                   102\n",
       "American Indian or Alaska Native        89\n",
       "Asian (Chinese)                         84\n",
       "Asian (Filipino)                        46\n",
       "Asian (Other)                           37\n",
       "Asian (Japanese)                        21\n",
       "Asian (Korean)                          14\n",
       "Asian (Vietnamese)                      13\n",
       "Pacific Islander (Native Hawaiian)      10\n",
       "Pacific Islander (Other)                 8\n",
       "Pacific Islander (Samoan)                3\n",
       "Pacific Islander (Guamanian)             1\n",
       "Name: race_ethnicity, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.race_ethnicity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College Degree (such as B.A., B.S.)           1477\n",
       "Completed some college, but no degree         1326\n",
       "High school graduate                          1078\n",
       "Masters degree                                 643\n",
       "Completed some high school                     638\n",
       "Associate Degree                               569\n",
       "Other post high school vocational training     324\n",
       "Completed some graduate, but no degree         237\n",
       "Doctorate degree                               146\n",
       "Middle School - Grades 4 - 8                    26\n",
       "3rd Grade or less                               11\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    3305\n",
       "Male      3170\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "South        2432\n",
       "West         1443\n",
       "Midwest      1349\n",
       "Northeast    1251\n",
       "Name: census_region, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.census_region.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of poll respondents by states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
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   "source": [
    "state_counts = df.groupby('state').size().reset_index().rename(columns={0: 'number of respondents'})\n",
    "\n",
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=state_counts['state'],\n",
    "    z=state_counts['number of respondents'].astype(float),\n",
    "    locationmode='USA-states',\n",
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    "    autocolorscale=False,\n",
    "    marker_line_color='white', # line markers between states\n",
    "    colorbar_title=\"Number of respondents\"\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Number of poll respondents by state<br>(Hover for breakdown)',\n",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notably, every state had respondents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Joe Biden                   2719\n",
       "Donald Trump                2481\n",
       "I am not sure/don't know     651\n",
       "I would not vote             374\n",
       "Someone else                 250\n",
       "Name: vote_2020, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.vote_2020.value_counts()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_df = df.copy()\n",
    "vote_map_dict = {'Donald Trump': 1, 'Joe Biden': 0, \"I am not sure/don't know\": 0, 'I would not vote': 0, 'Someone else': 0}\n",
    "dt_df['vote_2020'] = dt_df['vote_2020'].map(vote_map_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Respondents by state and race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_map_dict = {'White': 'White', 'Black, or African American': 'Black', 'Some other race': 'Others', \n",
    "            'Asian (Asian Indian)': 'Asian', 'Asian (Chinese)': 'Asian', 'Asian (Filipino)': 'Asian', 'Asian (Other)': 'Asian', \n",
    "            'Asian (Japanese)': 'Asian', 'Asian (Korean)': 'Asian', 'Asian (Vietnamese)': 'Asian', 'American Indian or Alaska Native': 'Others', \n",
    "            'Pacific Islander (Native Hawaiian)': 'Others', 'Pacific Islander (Other)': 'Others', \n",
    "            'Pacific Islander (Samoan)': 'Others', 'Pacific Islander (Guamanian)': 'Others'}\n",
    "\n",
    "dt_df['race'] = dt_df['race_ethnicity'].map(race_map_dict)"
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   "source": [
    "dt_race_df = dt_df.groupby(['state', 'race']).size().reset_index().rename(columns={0: 'Number of respondents'})\n",
    "\n",
    "fig = px.bar(dt_race_df, x=\"state\", y=\"Number of respondents\",\n",
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   "source": [
    "All the states had white respondents, several states had only white respondents, a number of states had no black or Asian respondents, or both, even more states had very few black and Asian respondents."
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  {
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>race</th>\n",
       "      <th>Number of respondents</th>\n",
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       "      <th>77</th>\n",
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       "   state   race  Number of respondents\n",
       "77    ME  White                     20"
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   "source": [
    "## Respondents by state and gender"
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   "cell_type": "code",
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   "source": [
    "dt_gender_df = dt_df.groupby(['state', 'gender']).size().reset_index().rename(columns=({0: 'Number of respondents'}))\n",
    "\n",
    "fig = px.bar(dt_gender_df, x=\"state\", y=\"Number of respondents\",\n",
    "             color='gender', barmode='group',\n",
    "             height=400)\n",
    "fig.update_layout(title_text='Number of poll respondents by state and gender')\n",
    "fig.show()"
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   "source": [
    "One state (WY) had no female respondent, all the other states had both male and female respondents."
   ]
  },
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   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>gender</th>\n",
       "      <th>Number of respondents</th>\n",
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       "      <th>100</th>\n",
       "      <td>WY</td>\n",
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       "    state  gender  Number of respondents\n",
       "100    WY  Female                      0\n",
       "101    WY    Male                      5"
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    "dt_gender_df.loc[dt_gender_df['state'] == 'WY']"
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   "metadata": {},
   "source": [
    "## Disaggregation Estimate support for Trump"
   ]
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  {
   "cell_type": "code",
   "execution_count": 141,
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       "                            \n",
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     "metadata": {},
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   ],
   "source": [
    "vote_dt_df = (dt_df.groupby('state')['vote_2020'].mean()*100).reset_index().rename(columns={'vote_2020': '% of vote'})\n",
    "\n",
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=vote_dt_df['state'],\n",
    "    z=vote_dt_df['% of vote'].astype(float),\n",
    "    locationmode='USA-states',\n",
    "    colorscale='Reds',\n",
    "    autocolorscale=False,\n",
    "    marker_line_color='white', # line markers between states\n",
    "    colorbar_title=\"% of support Donald Trump\"\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Disaggregation Estimate support for Donald Trump by state in 2020 election poll<br>(Hover for breakdown)',\n",
    "    geo = dict(\n",
    "        scope='usa',\n",
    "        projection=go.layout.geo.Projection(type = 'albers usa'),\n",
    "        showlakes=True, # lakes\n",
    "        lakecolor='rgb(255, 255, 255)'),\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By age & gender\n",
    "\n",
    "Around 35-45 years old male are the largest voting block for Trump."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/plotly/express/_core.py:383: FutureWarning:\n",
      "\n",
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{\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Support for Trump by age and gender\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"age\"}}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"matches\": \"x\", \"showgrid\": true, \"showticklabels\": false}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 0.7326], \"title\": {\"text\": \"count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.7426, 1.0], \"matches\": \"y2\", \"showgrid\": false, \"showline\": false, \"showticklabels\": false, \"ticks\": \"\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('d7f7b321-4ba9-4384-a57b-d51678c4105b');\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(dt_df.loc[dt_df['vote_2020']==1], x=\"age\", color=\"gender\",\n",
    "                   marginal=\"box\", # or violin, rug\n",
    "                   hover_data=dt_df.loc[dt_df['vote_2020']==1].columns)\n",
    "fig.update_layout(title_text='Support for Trump by age and gender')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simplify education and age variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_df.loc[(dt_df['education'] == 'College Degree (such as B.A., B.S.)'), 'edu_cat'] = 'Post secondary or higher'\n",
    "dt_df.loc[(dt_df['education'] == 'Completed some college, but no degree'), 'edu_cat'] = 'Some post secondary'\n",
    "dt_df.loc[(dt_df['education'] == 'High school graduate'), 'edu_cat'] = 'High school or less'\n",
    "dt_df.loc[(dt_df['education'] == 'Masters degree'), 'edu_cat'] = 'Graduate degree'\n",
    "dt_df.loc[(dt_df['education'] == 'Completed some high school'), 'edu_cat'] = 'High school or less'\n",
    "dt_df.loc[(dt_df['education'] == 'Associate Degree'), 'edu_cat'] = 'Post secondary or higher'\n",
    "dt_df.loc[(dt_df['education'] == 'Other post high school vocational training'), 'edu_cat'] = 'Some post secondary'\n",
    "dt_df.loc[(dt_df['education'] == 'Completed some graduate, but no degree'), 'edu_cat'] = 'Post secondary or higher'\n",
    "dt_df.loc[(dt_df['education'] == 'Doctorate degree'), 'edu_cat'] = 'Graduate degree'\n",
    "dt_df.loc[(dt_df['education'] == 'Middle School - Grades 4 - 8'), 'edu_cat'] = 'High school or less'\n",
    "dt_df.loc[(dt_df['education'] == '3rd Grade or less'), 'edu_cat'] = 'High school or less'\n",
    "\n",
    "dt_df.loc[(dt_df['age'] <= 29),  'age_cat'] = 'age_18-29'\n",
    "dt_df.loc[(dt_df['age'] <= 44) & (dt_df['age'] > 29),  'age_cat'] = 'age_30-44'\n",
    "dt_df.loc[(dt_df['age'] <= 59) & (dt_df['age'] > 44),  'age_cat'] = 'age_45-59'\n",
    "dt_df.loc[(dt_df['age'] >= 60),  'age_cat'] = 'age_60_or_more'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Among Trump's voters who have graduate degree, age 30-44 is the age group that he had the most supporters, among Trump's voters who have post secondary or higher degrees, age 60 and over is the age group that he had the most supporters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In almost all age groups, highest number of Trump's voters come from post secondary or higher education category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>1057</td>\n",
       "      <td>42.603789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>1424</td>\n",
       "      <td>57.396211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  vote_2020  percentage\n",
       "0  Female       1057   42.603789\n",
       "1    Male       1424   57.396211"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_group = dt_df.loc[dt_df['vote_2020'] == 1].groupby('gender')['vote_2020'].count().reset_index()\n",
    "gender_group['percentage'] = 100 * gender_group['vote_2020']  / gender_group['vote_2020'].sum()\n",
    "gender_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By age category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_cat</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age_18-29</td>\n",
       "      <td>307</td>\n",
       "      <td>12.374043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age_30-44</td>\n",
       "      <td>820</td>\n",
       "      <td>33.051189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age_45-59</td>\n",
       "      <td>619</td>\n",
       "      <td>24.949617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>735</td>\n",
       "      <td>29.625151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age_cat  vote_2020  percentage\n",
       "0       age_18-29        307   12.374043\n",
       "1       age_30-44        820   33.051189\n",
       "2       age_45-59        619   24.949617\n",
       "3  age_60_or_more        735   29.625151"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_cat = dt_df.loc[dt_df['vote_2020'] == 1].groupby('age_cat')['vote_2020'].count().reset_index()\n",
    "age_cat['percentage'] = 100 * age_cat['vote_2020']  / age_cat['vote_2020'].sum()\n",
    "age_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30-44 years olds are the largest voting block, followed by 60 years old and over."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By age & gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_cat</th>\n",
       "      <th>gender</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age_18-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>160</td>\n",
       "      <td>6.449012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age_18-29</td>\n",
       "      <td>Male</td>\n",
       "      <td>147</td>\n",
       "      <td>5.925030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>age_30-44</td>\n",
       "      <td>Female</td>\n",
       "      <td>274</td>\n",
       "      <td>11.043934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age_30-44</td>\n",
       "      <td>Male</td>\n",
       "      <td>546</td>\n",
       "      <td>22.007255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>age_45-59</td>\n",
       "      <td>Female</td>\n",
       "      <td>284</td>\n",
       "      <td>11.446997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>age_45-59</td>\n",
       "      <td>Male</td>\n",
       "      <td>335</td>\n",
       "      <td>13.502620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>Female</td>\n",
       "      <td>339</td>\n",
       "      <td>13.663845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>Male</td>\n",
       "      <td>396</td>\n",
       "      <td>15.961306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age_cat  gender  vote_2020  percentage\n",
       "0       age_18-29  Female        160    6.449012\n",
       "1       age_18-29    Male        147    5.925030\n",
       "2       age_30-44  Female        274   11.043934\n",
       "3       age_30-44    Male        546   22.007255\n",
       "4       age_45-59  Female        284   11.446997\n",
       "5       age_45-59    Male        335   13.502620\n",
       "6  age_60_or_more  Female        339   13.663845\n",
       "7  age_60_or_more    Male        396   15.961306"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_gender = dt_df.loc[dt_df['vote_2020'] == 1].groupby(['age_cat', 'gender'])['vote_2020'].count().reset_index()\n",
    "age_gender['percentage'] = 100 * age_gender['vote_2020']  / age_gender['vote_2020'].sum()\n",
    "age_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asian</td>\n",
       "      <td>75</td>\n",
       "      <td>3.022975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black</td>\n",
       "      <td>69</td>\n",
       "      <td>2.781137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Others</td>\n",
       "      <td>145</td>\n",
       "      <td>5.844418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>White</td>\n",
       "      <td>2192</td>\n",
       "      <td>88.351471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     race  vote_2020  percentage\n",
       "0   Asian         75    3.022975\n",
       "1   Black         69    2.781137\n",
       "2  Others        145    5.844418\n",
       "3   White       2192   88.351471"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_cat = dt_df.loc[dt_df['vote_2020'] == 1].groupby('race')['vote_2020'].count().reset_index()\n",
    "race_cat['percentage'] = 100 * race_cat['vote_2020']  / race_cat['vote_2020'].sum()\n",
    "race_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By race & gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>1.289802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>1.733172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>1.249496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>38</td>\n",
       "      <td>1.531640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Others</td>\n",
       "      <td>Female</td>\n",
       "      <td>81</td>\n",
       "      <td>3.264813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Others</td>\n",
       "      <td>Male</td>\n",
       "      <td>64</td>\n",
       "      <td>2.579605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>913</td>\n",
       "      <td>36.799678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>1279</td>\n",
       "      <td>51.551794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     race  gender  vote_2020  percentage\n",
       "0   Asian  Female         32    1.289802\n",
       "1   Asian    Male         43    1.733172\n",
       "2   Black  Female         31    1.249496\n",
       "3   Black    Male         38    1.531640\n",
       "4  Others  Female         81    3.264813\n",
       "5  Others    Male         64    2.579605\n",
       "6   White  Female        913   36.799678\n",
       "7   White    Male       1279   51.551794"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_gender = dt_df.loc[dt_df['vote_2020'] == 1].groupby(['race', 'gender'])['vote_2020'].count().reset_index()\n",
    "race_gender['percentage'] = 100 * race_gender['vote_2020']  / race_gender['vote_2020'].sum()\n",
    "race_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By race & age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>race</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>17</td>\n",
       "      <td>0.685208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asian</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>30</td>\n",
       "      <td>1.209190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asian</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>15</td>\n",
       "      <td>0.604595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asian</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>13</td>\n",
       "      <td>0.523982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Black</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>24</td>\n",
       "      <td>0.967352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Black</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>21</td>\n",
       "      <td>0.846433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Black</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>17</td>\n",
       "      <td>0.685208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Black</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>7</td>\n",
       "      <td>0.282144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Others</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>35</td>\n",
       "      <td>1.410721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Others</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>50</td>\n",
       "      <td>2.015316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Others</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>38</td>\n",
       "      <td>1.531640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Others</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>22</td>\n",
       "      <td>0.886739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>White</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>231</td>\n",
       "      <td>9.310762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>White</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>719</td>\n",
       "      <td>28.980250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>White</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>549</td>\n",
       "      <td>22.128174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>White</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>693</td>\n",
       "      <td>27.932285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      race         age_cat  vote_2020  percentage\n",
       "0    Asian       age_18-29         17    0.685208\n",
       "1    Asian       age_30-44         30    1.209190\n",
       "2    Asian       age_45-59         15    0.604595\n",
       "3    Asian  age_60_or_more         13    0.523982\n",
       "4    Black       age_18-29         24    0.967352\n",
       "5    Black       age_30-44         21    0.846433\n",
       "6    Black       age_45-59         17    0.685208\n",
       "7    Black  age_60_or_more          7    0.282144\n",
       "8   Others       age_18-29         35    1.410721\n",
       "9   Others       age_30-44         50    2.015316\n",
       "10  Others       age_45-59         38    1.531640\n",
       "11  Others  age_60_or_more         22    0.886739\n",
       "12   White       age_18-29        231    9.310762\n",
       "13   White       age_30-44        719   28.980250\n",
       "14   White       age_45-59        549   22.128174\n",
       "15   White  age_60_or_more        693   27.932285"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_age = dt_df.loc[dt_df['vote_2020'] == 1].groupby(['race', 'age_cat'])['vote_2020'].count().reset_index()\n",
    "race_age['percentage'] = 100 * race_age['vote_2020']  / race_age['vote_2020'].sum()\n",
    "race_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By eduacation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>edu_cat</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>373</td>\n",
       "      <td>15.034260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>616</td>\n",
       "      <td>24.828698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>869</td>\n",
       "      <td>35.026199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>623</td>\n",
       "      <td>25.110842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    edu_cat  vote_2020  percentage\n",
       "0           Graduate degree        373   15.034260\n",
       "1       High school or less        616   24.828698\n",
       "2  Post secondary or higher        869   35.026199\n",
       "3       Some post secondary        623   25.110842"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_cat = dt_df.loc[dt_df['vote_2020'] == 1].groupby('edu_cat')['vote_2020'].count().reset_index()\n",
    "edu_cat['percentage'] = 100 * edu_cat['vote_2020']  / edu_cat['vote_2020'].sum()\n",
    "edu_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some post secondaryor higher are the largest voting block."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By education & gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>edu_cat</th>\n",
       "      <th>gender</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>Female</td>\n",
       "      <td>86</td>\n",
       "      <td>3.466344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>Male</td>\n",
       "      <td>287</td>\n",
       "      <td>11.567916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>Female</td>\n",
       "      <td>323</td>\n",
       "      <td>13.018944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>Male</td>\n",
       "      <td>293</td>\n",
       "      <td>11.809754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>Female</td>\n",
       "      <td>343</td>\n",
       "      <td>13.825071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>Male</td>\n",
       "      <td>526</td>\n",
       "      <td>21.201129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>Female</td>\n",
       "      <td>305</td>\n",
       "      <td>12.293430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>Male</td>\n",
       "      <td>318</td>\n",
       "      <td>12.817412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    edu_cat  gender  vote_2020  percentage\n",
       "0           Graduate degree  Female         86    3.466344\n",
       "1           Graduate degree    Male        287   11.567916\n",
       "2       High school or less  Female        323   13.018944\n",
       "3       High school or less    Male        293   11.809754\n",
       "4  Post secondary or higher  Female        343   13.825071\n",
       "5  Post secondary or higher    Male        526   21.201129\n",
       "6       Some post secondary  Female        305   12.293430\n",
       "7       Some post secondary    Male        318   12.817412"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_gender = dt_df.loc[dt_df['vote_2020'] == 1].groupby(['edu_cat', 'gender'])['vote_2020'].count().reset_index()\n",
    "edu_gender['percentage'] = 100 * edu_gender['vote_2020']  / edu_gender['vote_2020'].sum()\n",
    "edu_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By education & race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edu_cat</th>\n",
       "      <th>race</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>Asian</td>\n",
       "      <td>9</td>\n",
       "      <td>0.362757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>Black</td>\n",
       "      <td>5</td>\n",
       "      <td>0.201532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>Others</td>\n",
       "      <td>8</td>\n",
       "      <td>0.322451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>White</td>\n",
       "      <td>351</td>\n",
       "      <td>14.147521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>Asian</td>\n",
       "      <td>13</td>\n",
       "      <td>0.523982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>Black</td>\n",
       "      <td>20</td>\n",
       "      <td>0.806127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>Others</td>\n",
       "      <td>45</td>\n",
       "      <td>1.813785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>White</td>\n",
       "      <td>538</td>\n",
       "      <td>21.684805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>Asian</td>\n",
       "      <td>40</td>\n",
       "      <td>1.612253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>Black</td>\n",
       "      <td>25</td>\n",
       "      <td>1.007658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>Others</td>\n",
       "      <td>40</td>\n",
       "      <td>1.612253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>White</td>\n",
       "      <td>764</td>\n",
       "      <td>30.794035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>Asian</td>\n",
       "      <td>13</td>\n",
       "      <td>0.523982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>Black</td>\n",
       "      <td>19</td>\n",
       "      <td>0.765820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>Others</td>\n",
       "      <td>52</td>\n",
       "      <td>2.095929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>White</td>\n",
       "      <td>539</td>\n",
       "      <td>21.725111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     edu_cat    race  vote_2020  percentage\n",
       "0            Graduate degree   Asian          9    0.362757\n",
       "1            Graduate degree   Black          5    0.201532\n",
       "2            Graduate degree  Others          8    0.322451\n",
       "3            Graduate degree   White        351   14.147521\n",
       "4        High school or less   Asian         13    0.523982\n",
       "5        High school or less   Black         20    0.806127\n",
       "6        High school or less  Others         45    1.813785\n",
       "7        High school or less   White        538   21.684805\n",
       "8   Post secondary or higher   Asian         40    1.612253\n",
       "9   Post secondary or higher   Black         25    1.007658\n",
       "10  Post secondary or higher  Others         40    1.612253\n",
       "11  Post secondary or higher   White        764   30.794035\n",
       "12       Some post secondary   Asian         13    0.523982\n",
       "13       Some post secondary   Black         19    0.765820\n",
       "14       Some post secondary  Others         52    2.095929\n",
       "15       Some post secondary   White        539   21.725111"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_race = dt_df.loc[dt_df['vote_2020'] == 1].groupby(['edu_cat', 'race'])['vote_2020'].count().reset_index()\n",
    "edu_race['percentage'] = 100 * edu_race['vote_2020']  / edu_race['vote_2020'].sum()\n",
    "edu_race"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## By education & age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>edu_cat</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>vote_2020</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>14</td>\n",
       "      <td>0.564289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>222</td>\n",
       "      <td>8.948005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>64</td>\n",
       "      <td>2.579605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>73</td>\n",
       "      <td>2.942362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>136</td>\n",
       "      <td>5.481661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>173</td>\n",
       "      <td>6.972995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>146</td>\n",
       "      <td>5.884724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>High school or less</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>161</td>\n",
       "      <td>6.489319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>77</td>\n",
       "      <td>3.103587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>263</td>\n",
       "      <td>10.600564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>239</td>\n",
       "      <td>9.633212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>290</td>\n",
       "      <td>11.688835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>80</td>\n",
       "      <td>3.224506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>162</td>\n",
       "      <td>6.529625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>age_45-59</td>\n",
       "      <td>170</td>\n",
       "      <td>6.852076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>age_60_or_more</td>\n",
       "      <td>211</td>\n",
       "      <td>8.504635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     edu_cat         age_cat  vote_2020  percentage\n",
       "0            Graduate degree       age_18-29         14    0.564289\n",
       "1            Graduate degree       age_30-44        222    8.948005\n",
       "2            Graduate degree       age_45-59         64    2.579605\n",
       "3            Graduate degree  age_60_or_more         73    2.942362\n",
       "4        High school or less       age_18-29        136    5.481661\n",
       "5        High school or less       age_30-44        173    6.972995\n",
       "6        High school or less       age_45-59        146    5.884724\n",
       "7        High school or less  age_60_or_more        161    6.489319\n",
       "8   Post secondary or higher       age_18-29         77    3.103587\n",
       "9   Post secondary or higher       age_30-44        263   10.600564\n",
       "10  Post secondary or higher       age_45-59        239    9.633212\n",
       "11  Post secondary or higher  age_60_or_more        290   11.688835\n",
       "12       Some post secondary       age_18-29         80    3.224506\n",
       "13       Some post secondary       age_30-44        162    6.529625\n",
       "14       Some post secondary       age_45-59        170    6.852076\n",
       "15       Some post secondary  age_60_or_more        211    8.504635"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_age = dt_df.loc[dt_df['vote_2020'] == 1].groupby(['edu_cat', 'age_cat'])['vote_2020'].count().reset_index()\n",
    "edu_age['percentage'] = 100 * edu_age['vote_2020']  / edu_age['vote_2020'].sum()\n",
    "edu_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# categorical_feature_mask = dt_df.dtypes==object\n",
    "# filter categorical columns using mask and turn it into a list\n",
    "# categorical_cols = dt_df.columns[categorical_feature_mask].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dt_df[categorical_cols] = dt_df[categorical_cols].apply(lambda col: le.fit_transform(col))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dt_df['region_num'] = le.fit_transform(dt_df['census_region'])\n",
    "dt_df['gender_num'] = le.fit_transform(dt_df['gender'])\n",
    "dt_df['race_num'] = le.fit_transform(dt_df['race'])\n",
    "dt_df['edu_num'] = le.fit_transform(dt_df['edu_cat'])\n",
    "dt_df['age_num'] = le.fit_transform(dt_df['age_cat'])\n",
    "dt_df['state_num'] = le.fit_transform(dt_df['state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniq_dt_df = dt_df.groupby(['race_num', 'gender_num', 'edu_num', 'age_num', 'region_num', 'state_num']).agg({'vote_2020': 'sum'}).reset_index()\n",
    "\n",
    "uniq_dt_df['n'] = dt_df.groupby(['race_num', 'gender_num', 'edu_num', 'age_num', 'region_num', 'state_num'], as_index=False).size()['size']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3172200772200772"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniq_dt_df.shape[0] / dt_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_gender_race(gender, race):\n",
    "    return (4 * gender + race).values\n",
    "\n",
    "def encode_age_edu(age, edu):\n",
    "    return (4 * age + edu).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_race = encode_gender_race(uniq_dt_df.gender_num, uniq_dt_df.race_num)\n",
    "n_gender_race = len(set(gender_race))\n",
    "\n",
    "age = uniq_dt_df.age_num.values\n",
    "n_age = len(set(age))\n",
    "\n",
    "edu = uniq_dt_df.edu_num.values\n",
    "n_edu = len(set(edu))\n",
    "\n",
    "age_edu = encode_age_edu(uniq_dt_df.age_num, uniq_dt_df.edu_num)\n",
    "n_age_edu = len(set(age_edu))\n",
    "\n",
    "region = uniq_dt_df.region_num.values\n",
    "n_region = len(set(region))\n",
    "\n",
    "state = uniq_dt_df.state_num.values\n",
    "n_state = len(set(state))\n",
    "\n",
    "n = uniq_dt_df.n.values\n",
    "vote_2020 = uniq_dt_df.vote_2020.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_race_ = shared(gender_race)\n",
    "age_ = shared(age)\n",
    "edu_ = shared(edu)\n",
    "age_edu_ = shared(age_edu)\n",
    "state_ = shared(state)\n",
    "use_poll_ = shared(1)\n",
    "n_ = shared(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hierarchical_normal(name, shape, μ=0.):\n",
    "    Δ = pm.Normal('Δ_{}'.format(name), 0., 1., shape=shape)\n",
    "    σ = pm.HalfCauchy('σ_{}'.format(name), 5.)\n",
    "    \n",
    "    return pm.Deterministic(name, μ + Δ * σ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_df = dt_df.groupby(['region_num', 'state']).size().reset_index().rename(columns={0: 'count'}).drop_duplicates(subset =\"state\",)\n",
    "state_region = state_df.region_num.values\n",
    "\n",
    "with pm.Model() as model:\n",
    "    α_region = hierarchical_normal('region', n_region)\n",
    "    μ_state = α_region[state_region]\n",
    "    α_state = hierarchical_normal('state', n_state, μ=μ_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model: \n",
    "    β0 = pm.Normal('β0', 0., 5., testval = sp.special.logit(dt_df.vote_2020.mean()))\n",
    "    α_gender_race = hierarchical_normal('gender_race', n_gender_race)\n",
    "    α_age = hierarchical_normal('age', n_age)\n",
    "    α_edu = hierarchical_normal('edu', n_edu)\n",
    "    α_age_edu = hierarchical_normal('age_edu', n_age_edu)\n",
    "    \n",
    "    η = β0 + α_gender_race[gender_race_] + α_age[age_] + α_edu[edu_] + α_age_edu[age_edu_] + α_state[state_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "with model:\n",
    "    p = pm.math.sigmoid(η)\n",
    "    obs = pm.Binomial('obs', n_, p, observed=vote_2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using adapt_diag...\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "NUTS: [σ_age_edu, Δ_age_edu, σ_edu, Δ_edu, σ_age, Δ_age, σ_gender_race, Δ_gender_race, β0, σ_state, Δ_state, σ_region, Δ_region]\n",
      "Sampling 2 chains: 100%|██████████| 3000/3000 [04:18<00:00, 11.61draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "NUTS_KWARGS = {'target_accept': 0.99}\n",
    "\n",
    "with model: \n",
    "    trace = pm.sample(draws=1000, nuts_kwargs=NUTS_KWARGS, init='adapt_diag')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.energyplot(trace);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bfmi = pm.bfmi(trace)\n",
    "max_gr = max(np.max(gr_stats) for gr_stats in pm.gelman_rubin(trace).values())\n",
    "\n",
    "(pm.energyplot(trace, legend=False, figsize=(6, 4)).set_title(\"BFMI = {}\\nGelman-Rubin = {}\".format(bfmi, max_gr)));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post-stratification data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "census_df = pd.read_stata('data/usa_00002.dta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>sample</th>\n",
       "      <th>serial</th>\n",
       "      <th>hhwt</th>\n",
       "      <th>cluster</th>\n",
       "      <th>stateicp</th>\n",
       "      <th>strata</th>\n",
       "      <th>pernum</th>\n",
       "      <th>perwt</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>race</th>\n",
       "      <th>raced</th>\n",
       "      <th>educ</th>\n",
       "      <th>educd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019 acs</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2.019000e+12</td>\n",
       "      <td>alabama</td>\n",
       "      <td>220001</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>39</td>\n",
       "      <td>black/african american/negro</td>\n",
       "      <td>black/african american/negro</td>\n",
       "      <td>grade 10</td>\n",
       "      <td>grade 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019 acs</td>\n",
       "      <td>2</td>\n",
       "      <td>70</td>\n",
       "      <td>2.019000e+12</td>\n",
       "      <td>alabama</td>\n",
       "      <td>100001</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>female</td>\n",
       "      <td>21</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>grade 10</td>\n",
       "      <td>grade 10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019 acs</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>2.019000e+12</td>\n",
       "      <td>alabama</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>19</td>\n",
       "      <td>black/african american/negro</td>\n",
       "      <td>black/african american/negro</td>\n",
       "      <td>1 year of college</td>\n",
       "      <td>1 or more years of college credit, no degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019 acs</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>2.019000e+12</td>\n",
       "      <td>alabama</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>male</td>\n",
       "      <td>77</td>\n",
       "      <td>white</td>\n",
       "      <td>white</td>\n",
       "      <td>grade 9</td>\n",
       "      <td>grade 9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019 acs</td>\n",
       "      <td>5</td>\n",
       "      <td>53</td>\n",
       "      <td>2.019000e+12</td>\n",
       "      <td>alabama</td>\n",
       "      <td>270101</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>41</td>\n",
       "      <td>black/african american/negro</td>\n",
       "      <td>black/african american/negro</td>\n",
       "      <td>grade 9</td>\n",
       "      <td>grade 9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample  serial  hhwt       cluster stateicp  strata  pernum  perwt  \\\n",
       "0  2019 acs       1    11  2.019000e+12  alabama  220001       1     11   \n",
       "1  2019 acs       2    70  2.019000e+12  alabama  100001       1     70   \n",
       "2  2019 acs       3    20  2.019000e+12  alabama  110001       1     20   \n",
       "3  2019 acs       4    79  2.019000e+12  alabama  110001       1     79   \n",
       "4  2019 acs       5    53  2.019000e+12  alabama  270101       1     53   \n",
       "\n",
       "      sex age                          race                         raced  \\\n",
       "0    male  39  black/african american/negro  black/african american/negro   \n",
       "1  female  21                         white                         white   \n",
       "2    male  19  black/african american/negro  black/african american/negro   \n",
       "3    male  77                         white                         white   \n",
       "4    male  41  black/african american/negro  black/african american/negro   \n",
       "\n",
       "                educ                                         educd  \n",
       "0           grade 10                                      grade 10  \n",
       "1           grade 10                                      grade 10  \n",
       "2  1 year of college  1 or more years of college credit, no degree  \n",
       "3            grade 9                                       grade 9  \n",
       "4            grade 9                                       grade 9  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "census_df.age[census_df.age=='less than 1 year old']='1'\n",
    "census_df.age[census_df.age=='90 (90+ in 1980 and 1990)']='91'\n",
    "census_df[['age']] = census_df[['age']].apply(pd.to_numeric)\n",
    "\n",
    "census_df.loc[(census_df['age'] <= 29),  'age_cat'] = 'age_18-29'\n",
    "census_df.loc[(census_df['age'] <= 44) & (census_df['age'] > 29),  'age_cat'] = 'age_30-44'\n",
    "census_df.loc[(census_df['age'] <= 59) & (census_df['age'] > 44),  'age_cat'] = 'age_45-59'\n",
    "census_df.loc[(census_df['age'] >= 60),  'age_cat'] = 'age_60_or_more'\n",
    "\n",
    "census_df.loc[(census_df['educ'] == 'grade 12'), 'edu_cat'] = 'High school or less'\n",
    "census_df.loc[(census_df['educ'] == '4 years of college'), 'edu_cat'] = 'Post secondary or higher'\n",
    "census_df.loc[(census_df['educ'] == '1 year of college'), 'edu_cat'] = 'Some post secondary'\n",
    "census_df.loc[(census_df['educ'] == '5+ years of college'), 'edu_cat'] = 'Graduate degree'\n",
    "census_df.loc[(census_df['educ'] == 'nursery school to grade 4'), 'edu_cat'] = 'High school or less'\n",
    "census_df.loc[(census_df['educ'] == '2 years of college'), 'edu_cat'] = 'Some post secondary'\n",
    "census_df.loc[(census_df['educ'] == 'grade 5, 6, 7, or 8'), 'edu_cat'] = 'High school or less'\n",
    "census_df.loc[(census_df['educ'] == 'n/a or no schooling'), 'edu_cat'] = 'High school or less'\n",
    "census_df.loc[(census_df['educ'] == 'grade 11'), 'edu_cat'] = 'High school or less'\n",
    "census_df.loc[(census_df['educ'] == 'grade 10'), 'edu_cat'] = 'High school or less'\n",
    "census_df.loc[(census_df['educ'] == 'grade 9'), 'edu_cat'] = 'High school or less'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "High school or less         1815689\n",
       "Some post secondary          595884\n",
       "Post secondary or higher     507242\n",
       "Graduate degree              320738\n",
       "Name: edu_cat, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_df.edu_cat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "us_state_abbrev = {'california': 'CA', 'texas': 'TX', 'florida': 'FL', 'new york': 'NY', 'pennsylvania': 'PA', 'illinois': 'IL', \n",
    "                   'ohio': 'OH', 'north carolina': 'NC', 'georgia': 'GA', 'michigan': 'MI', 'new jersey': 'NJ', 'virginia': 'VA', \n",
    "                   'washington': 'WA', 'arizona': 'AZ', 'massachusetts': 'MA', 'tennessee': 'TN', 'indiana': 'IN', 'missouri': 'MO',\n",
    "                   'maryland': 'MD', 'wisconsin': 'WI', 'colorado': 'CO', 'minnesota': 'MN', 'south carolina': 'SC', 'alabama': 'AL', \n",
    "                   'kentucky': 'KY', 'louisiana': 'LA', 'oregon': 'OR', 'oklahoma': 'OK', 'connecticut': 'CT', 'iowa': 'IA',\n",
    "                   'utah': 'UT', 'arkansas': 'AR', 'kansas': 'KS', 'nevada': 'NV', 'mississippi': 'MS', 'nebraska': 'NE', \n",
    "                   'new mexico': 'NM', 'west virginia': 'WV', 'idaho': 'ID', 'hawaii': 'HI', 'new hampshire': 'NH', 'maine': 'ME', \n",
    "                   'montana': 'MT', 'rhode island': 'RI', 'delaware': 'DE', 'south dakota': 'SD', 'north dakota': 'ND', 'alaska': 'AK', \n",
    "                   'district of columbia': 'DC', 'vermont': 'VT', 'wyoming': 'WY'}\n",
    "\n",
    "census_df['state'] = census_df['stateicp'].map(us_state_abbrev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_dict = {'white': 'White', 'black/african american/negro': 'Black', 'other asian or pacific islander': 'Asian', \n",
    "            'other race, nec': 'Others', 'two major races': 'Others', 'chinese': 'Asian', 'american indian or alaska native': 'Others', \n",
    "            'three or more major races': 'Others', 'japanese': 'Asian'}\n",
    "census_df['race_cat'] = census_df['race'].map(map_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "census_df = census_df[['sex', 'race_cat', 'age_cat', 'edu_cat', 'state']]\n",
    "census_df = census_df.groupby(['state', 'sex', 'race_cat', 'age_cat', 'edu_cat']).size().reset_index().rename(columns={0: 'freq'})\n",
    "census_df['percentage'] = 100 * census_df['freq']  / census_df['freq'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>sex</th>\n",
       "      <th>race_cat</th>\n",
       "      <th>age_cat</th>\n",
       "      <th>edu_cat</th>\n",
       "      <th>freq</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>High school or less</td>\n",
       "      <td>206</td>\n",
       "      <td>0.006359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>Post secondary or higher</td>\n",
       "      <td>41</td>\n",
       "      <td>0.001266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
       "      <td>Some post secondary</td>\n",
       "      <td>33</td>\n",
       "      <td>0.001019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>96</td>\n",
       "      <td>0.002963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state   sex race_cat    age_cat                   edu_cat  freq  percentage\n",
       "0    CT  male    Asian  age_18-29           Graduate degree    32    0.000988\n",
       "1    CT  male    Asian  age_18-29       High school or less   206    0.006359\n",
       "2    CT  male    Asian  age_18-29  Post secondary or higher    41    0.001266\n",
       "3    CT  male    Asian  age_18-29       Some post secondary    33    0.001019\n",
       "4    CT  male    Asian  age_30-44           Graduate degree    96    0.002963"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_df = pd.merge(census_df, state_df[['state', 'region_num']], on='state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>sex</th>\n",
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       "      <th>age_cat</th>\n",
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       "      <th>0</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_18-29</td>\n",
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       "      <td>33</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CT</td>\n",
       "      <td>male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>age_30-44</td>\n",
       "      <td>Graduate degree</td>\n",
       "      <td>96</td>\n",
       "      <td>0.002963</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state   sex race_cat    age_cat                   edu_cat  freq  percentage  \\\n",
       "0    CT  male    Asian  age_18-29           Graduate degree    32    0.000988   \n",
       "1    CT  male    Asian  age_18-29       High school or less   206    0.006359   \n",
       "2    CT  male    Asian  age_18-29  Post secondary or higher    41    0.001266   \n",
       "3    CT  male    Asian  age_18-29       Some post secondary    33    0.001019   \n",
       "4    CT  male    Asian  age_30-44           Graduate degree    96    0.002963   \n",
       "\n",
       "   region_num  \n",
       "0           1  \n",
       "1           1  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_df['gender_num'] = le.fit_transform(ps_df['sex'])\n",
    "ps_df['race_num'] = le.fit_transform(ps_df['race_cat'])\n",
    "ps_df['edu_num'] = le.fit_transform(ps_df['edu_cat'])\n",
    "ps_df['age_num'] = le.fit_transform(ps_df['age_cat'])\n",
    "ps_df['state_num'] = le.fit_transform(ps_df['state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_gender_race = encode_gender_race(ps_df.gender_num, ps_df.race_num)\n",
    "ps_age = ps_df.age_num.values\n",
    "ps_edu = ps_df.edu_num.values\n",
    "ps_age_edu = encode_age_edu(ps_df.age_num, ps_df.edu_num)\n",
    "ps_region = ps_df.region_num.values\n",
    "ps_state = ps_df.state_num.values\n",
    "ps_n = ps_df.freq.values.astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_race_.set_value(ps_gender_race)\n",
    "age_.set_value(ps_age)\n",
    "edu_.set_value(ps_edu)\n",
    "age_edu_.set_value(ps_age_edu)\n",
    "state_.set_value(ps_state)\n",
    "use_poll_.set_value(0)\n",
    "n_.set_value(ps_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning:\n",
      "\n",
      "sample_ppc() is deprecated.  Please use sample_posterior_predictive()\n",
      "\n",
      "100%|██████████| 2000/2000 [00:10<00:00, 187.95it/s]\n"
     ]
    }
   ],
   "source": [
    "with model:\n",
    "    pp_trace = pm.sample_ppc(trace)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP_COLS = ['pp_vote_2020_{}'.format(i) for i in range(pp_trace['obs'].shape[0])]\n",
    "pp_df = pd.merge(ps_df, pd.DataFrame(pp_trace['obs'].T, columns=PP_COLS), left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_prob = pp_df.groupby('state').apply(lambda df: df[PP_COLS].sum(axis=0) / df.freq.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps_mean = ps_prob.mean(axis=1)\n",
    "ps_mean = ps_mean.reset_index().rename(columns = {0: 'mean'})\n",
    "ps_mean['mean %'] = ps_mean['mean'] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>0.374558</td>\n",
       "      <td>37.455771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>0.400471</td>\n",
       "      <td>40.047112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>0.442948</td>\n",
       "      <td>44.294767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0.410059</td>\n",
       "      <td>41.005896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>0.301691</td>\n",
       "      <td>30.169066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state      mean     mean %\n",
       "0    AK  0.374558  37.455771\n",
       "1    AL  0.400471  40.047112\n",
       "2    AR  0.442948  44.294767\n",
       "3    AZ  0.410059  41.005896\n",
       "4    CA  0.301691  30.169066"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
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       "                            \n",
       "var gd = document.getElementById('3b3328f8-7f5d-4ab9-90a0-9cdff29f0288');\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "        </div>"
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     "metadata": {},
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   ],
   "source": [
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=ps_mean['state'],\n",
    "    z=ps_mean['mean %'].astype(float),\n",
    "    locationmode='USA-states',\n",
    "    colorscale='Reds',\n",
    "    autocolorscale=False,\n",
    "    marker_line_color='white', # line markers between states\n",
    "    colorbar_title=\"Mean %\"\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='MRP estimate support for Trump by state in 2020 election poll<br>(Hover for breakdown)',\n",
    "    geo = dict(\n",
    "        scope='usa',\n",
    "        projection=go.layout.geo.Projection(type = 'albers usa'),\n",
    "        showlakes=True, # lakes\n",
    "        lakecolor='rgb(255, 255, 255)'),\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
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   "source": [
    "fig = go.Figure(data=go.Scatter(x=vote_dt_df['% of vote'],\n",
    "                                y=ps_mean['mean %'],\n",
    "                                mode='markers',\n",
    "                                text=ps_mean['state'])) # hover text goes here\n",
    "\n",
    "fig.update_layout(title='Estimated vote for Trump in the poll', xaxis_title=\"Disaggregation estimate\", yaxis_title=\"MRP estimate\")\n",
    "fig.show();"
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  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 12))\n",
    "\n",
    "plt.scatter(ps_mean['mean %'], ps_mean['state'], s=20, c='b', marker=\"o\", label='MRP estimate')\n",
    "plt.scatter(vote_dt_df['% of vote'],vote_dt_df['state'], s=20, c='r', marker=\"o\", label='Disaggregation estimate')\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('MRP estimate vs. Disaggregation estimate by State')\n",
    "plt.xlabel('Estimated Percentage Support for Trump by state', fontsize=12)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "autocolorscale": false,
         "colorbar": {
          "title": {
           "text": "% of vote"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(255,245,240)"
          ],
          [
           0.125,
           "rgb(254,224,210)"
          ],
          [
           0.25,
           "rgb(252,187,161)"
          ],
          [
           0.375,
           "rgb(252,146,114)"
          ],
          [
           0.5,
           "rgb(251,106,74)"
          ],
          [
           0.625,
           "rgb(239,59,44)"
          ],
          [
           0.75,
           "rgb(203,24,29)"
          ],
          [
           0.875,
           "rgb(165,15,21)"
          ],
          [
           1,
           "rgb(103,0,13)"
          ]
         ],
         "locationmode": "USA-states",
         "locations": [
          "AL",
          "AR",
          "AZ",
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       "                            \n",
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       "        </div>"
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     },
     "metadata": {},
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   ],
   "source": [
    "black_men_disagg_p = dt_df.loc[(dt_df['gender'] == 'Male') & (dt_df['race'] == 'Black')].groupby('state')['vote_2020'].mean().reset_index()\n",
    "black_men_disagg_p['% of vote'] = black_men_disagg_p['vote_2020'] * 100\n",
    "\n",
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=black_men_disagg_p['state'],\n",
    "    z=black_men_disagg_p['% of vote'].astype(float),\n",
    "    locationmode='USA-states',\n",
    "    colorscale='Reds',\n",
    "    autocolorscale=False,\n",
    "#    text=support_df['text'], # hover text\n",
    "    marker_line_color='white', # line markers between states\n",
    "    colorbar_title=\"% of vote\"\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='Disaggregation Estimate support for Trump among black men by state in 2020 election poll<br>(Hover for breakdown)',\n",
    "    geo = dict(\n",
    "        scope='usa',\n",
    "        projection=go.layout.geo.Projection(type = 'albers usa'),\n",
    "        showlakes=True, # lakes\n",
    "        lakecolor='rgb(255, 255, 255)'),\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
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       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "black_men_ps_mean = pp_df.loc[(pp_df['race_cat'] == 'Black') & (pp_df['sex'] == 'male')].groupby('state').apply(lambda df: (df[PP_COLS].sum(axis=0) / df.freq.sum())).mean(axis=1).reset_index().rename(columns={0: 'mean'})\n",
    "black_men_ps_mean['% mean'] = black_men_ps_mean['mean'] * 100\n",
    "\n",
    "fig = go.Figure(data=go.Choropleth(\n",
    "    locations=black_men_ps_mean['state'],\n",
    "    z=black_men_ps_mean['% mean'].astype(float),\n",
    "    locationmode='USA-states',\n",
    "    colorscale='Reds',\n",
    "    autocolorscale=False,\n",
    "#    text=support_df['text'], # hover text\n",
    "    marker_line_color='white', # line markers between states\n",
    "    colorbar_title=\"mean %\"\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    title_text='MRP Estimate support for Trump among black men by state in 2020 election poll<br>(Hover for breakdown)',\n",
    "    geo = dict(\n",
    "        scope='usa',\n",
    "        projection=go.layout.geo.Projection(type = 'albers usa'),\n",
    "        showlakes=True, # lakes\n",
    "        lakecolor='rgb(255, 255, 255)'),\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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