diff --git a/Multilevel regression with post-stratification_election2020.ipynb b/Multilevel regression with post-stratification_election2020.ipynb new file mode 100644 index 0000000..dea8f06 --- /dev/null +++ b/Multilevel regression with post-stratification_election2020.ipynb @@ -0,0 +1,42583 @@ +{ + "cells": [ + { + "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": [ + { + "data": { + "text/html": [ + "
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0Donald TrumpWhiteAssociate DegreeWIFemale49.0Midwest
1I am not sure/don't knowWhiteCollege Degree (such as B.A., B.S.)VAFemale39.0South
2Donald TrumpWhiteCollege Degree (such as B.A., B.S.)VAFemale46.0South
3Donald TrumpWhiteHigh school graduateTXFemale75.0South
4Donald TrumpWhiteHigh school graduateWAFemale52.0West
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" + ], + "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", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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", + " colorscale='Reds',\n", + " 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
(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": "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" + } + ], + "source": [ + "df.vote_2020.value_counts()" + ] + }, + { + "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)" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "race=Others
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(Hover for breakdown)" + } + } + }, + "text/html": [ + "
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(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": "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": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/tljh/user/lib/python3.6/site-packages/plotly/express/_core.py:383: FutureWarning:\n", + "\n", + "Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version. Convert to a numpy array before indexing instead.\n", + "\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "gender=Female
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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, + 1 + ], + "title": { + "text": "age" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 1 + ], + "matches": "x", + "showgrid": true, + "showticklabels": false + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.7326 + ], + "title": { + "text": "count" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.7426, + 1 + ], + "matches": "y2", + "showgrid": false, + "showline": false, + "showticklabels": false, + "ticks": "" + } + } + }, + "text/html": [ + "
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" + ] + }, + "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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gendervote_2020percentage
0Female105742.603789
1Male142457.396211
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" + ], + "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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age_catvote_2020percentage
0age_18-2930712.374043
1age_30-4482033.051189
2age_45-5961924.949617
3age_60_or_more73529.625151
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" + ], + "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": { + "text/html": [ + "
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age_catgendervote_2020percentage
0age_18-29Female1606.449012
1age_18-29Male1475.925030
2age_30-44Female27411.043934
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6age_60_or_moreFemale33913.663845
7age_60_or_moreMale39615.961306
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" + ], + "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": [ + "
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racevote_2020percentage
0Asian753.022975
1Black692.781137
2Others1455.844418
3White219288.351471
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" + ], + "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": [ + { + "data": { + "text/html": [ + "
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racegendervote_2020percentage
0AsianFemale321.289802
1AsianMale431.733172
2BlackFemale311.249496
3BlackMale381.531640
4OthersFemale813.264813
5OthersMale642.579605
6WhiteFemale91336.799678
7WhiteMale127951.551794
\n", + "
" + ], + "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": [ + "
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raceage_catvote_2020percentage
0Asianage_18-29170.685208
1Asianage_30-44301.209190
2Asianage_45-59150.604595
3Asianage_60_or_more130.523982
4Blackage_18-29240.967352
5Blackage_30-44210.846433
6Blackage_45-59170.685208
7Blackage_60_or_more70.282144
8Othersage_18-29351.410721
9Othersage_30-44502.015316
10Othersage_45-59381.531640
11Othersage_60_or_more220.886739
12Whiteage_18-292319.310762
13Whiteage_30-4471928.980250
14Whiteage_45-5954922.128174
15Whiteage_60_or_more69327.932285
\n", + "
" + ], + "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": [ + "
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edu_catvote_2020percentage
0Graduate degree37315.034260
1High school or less61624.828698
2Post secondary or higher86935.026199
3Some post secondary62325.110842
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" + ], + "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": [ + "
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edu_catgendervote_2020percentage
0Graduate degreeFemale863.466344
1Graduate degreeMale28711.567916
2High school or lessFemale32313.018944
3High school or lessMale29311.809754
4Post secondary or higherFemale34313.825071
5Post secondary or higherMale52621.201129
6Some post secondaryFemale30512.293430
7Some post secondaryMale31812.817412
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" + ], + "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": [ + "
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edu_catracevote_2020percentage
0Graduate degreeAsian90.362757
1Graduate degreeBlack50.201532
2Graduate degreeOthers80.322451
3Graduate degreeWhite35114.147521
4High school or lessAsian130.523982
5High school or lessBlack200.806127
6High school or lessOthers451.813785
7High school or lessWhite53821.684805
8Post secondary or higherAsian401.612253
9Post secondary or higherBlack251.007658
10Post secondary or higherOthers401.612253
11Post secondary or higherWhite76430.794035
12Some post secondaryAsian130.523982
13Some post secondaryBlack190.765820
14Some post secondaryOthers522.095929
15Some post secondaryWhite53921.725111
\n", + "
" + ], + "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": [ + "
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edu_catage_catvote_2020percentage
0Graduate degreeage_18-29140.564289
1Graduate degreeage_30-442228.948005
2Graduate degreeage_45-59642.579605
3Graduate degreeage_60_or_more732.942362
4High school or lessage_18-291365.481661
5High school or lessage_30-441736.972995
6High school or lessage_45-591465.884724
7High school or lessage_60_or_more1616.489319
8Post secondary or higherage_18-29773.103587
9Post secondary or higherage_30-4426310.600564
10Post secondary or higherage_45-592399.633212
11Post secondary or higherage_60_or_more29011.688835
12Some post secondaryage_18-29803.224506
13Some post secondaryage_30-441626.529625
14Some post secondaryage_45-591706.852076
15Some post secondaryage_60_or_more2118.504635
\n", + "
" + ], + "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", 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+ "text/plain": [ + "
" + ] + }, + "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": [ + "
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sampleserialhhwtclusterstateicpstratapernumperwtsexageraceracededuceducd
02019 acs1112.019000e+12alabama220001111male39black/african american/negroblack/african american/negrograde 10grade 10
12019 acs2702.019000e+12alabama100001170female21whitewhitegrade 10grade 10
22019 acs3202.019000e+12alabama110001120male19black/african american/negroblack/african american/negro1 year of college1 or more years of college credit, no degree
32019 acs4792.019000e+12alabama110001179male77whitewhitegrade 9grade 9
42019 acs5532.019000e+12alabama270101153male41black/african american/negroblack/african american/negrograde 9grade 9
\n", + "
" + ], + "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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statesexrace_catage_catedu_catfreqpercentage
0CTmaleAsianage_18-29Graduate degree320.000988
1CTmaleAsianage_18-29High school or less2060.006359
2CTmaleAsianage_18-29Post secondary or higher410.001266
3CTmaleAsianage_18-29Some post secondary330.001019
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statesexrace_catage_catedu_catfreqpercentageregion_num
0CTmaleAsianage_18-29Graduate degree320.0009881
1CTmaleAsianage_18-29High school or less2060.0063591
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4CTmaleAsianage_30-44Graduate degree960.0029631
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" + ], + "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": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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();" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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"white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "MRP Estimate support for Trump among black men by state in 2020 election poll
(Hover for breakdown)" + } + } + }, + "text/html": [ + "
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" + ] + }, + "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
(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": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}