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divergentdave/Uninsurance Spells.ipynb

Created March 19, 2023 00:41
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Health insurance rates over time from SIPP data
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Uninsurance Spells.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "77656d2f",
"metadata": {},
"source": [
"This notebook calculates the incidence of uninsurance spells over time using SIPP microdata, re-deriving the charts in [Over 20 Percent of People Lacked Health Insurance Between 2017 and 2020](https://www.peoplespolicyproject.org/2023/03/10/over-20-percent-of-people-lacked-health-insurance-between-2017-and-2020/) from People's Policy Project."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1a9511cb",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "47612138",
"metadata": {},
"outputs": [],
"source": [
"# Load schema files, which contain data types for each variable.\n",
"# (this is loosely based on the python input example script provided with the data files)\n",
"primary_data_file_2018_schema = pd.read_json(\"pu2018_schema.json\")\n",
"primary_data_file_2019_schema = pd.read_json(\"pu2019_schema.json\")\n",
"primary_data_file_2020_schema = pd.read_json(\"pu2020_schema.json\")\n",
"primary_data_file_2021_schema = pd.read_json(\"pu2021_schema.json\")\n",
"longitudinal_weight_4yr_schema = pd.read_json(\"lgtwgt2021yr4_schema.json\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "852b1f48",
"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>varnum</th>\n",
" <th>name</th>\n",
" <th>label</th>\n",
" <th>dtype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>SSUID</td>\n",
" <td>Sample unit identifier. This identifier is cre...</td>\n",
" <td>string</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>SHHADID</td>\n",
" <td>Household address ID. Used to differentiate ho...</td>\n",
" <td>string</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>SPANEL</td>\n",
" <td>Panel year</td>\n",
" <td>integer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>SWAVE</td>\n",
" <td>Wave number of interview</td>\n",
" <td>integer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>GHLFSAM</td>\n",
" <td>Half sample code. A code used to divide the sa...</td>\n",
" <td>string</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" varnum name label dtype\n",
"0 1 SSUID Sample unit identifier. This identifier is cre... string\n",
"1 2 SHHADID Household address ID. Used to differentiate ho... string\n",
"2 3 SPANEL Panel year integer\n",
"3 4 SWAVE Wave number of interview integer\n",
"4 5 GHLFSAM Half sample code. A code used to divide the sa... string"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"primary_data_file_2018_schema.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b7c9cf7c",
"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 thead th {\n",
" text-align: right;\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>varnum</th>\n",
" <th>name</th>\n",
" <th>label</th>\n",
" <th>dtype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>SSUID</td>\n",
" <td></td>\n",
" <td>string</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>PNUM</td>\n",
" <td></td>\n",
" <td>integer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>SPANEL</td>\n",
" <td></td>\n",
" <td>integer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>FINYR4</td>\n",
" <td></td>\n",
" <td>float</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" varnum name label dtype\n",
"0 1 SSUID string\n",
"1 2 PNUM integer\n",
"2 3 SPANEL integer\n",
"3 4 FINYR4 float"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"longitudinal_weight_4yr_schema"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "966d3ce2",
"metadata": {},
"outputs": [],
"source": [
"# Rewrite the \"dtype\" column of each schema dataframe for use in read_csv().\n",
"def rewrite_dtype(dtype):\n",
" if dtype == \"integer\":\n",
" return \"Int64\"\n",
" elif dtype == \"string\":\n",
" return \"object\"\n",
" elif dtype == \"float\":\n",
" return \"Float64\"\n",
" else:\n",
" return \"ERROR\"\n",
"\n",
"primary_data_file_2018_schema[\"dtype\"] = primary_data_file_2018_schema[\"dtype\"].apply(rewrite_dtype)\n",
"primary_data_file_2019_schema[\"dtype\"] = primary_data_file_2019_schema[\"dtype\"].apply(rewrite_dtype)\n",
"primary_data_file_2020_schema[\"dtype\"] = primary_data_file_2020_schema[\"dtype\"].apply(rewrite_dtype)\n",
"primary_data_file_2021_schema[\"dtype\"] = primary_data_file_2021_schema[\"dtype\"].apply(rewrite_dtype)\n",
"longitudinal_weight_4yr_schema[\"dtype\"] = longitudinal_weight_4yr_schema[\"dtype\"].apply(rewrite_dtype)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c0d9db16",
"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",
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"\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>varnum</th>\n",
" <th>name</th>\n",
" <th>label</th>\n",
" <th>dtype</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>SSUID</td>\n",
" <td></td>\n",
" <td>object</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>PNUM</td>\n",
" <td></td>\n",
" <td>Int64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>SPANEL</td>\n",
" <td></td>\n",
" <td>Int64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>FINYR4</td>\n",
" <td></td>\n",
" <td>Float64</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" varnum name label dtype\n",
"0 1 SSUID object\n",
"1 2 PNUM Int64\n",
"2 3 SPANEL Int64\n",
"3 4 FINYR4 Float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"longitudinal_weight_4yr_schema"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3e658ee0",
"metadata": {},
"outputs": [],
"source": [
"# Load microdata. `usecols` is set to only load the columns of interest, in order to limit memory consumption.\n",
"usecols = [\"SSUID\", \"PNUM\", \"MONTHCODE\", \"SPANEL\", \"RIN_UNIV\", \"RHLTHMTH\", \"TAGE\", \"RPUBTYPE2\"]\n",
"primary_data_2018 = pd.read_csv(\n",
" \"pu2018.csv\",\n",
" names=primary_data_file_2018_schema[\"name\"],\n",
" dtype=dict(zip(primary_data_file_2018_schema[\"name\"], primary_data_file_2018_schema[\"dtype\"])),\n",
" sep=\"|\",\n",
" header=0,\n",
" usecols=usecols,\n",
")\n",
"primary_data_2019 = pd.read_csv(\n",
" \"pu2019.csv\",\n",
" names=primary_data_file_2019_schema[\"name\"],\n",
" dtype=dict(zip(primary_data_file_2019_schema[\"name\"], primary_data_file_2019_schema[\"dtype\"])),\n",
" sep=\"|\",\n",
" header=0,\n",
" usecols=usecols,\n",
")\n",
"primary_data_2020 = pd.read_csv(\n",
" \"pu2020.csv\",\n",
" names=primary_data_file_2020_schema[\"name\"],\n",
" dtype=dict(zip(primary_data_file_2020_schema[\"name\"], primary_data_file_2020_schema[\"dtype\"])),\n",
" sep=\"|\",\n",
" header=0,\n",
" usecols=usecols,\n",
")\n",
"primary_data_2021 = pd.read_csv(\n",
" \"pu2021.csv\",\n",
" names=primary_data_file_2021_schema[\"name\"],\n",
" dtype=dict(zip(primary_data_file_2021_schema[\"name\"], primary_data_file_2021_schema[\"dtype\"])),\n",
" sep=\"|\",\n",
" header=0,\n",
" usecols=usecols,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "739f1033",
"metadata": {
"scrolled": true
},
"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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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SSUID</th>\n",
" <th>SPANEL</th>\n",
" <th>PNUM</th>\n",
" <th>MONTHCODE</th>\n",
" <th>RPUBTYPE2</th>\n",
" <th>RHLTHMTH</th>\n",
" <th>RIN_UNIV</th>\n",
" <th>TAGE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00011413607018</td>\n",
" <td>2018</td>\n",
" <td>101</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00011413607018</td>\n",
" <td>2018</td>\n",
" <td>101</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00011413607018</td>\n",
" <td>2018</td>\n",
" <td>101</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00011413607018</td>\n",
" <td>2018</td>\n",
" <td>101</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>00011413607018</td>\n",
" <td>2018</td>\n",
" <td>101</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SSUID SPANEL PNUM MONTHCODE RPUBTYPE2 RHLTHMTH RIN_UNIV \\\n",
"0 00011413607018 2018 101 1 2 2 1 \n",
"1 00011413607018 2018 101 2 2 2 1 \n",
"2 00011413607018 2018 101 3 2 2 1 \n",
"3 00011413607018 2018 101 4 2 2 1 \n",
"4 00011413607018 2018 101 5 2 2 1 \n",
"\n",
" TAGE \n",
"0 33 \n",
"1 33 \n",
"2 33 \n",
"3 33 \n",
"4 33 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"primary_data_2018.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2af763e6",
"metadata": {},
"outputs": [],
"source": [
"# Load longitudinal weights.\n",
"longitudinal_weight_4yr = pd.read_csv(\n",
" \"lgtwgt2021yr4.csv\",\n",
" names=longitudinal_weight_4yr_schema[\"name\"],\n",
" dtype=dict(zip(longitudinal_weight_4yr_schema[\"name\"], longitudinal_weight_4yr_schema[\"dtype\"])),\n",
" sep=\"|\",\n",
" header=0,\n",
" usecols=[\"SSUID\", \"PNUM\", \"SPANEL\", \"FINYR4\"],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "87019db5",
"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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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>SSUID</th>\n",
" <th>PNUM</th>\n",
" <th>SPANEL</th>\n",
" <th>FINYR4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00011455225318</td>\n",
" <td>101</td>\n",
" <td>2018</td>\n",
" <td>32552.637126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00011481677018</td>\n",
" <td>101</td>\n",
" <td>2018</td>\n",
" <td>9546.924851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00011481677018</td>\n",
" <td>102</td>\n",
" <td>2018</td>\n",
" <td>12955.20757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00013345043118</td>\n",
" <td>101</td>\n",
" <td>2018</td>\n",
" <td>32997.209918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>00013355998818</td>\n",
" <td>101</td>\n",
" <td>2018</td>\n",
" <td>15533.359342</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SSUID PNUM SPANEL FINYR4\n",
"0 00011455225318 101 2018 32552.637126\n",
"1 00011481677018 101 2018 9546.924851\n",
"2 00011481677018 102 2018 12955.20757\n",
"3 00013345043118 101 2018 32997.209918\n",
"4 00013355998818 101 2018 15533.359342"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"longitudinal_weight_4yr.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c38cfaeb",
"metadata": {},
"outputs": [],
"source": [
"# The combination of SPANEL, SSUID, and PNUM uniquely identifies each survey respondent.\n",
"# Move these columns to the dataframe's index.\n",
"longitudinal_weight_4yr = longitudinal_weight_4yr.set_index([\"SPANEL\", \"SSUID\", \"PNUM\"])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "09f63f85",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th>FINYR4</th>\n",
" </tr>\n",
" <tr>\n",
" <th>SPANEL</th>\n",
" <th>SSUID</th>\n",
" <th>PNUM</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2018</th>\n",
" <th>00011455225318</th>\n",
" <th>101</th>\n",
" <td>32552.637126</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">00011481677018</th>\n",
" <th>101</th>\n",
" <td>9546.924851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>12955.20757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00013345043118</th>\n",
" <th>101</th>\n",
" <td>32997.209918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00013355998818</th>\n",
" <th>101</th>\n",
" <td>15533.359342</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" FINYR4\n",
"SPANEL SSUID PNUM \n",
"2018 00011455225318 101 32552.637126\n",
" 00011481677018 101 9546.924851\n",
" 102 12955.20757\n",
" 00013345043118 101 32997.209918\n",
" 00013355998818 101 15533.359342"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"longitudinal_weight_4yr.head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "710d04f4",
"metadata": {},
"outputs": [],
"source": [
"# Pivot the primary data, moving the same columns to the index, and moving the month to hierarchical columns\n",
"# below each other variable.\n",
"primary_data_2018_pivoted = primary_data_2018.pivot(index=[\"SPANEL\", \"SSUID\", \"PNUM\"], columns=\"MONTHCODE\")\n",
"primary_data_2019_pivoted = primary_data_2019.pivot(index=[\"SPANEL\", \"SSUID\", \"PNUM\"], columns=\"MONTHCODE\")\n",
"primary_data_2020_pivoted = primary_data_2020.pivot(index=[\"SPANEL\", \"SSUID\", \"PNUM\"], columns=\"MONTHCODE\")\n",
"primary_data_2021_pivoted = primary_data_2021.pivot(index=[\"SPANEL\", \"SSUID\", \"PNUM\"], columns=\"MONTHCODE\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "bdb962fc",
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"10\" halign=\"left\">RPUBTYPE2</th>\n",
" <th>...</th>\n",
" <th colspan=\"10\" halign=\"left\">TAGE</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>MONTHCODE</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>...</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" <th>12</th>\n",
" </tr>\n",
" <tr>\n",
" <th>SPANEL</th>\n",
" <th>SSUID</th>\n",
" <th>PNUM</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">2018</th>\n",
" <th>00011413607018</th>\n",
" <th>101</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00011413613418</th>\n",
" <th>101</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">00011413646518</th>\n",
" <th>101</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 48 columns</p>\n",
"</div>"
],
"text/plain": [
" RPUBTYPE2 ... TAGE \\\n",
"MONTHCODE 1 2 3 4 5 6 7 8 9 10 ... 3 4 \n",
"SPANEL SSUID PNUM ... \n",
"2018 00011413607018 101 2 2 2 2 2 2 2 2 2 2 ... 33 33 \n",
" 00011413613418 101 2 2 2 2 2 2 2 2 2 2 ... 36 36 \n",
" 00011413646518 101 2 2 2 2 2 2 2 2 2 2 ... 35 35 \n",
" 102 1 1 1 1 1 1 1 1 1 1 ... 52 52 \n",
" 103 1 1 1 1 1 1 1 1 1 1 ... 48 48 \n",
"\n",
" \n",
"MONTHCODE 5 6 7 8 9 10 11 12 \n",
"SPANEL SSUID PNUM \n",
"2018 00011413607018 101 33 33 33 33 33 33 33 33 \n",
" 00011413613418 101 36 36 36 36 36 36 36 36 \n",
" 00011413646518 101 35 35 35 35 35 35 35 35 \n",
" 102 52 52 52 52 52 52 52 52 \n",
" 103 48 48 48 48 48 48 48 48 \n",
"\n",
"[5 rows x 48 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"primary_data_2018_pivoted.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e1ce25ee",
"metadata": {},
"outputs": [],
"source": [
"# Rename the columns at the month level to include the year, in preparation for\n",
"# concatenating all the dataframes.\n",
"primary_data_2018_pivoted = primary_data_2018_pivoted.rename(columns=lambda monthcode: f\"2017-{monthcode:02}\", level=1)\n",
"primary_data_2018_pivoted = primary_data_2018_pivoted.rename_axis([\"Variable\", \"Month\"], axis=\"columns\")\n",
"primary_data_2019_pivoted = primary_data_2019_pivoted.rename(columns=lambda monthcode: f\"2018-{monthcode:02}\", level=1)\n",
"primary_data_2019_pivoted = primary_data_2019_pivoted.rename_axis([\"Variable\", \"Month\"], axis=\"columns\")\n",
"primary_data_2020_pivoted = primary_data_2020_pivoted.rename(columns=lambda monthcode: f\"2019-{monthcode:02}\", level=1)\n",
"primary_data_2020_pivoted = primary_data_2020_pivoted.rename_axis([\"Variable\", \"Month\"], axis=\"columns\")\n",
"primary_data_2021_pivoted = primary_data_2021_pivoted.rename(columns=lambda monthcode: f\"2020-{monthcode:02}\", level=1)\n",
"primary_data_2021_pivoted = primary_data_2021_pivoted.rename_axis([\"Variable\", \"Month\"], axis=\"columns\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "785e3c9b",
"metadata": {},
"outputs": [],
"source": [
"# Combine all four years of primary data, along the column axis.\n",
"primary_data_all_years = pd.concat(\n",
" [primary_data_2018_pivoted, primary_data_2019_pivoted, primary_data_2020_pivoted, primary_data_2021_pivoted],\n",
" axis=\"columns\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "e027707c",
"metadata": {},
"outputs": [
{
"data": {
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" <td>19</td>\n",
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"</table>\n",
"<p>118393 rows × 192 columns</p>\n",
"</div>"
],
"text/plain": [
"Variable RPUBTYPE2 \\\n",
"Month 2017-01 2017-02 2017-03 2017-04 2017-05 2017-06 \n",
"SPANEL SSUID PNUM \n",
"2018 00011413607018 101 2 2 2 2 2 2 \n",
" 00011413613418 101 2 2 2 2 2 2 \n",
" 00011413646518 101 2 2 2 2 2 2 \n",
" 102 1 1 1 1 1 1 \n",
" 103 1 1 1 1 1 1 \n",
"... ... ... ... ... ... ... \n",
"2021 88199596762221 102 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 103 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 88199599566821 101 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 102 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 103 <NA> <NA> <NA> <NA> <NA> <NA> \n",
"\n",
"Variable ... TAGE \\\n",
"Month 2017-07 2017-08 2017-09 2017-10 ... 2020-03 \n",
"SPANEL SSUID PNUM ... \n",
"2018 00011413607018 101 2 2 2 2 ... <NA> \n",
" 00011413613418 101 2 2 2 2 ... <NA> \n",
" 00011413646518 101 2 2 2 2 ... <NA> \n",
" 102 1 1 1 1 ... <NA> \n",
" 103 1 1 1 1 ... <NA> \n",
"... ... ... ... ... ... ... \n",
"2021 88199596762221 102 <NA> <NA> <NA> <NA> ... 39 \n",
" 103 <NA> <NA> <NA> <NA> ... 1 \n",
" 88199599566821 101 <NA> <NA> <NA> <NA> ... 55 \n",
" 102 <NA> <NA> <NA> <NA> ... 22 \n",
" 103 <NA> <NA> <NA> <NA> ... 19 \n",
"\n",
"Variable \\\n",
"Month 2020-04 2020-05 2020-06 2020-07 2020-08 2020-09 \n",
"SPANEL SSUID PNUM \n",
"2018 00011413607018 101 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 00011413613418 101 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 00011413646518 101 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 102 <NA> <NA> <NA> <NA> <NA> <NA> \n",
" 103 <NA> <NA> <NA> <NA> <NA> <NA> \n",
"... ... ... ... ... ... ... \n",
"2021 88199596762221 102 39 39 39 39 39 39 \n",
" 103 1 1 1 1 1 1 \n",
" 88199599566821 101 55 55 55 55 55 55 \n",
" 102 22 22 22 22 22 22 \n",
" 103 19 19 19 19 19 19 \n",
"\n",
"Variable \n",
"Month 2020-10 2020-11 2020-12 \n",
"SPANEL SSUID PNUM \n",
"2018 00011413607018 101 <NA> <NA> <NA> \n",
" 00011413613418 101 <NA> <NA> <NA> \n",
" 00011413646518 101 <NA> <NA> <NA> \n",
" 102 <NA> <NA> <NA> \n",
" 103 <NA> <NA> <NA> \n",
"... ... ... ... \n",
"2021 88199596762221 102 39 39 39 \n",
" 103 1 1 1 \n",
" 88199599566821 101 55 55 55 \n",
" 102 22 22 22 \n",
" 103 19 19 19 \n",
"\n",
"[118393 rows x 192 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"primary_data_all_years"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1e4b2e10",
"metadata": {},
"outputs": [],
"source": [
"# Add a level of columns to the longitudinal weights so they can be cleanly merged with the pivoted and\n",
"# concatenated dataframe.\n",
"longitudinal_weight_4yr.columns = pd.MultiIndex.from_product([longitudinal_weight_4yr.columns, [\"N/A\"]])\n",
"longitudinal_weight_4yr = longitudinal_weight_4yr.rename_axis([\"Variable\", \"Month\"], axis=\"columns\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ee4aab03",
"metadata": {},
"outputs": [],
"source": [
"# Join the longitudinal weight with the primary data. (join on their indices, which each\n",
"# uniquely identify respondents with SPANEL, SSUID, and PNUM)\n",
"joined = longitudinal_weight_4yr.join(primary_data_all_years)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "25bc8720",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Variable</th>\n",
" <th>FINYR4</th>\n",
" <th colspan=\"9\" halign=\"left\">RPUBTYPE2</th>\n",
" <th>...</th>\n",
" <th colspan=\"10\" halign=\"left\">TAGE</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>Month</th>\n",
" <th>N/A</th>\n",
" <th>2017-01</th>\n",
" <th>2017-02</th>\n",
" <th>2017-03</th>\n",
" <th>2017-04</th>\n",
" <th>2017-05</th>\n",
" <th>2017-06</th>\n",
" <th>2017-07</th>\n",
" <th>2017-08</th>\n",
" <th>2017-09</th>\n",
" <th>...</th>\n",
" <th>2020-03</th>\n",
" <th>2020-04</th>\n",
" <th>2020-05</th>\n",
" <th>2020-06</th>\n",
" <th>2020-07</th>\n",
" <th>2020-08</th>\n",
" <th>2020-09</th>\n",
" <th>2020-10</th>\n",
" <th>2020-11</th>\n",
" <th>2020-12</th>\n",
" </tr>\n",
" <tr>\n",
" <th>SPANEL</th>\n",
" <th>SSUID</th>\n",
" <th>PNUM</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"11\" valign=\"top\">2018</th>\n",
" <th>00011455225318</th>\n",
" <th>101</th>\n",
" <td>32552.637126</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">00011481677018</th>\n",
" <th>101</th>\n",
" <td>9546.924851</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>12955.20757</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00013345043118</th>\n",
" <th>101</th>\n",
" <td>32997.209918</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>00013355998818</th>\n",
" <th>101</th>\n",
" <td>15533.359342</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">91092560676218</th>\n",
" <th>101</th>\n",
" <td>40351.217934</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" <td>39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>26145.404593</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">91092560931018</th>\n",
" <th>101</th>\n",
" <td>21224.01903</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
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" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>20324.868983</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91092560938318</th>\n",
" <th>101</th>\n",
" <td>17590.923465</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15070 rows × 193 columns</p>\n",
"</div>"
],
"text/plain": [
"Variable FINYR4 RPUBTYPE2 \\\n",
"Month N/A 2017-01 2017-02 2017-03 2017-04 \n",
"SPANEL SSUID PNUM \n",
"2018 00011455225318 101 32552.637126 2 2 2 2 \n",
" 00011481677018 101 9546.924851 2 2 2 2 \n",
" 102 12955.20757 2 2 2 2 \n",
" 00013345043118 101 32997.209918 2 2 2 2 \n",
" 00013355998818 101 15533.359342 2 2 2 2 \n",
"... ... ... ... ... ... \n",
" 91092560676218 101 40351.217934 2 2 2 2 \n",
" 102 26145.404593 2 2 2 2 \n",
" 91092560931018 101 21224.01903 2 2 2 2 \n",
" 102 20324.868983 2 2 2 2 \n",
" 91092560938318 101 17590.923465 1 1 1 1 \n",
"\n",
"Variable ... \\\n",
"Month 2017-05 2017-06 2017-07 2017-08 2017-09 ... \n",
"SPANEL SSUID PNUM ... \n",
"2018 00011455225318 101 2 2 2 2 2 ... \n",
" 00011481677018 101 2 2 2 2 2 ... \n",
" 102 2 2 2 2 2 ... \n",
" 00013345043118 101 2 2 2 2 2 ... \n",
" 00013355998818 101 2 2 2 2 2 ... \n",
"... ... ... ... ... ... ... \n",
" 91092560676218 101 2 2 2 2 2 ... \n",
" 102 2 2 2 2 2 ... \n",
" 91092560931018 101 2 2 2 2 2 ... \n",
" 102 2 2 2 2 2 ... \n",
" 91092560938318 101 1 1 1 1 1 ... \n",
"\n",
"Variable TAGE \\\n",
"Month 2020-03 2020-04 2020-05 2020-06 2020-07 2020-08 \n",
"SPANEL SSUID PNUM \n",
"2018 00011455225318 101 27 27 27 27 27 27 \n",
" 00011481677018 101 60 60 60 60 60 60 \n",
" 102 60 60 60 60 60 60 \n",
" 00013345043118 101 24 24 24 24 24 24 \n",
" 00013355998818 101 22 22 22 22 22 22 \n",
"... ... ... ... ... ... ... \n",
" 91092560676218 101 39 39 39 39 39 39 \n",
" 102 55 55 55 55 55 55 \n",
" 91092560931018 101 88 88 88 88 88 88 \n",
" 102 90 90 90 90 90 90 \n",
" 91092560938318 101 87 87 87 87 87 87 \n",
"\n",
"Variable \n",
"Month 2020-09 2020-10 2020-11 2020-12 \n",
"SPANEL SSUID PNUM \n",
"2018 00011455225318 101 27 27 27 27 \n",
" 00011481677018 101 60 60 60 60 \n",
" 102 60 60 60 60 \n",
" 00013345043118 101 24 24 24 24 \n",
" 00013355998818 101 22 22 22 22 \n",
"... ... ... ... ... \n",
" 91092560676218 101 39 39 39 39 \n",
" 102 55 55 55 55 \n",
" 91092560931018 101 88 88 88 88 \n",
" 102 90 90 90 90 \n",
" 91092560938318 101 87 87 87 87 \n",
"\n",
"[15070 rows x 193 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"joined"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "f73a7bd2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['FINYR4', 'RHLTHMTH', 'RIN_UNIV', 'RPUBTYPE2', 'TAGE'], dtype='object', name='Variable')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"joined.columns.levels[0]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1df182ca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"SPANEL SSUID PNUM\n",
"2018 00011455225318 101 32552.637126\n",
" 00011481677018 101 9546.924851\n",
" 102 12955.20757\n",
" 00013345043118 101 32997.209918\n",
" 00013355998818 101 15533.359342\n",
"Name: (FINYR4, N/A), dtype: Float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = joined[\"FINYR4\", \"N/A\"]\n",
"weights.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fe909341",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Month\n",
"2017-01 3.181672e+08\n",
"2017-02 3.188061e+08\n",
"2017-03 3.192358e+08\n",
"2017-04 3.195537e+08\n",
"2017-05 3.202195e+08\n",
"2017-06 3.209601e+08\n",
"2017-07 3.215314e+08\n",
"2017-08 3.221478e+08\n",
"2017-09 3.231189e+08\n",
"2017-10 3.243841e+08\n",
"2017-11 3.250090e+08\n",
"2017-12 3.256041e+08\n",
"2018-01 3.252889e+08\n",
"2018-02 3.252889e+08\n",
"2018-03 3.252889e+08\n",
"2018-04 3.252889e+08\n",
"2018-05 3.254667e+08\n",
"2018-06 3.254667e+08\n",
"2018-07 3.254667e+08\n",
"2018-08 3.254667e+08\n",
"2018-09 3.254667e+08\n",
"2018-10 3.256041e+08\n",
"2018-11 3.256041e+08\n",
"2018-12 3.256041e+08\n",
"2019-01 3.256041e+08\n",
"2019-02 3.256041e+08\n",
"2019-03 3.256041e+08\n",
"2019-04 3.256041e+08\n",
"2019-05 3.256041e+08\n",
"2019-06 3.256041e+08\n",
"2019-07 3.256041e+08\n",
"2019-08 3.256041e+08\n",
"2019-09 3.256041e+08\n",
"2019-10 3.256041e+08\n",
"2019-11 3.256041e+08\n",
"2019-12 3.256041e+08\n",
"2020-01 3.256041e+08\n",
"2020-02 3.256041e+08\n",
"2020-03 3.256041e+08\n",
"2020-04 3.256041e+08\n",
"2020-05 3.256041e+08\n",
"2020-06 3.256041e+08\n",
"2020-07 3.256041e+08\n",
"2020-08 3.256041e+08\n",
"2020-09 3.256041e+08\n",
"2020-10 3.256041e+08\n",
"2020-11 3.256041e+08\n",
"2020-12 3.256041e+08\n",
"dtype: float64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the total population of the survey question's universe. Take the health insurance question,\n",
"# find all NaN values, negate to get a series that's true for every non-NaN value, and multiply\n",
"# the resulting boolean series mask against the weights, broadcasting across the month level of\n",
"# the columns. Then sum each column to get the population in each month.\n",
"population_time_series = (~joined[\"RHLTHMTH\"].isna()).mul(weights, axis=\"index\").sum()\n",
"population_time_series"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "32ad44fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Month\n",
"2017-01 3.275508e+07\n",
"2017-02 3.185828e+07\n",
"2017-03 3.095838e+07\n",
"2017-04 3.076299e+07\n",
"2017-05 3.055056e+07\n",
"2017-06 3.011647e+07\n",
"2017-07 3.063364e+07\n",
"2017-08 2.996161e+07\n",
"2017-09 2.985697e+07\n",
"2017-10 2.916041e+07\n",
"2017-11 2.923747e+07\n",
"2017-12 2.880347e+07\n",
"2018-01 2.561511e+07\n",
"2018-02 2.524192e+07\n",
"2018-03 2.501206e+07\n",
"2018-04 2.488424e+07\n",
"2018-05 2.534009e+07\n",
"2018-06 2.568370e+07\n",
"2018-07 2.594438e+07\n",
"2018-08 2.555507e+07\n",
"2018-09 2.575076e+07\n",
"2018-10 2.542271e+07\n",
"2018-11 2.524205e+07\n",
"2018-12 2.579633e+07\n",
"2019-01 2.506074e+07\n",
"2019-02 2.505425e+07\n",
"2019-03 2.483905e+07\n",
"2019-04 2.515795e+07\n",
"2019-05 2.576619e+07\n",
"2019-06 2.633306e+07\n",
"2019-07 2.698924e+07\n",
"2019-08 2.593948e+07\n",
"2019-09 2.536369e+07\n",
"2019-10 2.529777e+07\n",
"2019-11 2.460782e+07\n",
"2019-12 2.454058e+07\n",
"2020-01 2.301533e+07\n",
"2020-02 2.303868e+07\n",
"2020-03 2.238515e+07\n",
"2020-04 2.243224e+07\n",
"2020-05 2.265058e+07\n",
"2020-06 2.261034e+07\n",
"2020-07 2.250228e+07\n",
"2020-08 2.288773e+07\n",
"2020-09 2.293478e+07\n",
"2020-10 2.239720e+07\n",
"2020-11 2.222852e+07\n",
"2020-12 2.252707e+07\n",
"dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the point-in-time uninsured population each month. Filter for entries where RHLTHMTH equals 2, meaning\n",
"# no health insurance coverage. Then, multiply by the weights as before, and sum.\n",
"point_in_time_uninsured_time_series = (joined[\"RHLTHMTH\"] == 2).mul(weights, axis=\"index\").sum()\n",
"point_in_time_uninsured_time_series"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "2f87e38d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Month\n",
"2017-01 10.294926\n",
"2017-02 9.992995\n",
"2017-03 9.697654\n",
"2017-04 9.626863\n",
"2017-05 9.540504\n",
"2017-06 9.383243\n",
"2017-07 9.527418\n",
"2017-08 9.300579\n",
"2017-09 9.240243\n",
"2017-10 8.989470\n",
"2017-11 8.995895\n",
"2017-12 8.846161\n",
"2018-01 7.874571\n",
"2018-02 7.759846\n",
"2018-03 7.689182\n",
"2018-04 7.649889\n",
"2018-05 7.785769\n",
"2018-06 7.891344\n",
"2018-07 7.971438\n",
"2018-08 7.851824\n",
"2018-09 7.911949\n",
"2018-10 7.807858\n",
"2018-11 7.752376\n",
"2018-12 7.922604\n",
"2019-01 7.696689\n",
"2019-02 7.694695\n",
"2019-03 7.628605\n",
"2019-04 7.726544\n",
"2019-05 7.913348\n",
"2019-06 8.087446\n",
"2019-07 8.288975\n",
"2019-08 7.966570\n",
"2019-09 7.789733\n",
"2019-10 7.769488\n",
"2019-11 7.557587\n",
"2019-12 7.536938\n",
"2020-01 7.068500\n",
"2020-02 7.075673\n",
"2020-03 6.874960\n",
"2020-04 6.889421\n",
"2020-05 6.956477\n",
"2020-06 6.944120\n",
"2020-07 6.910933\n",
"2020-08 7.029313\n",
"2020-09 7.043762\n",
"2020-10 6.878659\n",
"2020-11 6.826856\n",
"2020-12 6.918546\n",
"dtype: float64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Compute percentage for point-in-time uninsurance.\n",
"point_in_time_uninsured_percent = point_in_time_uninsured_time_series / population_time_series * 100\n",
"point_in_time_uninsured_percent"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "26f3511e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Month\n",
"2017-01 3.275508e+07\n",
"2017-02 3.282785e+07\n",
"2017-03 3.302439e+07\n",
"2017-04 3.318287e+07\n",
"2017-05 3.341521e+07\n",
"2017-06 3.364884e+07\n",
"2017-07 3.439155e+07\n",
"2017-08 3.471736e+07\n",
"2017-09 3.518055e+07\n",
"2017-10 3.537844e+07\n",
"2017-11 3.590747e+07\n",
"2017-12 3.619457e+07\n",
"2018-01 4.347808e+07\n",
"2018-02 4.377880e+07\n",
"2018-03 4.417637e+07\n",
"2018-04 4.488855e+07\n",
"2018-05 4.546746e+07\n",
"2018-06 4.621783e+07\n",
"2018-07 4.706553e+07\n",
"2018-08 4.763423e+07\n",
"2018-09 4.817349e+07\n",
"2018-10 4.845368e+07\n",
"2018-11 4.858309e+07\n",
"2018-12 4.924080e+07\n",
"2019-01 5.518682e+07\n",
"2019-02 5.535343e+07\n",
"2019-03 5.556472e+07\n",
"2019-04 5.631805e+07\n",
"2019-05 5.746341e+07\n",
"2019-06 5.851176e+07\n",
"2019-07 5.959678e+07\n",
"2019-08 5.974034e+07\n",
"2019-09 5.982183e+07\n",
"2019-10 6.019504e+07\n",
"2019-11 6.041176e+07\n",
"2019-12 6.065100e+07\n",
"2020-01 6.411746e+07\n",
"2020-02 6.445272e+07\n",
"2020-03 6.448997e+07\n",
"2020-04 6.488021e+07\n",
"2020-05 6.524110e+07\n",
"2020-06 6.546834e+07\n",
"2020-07 6.566136e+07\n",
"2020-08 6.587656e+07\n",
"2020-09 6.623099e+07\n",
"2020-10 6.626312e+07\n",
"2020-11 6.637401e+07\n",
"2020-12 6.681183e+07\n",
"dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the cumulatively uninsured population as of each month. Start by getting a mask dataframe for\n",
"# uninsurance at each month, then smear true values right using `cummax()`. Do a broadcast\n",
"# multiplication by the weight column as before, and sum.\n",
"cumulative_uninsured_time_series = (joined[\"RHLTHMTH\"] == 2).cummax(axis=\"columns\").mul(weights, axis=\"index\").sum()\n",
"cumulative_uninsured_time_series"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "7ad41997",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Month\n",
"2017-01 10.294926\n",
"2017-02 10.297121\n",
"2017-03 10.344826\n",
"2017-04 10.384131\n",
"2017-05 10.435095\n",
"2017-06 10.483806\n",
"2017-07 10.696172\n",
"2017-08 10.776845\n",
"2017-09 10.887803\n",
"2017-10 10.906342\n",
"2017-11 11.048146\n",
"2017-12 11.116127\n",
"2018-01 13.365990\n",
"2018-02 13.458436\n",
"2018-03 13.580655\n",
"2018-04 13.799594\n",
"2018-05 13.969926\n",
"2018-06 14.200476\n",
"2018-07 14.460933\n",
"2018-08 14.635667\n",
"2018-09 14.801357\n",
"2018-10 14.881162\n",
"2018-11 14.920908\n",
"2018-12 15.122903\n",
"2019-01 16.949054\n",
"2019-02 17.000223\n",
"2019-03 17.065116\n",
"2019-04 17.296478\n",
"2019-05 17.648242\n",
"2019-06 17.970213\n",
"2019-07 18.303446\n",
"2019-08 18.347538\n",
"2019-09 18.372564\n",
"2019-10 18.487186\n",
"2019-11 18.553746\n",
"2019-12 18.627221\n",
"2020-01 19.691844\n",
"2020-02 19.794812\n",
"2020-03 19.806251\n",
"2020-04 19.926103\n",
"2020-05 20.036940\n",
"2020-06 20.106728\n",
"2020-07 20.166011\n",
"2020-08 20.232102\n",
"2020-09 20.340957\n",
"2020-10 20.350824\n",
"2020-11 20.384881\n",
"2020-12 20.519345\n",
"dtype: float64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Compute percentage for cumulative uninsurance.\n",
"cumulative_uninsured_percent = cumulative_uninsured_time_series / population_time_series * 100\n",
"cumulative_uninsured_percent"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7a386e8a",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xticks(rotation=90)\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Uninsurance (%)\")\n",
"plt.plot(point_in_time_uninsured_percent)\n",
"plt.plot(cumulative_uninsured_percent)\n",
"plt.legend([\"Point-in-time Uninsurance\", \"Cumulative Uninsurance\"])\n",
"plt.title(\"Percent of Population Without Health Insurance, 2017-2020\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "23d34196",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Repeat the same calculations, but limited to the universe of non-elderly adults, ages 18 to 60.\n",
"age_mask = (joined[\"TAGE\"] >= 18) & (joined[\"TAGE\"] <= 60)\n",
"denominator = (~joined[\"RHLTHMTH\"].isna() & age_mask).mul(weights, axis=\"index\").sum()\n",
"numerator_pit = ((joined[\"RHLTHMTH\"] == 2) & age_mask).mul(weights, axis=\"index\").sum()\n",
"numerator_cumulative = ((joined[\"RHLTHMTH\"] == 2).cummax(axis=\"columns\") & age_mask).mul(weights, axis=\"index\").sum()\n",
"\n",
"plt.xticks(rotation=90)\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Uninsurance (%)\")\n",
"plt.plot(numerator_pit / denominator * 100)\n",
"plt.plot(numerator_cumulative / denominator * 100)\n",
"plt.legend([\"Point-in-time Uninsurance\", \"Cumulative Uninsurance\"])\n",
"plt.title(\"Percent of Non-Eldery Adults Without Health Insurance, 2017-2020\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "dac5356c",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Repeat the same calculations, but limited to those who were on Medicaid in January 2017.\n",
"has_medicaid_2017_01 = joined[\"RPUBTYPE2\", \"2017-01\"] == 1\n",
"denominator = (~joined[\"RHLTHMTH\"].isna()).mul(has_medicaid_2017_01, axis=\"index\").mul(weights, axis=\"index\").sum()\n",
"numerator_pit = (joined[\"RHLTHMTH\"] == 2).mul(has_medicaid_2017_01, axis=\"index\").mul(weights, axis=\"index\").sum()\n",
"numerator_cumulative = (joined[\"RHLTHMTH\"] == 2).cummax(axis=\"columns\").mul(has_medicaid_2017_01, axis=\"index\").mul(weights, axis=\"index\").sum()\n",
"\n",
"plt.xticks(rotation=90)\n",
"plt.xlabel(\"Month\")\n",
"plt.ylabel(\"Uninsurance (%)\")\n",
"plt.plot(numerator_pit / denominator * 100)\n",
"plt.plot(numerator_cumulative / denominator * 100)\n",
"plt.legend([\"Point-in-time Uninsurance\", \"Cumulative Uninsurance\"])\n",
"plt.title(\"Percent of People Who Were on Medicaid in January 2017, and Later Were Without Health Insurance, 2017-2020\", wrap=True)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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