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eedrummer/Synthea-ws.ipynb

Created October 11, 2019 16:02
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Synthea-ws.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from ipyleaflet import Map, Marker\n",
"import geocoder\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34491, 47)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographics = pd.read_csv(\"/Users/andrewg/projects/synthea/src/main/resources/geography/demographics.csv\")\n",
"demographics.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(87114, 7)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"existing_zips = pd.read_csv(\"/Users/andrewg/projects/synthea/src/main/resources/geography/zipcodes.csv\", dtype={'ZCTA5': 'str'})\n",
"existing_zips.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The exisiting zip codes file has 87114 rows in it. The demographics file has 34491 cities/states.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(87102, 7)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"existing_zips.drop_duplicates().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The existing zip code file has 12 exact duplicates in the file\n",
"\n",
"Next, we'll check the integrity of the existing files by building DataFrames of just city and state names from both. This is how the look up happens in code."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(45841, 2)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ez_city_state = existing_zips[['USPS', 'NAME']].drop_duplicates()\n",
"demo_city_state = demographics[['STNAME', 'NAME']].drop_duplicates()\n",
"ez_city_state.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(28442, 2)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demo_city_state.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17399, 4)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demo_ez = ez_city_state.merge(demo_city_state, how='outer', left_on=['USPS', 'NAME'], right_on=['STNAME', 'NAME'], indicator=True)\n",
"demo_ez[demo_ez._merge == 'left_only'].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After building a DataFrame that is an outer join on the demographics city and state combinations we can see that the zip code file has 17399 city and state combinations that do not appear in the demographics file. From the Synthea code base, these are essentially dead.\n",
"\n",
"We can now create a DataFrame based on the zip code file, but with only cities and states that appear in the demographics file."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"merged_zip = demo_city_state.merge(existing_zips.drop_duplicates(), how='outer', right_on=['USPS', 'NAME'], left_on=['STNAME', 'NAME'], indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(60034, 6)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"in_demo_file = merged_zip[merged_zip._merge == 'both'][['NAME', 'USPS', 'ST', 'ZCTA5', 'LAT', 'LON']]\n",
"in_demo_file.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The resulting DataFrame will contain rows only for matches against the demographic file and does not have any exact duplicates. It will still contain entries without a zip code."
]
},
{
"cell_type": "code",
"execution_count": 10,
"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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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>USPS</th>\n",
" <th>ST</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Abbeville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>NaN</td>\n",
" <td>31.596562</td>\n",
" <td>-85.304308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Abbeville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>36310</td>\n",
" <td>31.595148</td>\n",
" <td>-85.208852</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Adamsville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Adamsville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35060</td>\n",
" <td>33.557750</td>\n",
" <td>-86.951726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Adamsville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35062</td>\n",
" <td>33.724920</td>\n",
" <td>-87.010252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME USPS ST ZCTA5 LAT LON\n",
"0 Abbeville Alabama AL NaN 31.596562 -85.304308\n",
"1 Abbeville Alabama AL 36310 31.595148 -85.208852\n",
"2 Adamsville Alabama AL 35005 33.595950 -87.000649\n",
"3 Adamsville Alabama AL 35060 33.557750 -86.951726\n",
"4 Adamsville Alabama AL 35062 33.724920 -87.010252"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"in_demo_file.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's look at cities with zip codes."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"filtered_zip = in_demo_file[in_demo_file.ZCTA5.notna()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's load up the zip code gazetteer from the US Census - https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2019_Gazetteer/2019_Gaz_zcta_national.zip"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"zip_g = pd.read_csv(\"/Users/andrewg/Downloads/2019_Gaz_zcta_national.txt\", encoding='latin-1', dtype={'GEOID': 'str'}, sep='\\t').rename(columns={'GEOID': 'ZCTA5', 'INTPTLAT': 'LAT', 'INTPTLONG': 'LON'})"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\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>ZCTA5</th>\n",
" <th>ALAND</th>\n",
" <th>AWATER</th>\n",
" <th>ALAND_SQMI</th>\n",
" <th>AWATER_SQMI</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>00601</td>\n",
" <td>166659747</td>\n",
" <td>799292</td>\n",
" <td>64.348</td>\n",
" <td>0.309</td>\n",
" <td>18.180555</td>\n",
" <td>-66.749961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>00602</td>\n",
" <td>79307534</td>\n",
" <td>4428428</td>\n",
" <td>30.621</td>\n",
" <td>1.710</td>\n",
" <td>18.361945</td>\n",
" <td>-67.175597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>00603</td>\n",
" <td>81887188</td>\n",
" <td>181412</td>\n",
" <td>31.617</td>\n",
" <td>0.070</td>\n",
" <td>18.455183</td>\n",
" <td>-67.119887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>00606</td>\n",
" <td>109579993</td>\n",
" <td>12487</td>\n",
" <td>42.309</td>\n",
" <td>0.005</td>\n",
" <td>18.158327</td>\n",
" <td>-66.932928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>00610</td>\n",
" <td>93013428</td>\n",
" <td>4172059</td>\n",
" <td>35.913</td>\n",
" <td>1.611</td>\n",
" <td>18.294032</td>\n",
" <td>-67.127156</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ZCTA5 ALAND AWATER ALAND_SQMI AWATER_SQMI LAT LON\n",
"0 00601 166659747 799292 64.348 0.309 18.180555 -66.749961\n",
"1 00602 79307534 4428428 30.621 1.710 18.361945 -67.175597\n",
"2 00603 81887188 181412 31.617 0.070 18.455183 -67.119887\n",
"3 00606 109579993 12487 42.309 0.005 18.158327 -66.932928\n",
"4 00610 93013428 4172059 35.913 1.611 18.294032 -67.127156"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zip_g.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"33144"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zip_g.ZCTA5.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23445"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_zip.ZCTA5.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 23445.000000\n",
"mean 1.535423\n",
"std 1.038375\n",
"min 1.000000\n",
"25% 1.000000\n",
"50% 1.000000\n",
"75% 2.000000\n",
"max 23.000000\n",
"Name: NAME, dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_zip.groupby('ZCTA5').count().NAME.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The gazetteer has 33144 unique zip codes. The filtered set from the zip codes file has 23445. The majority represent a single city. Some are present for more than one city as a zip code can be split across multiple cities. The largest example of this is `63121` which is somewhere in/near St. Louis. Searching the web on this code appears to verify that this is correct."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 20223.000000\n",
"mean 1.780052\n",
"std 3.157737\n",
"min 1.000000\n",
"25% 1.000000\n",
"50% 1.000000\n",
"75% 2.000000\n",
"max 214.000000\n",
"Name: ZCTA5, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filtered_zip.groupby(['NAME', 'USPS']).count().ZCTA5.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most cities have a single zip code. Larger cities have many. New York City leads the count with 214.\n",
"\n",
"Now we can merge the filtered zip code DataFrame with the Gazetteer to check the accuracy of the existing zip code file."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>USPS</th>\n",
" <th>ST</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT_x</th>\n",
" <th>LON_x</th>\n",
" <th>LAT_y</th>\n",
" <th>LON_y</th>\n",
" <th>_merge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Abbeville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>36310</td>\n",
" <td>31.595148</td>\n",
" <td>-85.208852</td>\n",
" <td>31.595139</td>\n",
" <td>-85.208847</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Adamsville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Birmingham</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Graysville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Maytown</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME USPS ST ZCTA5 LAT_x LON_x LAT_y LON_y \\\n",
"0 Abbeville Alabama AL 36310 31.595148 -85.208852 31.595139 -85.208847 \n",
"1 Adamsville Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"2 Birmingham Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"3 Graysville Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"4 Maytown Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"\n",
" _merge \n",
"0 both \n",
"1 both \n",
"2 both \n",
"3 both \n",
"4 both "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz = filtered_zip.merge(zip_g[['ZCTA5', 'LAT', 'LON']], how='outer', on='ZCTA5', indicator=True)\n",
"merged_with_gaz[merged_with_gaz._merge == 'both'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After performing the merge, we can look for zip codes that exist in Synthea, but not in the Gazetteer:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>USPS</th>\n",
" <th>ST</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT_x</th>\n",
" <th>LON_x</th>\n",
" <th>LAT_y</th>\n",
" <th>LON_y</th>\n",
" <th>_merge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2422</th>\n",
" <td>Southside</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>72501.0</td>\n",
" <td>35.698836</td>\n",
" <td>-91.623393</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2514</th>\n",
" <td>Alpine County</td>\n",
" <td>California</td>\n",
" <td>CA</td>\n",
" <td>96120.0</td>\n",
" <td>38.684002</td>\n",
" <td>-119.822738</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3547</th>\n",
" <td>Jurupa Valley</td>\n",
" <td>California</td>\n",
" <td>CA</td>\n",
" <td>92509.0</td>\n",
" <td>33.996667</td>\n",
" <td>-117.485358</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3837</th>\n",
" <td>Mariposa County</td>\n",
" <td>California</td>\n",
" <td>CA</td>\n",
" <td>95338.0</td>\n",
" <td>37.464995</td>\n",
" <td>-119.995226</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4339</th>\n",
" <td>Trinity County</td>\n",
" <td>California</td>\n",
" <td>CA</td>\n",
" <td>96093.0</td>\n",
" <td>40.946884</td>\n",
" <td>-122.812172</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME USPS ST ZCTA5 LAT_x LON_x LAT_y \\\n",
"2422 Southside Arkansas AR 72501.0 35.698836 -91.623393 NaN \n",
"2514 Alpine County California CA 96120.0 38.684002 -119.822738 NaN \n",
"3547 Jurupa Valley California CA 92509.0 33.996667 -117.485358 NaN \n",
"3837 Mariposa County California CA 95338.0 37.464995 -119.995226 NaN \n",
"4339 Trinity County California CA 96093.0 40.946884 -122.812172 NaN \n",
"\n",
" LON_y _merge \n",
"2422 NaN left_only \n",
"2514 NaN left_only \n",
"3547 NaN left_only \n",
"3837 NaN left_only \n",
"4339 NaN left_only "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz[(merged_with_gaz._merge == 'left_only') & (merged_with_gaz.ZCTA5.notna())].head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(59, 9)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz[(merged_with_gaz._merge == 'left_only') & (merged_with_gaz.ZCTA5.notna())].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There happens to be 59 rows in the existing zip code file where the zip code has .0 added to the end of the code. We can clean that up and re-merge."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"in_demo_file.ZCTA5 = in_demo_file.ZCTA5.str.replace('\\.0', '')\n",
"filtered_zip = in_demo_file[in_demo_file.ZCTA5.notna()]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 9)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz = filtered_zip.merge(zip_g[['ZCTA5', 'LAT', 'LON']], how='outer', on='ZCTA5', indicator=True)\n",
"merged_with_gaz[(merged_with_gaz._merge == 'left_only') & (merged_with_gaz.ZCTA5.notna())].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This fixed the issues with those rows. Now we can check the existing file against the Gazetteer by looking at the differences in lat/lon:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"merged_with_gaz['lat_diff'] = merged_with_gaz.LAT_x - merged_with_gaz.LAT_y\n",
"merged_with_gaz['lon_diff'] = merged_with_gaz.LON_x - merged_with_gaz.LON_y"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 35998.000000\n",
"mean 0.000123\n",
"std 0.016812\n",
"min -0.857257\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 0.000000\n",
"max 1.050104\n",
"Name: lat_diff, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz['lat_diff'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 35998.000000\n",
"mean 0.000952\n",
"std 0.024304\n",
"min -1.463312\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 0.000000\n",
"max 1.358265\n",
"Name: lon_diff, dtype: float64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz['lon_diff'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(286, 11)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz[(np.absolute(merged_with_gaz.lon_diff) > 0.1) | (np.absolute(merged_with_gaz.lat_diff) > 0.1)].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Gazetteer is largely in agreement with the existing file. The vast majority of zip codes have no change in lat/lon. There are only 286 cases where the difference is greater than 0.1 absolute degrees of either latitude or longitude. A small selection of these are listed below:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>USPS</th>\n",
" <th>ST</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT_x</th>\n",
" <th>LON_x</th>\n",
" <th>LAT_y</th>\n",
" <th>LON_y</th>\n",
" <th>_merge</th>\n",
" <th>lat_diff</th>\n",
" <th>lon_diff</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>Athens</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35614</td>\n",
" <td>34.853722</td>\n",
" <td>-86.990112</td>\n",
" <td>34.875919</td>\n",
" <td>-87.229253</td>\n",
" <td>both</td>\n",
" <td>-0.022197</td>\n",
" <td>0.239141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>Bakerhill</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>36027</td>\n",
" <td>32.060643</td>\n",
" <td>-85.116624</td>\n",
" <td>31.822420</td>\n",
" <td>-85.378527</td>\n",
" <td>both</td>\n",
" <td>0.238223</td>\n",
" <td>0.261903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>Eufaula</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>36027</td>\n",
" <td>32.060643</td>\n",
" <td>-85.116624</td>\n",
" <td>31.822420</td>\n",
" <td>-85.378527</td>\n",
" <td>both</td>\n",
" <td>0.238223</td>\n",
" <td>0.261903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>535</th>\n",
" <td>Cullman</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35057</td>\n",
" <td>34.180462</td>\n",
" <td>-86.934257</td>\n",
" <td>33.969944</td>\n",
" <td>-86.944303</td>\n",
" <td>both</td>\n",
" <td>0.210518</td>\n",
" <td>0.010046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>536</th>\n",
" <td>Dodge City</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35057</td>\n",
" <td>34.180462</td>\n",
" <td>-86.934257</td>\n",
" <td>33.969944</td>\n",
" <td>-86.944303</td>\n",
" <td>both</td>\n",
" <td>0.210518</td>\n",
" <td>0.010046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\n",
" <td>Good Hope</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35057</td>\n",
" <td>34.180462</td>\n",
" <td>-86.934257</td>\n",
" <td>33.969944</td>\n",
" <td>-86.944303</td>\n",
" <td>both</td>\n",
" <td>0.210518</td>\n",
" <td>0.010046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>538</th>\n",
" <td>West Point</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35057</td>\n",
" <td>34.180462</td>\n",
" <td>-86.934257</td>\n",
" <td>33.969944</td>\n",
" <td>-86.944303</td>\n",
" <td>both</td>\n",
" <td>0.210518</td>\n",
" <td>0.010046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>671</th>\n",
" <td>Fort Payne</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35968</td>\n",
" <td>34.551098</td>\n",
" <td>-85.738915</td>\n",
" <td>34.382994</td>\n",
" <td>-85.803307</td>\n",
" <td>both</td>\n",
" <td>0.168104</td>\n",
" <td>0.064392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>672</th>\n",
" <td>Henagar</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35968</td>\n",
" <td>34.551098</td>\n",
" <td>-85.738915</td>\n",
" <td>34.382994</td>\n",
" <td>-85.803307</td>\n",
" <td>both</td>\n",
" <td>0.168104</td>\n",
" <td>0.064392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>673</th>\n",
" <td>Pine Ridge</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35968</td>\n",
" <td>34.551098</td>\n",
" <td>-85.738915</td>\n",
" <td>34.382994</td>\n",
" <td>-85.803307</td>\n",
" <td>both</td>\n",
" <td>0.168104</td>\n",
" <td>0.064392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>674</th>\n",
" <td>Rainsville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35968</td>\n",
" <td>34.551098</td>\n",
" <td>-85.738915</td>\n",
" <td>34.382994</td>\n",
" <td>-85.803307</td>\n",
" <td>both</td>\n",
" <td>0.168104</td>\n",
" <td>0.064392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>675</th>\n",
" <td>Sylvania</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35968</td>\n",
" <td>34.551098</td>\n",
" <td>-85.738915</td>\n",
" <td>34.382994</td>\n",
" <td>-85.803307</td>\n",
" <td>both</td>\n",
" <td>0.168104</td>\n",
" <td>0.064392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>713</th>\n",
" <td>Gadsden</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35905</td>\n",
" <td>33.961400</td>\n",
" <td>-85.922422</td>\n",
" <td>33.856532</td>\n",
" <td>-85.912603</td>\n",
" <td>both</td>\n",
" <td>0.104868</td>\n",
" <td>-0.009819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>714</th>\n",
" <td>Glencoe</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35905</td>\n",
" <td>33.961400</td>\n",
" <td>-85.922422</td>\n",
" <td>33.856532</td>\n",
" <td>-85.912603</td>\n",
" <td>both</td>\n",
" <td>0.104868</td>\n",
" <td>-0.009819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>715</th>\n",
" <td>Hokes Bluff</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35905</td>\n",
" <td>33.961400</td>\n",
" <td>-85.922422</td>\n",
" <td>33.856532</td>\n",
" <td>-85.912603</td>\n",
" <td>both</td>\n",
" <td>0.104868</td>\n",
" <td>-0.009819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>716</th>\n",
" <td>Southside</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35905</td>\n",
" <td>33.961400</td>\n",
" <td>-85.922422</td>\n",
" <td>33.856532</td>\n",
" <td>-85.912603</td>\n",
" <td>both</td>\n",
" <td>0.104868</td>\n",
" <td>-0.009819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1072</th>\n",
" <td>Akhiok</td>\n",
" <td>Alaska</td>\n",
" <td>AK</td>\n",
" <td>99615</td>\n",
" <td>57.650481</td>\n",
" <td>-153.261105</td>\n",
" <td>57.682717</td>\n",
" <td>-153.378849</td>\n",
" <td>both</td>\n",
" <td>-0.032236</td>\n",
" <td>0.117744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1073</th>\n",
" <td>Kodiak</td>\n",
" <td>Alaska</td>\n",
" <td>AK</td>\n",
" <td>99615</td>\n",
" <td>57.650481</td>\n",
" <td>-153.261105</td>\n",
" <td>57.682717</td>\n",
" <td>-153.378849</td>\n",
" <td>both</td>\n",
" <td>-0.032236</td>\n",
" <td>0.117744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1074</th>\n",
" <td>Larsen Bay</td>\n",
" <td>Alaska</td>\n",
" <td>AK</td>\n",
" <td>99615</td>\n",
" <td>57.650481</td>\n",
" <td>-153.261105</td>\n",
" <td>57.682717</td>\n",
" <td>-153.378849</td>\n",
" <td>both</td>\n",
" <td>-0.032236</td>\n",
" <td>0.117744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1079</th>\n",
" <td>Aleknagik</td>\n",
" <td>Alaska</td>\n",
" <td>AK</td>\n",
" <td>99576</td>\n",
" <td>60.261108</td>\n",
" <td>-159.092876</td>\n",
" <td>60.274057</td>\n",
" <td>-158.668434</td>\n",
" <td>both</td>\n",
" <td>-0.012949</td>\n",
" <td>-0.424442</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME USPS ST ZCTA5 LAT_x LON_x LAT_y \\\n",
"156 Athens Alabama AL 35614 34.853722 -86.990112 34.875919 \n",
"195 Bakerhill Alabama AL 36027 32.060643 -85.116624 31.822420 \n",
"196 Eufaula Alabama AL 36027 32.060643 -85.116624 31.822420 \n",
"535 Cullman Alabama AL 35057 34.180462 -86.934257 33.969944 \n",
"536 Dodge City Alabama AL 35057 34.180462 -86.934257 33.969944 \n",
"537 Good Hope Alabama AL 35057 34.180462 -86.934257 33.969944 \n",
"538 West Point Alabama AL 35057 34.180462 -86.934257 33.969944 \n",
"671 Fort Payne Alabama AL 35968 34.551098 -85.738915 34.382994 \n",
"672 Henagar Alabama AL 35968 34.551098 -85.738915 34.382994 \n",
"673 Pine Ridge Alabama AL 35968 34.551098 -85.738915 34.382994 \n",
"674 Rainsville Alabama AL 35968 34.551098 -85.738915 34.382994 \n",
"675 Sylvania Alabama AL 35968 34.551098 -85.738915 34.382994 \n",
"713 Gadsden Alabama AL 35905 33.961400 -85.922422 33.856532 \n",
"714 Glencoe Alabama AL 35905 33.961400 -85.922422 33.856532 \n",
"715 Hokes Bluff Alabama AL 35905 33.961400 -85.922422 33.856532 \n",
"716 Southside Alabama AL 35905 33.961400 -85.922422 33.856532 \n",
"1072 Akhiok Alaska AK 99615 57.650481 -153.261105 57.682717 \n",
"1073 Kodiak Alaska AK 99615 57.650481 -153.261105 57.682717 \n",
"1074 Larsen Bay Alaska AK 99615 57.650481 -153.261105 57.682717 \n",
"1079 Aleknagik Alaska AK 99576 60.261108 -159.092876 60.274057 \n",
"\n",
" LON_y _merge lat_diff lon_diff \n",
"156 -87.229253 both -0.022197 0.239141 \n",
"195 -85.378527 both 0.238223 0.261903 \n",
"196 -85.378527 both 0.238223 0.261903 \n",
"535 -86.944303 both 0.210518 0.010046 \n",
"536 -86.944303 both 0.210518 0.010046 \n",
"537 -86.944303 both 0.210518 0.010046 \n",
"538 -86.944303 both 0.210518 0.010046 \n",
"671 -85.803307 both 0.168104 0.064392 \n",
"672 -85.803307 both 0.168104 0.064392 \n",
"673 -85.803307 both 0.168104 0.064392 \n",
"674 -85.803307 both 0.168104 0.064392 \n",
"675 -85.803307 both 0.168104 0.064392 \n",
"713 -85.912603 both 0.104868 -0.009819 \n",
"714 -85.912603 both 0.104868 -0.009819 \n",
"715 -85.912603 both 0.104868 -0.009819 \n",
"716 -85.912603 both 0.104868 -0.009819 \n",
"1072 -153.378849 both -0.032236 0.117744 \n",
"1073 -153.378849 both -0.032236 0.117744 \n",
"1074 -153.378849 both -0.032236 0.117744 \n",
"1079 -158.668434 both -0.012949 -0.424442 "
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_with_gaz[(np.absolute(merged_with_gaz.lon_diff) > 0.1) | (np.absolute(merged_with_gaz.lat_diff) > 0.1)].head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create the new file, we will use the lat / lon values from the Gatetteer and keep the old values for places without a zip code.\n",
"\n",
"To do this, we will fill in the `LAT_y` and `LON_y` columns where there are null with the values from the original zipcode file. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"filled_all_values = merged_with_gaz.fillna({'LAT_y': merged_with_gaz.LAT_x, 'LON_y': merged_with_gaz.LON_x})"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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" <th>ST</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Abbeville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>36310</td>\n",
" <td>31.595148</td>\n",
" <td>-85.208852</td>\n",
" <td>31.595139</td>\n",
" <td>-85.208847</td>\n",
" <td>both</td>\n",
" <td>0.000009</td>\n",
" <td>-0.000005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Adamsville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Birmingham</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Graysville</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Maytown</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" <td>both</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME USPS ST ZCTA5 LAT_x LON_x LAT_y LON_y \\\n",
"0 Abbeville Alabama AL 36310 31.595148 -85.208852 31.595139 -85.208847 \n",
"1 Adamsville Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"2 Birmingham Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"3 Graysville Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"4 Maytown Alabama AL 35005 33.595950 -87.000649 33.595950 -87.000649 \n",
"\n",
" _merge lat_diff lon_diff \n",
"0 both 0.000009 -0.000005 \n",
"1 both 0.000000 0.000000 \n",
"2 both 0.000000 0.000000 \n",
"3 both 0.000000 0.000000 \n",
"4 both 0.000000 0.000000 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filled_all_values.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can prepare the DataFrame of cities with zip codes for export by dropping the columns we no longer need and renaming columns so they match the correct zip code file format."
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>USPS</th>\n",
" <th>ST</th>\n",
" <th>NAME</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Abbeville</td>\n",
" <td>36310</td>\n",
" <td>31.595139</td>\n",
" <td>-85.208847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Adamsville</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Birmingham</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Graysville</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Maytown</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Mulga</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Sylvan Springs</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Adamsville</td>\n",
" <td>35060</td>\n",
" <td>33.559629</td>\n",
" <td>-86.953465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Adamsville</td>\n",
" <td>35062</td>\n",
" <td>33.724920</td>\n",
" <td>-87.010252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Dora</td>\n",
" <td>35062</td>\n",
" <td>33.724920</td>\n",
" <td>-87.010252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" USPS ST NAME ZCTA5 LAT LON\n",
"0 Alabama AL Abbeville 36310 31.595139 -85.208847\n",
"1 Alabama AL Adamsville 35005 33.595950 -87.000649\n",
"2 Alabama AL Birmingham 35005 33.595950 -87.000649\n",
"3 Alabama AL Graysville 35005 33.595950 -87.000649\n",
"4 Alabama AL Maytown 35005 33.595950 -87.000649\n",
"5 Alabama AL Mulga 35005 33.595950 -87.000649\n",
"6 Alabama AL Sylvan Springs 35005 33.595950 -87.000649\n",
"7 Alabama AL Adamsville 35060 33.559629 -86.953465\n",
"8 Alabama AL Adamsville 35062 33.724920 -87.010252\n",
"9 Alabama AL Dora 35062 33.724920 -87.010252"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"only_needed_columns = filled_all_values[filled_all_values._merge == 'both'].drop(columns=['LAT_x', 'LON_x', '_merge', 'lat_diff', 'lon_diff'])\n",
"zip_export_df = only_needed_columns.rename(columns={'LAT_y': 'LAT', 'LON_y': 'LON'})\n",
"zip_export_df = zip_export_df[['USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]\n",
"zip_export_df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35998, 6)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zip_export_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's work on the cities without zip codes. This attempts to check / fix cities with no zip code using the [US Census National Places Gazetteer File](https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2019_Gazetteer/2019_Gaz_place_national.zip)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"no_zip = in_demo_file[in_demo_file.ZCTA5.isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(24036, 6)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_zip.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One thing that is a problem with the existing zip code file is that there are duplicate entries for cities with no zip code that have differing lat / lon values."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>NAME</th>\n",
" <th>USPS</th>\n",
" <th>ST</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1168</th>\n",
" <td>Tallassee</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>NaN</td>\n",
" <td>32.512019</td>\n",
" <td>-85.985882</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1169</th>\n",
" <td>Tallassee</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>NaN</td>\n",
" <td>32.623945</td>\n",
" <td>-85.809392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1892</th>\n",
" <td>St. Johns</td>\n",
" <td>Arizona</td>\n",
" <td>AZ</td>\n",
" <td>NaN</td>\n",
" <td>34.455877</td>\n",
" <td>-109.399306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1893</th>\n",
" <td>St. Johns</td>\n",
" <td>Arizona</td>\n",
" <td>AZ</td>\n",
" <td>NaN</td>\n",
" <td>33.263248</td>\n",
" <td>-112.116361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2029</th>\n",
" <td>Beaver</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>NaN</td>\n",
" <td>36.470907</td>\n",
" <td>-93.801892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2030</th>\n",
" <td>Beaver</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>NaN</td>\n",
" <td>34.719002</td>\n",
" <td>-92.587489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2050</th>\n",
" <td>Benton</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>NaN</td>\n",
" <td>35.320424</td>\n",
" <td>-92.228274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2051</th>\n",
" <td>Benton</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>NaN</td>\n",
" <td>36.372923</td>\n",
" <td>-91.846359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2067</th>\n",
" <td>Black Oak</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>NaN</td>\n",
" <td>35.777038</td>\n",
" <td>-90.353510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2068</th>\n",
" <td>Black Oak</td>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>NaN</td>\n",
" <td>35.388468</td>\n",
" <td>-90.423326</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" NAME USPS ST ZCTA5 LAT LON\n",
"1168 Tallassee Alabama AL NaN 32.512019 -85.985882\n",
"1169 Tallassee Alabama AL NaN 32.623945 -85.809392\n",
"1892 St. Johns Arizona AZ NaN 34.455877 -109.399306\n",
"1893 St. Johns Arizona AZ NaN 33.263248 -112.116361\n",
"2029 Beaver Arkansas AR NaN 36.470907 -93.801892\n",
"2030 Beaver Arkansas AR NaN 34.719002 -92.587489\n",
"2050 Benton Arkansas AR NaN 35.320424 -92.228274\n",
"2051 Benton Arkansas AR NaN 36.372923 -91.846359\n",
"2067 Black Oak Arkansas AR NaN 35.777038 -90.353510\n",
"2068 Black Oak Arkansas AR NaN 35.388468 -90.423326"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_zip_dupes = no_zip[no_zip.duplicated(subset=['USPS', 'ST', 'NAME'], keep=False)]\n",
"no_zip_dupes.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the national place gazetteer from the US Census."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"place_g = pd.read_csv(\"/Users/andrewg/Downloads/2019_Gaz_place_national.txt\", encoding='latin-1', dtype={'GEOID': 'str'}, sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>USPS</th>\n",
" <th>GEOID</th>\n",
" <th>ANSICODE</th>\n",
" <th>NAME</th>\n",
" <th>LSAD</th>\n",
" <th>FUNCSTAT</th>\n",
" <th>ALAND</th>\n",
" <th>AWATER</th>\n",
" <th>ALAND_SQMI</th>\n",
" <th>AWATER_SQMI</th>\n",
" <th>INTPTLAT</th>\n",
" <th>INTPTLONG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AL</td>\n",
" <td>0100100</td>\n",
" <td>2582661</td>\n",
" <td>Abanda CDP</td>\n",
" <td>57</td>\n",
" <td>S</td>\n",
" <td>7764034</td>\n",
" <td>34284</td>\n",
" <td>2.998</td>\n",
" <td>0.013</td>\n",
" <td>33.091627</td>\n",
" <td>-85.527029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AL</td>\n",
" <td>0100124</td>\n",
" <td>2403054</td>\n",
" <td>Abbeville city</td>\n",
" <td>25</td>\n",
" <td>A</td>\n",
" <td>40255362</td>\n",
" <td>107642</td>\n",
" <td>15.543</td>\n",
" <td>0.042</td>\n",
" <td>31.564689</td>\n",
" <td>-85.259124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AL</td>\n",
" <td>0100460</td>\n",
" <td>2403063</td>\n",
" <td>Adamsville city</td>\n",
" <td>25</td>\n",
" <td>A</td>\n",
" <td>65193211</td>\n",
" <td>14122</td>\n",
" <td>25.171</td>\n",
" <td>0.005</td>\n",
" <td>33.605748</td>\n",
" <td>-86.974649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AL</td>\n",
" <td>0100484</td>\n",
" <td>2405123</td>\n",
" <td>Addison town</td>\n",
" <td>43</td>\n",
" <td>A</td>\n",
" <td>9753293</td>\n",
" <td>83417</td>\n",
" <td>3.766</td>\n",
" <td>0.032</td>\n",
" <td>34.202681</td>\n",
" <td>-87.178004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AL</td>\n",
" <td>0100676</td>\n",
" <td>2405125</td>\n",
" <td>Akron town</td>\n",
" <td>43</td>\n",
" <td>A</td>\n",
" <td>1776163</td>\n",
" <td>13849</td>\n",
" <td>0.686</td>\n",
" <td>0.005</td>\n",
" <td>32.879495</td>\n",
" <td>-87.741679</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" USPS GEOID ANSICODE NAME LSAD FUNCSTAT ALAND AWATER \\\n",
"0 AL 0100100 2582661 Abanda CDP 57 S 7764034 34284 \n",
"1 AL 0100124 2403054 Abbeville city 25 A 40255362 107642 \n",
"2 AL 0100460 2403063 Adamsville city 25 A 65193211 14122 \n",
"3 AL 0100484 2405123 Addison town 43 A 9753293 83417 \n",
"4 AL 0100676 2405125 Akron town 43 A 1776163 13849 \n",
"\n",
" ALAND_SQMI AWATER_SQMI INTPTLAT INTPTLONG \n",
"0 2.998 0.013 33.091627 -85.527029 \n",
"1 15.543 0.042 31.564689 -85.259124 \n",
"2 25.171 0.005 33.605748 -86.974649 \n",
"3 3.766 0.032 34.202681 -87.178004 \n",
"4 0.686 0.005 32.879495 -87.741679 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"place_g.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clean up the place names by removing \"city\", \"CDP\" and \"town\"."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"place_g['NAME'] = place_g['NAME'].str.replace('city', '').str.replace('CDP', '').str.replace('town', '').str.rstrip()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first line in the next cell is a little crazy. First, we get a DataFrame of all the duplicates. The problem is that the `duplicated` function doesn't have a nice way to be used to pull a single instance of duplicates. So the DataFrame that is returned can still have duplicate cities if there are 3 or more duplicates. That DataFrame is reduced to just city and state and the duplicates of that are dropped, which should give us the set of cities without zip codes that have multiple entries with conflicting lat / lon."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"no_zip_dupes = no_zip[no_zip.duplicated(subset=['USPS', 'ST', 'NAME'])][['USPS', 'ST', 'NAME']].drop_duplicates()\n",
"merge_with_place = no_zip_dupes.merge(place_g[['USPS', 'NAME', 'INTPTLAT', 'INTPTLONG']], how='outer', left_on=['ST', 'NAME'], right_on=['USPS', 'NAME'], indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2734, 3)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_zip_dupes.shape"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1428, 7)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merge_with_place[merge_with_place._merge == 'both'].shape"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1322, 7)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merge_with_place[merge_with_place._merge == 'left_only'].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 2734 unique cities in the zip code file that have conflicting lat / lon. Merging those with the place gazetteer will resolve 1428 with definitive lat / lon. There are 1322 without a match in the place file."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"cities_with_no_zip_and_place = merge_with_place[merge_with_place._merge == 'left_only'].drop(columns=['_merge'])"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>USPS_x</th>\n",
" <th>ST</th>\n",
" <th>NAME</th>\n",
" <th>USPS_y</th>\n",
" <th>INTPTLAT</th>\n",
" <th>INTPTLONG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Arkansas</td>\n",
" <td>AR</td>\n",
" <td>Lake</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Ash Grove</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Banner</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Blue Mound</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Brooklyn</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Browning</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Burton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Cincinnati</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Clayton</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>Illinois</td>\n",
" <td>IL</td>\n",
" <td>Concord</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" USPS_x ST NAME USPS_y INTPTLAT INTPTLONG\n",
"22 Arkansas AR Lake NaN NaN NaN\n",
"53 Illinois IL Ash Grove NaN NaN NaN\n",
"55 Illinois IL Banner NaN NaN NaN\n",
"57 Illinois IL Blue Mound NaN NaN NaN\n",
"58 Illinois IL Brooklyn NaN NaN NaN\n",
"59 Illinois IL Browning NaN NaN NaN\n",
"60 Illinois IL Burton NaN NaN NaN\n",
"62 Illinois IL Cincinnati NaN NaN NaN\n",
"63 Illinois IL Clayton NaN NaN NaN\n",
"64 Illinois IL Concord NaN NaN NaN"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cities_with_no_zip_and_place.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"any_with_matching_zip = cities_with_no_zip_and_place.merge(zip_export_df[['ST', 'NAME']], how=\"left\", on=['ST', 'NAME'], indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(839, 7)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"any_with_matching_zip[any_with_matching_zip._merge == 'left_only'].shape"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(483, 7)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"any_with_matching_zip[any_with_matching_zip._merge == 'both'].drop_duplicates().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of the 1322 cities that do not have a corresponding place, 483 have an existing row with a zip code. Since the data is conflicting and we are unable to verify it, it seems best to drop these from the new data set. The remaining 839 cities represent places that exist in the demographics file, do not have a row with a zip code, could not be matched to the place gazetteer and have multiple rows with conflicting zip codes.\n",
"\n",
"Moving on to the remaining 839 without a corresponding zip code, let's look at them by population."
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>ST</th>\n",
" <th>NAME</th>\n",
" <th>POPESTIMATE2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>919</th>\n",
" <td>NJ</td>\n",
" <td>Hamilton</td>\n",
" <td>89030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1037</th>\n",
" <td>OH</td>\n",
" <td>Colerain</td>\n",
" <td>58838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>931</th>\n",
" <td>NJ</td>\n",
" <td>Union</td>\n",
" <td>58128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1428</th>\n",
" <td>PA</td>\n",
" <td>Millcreek</td>\n",
" <td>54003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>925</th>\n",
" <td>NJ</td>\n",
" <td>Monroe</td>\n",
" <td>44141</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ST NAME POPESTIMATE2015\n",
"919 NJ Hamilton 89030\n",
"1037 OH Colerain 58838\n",
"931 NJ Union 58128\n",
"1428 PA Millcreek 54003\n",
"925 NJ Monroe 44141"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"any_with_matching_zip[any_with_matching_zip._merge == 'left_only'].merge(demographics, how='left', left_on=['USPS_x', 'NAME'], right_on=['STNAME', 'NAME']).sort_values('POPESTIMATE2015', ascending=False)[['ST', 'NAME', 'POPESTIMATE2015']].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot the points for the top 3 places on a map to see how things look."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "938fb4e336a845ffa34565eea4c5a1a9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"h_df = existing_zips[(existing_zips.ST == 'NJ') & (existing_zips.NAME == 'Hamilton')]\n",
"center = (h_df.LAT.mean(), h_df.LON.mean())\n",
"\n",
"m = Map(center=center, zoom=8)\n",
"\n",
"for row in h_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d93ad83d352b48d2b254b9d709eb4651",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"c_df = existing_zips[(existing_zips.ST == 'OH') & (existing_zips.NAME == 'Colerain')]\n",
"center = (c_df.LAT.mean(), c_df.LON.mean())\n",
"\n",
"m = Map(center=center, zoom=8)\n",
"\n",
"for row in c_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b340bd83cd1e4502a3eadf0ebf6f786f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"u_df = existing_zips[(existing_zips.ST == 'NJ') & (existing_zips.NAME == 'Union')]\n",
"center = (u_df.LAT.mean(), u_df.LON.mean())\n",
"\n",
"m = Map(center=center, zoom=10)\n",
"\n",
"for row in u_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately, in our top three cases, things are wrong. There appears to be a point that makes sense and then points that very much do not make sense.\n",
"\n",
"We can use a geocoder to try to sort some of this out. In this case, I'll limit the geocoder to look only at cities with more than 18900 people. This cutoff was chosen because this is where the data starts to get messy. The first town under that threshold in the zip code file is Park, MI. I could not find this town through simple web search. "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"sorted_by_pop = any_with_matching_zip[any_with_matching_zip._merge == 'left_only'].merge(demographics, how='left', left_on=['USPS_x', 'NAME'], right_on=['STNAME', 'NAME']).sort_values('POPESTIMATE2015', ascending=False)[['ST', 'NAME', 'STNAME','POPESTIMATE2015']]\n",
"to_code = sorted_by_pop[sorted_by_pop.POPESTIMATE2015 > 18900]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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>ST</th>\n",
" <th>NAME</th>\n",
" <th>STNAME</th>\n",
" <th>POPESTIMATE2015</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>919</th>\n",
" <td>NJ</td>\n",
" <td>Hamilton</td>\n",
" <td>New Jersey</td>\n",
" <td>89030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1037</th>\n",
" <td>OH</td>\n",
" <td>Colerain</td>\n",
" <td>Ohio</td>\n",
" <td>58838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>931</th>\n",
" <td>NJ</td>\n",
" <td>Union</td>\n",
" <td>New Jersey</td>\n",
" <td>58128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1428</th>\n",
" <td>PA</td>\n",
" <td>Millcreek</td>\n",
" <td>Pennsylvania</td>\n",
" <td>54003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>925</th>\n",
" <td>NJ</td>\n",
" <td>Monroe</td>\n",
" <td>New Jersey</td>\n",
" <td>44141</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ST NAME STNAME POPESTIMATE2015\n",
"919 NJ Hamilton New Jersey 89030\n",
"1037 OH Colerain Ohio 58838\n",
"931 NJ Union New Jersey 58128\n",
"1428 PA Millcreek Pennsylvania 54003\n",
"925 NJ Monroe New Jersey 44141"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_code.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"to_code = to_code[['ST', 'NAME', 'STNAME']].drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"coded_items = []\n",
"for row in to_code.itertuples():\n",
" state = row.ST\n",
" city = row.NAME\n",
" state_name = row.STNAME\n",
" g = geocoder.osm(f'{city}, {state}')\n",
" coded_items.append({'ST': state, 'NAME': city, 'LAT': g.y, 'LON': g.x, 'STNAME': state_name, 'confidence': g.confidence})\n",
" time.sleep(2)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"coded_df = pd.DataFrame.from_records(coded_items)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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>ST</th>\n",
" <th>NAME</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" <th>STNAME</th>\n",
" <th>confidence</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NJ</td>\n",
" <td>Hamilton</td>\n",
" <td>40.207057</td>\n",
" <td>-74.081251</td>\n",
" <td>New Jersey</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>OH</td>\n",
" <td>Colerain</td>\n",
" <td>40.125904</td>\n",
" <td>-80.808697</td>\n",
" <td>Ohio</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NJ</td>\n",
" <td>Union</td>\n",
" <td>40.665225</td>\n",
" <td>-74.304528</td>\n",
" <td>New Jersey</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>PA</td>\n",
" <td>Millcreek</td>\n",
" <td>42.101078</td>\n",
" <td>-80.020708</td>\n",
" <td>Pennsylvania</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NJ</td>\n",
" <td>Monroe</td>\n",
" <td>40.812878</td>\n",
" <td>-74.442376</td>\n",
" <td>New Jersey</td>\n",
" <td>6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ST NAME LAT LON STNAME confidence\n",
"0 NJ Hamilton 40.207057 -74.081251 New Jersey 6\n",
"1 OH Colerain 40.125904 -80.808697 Ohio 6\n",
"2 NJ Union 40.665225 -74.304528 New Jersey 1\n",
"3 PA Millcreek 42.101078 -80.020708 Pennsylvania 2\n",
"4 NJ Monroe 40.812878 -74.442376 New Jersey 6"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coded_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's start to check and clean up all of the cities with no zip codes and duplicate entries in the zip code file. First let's look at the ones with a matching place. We can see how their existing lat/lon values compare to the ones in the gazetteer. We had to drop the lat/lon to find the duplicates, so now we need to merge the original lat/lon values back in:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"no_original_lat_lon = merge_with_place[merge_with_place._merge == 'both'].drop(columns=['_merge', 'USPS_y']).rename(columns={'USPS_x': 'USPS'})\n",
"original_lat_lon = no_zip[no_zip.duplicated(subset=['USPS', 'ST', 'NAME'], keep=False)]\n",
"dupes_with_both_lat_lon = original_lat_lon.merge(no_original_lat_lon, how='inner', on=['USPS', 'ST', 'NAME'], indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"dupes_with_both_lat_lon['lat_diff'] = dupes_with_both_lat_lon.LAT - dupes_with_both_lat_lon.INTPTLAT\n",
"dupes_with_both_lat_lon['lon_diff'] = dupes_with_both_lat_lon.LON - dupes_with_both_lat_lon.INTPTLONG"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 4299.000000\n",
"mean -0.081105\n",
"std 1.386245\n",
"min -6.471268\n",
"25% -0.088025\n",
"50% 0.000000\n",
"75% 0.047225\n",
"max 6.479795\n",
"Name: lon_diff, dtype: float64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dupes_with_both_lat_lon.lon_diff.describe()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 4299.000000\n",
"mean 0.130407\n",
"std 0.891109\n",
"min -4.591193\n",
"25% -0.025385\n",
"50% 0.000000\n",
"75% 0.191900\n",
"max 4.439422\n",
"Name: lat_diff, dtype: float64"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dupes_with_both_lat_lon.lat_diff.describe()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"cities_with_reasonable_points = dupes_with_both_lat_lon[(np.absolute(dupes_with_both_lat_lon.lat_diff) < 0.1) | (np.absolute(dupes_with_both_lat_lon.lon_diff) < 0.1)][['ST', 'NAME']].drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"flag_for_no_reasonable = dupes_with_both_lat_lon.drop(columns=['_merge']).merge(cities_with_reasonable_points, how='left', indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(76, 2)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flag_for_no_reasonable[flag_for_no_reasonable._merge == 'left_only'][['ST', 'NAME']].drop_duplicates().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With duplicate cities that have a matching place in the gazetteer, we find much more messy data. We will keep all rows from the existing zip code file that are less than 0.1 degrees different for both lat and lon. Throwing out the rest leaves us with 76 cities that do not have an entry in the zip code file. For these cities, we will just take the entry in the place gazetteer. We can now build a DataFrame that is ready for export for all cities that had no zip code, but multiple entries in the zip code file."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"geocoded_export_df = coded_df.drop(columns=['confidence']).rename(columns={'STNAME': 'USPS'})\n",
"geocoded_export_df['ZCTA5'] = np.nan\n",
"geocoded_export_df = geocoded_export_df[['USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]\n",
"reasonable_places_export_df = dupes_with_both_lat_lon[(np.absolute(dupes_with_both_lat_lon.lat_diff) < 0.1) | (np.absolute(dupes_with_both_lat_lon.lon_diff) < 0.1)]\n",
"reasonable_places_export_df = reasonable_places_export_df.drop(columns=['INTPTLAT', 'INTPTLONG', '_merge', 'lat_diff', 'lon_diff'])\n",
"reasonable_places_export_df = reasonable_places_export_df[['USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Getting the \"messy\" duplicate cities with no zip code and no place and less than 18900 residents is somewhat tricky because it contains entries for parts of cities that were geocoded, so we need to remove those."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"low_pop_leave_alone = sorted_by_pop[sorted_by_pop.POPESTIMATE2015 < 18900]\n",
"low_pop_leave_alone = low_pop_leave_alone.merge(coded_df, how='outer', on=['ST', 'NAME', 'STNAME'], indicator=True)\n",
"low_pop_leave_alone = low_pop_leave_alone[low_pop_leave_alone._merge == 'left_only'][['ST', 'NAME', 'STNAME']].drop_duplicates()\n",
"low_pop_leave_alone = low_pop_leave_alone.merge(existing_zips, how='inner', left_on=['ST', 'NAME', 'STNAME'], right_on=['ST', 'NAME', 'USPS'], indicator=True)\n",
"low_pop_leave_alone = low_pop_leave_alone.drop(columns=['Unnamed: 0', '_merge'])\n",
"low_pop_export_df = low_pop_leave_alone[['USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now get the last 76 cities with no reasonable existing points and pull locations from the place gazetteer"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"no_reasonable_points = flag_for_no_reasonable[flag_for_no_reasonable._merge == 'left_only'][['ST', 'NAME', 'USPS']].drop_duplicates()\n",
"backfill = no_reasonable_points.merge(place_g, how='inner', left_on=['ST', 'NAME'], right_on=['USPS', 'NAME'])\n",
"backfill = backfill[['USPS_x', 'ST', 'NAME', 'INTPTLAT', 'INTPTLONG']]\n",
"backfill['ZCTA5'] = np.nan\n",
"backfill_export_df = backfill.rename(columns={'USPS_x': 'USPS', 'INTPTLAT': 'LAT', 'INTPTLONG': 'LON'})[['USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create the DataFrame for all rows representing cities without a zip code that had multiple entries in the original zip code file"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"multi_no_zip_export_df = pd.concat([geocoded_export_df, reasonable_places_export_df, low_pop_export_df, backfill_export_df])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can look at places in the zip code file that have only a single row, but no zip code."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"non_dupes = no_zip[~no_zip.duplicated(subset=['USPS', 'ST', 'NAME'], keep=False)]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(15671, 6)"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_dupes.shape"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"non_with_place = non_dupes.merge(place_g[['USPS', 'NAME', 'INTPTLAT', 'INTPTLONG']], how='outer', left_on=['ST', 'NAME'], right_on=['USPS', 'NAME'], indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6122, 10)"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_with_place[non_with_place._merge == 'both'].shape"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"non_with_place['lat_diff'] = non_with_place.LAT - non_with_place.INTPTLAT\n",
"non_with_place['lon_diff'] = non_with_place.LON - non_with_place.INTPTLONG"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 6122.000000\n",
"mean 0.010262\n",
"std 0.297057\n",
"min -4.317369\n",
"25% -0.012250\n",
"50% 0.000000\n",
"75% 0.015475\n",
"max 4.954432\n",
"Name: lat_diff, dtype: float64"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_with_place.lat_diff.describe()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 6122.000000\n",
"mean -0.009884\n",
"std 0.429038\n",
"min -5.993643\n",
"25% -0.018097\n",
"50% 0.000000\n",
"75% 0.015364\n",
"max 5.523079\n",
"Name: lon_diff, dtype: float64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_with_place.lon_diff.describe()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(819, 12)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_with_place[(np.absolute(non_with_place.lat_diff) > 0.1) | (np.absolute(non_with_place.lon_diff) > 0.1)].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This data appears to be less messy than the previous data set. The mean difference is fairly small. There are 819 cases where the data is off by more than 0.1. We can use the gazetter lat / lon for these places. Next, we can look at the entries that do not have a match in the place gazetteer. First we will eliminate those that have an row in the zip code file with a zip code"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"no_zip_no_place_singles = non_with_place[non_with_place._merge == 'left_only'].drop(columns='_merge')\n",
"no_zip_no_place_singles = no_zip_no_place_singles.merge(zip_export_df, how='outer', left_on=['ST', 'NAME', 'USPS_x'], right_on=['ST', 'NAME', 'USPS'], indicator=True)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7344, 16)"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_zip_no_place_singles[no_zip_no_place_singles._merge == 'left_only'].shape"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2223, 3)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_zip_no_place_singles[no_zip_no_place_singles._merge == 'both'][['ST', 'NAME', 'USPS']].drop_duplicates().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have 7344 cities that have only a single entry in the zip code file with out a zip code and no corresponding entry with a zip code. 2223 have existing matches in the zip code file."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"nznpnm = no_zip_no_place_singles[no_zip_no_place_singles._merge == 'left_only']"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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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" 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>NAME</th>\n",
" <th>USPS_x</th>\n",
" <th>ID</th>\n",
" <th>COUNTY</th>\n",
" <th>STNAME</th>\n",
" <th>POPESTIMATE2015</th>\n",
" <th>CTYNAME</th>\n",
" <th>TOT_POP</th>\n",
" <th>TOT_MALE</th>\n",
" <th>TOT_FEMALE</th>\n",
" <th>...</th>\n",
" <th>35..50</th>\n",
" <th>50..75</th>\n",
" <th>75..100</th>\n",
" <th>100..150</th>\n",
" <th>150..200</th>\n",
" <th>200..999</th>\n",
" <th>LESS_THAN_HS</th>\n",
" <th>HS_DEGREE</th>\n",
" <th>SOME_COLLEGE</th>\n",
" <th>BS_DEGREE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>Honolulu</td>\n",
" <td>Hawaii</td>\n",
" <td>3301</td>\n",
" <td>3</td>\n",
" <td>Hawaii</td>\n",
" <td>998714</td>\n",
" <td>Honolulu County</td>\n",
" <td>998714</td>\n",
" <td>0.508581</td>\n",
" <td>0.491419</td>\n",
" <td>...</td>\n",
" <td>0.12</td>\n",
" <td>0.18</td>\n",
" <td>0.13</td>\n",
" <td>0.16</td>\n",
" <td>0.06</td>\n",
" <td>0.07</td>\n",
" <td>0.07</td>\n",
" <td>0.28</td>\n",
" <td>0.52</td>\n",
" <td>0.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6786</th>\n",
" <td>Henrico County</td>\n",
" <td>Virginia</td>\n",
" <td>31776</td>\n",
" <td>87</td>\n",
" <td>Virginia</td>\n",
" <td>325155</td>\n",
" <td>Henrico County</td>\n",
" <td>325155</td>\n",
" <td>0.472190</td>\n",
" <td>0.527810</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>Augusta</td>\n",
" <td>Georgia</td>\n",
" <td>3169</td>\n",
" <td>245</td>\n",
" <td>Georgia</td>\n",
" <td>197182</td>\n",
" <td>Richmond County</td>\n",
" <td>201793</td>\n",
" <td>0.484754</td>\n",
" <td>0.515246</td>\n",
" <td>...</td>\n",
" <td>0.15</td>\n",
" <td>0.15</td>\n",
" <td>0.08</td>\n",
" <td>0.06</td>\n",
" <td>0.02</td>\n",
" <td>0.01</td>\n",
" <td>0.20</td>\n",
" <td>0.35</td>\n",
" <td>0.34</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td>Athens</td>\n",
" <td>Georgia</td>\n",
" <td>2805</td>\n",
" <td>59</td>\n",
" <td>Georgia</td>\n",
" <td>122604</td>\n",
" <td>Clarke County</td>\n",
" <td>123912</td>\n",
" <td>0.475450</td>\n",
" <td>0.524550</td>\n",
" <td>...</td>\n",
" <td>0.12</td>\n",
" <td>0.13</td>\n",
" <td>0.07</td>\n",
" <td>0.07</td>\n",
" <td>0.02</td>\n",
" <td>0.03</td>\n",
" <td>0.02</td>\n",
" <td>0.22</td>\n",
" <td>0.61</td>\n",
" <td>0.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3652</th>\n",
" <td>Edison</td>\n",
" <td>New Jersey</td>\n",
" <td>18663</td>\n",
" <td>23</td>\n",
" <td>New Jersey</td>\n",
" <td>102701</td>\n",
" <td>Middlesex County</td>\n",
" <td>840900</td>\n",
" <td>0.492243</td>\n",
" <td>0.507757</td>\n",
" <td>...</td>\n",
" <td>0.08</td>\n",
" <td>0.15</td>\n",
" <td>0.13</td>\n",
" <td>0.22</td>\n",
" <td>0.11</td>\n",
" <td>0.12</td>\n",
" <td>0.09</td>\n",
" <td>0.21</td>\n",
" <td>0.45</td>\n",
" <td>0.23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 48 columns</p>\n",
"</div>"
],
"text/plain": [
" NAME USPS_x ID COUNTY STNAME POPESTIMATE2015 \\\n",
"109 Honolulu Hawaii 3301 3 Hawaii 998714 \n",
"6786 Henrico County Virginia 31776 87 Virginia 325155 \n",
"108 Augusta Georgia 3169 245 Georgia 197182 \n",
"107 Athens Georgia 2805 59 Georgia 122604 \n",
"3652 Edison New Jersey 18663 23 New Jersey 102701 \n",
"\n",
" CTYNAME TOT_POP TOT_MALE TOT_FEMALE ... 35..50 50..75 \\\n",
"109 Honolulu County 998714 0.508581 0.491419 ... 0.12 0.18 \n",
"6786 Henrico County 325155 0.472190 0.527810 ... NaN NaN \n",
"108 Richmond County 201793 0.484754 0.515246 ... 0.15 0.15 \n",
"107 Clarke County 123912 0.475450 0.524550 ... 0.12 0.13 \n",
"3652 Middlesex County 840900 0.492243 0.507757 ... 0.08 0.15 \n",
"\n",
" 75..100 100..150 150..200 200..999 LESS_THAN_HS HS_DEGREE \\\n",
"109 0.13 0.16 0.06 0.07 0.07 0.28 \n",
"6786 NaN NaN NaN NaN NaN NaN \n",
"108 0.08 0.06 0.02 0.01 0.20 0.35 \n",
"107 0.07 0.07 0.02 0.03 0.02 0.22 \n",
"3652 0.13 0.22 0.11 0.12 0.09 0.21 \n",
"\n",
" SOME_COLLEGE BS_DEGREE \n",
"109 0.52 0.11 \n",
"6786 NaN NaN \n",
"108 0.34 0.09 \n",
"107 0.61 0.14 \n",
"3652 0.45 0.23 \n",
"\n",
"[5 rows x 48 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nznpnm[['NAME', 'USPS_x']].merge(demographics, how='left', left_on=['USPS_x', 'NAME'], right_on=['STNAME', 'NAME']).sort_values('POPESTIMATE2015', ascending=False).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A quick check by hand of the top entries by population showed these to be reasonable. We will leave them in the file as we have no other way to check them and we would lose information otherwise. It is clear that some of the data in the demographics file could use some updating. The population listed for Honolulu is for the county, not the city.\n",
"\n",
"For those with a matching zip code entry, we can check how close the entry in the existing zip code file is to other entries in the file."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"single_with_zip_match = no_zip_no_place_singles[no_zip_no_place_singles._merge == 'both'][['USPS', 'NAME', 'ST', 'ZCTA5_y', 'LAT_x', 'LAT_y', 'LON_x', 'LON_y']]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"single_with_zip_match['lat_diff'] = single_with_zip_match.LAT_x - single_with_zip_match.LAT_y\n",
"single_with_zip_match['lon_diff'] = single_with_zip_match.LON_x - single_with_zip_match.LON_y"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(115, 2)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"single_with_zip_match[(np.absolute(single_with_zip_match.lat_diff) > 0.3) | (np.absolute(single_with_zip_match.lon_diff) > 0.3)][['ST', 'NAME']].drop_duplicates().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It appears that the vast majority of the cities are within 0.3 degrees of a corresponding record. That's roughly within 18 miles, but obviously highly dependent on the locations of the points. We will keep the points that fall within the threshold.\n",
"\n",
"We can now get the single row, no zip code cities ready for export. "
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"nwp_close_export = non_with_place[(np.absolute(non_with_place.lat_diff) < 0.1) & (np.absolute(non_with_place.lon_diff) < 0.1)]\n",
"nwp_close_export = nwp_close_export[['USPS_x', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]\n",
"nwp_close_export = nwp_close_export.rename(columns={'USPS_x': 'USPS'})\n",
"nwp_far_export = non_with_place[(np.absolute(non_with_place.lat_diff) > 0.1) | (np.absolute(non_with_place.lon_diff) > 0.1)]\n",
"nwp_far_export = nwp_far_export[['USPS_x', 'ST', 'NAME', 'ZCTA5', 'INTPTLAT', 'INTPTLONG']]\n",
"nwp_far_export = nwp_far_export.rename(columns={'USPS_x': 'USPS', 'INTPTLAT': 'LAT', 'INTPTLONG': 'LON'})\n",
"\n",
"nznpnm_export = nznpnm[['USPS_x', 'ST', 'NAME', 'ZCTA5_x', 'LAT_x', 'LON_x']]\n",
"nznpnm_export = nznpnm_export.rename(columns={'USPS_x': 'USPS', 'ZCTA5_x': 'ZCTA5', 'LAT_x': 'LAT', 'LON_x': 'LON'})\n",
"nznpmut = single_with_zip_match[(np.absolute(single_with_zip_match.lat_diff) < 0.3) & (np.absolute(single_with_zip_match.lon_diff) < 0.3)]\n",
"nznpmut_export = nznpmut[['USPS', 'ST', 'NAME', 'LAT_x', 'LON_x']].drop_duplicates()\n",
"nznpmut_export = nznpmut_export.rename(columns={'LAT_x': 'LAT', 'LON_x': 'LON'})\n",
"nznpmut_export['ZCTA5'] = np.nan\n",
"nznpmut_export = nznpmut_export[['USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]\n",
"\n",
"single_no_zips_export = pd.concat([nwp_close_export, nwp_far_export, nznpnm_export, nznpmut_export])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can bring all of our export DataFrames together"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"full_export_df = pd.concat([zip_export_df, multi_no_zip_export_df, single_no_zips_export]).drop_duplicates()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can apply the correct sort and add the mysterious empty column."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th></th>\n",
" <th>USPS</th>\n",
" <th>ST</th>\n",
" <th>NAME</th>\n",
" <th>ZCTA5</th>\n",
" <th>LAT</th>\n",
" <th>LON</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Abbeville</td>\n",
" <td>36310</td>\n",
" <td>31.595139</td>\n",
" <td>-85.208847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Abbeville</td>\n",
" <td>NaN</td>\n",
" <td>31.596562</td>\n",
" <td>-85.304308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Adamsville</td>\n",
" <td>35005</td>\n",
" <td>33.595950</td>\n",
" <td>-87.000649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Adamsville</td>\n",
" <td>35060</td>\n",
" <td>33.559629</td>\n",
" <td>-86.953465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>Alabama</td>\n",
" <td>AL</td>\n",
" <td>Adamsville</td>\n",
" <td>35062</td>\n",
" <td>33.724920</td>\n",
" <td>-87.010252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" USPS ST NAME ZCTA5 LAT LON\n",
"0 NaN Alabama AL Abbeville 36310 31.595139 -85.208847\n",
"0 NaN Alabama AL Abbeville NaN 31.596562 -85.304308\n",
"1 NaN Alabama AL Adamsville 35005 33.595950 -87.000649\n",
"7 NaN Alabama AL Adamsville 35060 33.559629 -86.953465\n",
"8 NaN Alabama AL Adamsville 35062 33.724920 -87.010252"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_export_df[''] = np.nan\n",
"full_export_df = full_export_df[['', 'USPS', 'ST', 'NAME', 'ZCTA5', 'LAT', 'LON']]\n",
"full_export_df = full_export_df.sort_values(['USPS', 'NAME', 'ZCTA5'])\n",
"full_export_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(56933, 7)"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check for integrity with the demographics file"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 54)"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"check_df = demographics.merge(full_export_df, how='outer', left_on=['STNAME', 'NAME'], right_on=['USPS', 'NAME'], indicator=True)\n",
"check_df[check_df._merge == 'left_only'].shape"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 89,
"metadata": {},
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],
"source": [
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The merge shows that there are no cases where there is a row in the demographics file, but no corresponding row in the zip codes and likewise, there is no place in the zip code file, but no row in demographics. We can now look at plots of larger cities to see if it looks OK.\n",
"\n",
"New York City"
]
},
{
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"execution_count": 92,
"metadata": {},
"outputs": [
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]
},
"metadata": {},
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"check_df = full_export_df[(full_export_df.ST == 'NY') & (full_export_df.NAME == 'New York')]\n",
"center = (check_df.LAT.mean(), check_df.LON.mean())\n",
"\n",
"m = Map(center=center, zoom=10)\n",
"\n",
"for row in check_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Los Angeles"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
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},
"metadata": {},
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"check_df = full_export_df[(full_export_df.ST == 'CA') & (full_export_df.NAME == 'Los Angeles')]\n",
"center = (check_df.LAT.mean(), check_df.LON.mean())\n",
"\n",
"m = Map(center=center, zoom=10)\n",
"\n",
"for row in check_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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]
},
{
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"execution_count": 95,
"metadata": {},
"outputs": [
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},
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"\n",
"m = Map(center=center, zoom=10)\n",
"\n",
"for row in check_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
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},
{
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"metadata": {},
"source": [
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]
},
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"execution_count": 96,
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},
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"center = (check_df.LAT.mean(), check_df.LON.mean())\n",
"\n",
"m = Map(center=center, zoom=10)\n",
"\n",
"for row in check_df.itertuples():\n",
" m.add_layer(Marker(location=(row.LAT,row.LON), draggable=False))\n",
"\n",
"m"
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},
{
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},
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