Find Census Tract from Point Coordinates

M. Fawcett February 18, 2024
This notebook explores finding which Delaware Census Tract a street address longitude/latitude coordinate falls within.

Uses Python Shapely and Geopandas packages.

Addresses and their associated longitude and latitude coordinates for Delaware were found here:
https://www.transportation.gov/gis/national-address-database

There is code in this notebook that shows how the Delaware addresses were extracted from the national address CSV file and placed into a separate new CSV file called NAD_r15_Delaware.csv.

# Import packages needed for this analysis
import requests  # to make http requests for data using census web API
import pandas as pd  # to make dataframes
import numpy as np  # to do numerical stuff
import matplotlib.pyplot as plt
import geopandas as gpd  # Extension of pandas for creating *geospatial* dataframes
import json  # for interactive maps using Bokeh
import bokeh  # for interactive maps using json
import csv

# See: https://www.matecdev.com/posts/point-in-polygon.html
from shapely.geometry import Point, Polygon  

### Import this custom config file containing MY google maps api key needed by Bokeh to show maps.
### (do not share this key!)
import config

# Set option to make Pandas dataframe output more readable
pd.set_option('display.max_columns', None)

### Set some global variables
# Working directory path - This is my path on a Mac
mydir = "~/Documents/Data Science/Points Inside Census Tract"

### Test set up by getting Delaware census tract populations for 2017

# Base URL
# Components of the base URL go into variables.
HOST = "https://api.census.gov/data"
year = "2017"
dataset = "acs/acs5"  # The data product

# Code to join the base url components
base_url = "/".join([HOST, year, dataset])

# Form the predicates dictionary
predicates = {}  # initialize empty dictionary
get_vars = ["NAME", "B01003_001E"]   # Get Name of the tract and the population. The "E" indicates estimate.
                                     # The "B" indicates it is a base table. Base tables are the most detailed.
predicates["get"] = ",".join(get_vars)
predicates["for"] = "tract:*"   # Census tracts are the desired geography. "*" means all tracts.
predicates["in"] = "state:10"   # Delaware only

# The table code does not need to be supplied.  Variable codes are unique within data products.
# Specifying the data product (ACS 5 year), year and variable code is sufficient.  

# Make the request.  Results get loaded into the variable "response_population".
response_population = requests.get(base_url, params = predicates)

# Display the result
print(response_population.text)

# When the results are displayed there are going to be five values for each tract.  This appears contrary 
# to the get_vars list only specifying two variables.  The results will have a Name, population (B01003_001E), 
# state code, county code and tract code.  This is just the way it works.  You will need to examine the results
# and then decide which columns to use, or rename, when moving the data into a dataframe.

[["NAME","B01003_001E","state","county","tract"],
["Census Tract 4, New Castle County, Delaware","2940","10","003","000400"],
["Census Tract 13, New Castle County, Delaware","3503","10","003","001300"],
["Census Tract 26, New Castle County, Delaware","3968","10","003","002600"],
["Census Tract 102, New Castle County, Delaware","2144","10","003","010200"],
["Census Tract 103, New Castle County, Delaware","3306","10","003","010300"],
["Census Tract 108, New Castle County, Delaware","4906","10","003","010800"],
["Census Tract 6.02, New Castle County, Delaware","3247","10","003","000602"],
["Census Tract 16, New Castle County, Delaware","2112","10","003","001600"],
["Census Tract 23, New Castle County, Delaware","2814","10","003","002300"],
["Census Tract 120, New Castle County, Delaware","4716","10","003","012000"],
["Census Tract 122, New Castle County, Delaware","4481","10","003","012200"],
["Census Tract 129, New Castle County, Delaware","4593","10","003","012900"],
["Census Tract 132, New Castle County, Delaware","2744","10","003","013200"],
["Census Tract 136.04, New Castle County, Delaware","4458","10","003","013604"],
["Census Tract 142, New Castle County, Delaware","2152","10","003","014200"],
["Census Tract 147.02, New Castle County, Delaware","1785","10","003","014702"],
["Census Tract 147.05, New Castle County, Delaware","5482","10","003","014705"],
["Census Tract 12, New Castle County, Delaware","1618","10","003","001200"],
["Census Tract 114, New Castle County, Delaware","3426","10","003","011400"],
["Census Tract 126, New Castle County, Delaware","2855","10","003","012600"],
["Census Tract 127, New Castle County, Delaware","4027","10","003","012700"],
["Census Tract 136.08, New Castle County, Delaware","2112","10","003","013608"],
["Census Tract 136.11, New Castle County, Delaware","5478","10","003","013611"],
["Census Tract 11, New Castle County, Delaware","3234","10","003","001100"],
["Census Tract 27, New Castle County, Delaware","2433","10","003","002700"],
["Census Tract 104, New Castle County, Delaware","4650","10","003","010400"],
["Census Tract 112.04, New Castle County, Delaware","3434","10","003","011204"],
["Census Tract 119, New Castle County, Delaware","3434","10","003","011900"],
["Census Tract 125, New Castle County, Delaware","5269","10","003","012500"],
["Census Tract 131, New Castle County, Delaware","2468","10","003","013100"],
["Census Tract 133, New Castle County, Delaware","1917","10","003","013300"],
["Census Tract 138, New Castle County, Delaware","5401","10","003","013800"],
["Census Tract 140, New Castle County, Delaware","5192","10","003","014000"],
["Census Tract 141, New Castle County, Delaware","4269","10","003","014100"],
["Census Tract 144.03, New Castle County, Delaware","5809","10","003","014403"],
["Census Tract 148.03, New Castle County, Delaware","4129","10","003","014803"],
["Census Tract 150, New Castle County, Delaware","5824","10","003","015000"],
["Census Tract 159, New Castle County, Delaware","3893","10","003","015900"],
["Census Tract 164.01, New Castle County, Delaware","6641","10","003","016401"],
["Census Tract 136.12, New Castle County, Delaware","6306","10","003","013612"],
["Census Tract 145.02, New Castle County, Delaware","6165","10","003","014502"],
["Census Tract 147.03, New Castle County, Delaware","4585","10","003","014703"],
["Census Tract 151, New Castle County, Delaware","3474","10","003","015100"],
["Census Tract 160, New Castle County, Delaware","2850","10","003","016000"],
["Census Tract 510.05, Sussex County, Delaware","4912","10","005","051005"],
["Census Tract 510.06, Sussex County, Delaware","3046","10","005","051006"],
["Census Tract 510.07, Sussex County, Delaware","5019","10","005","051007"],
["Census Tract 511.01, Sussex County, Delaware","742","10","005","051101"],
["Census Tract 511.02, Sussex County, Delaware","946","10","005","051102"],
["Census Tract 511.03, Sussex County, Delaware","959","10","005","051103"],
["Census Tract 512.01, Sussex County, Delaware","1500","10","005","051201"],
["Census Tract 512.02, Sussex County, Delaware","903","10","005","051202"],
["Census Tract 512.03, Sussex County, Delaware","727","10","005","051203"],
["Census Tract 512.04, Sussex County, Delaware","577","10","005","051204"],
["Census Tract 512.05, Sussex County, Delaware","748","10","005","051205"],
["Census Tract 513.05, Sussex County, Delaware","2652","10","005","051305"],
["Census Tract 513.06, Sussex County, Delaware","2854","10","005","051306"],
["Census Tract 9900, Sussex County, Delaware","0","10","005","990000"],
["Census Tract 109, New Castle County, Delaware","2630","10","003","010900"],
["Census Tract 124, New Castle County, Delaware","4617","10","003","012400"],
["Census Tract 111, New Castle County, Delaware","2900","10","003","011100"],
["Census Tract 112.02, New Castle County, Delaware","4106","10","003","011202"],
["Census Tract 118, New Castle County, Delaware","3991","10","003","011800"],
["Census Tract 134, New Castle County, Delaware","2585","10","003","013400"],
["Census Tract 136.13, New Castle County, Delaware","5814","10","003","013613"],
["Census Tract 144.02, New Castle County, Delaware","3771","10","003","014402"],
["Census Tract 145.01, New Castle County, Delaware","2130","10","003","014501"],
["Census Tract 148.05, New Castle County, Delaware","10261","10","003","014805"],
["Census Tract 161, New Castle County, Delaware","1850","10","003","016100"],
["Census Tract 163.02, New Castle County, Delaware","7470","10","003","016302"],
["Census Tract 168.01, New Castle County, Delaware","5720","10","003","016801"],
["Census Tract 22, New Castle County, Delaware","2527","10","003","002200"],
["Census Tract 110, New Castle County, Delaware","3286","10","003","011000"],
["Census Tract 116, New Castle County, Delaware","3505","10","003","011600"],
["Census Tract 130, New Castle County, Delaware","1850","10","003","013000"],
["Census Tract 166.04, New Castle County, Delaware","12424","10","003","016604"],
["Census Tract 147.06, New Castle County, Delaware","2930","10","003","014706"],
["Census Tract 148.07, New Castle County, Delaware","8885","10","003","014807"],
["Census Tract 154, New Castle County, Delaware","3105","10","003","015400"],
["Census Tract 2, New Castle County, Delaware","6576","10","003","000200"],
["Census Tract 9, New Castle County, Delaware","2256","10","003","000900"],
["Census Tract 101.01, New Castle County, Delaware","4126","10","003","010101"],
["Census Tract 112.05, New Castle County, Delaware","1969","10","003","011205"],
["Census Tract 135.01, New Castle County, Delaware","6791","10","003","013501"],
["Census Tract 143, New Castle County, Delaware","6725","10","003","014300"],
["Census Tract 139.01, New Castle County, Delaware","3241","10","003","013901"],
["Census Tract 24, New Castle County, Delaware","4835","10","003","002400"],
["Census Tract 152, New Castle County, Delaware","5798","10","003","015200"],
["Census Tract 15, New Castle County, Delaware","2116","10","003","001500"],
["Census Tract 112.03, New Castle County, Delaware","4790","10","003","011203"],
["Census Tract 14, New Castle County, Delaware","2367","10","003","001400"],
["Census Tract 121, New Castle County, Delaware","2732","10","003","012100"],
["Census Tract 136.07, New Castle County, Delaware","5751","10","003","013607"],
["Census Tract 156, New Castle County, Delaware","2947","10","003","015600"],
["Census Tract 112.06, New Castle County, Delaware","4362","10","003","011206"],
["Census Tract 5, New Castle County, Delaware","3375","10","003","000500"],
["Census Tract 135.03, New Castle County, Delaware","7525","10","003","013503"],
["Census Tract 501.01, Sussex County, Delaware","3861","10","005","050101"],
["Census Tract 504.01, Sussex County, Delaware","3781","10","005","050401"],
["Census Tract 506.01, Sussex County, Delaware","5410","10","005","050601"],
["Census Tract 503.01, Sussex County, Delaware","8341","10","005","050301"],
["Census Tract 505.01, Sussex County, Delaware","3965","10","005","050501"],
["Census Tract 513.03, Sussex County, Delaware","5069","10","005","051303"],
["Census Tract 504.03, Sussex County, Delaware","3639","10","005","050403"],
["Census Tract 508.03, Sussex County, Delaware","8073","10","005","050803"],
["Census Tract 515, Sussex County, Delaware","4918","10","005","051500"],
["Census Tract 517.02, Sussex County, Delaware","5237","10","005","051702"],
["Census Tract 519, Sussex County, Delaware","4260","10","005","051900"],
["Census Tract 506.02, Sussex County, Delaware","6488","10","005","050602"],
["Census Tract 518.01, Sussex County, Delaware","4565","10","005","051801"],
["Census Tract 503.02, Sussex County, Delaware","5277","10","005","050302"],
["Census Tract 507.01, Sussex County, Delaware","4271","10","005","050701"],
["Census Tract 513.01, Sussex County, Delaware","6565","10","005","051301"],
["Census Tract 513.02, Sussex County, Delaware","3708","10","005","051302"],
["Census Tract 517.01, Sussex County, Delaware","3872","10","005","051701"],
["Census Tract 518.02, Sussex County, Delaware","4716","10","005","051802"],
["Census Tract 501.03, Sussex County, Delaware","4589","10","005","050103"],
["Census Tract 502, Sussex County, Delaware","3894","10","005","050200"],
["Census Tract 510.03, Sussex County, Delaware","5740","10","005","051003"],
["Census Tract 508.02, Sussex County, Delaware","5798","10","005","050802"],
["Census Tract 514, Sussex County, Delaware","3874","10","005","051400"],
["Census Tract 508.01, Sussex County, Delaware","3750","10","005","050801"],
["Census Tract 428, Kent County, Delaware","6877","10","001","042800"],
["Census Tract 422.02, Kent County, Delaware","9532","10","001","042202"],
["Census Tract 431, Kent County, Delaware","2867","10","001","043100"],
["Census Tract 402.01, Kent County, Delaware","5311","10","001","040201"],
["Census Tract 416, Kent County, Delaware","2060","10","001","041600"],
["Census Tract 421, Kent County, Delaware","3766","10","001","042100"],
["Census Tract 430, Kent County, Delaware","5332","10","001","043000"],
["Census Tract 417.02, Kent County, Delaware","4405","10","001","041702"],
["Census Tract 402.02, Kent County, Delaware","15267","10","001","040202"],
["Census Tract 402.03, Kent County, Delaware","6055","10","001","040203"],
["Census Tract 409, Kent County, Delaware","3016","10","001","040900"],
["Census Tract 413, Kent County, Delaware","2273","10","001","041300"],
["Census Tract 417.01, Kent County, Delaware","6097","10","001","041701"],
["Census Tract 420, Kent County, Delaware","3039","10","001","042000"],
["Census Tract 429, Kent County, Delaware","4810","10","001","042900"],
["Census Tract 407, Kent County, Delaware","4649","10","001","040700"],
["Census Tract 414, Kent County, Delaware","3622","10","001","041400"],
["Census Tract 415, Kent County, Delaware","4317","10","001","041500"],
["Census Tract 418.01, Kent County, Delaware","10075","10","001","041801"],
["Census Tract 418.02, Kent County, Delaware","5313","10","001","041802"],
["Census Tract 401, Kent County, Delaware","6849","10","001","040100"],
["Census Tract 411, Kent County, Delaware","3604","10","001","041100"],
["Census Tract 412, Kent County, Delaware","4719","10","001","041200"],
["Census Tract 419, Kent County, Delaware","5485","10","001","041900"],
["Census Tract 422.01, Kent County, Delaware","11915","10","001","042201"],
["Census Tract 425, Kent County, Delaware","3837","10","001","042500"],
["Census Tract 410, Kent County, Delaware","6445","10","001","041000"],
["Census Tract 144.04, New Castle County, Delaware","3894","10","003","014404"],
["Census Tract 149.03, New Castle County, Delaware","7561","10","003","014903"],
["Census Tract 3, New Castle County, Delaware","2794","10","003","000300"],
["Census Tract 6.01, New Castle County, Delaware","3080","10","003","000601"],
["Census Tract 21, New Castle County, Delaware","1968","10","003","002100"],
["Census Tract 25, New Castle County, Delaware","3061","10","003","002500"],
["Census Tract 112.01, New Castle County, Delaware","2428","10","003","011201"],
["Census Tract 113, New Castle County, Delaware","2147","10","003","011300"],
["Census Tract 115, New Castle County, Delaware","3015","10","003","011500"],
["Census Tract 117, New Castle County, Delaware","3992","10","003","011700"],
["Census Tract 123, New Castle County, Delaware","2646","10","003","012300"],
["Census Tract 148.08, New Castle County, Delaware","6461","10","003","014808"],
["Census Tract 136.10, New Castle County, Delaware","6250","10","003","013610"],
["Census Tract 137, New Castle County, Delaware","4228","10","003","013700"],
["Census Tract 149.04, New Castle County, Delaware","5045","10","003","014904"],
["Census Tract 162, New Castle County, Delaware","2485","10","003","016200"],
["Census Tract 163.01, New Castle County, Delaware","5418","10","003","016301"],
["Census Tract 166.01, New Castle County, Delaware","12533","10","003","016601"],
["Census Tract 166.02, New Castle County, Delaware","8455","10","003","016602"],
["Census Tract 169.01, New Castle County, Delaware","2117","10","003","016901"],
["Census Tract 405.01, Kent County, Delaware","4490","10","001","040501"],
["Census Tract 405.02, Kent County, Delaware","2252","10","001","040502"],
["Census Tract 432.02, Kent County, Delaware","4219","10","001","043202"],
["Census Tract 433, Kent County, Delaware","6085","10","001","043300"],
["Census Tract 434, Kent County, Delaware","4562","10","001","043400"],
["Census Tract 9900, Kent County, Delaware","0","10","001","990000"],
["Census Tract 19.02, New Castle County, Delaware","1885","10","003","001902"],
["Census Tract 28, New Castle County, Delaware","1527","10","003","002800"],
["Census Tract 29, New Castle County, Delaware","3587","10","003","002900"],
["Census Tract 30.02, New Castle County, Delaware","3453","10","003","003002"],
["Census Tract 101.04, New Castle County, Delaware","3752","10","003","010104"],
["Census Tract 105.02, New Castle County, Delaware","6121","10","003","010502"],
["Census Tract 107.02, New Castle County, Delaware","5439","10","003","010702"],
["Census Tract 135.05, New Castle County, Delaware","2924","10","003","013505"],
["Census Tract 135.06, New Castle County, Delaware","4466","10","003","013506"],
["Census Tract 136.14, New Castle County, Delaware","3105","10","003","013614"],
["Census Tract 136.15, New Castle County, Delaware","3916","10","003","013615"],
["Census Tract 139.03, New Castle County, Delaware","5356","10","003","013903"],
["Census Tract 139.04, New Castle County, Delaware","8290","10","003","013904"],
["Census Tract 148.09, New Castle County, Delaware","8127","10","003","014809"],
["Census Tract 148.10, New Castle County, Delaware","7820","10","003","014810"],
["Census Tract 149.06, New Castle County, Delaware","4842","10","003","014906"],
["Census Tract 149.07, New Castle County, Delaware","4889","10","003","014907"],
["Census Tract 149.08, New Castle County, Delaware","2088","10","003","014908"],
["Census Tract 149.09, New Castle County, Delaware","6386","10","003","014909"],
["Census Tract 155.02, New Castle County, Delaware","2580","10","003","015502"],
["Census Tract 158.02, New Castle County, Delaware","2354","10","003","015802"],
["Census Tract 163.05, New Castle County, Delaware","8230","10","003","016305"],
["Census Tract 164.04, New Castle County, Delaware","3412","10","003","016404"],
["Census Tract 166.08, New Castle County, Delaware","4738","10","003","016608"],
["Census Tract 168.04, New Castle County, Delaware","7334","10","003","016804"],
["Census Tract 169.04, New Castle County, Delaware","3954","10","003","016904"],
["Census Tract 9801, New Castle County, Delaware","0","10","003","980100"],
["Census Tract 9901, New Castle County, Delaware","0","10","003","990100"],
["Census Tract 501.04, Sussex County, Delaware","4520","10","005","050104"],
["Census Tract 501.05, Sussex County, Delaware","6006","10","005","050105"],
["Census Tract 504.05, Sussex County, Delaware","3983","10","005","050405"],
["Census Tract 504.06, Sussex County, Delaware","5081","10","005","050406"],
["Census Tract 504.07, Sussex County, Delaware","5310","10","005","050407"],
["Census Tract 504.08, Sussex County, Delaware","4488","10","005","050408"],
["Census Tract 505.03, Sussex County, Delaware","5587","10","005","050503"],
["Census Tract 505.04, Sussex County, Delaware","6177","10","005","050504"],
["Census Tract 507.03, Sussex County, Delaware","2021","10","005","050703"],
["Census Tract 509.01, Sussex County, Delaware","2404","10","005","050901"],
["Census Tract 507.04, Sussex County, Delaware","4357","10","005","050704"],
["Census Tract 507.05, Sussex County, Delaware","4994","10","005","050705"],
["Census Tract 507.06, Sussex County, Delaware","1769","10","005","050706"],
["Census Tract 509.02, Sussex County, Delaware","4814","10","005","050902"],
["Census Tract 510.04, Sussex County, Delaware","4794","10","005","051004"]]

### I downloaded the TIGER/Line shape zip file containing Delaware census tract boundaries from US Census.
# See https://catalog.data.gov/dataset/tiger-line-shapefile-2017-state-delaware-current-census-tract-state-based
# Look for download link to tl_2017_10_tract.zip
# "tl" = Tiger line file
# "10" = Delaware
# "tract" = Census tract boundaries

# The zip file was extracted into the working directory for this project

### Create a geospatial dataframe out of the .shp file 

# Load the tracts shapefile into a geopandas dataframe
gdf_tract = gpd.read_file("/Users/mitchellfawcett/Documents/Data Science/Points Inside Census Tract/tl_2017_10_tract.shp")
# Note - you'll get an error if the other files in the shape file set are not in the same folder as the .shp file. These act
# as a complete set and all files are necessary to get the shapes.

# Examine the geo dataframe.
print(gdf_tract.head())

  STATEFP COUNTYFP TRACTCE        GEOID    NAME             NAMELSAD  MTFCC  \
0      10      001  040502  10001040502  405.02  Census Tract 405.02  G5020   
1      10      001  043202  10001043202  432.02  Census Tract 432.02  G5020   
2      10      001  040501  10001040501  405.01  Census Tract 405.01  G5020   
3      10      001  990000  10001990000    9900    Census Tract 9900  G5020   
4      10      001  043400  10001043400     434     Census Tract 434  G5020   

  FUNCSTAT      ALAND     AWATER     INTPTLAT      INTPTLON  \
0        S   29380975     461377  +39.2124212  -075.5318919   
1        S  295721374   46419973  +39.1456066  -075.4324489   
2        S   10394105          0  +39.1995840  -075.5439015   
3        S          0  495919229  +39.1258693  -075.3111928   
4        S   66115677     653023  +38.9723618  -075.4729061   

                                            geometry  
0  POLYGON ((-75.58160 39.24208, -75.58145 39.242...  
1  POLYGON ((-75.54956 39.35384, -75.54939 39.353...  
2  POLYGON ((-75.58810 39.24403, -75.58806 39.244...  
3  POLYGON ((-75.51194 39.36595, -75.51178 39.366...  
4  POLYGON ((-75.52892 39.01019, -75.52871 39.010...  

### Only keep tracts that have non-zero land.  Zero land area tracts represent the Delaware River.
gdf_tract = gdf_tract[gdf_tract['ALAND'] != 0]

### Plot the tract shape file using bokeh.

# Load bokeh libraries
from bokeh.io import output_notebook, show, output_file
from bokeh.plotting import figure
from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar
from bokeh.palettes import brewer

### Bokeh requires json format data to plot

#Read data to json.  
gdf_tract_json = json.loads(gdf_tract.to_json())

#Convert to String like object.
gdf_tract_json_data = json.dumps(gdf_tract_json)

### Create the bokeh figure to visualize Delaware Census Tracts

#Input GeoJSON source that contains features to be plotted.
geosource = GeoJSONDataSource(geojson = gdf_tract_json_data)

# Create the figure object.
p = figure(title = 'Delaware Census Tracts', height = 600 , width = 300, toolbar_location = None)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None

# Add patch renderer to figure. 
p.patches('xs','ys', 
          source = geosource, 
          line_color = 'black', 
          line_width = 0.25, 
          fill_color = None,
          fill_alpha = 1)

# Display Delaware census tracts figure inline in Jupyter Notebook.
output_notebook()
show(p)

Loading BokehJS ...

# Cite https://www.matecdev.com/posts/point-in-polygon.html

# Do a quick test of Point in Polygon.
# Create Point objects
p1 = Point(24.82, 60.24)
p2 = Point(24.895, 60.05)

# Create a square
coords = [(24.89, 60.06), (24.75, 60.06), (24.75, 60.30), (24.89, 60.30)]
poly = Polygon(coords)

# PIP test with 'within'
p1.within(poly)     # True
p2.within(poly)     # False

# PIP test with 'contains'
poly.contains(p1)   # True
poly.contains(p2)   # False </code></pre>

False

p1.within(poly)     # True

True

poly.contains(p2)   # False

False

# Use Delaware shape file
# Put the shapefile into a geopandas geo dataframe
de_tracts_gdf = gpd.GeoDataFrame.from_file(mydir + "/tl_2017_10_tract.shp")

de_tracts_gdf.head()
# The "geometry" column has the point paths of census tract boundaries

	STATEFP	COUNTYFP	TRACTCE	GEOID	NAME	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON	geometry
0	10	001	040502	10001040502	405.02	Census Tract 405.02	G5020	S	29380975	461377	+39.2124212	-075.5318919	POLYGON ((-75.58160 39.24208, -75.58145 39.242...
1	10	001	043202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374	46419973	+39.1456066	-075.4324489	POLYGON ((-75.54956 39.35384, -75.54939 39.353...
2	10	001	040501	10001040501	405.01	Census Tract 405.01	G5020	S	10394105	0	+39.1995840	-075.5439015	POLYGON ((-75.58810 39.24403, -75.58806 39.244...
3	10	001	990000	10001990000	9900	Census Tract 9900	G5020	S	0	495919229	+39.1258693	-075.3111928	POLYGON ((-75.51194 39.36595, -75.51178 39.366...
4	10	001	043400	10001043400	434	Census Tract 434	G5020	S	66115677	653023	+38.9723618	-075.4729061	POLYGON ((-75.52892 39.01019, -75.52871 39.010...

# display the census tract boundaries
base = de_tracts_gdf.boundary.plot(linewidth=1, edgecolor="black", figsize=(15,15))

# Generate some random points for another test demo
# https://www.matecdev.com/posts/point-in-polygon.html

N=1000
lat = np.random.uniform(38.4,40.0,N)  # some random latitudes near Delaware
lon = np.random.uniform(-75.8,-75.0,N) # some random longitudes near Delaware

# Create geodataframe from numpy arrays
df = pd.DataFrame({'lon':lon, 'lat':lat})
df['coords'] = list(zip(df['lon'],df['lat']))  # Zip() combines two or more lists, or other Iterable objects, into one entity
df['coords'] = df['coords'].apply(Point)
points = gpd.GeoDataFrame(df, geometry='coords', crs=de_tracts_gdf.crs)

points.head()

	lon	lat	coords
0	-75.633827	39.112459	POINT (-75.63383 39.11246)
1	-75.231496	39.269592	POINT (-75.23150 39.26959)
2	-75.076523	39.071421	POINT (-75.07652 39.07142)
3	-75.269321	39.153624	POINT (-75.26932 39.15362)
4	-75.452774	39.730990	POINT (-75.45277 39.73099)

# Perform spatial join to match points and polygons
pointInPolys = gpd.tools.sjoin(points, de_tracts_gdf, predicate="within", how='left')

# Plot map with points 
base = de_tracts_gdf.boundary.plot(linewidth=1, edgecolor="black", figsize=(15,15))
points.plot(ax=base, linewidth=1, color="blue", markersize=1)

plt.show()

# Examine the generated points data frame showing what Census Tract they are in
pointInPolys.head(100)

	lon	lat	coords	index_right	STATEFP	COUNTYFP	TRACTCE	GEOID	NAME	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON
0	-75.688211	38.992596	POINT (-75.68821 38.99260)	193.0	10	001	042800	10001042800	428	Census Tract 428	G5020	S	147899008.0	542235.0	+38.9957512	-075.6283501
1	-75.660924	39.496872	POINT (-75.66092 39.49687)	38.0	10	003	016602	10003016602	166.02	Census Tract 166.02	G5020	S	51293992.0	899748.0	+39.5185461	-075.6880137
2	-75.352929	38.968923	POINT (-75.35293 38.96892)	1.0	10	001	043202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374.0	46419973.0	+39.1456066	-075.4324489
3	-75.285362	38.995251	POINT (-75.28536 38.99525)	3.0	10	001	990000	10001990000	9900	Census Tract 9900	G5020	S	0.0	495919229.0	+39.1258693	-075.3111928
4	-75.200084	38.832075	POINT (-75.20008 38.83207)	217.0	10	005	990000	10005990000	9900	Census Tract 9900	G5020	S	0.0	534627888.0	+38.8648060	-075.2250098
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	-75.427061	39.189003	POINT (-75.42706 39.18900)	1.0	10	001	043202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374.0	46419973.0	+39.1456066	-075.4324489
96	-75.131307	38.753294	POINT (-75.13131 38.75329)	24.0	10	005	051003	10005051003	510.03	Census Tract 510.03	G5020	S	20037219.0	39992.0	+38.7380987	-075.1102568
97	-75.372738	39.467211	POINT (-75.37274 39.46721)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
98	-75.031915	39.602897	POINT (-75.03191 39.60290)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
99	-75.334355	39.728025	POINT (-75.33435 39.72802)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

100 rows × 16 columns

# Example use: get points in Census Tract '429'
pnt_429 = points[pointInPolys.NAME=='429']

# Plot map again with Census Tract 429 points in Red, all others in Blue
base = de_tracts_gdf.boundary.plot(linewidth=1, edgecolor="black", figsize=(15,15))
points.plot(ax=base, linewidth=1, color="blue", markersize=1)
pnt_429.plot(ax=base, linewidth=1, color="red", markersize=8)
plt.show()

Use the National Address Database to build a dataframe of Delaware street addresses

This assumes an ASCII text file of addresses was downloaded from https://www.transportation.gov/mission/open/gis/national-address-database/national-address-database-nad-disclaimer

A copy of the file was stored locally at "~/Documents/National Address Database 2024 r15/TXT/NAD.txt"

There are earlier versions of the NAD stored on this computrer, but they have different schemas.

# Look at the first 100 rows of the addresses
firstn_df = pd.read_csv("~/Documents/National Address Database 2024 r15/TXT/NAD.txt", nrows = 100)

firstn_df

	OID	AddNum_Pre	Add_Number	AddNum_Suf	AddNo_Full	St_PreMod	St_PreDir	St_PreTyp	St_PreSep	St_Name	St_PosTyp	St_PosDir	St_PosMod	StNam_Full	Building	Floor	Unit	Room	Seat	Addtl_Loc	SubAddress	LandmkName	County	Inc_Muni	Post_City	Census_Plc	Uninc_Comm	Nbrhd_Comm	NatAmArea	NatAmSub	Urbnztn_PR	PlaceOther	PlaceNmTyp	State	Zip_Code	Plus_4	UUID	AddAuth	AddrRefSys	Longitude	Latitude	NatGrid	Elevation	Placement	AddrPoint	Related_ID	RelateType	ParcelSrc	Parcel_ID	AddrClass	Lifecycle	Effective	Expire	DateUpdate	AnomStatus	LocatnDesc	Addr_Type	DeliverTyp	NAD_Source	DataSet_ID
0	-1	NaN	26800	NaN	26800	NaN	NaN	NaN	NaN	EKLUTNA VILLAGE	Road	NaN	NaN	EKLUTNA VILLAGE Road	NaN	NaN	TRLR 26800	NaN	NaN	NaN	TRLR 26800	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	Eklutna ANVSA	NaN	NaN	NaN	NaN	AK	99567	NaN	{F7DE0555-9BE8-44A9-89A7-C9FA22B17866}	Municipality of Anchorage, Alaska	NaN	-149.375648	61.465525	06VUP7341516947	NaN	Unknown	-149.37564802268 61.4655246739195	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
1	-1	NaN	26960	NaN	26960	NaN	NaN	NaN	NaN	ADAK	Circle	NaN	NaN	ADAK Circle	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Eagle River	NaN	Northern Communities	NaN	Eklutna ANVSA	NaN	NaN	NaN	NaN	AK	99577	NaN	{602F8773-0D6B-42C5-90B6-64FD2B8EFD6E}	Municipality of Anchorage, Alaska	NaN	-149.360392	61.463624	06VUN6488599795	NaN	Unknown	-149.360391525476 61.4636243831014	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
2	-1	NaN	26636	NaN	26636	NaN	NaN	NaN	NaN	EKLUTNA VILLAGE	Road	NaN	NaN	EKLUTNA VILLAGE Road	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	Eklutna ANVSA	NaN	NaN	NaN	NaN	AK	99567	NaN	{612F3150-A5E0-4F6A-8757-0CA74C1AB958}	Municipality of Anchorage, Alaska	NaN	-149.361471	61.460711	06VUP7415116384	NaN	Unknown	-149.36147119804 61.460711123149	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
3	-1	NaN	26585	NaN	26585	NaN	NaN	NaN	NaN	EKLUTNA VILLAGE	Road	NaN	NaN	EKLUTNA VILLAGE Road	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	Eklutna ANVSA	NaN	NaN	NaN	NaN	AK	99567	NaN	{3B590E65-520D-45C3-B3B5-8DDB966EDDEF}	Municipality of Anchorage, Alaska	NaN	-149.360808	61.460619	06VUP7418616372	NaN	Unknown	-149.360808398565 61.460619411486	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
4	-1	NaN	26612	NaN	26612	NaN	NaN	NaN	NaN	EKLUTNA VILLAGE	Road	NaN	NaN	EKLUTNA VILLAGE Road	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	Eklutna ANVSA	NaN	NaN	NaN	NaN	AK	99567	NaN	{8903A38A-A49D-4985-849D-4433BDF0CCDF}	Municipality of Anchorage, Alaska	NaN	-149.361635	61.460386	06VUP7414116348	NaN	Unknown	-149.361634630336 61.4603857255667	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	-1	NaN	24707	NaN	24707	NaN	NaN	NaN	NaN	THUNDERBIRD	Drive	NaN	NaN	THUNDERBIRD Drive	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	NaN	NaN	NaN	NaN	NaN	AK	99567	NaN	{87288261-1F12-4638-ABA1-92E6CBAD86F1}	Municipality of Anchorage, Alaska	NaN	-149.368321	61.443466	06VUP7371614477	NaN	Unknown	-149.368321425206 61.4434660266244	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
96	-1	NaN	24643	NaN	24643	NaN	NaN	NaN	NaN	TEAL	Loop	NaN	NaN	TEAL Loop	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	NaN	NaN	NaN	NaN	NaN	AK	99567	NaN	{B501946B-63FB-4A88-89EC-977D4E725C8C}	Municipality of Anchorage, Alaska	NaN	-149.364682	61.443099	06VUP7390914429	NaN	Unknown	-149.364681587321 61.443098685482	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
97	-1	NaN	27627	NaN	27627	NaN	NaN	NaN	NaN	MALLARD	Court	NaN	NaN	MALLARD Court	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	NaN	NaN	NaN	NaN	NaN	AK	99567	NaN	{90B13D6E-8C24-46E5-A940-005968E2AA49}	Municipality of Anchorage, Alaska	NaN	-149.366674	61.443128	06VUP7380214436	NaN	Unknown	-149.366674350658 61.4431283177893	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
98	-1	NaN	27617	NaN	27617	NaN	NaN	NaN	NaN	MALLARD	Court	NaN	NaN	MALLARD Court	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	NaN	NaN	NaN	NaN	NaN	AK	99567	NaN	{94AEEFF9-F937-4A19-8A72-C13CF9AF8CF1}	Municipality of Anchorage, Alaska	NaN	-149.366948	61.443146	06VUP7378814438	NaN	Unknown	-149.366948055647 61.4431457965646	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN
99	-1	NaN	24636	NaN	24636	NaN	NaN	NaN	NaN	TEAL	Loop	NaN	NaN	TEAL Loop	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Anchorage Municipality	Unincorporated	Chugiak	NaN	Northern Communities	NaN	NaN	NaN	NaN	NaN	NaN	AK	99567	NaN	{1AD66292-4601-4C45-896B-721B8A3BB9A4}	Municipality of Anchorage, Alaska	NaN	-149.365157	61.443107	06VUP7388314431	NaN	Unknown	-149.36515668202 61.4431066667334	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	4/26/2022 0:00:00	NaN	NaN	Unknown	NaN	Municipality of Anchorage, Alaska	NaN

100 rows × 60 columns

print(firstn_df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 60 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   OID         100 non-null    int64  
 1   AddNum_Pre  0 non-null      float64
 2   Add_Number  100 non-null    int64  
 3   AddNum_Suf  0 non-null      float64
 4   AddNo_Full  100 non-null    int64  
 5   St_PreMod   0 non-null      float64
 6   St_PreDir   0 non-null      float64
 7   St_PreTyp   0 non-null      float64
 8   St_PreSep   0 non-null      float64
 9   St_Name     100 non-null    object 
 10  St_PosTyp   100 non-null    object 
 11  St_PosDir   0 non-null      float64
 12  St_PosMod   0 non-null      float64
 13  StNam_Full  100 non-null    object 
 14  Building    0 non-null      float64
 15  Floor       0 non-null      float64
 16  Unit        2 non-null      object 
 17  Room        0 non-null      float64
 18  Seat        0 non-null      float64
 19  Addtl_Loc   0 non-null      float64
 20  SubAddress  2 non-null      object 
 21  LandmkName  0 non-null      float64
 22  County      100 non-null    object 
 23  Inc_Muni    100 non-null    object 
 24  Post_City   100 non-null    object 
 25  Census_Plc  0 non-null      float64
 26  Uninc_Comm  100 non-null    object 
 27  Nbrhd_Comm  0 non-null      float64
 28  NatAmArea   24 non-null     object 
 29  NatAmSub    0 non-null      float64
 30  Urbnztn_PR  0 non-null      float64
 31  PlaceOther  0 non-null      float64
 32  PlaceNmTyp  0 non-null      float64
 33  State       100 non-null    object 
 34  Zip_Code    100 non-null    int64  
 35  Plus_4      0 non-null      float64
 36  UUID        100 non-null    object 
 37  AddAuth     100 non-null    object 
 38  AddrRefSys  0 non-null      float64
 39  Longitude   100 non-null    float64
 40  Latitude    100 non-null    float64
 41  NatGrid     100 non-null    object 
 42  Elevation   0 non-null      float64
 43  Placement   100 non-null    object 
 44  AddrPoint   100 non-null    object 
 45  Related_ID  0 non-null      float64
 46  RelateType  0 non-null      float64
 47  ParcelSrc   0 non-null      float64
 48  Parcel_ID   0 non-null      float64
 49  AddrClass   100 non-null    object 
 50  Lifecycle   0 non-null      float64
 51  Effective   0 non-null      float64
 52  Expire      0 non-null      float64
 53  DateUpdate  100 non-null    object 
 54  AnomStatus  0 non-null      float64
 55  LocatnDesc  0 non-null      float64
 56  Addr_Type   100 non-null    object 
 57  DeliverTyp  0 non-null      float64
 58  NAD_Source  100 non-null    object 
 59  DataSet_ID  0 non-null      float64
dtypes: float64(36), int64(4), object(20)
memory usage: 47.0+ KB
None

# DO NOT RUN THIS CODE UNLESS YOU REALLY NEED TO RECREATE THE DELAWARE ADDRESS CSV FILE # CONVERT THIS CELL FROM "RAW" TO "CODE" CELL TO EXECUTE IT. # Select just the Delaware address from the NAD data and put them into a new CSV file # This builds a csv file consisting of all the Delaware addresses. # /Users/mitchellfawcett/Documents/National Address Database 2024 r15/TXT/NAD.txt is source file. # NAD_r15_Delaware.csv" is output file. # ~ 4 minutes with open("/Users/mitchellfawcett/Documents/National Address Database 2024 r15/TXT/NAD.txt", 'r') as infile, open("/Users/mitchellfawcett/Documents/National Address Database 2024 r15/TXT/NAD_r15_Delaware.csv", 'w') as outfile: reader = csv.reader(infile) writer = csv.writer(outfile) line_count = 0 for row in reader: if line_count == 0: writer.writerow(row) # header line_count += 1 else: if row[33] == "DE": writer.writerow(row) line_count += 1

# Load the Delaware address CSV file into a dataframe & examine
delaware_df = pd.read_csv("~/Documents/National Address Database 2024 r15/TXT/NAD_r15_Delaware.csv")
delaware_df.head()

/var/folders/9v/165788x50k7_g0vrrn1__z1h0000gn/T/ipykernel_8757/186015339.py:2: DtypeWarning: Columns (3,4,7,11,12,14,15,16,17,19,20,25,26,27,28,37) have mixed types. Specify dtype option on import or set low_memory=False.
  delaware_df = pd.read_csv("~/Documents/National Address Database 2024 r15/TXT/NAD_r15_Delaware.csv")

	OID	AddNum_Pre	Add_Number	AddNum_Suf	AddNo_Full	St_PreMod	St_PreDir	St_PreTyp	St_PreSep	St_Name	St_PosTyp	St_PosDir	St_PosMod	StNam_Full	Building	Floor	Unit	Room	Seat	Addtl_Loc	SubAddress	LandmkName	County	Inc_Muni	Post_City	Census_Plc	Uninc_Comm	Nbrhd_Comm	NatAmArea	NatAmSub	Urbnztn_PR	PlaceOther	PlaceNmTyp	State	Zip_Code	Plus_4	UUID	AddAuth	AddrRefSys	Longitude	Latitude	NatGrid	Elevation	Placement	AddrPoint	Related_ID	RelateType	ParcelSrc	Parcel_ID	AddrClass	Lifecycle	Effective	Expire	DateUpdate	AnomStatus	LocatnDesc	Addr_Type	DeliverTyp	NAD_Source	DataSet_ID
0	-1	NaN	216	NaN	216	NaN	NaN	NaN	NaN	KING	Street	NaN	NaN	KING Street	NaN	NaN	APT B	NaN	NaN	NaN	APT B	NaN	Kent	Unincorporated	Smyrna	NaN	NaN	Smyrna	NaN	NaN	NaN	NaN	NaN	DE	19977.0	NaN	{090F51C5-7577-412C-B1E7-E18905DF905F}	NaN	NaN	-75.589069	39.309325	18SVJ4921451269	NaN	Unknown	-75.5890689551341 39.3093249142128	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	7/1/2022 0:00:00	NaN	NaN	Unknown	NaN	Kent County, Delaware	NaN
1	-1	NaN	978	NaN	978	NaN	NaN	NaN	NaN	CAVEAT	Road	NaN	NaN	CAVEAT Road	NaN	NaN	APT B	NaN	NaN	NaN	APT B	NaN	Kent	Unincorporated	Magnolia	NaN	NaN	Magnolia	NaN	NaN	NaN	NaN	NaN	DE	19962.0	NaN	{BAD7810A-EFA0-4591-8C1E-F203665DAF3B}	NaN	NaN	-75.568466	39.250083	18SVJ5946322577	NaN	Unknown	-75.5684660865876 39.2500827735636	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	7/1/2022 0:00:00	NaN	NaN	Unknown	NaN	Kent County, Delaware	NaN
2	-1	NaN	14	NaN	14	NaN	NaN	NaN	NaN	BURNSIDE	Drive	NaN	NaN	BURNSIDE Drive	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Kent	Unincorporated	Smyrna	NaN	NaN	Wicksfield	NaN	NaN	NaN	NaN	NaN	DE	19977.0	NaN	{9E4CD656-EEB1-4DA6-A62C-2036F209F158}	NaN	NaN	-75.616076	39.275783	18SVJ4686047562	NaN	Unknown	-75.6160764495596 39.2757829759585	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	11/13/2012 0:00:00	NaN	NaN	Unknown	NaN	Kent County, Delaware	NaN
3	-1	NaN	347	NaN	347	NaN	NaN	NaN	NaN	WILLOWWOOD	Drive	NaN	NaN	WILLOWWOOD Drive	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Kent	Unincorporated	Smyrna	NaN	NaN	Willowwood	NaN	NaN	NaN	NaN	NaN	DE	19977.0	NaN	{C63BC5D6-FC2F-47CC-8CAB-81F09AF50CF1}	NaN	NaN	-75.596984	39.260780	18SVJ4849645886	NaN	Unknown	-75.5969835035535 39.2607799993354	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	5/2/2012 0:00:00	NaN	NaN	Unknown	NaN	Kent County, Delaware	NaN
4	-1	NaN	22	NaN	22	NaN	NaN	NaN	NaN	MALCOLMS MILL	Drive	NaN	NaN	MALCOLMS MILL Drive	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Kent	Unincorporated	Smyrna	NaN	NaN	Smyrna	NaN	NaN	NaN	NaN	NaN	DE	19977.0	NaN	{95EF8E9D-A53F-4099-B57A-5CC45D5F3AD7}	NaN	NaN	-75.616818	39.240314	18SVJ4676943626	NaN	Unknown	-75.6168176827436 39.2403144684381	NaN	NaN	NaN	NaN	Numbered Thoroughfare Address	NaN	NaN	NaN	5/8/2012 0:00:00	NaN	NaN	Unknown	NaN	Kent County, Delaware	NaN

delaware_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 534407 entries, 0 to 534406
Data columns (total 60 columns):
 #   Column      Non-Null Count   Dtype  
---  ------      --------------   -----  
 0   OID         534407 non-null  int64  
 1   AddNum_Pre  0 non-null       float64
 2   Add_Number  534407 non-null  int64  
 3   AddNum_Suf  335 non-null     object 
 4   AddNo_Full  534407 non-null  object 
 5   St_PreMod   0 non-null       float64
 6   St_PreDir   67506 non-null   object 
 7   St_PreTyp   4 non-null       object 
 8   St_PreSep   0 non-null       float64
 9   St_Name     534407 non-null  object 
 10  St_PosTyp   531475 non-null  object 
 11  St_PosDir   3000 non-null    object 
 12  St_PosMod   4 non-null       object 
 13  StNam_Full  534407 non-null  object 
 14  Building    741 non-null     object 
 15  Floor       12 non-null      object 
 16  Unit        59872 non-null   object 
 17  Room        53 non-null      object 
 18  Seat        0 non-null       float64
 19  Addtl_Loc   96 non-null      object 
 20  SubAddress  60774 non-null   object 
 21  LandmkName  0 non-null       float64
 22  County      534407 non-null  object 
 23  Inc_Muni    534407 non-null  object 
 24  Post_City   534407 non-null  object 
 25  Census_Plc  67533 non-null   object 
 26  Uninc_Comm  266780 non-null  object 
 27  Nbrhd_Comm  267515 non-null  object 
 28  NatAmArea   9363 non-null    object 
 29  NatAmSub    0 non-null       float64
 30  Urbnztn_PR  0 non-null       float64
 31  PlaceOther  0 non-null       float64
 32  PlaceNmTyp  0 non-null       float64
 33  State       534407 non-null  object 
 34  Zip_Code    534162 non-null  float64
 35  Plus_4      0 non-null       float64
 36  UUID        534406 non-null  object 
 37  AddAuth     186840 non-null  object 
 38  AddrRefSys  0 non-null       float64
 39  Longitude   534407 non-null  float64
 40  Latitude    534407 non-null  float64
 41  NatGrid     534406 non-null  object 
 42  Elevation   0 non-null       float64
 43  Placement   534406 non-null  object 
 44  AddrPoint   534407 non-null  object 
 45  Related_ID  0 non-null       float64
 46  RelateType  0 non-null       float64
 47  ParcelSrc   0 non-null       float64
 48  Parcel_ID   0 non-null       float64
 49  AddrClass   534407 non-null  object 
 50  Lifecycle   0 non-null       float64
 51  Effective   0 non-null       float64
 52  Expire      0 non-null       float64
 53  DateUpdate  534407 non-null  object 
 54  AnomStatus  0 non-null       float64
 55  LocatnDesc  0 non-null       float64
 56  Addr_Type   534406 non-null  object 
 57  DeliverTyp  0 non-null       float64
 58  NAD_Source  534407 non-null  object 
 59  DataSet_ID  0 non-null       float64
dtypes: float64(26), int64(2), object(32)
memory usage: 244.6+ MB

# Select the columns necessary for the point-in-census-tract analysis.
# I want just the address columns and longitude and latitude columns.
# Also keeping the UUID column to use for easier joins to form the final dataset.
de_address_df = delaware_df[['AddNo_Full', 'St_Name', 'St_PosTyp', 'St_PosDir', 'St_PosMod', 'County', 'Post_City', \
                             'State', 'Zip_Code', 'Longitude', 'Latitude', 'AddrPoint', 'UUID']].copy()

# Examnine results
de_address_df.head()

	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID
0	216	KING	Street	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.589069	39.309325	-75.5890689551341 39.3093249142128	{090F51C5-7577-412C-B1E7-E18905DF905F}
1	978	CAVEAT	Road	NaN	NaN	Kent	Magnolia	DE	19962.0	-75.568466	39.250083	-75.5684660865876 39.2500827735636	{BAD7810A-EFA0-4591-8C1E-F203665DAF3B}
2	14	BURNSIDE	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616076	39.275783	-75.6160764495596 39.2757829759585	{9E4CD656-EEB1-4DA6-A62C-2036F209F158}
3	347	WILLOWWOOD	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.596984	39.260780	-75.5969835035535 39.2607799993354	{C63BC5D6-FC2F-47CC-8CAB-81F09AF50CF1}
4	22	MALCOLMS MILL	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616818	39.240314	-75.6168176827436 39.2403144684381	{95EF8E9D-A53F-4099-B57A-5CC45D5F3AD7}

len(de_address_df)

534407

# Get some coordinate points from the delaware address data to test this process
point_sample_df = de_address_df.sample(n=1000)

# Zip() combines two or more lists, or other Iterable objects, into one entity, here called 'coords'
point_sample_df['coords'] = list(zip(point_sample_df['Longitude'],point_sample_df['Latitude']))

# Convert the 'coords' entity into a geometry Point object needed to use with the 'within' polygon function.
point_sample_df['coords'] = point_sample_df['coords'].apply(Point)

# Examine the dataframe with the newly added 'coords' Point column (right-most column)
point_sample_df.head()

	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID	coords
363168	29556	MAPLE	Road	NaN	NaN	Sussex	LEWES	DE	19958.0	-75.234757	38.737225	-75.2347568123153 38.7372245335924	{D8CEF7A8-7E04-4D8D-98DB-6F0D4D15188A}	POINT (-75.2347568123153 38.7372245335924)
505387	18888	BETHPAGE	Drive	NaN	NaN	Sussex	LEWES	DE	19958.0	-75.144634	38.732908	-75.1446337160359 38.7329081540269	{09EBB22B-5974-4202-982A-F84F2FFDA5F7}	POINT (-75.1446337160359 38.73290815402689)
259712	303	WALDEN	Road	NaN	NaN	New Castle	WILMINGTON	DE	19803.0	-75.552691	39.797975	-75.5526910174496 39.797974732606	{981FC722-E078-4512-9546-394E91B3525B}	POINT (-75.5526910174496 39.797974732606)
423799	1004	CAPTAIN HOOK	Trail	NaN	NaN	Sussex	SELBYVILLE	DE	19975.0	-75.081028	38.465249	-75.0810278397987 38.4652492117774	{D5234580-9844-48D9-B56D-3E724CA7A465}	POINT (-75.08102783979869 38.4652492117774)
342634	18	RIVERS END EAST	Drive	NaN	NaN	New Castle	CLAYMONT	DE	19703.0	-75.465175	39.793302	-75.4651749368478 39.79330240308	{CF4467DE-79F4-4D4D-91E9-74BAA53C5D56}	POINT (-75.4651749368478 39.79330240308)

# Create the geodataframe object needed for matching points to polygons in the final step
de_points_gdf = gpd.GeoDataFrame(point_sample_df, geometry='coords', crs=de_tracts_gdf.crs)

de_points_gdf.info()  # Note, this must be a 'geodataframe' object for point-polygon matching to work.

<class 'geopandas.geodataframe.GeoDataFrame'>
Index: 1000 entries, 363168 to 223009
Data columns (total 14 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   AddNo_Full  1000 non-null   object  
 1   St_Name     1000 non-null   object  
 2   St_PosTyp   990 non-null    object  
 3   St_PosDir   3 non-null      object  
 4   St_PosMod   0 non-null      object  
 5   County      1000 non-null   object  
 6   Post_City   1000 non-null   object  
 7   State       1000 non-null   object  
 8   Zip_Code    1000 non-null   float64 
 9   Longitude   1000 non-null   float64 
 10  Latitude    1000 non-null   float64 
 11  AddrPoint   1000 non-null   object  
 12  UUID        1000 non-null   object  
 13  coords      1000 non-null   geometry
dtypes: float64(3), geometry(1), object(10)
memory usage: 117.2+ KB

# Final step - Perform spatial join to match points and polygons.
# Put results into a geodataframe
pointInTracts_gdf = gpd.tools.sjoin(de_points_gdf, de_tracts_gdf, predicate="within", how='left')

# Examine the results.  'NAME' column has the census tract that each address point was found.
pointInTracts_gdf.head()

	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID	coords	index_right	STATEFP	COUNTYFP	TRACTCE	GEOID	NAME	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON
363168	29556	MAPLE	Road	NaN	NaN	Sussex	LEWES	DE	19958.0	-75.234757	38.737225	-75.2347568123153 38.7372245335924	{D8CEF7A8-7E04-4D8D-98DB-6F0D4D15188A}	POINT (-75.23476 38.73722)	20	10	005	050803	10005050803	508.03	Census Tract 508.03	G5020	S	45222333	831841	+38.7601194	-075.2551308
505387	18888	BETHPAGE	Drive	NaN	NaN	Sussex	LEWES	DE	19958.0	-75.144634	38.732908	-75.1446337160359 38.7329081540269	{09EBB22B-5974-4202-982A-F84F2FFDA5F7}	POINT (-75.14463 38.73291)	164	10	005	051004	10005051004	510.04	Census Tract 510.04	G5020	S	20319511	336020	+38.7287224	-075.1684692
259712	303	WALDEN	Road	NaN	NaN	New Castle	WILMINGTON	DE	19803.0	-75.552691	39.797975	-75.5526910174496 39.797974732606	{981FC722-E078-4512-9546-394E91B3525B}	POINT (-75.55269 39.79797)	97	10	003	011700	10003011700	117	Census Tract 117	G5020	S	19458464	0	+39.8023473	-075.5597904
423799	1004	CAPTAIN HOOK	Trail	NaN	NaN	Sussex	SELBYVILLE	DE	19975.0	-75.081028	38.465249	-75.0810278397987 38.4652492117774	{D5234580-9844-48D9-B56D-3E724CA7A465}	POINT (-75.08103 38.46525)	116	10	005	051306	10005051306	513.06	Census Tract 513.06	G5020	S	11824830	2881388	+38.4787667	-075.1012742
342634	18	RIVERS END EAST	Drive	NaN	NaN	New Castle	CLAYMONT	DE	19703.0	-75.465175	39.793302	-75.4651749368478 39.79330240308	{CF4467DE-79F4-4D4D-91E9-74BAA53C5D56}	POINT (-75.46517 39.79330)	136	10	003	010104	10003010104	101.04	Census Tract 101.04	G5020	S	3629092	2863836	+39.7961063	-075.4534792

# Plot the results of random sample of address points
# Plot map with points 
base = de_tracts_gdf.boundary.plot(linewidth=1, edgecolor="black", figsize=(15,15))
de_points_gdf.plot(ax=base, linewidth=1, color="blue", markersize=1)

plt.show()

# Plot again, this time with one census tract points in red
# Example use: get points in Census Tract '141'
pnt_141 = de_points_gdf[pointInTracts_gdf.NAME=='141']

pnt_141

	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID	coords
299838	41	AUGUSTA	Drive	NaN	NaN	New Castle	NEWARK	DE	19713.0	-75.707925	39.678196	-75.7079253368914 39.6781956215546	{8F0F744D-2C60-42FB-BFD4-59372F60A045}	POINT (-75.70793 39.67820)
129240	511	BANYAN	Road	NaN	NaN	New Castle	NEWARK	DE	19713.0	-75.717448	39.677296	-75.7174478435114 39.6772958879247	{C1F6A7FC-42D6-4F95-8C77-18FB16A5016D}	POINT (-75.71745 39.67730)
297341	8	CAROLE	Road	NaN	NaN	New Castle	NEWARK	DE	19713.0	-75.710418	39.679249	-75.7104183066428 39.6792487781724	{4F5AEEAF-126A-462C-A4FF-32983372EBDC}	POINT (-75.71042 39.67925)
196225	17	MALVERN	Road	NaN	NaN	New Castle	NEWARK	DE	19713.0	-75.702286	39.676410	-75.7022857071547 39.6764101711002	{83357730-D9E0-4652-8586-C9E466E35195}	POINT (-75.70229 39.67641)
233945	8	GREENBRIDGE	Drive	NaN	NaN	New Castle	NEWARK	DE	19713.0	-75.723115	39.674842	-75.7231149917752 39.6748423692452	{2859990B-B2CA-41A1-98EE-CB8A4BAFF4CD}	POINT (-75.72311 39.67484)

# Plot map again with Census Tract 141 points in Red, all others in Blue
base = de_tracts_gdf.boundary.plot(linewidth=1, edgecolor="black", figsize=(15,15))
de_points_gdf.plot(ax=base, linewidth=1, color="blue", markersize=1)
pnt_141.plot(ax=base, linewidth=1, color="red", markersize=8)
plt.show()

# The above matched point-in-polygons for 1000 sample addresses.  Repeat the matching for all Delaware addresses.
# Get some coordinate points from the delaware address data
# point_sample_df = de_address_df.sample(n=1000)

# Zip() combines two or more lists, or other Iterable objects, into one entity, here called 'coords'
de_address_df['coords'] = list(zip(de_address_df['Longitude'],de_address_df['Latitude']))

# Convert the 'coords' entity into a geometry Point object needed to use with the 'within' polygon function.
de_address_df['coords'] = de_address_df['coords'].apply(Point)

# Examine the dataframe with the newly added 'coords' Point column (right-most column)
de_address_df.head()

	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID	coords
0	216	KING	Street	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.589069	39.309325	-75.5890689551341 39.3093249142128	{090F51C5-7577-412C-B1E7-E18905DF905F}	POINT (-75.5890689551341 39.3093249142128)
1	978	CAVEAT	Road	NaN	NaN	Kent	Magnolia	DE	19962.0	-75.568466	39.250083	-75.5684660865876 39.2500827735636	{BAD7810A-EFA0-4591-8C1E-F203665DAF3B}	POINT (-75.56846608658761 39.2500827735636)
2	14	BURNSIDE	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616076	39.275783	-75.6160764495596 39.2757829759585	{9E4CD656-EEB1-4DA6-A62C-2036F209F158}	POINT (-75.6160764495596 39.2757829759585)
3	347	WILLOWWOOD	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.596984	39.260780	-75.5969835035535 39.2607799993354	{C63BC5D6-FC2F-47CC-8CAB-81F09AF50CF1}	POINT (-75.5969835035535 39.2607799993354)
4	22	MALCOLMS MILL	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616818	39.240314	-75.6168176827436 39.2403144684381	{95EF8E9D-A53F-4099-B57A-5CC45D5F3AD7}	POINT (-75.6168176827436 39.24031446843809)

print("There are", len(de_address_df), "rows of addresses and coordinate points")

There are 534407 rows of addresses and coordinate points

# Create the geodataframe object needed for matching points to polygons in the final step
de_all_points_gdf = gpd.GeoDataFrame(de_address_df, geometry='coords', crs=de_tracts_gdf.crs)

# Final step - Perform spatial join to match points and polygons.
# Put results into a geodataframe
all_DE_pointInTracts_gdf = gpd.tools.sjoin(de_all_points_gdf, de_tracts_gdf, predicate="within", how='left')

# Examine the results.  'NAME' column has the census tract that each address point was found.
all_DE_pointInTracts_gdf.head()

	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID	coords	index_right	STATEFP	COUNTYFP	TRACTCE	GEOID	NAME	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON
0	216	KING	Street	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.589069	39.309325	-75.5890689551341 39.3093249142128	{090F51C5-7577-412C-B1E7-E18905DF905F}	POINT (-75.58907 39.30932)	195.0	10	001	040203	10001040203	402.03	Census Tract 402.03	G5020	S	59368816.0	1534599.0	+39.2858685	-075.5508360
1	978	CAVEAT	Road	NaN	NaN	Kent	Magnolia	DE	19962.0	-75.568466	39.250083	-75.5684660865876 39.2500827735636	{BAD7810A-EFA0-4591-8C1E-F203665DAF3B}	POINT (-75.56847 39.25008)	195.0	10	001	040203	10001040203	402.03	Census Tract 402.03	G5020	S	59368816.0	1534599.0	+39.2858685	-075.5508360
2	14	BURNSIDE	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616076	39.275783	-75.6160764495596 39.2757829759585	{9E4CD656-EEB1-4DA6-A62C-2036F209F158}	POINT (-75.61608 39.27578)	208.0	10	001	040202	10001040202	402.02	Census Tract 402.02	G5020	S	31959485.0	669328.0	+39.2597299	-075.6117335
3	347	WILLOWWOOD	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.596984	39.260780	-75.5969835035535 39.2607799993354	{C63BC5D6-FC2F-47CC-8CAB-81F09AF50CF1}	POINT (-75.59698 39.26078)	208.0	10	001	040202	10001040202	402.02	Census Tract 402.02	G5020	S	31959485.0	669328.0	+39.2597299	-075.6117335
4	22	MALCOLMS MILL	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616818	39.240314	-75.6168176827436 39.2403144684381	{95EF8E9D-A53F-4099-B57A-5CC45D5F3AD7}	POINT (-75.61682 39.24031)	208.0	10	001	040202	10001040202	402.02	Census Tract 402.02	G5020	S	31959485.0	669328.0	+39.2597299	-075.6117335

# Save results to a CSV file
all_DE_pointInTracts_gdf.to_csv('DelawarePointsInCensusTract.csv')

# Read back a few rows of the CSV to see how it looks
pd.read_csv('DelawarePointsInCensusTract.csv', nrows = 100)

	Unnamed: 0	AddNo_Full	St_Name	St_PosTyp	St_PosDir	St_PosMod	County	Post_City	State	Zip_Code	Longitude	Latitude	AddrPoint	UUID	coords	index_right	STATEFP	COUNTYFP	TRACTCE	GEOID	NAME	NAMELSAD	MTFCC	FUNCSTAT	ALAND	AWATER	INTPTLAT	INTPTLON
0	0	216	KING	Street	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.589069	39.309325	-75.5890689551341 39.3093249142128	{090F51C5-7577-412C-B1E7-E18905DF905F}	POINT (-75.5890689551341 39.3093249142128)	195.0	10	1	40203	10001040203	402.03	Census Tract 402.03	G5020	S	59368816.0	1534599.0	39.285868	-75.550836
1	1	978	CAVEAT	Road	NaN	NaN	Kent	Magnolia	DE	19962.0	-75.568466	39.250083	-75.5684660865876 39.2500827735636	{BAD7810A-EFA0-4591-8C1E-F203665DAF3B}	POINT (-75.56846608658761 39.2500827735636)	195.0	10	1	40203	10001040203	402.03	Census Tract 402.03	G5020	S	59368816.0	1534599.0	39.285868	-75.550836
2	2	14	BURNSIDE	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616076	39.275783	-75.6160764495596 39.2757829759585	{9E4CD656-EEB1-4DA6-A62C-2036F209F158}	POINT (-75.6160764495596 39.2757829759585)	208.0	10	1	40202	10001040202	402.02	Census Tract 402.02	G5020	S	31959485.0	669328.0	39.259730	-75.611733
3	3	347	WILLOWWOOD	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.596984	39.260780	-75.5969835035535 39.2607799993354	{C63BC5D6-FC2F-47CC-8CAB-81F09AF50CF1}	POINT (-75.5969835035535 39.2607799993354)	208.0	10	1	40202	10001040202	402.02	Census Tract 402.02	G5020	S	31959485.0	669328.0	39.259730	-75.611733
4	4	22	MALCOLMS MILL	Drive	NaN	NaN	Kent	Smyrna	DE	19977.0	-75.616818	39.240314	-75.6168176827436 39.2403144684381	{95EF8E9D-A53F-4099-B57A-5CC45D5F3AD7}	POINT (-75.6168176827436 39.24031446843809)	208.0	10	1	40202	10001040202	402.02	Census Tract 402.02	G5020	S	31959485.0	669328.0	39.259730	-75.611733
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	95	3089	KITTS HUMMOCK	Road	NaN	NaN	Kent	Dover	DE	19901.0	-75.401731	39.102705	-75.4017313264713 39.1027047760118	{6F9DDB72-F731-4C9D-AE92-344D61247B09}	POINT (-75.40173132647129 39.1027047760118)	1.0	10	1	43202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374.0	46419973.0	39.145607	-75.432449
96	96	3083	KITTS HUMMOCK	Road	NaN	NaN	Kent	Dover	DE	19901.0	-75.401741	39.102821	-75.401740998113 39.1028206745749	{4F9A62B6-83FA-4B7D-99E0-AFFA724439EC}	POINT (-75.40174099811301 39.10282067457489)	1.0	10	1	43202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374.0	46419973.0	39.145607	-75.432449
97	97	3073	KITTS HUMMOCK	Road	NaN	NaN	Kent	Dover	DE	19901.0	-75.402067	39.102718	-75.4020668866337 39.1027181743677	{E7BD28BE-99CE-4255-AED3-8774B12970A3}	POINT (-75.40206688663369 39.10271817436769)	1.0	10	1	43202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374.0	46419973.0	39.145607	-75.432449
98	98	1970	LITTLE CREEK	Road	NaN	NaN	Kent	Dover	DE	19901.0	-75.476258	39.154503	-75.4762582016554 39.1545030351478	{C8A50559-E5F9-4F4B-8D9F-78E69CEA5CA2}	POINT (-75.4762582016554 39.1545030351478)	172.0	10	1	41000	10001041000	410.00	Census Tract 410	G5020	S	26732724.0	21809.0	39.158842	-75.471365
99	99	359	BAY	Drive	NaN	NaN	Kent	Dover	DE	19901.0	-75.400671	39.097586	-75.4006705151962 39.0975855043087	{89D9155F-D97A-4859-8FD8-E0AB6A51BCE8}	POINT (-75.4006705151962 39.0975855043087)	1.0	10	1	43202	10001043202	432.02	Census Tract 432.02	G5020	S	295721374.0	46419973.0	39.145607	-75.432449

100 rows × 28 columns

# Create a sample CSV file for iReach to test
all_DE_pointInTracts_gdf.sample(n=1000).to_csv('SampleDelawarePointsInCensusTract.csv')