Intro to Census Data with Python

Intro to Census Data with Python#

To run the code:

Make sure you have this notebook open in Google Colab (if you are starting from the digital textbook, click on the icon to the top right and click Colab). Each block of code is called a cell. To run a cell, hover over it and click the arrow in the top left of the cell, or click inside of the cell and press Shift + Enter.

Note: When you run a block of code for the first time, Google Colab will say Warning: This notebook was not authored by Google. Please click Run Anyway.

Sign up for a Census API key here. It may take some time for you to receive the email to activate the API key, so this tutorial notebook is divided into two sections: 1. Without API Key and 2. With API Key.

Without API Key: pygris#

More information about pygris

#@title Import modules
!pip install -q pygris
from pygris import validate_state, tracts
from pygris.data import get_census
import matplotlib.pyplot as plt

Get outlines of Census Tracts within Orange County

tracts = tracts(state = "FL",
                county = ['Orange'],
                year = 2023)

tracts.plot()

Using FIPS code '12' for input 'FL'
Using FIPS code '095' for input 'Orange'

<Axes: >

../_images/0a968bbd1c3de1808f037dbaa6427784e5eb34b5ddb8b274de65627c7bee1833.png

In Census data, there are unique variable names for hundreds of different datasets. Go to this list of Census variables and find the codes that align with your topic. Add these codes, along with what you want the column name to be, using the format below:

census_vars = {
    "CENSUS_CODE": "NAME_OF_YOUR_COLUMN",
    "CENSUS_CODE": "NAME_OF_YOUR_COLUMN",
    etc...
}

If you’re not sure which Census variables to use, here is a starting point

census_vars = {
    "B01003_001E": 'TOT_POP',
    "C17002_001E": 'INCOME_POVERTY_RATIO',
    "B19058_001E": 'TOT_FOOD_ASSIST',
    "B01002_001E": 'MEDIAN_AGE',
    "B19013_001E": 'MEDIAN_HOUSEHOLD_INCOME',
}

Census Variable	Description
B01003_001E	Total Population
C17002_001E	Ratio of Income to Poverty Level in the Past 12 Months
B19058_001E	Total Public Assistance Income or Food Stamps/SNAP in the Past 12 Months for Households
B01002_001E	Median Age
B19013_001E	Median Household Income in the Past 12 Months (in 2023 Inflation-Adjusted Dollars)

# Enter your code here
census_vars = {
    
}

census_data = get_census(dataset = "acs/acs5",
                           variables = list(census_vars.keys()),
                           year = 2023,
                           params = {
                             "for": "tract:*",
                             "in": f"state:{validate_state('FL')}"},
                           guess_dtypes = True,
                           return_geoid = True).rename(columns=census_vars)

census_data.head()

Example:#

census_vars = {
    "B01003_001E": 'TOT_POP',
    "C17002_001E": 'INCOME_POVERTY_RATIO',
    "B19058_001E": 'TOT_FOOD_ASSIST',
    "B01002_001E": 'MEDIAN_AGE',
    "B19013_001E": 'MEDIAN_HOUSEHOLD_INCOME',
}

census_data = get_census(dataset = "acs/acs5",
                           variables = list(census_vars.keys()),
                           year = 2023,
                           params = {
                             "for": "tract:*",
                             "in": f"state:{validate_state('FL')}"},
                           guess_dtypes = True,
                           return_geoid = True).rename(columns=census_vars)

census_data.head()

Using FIPS code '12' for input 'FL'

	TOT_POP	INCOME_POVERTY_RATIO	TOT_FOOD_ASSIST	MEDIAN_AGE	MEDIAN_HOUSEHOLD_INCOME	GEOID
0	5187	5006	2319	21.5	18657.0	12001000201
1	5897	4509	1897	21.4	17609.0	12001000202
2	3703	3703	1855	27.5	47813.0	12001000301
3	2500	2500	1255	47.0	39583.0	12001000302
4	5736	5736	2414	31.8	51266.0	12001000400

To visualize these variables on a map, we can merge census_data with tracts.

data = tracts[['geometry', 'GEOID']].merge(census_data, on = "GEOID")
data

	geometry	GEOID	TOT_POP	INCOME_POVERTY_RATIO	TOT_FOOD_ASSIST	MEDIAN_AGE	MEDIAN_HOUSEHOLD_INCOME
0	POLYGON ((-81.44831 28.59645, -81.44831 28.596...	12095012405	5004	5003	2029	31.9	43411.0
1	POLYGON ((-81.55755 28.7129, -81.55751 28.7129...	12095017812	7675	7675	2823	41.6	106974.0
2	POLYGON ((-81.16011 28.5043, -81.16001 28.5043...	12095016750	12628	12600	4188	38.2	113182.0
3	POLYGON ((-81.27566 28.46595, -81.27561 28.465...	12095016756	2851	2851	953	42.5	150532.0
4	POLYGON ((-81.51742 28.57588, -81.51741 28.576...	12095015005	3061	3015	1085	44.9	102202.0
...	...	...	...	...	...	...	...
262	POLYGON ((-81.24514 28.58933, -81.24514 28.589...	12095016504	5370	5180	1886	32.7	58125.0
263	POLYGON ((-81.245 28.57761, -81.24464 28.57761...	12095016505	2574	2574	1059	29.6	59735.0
264	POLYGON ((-81.43318 28.49325, -81.43289 28.493...	12095014504	5583	5556	2564	31.5	59000.0
265	POLYGON ((-81.21225 28.52589, -81.21224 28.526...	12095016731	7530	5111	1874	32.3	90625.0
266	POLYGON ((-81.52343 28.43885, -81.52282 28.442...	12095017109	5450	5450	2018	51.0	159821.0

267 rows × 7 columns

# Plot the data
data.plot(
    column = "MEDIAN_HOUSEHOLD_INCOME",
    cmap = "viridis",
    figsize = (8, 6),
    legend = True
)

plt.title("Median Household Income in Orange County, FL (2023)")

Text(0.5, 1.0, 'Median Household Income in Orange County, FL (2023)')

../_images/e322a5318757a410cef42f73d6e4254e55748ac3a1df37967be35a3b1605bef9.png

With API Key: cenpy#

More information about cenpy
Sign up for a Census API key here.

#@title Import modules
!pip install -q pytidycensus
import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
import pytidycensus as tc
import os

# Set your API key (http://api.census.gov/data/key_signup.html)
tc.set_census_api_key("CENSUS API KEY HERE")

Census API key has been set for this session.