U.S. Census Data API in Python#
by Avery Fernandez
U.S. Census API documentation: https://www.census.gov/data/developers/about.html
U.S. Census Data Discovery Tool: https://api.census.gov/data.html
These recipe examples were tested on March 16, 2022.
See also the U.S. Census API Terms of Service
Attribution: This tutorial uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.
API Key Information#
While an API key is not required to use the U.S. Census Data API, you may consider registering for an API key as the API is limited to 500 calls a day without a key. Sign up can be found here: https://api.census.gov/data/key_signup.html
This tutorial does not use an API key:
key = ""
However, if you want to add in your API Key, save the API key to a file api_key and run the following code:
from api_key import key
1. Get population estimates of counties by state#
Note: includes Washington, D.C. and Puerto Rico
First, import several libraries:
from time import sleep
import requests
from pprint import pprint
For obtaining data from the Census API, it is helpful to first obtain a list of state IDs:
# define root Census API
api = 'https://api.census.gov/data/'
# define api url for the state ids
# we will use the Population Estimates from 2019 dataset:
# https://api.census.gov/data/2019/pep/population/examples.html
state_ids = api + "2019/pep/population?get=NAME&for=state:*" + key
# read the url and import JSON data
state_ids = requests.get(state_ids).json()
print(len(state_ids))
53
pprint(state_ids[0:10]) # show first 10
[['NAME', 'state'],
['Alabama', '01'],
['Alaska', '02'],
['Arizona', '04'],
['Arkansas', '05'],
['California', '06'],
['Colorado', '08'],
['Delaware', '10'],
['District of Columbia', '11'],
['Idaho', '16']]
# remove heading
state_ids = state_ids[1:]
pprint(state_ids[0:10])
[['Alabama', '01'],
['Alaska', '02'],
['Arizona', '04'],
['Arkansas', '05'],
['California', '06'],
['Colorado', '08'],
['Delaware', '10'],
['District of Columbia', '11'],
['Idaho', '16'],
['Connecticut', '09']]
Now we can loop through each state and pull their individual population data:
# Extract out ids from the list to create a dictionary of state ids
for states in state_ids: # loops through the list of states
stateName = states[0] # grabs the name of the state
# grabs the name of each county and their population
request = requests.get(api + "2019/pep/population?get=NAME,POP&for=county:*&in=state:"+states[1]+key).json()[1:]
sleep(1)
# creates lists to add the populations and counties
counties = []
countyPopulation = []
for county in request: # loops through the json data, extracting the county name and their population
counties.append(county[0][:-(len(stateName)+2)]) # adds the county's name and deletes the state name from the end of the county string
countyPopulation.append(int(county[1] or 'nan')) # adds the county's population
# converts the name of the state into a variable and stores the county data into the given name for easy reference
globals()[stateName] = {counties[i]: countyPopulation[i] for i in range(len(counties))}
# show data for Alabama
pprint(Alabama)
{'Autauga County': 55869,
'Baldwin County': 223234,
'Barbour County': 24686,
'Bibb County': 22394,
'Blount County': 57826,
'Bullock County': 10101,
'Butler County': 19448,
'Calhoun County': 113605,
'Chambers County': 33254,
'Cherokee County': 26196,
'Chilton County': 44428,
'Choctaw County': 12589,
'Clarke County': 23622,
'Clay County': 13235,
'Cleburne County': 14910,
'Coffee County': 52342,
'Colbert County': 55241,
'Conecuh County': 12067,
'Coosa County': 10663,
'Covington County': 37049,
'Crenshaw County': 13772,
'Cullman County': 83768,
'Dale County': 49172,
'Dallas County': 37196,
'DeKalb County': 71513,
'Elmore County': 81209,
'Escambia County': 36633,
'Etowah County': 102268,
'Fayette County': 16302,
'Franklin County': 31362,
'Geneva County': 26271,
'Greene County': 8111,
'Hale County': 14651,
'Henry County': 17205,
'Houston County': 105882,
'Jackson County': 51626,
'Jefferson County': 658573,
'Lamar County': 13805,
'Lauderdale County': 92729,
'Lawrence County': 32924,
'Lee County': 164542,
'Limestone County': 98915,
'Lowndes County': 9726,
'Macon County': 18068,
'Madison County': 372909,
'Marengo County': 18863,
'Marion County': 29709,
'Marshall County': 96774,
'Mobile County': 413210,
'Monroe County': 20733,
'Montgomery County': 226486,
'Morgan County': 119679,
'Perry County': 8923,
'Pickens County': 19930,
'Pike County': 33114,
'Randolph County': 22722,
'Russell County': 57961,
'Shelby County': 217702,
'St. Clair County': 89512,
'Sumter County': 12427,
'Talladega County': 79978,
'Tallapoosa County': 40367,
'Tuscaloosa County': 209355,
'Walker County': 63521,
'Washington County': 16326,
'Wilcox County': 10373,
'Winston County': 23629}
2. Get population estimates over a range of years#
We can use similar code as before, but now loop through different population estimate datasets by year. Here are the specific APIs used:
Vintage 2015 Population Estimates: https://api.census.gov/data/2015/pep/population/examples.html
Vintage 2016 Population Estimates: https://api.census.gov/data/2016/pep/population/examples.html
Vintage 2017 Population Estimates: https://api.census.gov/data/2017/pep/population/examples.html
# first we must clear the global variables
for states in state_ids:
stateName = states[0]
try:
del globals()[stateName]
except:
pass
globals()[stateName] = {}
for years in range(2015,2018): # goes through datasets for 2015,2016,2017
for states in state_ids: # using the state_ids from earlier
stateName = states[0]
request = requests.get(api+str(years)+"/pep/population?get=GEONAME,POP&for=county:*&in=state:"+states[1]+key).json()[1:]
sleep(1)
counties = []
countyPopulation = []
for county in request:
counties.append((county[0]).split(", ")[0])
countyPopulation.append(int(county[1] or 'nan'))
globals()[stateName][years] = {counties[i]: countyPopulation[i] for i in range(len(counties))}
# show Alabama data
pprint(Alabama)
{2015: {'Autauga County': 55347,
'Baldwin County': 203709,
'Barbour County': 26489,
'Bibb County': 22583,
'Blount County': 57673,
'Bullock County': 10696,
'Butler County': 20154,
'Calhoun County': 115620,
'Chambers County': 34123,
'Cherokee County': 25859,
'Chilton County': 43943,
'Choctaw County': 13170,
'Clarke County': 24675,
'Clay County': 13555,
'Cleburne County': 15018,
'Coffee County': 51211,
'Colbert County': 54354,
'Conecuh County': 12672,
'Coosa County': 10724,
'Covington County': 37835,
'Crenshaw County': 13963,
'Cullman County': 82005,
'Dale County': 49565,
'Dallas County': 41131,
'DeKalb County': 71130,
'Elmore County': 81468,
'Escambia County': 37789,
'Etowah County': 103057,
'Fayette County': 16759,
'Franklin County': 31696,
'Geneva County': 26777,
'Greene County': 8479,
'Hale County': 15068,
'Henry County': 17221,
'Houston County': 104173,
'Jackson County': 52419,
'Jefferson County': 660367,
'Lamar County': 13886,
'Lauderdale County': 92596,
'Lawrence County': 33115,
'Lee County': 156993,
'Limestone County': 91663,
'Lowndes County': 10458,
'Macon County': 19105,
'Madison County': 353089,
'Marengo County': 20028,
'Marion County': 30168,
'Marshall County': 94725,
'Mobile County': 415395,
'Monroe County': 21673,
'Montgomery County': 226519,
'Morgan County': 119565,
'Perry County': 9652,
'Pickens County': 20864,
'Pike County': 33046,
'Randolph County': 22696,
'Russell County': 59660,
'Shelby County': 208713,
'St. Clair County': 87074,
'Sumter County': 13103,
'Talladega County': 80862,
'Tallapoosa County': 40844,
'Tuscaloosa County': 203976,
'Walker County': 65294,
'Washington County': 16804,
'Wilcox County': 11059,
'Winston County': 23877},
2016: {'Autauga County': 55416,
'Baldwin County': 208563,
'Barbour County': 25965,
'Bibb County': 22643,
'Blount County': 57704,
'Bullock County': 10362,
'Butler County': 19998,
'Calhoun County': 114611,
'Chambers County': 33843,
'Cherokee County': 25725,
'Chilton County': 43941,
'Choctaw County': 12993,
'Clarke County': 24392,
'Clay County': 13492,
'Cleburne County': 14924,
'Coffee County': 51226,
'Colbert County': 54216,
'Conecuh County': 12395,
'Coosa County': 10581,
'Covington County': 37458,
'Crenshaw County': 13913,
'Cullman County': 82471,
'Dale County': 49226,
'Dallas County': 40008,
'DeKalb County': 70900,
'Elmore County': 81799,
'Escambia County': 37728,
'Etowah County': 102564,
'Fayette County': 16546,
'Franklin County': 31628,
'Geneva County': 26614,
'Greene County': 8422,
'Hale County': 14952,
'Henry County': 17164,
'Houston County': 104056,
'Jackson County': 52138,
'Jefferson County': 659521,
'Lamar County': 13918,
'Lauderdale County': 92318,
'Lawrence County': 33244,
'Lee County': 158991,
'Limestone County': 92753,
'Lowndes County': 10358,
'Macon County': 18963,
'Madison County': 356967,
'Marengo County': 19673,
'Marion County': 29998,
'Marshall County': 95157,
'Mobile County': 414836,
'Monroe County': 21530,
'Montgomery County': 226349,
'Morgan County': 119012,
'Perry County': 9574,
'Pickens County': 20324,
'Pike County': 33286,
'Randolph County': 22652,
'Russell County': 58172,
'Shelby County': 210622,
'St. Clair County': 88019,
'Sumter County': 13040,
'Talladega County': 80103,
'Tallapoosa County': 40727,
'Tuscaloosa County': 206102,
'Walker County': 64967,
'Washington County': 16756,
'Wilcox County': 10986,
'Winston County': 23805},
2017: {'Autauga County': 55504,
'Baldwin County': 212628,
'Barbour County': 25270,
'Bibb County': 22668,
'Blount County': 58013,
'Bullock County': 10309,
'Butler County': 19825,
'Calhoun County': 114728,
'Chambers County': 33713,
'Cherokee County': 25857,
'Chilton County': 44067,
'Choctaw County': 12945,
'Clarke County': 24083,
'Clay County': 13367,
'Cleburne County': 14900,
'Coffee County': 51874,
'Colbert County': 54500,
'Conecuh County': 12469,
'Coosa County': 10754,
'Covington County': 37092,
'Crenshaw County': 13871,
'Cullman County': 82755,
'Dale County': 49226,
'Dallas County': 39215,
'DeKalb County': 71617,
'Elmore County': 81677,
'Escambia County': 37447,
'Etowah County': 102755,
'Fayette County': 16468,
'Franklin County': 31495,
'Geneva County': 26421,
'Greene County': 8330,
'Hale County': 14812,
'Henry County': 17147,
'Houston County': 104346,
'Jackson County': 51909,
'Jefferson County': 659197,
'Lamar County': 13946,
'Lauderdale County': 92538,
'Lawrence County': 33049,
'Lee County': 161604,
'Limestone County': 94402,
'Lowndes County': 10076,
'Macon County': 18755,
'Madison County': 361046,
'Marengo County': 19375,
'Marion County': 29833,
'Marshall County': 95548,
'Mobile County': 413955,
'Monroe County': 21327,
'Montgomery County': 226646,
'Morgan County': 118818,
'Perry County': 9339,
'Pickens County': 20176,
'Pike County': 33267,
'Randolph County': 22670,
'Russell County': 57045,
'Shelby County': 213605,
'St. Clair County': 88199,
'Sumter County': 12687,
'Talladega County': 80065,
'Tallapoosa County': 40681,
'Tuscaloosa County': 207811,
'Walker County': 64058,
'Washington County': 16531,
'Wilcox County': 10719,
'Winston County': 23722}}
3. Plot Population Change#
This data is based off the 2021 Population Estimates dataset:
https://api.census.gov/data/2021/pep/population/variables.html
The percentage change in population is from July 1, 2020 to July 1, 2021 for states (includes Washington, D.C. and Puerto Rico)
request = requests.get(api+"2021/pep/population?get=NAME,POP_2021,PPOPCHG_2021&for=state:*"+key).json()[1:]
request.sort()
print(len(request)) # shows the size of data
52
pprint(request[0:10]) # shows first 10
[['Alabama', '5039877', '0.2999918604', '01'],
['Alaska', '732673', '0.0316749062', '02'],
['Arizona', '7276316', '1.3698828613', '04'],
['Arkansas', '3025891', '0.4534511286', '05'],
['California', '39237836', '-0.6630474360', '06'],
['Colorado', '5812069', '0.4799364073', '08'],
['Connecticut', '3605597', '0.1482392938', '09'],
['Delaware', '1003384', '1.1592057958', '10'],
['District of Columbia', '670050', '-2.9043911470', '11'],
['Florida', '21781128', '0.9791222337', '12']]
# prepare data for plotting
stateName = []
population = []
populationChange = []
for states in request:
stateName.append(states[0])
population.append(int(states[1]) or 'nan')
populationChange.append(float(states[2] or 'nan'))
# plot data
import matplotlib.pyplot as plt
plt.figure(figsize=(10,15))
plt.scatter(populationChange, stateName, c="blue", alpha=0.5, label="states")
plt.title("Population Change from 2020 to 2021", c="k")
plt.xlabel("% Population Change",c="k")
plt.ylabel("States (including Washington DC and Puerto Rico)")
plt.show()
