Get Population By Zip Code

Get US Population By Zip Code

Understanding ZCTAs

ZIP Code Tabulation Areas (ZCTAs) serve as a statistical approximation of U.S. Postal Service ZIP codes, providing a unique lens through which to analyze population data. Unlike traditional ZIP codes, ZCTAs are designed by the Census Bureau to represent geographical areas, making them a valuable tool for granular demographic insights.

The MetadAPI Population by Zip Code Dataset

The Census Bureau's ZCTAs population data is released every 10 years as a result of the decennial census. The U.S. Census Bureau conducts a census every 10 years to determine the number of people living in the United States. The census counts each resident of the country, where they live on April 1, every ten years ending in zero. This data is provided using the ZCTAs tabulation approximations to the USPS Zip Codes. Metadapi takes this information and produces a user friendly data model within the JSON response for the zip code information. The array zipCodeStatistics contains a model with the year, totalPopulation, malePopulation and femalePopulation for each zip code as highlighted on the example below.

  "year": 2020,
  "totalPopulation": 52943,
  "malePopulation": 25206,
  "femalePopulation": 27737

The full JSON model for Houston Texas zip code 77070 is presented below (where you can see the population for this zip code for the years 2000, 2010 and 2020).

    "links": {
        "self": ""
    "meta": {
        "count": 1
    "data": {
        "zipCode": "77070",
        "uspsMainCityKey": "W24130",
        "uspsMainCityName": "HOUSTON",
        "titleCaseCityName": "Houston",
        "zipClassificationCode": "N",
        "zipClassificationDesc": "Non-Unique Zip",
        "uspsFacilityCode": "P",
        "uspsFacilityName": "Post Office",
        "uspsCityMailingInd": true,
        "uspsDeliveryCode": "Y",
        "uspsDeliveryDesc": "Office Has City-Delivery Carrier Routes",
        "uspsCarrierRouteSortCode": "D",
        "uspsCarrierRouteRateSortDesc": "Carrier Route. Sortation/Rates. Do Not Apply-Merging Not Permitted",
        "uniqueZipNameInd": false,
        "uspsFinanceNumber": "484145",
        "stateCode": "TX",
        "stateName": "Texas",
        "stateFipsCode": "48",
        "stateAbbr": "Tex.",
        "countyFipsCode": "201",
        "uspsCountyName": "HARRIS",
        "titleCaseCountyName": "Harris",
        "latitude": 29.979456,
        "longitude": -95.57332,
        "landAreaMi2": 13.035,
        "waterAreaMi2": 0.128,
        "landAreaKm2": 33.76091,
        "waterAreaKm2": 0.330637,
        "divisionCode": "7",
        "divisionName": "West South Central Division",
        "regionCode": "3",
        "regionName": "South Region",
        "msaCode": "26420",
        "msaName": "Houston-The Woodlands-Sugar Land, TX Metro Area",
        "cityAliases": [
                "uspsCityKey": "W24130",
                "uspsCityName": "HOUSTON",
                "titleCaseCityName": "Houston",
                "uspsCityAbbr": null,
                "uspsCityMailingInd": true,
                "uspsMainCityInd": true
        "zipCodeStatistics": [
                "year": 2000,
                "totalPopulation": 32385,
                "malePopulation": 15812,
                "femalePopulation": 16573
                "year": 2010,
                "totalPopulation": 46017,
                "malePopulation": 22355,
                "femalePopulation": 23662
                "year": 2020,
                "totalPopulation": 52943,
                "malePopulation": 25206,
                "femalePopulation": 27737

Population by Zip Code Use Cases

Analyzing census population by zip code can help with urban planning, resource allocation, and targeted community outreach. It's valuable for:

  1. Resource Distribution: Efficiently allocating public services, such as schools, healthcare facilities, and emergency services based on population density in specific zip codes.
  2. Business Planning: Understanding demographics helps businesses tailor their products/services and target marketing campaigns to specific regions.
  3. Infrastructure Development: Planning and prioritizing infrastructure projects like roads, public transportation, and utilities based on population density and growth.
  4. Policy Making: Informing policymakers about the unique needs of different communities, influencing decisions related to zoning, housing, and social services.
  5. Healthcare Planning: Identifying areas with specific health needs, enabling better distribution of medical resources and public health interventions.
  6. Education Planning: Tailoring educational programs and allocating resources based on the demographics and needs of different zip codes.
  7. Emergency Response: Enhancing emergency response planning by understanding population distribution, ensuring effective evacuation plans, and allocating resources for disaster management.
  8. Demographic Studies: Conducting detailed demographic studies to understand trends, social disparities, and changes over time.

By leveraging census population data at the zip code level, communities and organizations can make more informed decisions to address specific challenges and promote well-being.

Population by Zip Code Python Example

To bring theory into practice, let's take a look at the following Python code example.  The Python code will send a list of zip codes to the List All Zip Codes endpoint to get the details for each of them. We are going to plot the population results of each zip code to compare them together. 

The first part is to import the 2 libraries we'll be using on this example, "requests" to call API endpoints and matplotlib to create a chart with the results.

import requests
import matplotlib.pyplot as plt

Next we create a function to call the endpoint to get a list of zip codes. (in the example below you need to replace the text with your API key from metadapi). 

def fetch_data_from_api():
  vheaders = {"Ocp-Apim-Subscription-Key": ""}
  url = f",33967,48201"
  response = requests.get(url,headers=vheaders)  if response.status_code == 200: 
    return response.json()
    print(f"Failed to fetch data. Status code: {response.status_code}")
    return None

Next step we create a function that plots the population data for each zip code:

def plot_population_data(data):
  for zipcodes in data['data']:
    years = []
    populations = []
    for zipstats in zipcodes['zipCodeStatistics']:
      plt.plot(years,populations, label = zipcodes['zipCode'])
plt.title("Population Over Years")
plt.ylabel("Total Population")

To download the full source code visit the zip code github

Running this code will give you the following graph showing the population for each of the zip codes provided:


The utilization of an API providing population data by zip code offers invaluable insights and benefits for various applications. This comprehensive data source empowers businesses, researchers, and policymakers to make informed decisions by understanding demographic trends and population dynamics at a localized level. Whether optimizing marketing strategies, enhancing urban planning, or tailoring public services, the precision and granularity of zip code-specific population information enable more accurate analyses and targeted initiatives. 

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