cendat is a Python library designed to simplify the process of exploring and retrieving data from the U.S. Census Bureau’s API. It provides a high-level, intuitive workflow for discovering available datasets, filtering geographies and variables, and fetching data concurrently.

The library handles the complexities of the Census API’s structure, such as geographic hierarchies and inconsistent product naming, allowing you to focus on getting the data you need.

You can find regular cendat updates and musings on the developer blog.

The library is designed around a simple, four-step “List -> Set -> Get -> Convert/Analyze” workflow:

1. List: Use the list_* methods (list_products, list_geos, list_groups, list_variables) with patterns to explore what’s available and filter down to what you need.
2. Set: Use the set_* methods (set_products, set_geos, set_groups, set_variables) to lock in your selections. You can call these methods without arguments to use the results from your last “List” call. The describe_groups method is especially helpful for variable selection in programs with many variables, like the ACS.
3. Get: Call the get_data() method to build and execute all the necessary API calls. This method handles complex geographic requirements automatically and utilizes thread pooling for speed.
4. Convert & Analyze: Use the to_polars() or to_pandas() methods on the response object to get your data in a ready-to-use DataFrame format. The response object also includes a powerful tabulate() method for quick, Stata-like frequency tables.

You can install cendat using pip.

pip install cendat

The library has optional dependencies for converting the response data into pandas or polars DataFrames. You can install the support you need:

pip install cendat[pandas]

pip install cendat[polars]

pip install cendat[all]

This is the main class for building and executing queries.

Initializes the helper object.

• years (int | list[int], optional): The year or years of interest. Can be a single integer or a list of integers. Defaults to None.
• key (str, optional): Your Census API key. Providing a key is recommended to avoid strict rate limits. Defaults to None.

Sets the primary year or years for data queries.

• years (int | list[int]): The year or years to set.

Loads a Census API key for authenticated requests.

• key (str, optional): The API key to load.

Lists available data products, filtered by year and search patterns.

• years (int | list[int], optional): Filters products available for the specified year(s). Defaults to the years set on the object.
• patterns (str | list[str], optional): Regex pattern(s) to search for within the product metadata.
• to_dicts (bool): If True (default), returns a list of dictionaries with full product details. If False, returns a list of product titles.
• logic (callable): The logic to use when multiple patterns are provided. Can be all (default) or any.
• match_in (str): The field to match patterns against. Can be 'title' (default) or 'desc'.

Sets the active data products for the session.

• titles (str | list[str], optional): The title or list of titles of the products to set. If None, it sets all products from the last list_products() call.

Lists available geographies for the currently set products.

• to_dicts (bool): If True, returns a list of dictionaries with full geography details. If False (default), returns a list of unique summary level (sumlev) strings.
• patterns (str | list[str], optional): Regex pattern(s) to search for within the geography description.
• logic (callable): The logic to use when multiple patterns are provided. Can be all (default) or any.

Sets the active geographies for the session.

• values (str | list[str], optional): The geography values to set. If None, sets all geos from the last list_geos() call.
• by (str): The key to use for matching values. Must be either 'sumlev' (default) or 'desc'.

Lists available variable groups for the currently set products. Not all products have groups, in which case the resulting list will be empty.

• to_dicts (bool): If True (default), returns a list of dictionaries with full group details. If False, returns a list of unique group names.
• patterns (str | list[str], optional): Regex pattern(s) to search for within the group metadata.
• logic (callable): The logic to use when multiple patterns are provided. Can be all (default) or any.
• match_in (str): The field to match patterns against. Can be 'description' (default) or 'name'.

Sets the active variable groups for the session. If the call to set_groups results in a single group for each product vintage and all group variables are wanted, set_variables may be skipped.

• names (str | list[str], optional): The name or list of names of the groups to set. If None, sets all groups from the last list_groups() call.

Print hierarchically-nested group descriptions to facilitate variable selection.

• groups (str | list[str], optional): The name or list of names of the groups to describe. If None, describes all set groups or reports an error with instructions to set groups or use the groups parameter.

Lists available variables for the currently set products.

• to_dicts (bool): If True (default), returns a list of dictionaries with full variable details. If False, returns a list of unique variable names.
• patterns (str | list[str], optional): Regex pattern(s) to search for within the variable metadata.
• logic (callable): The logic to use when multiple patterns are provided. Can be all (default) or any.
• match_in (str): The field to match patterns against. Can be 'label' (default), 'name' or 'concept'.
• groups (str | list[str], optional): Variable groups within which the listing will be limited. Groups provided here override whatever groups may be set, and set groups will be used if this is None.

Sets the active variables for the session. If exactly one group is set for each product vintage and all group variables are wanted, set_variables may be skipped. Doing so allows for more than the standard API max of 50 variables.

• names (str | list[str], optional): The name or list of names of the variables to set. If None, sets all variables from the last list_variables() call.

Executes the API calls based on the set parameters and retrieves the data.

• within (str | dict | list[dict], optional): Defines the geographic scope of the query.
  • For aggregate data, this can be a dictionary filtering parent geographies (e.g., {'state': '06'} for California). A list of dictionaries can be provided to query multiple scopes.
  • For microdata, this must be a dictionary specifying the target geography and its codes (e.g., {'public use microdata area': ['7701', '7702']}).
  • Defaults to 'us' for nationwide data where applicable.
• max_workers (int, optional): The maximum number of concurrent threads to use for making API calls. For requests generating thousands of calls, it's wise to keep this value lower (e.g., < 100) to avoid server-side connection issues. Defaults to 100.
• timeout (int, optional): Request timeout in seconds for each API call. Defaults to 30.
• preview_only (bool, optional): If True, builds the list of API calls but does not execute them. Useful for debugging. Defaults to False.
• include_names (bool, optional): If True, includes geography name (NAME) in API request--this variable is a special keyword understood by the data endpoint but is not included in variables.json and is therefore not discoverable through list_variables(). Note that NAME requests for microdata products will be ignored (with a message). Defaults to False.
• include_geoids (bool, optional): If True, includes geography ID (GEO_ID) in API request--this variable is a special keyword understood by the data endpoint but is not included in variables.json and is therefore not discoverable through list_variables(). Note that GEO_ID requests for microdata products will be ignored (with a message). Defaults to False.
• include_attributes (bool, optional): If True, includes attributes associated with set variables (e.g., margins of error) in API request if available. Defaults to False.

A container for the data returned by CenDatHelper.get_data().

Converts the raw response data into a list of Polars DataFrames.

• schema_overrides (dict, optional): A dictionary mapping column names to Polars data types to override the inferred schema. Example: {'POP': pl.Int64}.
• concat (bool): If True, concatenates all resulting DataFrames into a single DataFrame. Defaults to False.
• destring (bool): If True, attempts to convert string representations of numbers into native numeric types. Defaults to False.

Converts the raw response data into a list of Pandas DataFrames.

• dtypes (dict, optional): A dictionary mapping column names to Pandas data types, which is passed to the .astype() method. Example: {'POP': 'int64'}.
• concat (bool): If True, concatenates all resulting DataFrames into a single DataFrame. Defaults to False.
• destring (bool): If True, attempts to convert string representations of numbers into native numeric types. Defaults to False.

Generates and prints a frequency table.

• *variables (str): One or more column names to include in the tabulation.
• strat_by (str, optional): A column name to stratify the results by. Percentages and cumulative stats will be calculated within each stratum. Defaults to None.
• weight_var (str, optional): The name of the column to use for weighting. If None, each row has a weight of 1. Defaults to None.
• weight_div (int, optional): A positive integer to divide the weight by, useful for pooled tabulations across multiple product vintages. weight_var must be provided if this is used. Defaults to None.
• where (str | list[str], optional): A string or list of strings representing conditions to filter the data before tabulation. Each condition should be in a format like "variable operator value" (e.g., "AGE > 30") or "variable1 / variable2 operator value" (e.g., "B17001_002E / B17001_001E < 0.01"). Defaults to None.
• logic (callable): The function to apply when multiple where conditions are provided. Use all for AND logic (default) or any for OR logic.
• digits (int): The number of decimal places to display for floating-point numbers in the output table. Defaults to 1.

import os
from cendat import CenDatHelper
from dotenv import load_dotenv

load_dotenv()

# --- ACS PUMS ANALYSIS ---

# 1. Initialize and set up the query
cdh = CenDatHelper(years=[2022], key=os.getenv("CENSUS_API_KEY"))
cdh.list_products(patterns=r"acs/acs1/pums\b")
cdh.set_products()
cdh.set_geos(values="state", by="desc")
cdh.set_variables(names=["SEX", "AGEP", "ST", "PWGTP"])

# 2. Get data for two states
response = cdh.get_data(
    within={"state": ["06", "48"]}, # California and Texas
)

# 3. Create a stratified tabulation
print("Age Distribution by Sex, Stratified by State")
response.tabulate(
    "SEX", "AGEP",
    strat_by="ST",
    weight_var="PWGTP",
    where="AGEP > 17" # Filter for adults
)

# 4. Convert to DataFrame for further analysis
df = response.to_polars(concat=True, destring=True)
print(df.head())

# --- ACS 5YR AGGREGATE ANALYSIS ---

cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY"))
cdh.list_products(years=[2023], patterns=r"acs/acs5\)")
cdh.set_products()
cdh.list_groups(patterns="sex by age")
cdh.set_groups("B17001")
cdh.describe_groups()
cdh.set_variables(["B17001_001E", "B17001_002E"])
cdh.set_geos(["160"])
response = cdh.get_data(
    include_names=True,
    include_attributes=True,
)
df = response.to_polars(concat=True, destring=True)
df.glimpse()

# --- ACS 5YR AGGREGATE ANALYSIS ---

cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY"))
cdh.list_products(years=[2023], patterns=r"/acs/acs5\)")
cdh.set_products()
cdh.set_variables("B01001_001E")  # total population
cdh.set_geos("150")
response = cdh.get_data()

# how many counties
response.tabulate("state", where="B01001_001E > 10_000")

# how many people in those counties
response.tabulate("state", weight_var="B01001_001E", where="B01001_001E > 10_000")

# --- CPS MICRODATA ANALYSIS ---

cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY"))
cdh.list_products(years=[2022, 2023], patterns="/cps/tobacco")
cdh.set_products()
cdh.list_groups()
cdh.set_variables(["PEA1", "PEA3", "PWNRWGT"])
cdh.set_geos("state", "desc")
response = cdh.get_data(within={"state": ["06", "48"]})
response.tabulate(
    "PEA1",
    "PEA3",
    strat_by="state",
    weight_var="PWNRWGT",
    weight_div=3,
)

# --- ACS ANALYSIS: see Colorado incorporated places with very low poverty across years ---

cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY"))
cdh.list_products(years=[2020, 2021, 2022, 2023], patterns=r"acs/acs5\)")
cdh.set_products()
cdh.list_groups(patterns="sex by age")
cdh.set_groups(["B17001"])
cdh.describe_groups()
cdh.set_geos(["160"])
response = cdh.get_data(
    include_names=True,
    within={"state": "08"},
)

response.tabulate(
    "NAME",
    "B17001_002E",
    "B17001_001E",
    where=[
        "B17001_001E > 1_000",
        "B17001_002E / B17001_001E < 0.01",
        "'CDP' not in NAME",
    ],
    weight_var="B17001_001E",
    strat_by="vintage",
)