acs-nativity

acs-nativity is a Python package for analyzing immigration trends in the United States using data from the American Community Survey (ACS). It provides a simple interface for downloading and visualizing data on the native-born and foreign-born population.

The package provides data as a time series covering the full span of ACS 1-year estimates (2005-2024). Under the hood, the package downloads and harmonizes two ACS tables: B05002 (2005-2008) and B05012 (2009 onward). The 2025 ACS 1-year estimates are expected to be released in September 2026. The Census Bureau did not release ACS 1-year estimates in 2020.

acs-nativity makes it easy to work with data for several geographies covered by the ACS 1-year estimates. This includes the nation, all states, the District of Columbia, all metropolitan statistical areas, and all counties and places (i.e., towns or cities) with populations of 65,000 or more.

The package exposes three functions:

• get_nativity_timeseries() - Downloads ACS nativity data and returns a dataframe covering all available ACS 1-year estimates for the given geography.
• plot_nativity_timeseries() - Creates a time series visualization of nativity data.
• plot_nativity_change() - Creates a bar chart showing the year-to-year change in a nativity measure.

Installation

Install acs-nativity with pip:

pip install acs-nativity

Example Workflow

The example below walks you through getting historic nativity data for the entire country, graphing it as a time series, and graphing the year-over-year changes.

Getting Data

The code below will get nativity data for the entire country over the course of the entire ACS (2005-2024):

from acs_nativity import (
    get_nativity_timeseries,
    plot_nativity_timeseries,
    plot_nativity_change,
)

df = get_nativity_timeseries(us="*")
df.head(1)

   Name           Year  Total      Native     Foreign-born  Percent Foreign-born
0  United States  2005  288378137  252688295  35689842      12.376057

The parameter us="*" tells get_nativity_timeseries to return data for the entire country. The key columns are Total, Native, Foreign-born, and Percent Foreign-born. Those columns are provided for all geographies.

Graphing Time Series

To plot a time series of the dataframe, call plot_nativity_timeseries and specify the column you want to chart. Most chart details (e.g., title and axis labels) are handled automatically, and annotations show when presidential administrations changed.

fig = plot_nativity_timeseries(df, column="Foreign-born")
fig.show()

Note: In Jupyter notebooks, you can simply call plot_nativity_timeseries(df, column="Foreign-born") as the last line of a cell and the figure will render automatically. In a Python REPL or script, assign the figure to a variable and call .show().

This graph shows that the foreign-born population has increased steadily since 2005, with a particularly large increase during the Biden administration.

Graphing Year-over-Year Change

Sometimes it is helpful to show the year-over-year changes instead of raw values. To do that, call plot_nativity_change with a dataframe and a column:

fig = plot_nativity_change(df, column="Foreign-born")
fig.show()

Note: In Jupyter notebooks, you can simply call plot_nativity_change(df, column="Foreign-born") as the last line of a cell and the figure will render automatically. In a Python REPL or script, assign the figure to a variable and call .show().

This chart makes it clear that the only year when the foreign-born population decreased was 2008.

Choosing a Geography

get_nativity_timeseries can provide data for several geographies covered by the ACS 1-year estimates. This includes the nation, all states, the District of Columbia, all metropolitan statistical areas, and all counties and places (i.e., towns or cities) with populations of 65,000 or more.

To specify a geography, acs-nativity follows the same conventions as the censusdis package, which provides convenient constants for identifying Census geographies.

A geography is specified using a keyword argument where:

• the keyword identifies the geography type (such as state)
• the value is a constant imported from a censusdis module (such as censusdis.states)

Installing acs-nativity automatically installs censusdis, so these constants are available once the package is installed.

Below are examples for several common geographies.

Geography	Keyword	Module for Value	Example
United States	us	—	df = get_nativity_timeseries(us="*")
State	state	censusdis.states	from censusdis.states import MN df = get_nativity_timeseries(state=MN)
County	county	censusdis.counties.<state_name>	from censusdis.states import NY from censusdis.counties.new_york import NASSAU df = get_nativity_timeseries(state=NY, county=NASSAU)
City	place	censusdis.places.<state_name>	from censusdis.states import IL from censusdis.places.illinois import CHICAGO_CITY df = get_nativity_timeseries(state=IL, place=CHICAGO_CITY)
Metropolitan Statistical Area (MSA)	(see note below)	censusdis.msa_msa	from censusdis.msa_msa import EL_PASO_TX_METRO_AREA df = get_nativity_timeseries( metropolitan_statistical_area_micropolitan_statistical_area= EL_PASO_TX_METRO_AREA )

Note: The keyword for MSAs is metropolitan_statistical_area_micropolitan_statistical_area, which is too long to display cleanly inside the table.

You can learn more in the Additional Geographies section of the censusdis documentation.

Getting with the Latest Data

By default, get_nativity_timeseries() returns data for 2005–2024. These years were chosen because:

• 2005 is the first year of ACS 1‑year estimates
• 2024 is the most recent year available at the time this package was published

The Census Bureau is expected to release the 2025 ACS 1‑year estimates in July 2026. When that happens, you can retrieve the new data immediately—without updating the package—by setting end_year=2025. For example:

df = get_nativity_timeseries(end_year=2025, us="*")

This works because the package downloads data dynamically from the Census API; it does not store any data internally.