uszipinfo 1.0.1

ML-ready ZIP-code-level metadata for the United States.

uszipinfo

ML-ready ZIP-code-level metadata for the United States.

A single, fast, typed Python package providing demographic, geographic, and metro-context features for every US ZIP code — including PO Boxes, military APO/FPO/DPO ZIPs, and US territories. Designed for machine learning pipelines and data analysis where you need rich features per ZIP without re-implementing the Census API plumbing.

import uszipinfo

info = uszipinfo.lookup("98004")
print(info.population)               # 39161
print(info.urbanicity_tier)          # 'urban'
print(info.msa_name)                 # 'Seattle-Tacoma-Bellevue, WA'
print(info.median_household_income)  # 157784
print(info.pct_bachelors_or_higher)  # 0.7488

---

Why this exists

Existing PyPI ZIP packages each have gaps:

Package	Demographics	Recent data	ML features	PO Boxes	Military
uszipcode	Yes	Older ACS	Limited	Partial	No
pgeocode	No	Geographic only	No	No	No
pyzipcode	No	Basic only	No	No	No
zipcodes	No	Basic only	No	No	No
uszipinfo	Yes	Annual	Yes	Yes	Yes

uszipinfo combines recent Census ACS demographics, HUD-style ZIP crosswalks, Census Gazetteer geography, GeoNames postal coverage, and engineered ML features into a single package with a clean, typed Python API.

---

Coverage

The bundled data covers 41,994 ZIPs spanning all 50 states, DC, 5 US territories, and military APO/FPO/DPO addresses.

ZIP type	Count	Description
Standard	32,164	Residential ZIPs with full demographics
PO_Box	7,669	PO Box-only ZIPs (city/state/lat/lon, null demographics)
Unique	1,377	Institutional ZIPs (universities, large companies)
Military	784	APO/FPO/DPO with state ∈ {AA, AE, AP}

For Standard ZIPs, all 54 fields are populated (≥95% coverage on demographic columns). For non-residential ZIPs (PO Box, Unique, Military), demographics are deliberately null — they have no residential population to measure — but zip_type, state, primary_city, and geographic fields are populated.

---

Installation

pip install uszipinfo

The bundled Parquet data file (~7 MB) ships with the package — no API key, no separate download, no internet connection required at runtime.

Optional dependencies:

pip install uszipinfo[build]   # for rebuilding the dataset from sources
pip install uszipinfo[dev]     # pytest, ruff, mypy for development

---

Quick start

import uszipinfo

# Single ZIP lookup, returns a typed ZipInfo dataclass
info = uszipinfo.lookup("98004")
print(info.population)

# Bulk lookup, returns a DataFrame
df = uszipinfo.lookup_many(["98004", "98005", "98006"])

# Filter by criteria
urban_wa = uszipinfo.filter_zips(state="WA", urbanicity_tier="urban")
high_income = uszipinfo.filter_zips(min_median_household_income=100000)
nyc_metro = uszipinfo.filter_zips(msa_code="35620")

# Geographic queries
nearby = uszipinfo.nearest_zips("98004", n=10, max_distance_mi=20)
distance = uszipinfo.distance_mi("98004", "10001")  # great-circle miles

# Load the full DataFrame
df = uszipinfo.load()

---

API reference

uszipinfo.load(year=None) -> pd.DataFrame

Return the full ZIP metadata as a pandas DataFrame with all 54 columns.

Pass year= to load a specific ACS vintage (only relevant if multiple years are bundled; defaults to latest).

uszipinfo.lookup(zip_code, year=None) -> ZipInfo

Return a typed ZipInfo dataclass for a single ZIP.

• Accepts ZIP codes as 4- or 5-digit strings: "02139", "2139", 2139
• Accepts ZIP+4 format: "98004-1234" (suffix is dropped)
• Raises KeyError if the ZIP is not in the dataset
• Raises ValueError for malformed input

uszipinfo.lookup_many(zip_codes, year=None) -> pd.DataFrame

Bulk lookup. Returns a DataFrame in the order of the input. ZIPs not present in the dataset are silently dropped from the output.

uszipinfo.filter_zips(year=None, **criteria) -> pd.DataFrame

Filter the dataset by criteria. Supports three forms:

• Equality: state="WA", is_metro=True, urbanicity_tier="urban"
• Membership: state=["WA", "OR"]
• Range: min_population=1000, max_median_household_income=50000

Range filters use the prefix min_ (≥) or max_ (≤) on any numeric column.

uszipinfo.nearest_zips(zip_code, n=10, max_distance_mi=None) -> pd.DataFrame

Return the n nearest ZIPs to zip_code, sorted by great-circle distance. Optionally filter to within max_distance_mi. Excludes the source ZIP.

uszipinfo.distance_mi(zip_a, zip_b) -> float

Return great-circle distance in miles between two ZIPs.

Raises KeyError if either ZIP is unknown, or ValueError if either ZIP lacks coordinates (some PO Box ZIPs may have no lat/lon).

uszipinfo.haversine_mi(lat1, lon1, lat2, lon2) -> float

Direct great-circle distance from raw coordinates (degrees).

Module-level constants

uszipinfo.__version__   # package version, e.g., "1.0.0"
uszipinfo.DATA_YEAR     # ACS vintage of the bundled data
uszipinfo.COLUMNS       # list of all 54 column names
uszipinfo.ENUMS         # allowed values for enum-like columns

---

Schema

54 columns across 10 categories. All percentage fields are in 0–1 range (not 0–100).

The Nullable column indicates whether the field can be None (in Python) or NaN (in pandas). Non-nullable fields are guaranteed to have a value for every record. Nullable fields may be missing for ZIPs where the underlying data source is unavailable — typically PO Box, Unique, and Military ZIPs that have no residential population for the Census to measure.

Geographic identity

Field	Type	Nullable	Description
zip	str	No	5-digit ZIP, zero-padded. Primary key.
state	str	No	2-letter USPS state code (or AA/AE/AP for military)
state_name	str	No	Full state name
county	str	Yes	Dominant county name (when ZIP spans multiple)
county_fips	str	Yes	5-digit county FIPS code (state + county)
primary_city	str	Yes	Most-associated city name
lat	float	Yes	Interior point latitude
lon	float	Yes	Interior point longitude
timezone	str	Yes	IANA timezone (e.g., America/Los_Angeles)
land_area_sq_mi	float	Yes	Land area in square miles
water_area_sq_mi	float	Yes	Water area in square miles

Metro / region

Field	Type	Nullable	Description
cbsa_code	str	Yes	Core-Based Statistical Area code (5-digit)
cbsa_name	str	Yes	CBSA name (e.g., Seattle-Tacoma-Bellevue, WA)
cbsa_type	str	Yes	Metro / Micro
msa_code	str	Yes	Same as cbsa_code if Metro, else null
msa_name	str	Yes	Same as cbsa_name if Metro, else null
csa_code	str	Yes	Combined Statistical Area code (parent of CBSA)
csa_name	str	Yes	CSA name
is_metro	bool	No	True iff cbsa_type == Metro
census_region	str	Yes	Northeast / Midwest / South / West / Territories
census_division	str	Yes	One of 11 divisions (9 standard + 2 territory)

Population

Field	Type	Nullable	Description
population	int	Yes	Total population
population_density	float	Yes	Population per square mile of land
households	int	Yes	Total households
median_age	float	Yes	Median age in years
pct_under_18	float	Yes	Percent of population under 18
pct_65_plus	float	Yes	Percent 65 or older

Economic

Field	Type	Nullable	Description
median_household_income	int	Yes	USD
pct_below_poverty	float	Yes	Percent below federal poverty line
pct_employed	float	Yes	Labor force participation rate
mean_travel_time_to_work_minutes	float	Yes	Average commute time
pct_no_vehicles	float	Yes	Percent of households with no vehicles

Education

Field	Type	Nullable	Description
pct_bachelors_or_higher	float	Yes	Percent of adults 25+ with bachelor's or higher

Housing

Field	Type	Nullable	Description
total_housing_units	int	Yes	Total housing units
pct_owner_occupied	float	Yes	Percent of occupied units owner-occupied
pct_vacant	float	Yes	Percent of housing units vacant
pct_single_family	float	Yes	Percent that are 1-unit structures
pct_multi_family	float	Yes	Percent that are 5+ unit structures
median_home_value	int	Yes	USD
vacancy_for_seasonal_use	float	Yes	Percent vacant for seasonal use

Race / ethnicity

All percentages in 0–1 range, sourced from Census ACS B03002 table.

Field	Type	Nullable	Description
pct_white	float	Yes	Non-Hispanic white
pct_black	float	Yes	Black or African American
pct_hispanic	float	Yes	Hispanic or Latino (any race)
pct_asian	float	Yes	Asian
pct_native_american	float	Yes	American Indian / Alaska Native
pct_pacific_islander	float	Yes	Native Hawaiian / Pacific Islander

USPS classification

Field	Type	Nullable	Description
zip_type	str	No	Standard / PO_Box / Unique / Military (heuristic)

The official USPS classification is not redistributable. zip_type is inferred from population, area, and prefix conventions. Accuracy is high for residential ZIPs and military ZIPs but may misclassify some edge cases (e.g., very small Unique ZIPs as PO_Box).

Engineered features

Field	Type	Nullable	Description
urbanicity_tier	str	Yes	rural (<100/sq mi) / suburban (100–1000) / urban (1000–10000) / dense_urban (>10000)
climate_zone	str	Yes	tropical / subtropical / temperate / continental / cold (latitude-based)
is_college_town	bool	No	Heuristic: high education + moderate density + non-trivial population
is_resort_area	bool	No	Heuristic: high seasonal-housing-vacancy ratio

Build metadata

Field	Type	Nullable	Description
data_year	int	No	ACS vintage year
build_date	date	No	Date the artifact was built
build_version	str	No	Package version that built this artifact

---

Examples

Basic lookup

import uszipinfo

info = uszipinfo.lookup("90210")
print(f"{info.primary_city}, {info.state}")
# Beverly Hills, CA

print(f"Population: {info.population:,}")
# Population: 19,180

print(f"Median income: ${info.median_household_income:,}")
# Median income: $172,285

print(f"In MSA: {info.msa_name}")
# In MSA: Los Angeles-Long Beach-Anaheim, CA

Filter by demographic criteria

# All college-town ZIPs in Massachusetts
ma_college = uszipinfo.filter_zips(
    state="MA",
    is_college_town=True,
)
print(ma_college[["zip", "primary_city", "population"]])

# High-income suburban ZIPs nationwide
wealthy_suburbs = uszipinfo.filter_zips(
    urbanicity_tier="suburban",
    min_median_household_income=150000,
)

Bulk feature engineering for an ML model

import pandas as pd
import uszipinfo

# You have a DataFrame with a 'zip' column from your modeling pipeline
my_data = pd.DataFrame({"zip": ["98004", "98005", "98006", "10001"]})

# Enrich with ZIP metadata in one line
features = my_data.merge(uszipinfo.load(), on="zip", how="left")

# Use as model features:
# population, population_density, median_household_income,
# pct_bachelors_or_higher, pct_multi_family, urbanicity_tier, etc.

Geographic queries

# Find the 10 nearest ZIPs to Bellevue
nearby = uszipinfo.nearest_zips("98004", n=10)
print(nearby[["zip", "primary_city", "distance_mi"]])

# Distance between two ZIPs
miles = uszipinfo.distance_mi("98004", "10001")
print(f"Bellevue to Manhattan: {miles:.0f} miles")
# Bellevue to Manhattan: 2395 miles

# Nearby high-density ZIPs only
nyc_dense = uszipinfo.filter_zips(
    state="NY",
    urbanicity_tier="dense_urban",
)

Identifying non-standard ZIPs

# Check if a ZIP is a PO Box
info = uszipinfo.lookup("00501")  # IRS administrative ZIP
print(info.zip_type)
# 'PO_Box'

# Find all military ZIPs in the dataset
military = uszipinfo.filter_zips(zip_type="Military")
print(f"Military ZIPs: {len(military)}")

# Check that demographics are appropriately null for non-residential ZIPs
po_box = uszipinfo.lookup("10101")  # Manhattan PO Box
print(po_box.population)             # None
print(po_box.primary_city)           # 'New York'
print(po_box.zip_type)               # 'PO_Box'

---

Data sources

All sources are public-domain or permissively licensed:

Source	Provides	License	Refresh
US Census ACS 5-Year Estimates	Demographics, housing, economic indicators	Public domain	Annual (December)
US Census Gazetteer	Lat/lon, land/water area for ZCTAs	Public domain	Annual
US Census ZCTA-County Relationship	ZIP-to-county mapping	Public domain	Decennial
OMB CBSA Delineations	County-to-CBSA, MSA classification, CSA hierarchy	Public domain	Annual
GeoNames Postal Codes	Full ZIP coverage including PO Box / Military / Territory ZIPs, primary city, lat/lon	CC BY 4.0	Continuous

GeoNames attribution: data © GeoNames (https://www.geonames.org), used under CC BY 4.0.

USPS authoritative ZIP type classifications are NOT redistributable. The zip_type field is inferred from public signals; see DATA_LICENSE for details.

---

Building from source

The build pipeline is checked into the repo for transparency. Anyone can regenerate the bundled Parquet from primary sources.

# Get a free Census API key (recommended; avoids rate limiting):
# https://api.census.gov/data/key_signup.html
export CENSUS_API_KEY=your_key_here

# Run the build (downloads ~50 MB of source data, takes 1–2 minutes)
python -m pipeline.run \
    --year 2022 \
    --out src/uszipinfo/_data/zip_metadata_2022.parquet

# To guarantee coverage of a specific ZIP set
# (e.g., from a downstream system like AMD), pass --extra-zips:
python -m pipeline.run \
    --year 2022 \
    --extra-zips ./my_required_zips.csv \
    --out src/uszipinfo/_data/zip_metadata_2022.parquet

The --extra-zips flag accepts a CSV with a single zip column. Any ZIPs not covered by other sources will be added with synthesized records (military prefix detection or skeleton fallback). This guarantees uszipinfo.lookup(z) succeeds for every ZIP in your downstream system.

The build pipeline runs the following steps:

1. Fetch GeoNames (master ZIP list, primary city, lat/lon)
2. Fetch Census ACS (demographics)
3. Fetch Census Gazetteer (authoritative geography)
4. Fetch Census ZCTA-County (county FIPS)
5. Fetch OMB CBSA delineation (metro context)
6. Merge sources in priority order
7. Derive ZIP types heuristically
8. Engineer features (urbanicity, climate, college-town, resort)
9. Validate (schema, value ranges, coverage thresholds)

If validation fails, the build refuses to write the artifact and prints all detected problems.

---

Versioning

uszipinfo follows semver:

• Major (1.0.0 → 2.0.0): schema-breaking changes (field rename, type change, removal)
• Minor (1.0.0 → 1.1.0): new ACS vintage, new fields added (additive), new helper functions
• Patch (1.0.0 → 1.0.1): bug fixes, doc updates, no data or schema change

The data vintage is independent of the package version and accessible via uszipinfo.DATA_YEAR.

---

Performance

The bundled Parquet is loaded once on first call and cached in memory.

Operation	Cold	Warm
First import + lookup()	~150 ms	—
lookup() after first call	~1 ms	<1 ms
lookup_many(1000)	~10 ms	~5 ms
load() returning full DataFrame	~50 ms	~10 ms
filter_zips(...)	~20 ms	~10 ms
nearest_zips()	~30 ms	~30 ms

Memory footprint: ~20 MB for the in-memory DataFrame.

---

Testing

pip install uszipinfo[dev]
pytest tests/

The test suite includes:

• API correctness (lookup, filter, geo)
• Schema validation
• Coverage tests for known PO Box, Military, and Territory ZIPs
• Sanity checks against well-known ZIPs (90210, 10001, etc.)

---

Contributing

Issues and pull requests welcome. Areas where contributions are particularly valuable:

• Field additions: vehicle ownership detail, school district mapping, congressional district, etc.
• International expansion: a zipinfo umbrella package covering postal codes for other countries
• Vintage backfill: building artifacts for older ACS years for historical analysis
• Heuristic refinements: improvements to is_college_town, is_resort_area, climate banding, etc.

---

License

• Code: MIT
• Data: Public domain (US government work) + GeoNames CC BY 4.0 (attribution required)

See LICENSE and DATA_LICENSE for full text.

---

FAQ

Why aren't there ~42,000 USPS ZIPs in your data when USPS says there are ~42,000 ZIPs?

There are. We have 41,994 ZIPs covering all USPS-deliverable ZIPs that appear in any of our sources. USPS allocates ZIPs continuously; our annual rebuild may lag a few months behind brand-new ZIP allocations.

Why are some demographic fields null?

Three reasons:

1. PO Box / Unique / Military ZIPs have no residential population, so Census doesn't tabulate demographics for them. The zip_type field tells you which kind.
2. Newly-allocated ZIPs may not yet have ACS data. They show up with Standard zip_type but null demographics until the next ACS release.
3. Very small ZCTAs sometimes have null Census fields due to privacy suppression. This affects <1% of Standard ZIPs.

Why does urbanicity_tier say urban for my suburban ZIP?

Tiers are based on population density:

• rural: <100 / sq mi
• suburban: 100–1000 / sq mi
• urban: 1000–10000 / sq mi
• dense_urban: >10000 / sq mi

These bands are coarse on purpose. ML models often want a categorical density signal; the boundaries are tuned to align with patterns in delivery, retail, and other practical use cases. If you need finer control, use the raw population_density field directly.

How accurate is is_college_town / is_resort_area?

These are heuristic flags with documented rules:

• is_college_town: pct_bachelors_or_higher > 0.40, population_density between 500 and 5000, population > 5000
• is_resort_area: vacancy_for_seasonal_use > 0.15

They catch most well-known examples but will have false positives and false negatives. If you need authoritative classifications, validate against your own source.

How is zip_type derived?

Heuristic, in order:

1. Military if state ∈ {AA, AE, AP} OR ZIP prefix is in known military ranges (090–099, 340, 962–966)
2. PO_Box if no ZCTA data + zero population + zero land area
3. Unique if non-zero population < 100 + measurable land area
4. Standard otherwise

This is approximate. If you need authoritative USPS classification, purchase the USPS Address Information System or licensed equivalent.

Can I use this for non-US postal codes?

Not yet — v1 is US-only (including all 50 states + DC + 5 territories + military). International support is a planned v2+ feature. For international postal data, see pgeocode or GeoNames directly.

How often is this updated?

Annually after the December ACS release. Patch releases for bug fixes ship as needed.

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Acknowledgments

Data sources:

• US Census Bureau (ACS, Gazetteer, ZCTA-County relationship files)
• Office of Management and Budget (CBSA delineations)
• GeoNames postal data (https://www.geonames.org)

This package is not affiliated with the US Census Bureau, USPS, OMB, or GeoNames.