pycen 0.1.0a3

Lightweight Python package for intuitively exploring and acquiring U.S. Census data with spatial integration

pycen

Lightweight Python package for exploring and acquiring U.S. Census data with intuitive spatial integration.

flowchart TD
    A[Need Census data?]

    A --> B & C

    subgraph PYCEN["<i>pycen</i>"]
        direction TB
        B[<b>`explore`</b><br/>Intuitive metadata<br/>keyword search]
        C[<b>`acquire`</b><br/>Data + boundaries<br/>in one call]

        C --> D
        C --> E

        D[<b>`quick_check`</b><br/>Quality validation]
        E[<b>`quick_viz`</b><br/>Instant maps]
    end

    B --> F
    D & E --> F[Domain analysis]

    style A fill:#94a3b8,stroke:#334155,stroke-width:2px,color:#000
    style B fill:#3b82f6,stroke:#1e40af,stroke-width:2px,color:#fff
    style C fill:#3b82f6,stroke:#1e40af,stroke-width:2px,color:#fff
    style D fill:#22c55e,stroke:#15803d,stroke-width:2px,color:#fff
    style E fill:#22c55e,stroke:#15803d,stroke-width:2px,color:#fff
    style F fill:#94a3b8,stroke:#334155,stroke-width:2px,color:#000
    style PYCEN fill:#1e293b,stroke:#64748b,stroke-width:2px,color:#fff

overview

pycen makes the exploration and acquisition of U.S. Census data accessible and intuitive for spatial workflows. The explore module presents browsable Census API metadata via topic-organized, interactive nested tables, with customizable themes to highlight curated variable recipes. It also supports natural‑language keyword searches for efficient variable discovery. The acquire module streamlines data processing: one function call returns both data and boundaries as a GeoDataFrame with built-in quality checks and rapid visualizations;simple tabular or boundaries-only downloads are separately callable. pycen pulls live data products with efficient local caches to keep iterations fast, smooth, and reproducible. The multi‑year fetch function enables longitudinal comparisons tracking change over time.

sample use

basic workflow

import pycen
from pycen import explore, acquire

# 1. Explore variables
# `browse` and `search` return interactive tables
# `lookup` returns details
explore.browse(year=2023, dataset="acs5").show()
explore.search("vehicle", year=2023, dataset="acs5").show()
explore.lookup("B08201_002E", year=2021, dataset="acs5")

# 2. Acquire data
## continental US income gini map
gdf = acquire.get_censhp(
    variables={"B19083_001E":"gini_index"},
    geography="place",               # if no state/county, gets nationwide
    dataset="acs5",
    year=2023,
)
acquire.quick_check(gdf)             # returns N/A summary
acquire.quick_viz(gdf, "gini_index") # returns map + distribution histogram
acquire.quick_viz(gdf, "gini_index", palette="viridis") # optional customizable palette
acquire.quick_viz(gdf, "gini_index",save_path='gini_index.png') # optional save

## finer scale
## Cook County income gini at tract level
gdf = acquire.get_censhp(
    variables={"B19083_001E":"gini_index"},
    geography="tract",
    county="Cook County County",
    state="IL",
    dataset="acs5",
    year=2023,
)
acquire.quick_viz(gdf, "gini_index")

## neighborhood analyses
## Chicago super commuters
gdf = acquire.get_censhp(
    variables={"B08303_012E":"commute_over_60min", "B08303_001E":"total_commuters"},
    geography="block group",
    place="Chicago city",
    #county="Cook County",  # optional, add for clarity
    state="IL",
    dataset="acs5",
    year=2023
)
gdf["pct_super_commuters"] = gdf["commute_over_60min"] / gdf["total_commuters"] * 100
acquire.quick_viz(gdf, "pct_super_commuters")

## decennial data supports block-scale (finest)
## Chicago housing vacancy rates at block level
select_var={
    "H001003": "vacant_hh",
    "H001001": "total_hh"
}
gdf = acquire.get_censhp(
    variables=select_var,
    geography="block",
    county="Cook County",
    state="IL",
    dataset="dec_pl",
    year=2010,
)
gdf['vacancy_rate'] = gdf['vacant_hh'] / gdf['total_hh'] * 100
acquire.quick_viz(gdf, "vacancy_rate")

tabular data workflow

# 3. Tabular data only
df = acquire.get_census(
    variables=["B25032_022E"],  # renter-occupied, mobile home
    geography="tract",
    state="CA",
    year=2021,
)

# 4. Single-year, multivariable tabular data for comparative analysis
import pandas as pd
import matplotlib.pyplot as plt
from pycen import acquire

vars_race = {
    'B03002_001E': 'total',
    'B03002_003E': 'nh_white',
    'B03002_004E': 'nh_black',
    'B03002_006E': 'nh_asian',
    'B03002_005E': 'nh_aian',
    'B03002_007E': 'nh_nhpi',
    'B03002_008E': 'nh_other',
    'B03002_009E': 'nh_two_or_more',
    'B03002_012E': 'hispanic',
}

df_race = acquire.get_census(
    variables=vars_race,
    geography='county',
    state='CA',
    county='Alameda',
    dataset='acs5',
    year=2023,
)

row = df_race.iloc[0]
other = row['nh_aian'] + row['nh_nhpi'] + row['nh_other'] + row['nh_two_or_more']
vals = {
    'White (NH)': row['nh_white'],
    'Black (NH)': row['nh_black'],
    'Asian (NH)': row['nh_asian'],
    'Other (NH)': other,
    'Hispanic (any race)': row['hispanic'],
}

pct = {k: v / row['total'] * 100 for k, v in vals.items()}

plt.figure(figsize=(7, 4))
plt.bar(pct.keys(), pct.values(), color=['#4c78a8', '#f58518', '#54a24b', '#b279a2', '#e45756'])
plt.ylabel('Population %')
plt.title('Alameda County, CA -— Race/Ethnicity (ACS 2023)')
plt.xticks(rotation=25, ha='right')
plt.tight_layout()
plt.show()

# 5. Multi-year tabular data for trend analysis
# comparative tracking of remote work surge (2019–2023)
from pycen import acquire
import matplotlib.pyplot as plt

# explore.search("work from home", year=2023, dataset="acs5").show()
# B08101_049E = worked from home
df_long = acquire.get_census(
    variables={'B08101_049E': 'wfh_workers', 'B08101_001E': 'total_workers'},
    geography='county',
    state='CA',
    years=[2019, 2020, 2021, 2022, 2023],
    merge='long'
)

df_long['wfh_pct'] = (df_long['wfh_workers'] / df_long['total_workers']) * 100
bay_area = df_long[df_long['NAME'].str.contains('San Francisco|Alameda|Santa Clara|Contra Costa|San Mateo')]

for county in bay_area['NAME'].unique():
    county_data = bay_area[bay_area['NAME'] == county]
    plt.plot(county_data['year'], county_data['wfh_pct'], marker='o', label=county)

plt.title('Bay Area WFH 2019-2023')
plt.ylabel('Work From Home (%)')
plt.xlabel('Year')
plt.xticks(sorted(major['year'].unique())) 
plt.legend()
plt.grid(alpha=0.3)
plt.tight_layout()

core functions

Explore

• explore.search(query, year, dataset) - supports exact term match and fuzzy keyword search
• explore.browse(year, dataset) - view all variables via interactive tree table with theme variable highlights
• explore.lookup(code, year, dataset) - inspect variable details

Acquire

• acquire.get_censhp(...) - data + boundaries --> GeoDataFrame
• acquire.get_census(...) - data only --> DataFrame
• acquire.get_boundaries(...) - boundaries only --> shp/gpkg
• acquire.quick_check(gdf) - N/A values summary
• acquire.quick_viz(gdf, column, palette, save_path) - exploratory map + distribution histogram for select variable

Info

• pycen.get_product() - list datasets and years
• pycen.get_geography() - list geography levels by dataset

Geo Helpers

from pycen import geography
geography.search('Oakland', state='CA') # most powerful, return all related info

# state and county lookup
geography.state('CA') # can also search by 'California' or fips code '06'
geography.county('Alameda', state='CA')

# list geographies
geography.list_places('CA', query='Oakland') # minimal search
pycen.geography.list_cbsa(query='new york',year=2023, limit=5) # specify year and return limit if multi-match
pycen.geography.list_csa(query='detroit',year=2023, limit=5) # look up csa name
geography.list_counties('CA')

Themes

• pycen.set_theme(name_or_dict) - set active theme name or register a custom theme (dict)
• pycen.get_theme_settings() - get active theme name (defaults to a general curation of useful variables)
• pycen.explore.get_theme(name=None) - get theme details (dict); defaults to active theme
• pycen.list_themes() - list available theme names (includes session custom themes)

Notes

• Datasets: acs5, acs1, dec_pl, dec_sf1
• Spatial features require: geopandas, pygris
• Geographies are resolved per dataset/year from Census geography metadata (live/cache/static)
• Optional: rich enables prettier terminal tables for explore.search().show()
• geography.search() uses a bundled 2020 snapshot by default; if a different vintage is requested, it attempts a live code-list fetch and falls back to 2020 if unavailable

API key for higher rate limits:

pycen.set_api_key("YOUR_KEY")  # get key at api.census.gov/data/key_signup.html