Tutorials

Step-by-step guides for common cendat workflows. Each tutorial is a fully executable Jupyter notebook with real Census API outputs.

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Interactive Tutorials

Tutorial	Description
ACS PUMS Microdata	Analyze person-level microdata with weighted frequency tables
ACS 5-Year Aggregate	Fetch aggregate statistics for places (cities, towns, CDPs)
Multi-Year Comparisons	Compare data across multiple years with complex filtering
Working with Geometries	Fetch geographic boundaries for mapping
CPS Microdata	Analyze CPS supplement data with pooled weights

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Tips and Best Practices

Finding the Right Product

Use regex patterns to filter products efficiently:

# ACS 5-year (main product, not profiles/subjects)
cdh.list_products(patterns=r"acs/acs5\)")

# ACS 1-year PUMS
cdh.list_products(patterns=r"acs/acs1/pums\b")

# Decennial Census
cdh.list_products(patterns="dec/")

Using Groups Effectively

For products with many variables (ACS, Decennial), always start with groups:

cdh.list_groups(patterns="your topic")
cdh.set_groups("GROUP_NAME")
cdh.describe_groups()  # See variable hierarchy

Rate Limiting

For large requests, adjust max_workers:

# For thousands of API calls, reduce concurrency
response = cdh.get_data(max_workers=25)

The in_place Option

For iterative work or building up complex queries:

# Keep data attached to the helper object
cdh.get_data(in_place=True)

# Access later via helper['params']