Frequently Asked Questions (FAQ)#
Here are answers to common questions about FedFred, the modern Python client for the FRED® API.
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What is FedFred?#
See Answer
FedFred is a feature-rich Python client for the St. Louis Federal Reserve Economic Database (FRED®).
Highlights:
Synchronous and asynchronous access to FRED datasets,
Native support for DataFrame formats: Pandas, Polars, and Dask,
Built-in caching, retry handling, and structured typed object models.
➔ See the Quick Start Guide for a simple example.
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How is FedFred different from fredapi and other FRED clients?#
See Answer
FedFred improves upon traditional FRED libraries like fredapi by offering:
Asynchronous Support: Concurrently fetch thousands of series via
fedfred.clients.FredAPI.AsyncAPI.Built-in Rate Limiting: Automatic compliance with FRED’s 120 requests/minute rule.
Configurable Local Caching: Rapid repeated queries via FIFO cache.
Structured Objects: Typed Python models like
fedfred.objects.Seriesandfedfred.objects.Release.Flexible Backends: Support for Polars and Dask DataFrame outputs.
➔ See the full library comparison in Comparison with Other FRED Clients.
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Does FedFred support GeoFRED (Maps API) data?#
See Answer
Yes! FedFred includes robust support for geographic datasets via:
fedfred.clients.FredAPI.MapsAPIfor synchronous access,fedfred.clients.FredAPI.AsyncAPI.AsyncMapsAPIfor asynchronous access.
You can retrieve:
Regional economic indicators,
Shapefiles for states, counties, metro areas,
Output ready for GIS tools as GeoDataFrame.
➔ Explore regional analysis examples in Example Use Cases.
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Is caching supported in FedFred?#
See Answer
Yes! FedFred supports local FIFO caching natively.
Stores recent API responses (default 256 items),
Fully async-aware: works seamlessly across sync and async clients.
Example usage:
import fedfred as fd
fred = fd.FredAPI(api_key="your_api_key_here", cache_mode=True, cache_size=1000)
➔ Learn more in the Advanced Usage Examples section.
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