FedFred: A Modern Python Client for FRED® API#
FedFred is a feature-rich Python package for interacting with the Federal Reserve Bank of St. Louis Economic Database (FRED®). It supports synchronous and asynchronous access to FRED data, along with DataFrame transformations, rate-limiting, local caching, and typed object models — making it the most modern FRED client available for Python developers.
Install FedFred#
pip install fedfred
conda install -c conda-forge fedfred
See :ref:installation for more options, including conda and optional dependencies.
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Badges#
GitHub Actions build status.
Code linting and static analysis checks.
Unit test coverage for critical components.
GitHub CodeQL security scanning.
Open Source Security Foundation (OpenSSF) Gold Badge.
Codecov test coverage report.
Security risk analysis via Socket.dev.
Repology packaging status across Linux distributions.
Latest version released on PyPI.
Download stats via Pepy.tech.
Conda-forge published version.
Number of downloads from Conda-Forge.
Key Features#
Flexible DataFrames
Output data as Pandas, Polars, or Dask DataFrames for seamless data manipulation.
GeoSpatial Support
Native output to GeoDataFrames using GeoPandas, Polars-ST, and Dask-GeoPandas.
Async Compatibility
Full async client (fedfred.clients.FredAPI.AsyncAPI) for non-blocking data pipelines.
Local Caching
FIFO local cache accelerates repeated queries dramatically.
Rate Limiting
Automatic throttling to comply with FRED’s API request limits.
Structured Models
Rich typed objects (fedfred.objects.Series, fedfred.objects.Release) representing FRED entities.
Resources#
Explore the documentation:
Get Started
Developer Reference