Basic Usage Examples#

This page shows how to quickly start using the fedfred package to interact with the FRED® API. You’ll learn how to initialize a client, fetch time series, retrieve metadata, and explore categories and tags.

If you’re new to FedFred, start here!

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Getting Started#

Initialize the Client
import fedfred as fd

fred = fd.FredAPI(api_key="your_api_key_here")

Initialize with your FRED API key. See Advanced Usage Examples for async and caching options.

Fetch Time Series Observations
import fedfred as fd
fred = fd.FredAPI(api_key="your_api_key")
data = fred.get_series_observations("GDP")

# Pandas DataFrame
print(data.head())

import fedfred as fd
fred = fd.FredAPI(api_key="your_api_key", dataframe_method="polars")
data = fred.get_series_observations("GDP")

# Polars DataFrame
print(data)

import fedfred as fd
fred = fd.FredAPI(api_key="your_api_key", dataframe_method="dask")
data = fred.get_series_observations("GDP")

# Dask DataFrame
print(data.head())

Fetch historical observations for a series. Output is a DataFrame (or Polars/Dask — see FedFred API Overview).

Retrieve Series Metadata
metadata = fred.get_series("GDP")
print(metadata[0].title)
print(metadata[0].frequency)
print(metadata[0].units)

Get structured metadata using fedfred.objects.Series.

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Exploring FRED® Data#

Explore Categories

FRED organizes economic data into a hierarchy of categories.

categories = fred.get_category_children(category_id=0)
for category in categories:
    print(f"Category: {category.name} (ID: {category.id})")

Useful for browsing thematic economic data collections.

Retrieve Tags for a Series

Tags describe concepts (e.g., “inflation”, “gdp”, “employment”).

tags = fred.get_series_tags("GDP")
for tag in tags:
    print(f"Tag: {tag.name}")

Useful for semantic searching and exploration.

Find Related Series

Discover related tags using text-based series search.

related_tags = fred.get_series_search_related_tags("mortgage rate", tag_names="frb")
for tag in related_tags:
   print(f"Related Tag: {tag.name}")

Great for exploratory economic research and model-building.

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