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#
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.
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).
metadata = fred.get_series("GDP")
print(metadata.title)
print(metadata.frequency)
print(metadata.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 similar series using tag-based relevance search.
related_series = fred.get_series_search_related_tags("GDP")
for series in related_series:
print(f"Related Series: {series.title}")
Great for exploratory economic research and model-building.
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