---
id: pandas-datareader
version: "0.11.1"
license: BSD-3-Clause
license_treatment: permissive
maintenance: active
---

# pandas-datareader — Pandas-compatible data readers. Formerly a component of pandas.

License: permissive · Maintenance: active · Downloads: 1.3M/mo

## What it is and what it does

pandas-datareader is a data-access library that retrieves macroeconomic, policy, and factor-oriented datasets from public sources and returns them as pandas DataFrames. It wraps APIs from providers like FRED (Federal Reserve Economic Data), Fama/French, World Bank, OECD, Eurostat, and others, letting you fetch economic indicators, interest rates, factor returns, and similar data programmatically without manual downloads.

The library is built on pandas, lxml, and requests, making it straightforward to integrate into data analysis workflows. It requires Python 3.11 or later and has been actively maintained since 2015. The public API is designed around convenience functions like `get_data_fred()` that abstract away the details of authentication and HTTP calls, so you can focus on the data itself.

Use it for:

- Fetch US Treasury yield curves (e.g., 10-year rates) from FRED for financial modeling.
- Download Fama/French factor returns to build multi-factor risk models.
- Retrieve World Bank development indicators for cross-country economic analysis.
- Access OECD macroeconomic statistics for policy research or forecasting.
- Automate periodic updates of economic datasets in a pandas-based analysis pipeline.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Fetches macroeconomic and factor data from remote sources like FRED, Fama/French, World Bank, and OECD, returning it as pandas DataFrames.

Yes. Low install friction, active maintenance, no known vulnerabilities, permissive license, and a focused scope make it a reliable choice for fetching macroeconomic data into pandas workflows. Install it if you need programmatic access to FRED, Fama/French, World Bank, or similar sources; skip it if you only need ad-hoc manual downloads or work with proprietary data feeds.

## Install

pip install pandas-datareader
uv add pandas-datareader
poetry add pandas-datareader

## Installing pandas-datareader

Before you install: Low friction: pure Python wheel with four runtime dependencies (lxml, pandas, requests, setuptools). Active maintenance—last commit 2026-07-21, released 2026-06-24, 51 days ago.

License in practice: BSD-3-Clause (permissive): you can use, modify, and distribute freely in commercial or private projects, provided you include the license text and do not hold the authors liable.

Quickstart:

pip install pandas-datareader

import pandas_datareader as pdr
data = pdr.get_data_fred('GS10')

Requires Python 3.11 or later; pandas>=1.5.3 must be installed first.

Verify before relying:

- Whether all advertised data sources (FRED, Fama/French, Bank of Canada, World Bank, OECD, Eurostat) are currently functional and up-to-date.
- Performance characteristics when fetching large datasets or making many concurrent requests.
- Rate limits or authentication requirements for specific data providers.

## Package facts

- License: BSD-3-Clause (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

macroeconomic data download, FRED data access, world bank data retrieval, fama french factors, economic indicators pandas, remote data sources, oecd data fetch, financial-data, economic-indicators, data-acquisition

[View on SkillFed](https://skillfed.io/packages/pandas-datareader) · [View on PyPI](https://pypi.org/project/pandas-datareader/)