pandas-datareader
Pandas-compatible data readers. Formerly a component of pandas.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later; pandas>=1.5.3 must be installed first.
- 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 · maintenance · safety
BSD-3-Clause (permissive) — 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.
last release 2026-06-24 (51 days) · last repo commit 2026-07-21 · 3,231 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,276,123 downloads/mo, #4,125 on PyPI
Alternatives
Verify before relying
pip install pandas-datareader
import pandas_datareader as pdr
data = pdr.get_data_fred('GS10')- 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.
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 on this page. Verify before relying on it.
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
pandas-datareader on PyPI
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.
Requires Python 3.11 or later; pandas>=1.5.3 must be installed first.
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')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 the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packageslxmlpandasrequestssetuptools |
| Maintenance | Actively maintained 51 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,276,123 / month, #4,125 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/Engineering |
Evidence: pandas_datareader-0.11.1-py3-none-any.whl
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