imfp
Python package for downloading economic data from the International Monetary Fund SDMX 3.0 API, following econdataverse conventions.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has low install friction (two common dependencies), carries no known vulnerabilities, and solves a specific problem—programmatic IMF data access—with a clean API. It is suitable for anyone needing IMF economic data in Python, especially if you work with pandas. The Apache 2.0 license is permissive and poses no restrictions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low install friction; pure Python wheel with only pandas and requests as runtime dependencies.
- Active maintenance with a release 7 days ago and last commit on 2026-08-10.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive; you may use, modify, and distribute the package freely provided you include the license and document any changes.
last release 2026-08-07 (7 days) · last repo commit 2026-08-10 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 118,022 downloads/mo, #12,138 on PyPI
Alternatives
Verify before relying
pip install imfp
import imfp
# Discover datasets
dataflows = imfp.imf_get_dataflows()
# Get data with dimension filters
df = imfp.imf_get(
"PCPS",
dimensions={"INDICATOR": ["PCOAL"], "FREQUENCY": ["A"]}
)- Rate limiting and bandwidth management behavior—how they work in practice and whether they require configuration.
- Whether the IMF API requires authentication or an API key.
- Typical response times and data volume limits for large queries.
What it is and what it does
imfp is a Python client for the International Monetary Fund's SDMX 3.0 API that retrieves economic data and returns it as pandas DataFrames. It provides four core functions: discovering available datasets, inspecting their dimensions, looking up valid codes for filtering, and fetching the actual data. The package handles the API interaction, response parsing, and data structuring so you work directly with tidy tabular output.
The package is designed for economists, financial analysts, and researchers who need programmatic access to IMF economic indicators. It follows econdataverse conventions and mirrors the R package imfapi, making it familiar to users of that ecosystem. Version 2.0.0 introduced a new API with functions like imf_get_dataflows and imf_get_datastructure; the old 1.x function names still work but emit deprecation warnings.
Use it for
- Fetch commodity price indices (like coal prices) at annual frequency for time-series analysis.
- Build a research dataset by discovering IMF dataflows, then filtering by specific indicators and time periods.
- Automate regular downloads of economic indicators for a financial dashboard or reporting pipeline.
- Compare economic metrics across countries by querying IMF datasets with country-level dimension filters.
- Integrate IMF data into a pandas-based data processing workflow without manual API calls.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has low install friction (two common dependencies), carries no known vulnerabilities, and solves a specific problem—programmatic IMF data access—with a clean API. It is suitable for anyone needing IMF economic data in Python, especially if you work with pandas. The Apache 2.0 license is permissive and poses no restrictions.
Install
imfp on PyPI
Before you install
Low install friction; pure Python wheel with only pandas and requests as runtime dependencies. Active maintenance with a release 7 days ago and last commit on 2026-08-10.
Requires Python 3.10 or later.
License in practice
Apache License 2.0 is permissive; you may use, modify, and distribute the package freely provided you include the license and document any changes.
Quickstart
pip install imfp
import imfp
# Discover datasets
dataflows = imfp.imf_get_dataflows()
# Get data with dimension filters
df = imfp.imf_get(
"PCPS",
dimensions={"INDICATOR": ["PCOAL"], "FREQUENCY": ["A"]}
)Verify before relying
- Rate limiting and bandwidth management behavior—how they work in practice and whether they require configuration.
- Whether the IMF API requires authentication or an API key.
- Typical response times and data volume limits for large queries.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagespandasrequests |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 118,022 / month, #12,138 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Financial and Insurance IndustryLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Typing :: Typed |
Evidence: imfp-2.0.0-py3-none-any.whl
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