cf-xarray
A convenience wrapper for using CF attributes on xarray objects
Decision gist · record as of 2026-08-14
Yes. cf-xarray is actively maintained, has no known vulnerabilities, carries a permissive Apache 2.0 license, and adds minimal install friction (pure Python, single dependency). It is worth installing if you work with CF-compliant xarray datasets and want to write code that is independent of dataset-specific naming schemes. If your datasets do not follow CF conventions or you do not use xarray, it offers no benefit.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires xarray to be installed; your dataset must have CF-compliant attributes (e.g., `standard_name` or `long_name`) set on dimensions or coordinates for the `.cf` accessor to recognize them.
- Low friction: pure Python wheel with a single runtime dependency on xarray.
- Repository is active with a recent commit on 2026-08-01 and 180 stars; last release was 2026-06-12, 63 days ago.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive; you may use, modify, and distribute the package freely provided you include a copy of the license and note any changes you make.
last release 2026-06-12 (63 days) · last repo commit 2026-08-01 · 180 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 353,374 downloads/mo, #7,303 on PyPI
Alternatives
Verify before relying
pip install cf-xarray
import xarray as xr
import cf_xarray
ds = xr.open_dataset('data.nc')
result = ds.cf.mean('latitude')- Which specific CF attributes (standard_name, long_name, etc.) the accessor recognizes and prioritizes
- Performance overhead of the accessor layer compared to direct xarray operations
- Scope of CF convention coverage (e.g., does it handle all CF standard names or a subset)
What it is and what it does
cf-xarray is a lightweight wrapper that extends xarray with a `.cf` accessor, allowing you to write operations using CF (Climate and Forecast) convention attribute names instead of dataset-specific dimension and coordinate names. Instead of needing to know that a particular file uses 'lat' or 'latitude' or 'y', you can write `.cf.mean('latitude')` and the accessor resolves the actual dimension name from CF metadata attributes on your dataset.
The package is designed for scientists and data engineers working with climate, weather, or other geospatial datasets that follow CF conventions. It reduces the friction of writing code that must work across multiple datasets with different naming schemes, and it depends only on xarray, making it a minimal addition to existing workflows. The package is in production-stable status, actively maintained, and supports Python 3.11, 3.12, and 3.13.
Use it for
- Write climate analysis code that works across multiple datasets without hardcoding dimension names like 'lat', 'lon', or 'time'.
- Automate data processing pipelines where input files may use different naming conventions but follow CF standards.
- Build reusable analysis functions that accept CF-compliant xarray datasets and operate on standard semantic dimensions.
- Simplify exploratory data analysis on unfamiliar CF-compliant netCDF or HDF5 files by using semantic names.
- Integrate with Pangeo or other Earth science data ecosystems where CF conventions are standard.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
cf-xarray is actively maintained, has no known vulnerabilities, carries a permissive Apache 2.0 license, and adds minimal install friction (pure Python, single dependency). It is worth installing if you work with CF-compliant xarray datasets and want to write code that is independent of dataset-specific naming schemes. If your datasets do not follow CF conventions or you do not use xarray, it offers no benefit.
Install
cf-xarray on PyPI
Before you install
Low friction: pure Python wheel with a single runtime dependency on xarray. Repository is active with a recent commit on 2026-08-01 and 180 stars; last release was 2026-06-12, 63 days ago.
Requires xarray to be installed; your dataset must have CF-compliant attributes (e.g., `standard_name` or `long_name`) set on dimensions or coordinates for the `.cf` accessor to recognize them.
License in practice
Apache License 2.0 is permissive; you may use, modify, and distribute the package freely provided you include a copy of the license and note any changes you make.
Quickstart
pip install cf-xarray
import xarray as xr
import cf_xarray
ds = xr.open_dataset('data.nc')
result = ds.cf.mean('latitude')Verify before relying
- Which specific CF attributes (standard_name, long_name, etc.) the accessor recognizes and prioritizes
- Performance overhead of the accessor layer compared to direct xarray operations
- Scope of CF convention coverage (e.g., does it handle all CF standard names or a subset)
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagexarray |
| Maintenance | Actively maintained 63 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 353,374 / month, #7,303 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/StableLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: cf_xarray-0.11.3-py3-none-any.whl
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