 argopy is a python library dedicated to Argo data access, visualisation and manipulation for regular users as well as Argo experts and operators |
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Documentation
The official documentation is hosted on ReadTheDocs.org here: https://argopy.readthedocs.io
If you can't wait, click the MyBinder button below to open a jupyter notebook ready to play with Argopy:
Installation
Binary installers for the latest released version are available at the Python Package Index (PyPI) and on Conda.
# PyPI
pip install argopy
# conda
conda install -c conda-forge argopy
Argopy is continuously tested to work under most OS (Linux, Mac, Windows) and with python versions 3.11 and 3.12.
Usage
# Import the main data fetcher:
from argopy import DataFetcher
# Define what you want to fetch...
# a region:
f = DataFetcher().region([-85,-45,10.,20.,0,10.])
# floats:
f = DataFetcher().float([6902746, 6902747, 6902757, 6902766])
# or specific profiles:
f = DataFetcher().profile(6902746, 34)
# then fetch and get data as xarray datasets:
ds = f.load().data
# or
ds = f.to_xarray()
# you can even plot some information:
f.plot('trajectory')
They are many more usages and fine-tuning to allow you to access and manipulate Argo data:
- filters at fetch time (standard vs expert users, automatically select QC flags or data mode, ...)
- select data sources (erddap, ftp, local, argovis, ...)
- manipulate data (points, profiles, interpolations, binning, ...)
- visualisation (trajectories, topography, histograms, ...)
- tools for Quality Control (OWC, figures, ...)
- access meta-data and other Argo-related datasets (reference tables, deployment plans, topography, DOIs, ...)
- improve performances (caching, parallel data fetching)
Just check out the documentation for more !
Tutorials
Some tutorials, as jupyter notebooks, are available to get you started.
See here more for all details: https://argopy.readthedocs.io/en/latest/tutorials.html
🌿 Energy impact of argopy development
The Argopy team is concerned about the environmental impact of your favorite software development. Starting June 1st 2024, we're experimenting with the Green Metrics Tools from Green Coding to get an estimate of the energy used and CO2eq emitted by our development activities on Github infrastructure. Results:
Development and contributions
See our software management dashboard here: https://github.com/orgs/euroargodev/projects/19
And if you want to get involved and help maintain or develop Argopy, please check out the contribution page.
2 maintainers
Metadata
2 maintainers
Release files for argopy 1.5.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| argopy-1.5.0.tar.gz | 2.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| argopy-1.5.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 6.3 MB
Release files / argopy-1.5.0.tar.gz
| Download URL | argopy-1.5.0.tar.gz |
|---|---|
| Size | 2.0 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / argopy-1.5.0-py3-none-any.whl
| Download URL | argopy-1.5.0-py3-none-any.whl |
|---|---|
| Size | 4.3 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 12, 2026.
Transparency log
