Skip to main content

GroupBy operations for dask.array

Project description

GitHub Workflow CI Status pre-commit.ci status image Documentation Status

PyPI Conda-forge

NASA-80NSSC18M0156 NASA-80NSSC22K0345

flox

This project explores strategies for fast GroupBy reductions with dask.array. It used to be called dask_groupby It was motivated by

  1. Dask Dataframe GroupBy blogpost
  2. numpy_groupies in Xarray issue

(See a presentation about this package, from the Pangeo Showcase).

Acknowledgements

This work was funded in part by

  1. NASA-ACCESS 80NSSC18M0156 "Community tools for analysis of NASA Earth Observing System Data in the Cloud" (PI J. Hamman, NCAR),
  2. NASA-OSTFL 80NSSC22K0345 "Enhancing analysis of NASA data with the open-source Python Xarray Library" (PIs Scott Henderson, University of Washington; Deepak Cherian, NCAR; Jessica Scheick, University of New Hampshire), and
  3. NCAR's Earth System Data Science Initiative.

It was motivated by very very many discussions in the Pangeo community.

API

There are two main functions

  1. flox.groupby_reduce(dask_array, by_dask_array, "mean") "pure" dask array interface
  2. flox.xarray.xarray_reduce(xarray_object, by_dataarray, "mean") "pure" xarray interface; though work is ongoing to integrate this package in xarray.

Implementation

See the documentation for details on the implementation.

Custom reductions

flox implements all common reductions provided by numpy_groupies in aggregations.py. It also allows you to specify a custom Aggregation (again inspired by dask.dataframe), though this might not be fully functional at the moment. See aggregations.py for examples.

mean = Aggregation(
    # name used for dask tasks
    name="mean",
    # operation to use for pure-numpy inputs
    numpy="mean",
    # blockwise reduction
    chunk=("sum", "count"),
    # combine intermediate results: sum the sums, sum the counts
    combine=("sum", "sum"),
    # generate final result as sum / count
    finalize=lambda sum_, count: sum_ / count,
    # Used when "reindexing" at combine-time
    fill_value=0,
    # Used when any member of `expected_groups` is not found
    final_fill_value=np.nan,
)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

flox-0.9.8.tar.gz (655.2 kB view details)

Uploaded Source

Built Distribution

flox-0.9.8-py3-none-any.whl (64.7 kB view details)

Uploaded Python 3

File details

Details for the file flox-0.9.8.tar.gz.

File metadata

  • Download URL: flox-0.9.8.tar.gz
  • Upload date:
  • Size: 655.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.3

File hashes

Hashes for flox-0.9.8.tar.gz
Algorithm Hash digest
SHA256 1236efb869ae3b9dec2066605dbacebae571721ab7d37507cbb0d96bbc3e47fd
MD5 4960b628078cf2fcff3ed2d682b25942
BLAKE2b-256 146b0deee5fed70dffc64a9caae127ebc66a5a30533d2337404425b5f0e5c17a

See more details on using hashes here.

File details

Details for the file flox-0.9.8-py3-none-any.whl.

File metadata

  • Download URL: flox-0.9.8-py3-none-any.whl
  • Upload date:
  • Size: 64.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.3

File hashes

Hashes for flox-0.9.8-py3-none-any.whl
Algorithm Hash digest
SHA256 9a74daff7e47d105c98fa506f25c55b52c38374e96f046ca906b49272baec03f
MD5 8e644d2d9f447fd0f9c77c172ac2d70f
BLAKE2b-256 af6bb347143072d0a6c564ee617068851cf4b3709a3f02d028816f90f32738d2

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page