Extremely lightweight compatibility layer between dataframe libraries

Navigation

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for anopsy from gravatar.com anopsy Avatar for dealeon from gravatar.com dealeon Avatar for EdoAbati from gravatar.com EdoAbati Avatar for FBruzzesi from gravatar.com FBruzzesi Avatar for MarcoGorelli from gravatar.com MarcoGorelli

Unverified details

These details have not been verified by PyPI
Meta
  • License: MIT License Copyright (c) 2024, Marco Gorelli Permission is hereby granted, free of charge, to any...
  • Author: Marco Gorelli
  • Tags cudf, dask, dataframes, interoperability, modin, pandas, polars, pyarrow
  • Requires: Python >=3.8
  • Provides-Extra: cudf, dask, dev, docs, extra, modin, pandas, polars, pyarrow, pyspark
Classifiers

Project description

Narwhals

narwhals_small

PyPI version Downloads Trusted publishing

Extremely lightweight and extensible compatibility layer between dataframe libraries!

  • Full API support: cuDF, Modin, pandas, Polars, PyArrow
  • Lazy-only support: Dask
  • Interchange-level support: DuckDB, Ibis, Vaex, anything which implements the DataFrame Interchange Protocol

Seamlessly support all, without depending on any!

  • ✅ Just use a subset of the Polars API, no need to learn anything new
  • ✅ Zero dependencies, Narwhals only uses what the user passes in so your library can stay lightweight
  • ✅ Separate lazy and eager APIs, use expressions
  • ✅ Support pandas' complicated type system and index, without either getting in the way
  • ✅ 100% branch coverage, tested against pandas and Polars nightly builds
  • ✅ Negligible overhead, see overhead
  • ✅ Let your IDE help you thanks to full static typing, see typing
  • ✅ Perfect backwards compatibility policy, see stable api for how to opt-in

Get started!

Table of contents

Installation

  • pip (recommended, as it's the most up-to-date) 
    pip install narwhals
  • conda-forge (also fine, but the latest version may take longer to appear) 
    conda install -c conda-forge narwhals

Usage

There are three steps to writing dataframe-agnostic code using Narwhals:

  1. use narwhals.from_native to wrap a pandas/Polars/Modin/cuDF/PyArrow DataFrame/LazyFrame in a Narwhals class

  2. use the subset of the Polars API supported by Narwhals

  3. use narwhals.to_native to return an object to the user in its original dataframe flavour. For example:

    • if you started with pandas, you'll get pandas back
    • if you started with Polars, you'll get Polars back
    • if you started with Modin, you'll get Modin back (and compute will be distributed)
    • if you started with cuDF, you'll get cuDF back (and compute will happen on GPU)
    • if you started with PyArrow, you'll get PyArrow back

narwhals_gif

Example

See the tutorial for several examples!

Scope

  • Do you maintain a dataframe-consuming library?
  • Do you have a specific Polars function in mind that you would like Narwhals to have in order to make your work easier?

If you said yes to both, we'd love to hear from you!

Roadmap

See roadmap discussion on GitHub for an up-to-date plan of future work.

Used by

Join the party!

Feel free to add your project to the list if it's missing, and/or chat with us on Discord if you'd like any support.

Sponsors and institutional partners

Narwhals is 100% independent, community-driven, and community-owned. We are extremely grateful to the following organisations for having provided some funding / development time:

If you contribute to Narwhals on your organization's time, please let us know. We'd be happy to add your employer to this list!

Appears on

Narwhals has been featured in several talks, podcasts, and blog posts:

Why "Narwhals"?

Coz they are so awesome.

Thanks to Olha Urdeichuk for the illustration!

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for anopsy from gravatar.com anopsy Avatar for dealeon from gravatar.com dealeon Avatar for EdoAbati from gravatar.com EdoAbati Avatar for FBruzzesi from gravatar.com FBruzzesi Avatar for MarcoGorelli from gravatar.com MarcoGorelli

Unverified details

These details have not been verified by PyPI
Meta
  • License: MIT License Copyright (c) 2024, Marco Gorelli Permission is hereby granted, free of charge, to any...
  • Author: Marco Gorelli
  • Tags cudf, dask, dataframes, interoperability, modin, pandas, polars, pyarrow
  • Requires: Python >=3.8
  • Provides-Extra: cudf, dask, dev, docs, extra, modin, pandas, polars, pyarrow, pyspark
Classifiers

Release history Release notifications | RSS feed

This version

1.20.1

1.20.0

1.19.1

1.19.0

1.18.4

1.18.3

1.18.2

1.18.1 yanked

Reason this release was yanked:

Sigstore failed in GitHub - we'll publish 1.18.2 and make sure it's signed

1.18.0 yanked

Reason this release was yanked:

Mistake in the release process, so this release didn't get signed properly on GitHub. We'll release 1.18.1 to remedy

1.17.0

1.16.0

1.15.2

1.15.1

1.15.0

1.14.3

1.14.2

1.14.1

1.14.0

1.13.5

1.13.4

1.13.3

1.13.2

1.13.1

1.12.1

1.12.0

1.11.1

1.11.0

1.10.0

1.9.4

1.9.3

1.9.2

1.9.1

1.9.0

1.8.4

1.8.3

1.8.2

1.8.1

1.8.0

1.7.0

1.6.4

1.6.3

1.6.2

1.6.1

1.6.0

1.5.5

1.5.4

1.5.3

1.5.2

1.5.1

1.5.0

1.4.2

1.4.1

1.4.0

1.3.0

1.2.0

1.1.9

1.1.8

1.1.7

1.1.6

1.1.5

1.1.4

1.1.3

1.1.2

1.1.1

1.1.0

1.0.6

1.0.5

1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

0.9.29

0.9.28

0.9.27

0.9.26

0.9.25

0.9.24

0.9.23

0.9.22

0.9.21

0.9.20

0.9.19

0.9.18

0.9.17

0.9.16

0.9.15

0.9.14

0.9.13

0.9.12

0.9.11

0.9.10

0.9.9

0.9.8

0.9.7

0.9.6

0.9.5

0.9.2

0.9.1

0.9.0

0.8.21

0.8.20

0.8.19

0.8.18

0.8.17

0.8.16

0.8.15

0.8.14

0.8.13

0.8.12

0.8.11

0.8.10

0.8.9

0.8.8

0.8.7

0.8.6

0.8.5

0.8.4

0.8.3

0.8.2

0.8.1

0.8.0

0.7.18

0.7.16

0.7.15

0.7.14

0.7.12

0.7.11

0.7.10

0.7.9

0.7.8

0.7.7

0.7.6

0.7.5

0.7.4

0.7.3

0.7.2

0.7.1

0.7.0

0.6.9

0.6.8

0.6.7

0.6.6

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.5.0

0.4.1

0.4.0

0.3.0

0.2.0

0.1.14

0.1.13

0.1.12

0.1.11

0.1.10

0.1.9

0.1.8

0.1.7

0.1.6

Download files

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

Source Distribution

narwhals-1.20.1.tar.gz (224.4 kB view details)

Uploaded Source

Built Distribution

narwhals-1.20.1-py3-none-any.whl (262.4 kB view details)

Uploaded Python 3

File details

Details for the file narwhals-1.20.1.tar.gz.

File metadata

  • Download URL: narwhals-1.20.1.tar.gz
  • Upload date:
  • Size: 224.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for narwhals-1.20.1.tar.gz
Algorithm Hash digest
SHA256 ffc6a44c1bc651531198c5f7fc38d349dff898ecfe51c1ef96aaaf429ec4dc19
MD5 fab4e8d9e467808767486f6a266bc43c
BLAKE2b-256 f7f03179615405104a90dc31a56fea27c9135f646cf4476e2904fcde125f5287

See more details on using hashes here.

Provenance

The following attestation bundles were made for narwhals-1.20.1.tar.gz:

Publisher: publish_to_pypi.yml on narwhals-dev/narwhals

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file narwhals-1.20.1-py3-none-any.whl.

File metadata

  • Download URL: narwhals-1.20.1-py3-none-any.whl
  • Upload date:
  • Size: 262.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for narwhals-1.20.1-py3-none-any.whl
Algorithm Hash digest
SHA256 77fc10fed31534a4ecf0c5e1e091c91c454cb2fa73937f36be3fcb0c2dfdabc6
MD5 e9506db8ac754a9b168519d00469ab90
BLAKE2b-256 3da2c91fedeb24e622b30d240e89e5ecf40cb3c2a8e50f61b5b28f0eb1fbb458

See more details on using hashes here.

Provenance

The following attestation bundles were made for narwhals-1.20.1-py3-none-any.whl:

Publisher: publish_to_pypi.yml on narwhals-dev/narwhals

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

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