Extremely lightweight compatibility layer between dataframe libraries

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  • License: MIT License
  • Author: Marco Gorelli
  • Tags cudf , dask , dataframes , interoperability , modin , pandas , polars , pyarrow
  • Requires: Python >=3.9
  • Provides-Extra: cudf , dask , duckdb , ibis , modin , pandas , polars , pyarrow , pyspark , pyspark-connect , sqlframe
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Project description

Narwhals

narwhals_small

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Extremely lightweight and extensible compatibility layer between dataframe libraries!

  • Full API support: cuDF, Modin, pandas, Polars, PyArrow.
  • Lazy-only support: Dask, DuckDB, Ibis, PySpark, SQLFrame. Work in progress: Daft.

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

Narwhals allows you to define dataframe-agnostic functions. For example:

import narwhals as nw
from narwhals.typing import IntoFrameT


def agnostic_function(
    df_native: IntoFrameT,
    date_column: str,
    price_column: str,
) -> IntoFrameT:
    return (
        nw.from_native(df_native)
        .group_by(nw.col(date_column).dt.truncate("1mo"))
        .agg(nw.col(price_column).mean())
        .sort(date_column)
        .to_native()
    )

You can then pass pandas.DataFrame, polars.DataFrame, polars.LazyFrame, duckdb.DuckDBPyRelation, pyspark.sql.DataFrame, pyarrow.Table, and more, to agnostic_function. In each case, no additional dependencies will be required, and computation will stay native to the input library:

import pandas as pd
import polars as pl
from datetime import datetime

data = {
    "date": [datetime(2020, 1, 1), datetime(2020, 1, 8), datetime(2020, 2, 3)],
    "price": [1, 4, 3],
}
print("pandas result:")
print(agnostic_function(pd.DataFrame(data), "date", "price"))
print()
print("Polars result:")
print(agnostic_function(pl.DataFrame(data), "date", "price"))

pandas result:
        date  price
0 2020-01-01    2.5
1 2020-02-01    3.0

Polars result:
shape: (2, 2)
┌─────────────────────┬───────┐
│ date                ┆ price │
│ ---                 ┆ ---   │
│ datetime[μs]        ┆ f64   │
╞═════════════════════╪═══════╡
│ 2020-01-01 00:00:00 ┆ 2.5   │
│ 2020-02-01 00:00:00 ┆ 3.0   │
└─────────────────────┴───────┘

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 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
  • Author: Marco Gorelli
  • Tags cudf , dask , dataframes , interoperability , modin , pandas , polars , pyarrow
  • Requires: Python >=3.9
  • Provides-Extra: cudf , dask , duckdb , ibis , modin , pandas , polars , pyarrow , pyspark , pyspark-connect , sqlframe
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