This release has been yanked by its maintainers, and will be ignored by installers, except when explicitly specified.
Consider using release 2.27.1 instead.
Reason given by maintainers: Accidental breakage in Pointblank
Narwhals
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, Daft via the narwhals-daft plugin, and Apache DataFusion via the narwhals-datafusion plugin.
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:
-
use
narwhals.from_nativeto wrap a pandas/Polars/Modin/cuDF/PyArrow DataFrame/LazyFrame in a Narwhals class -
use
narwhals.to_nativeto 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
Example
Narwhals allows you to define dataframe-agnostic functions. For example:
import narwhals as nw
from narwhals.typing import IntoDataFrameT, IntoLazyFrameT
def agnostic_function(
df_native: IntoDataFrameT | IntoLazyFrameT,
) -> IntoDataFrameT | IntoLazyFrameT:
return (
nw.from_native(df_native)
.with_columns(
category=nw.when(nw.col("animal").str.contains("whale"))
.then(nw.lit("whale"))
.otherwise(nw.lit("other"))
)
.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 duckdb
import polars as pl
import pandas as pd
data = {
"animal": ["blue whale", "orca", "dolphin", "humpback whale", "seal"],
"length_m": [30.0, 8, 2.5, 17, 2.2],
"weight_kg": [150000, 4000, 200, 30000, 85],
}
print("Polars result")
df_pl = pl.DataFrame(data)
print(agnostic_function(df_pl))
print("DuckDB result")
print(agnostic_function(duckdb.sql("select * from df_pl")))
print("pandas result")
df_pd = pd.DataFrame(data)
print(agnostic_function(df_pd))
Polars result
shape: (5, 4)
┌────────────────┬──────────┬───────────┬──────────┐
│ animal ┆ length_m ┆ weight_kg ┆ category │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ i64 ┆ str │
╞════════════════╪══════════╪═══════════╪══════════╡
│ blue whale ┆ 30.0 ┆ 150000 ┆ whale │
│ orca ┆ 8.0 ┆ 4000 ┆ other │
│ dolphin ┆ 2.5 ┆ 200 ┆ other │
│ humpback whale ┆ 17.0 ┆ 30000 ┆ whale │
│ seal ┆ 2.2 ┆ 85 ┆ other │
└────────────────┴──────────┴───────────┴──────────┘
DuckDB result
┌────────────────┬──────────┬───────────┬──────────┐
│ animal │ length_m │ weight_kg │ category │
│ varchar │ double │ int64 │ varchar │
├────────────────┼──────────┼───────────┼──────────┤
│ blue whale │ 30.0 │ 150000 │ whale │
│ orca │ 8.0 │ 4000 │ other │
│ dolphin │ 2.5 │ 200 │ other │
│ humpback whale │ 17.0 │ 30000 │ whale │
│ seal │ 2.2 │ 85 │ other │
└────────────────┴──────────┴───────────┴──────────┘
pandas result
animal length_m weight_kg category
0 blue whale 30.0 150000 whale
1 orca 8.0 4000 other
2 dolphin 2.5 200 other
3 humpback whale 17.0 30000 whale
4 seal 2.2 85 other
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!
- altair
- bokeh
- darts
- fairlearn
- formulaic
- gt-extras
- hierarchicalforecast
- lightgbm
- marimo
- metalearners
- mosaic
- pandera
- panel-graphic-walker
- plotly
- pointblank
- pymarginaleffects
- pyreadr
- pyreadstat
- py-shiny
- pysummaries
- rio
- scikit-learn
- scikit-lego
- scikit-playtime
- tabmat
- tea-tasting
- timebasedcv
- tubular
- Validoopsie
- vegafusion
- wimsey
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!
Support
If you'd like to say "thank you", please give us a ⭐ star ⭐.
Please contact hello_narwhals@proton.me if you would like to:
- Receive professional support (e.g., if you're using or would like to use Narwhals at your company).
- Have any Narwhals fixes / features prioritised.
- Commission any Narwhals plugins for new backends.
Appears on
Narwhals has been featured in several talks, podcasts, and blog posts:
-
Inspiring Computing Podcast The Rise of Narwhals in Open-Source
-
PyCon DE & PyData 2025 How Narwhals is silently bringing pandas, Polars, DuckDB, PyArrow, and more together
-
The Python Exchange March 2025 What Can Narwhals Do for You?
-
PyData London 2025 How Narwhals brings Polars, DuckDB, PyArrow, & pandas together
-
Talk Python to me Podcast Ahoy, Narwhals are bridging the data science APIs
-
Python Bytes Podcast Episode 402, topic #2
-
Super Data Science: ML & AI Podcast Narwhals: For Pandas-to-Polars DataFrame Compatibility
-
Sample Space Podcast | probabl How Narwhals has many end users ... that never use it directly. - Marco Gorelli
-
The Real Python Podcast Narwhals: Expanding DataFrame Compatibility Between Libraries
-
Pycon Lithuania 2024 Marco Gorelli - DataFrame interoperatiblity - what's been achieved, and what comes next?
-
Pycon Italy 2024 How you can write a dataframe-agnostic library - Marco Gorelli
-
Polars Blog Post Polars has a new lightweight plotting backend
-
Quansight Labs blog post (w/ Scikit-Lego) How Narwhals and scikit-lego came together to achieve dataframe-agnosticism
Why "Narwhals"?
Thanks to Olha Urdeichuk for the illustration!
3 maintainers
Metadata
3 maintainers
Release files for narwhals 2.27.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| narwhals-2.27.0.tar.gz | 708.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| narwhals-2.27.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / narwhals-2.27.0.tar.gz
| Download URL | narwhals-2.27.0.tar.gz |
|---|---|
| Size | 708.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
27a1b68b94c36bbe60ecc63fb9b37674a85a98bee3f45d179cbdc0a872bc9b8d
|
|
BLAKE2b-256 checksum How to use checksums |
f32e325e6682e3ccdef9b7c681adfe0273e0b6bf5a90c759ecddbad06e619b55
|
| Upload date | |
|
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 5, 2026.
Transparency logRelease files / narwhals-2.27.0-py3-none-any.whl
| Download URL | narwhals-2.27.0-py3-none-any.whl |
|---|---|
| Size | 483.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
9d5661f93a10cfb2b2cc2b3e6e866541dea1b05706a3bcd22f633d70cfae3067
|
|
BLAKE2b-256 checksum How to use checksums |
16671f0b582e73ce46511f2799cbdcdbdf9ca98f344f2aad42ff5ff5dde0d8fe
|
| Upload date | |
|
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 5, 2026.
Transparency log
