Extremely lightweight compatibility layer between pandas, Polars, cuDF, and Modin
Project description
Narwhals
Extremely lightweight and extensible compatibility layer between Polars, pandas, Modin, and cuDF (and more!).
Seamlessly support all, without depending on any!
- ✅ Just use a subset of the Polars API, no need to learn anything new
- ✅ Zero dependencies, zero 3rd-party imports: Narwhals only uses what the user passes in, so you can keep your library 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
Used by
Join the party!
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 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)
What about Ibis?
Like Ibis, Narwhals aims to enable dataframe-agnostic code. However, Narwhals comes with zero dependencies, is about as lightweight as it gets, and is aimed at library developers rather than at end users. It also does not aim to support as many backends, instead preferring to focus on dataframes. So, which should you use?
- If you need to run complicated analyses and aren't too bothered about package size: Ibis!
- If you're a library maintainer and want the thinnest-possible layer to get cross-dataframe library support: Narwhals!
Here is the package size increase which would result from installing each tool in a non-pandas environment:
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!
Note: You might suspect that this is a secret ploy to infiltrate the Polars API everywhere. Indeed, you may suspect that.
Why "Narwhals"?
Thanks to Olha Urdeichuk for the illustration!
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file narwhals-0.9.5.tar.gz.
File metadata
- Download URL: narwhals-0.9.5.tar.gz
- Upload date:
- Size: 342.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ee5d48377fe75e2a84012a7a87fb04b6651b697f446044fa253f5c382032ba07 |
|
| MD5 | dc8b3b6b9ebffa0b2028854b6ffddcc1 |
|
| BLAKE2b-256 | 0ed6ab7dc021b71b5b70adfcc7629c790bf24572c9b16a34613f0c210af0842d |
Provenance
File details
Details for the file narwhals-0.9.5-py3-none-any.whl.
File metadata
- Download URL: narwhals-0.9.5-py3-none-any.whl
- Upload date:
- Size: 61.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 6085830a564f5fbd96e07bb06d9a7324a888ba9c4684a18f21a3dd70d623c617 |
|
| MD5 | 7680a819867174065c281c52981fd013 |
|
| BLAKE2b-256 | 93dff584cfa77a24ac9fb0b7532cbedde2749114fd4d88c63df7f89a03d7b35d |
