Narwhals

Extremely lightweight and extensible compatibility layer between Polars, pandas, Modin, and cuDF (and more!).

• Read the documentation
• Chat with us on Discord!
• Join our community call
• Read the contributing guide

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

Used by / integrates with

Join the party!

• Hamilton
• scikit-lego
• scikit-playtime
• timebasedcv

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.

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 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)

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 a SQL frontend in Python: Ibis!
• If you're a library maintainer and want a lightweight and minimal-overhead 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"?

Coz they are so awesome.

Thanks to Olha Urdeichuk for the illustration!