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
- ✅ No dependencies (not even Polars), keep your library lightweight
- ✅ Separate lazy and eager APIs
- ✅ Use Polars Expressions
- ✅ 100% branch coverage, tested against pandas and Polars nightly builds!
Installation
pip install narwhals
Or just vendor it, it's only a bunch of pure-Python files.
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)
Package size
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, preferring to instead focus on dataframes.
The projects are not in competition, and the comparison is intended only to help you choose the right tool for the right task.
Here is the package size increase which would result from installing each tool in a non-pandas environment:
Example
Here's an example of a dataframe agnostic function:
from typing import Any
import pandas as pd
import polars as pl
import narwhals as nw
def my_agnostic_function(
suppliers_native,
parts_native,
):
suppliers = nw.from_native(suppliers_native)
parts = nw.from_native(parts_native)
result = (
suppliers.join(parts, left_on="city", right_on="city")
.filter(nw.col("weight") > 10)
.group_by("s")
.agg(
weight_mean=nw.col("weight").mean(),
weight_max=nw.col("weight").max(),
)
.sort("s")
)
return nw.to_native(result)
You can pass in a pandas or Polars dataframe, the output will be the same! Let's try it out:
suppliers = {
"s": ["S1", "S2", "S3", "S4", "S5"],
"sname": ["Smith", "Jones", "Blake", "Clark", "Adams"],
"status": [20, 10, 30, 20, 30],
"city": ["London", "Paris", "Paris", "London", "Athens"],
}
parts = {
"p": ["P1", "P2", "P3", "P4", "P5", "P6"],
"pname": ["Nut", "Bolt", "Screw", "Screw", "Cam", "Cog"],
"color": ["Red", "Green", "Blue", "Red", "Blue", "Red"],
"weight": [12.0, 17.0, 17.0, 14.0, 12.0, 19.0],
"city": ["London", "Paris", "Oslo", "London", "Paris", "London"],
}
print("pandas output:")
print(
my_agnostic_function(
pd.DataFrame(suppliers),
pd.DataFrame(parts),
)
)
print("\nPolars output:")
print(
my_agnostic_function(
pl.LazyFrame(suppliers),
pl.LazyFrame(parts),
).collect()
)
pandas output:
s weight_mean weight_max
0 S1 15.0 19.0
1 S2 14.5 17.0
2 S3 14.5 17.0
3 S4 15.0 19.0
Polars output:
shape: (4, 3)
┌─────┬─────────────┬────────────┐
│ s ┆ weight_mean ┆ weight_max │
│ --- ┆ --- ┆ --- │
│ str ┆ f64 ┆ f64 │
╞═════╪═════════════╪════════════╡
│ S1 ┆ 15.0 ┆ 19.0 │
│ S2 ┆ 14.5 ┆ 17.0 │
│ S3 ┆ 14.5 ┆ 17.0 │
│ S4 ┆ 15.0 ┆ 19.0 │
└─────┴─────────────┴────────────┘
Magic! 🪄
Scope
- Do you maintain a dataframe-consuming library?
- Is there a Polars function which you'd like Narwhals to have, which would make your work easier?
If, I'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!
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