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
At only 0.3 MB and with zero dependencies, Narwhals is about as lightweight as it gets. Here's a comparison with Ibis (though note that the two projects have different goals and are not in competition):
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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