Numpy

Polars expressions support NumPy ufuncs. See here for a list on all supported numpy functions.

This means that if a function is not provided by Polars, we can use NumPy and we still have fast columnar operation through the NumPy API.

Example

Python

DataFrame · log · Available on feature numpy

import polars as pl
import numpy as np

df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})

out = df.select(np.log(pl.all()).name.suffix("_log"))
print(out)

shape: (3, 2)
┌──────────┬──────────┐
│ a_log    ┆ b_log    │
│ ---      ┆ ---      │
│ f64      ┆ f64      │
╞══════════╪══════════╡
│ 0.0      ┆ 1.386294 │
│ 0.693147 ┆ 1.609438 │
│ 1.098612 ┆ 1.791759 │
└──────────┴──────────┘

Interoperability

Polars Series have support for NumPy universal functions (ufuncs) and generalized ufuncs. Element-wise functions such as np.exp(), np.cos(), np.div(), etc. all work with almost zero overhead.

However, as a Polars-specific remark: missing values are a separate bitmask and are not visible by NumPy. This can lead to a window function or a np.convolve() giving flawed or incomplete results, so an error will be raised if you pass a Series with missing data to a generalized ufunc.

Convert a Polars Series to a NumPy array with the .to_numpy() method. Missing values will be replaced by np.nan during the conversion.