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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

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()).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). 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.

Convert a Polars Series to a NumPy array with the .to_numpy() method. Missing values will be replaced by np.nan during the conversion. If the Series does not include missing values, or those values are not desired anymore, the .view() method can be used instead, providing a zero-copy NumPy array of the data.