Numpy functions #

Polars expressions support NumPy ufuncs. See the NumPy documentation for a list of 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 operations through the NumPy API.

Example #

{{code_block(‘user-guide/expressions/numpy-example’,api_functions=[‘DataFrame’,’np.log’])}}

--8<-- "python/user-guide/expressions/numpy-example.py"

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, bear in mind that Polars keeps track of missing values with a separate bitmask and NumPy does not receive this information. 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 function to_numpy. Missing values will be replaced by np.nan during the conversion.