polars.Series.nan_max#

Series.nan_max() → int | float | date | datetime | timedelta | str[source]#

Get maximum value, but propagate/poison encountered NaN values.

This differs from numpy’s nanmax as numpy defaults to propagating NaN values, whereas polars defaults to ignoring them.

Examples

>>> s = pl.Series("a", [1, 3, 4])
>>> s.nan_max()
4

>>> s = pl.Series("a", [1, float("nan"), 4])
>>> s.nan_max()
nan