polars.Expr.nan_max#

Expr.nan_max() Expr[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

>>> df = pl.DataFrame({"a": [0, float("nan")]})
>>> df.select(pl.col("a").nan_max())
shape: (1, 1)
┌─────┐
│ a   │
│ --- │
│ f64 │
╞═════╡
│ NaN │
└─────┘