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