polars.Expr.is_not_nan#

Expr.is_not_nan() → Self[source]#

Returns a boolean Series indicating which values are not NaN.

Notes

Floating point NaN (Not A Number) should not be confused with missing data represented as Null/None.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, None, 1, 5],
...         "b": [1.0, 2.0, float("nan"), 1.0, 5.0],
...     }
... )
>>> df.with_columns(pl.col(pl.Float64).is_not_nan().name.suffix("_is_not_nan"))
shape: (5, 3)
┌──────┬─────┬──────────────┐
│ a    ┆ b   ┆ b_is_not_nan │
│ ---  ┆ --- ┆ ---          │
│ i64  ┆ f64 ┆ bool         │
╞══════╪═════╪══════════════╡
│ 1    ┆ 1.0 ┆ true         │
│ 2    ┆ 2.0 ┆ true         │
│ null ┆ NaN ┆ false        │
│ 1    ┆ 1.0 ┆ true         │
│ 5    ┆ 5.0 ┆ true         │
└──────┴─────┴──────────────┘