daft.functions.is_nan#

def is_nan(expr: Expression) -> Expression:
    """Checks if values are NaN (a special float value indicating not-a-number).

    Returns:
        Expression: Boolean Expression indicating whether values are invalid.

    Note:
        Nulls will be propagated! I.e. this operation will return a null for null values.

    Examples:
        >>> import daft
        >>> from daft.functions import is_nan
        >>>
        >>> df = daft.from_pydict({"data": [1.0, None, float("nan")]})
        >>> df = df.select(is_nan(df["data"]))
        >>> df.collect()
        ╭───────╮
        │ data  │
        │ ---   │
        │ Bool  │
        ╞═══════╡
        │ false │
        ├╌╌╌╌╌╌╌┤
        │ None  │
        ├╌╌╌╌╌╌╌┤
        │ true  │
        ╰───────╯
        <BLANKLINE>
        (Showing first 3 of 3 rows)

    """
    return Expression._call_builtin_scalar_fn("is_nan", expr)