pyflink.dataframe.dataframe.DataFrame.drop_nan

DataFrame.drop_nan(subset: List[str] | None = None) → DataFrame

Drop rows containing NaN values in float columns.

Does NOT drop rows with null — use drop_null() for that. Non-float columns in subset are silently ignored since NaN only applies to float/double types.

Parameters:

subset – Column names to check. If None, checks all float columns.

Returns:

A new DataFrame with rows containing NaN removed.

Example::

>>> df.drop_nan()               # drop if any float column is NaN
>>> df.drop_nan(subset=["a"])    # drop if float column "a" is NaN