pyflink.dataframe.dataframe.DataFrame.drop_null#

DataFrame.drop_null(subset: List[str] | None = None) → DataFrame[source]#

Drop rows containing null values.

Does NOT drop rows with NaN — use drop_nan() for that.

Parameters:: subset – Column names to check. If None, checks all columns.
Returns:: A new DataFrame with rows containing nulls removed.

Example::

>>> df.drop_null()               # drop if any column is null
>>> df.drop_null(subset=["a"])    # drop if column "a" is null