pyspark.sql.DataFrame.dropna¶

DataFrame.dropna(how: str = 'any', thresh: Optional[int] = None, subset: Union[str, Tuple[str, …], List[str], None] = None) → pyspark.sql.dataframe.DataFrame[source]¶

Returns a new DataFrame omitting rows with null values. DataFrame.dropna() and DataFrameNaFunctions.drop() are aliases of each other.

New in version 1.3.1.

Changed in version 3.4.0: Supports Spark Connect.

Parameters

howstr, optional: ‘any’ or ‘all’. If ‘any’, drop a row if it contains any nulls. If ‘all’, drop a row only if all its values are null.
thresh: int, optional: default None If specified, drop rows that have less than thresh non-null values. This overwrites the how parameter.
subsetstr, tuple or list, optional: optional list of column names to consider.

Returns

DataFrame: DataFrame with null only rows excluded.

Examples

>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([
...     Row(age=10, height=80, name="Alice"),
...     Row(age=5, height=None, name="Bob"),
...     Row(age=None, height=None, name="Tom"),
...     Row(age=None, height=None, name=None),
... ])
>>> df.na.drop().show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10|    80|Alice|
+---+------+-----+

pyspark.sql.DataFrame.drop_duplicates pyspark.sql.DataFrame.dtypes