pyspark.sql.DataFrameNaFunctions.drop#

DataFrameNaFunctions.drop(how='any', thresh=None, subset=None)[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, the values that can be ‘any’ or ‘all’, default ‘any’.: 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),
... ])

Example 1: Drop the row if it contains any nulls.

>>> df.na.drop().show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10|    80|Alice|
+---+------+-----+

Example 2: Drop the row only if all its values are null.

>>> df.na.drop(how='all').show()
+----+------+-----+
| age|height| name|
+----+------+-----+
|  10|    80|Alice|
|   5|  NULL|  Bob|
|NULL|  NULL|  Tom|
+----+------+-----+

Example 3: Drop rows that have less than thresh non-null values.

>>> df.na.drop(thresh=2).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10|    80|Alice|
|  5|  NULL|  Bob|
+---+------+-----+

Example 4: Drop rows with non-null values in the specified columns.

>>> df.na.drop(subset=['age', 'name']).show()
+---+------+-----+
|age|height| name|
+---+------+-----+
| 10|    80|Alice|
|  5|  NULL|  Bob|
+---+------+-----+