pyspark.sql.DataFrame.dropDuplicates

DataFrame.dropDuplicates(subset: Optional[List[str]] = None) → pyspark.sql.dataframe.DataFrame

Return a new DataFrame with duplicate rows removed, optionally only considering certain columns.

For a static batch DataFrame, it just drops duplicate rows. For a streaming DataFrame, it will keep all data across triggers as intermediate state to drop duplicates rows. You can use withWatermark() to limit how late the duplicate data can be and system will accordingly limit the state. In addition, too late data older than watermark will be dropped to avoid any possibility of duplicates.

drop_duplicates() is an alias for dropDuplicates().

Examples

>>> from pyspark.sql import Row
>>> df = sc.parallelize([ \
...     Row(name='Alice', age=5, height=80), \
...     Row(name='Alice', age=5, height=80), \
...     Row(name='Alice', age=10, height=80)]).toDF()
>>> df.dropDuplicates().show()
+-----+---+------+
| name|age|height|
+-----+---+------+
|Alice|  5|    80|
|Alice| 10|    80|
+-----+---+------+

>>> df.dropDuplicates(['name', 'height']).show()
+-----+---+------+
| name|age|height|
+-----+---+------+
|Alice|  5|    80|
+-----+---+------+

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