dropDuplicates

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

Syntax

dropDuplicates(subset: Optional[List[str]] = None)

Parameters

Parameter	Type	Description
subset	list of column names, optional	List of columns to use for duplicate comparison (default All columns).

Returns

DataFrame: DataFrame without duplicates.

Notes

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 the system will accordingly limit the state. In addition, data older than watermark will be dropped to avoid any possibility of duplicates.

Examples

Python

from pyspark.sql import Row
df = spark.createDataFrame([
    Row(name='Alice', age=5, height=80),
    Row(name='Alice', age=5, height=80),
    Row(name='Alice', age=10, height=80)
])

df.dropDuplicates().show()
# +-----+---+------+
# | name|age|height|
# +-----+---+------+
# |Alice|  5|    80|
# |Alice| 10|    80|
# +-----+---+------+

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