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PySpark DataFrame | dropDuplicates method

Machine Learning
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PySpark
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PySpark DataFrame
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark DataFrame's dropDuplicates(~) returns a new DataFrame with duplicate rows removed. We can optionally specify columns to check for duplicates.

NOTE

dropDuplicates(~) is an alias for drop_duplicates(~).

Parameters

1. subset | string or list of string | optional

The columns by which to check for duplicates. By default, all columns will be checked.

Return Value

A new PySpark DataFrame.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([["Alex", 25], ["Bob", 30], ["Bob", 30], ["Cathy", 25]], ["name", "age"])
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 25|
| Bob| 30|
| Bob| 30|
|Cathy| 25|
+-----+---+

Dropping duplicate rows in PySpark DataFrame

To drop duplicate rows:

df.dropDuplicates().show()
+-----+---+
| name|age|
+-----+---+
| Alex| 25|
| Bob| 30|
|Cathy| 25|
+-----+---+

Note the following:

  • only the first occurrence is kept while subsequent occurrences are removed.

  • a new PySpark DataFrame is returned while the original is kept intact.

Dropping duplicate rows for certain columns

To drop duplicate rows based on the age column:

df.dropDuplicates(["age"]).show()
+----+---+
|name|age|
+----+---+
|Alex| 25|
| Bob| 30|
+----+---+

Again, only the first occurrence is kept while the latter duplicate rows are discarded.

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Published by Isshin Inada
Edited by 0 others
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