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PySpark SQL Functions | trim method

Machine Learning
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PySpark
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PySpark SQL Functions
schedule Jul 1, 2022
Last updated
local_offer PySpark
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PySpark SQL Functions' trim(~) method returns a new PySpark column with the string values trimmed, that is, with the leading and trailing spaces removed.

Parameters

1. col | string

The column of type string to trim.

Return Value

A new PySpark Column.

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([[" Alex ", 20], [" Bob", 30], ["Cathy ", 40]], ["name", "age"])
df.show()
+---------+---+
| name|age|
+---------+---+
| Alex | 20|
| Bob| 30|
|Cathy | 40|
+---------+---+

Here, the values in the name column have leading and trailing spaces.

Trimming columns in PySpark

To trim the name column, that is, to remove the leading and trailing spaces:

import pyspark.sql.functions as F
df.select(F.trim("name").alias("trimmed_name")).show()
+------------+
|trimmed_name|
+------------+
| Alex|
| Bob|
| Cathy|
+------------+

Here, the alias(~) method is used to assign a label to the Column returned by trim(~).

To get the original PySpark DataFrame but with the name column updated with the trimmed version, use the withColumn(~) method:

df.withColumn("name", F.trim("name").alias("trimmed_name")).show()
+-----+---+
| name|age|
+-----+---+
| Alex| 20|
| Bob| 30|
|Cathy| 40|
+-----+---+
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Published by Isshin Inada
Edited by 0 others
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