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PySpark DataFrame | where method

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PySpark DataFrame
schedule Jul 1, 2022
Last updated
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PySpark DataFrame's where(~) method returns rows of the DataFrame that satisfies the given condition.

NOTE

The where(~) method is an alias for the filter(~) method.

Parameters

1. condition | Column or string

A boolean mask (Column) or a SQL string expression.

Return Value

A new PySpark DataFrame.

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|
+-----+---+

To get rows where age is greater than 25:

df.where("age > 25").show()
+-----+---+
| name|age|
+-----+---+
| Bob| 30|
|Cathy| 40|
+-----+---+

Equivalently, we can pass a Column object that represents a boolean mask:

df.where(df.age > 25).show()
+-----+---+
| name|age|
+-----+---+
| Bob| 30|
|Cathy| 40|
+-----+---+

Equivalently, we can use the col(~) function of sql.functions to refer to the column:

from pyspark.sql import functions as F
df.where(F.col("age") > 25).show()
+-----+---+
| name|age|
+-----+---+
| Bob| 30|
|Cathy| 40|
+-----+---+
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Published by Isshin Inada
Edited by 0 others
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