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

Getting rows where values are contained in a list of values in PySpark DataFrame

To get rows where values for the name column is either "Cathy" or "Alex":


        
        
            
                
                
                    from pyspark.sql import functions as F
df.filter(F.col("name").isin("Cathy", "Alex")).show()
                
            
            +----+---+
|name|age|
+----+---+
|Alex| 20|
+----+---+

Here, F.col("name").isin("Cathy","Alex") returns a Column object of booleans:


        
        
            
                
                
                    from pyspark.sql import functions as F
df.select(F.col("name").isin("Cathy", "Alex")).show()
                
            
            +-----------------------+
|(name IN (Cathy, Alex))|
+-----------------------+
|                   true|
|                  false|
+-----------------------+

The filter(~) method fetches the rows that correspond to True.

Note that if you have a list of values instead, use the * operator to convert the list into positional arguments:


        
        
            
                
                
                    from pyspark.sql import functions as F
my_list = ["Cathy", "Alex"]
df.filter(F.col("name").isin(*my_list)).show()
                
            
            +----+---+
|name|age|
+----+---+
|Alex| 20|
+----+---+

Published by Isshin Inada

Edited by 0 others

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Official PySpark Documentation

https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.Column.isin.html

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