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

Getting rows where values match some regular expression in PySpark DataFrame

To get rows where values match some regex:


        
        
            
                
                
                    from pyspark.sql import functions as F
df.filter(F.col("name").rlike("^A")).show()
                
            
            +----+---+
|name|age|
+----+---+
|Alex| 20|
+----+---+

Here, the regular expression "^A" matches strings that begin with "A". Also, F.col("name").rlike("^A") returns a Column object of booleans:


        
        
            
                
                
                    from pyspark.sql import functions as F
df.select(F.col("name").rlike("^A")).show()
                
            
            +---------------+
|RLIKE(name, ^A)|
+---------------+
|           true|
|          false|
+---------------+

In our solution, we use the filter(~) method to fetch only the rows that correspond to True.

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.rlike.html

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