
        
        
            
                
                
                    df = spark.createDataFrame([("Alex", 20), ("Bob", 30), ("Cathy", 40)], ["col1", "col2"])
df.show()
                
            
            +-----+----+
| col1|col2|
+-----+----+
| Alex|  20|
|  Bob|  30|
|Cathy|  40|
+-----+----+

To select columns using regular expression, use the colRegex(~) method:


        
        
            
                
                
                    df.select(df.colRegex("`col[123]`")).show()
                
            
            +-----+----+
| col1|col2|
+-----+----+
| Alex|  20|
|  Bob|  30|
|Cathy|  40|
+-----+----+

Here, note the following:

we wrapped the column label using backticks ` - this is required otherwise PySpark will throw an error.
the regular expression col[123] matches columns with label col1, col2 or col3.
the select(~) method is used to convert the Column object into a PySpark DataFrame.

Getting column labels that match regular expression as list of strings in PySpark

To get column labels as a list of strings instead of PySpark Column objects:


        
        
            
                
                
                    df.select(df.colRegex("`col[123]`")).columns
                
            
            ['col1', 'col2']

Here, we are using the columns property of the PySpark DataFrame returned by select(~).

Published by Isshin Inada

Edited by 0 others

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Official PySpark Documentation

https://spark.apache.org/docs/3.2.0/api/python/reference/api/pyspark.sql.DataFrame.colRegex.html

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