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

Selecting data using SQL expressions in PySpark DataFrame

To get a new DataFrame where the values for the name column is uppercased:


        
        
            
                
                
                    df.selectExpr("upper(name) AS upper_name", "age * 2").show()
                
            
            +----------+---------+
|upper_name|(age * 2)|
+----------+---------+
|      ALEX|       40|
|       BOB|       60|
|     CATHY|       80|
+----------+---------+

We should use selectExpr(~) rather than select(~) to extract columns while performing some simple transformations on them - just as we have done here.

NOTE

There exists a similar method expr(~) in the pyspark.sql.functions library. expr(~) also takes in as argument a SQL expression, but the difference is that the return type is a PySpark Column. The following usage of selectExpr(~) and expr(~) are equivalent:


        
        
            
                
                
                    from pyspark.sql import functions as F
# The below is the same as df.selectExpr("upper(name)").show()
df.select(F.expr("upper(name)")).show()
                
            
            +-----------+
|upper(name)|
+-----------+
|       ALEX|
|        BOB|
|      CATHY|
+-----------+

In general, you should use selectExpr(~) rather than expr(~) because:

you won't have to import the pyspark.sql.functions library.
the syntax is shorter and clearer

Parsing more complex SQL expressions

Consider the following PySpark DataFrame:


        
        
            
                
                
                    df = spark.createDataFrame([['Alex',20],['Bob',60]], ['name','age'])
df.show()
                
            
            +----+---+
|name|age|
+----+---+
|Alex| 20|
| Bob| 60|
+----+---+

We can use classic SQL clauses like AND and LIKE to formulate more complicated expressions:


        
        
            
                
                
                    df.selectExpr('age < 30 AND name LIKE "A%" as result').show()
                
            
            +------+
|result|
+------+
|  true|
| false|
+------+

Here, we are checking for rows where age is less than 30 and the name starts with the letter A.

Note that we can implement the same logic like so:


        
        
            
                
                
                    col = ((df.age < 30) & (F.col('name').startswith('A'))).alias('result')
df.select(col).show()
                
            
            +------+
|result|
+------+
|  true|
| false|
+------+

I personally prefer using selectExpr(~) because the syntax is cleaner and the meaning is intuitive for those who are familiar with SQL.

Checking for the existence of values in PySpark column

Another application of selectExpr(~) is to check for the existence of values in a PySpark column. Please check out the recipe here.

Published by Isshin Inada

Edited by 0 others

Did you find this page useful?

thumb_up

thumb_down

Comment

Citation

Ask a question or leave a feedback...

Official PySpark Documentation

https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.selectExpr.html

thumb_up

thumb_down

chat_bubble_outline

settings

Enjoy our search

Hit / to insta-search docs and recipes!