
        
        
            
                
                
                    df = spark.createDataFrame([['Alex',30],['Bob',50]], ['name','age'])
df.show()
                
            
            +----+---+
|name|age|
+----+---+
|Alex| 30|
| Bob| 50|
+----+---+

Using the expr method to convert column values to uppercase

The expr(~) method takes in as argument a SQL expression, so we can use SQL functions such as upper(~):


        
        
            
                
                
                    import pyspark.sql.functions as F
df.select(F.expr('upper(name)')).show()
                
            
            +-----------+
|upper(name)|
+-----------+
|       ALEX|
|        BOB|
+-----------+

NOTE

The expr(~) method can often be more succinctly written using PySpark DataFrame's selectExpr(~) method. For instance, the above case can be rewritten as:


        
        
            
                
                
                    df.selectExpr('upper(name)').show()
                
            
            +-----------+
|upper(name)|
+-----------+
|       ALEX|
|        BOB|
+-----------+

I recommend that you use selectExpr(~) whenever possible because:

you won't have to import the SQL functions library (pyspark.sql.functions).
syntax is shorter

Parsing complex SQL expressions using expr method

Here's a more complex SQL expression using clauses like AND and LIKE:


        
        
            
                
                
                    df.select(F.expr('age > 40 AND name LIKE "B%"').alias('result')).show()
                
            
            +------+
|result|
+------+
| false|
|  true|
+------+

Note the following:

we are checking for rows where age is larger than 40 and name starts with B.
we are assigning the label 'result' to the Column returned by expr(~) using the alias(~) method.

Practical applications of boolean masks returned by expr method

As we can see in the above example, the expr(~) method can return a boolean mask depending on the SQL expression you supply:


        
        
            
                
                
                    df.select(F.expr('age > 40 AND name LIKE "B%"').alias('result')).show()
                
            
            +------+
|result|
+------+
| false|
|  true|
+------+

This allows us to check for the existence of rows that satisfy a given condition using any(~):


        
        
            
                
                
                    df.select(F.expr('any(age > 40 AND name LIKE "B%")').alias('exists?')).show()
                
            
            +-------+
|exists?|
+-------+
|   true|
+-------+

Here, we get True because there exists at least one True value in the boolean mask.

Mapping column values using expr method

We can map column values using CASE WHEN in the expr(~) method like so:


        
        
            
                
                
                    col = F.expr('CASE WHEN age < 40 THEN "JUNIOR" ELSE "SENIOR" END').alias('result')
df.withColumn('status', col).show()
                
            
            +----+---+------+
|name|age|status|
+----+---+------+
|Alex| 30|JUNIOR|
| Bob| 50|SENIOR|
+----+---+------+