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

Creating a column of constants in PySpark DataFrame

To create a new PySpark DataFrame with the name column of df and a new column called is_single made up of True values:


        
        
            
                
                
                    import pyspark.sql.functions as F
df2 = df.select(F.col("name"), F.lit(True).alias("is_single"))
df2.show()
                
            
            +----+---------+
|name|is_single|
+----+---------+
|Alex|     true|
| Bob|     true|
+----+---------+

Here, F.lit(True) returns a Column object, which has a method called alias(~) that assigns a label.

Note that you could append a new column of constants using the withColumn(~) method:


        
        
            
                
                
                    import pyspark.sql.functions as F
df = spark.createDataFrame([["Alex", 20], ["Bob", 30]], ["name", "age"])
df_new = df.withColumn("is_single",  F.lit(True))
df_new.show()
                
            
            +----+---+---------+
|name|age|is_single|
+----+---+---------+
|Alex| 20|     true|
| Bob| 30|     true|
+----+---+---------+

Creating a column whose values are based on a condition in PySpark

We can also use lit(~) to create a column whose values depend on some condition:


        
        
            
                
                
                    import pyspark.sql.functions as F
col = df.when(F.col("age") <= 20, F.lit("junior")).otherwise(F.lit("senior"))
df3 = df.withColumn("status", col)
df3.show()
                
            
            +----+---+------+
|name|age|status|
+----+---+------+
|Alex| 20|junior|
| Bob| 30|senior|
+----+---+------+

Note the following:

we are using the when(~) and otherwise(~) pattern to fill the values of the column conditionally.
we are using the withColumn(~) method to append a new column named status.
the F.lit("junior") can actually be replaced by "junior" - this is just to demonstrate one usage of lit(~).

Published by Isshin Inada

Edited by 0 others

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Official PySpark Documetation

https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.functions.lit.html

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