Creating a Constant Column in a Spark DataFrame

Adding a constant column to a Spark DataFrame with an arbitrary value that applies to all rows can be achieved in several ways. The withColumn method, intended for this purpose, can lead to errors when attempting to provide a direct value as its second argument.

Using Literal Values (Spark 1.3 )

To resolve this issue, use lit to create a literal representation of the desired value:

from pyspark.sql.functions import lit

df.withColumn('new_column', lit(10))

Creating Complex Columns (Spark 1.4 )

For more complex column types, such as arrays, structs, or maps, use the appropriate functions:

from pyspark.sql.functions import array, struct

df.withColumn('array_column', array(lit(1), lit(2)))
df.withColumn('struct_column', struct(lit('foo'), lit(1)))

Typed Literals (Spark 2.2 )

Spark 2.2 introduces typedLit, providing support for Seq, Map, and Tuples:

import org.apache.spark.sql.functions.typedLit

df.withColumn("some_array", typedLit(Seq(1, 2, 3)))

Using User-Defined Functions (UDFs)

Alternatively, create a UDF that returns the constant value:

from pyspark.sql import functions as F

def constant_column(value):
    def udf(df):
        return [value for _ in range(df.count())]
    return F.udf(udf)

df.withColumn('constant_column', constant_column(10))

Note:

These methods can also be used to pass constant arguments to UDFs or SQL functions.