How Do I Add a Constant Column to a Spark DataFrame?

Adding a Constant Column to a Spark DataFrame

When attempting to add a new column to a DataFrame using withColumn and a constant value, users may encounter an error due to mismatched data types.

Solution:

Spark 2.2 :

Use typedLit to directly assign constant values of various types:

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

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

Spark 1.3 :

Use lit to create a literal value:

from pyspark.sql.functions import lit

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

Spark 1.4 :

For complex columns, use function blocks like array, struct, and create_map:

from pyspark.sql.functions import array, struct, create_map

df.withColumn("some_array", array(lit(1), lit(2), lit(3)))

In Scala:

import org.apache.spark.sql.functions.{array, lit, map, struct}

df.withColumn("new_column", lit(10))
df.withColumn("map", map(lit("key1"), lit(1), lit("key2"), lit(2)))

For structs, use alias on each field or cast on the whole object to provide names:

df.withColumn(
    "some_struct",
    struct(lit("foo").alias("x"), lit(1).alias("y"), lit(0.3).alias("z"))
 )

Note:

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

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