Add an empty column to Spark DataFrame

Question

As mentioned in many other locations on the web, adding a new column to an existing DataFrame is not straightforward. Unfortunately it is important to have this functionality (even though it is inefficient in a distributed environment) especially when trying to concatenate two DataFrames using unionAll.

What is the most elegant workaround for adding a null column to a DataFrame to facilitate a unionAll?

My version goes like this:

from pyspark.sql.types import StringType
from pyspark.sql.functions import UserDefinedFunction
to_none = UserDefinedFunction(lambda x: None, StringType())
new_df = old_df.withColumn('new_column', to_none(df_old['any_col_from_old']))

Answer 1

All you need here is a literal and cast:

from pyspark.sql.functions import lit

new_df = old_df.withColumn('new_column', lit(None).cast(StringType()))

A full example:

df = sc.parallelize([row(1, "2"), row(2, "3")]).toDF()
df.printSchema()

## root
##  |-- foo: long (nullable = true)
##  |-- bar: string (nullable = true)

new_df = df.withColumn('new_column', lit(None).cast(StringType()))
new_df.printSchema()

## root
##  |-- foo: long (nullable = true)
##  |-- bar: string (nullable = true)
##  |-- new_column: string (nullable = true)

new_df.show()

## +---+---+----------+
## |foo|bar|new_column|
## +---+---+----------+
## |  1|  2|      null|
## |  2|  3|      null|
## +---+---+----------+

A Scala equivalent can be found here: Create new Dataframe with empty/null field values