Adding constant columns with lit and typedLit to PySpark DataFrames

This post explains how to add constant columns to PySpark DataFrames with lit and typedLit.

You’ll see examples where these functions are useful and when these functions are invoked implicitly.

lit and typedLit are easy to learn and all PySpark programmers need to be comfortable using them.

Simple lit example

Create a DataFrame with num and letter columns.

df = spark.createDataFrame([(1, "a"), (2, "b")], ["num", "letter"])
|  1|     a|
|  2|     b|

Add a cool column to the DataFrame with the constant value 23.

from pyspark.sql.functions import *

df.withColumn("cool", lit(23)).show()
|  1|     a|  23|
|  2|     b|  23|

Let’s try this code without using lit:

df.withColumn("cool", 23).show()

That’ll give you the following error:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/powers/spark/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/", line 2454, in withColumn
    assert isinstance(col, Column), "col should be Column"
AssertionError: col should be Column

The second argument to withColumn must be a Column object and cannot be an integer.

Add constant value to column

Let’s add 5 to the num column:

df.withColumn("num_plus_5", df.num + lit(5)).show()
|  1|     a|         6|
|  2|     b|         7|

df.num and lit(5) both return Column objects, as you can observe in the PySpark console.

>>> df.num
>>> lit(5)

The + operator works when both operands are Column objects.

The + operator will also work if one operand is a Column object and the other is an integer.

df.withColumn("num_plus_5", df.num + 5).show()
|  1|     a|         6|
|  2|     b|         7|

PySpark implicitly converts 5 (an integer) to a Column object and that’s why this code works. Let’s refresh our understanding of implicit conversions in Python.

Python type conversions

Let’s look at how integers and floating point numbers are added with Python to illustrate the implicit conversion behavior.

An integer cannot be added with a floating point value without type conversion. Language designers either need to throw an error when users add ints and floats or convert the int to a float and then perform the addition. Python language designers made the decision to implicitly convert integers to floating point values in this situation.

Here’s an example that uses implicit conversion.

3 + 1.2 # 4.2

Programmers can also explicitly convert integers to floating point values, so no implicit conversions are needed.

float(3) + 1.2 # 4.2

Python doesn’t always perform implicit type conversions. This code will error out for example:

"hi" + 3
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can only concatenate str (not "int") to str

You need to make an explicit type conversion if you’d like to concatenate a string with an integer in Python.

"hi" + str(3) # 'hi3'

PySpark implicit type conversions

Let’s look at how PySpark implicitly converts integers to Columns with some console experimentation.

# implicit conversion
>>> col("num") + 5
Column<'(num + 5)'>

# explicit conversion
>>> col("num") + lit(5)
Column<'(num + 5)'>

It’s best to use lit and perform explicit conversions, so the intentions of your code are clear. You should avoid relying on implicit conversion rules that may behave unexpectedly in certain situations.

Array constant column

The Scala API has a typedLit function to handle complex types like arrays, but there is no such method in the PySpark API, so hacks are required.

Here’s how to add a constant [5, 8] array column to the DataFrame.

df.withColumn("nums", array(lit(5), lit(8))).show()
|num|letter|  nums|
|  1|     a|[5, 8]|
|  2|     b|[5, 8]|

This code does not work.

df.withColumn("nums", lit([5, 8])).show()

It errors out as follows:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/powers/spark/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/", line 98, in lit
    return col if isinstance(col, Column) else _invoke_function("lit", col)
  File "/Users/powers/spark/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/", line 58, in _invoke_function
    return Column(jf(*args))
  File "/Users/powers/spark/spark-3.1.2-bin-hadoop3.2/python/lib/", line 1304, in __call__
  File "/Users/powers/spark/spark-3.1.2-bin-hadoop3.2/python/pyspark/sql/", line 111, in deco
    return f(*a, **kw)
  File "/Users/powers/spark/spark-3.1.2-bin-hadoop3.2/python/lib/", line 326, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.sql.functions.lit.
: java.lang.RuntimeException: Unsupported literal type class java.util.ArrayList [5, 8]
    at org.apache.spark.sql.catalyst.expressions.Literal$.apply(literals.scala:90)
    at org.apache.spark.sql.catalyst.expressions.Literal$.$anonfun$create$2(literals.scala:152)
    at scala.util.Failure.getOrElse(Try.scala:222)
    at org.apache.spark.sql.catalyst.expressions.Literal$.create(literals.scala:152)
    at org.apache.spark.sql.functions$.typedLit(functions.scala:131)
    at org.apache.spark.sql.functions$.lit(functions.scala:114)
    at org.apache.spark.sql.functions.lit(functions.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(
    at java.lang.reflect.Method.invoke(
    at py4j.reflection.MethodInvoker.invoke(
    at py4j.reflection.ReflectionEngine.invoke(
    at py4j.Gateway.invoke(
    at py4j.commands.AbstractCommand.invokeMethod(
    at py4j.commands.CallCommand.execute(

Next steps

You’ve learned how to add constant columns to DataFrames in this post. You’ve also learned about type conversion in PySpark and how the lit function is used implicitly in certain situations.

There are times when you can omit lit and rely on implicit type conversions, but it’s better to write explicit PySpark code and invoke lit whenever it’s needed.


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