How to Apply a Function to Two Columns of a DataFrame?

When a function is applied to a column of a DataFrame, the values in all the rows are affected by the operation that function does. In this short how-to article, we will learn how to apply a function to two columns in Pandas and PySpark DataFrames.

Pandas

Consider we have a user-defined function f that takes two input values. We can apply this function to two columns of a DataFrame using the apply function and a lambda expression. If this function returns a single value, we can even create a new column with the values it returns.

Let’s say we have first and last name columns and want to create a new column containing the initials. The find_initials function defined below does this operation. We can apply it to the first name and last name columns as follows:

# Defining the function
def find_initials(fname, lname):
    return fname[0] + lname[0]

# Applying it to two columns
df["initials"] = df.apply(lambda x: find_initials(x["fname"], x["lname"]), axis=1)

PySpark

The operations are similar but we need an additional step to create a user-defined function (udf).

# importing necessary modules
from pyspark.sql import functions as F
from pyspark.sql.types import StringType

# Defining the function
def take_initials(fname, lname):
    return fname[0] + lname[0]

# Creating a udf
udf = F.udf(take_initials, StringType())

# Applying it to two columns
df = df.withColumn("initials", udf(F.col("fname"), F.col("lname")))

It is important to note that performing a row-wise operation in both Pandas and PySpark is expensive and not preferred if there is another way. The alternative is to use vectorized operations. 

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