PySpark - Count Distinct Values of a DataFrame Column

Introduction

In this tutorial, we want to count the distinct values of a PySpark DataFrame column. In order to do this, we use the distinct().count() method and the  countDistinct() function of PySpark.

Import Libraries

First, we import the following python modules:

from pyspark.sql import SparkSession
from pyspark.sql.functions import countDistinct

Create SparkSession

Before we can work with Pyspark, we need to create a SparkSession. A SparkSession is the entry point into all functionalities of Spark.

In order to create a basic SparkSession programmatically, we use the following command:

spark = SparkSession \
    .builder \
    .appName("Python PySpark Example") \
    .getOrCreate()

Create PySpark DataFrame

Next, we create the PySpark DataFrame with some example data from a list. To do this, we use the createDataFrame() method and pass the data and the column names as arguments.

column_names = ["language", "framework", "users"]
data = [
    ("Python", "Django", 20000),
    ("Python", "FastAPI", 9000),
    ("Java", "Spring", 7000),
    ("JavaScript", "ReactJS", 5000)
]
df = spark.createDataFrame(data, column_names)
df.show()

Count Distinct Values

Let's count the distinct values of the PySpark DataFrame column "language". We will explore two different possibilities to count the distinct values.

Option 1: distinct().count()

The first option we will explore is using the distinct() method combined with count().