Pyspark - Count Distinct Values in a Column

In this tutorial, we will look at how to get a count of the distinct values in a column of a Pyspark dataframe with the help of examples.

How to count unique values in a Pyspark dataframe column?

You can use the Pyspark count_distinct() function to get a count of the distinct values in a column of a Pyspark dataframe. Pass the column name as an argument. The following is the syntax –

count_distinct("column")

It returns the total distinct value count for the column.

Examples

Let’s look at some examples of getting the count of unique values in a Pyspark dataframe column. First, let’s create a Pyspark dataframe that we’ll be using throughout this tutorial.

#import the pyspark module
import pyspark
  
# import the  sparksession class  from pyspark.sql
from pyspark.sql import SparkSession

# create an app from SparkSession class
spark = SparkSession.builder.appName('datascience_parichay').getOrCreate()

# books data as list of lists
df = [[1, "PHP", "Sravan", 250],
        [2, "SQL", "Chandra", 300],
        [3, "Python", "Harsha", 250],
        [4, "R", "Rohith", 1200],
        [5, "Hadoop", "Manasa", 700],
        ]
  
# creating dataframe from books data
dataframe = spark.createDataFrame(df, ['Book_Id', 'Book_Name', 'Author', 'Price'])

# display the dataframe
dataframe.show()

Output:

+-------+---------+-------+-----+
|Book_Id|Book_Name| Author|Price|
+-------+---------+-------+-----+
|      1|      PHP| Sravan|  250|
|      2|      SQL|Chandra|  300|
|      3|   Python| Harsha|  250|
|      4|        R| Rohith| 1200|
|      5|   Hadoop| Manasa|  700|
+-------+---------+-------+-----+

We now have a dataframe with 5 rows and 4 columns containing information on some books.

Count distinct values in a column

Let’s count the distinct values in the “Price” column. For this, use the following steps –

Import the count_distinct() function from pyspark.sql.functions.
Use the count_distinct() function along with the Pyspark dataframe select() function to count the unique values in the given column.

# import count_distinct function 
from pyspark.sql.functions import count_distinct

# distinct value count in the Price column
dataframe.select(count_distinct("Price")).show()

Output:

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+---------------------+
|count(DISTINCT Price)|
+---------------------+
|                    4|
+---------------------+

We find that the “Price” column has 4 distinct values. If you see the dataframe above, you can see that two books have the same price of 250, and the other three books have different prices.

Count distinct values in multiple columns in Pyspark

You can also get the distinct value count for multiple columns in a Pyspark dataframe. Let’s count the unique values in the “Author” and the “Price” columns of the above dataframe.

# import count_distinct function 
from pyspark.sql.functions import count_distinct

# distinct value count in the Author and the Price columns 
dataframe.select(count_distinct("Author"), count_distinct("Price")).show()

Output:

+----------------------+---------------------+
|count(DISTINCT Author)|count(DISTINCT Price)|
+----------------------+---------------------+
|                     5|                    4|
+----------------------+---------------------+

Here, we use a count_distinct() function for each column we want to compute the distinct count of inside the select() function. You can see that the “Author” column has 5 distinct values whereas the “Price” column has 4 distinct values.

Authors

Piyush Raj

Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

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Gottumukkala Sravan Kumar

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