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Filtering a PySpark DataFrame using isin by exclusion

  • Last Updated : 29 Jun, 2021

In this article, we will discuss how to filter the pyspark dataframe using isin by exclusion.

isin(): This is used to find the elements contains in a given dataframe, it takes the elements and gets the elements to match the data.

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Syntax: isin([element1,element2,.,element n)

Creating Dataframe for demonstration:


# importing module
import pyspark
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
# list  of students  data  with null values
# we can define null values with none
data = [[1, "sravan", "vignan"],
        [2, "ramya", "vvit"],
        [3, "rohith", "klu"],
        [4, "sridevi", "vignan"],
        [5, "gnanesh", "iit"]]
# specify column names
columns = ['ID', 'NAME', 'college']
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)


Method 1: Using filter()

filter(): This clause is used to check the condition and give the results, Both are similar

Syntax: dataframe.filter(condition)

Example 1: Get the particular ID’s with filter() clause


# get the ID : 1,2,3 from dataframe


Example 2: Get names from dataframe columns.


# get name as sravan


Method 2: Using Where()

where(): This clause is used to check the condition and give the results

Syntax: dataframe.where(condition)

Example 1: Get the particular colleges with where() clause.


# get college as vignan


Example 2: Get ID except 5 from dataframe.


# get ID except 1


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