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Python PySpark – DataFrame filter on multiple columns

Last Updated : 14 Sep, 2021
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In this article, we are going to filter the dataframe on multiple columns by using filter() and where() function in Pyspark in Python.

Creating Dataframe for demonestration:

Python3




# 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 employee data
data = [[1, "sravan", "company 1"],
        [2, "ojaswi", "company 1"], 
        [3, "rohith", "company 2"],
        [4, "sridevi", "company 1"],
        [1, "sravan", "company 1"],
        [4, "sridevi", "company 1"]]
  
# specify column names
columns = ['ID', 'NAME', 'Company']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# display dataframe
dataframe.show()


Output:

Method 1: Using filter() Method

filter() is used to return the dataframe based on the given condition by removing the rows in the dataframe or by extracting the particular rows or columns from the dataframe. We are going to filter the dataframe on multiple columns. It can take a condition and returns the dataframe.

Syntax:

filter(dataframe.column condition)

Example 1: Conditional operator includes boolean or logical or relational operators.

Python3




# select dataframe where ID less than 3
dataframe.filter(dataframe.ID < 3).show()


Output:

Example 2: Python program to filter data based on two columns. In this example, we created a pyspark dataframe and select dataframe where ID less than 3 or name is Sridevi

Python3




# select dataframe where ID less than
# 3 or name is sridevi
dataframe.filter((dataframe.ID < 3) | 
                 (dataframe.NAME == 'sridevi')).show()


Output:

Example 3: Multiple columns filtering

Python3




# select dataframe where ID less than
# 3 or name is sridevi and comapny 1
dataframe.filter((dataframe.ID < 3) | (
    (dataframe.NAME == 'sridevi') & 
  (dataframe.Company == 'company 1'))).show()


Output:

Method 2: where() method

Where: where is similar to filter() function that is used to return the dataframe based on the given condition by removing the rows in the dataframe or by extracting the particular rows or columns from the dataframe. It can take a condition and returns the dataframe.

where(dataframe.column condition)

Example 1: Python program to filter on multiple columns

Python3




# select dataframe where ID less than
# 3 or name is sridevi and comapny 1
dataframe.where((dataframe.ID < 3) | (
    (dataframe.NAME == 'sridevi') &
  (dataframe.Company == 'company 1'))).show()


Output:



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