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 moduleimport pyspark # importing sparksession from pyspark.sql modulefrom pyspark.sql import SparkSession # creating sparksession and giving an app namespark = SparkSession.builder.appName('sparkdf').getOrCreate() # list of employee datadata = [[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 namescolumns = ['ID', 'NAME', 'Company'] # creating a dataframe from the lists of datadataframe = spark.createDataFrame(data, columns) # display dataframedataframe.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 3dataframe.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 sridevidataframe.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 1dataframe.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 1dataframe.where((dataframe.ID < 3) | ( (dataframe.NAME == 'sridevi') & (dataframe.Company == 'company 1'))).show() |
Output:
