Count rows based on condition in Pyspark Dataframe

Last Updated : 29 Jun, 2021

In this article, we will discuss how to count rows based on conditions in Pyspark dataframe.

For this, we are going to use these methods:

Using where() function.
Using filter() function.

Creating Dataframe for demonstration:

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 students  data  
data =[["1","sravan","vignan"], 
       ["2","ojaswi","vvit"], 
       ["3","rohith","vvit"], 
       ["4","sridevi","vignan"], 
       ["1","sravan","vignan"],  
       ["5","gnanesh","iit"]] 
  
# specify column names 
columns = ['ID','NAME','college'] 
  
# creating a dataframe from the lists of data 
dataframe = spark.createDataFrame(data,columns) 
  
print('Actual data in dataframe') 
dataframe.show() 

Output:

Note: If we want to get all row count we can use count() function

Syntax: dataframe.count()

Where, dataframe is the pyspark input dataframe

Example: Python program to get all row count

Python3

print('Total rows in dataframe') 
dataframe.count()

Output:

Total rows in dataframe
6

Method 1: using where()

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

Syntax: dataframe.where(condition)

Where the condition is the dataframe condition

Example 1: Condition to get rows in dataframe where ID =1

Python3

# condition to get rows in dataframe  
# where ID =1 
print('Total rows in dataframe where\ 
ID = 1 with where clause') 
print(dataframe.where(dataframe.ID == '1').count()) 
  
print('They are  ') 
dataframe.where(dataframe.ID == '1').show() 

Output:

Example 2: Condition to get rows in dataframe with multiple conditions.

Python3

# condition to get rows in dataframe 
# where ID not equal to 1 
print('Total rows in dataframe where\ 
ID except 1 with where clause') 
  
print(dataframe.where(dataframe.ID != '1').count()) 
  
# condition to get rows in dataframe 
# where college is equal to vignan 
print('Total rows in dataframe where\ 
college is vignan with where clause') 
print(dataframe.where(dataframe.college == 'vignan').count()) 
  
  
# condition to get rows in dataframe 
# where id greater than 2 
print('Total rows in dataframe where ID greater\ 
than 2 with where clause') 
print(dataframe.where(dataframe.ID > 2).count()) 

Output:

Total rows in dataframe where ID except 1 with where clause

4

Total rows in dataframe where college is vignan with where clause

3

Total rows in dataframe where ID greater than 2 with where clause

3

Example 3: Python program for multiple conditions

Python3

# condition to get rows in dataframe 
# where ID not equal to 1 and name is sridevi 
print('Total rows in dataframe where ID \ 
not equal to 1 and name is sridevi') 
print(dataframe.where((dataframe.ID != '1') & 
                      (dataframe.NAME == 'sridevi') 
                     ).count()) 
  
# condition to get rows in dataframe 
# where college is equal to vignan or iit 
print('Total rows in dataframe where college is\ 
vignan or iit with where clause') 
print(dataframe.where((dataframe.college == 'vignan') | 
                      (dataframe.college == 'iit')).count()) 

Output:

Total rows in dataframe where ID not equal to 1 and name is sridevi

1

Total rows in dataframe where college is vignan or iit with where clause

4

Method 2: Using filter()

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

Syntax: dataframe.filter(condition)

Example 1: Python program to get rows where id = 1

Python3

# condition to get rows in 
# dataframe where ID =1 
print('Total rows in dataframe where\ 
ID = 1 with filter clause') 
print(dataframe.filter(dataframe.ID == '1').count()) 
  
print('They are  ') 
dataframe.filter(dataframe.ID == '1').show() 

Output:

Example 2: Python program for multiple conditions

Python3

# condition to get rows in dataframe 
# where ID not equal to 1 and name is sridevi 
print('Total rows in dataframe where ID not\ 
equal to 1 and name is sridevi') 
print(dataframe.filter((dataframe.ID != '1') & 
                       (dataframe.NAME == 'sridevi')).count()) 
  
# condition to get rows in dataframe 
# where college is equal to vignan or iit 
print('Total rows in dataframe where college\ 
is vignan or iit with filter clause') 
print(dataframe.filter((dataframe.college == 'vignan') | 
                       (dataframe.college == 'iit')).count()) 

Output:

Total rows in dataframe where ID not equal to 1 and name is sridevi

1

Total rows in dataframe where college is vignan or iit with filter clause

4

Delete rows in PySpark dataframe based on multiple conditions

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Count rows based on condition in Pyspark Dataframe

Python3

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Method 1: using where()

Python3

Python3

Python3

Method 2: Using filter()

Python3

Python3

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