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())