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:
# 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
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
# 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.
# 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
# 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
# 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
# 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