How to use list comprehension In Python

This will act as a loop to get each row and finally we can use for loop to get particular columns, we are going to iterate the data in the given column using the collect() method through rdd.

Syntax: dataframe.rdd.collect()

Example: Here we are going to iterate rows in NAME column.

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"],
        ["5", "bobby", "company 1"]]
 
# specify column names
columns = ['ID', 'NAME', 'Company']
 
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
 
# select name column
for i in [j["NAME"] for j in dataframe.rdd.collect()]:
    print(i)

Output:

sravan
ojaswi
rohith
sridevi
bobby

How to Iterate over rows and columns in PySpark dataframe

In this article, we will discuss how to iterate rows and columns in PySpark dataframe.

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