Convert PySpark dataframe to list of tuples

In this article, we are going to convert the Pyspark dataframe into a list of tuples.

The rows in the dataframe are stored in the list separated by a comma operator. So we are going to create a dataframe by using a nested list

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", 67, 89],
        ["2", "ojaswi", "vvit", 78, 89],
        ["3", "rohith", "vvit", 100, 80],
        ["4", "sridevi", "vignan", 78, 80],
        ["1", "sravan", "vignan", 89, 98],
        ["5", "gnanesh", "iit", 94, 98]]

# specify column names
columns = ['student ID', 'student NAME',
           'college', 'subject1', 'subject2']

# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)

# display
dataframe.show()

Output:

Method 1: Using collect() method

By converting each row into a tuple and by appending the rows to a list, we can get the data in the list of tuple format.

tuple(): It is used to convert data into tuple format

Syntax: tuple(rows)

Example: Converting dataframe into a list of tuples.

Python3

# define a list
l=[]

# collect data from the  dataframe
for i in dataframe.collect():
   l.append(tuple(i))
   # convert to tuple and append to list

# print list of data
print(l)

Output:

[(‘1’, ‘sravan’, ‘vignan’, 67, 89), (‘2’, ‘ojaswi’, ‘vvit’, 78, 89), 

(‘3’, ‘rohith’, ‘vvit’, 100, 80), (‘4’, ‘sridevi’, ‘vignan’, 78, 80),

 (‘1’, ‘sravan’, ‘vignan’, 89, 98), (‘5’, ‘gnanesh’, ‘iit’, 94, 98)]

Method 2: Using tuple() with rdd

Convert rdd to a tuple using map() function, we are using map() and tuple() functions to convert from rdd

Syntax: rdd.map(tuple)

Example: Using RDD

Python3

# convert dataframe to rdd
rdd = dataframe.rdd

# convert rdd to tuple
data = rdd.map(tuple)

# display data
data.collect()

Output:

[('1', 'sravan', 'vignan', 67, 89),
('2', 'ojaswi', 'vvit', 78, 89),
('3', 'rohith', 'vvit', 100, 80),
('4', 'sridevi', 'vignan', 78, 80),
('1', 'sravan', 'vignan', 89, 98),
('5', 'gnanesh', 'iit', 94, 98)]