How to use collect() method In Python
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)]
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:
Contact Us