Create a dataframe in Pyspark using random values from a list

November 9, 2021

I need to convert this code into PySpark equivalent. I can not use pandas to create the dataframe.

This is how I create the dataframe using Pandas:

df['Name'] = np.random.choice(["Alex","James","Michael","Peter","Harry"], size=3)
df['ID'] = np.random.randint(1, 10, 3)
df['Fruit'] = np.random.choice(["Apple","Grapes","Orange","Pear","Kiwi"], size=3)

The dataframe should look like this in PySpark:

Name   ID  Fruit
Alex   3   Apple
James  6   Grapes
Harry  5   Pear

I have tried the following for 1 column:

sdf1 = spark.createDataFrame([(k,) for k in ['Alex','James', 'Harry']]).orderBy(rand()).limit(6).show()

>Solution :

You can first create pandas dataframe then convert it into Pyspark dataframe. Or you can zip the 3 random numpy arrays and create spark dataframe like this:

import numpy as np

names = [str(x) for x in np.random.choice(["Alex", "James", "Michael", "Peter", "Harry"], size=3)]
ids = [int(x) for x in np.random.randint(1, 10, 3)]
fruits = [str(x) for x in np.random.choice(["Apple", "Grapes", "Orange", "Pear", "Kiwi"], size=3)]

df = spark.createDataFrame(list(zip(names, ids, fruits)), ["Name", "ID", "Fruit"])

df.show()

#+-------+---+------+
#|   Name| ID| Fruit|
#+-------+---+------+
#|  Peter|  8|  Pear|
#|Michael|  7|  Kiwi|
#|  Harry|  4|Orange|
#+-------+---+------+