PySpark - Concatenate DataFrames

Introduction

In this tutorial, we want to concatenate multiple PySpark DataFrames. In order to do this, we use the the union() method of PySpark.

Import Libraries

First, we import the following python modules:

from pyspark.sql import SparkSession

Create SparkSession

Before we can work with Pyspark, we need to create a SparkSession. A SparkSession is the entry point into all functionalities of Spark.

In order to create a basic SparkSession programmatically, we use the following command:

spark = SparkSession \
    .builder \
    .appName("Python PySpark Example") \
    .getOrCreate()

Create PySpark DataFrames

We create two PySpark DataFrames with some example data from lists. To do this, we use the method createDataFrame() and pass the data and the column names as arguments.

First, we create the PySpark DataFrame "df1":

column_names = ["language", "framework", "users"]
data = [
    ("Python", "FastAPI", 9000),
    ("JavaScript", "ReactJS", 7000),
]
df1 = spark.createDataFrame(data, column_names)
df1.show()

Next, we create the PySpark DataFrame "df2". The DataFrame has exactly the same schema like DataFrame "df1":

column_names = ["language", "framework", "users"]
data = [
    ("Python", "FastAPI", 9000),
    ("Python", "Django", 20000),
    ("Java", "Spring", 12000),
]
df2 = spark.createDataFrame(data, column_names)
df2.show()