In this tutorial, we want to convert a PySpark DataFrame into a Pandas DataFrame with a specific schema. In order to do this, we use the the toPandas() 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") \

Create PySpark DataFrame

First, we define the column names and the data of the  Pyspark DataFrame:

column_names = ["language", "framework", "users"]

data = [
    ("Python", "Django", 20000),
    ("Python", "FastAPI", 9000),
    ("Java", "Spring", 7000),
    ("JavaScript", "ReactJS", 5000)

Next, we create the PySpark DataFrame from the list. To do this, we use the method createDataFrame() and pass the defined data and column names as arguments:

pyspark_df = spark.createDataFrame(data, column_names)

Convert to Pandas DataFrame

Finally, we convert the PySpark DataFrame into a Pandas DataFrame. To do this, we use the method toPandas():

pandas_df = pyspark_df.toPandas()


Congratulations! Now you are one step closer to become an AI Expert. You have seen that it is very easy to convert a PySpark DataFrame into a Pandas DataFrame. We can simply use the toPandas() method of PySpark. Try it yourself!


Also check out our Instagram page. We appreciate your like or comment. Feel free to share this post with your friends.