Introduction

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") \
    .getOrCreate()

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()

Conclusion

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!

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