How To Convert DataFrame To Pandas in Databricks in PySpark?

This recipe helps you convert DataFrame to Pandas in Databricks in PySpark.

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Learn how to convert DataFrames to Pandas in Databricks using PySpark with this easy-to-follow recipe and elevate your data game!

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How To Convert PySpark DataFrame To Pandas in Databricks?

This section will show you how to convert a Spark dataframe to a Pandas dataframe in Databricks.

System Requirements

  • Python (3.0 version)

  • Apache Spark (3.1.1 version)

Converting DataFrame to Pandas in Databricks in PySpark

Before moving on to the code, let us quickly get an overview of the steps you need to convert a Spark dataframe to a Pandas dataframe in Databricks.

  • Install pandas in Databricks by running the command !pip install pandas in a Databricks notebook or cluster.

  • Import pandas and PySpark in your notebook using the following commands:

import pandas as pd

from pyspark.sql.functions import col

  • Create a PySpark DataFrame using any of the available methods in PySpark, such as spark.read.csv() or spark.read.parquet().

  • Use the .toPandas() method on your PySpark DataFrame to convert it to a Pandas DataFrame. For example:

pyspark_df = spark.read.csv('file_path')

pandas_df = pyspark_df.toPandas()

# Importing packages import pyspark from pyspark.sql import SparkSession

databricks to pandas

The PySpark SQL package is imported into the environment to convert PySpark Dataframe to Pandas dataframe.

# Implementing conversion of DataFrame to Pandas in Databricks in PySpark spark = SparkSession.builder.appName('Spark Dataframe to Pandas PySpark').getOrCreate() SampleData = [("Ravi","","Gupta","36636","M",70000), ("Ram","Aggarwal","","40288","M",80000), ("Shyam","","Shinde","42114","",500000), ("Sarla","Priya","Gupta","39192","F",600000), ("Monica","Garg","Brown","","F",0)] DataColumns = ["first_name","middle_name","last_name","dob","gender","salary"] PysparkDF = spark.createDataFrame(data = SampleData, schema = DataColumns) PysparkDF.printSchema() PysparkDF.show(truncate=False) # Converting dataframe to pandas PandasDF = PysparkDF.toPandas() print(PandasDF)

convert large spark dataframe to pandas

convert spark dataframe to pandas

pyspark dataframe to pandas

The Spark Session is defined with 'Spark Dataframe to Pandas PySpark' as the App name. The "SampleData" value is created in which data is input. The "DataColumns" is defined, which contains the columns of the dataframe created. The "PySparkDF" is defined to create a dataframe using the .createDataFrame() function using "SampleData" and "DataColumns" as defined. The "PandasDF" is defined, which contains the value of conversion of Dataframe to Pandas using the "toPandas()" function.

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FAQs

To convert a DataFrame from Pandas to PySpark, you can use the createDataFrame() method in PySpark's SQL context. First, create a Pandas DataFrame and then pass it to the createDataFrame() method. The resulting PySpark DataFrame will have the same schema as the Pandas DataFrame.

We can convert a pandas DataFrame to a Spark DataFrame using the createDataFrame() function in PySpark. First, we need to create an RDD from the pandas DataFrame, and we can use the createDataFrame() function to create a Spark DataFrame. We can also specify the schema of the Spark DataFrame using the StructType and StructField classes.

To convert a DataFrame to a table in Databricks, use the .createOrReplaceTempView() method in PySpark. This method creates a temporary view of the DataFrame as a table, which can be queried using SQL. Simply call this method on your DataFrame and provide a name for the table. For example: my_dataframe.createOrReplaceTempView("my_table")

 

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