Method 2 : Using parallelize()
We are going to use parallelize() to create an RDD. Parallelize means to copy the elements present in a pre-defined collection to a distributed dataset on which we can operate in parallel. Here is the syntax of parallelize() :
Syntax : sc.parallelize(data,numSlices)
sc : Spark Context Object
Parameters :
- data : data for which RDD is to be made.
- numSlices : number of partitions that need to be made. This is an optional parameter.
Example:
In this example, we will then use createDataFrame() to create a PySpark DataFrame and then use toPandas() to get a Pandas DataFrame.
Python
# Importing PySpark and importantly# Row from pyspark.sqlimport pysparkfrom pyspark.sql import SparkSessionfrom pyspark.sql import Row# PySpark Sessionrow_pandas_session = SparkSession.builder.appName( 'row_pandas_session').getOrCreate()# List of Sample Row objectsrow_object_list = [Row(Topic='Dynamic Programming', Difficulty=10), Row(Topic='Arrays', Difficulty=5), Row(Topic='Sorting', Difficulty=6), Row(Topic='Binary Search', Difficulty=7)]# Creating an RDDrdd = row_pandas_session.sparkContext.parallelize(row_object_list)# DataFrame created using RDDdf = row_pandas_session.createDataFrame(rdd)# Checking the DataFramedf.show()# Conversion of DataFramedf2 = df.toPandas()# Final DataFrame neededprint(df2) |
Output :
Convert PySpark Row List to Pandas DataFrame
In this article, we will convert a PySpark Row List to Pandas Data Frame. A Row object is defined as a single Row in a PySpark DataFrame. Thus, a Data Frame can be easily represented as a Python List of Row objects.