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.
Method 1 : Use createDataFrame() method and use toPandas() method
Here is the syntax of the createDataFrame() method :
Syntax : current_session.createDataFrame(data, schema=None, samplingRatio=None, verifySchema=True)
Parameters :
- data : a resilient distributed dataset or data in form of MySQL/SQL datatypes
- schema : string or list of columns names for the DataFrame.
- samplingRatio -> float: a sample ratio of the rows
- verifySchema -> bool: check if the datatypes of the rows is as specified in the schema
Returns : PySpark DataFrame object.
Example:
In this example, we will pass the Row list as data and create a PySpark DataFrame. We will then use the toPandas() method 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 PySpark DataFrame using createDataFrame()df = row_pandas_session.createDataFrame(row_object_list)# Printing the Spark DataFramedf.show()# Conversion to Pandas DataFramepandas_df = df.toPandas()# Final Resultprint(pandas_df) |
Output :
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 :
Method 3: Iteration through Row list
In this method, we will traverse through the Row list, and convert each row object to a DataFrame using createDataFrame(). We will then append() this DataFrame to an accumulative final DataFrame which will be our final answer. The details of append() are given below :
Syntax: df.append(other, ignore_index=False, verify_integrity=False, sort=None)
df : Pandas DataFrame
Parameters :
- other : Pandas DataFrame, Numpy Array, Numpy Series etc.
- ignore_index : Checks if index labels are to be used or not.
- verify_integrity : If True, raise ValueError on creating index with duplicates.
- sort : Sort columns if the columns of df and other are unaligned.
Returns: A new appended DataFrame
Example:
In this example, we will then use createDataFrame() to create a PySpark DataFrame and then use append() to get a Pandas DataFrame.
Python
# Importing PySpark# Importing Pandas for append()import pysparkimport pandasfrom 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)]# Our final DataFrame initializedmega_df = pandas.DataFrame()# Traversing through the listfor i in range(len(row_object_list)): # Creating a Spark DataFrame of a single row small_df = row_pandas_session.createDataFrame([row_object_list[i]]) # appending the Pandas version of small_df # to mega_df mega_df = mega_df.append(small_df.toPandas(), ignore_index=True)# Printing our desired DataFrameprint(mega_df) |
Output :
