In this article, we will discuss how to convert Python Dictionary List to Pyspark DataFrame.
It can be done in these ways:
- Using Infer schema.
- Using Explicit schema
- Using SQL Expression
Method 1: Infer schema from the dictionary
We will pass the dictionary directly to the createDataFrame() method.
Syntax: spark.createDataFrame(data)
Example: Python code to create pyspark dataframe from dictionary list using this method
Python3
# import the modules from pyspark.sql import SparkSession # Create Spark session app name # is GFG and master name is local spark = SparkSession.builder.appName("GFG").master("local") .getOrCreate() # dictionary list of college data data = [{"Name": 'sravan kumar', "ID": 1, "Percentage": 94.29}, {"Name": 'sravani', "ID": 2, "Percentage": 84.29}, {"Name": 'kumar', "ID": 3, "Percentage": 94.29} ] # Create data frame from dictionary list df = spark.createDataFrame(data) # display df.show() |
Output:
Method 2: Using Explicit schema
Here we are going to create a schema and pass the schema along with the data to createdataframe() method.
Schema structure:
schema = StructType([
StructField(‘column_1’, DataType(), False),
StructField(‘column_2’, DataType(), False)])
Where columns are the name of the columns of the dictionary to get in pyspark dataframe and Datatype is the data type of the particular column.
Syntax: spark.createDataFrame(data, schema)
Where,
- data is the dictionary list
- schema is the schema of the dataframe
Python program to create pyspark dataframe from dictionary lists using this method.
Python3
# import the modules from pyspark.sql import SparkSession from pyspark.sql.types import StructField, StructType, StringType, IntegerType, FloatType # Create Spark session app name is # GFG and master name is local spark = SparkSession.builder.appName("GFG").master("local") .getOrCreate() # dictionary list of college data data = [{"Name": 'sravan kumar', "ID": 1, "Percentage": 94.29}, {"Name": 'sravani', "ID": 2, "Percentage": 84.29}, {"Name": 'kumar', "ID": 3, "Percentage": 94.29} ] # specify the schema schema = StructType([ StructField('Name', StringType(), False), StructField('ID', IntegerType(), False), StructField('Percentage', FloatType(), True) ]) # Create data frame from # dictionary list through the schema df = spark.createDataFrame(data, schema) # display df.show() |
Output:
Method 3: Using SQL Expression
Here we are using the Row function to convert the python dictionary list to pyspark dataframe.
Syntax: spark.createDataFrame([Row(**iterator) for iterator in data])
where:
- createDataFrame() is the method to create the dataframe
- Row(**iterator) to iterate the dictionary list.
- data is the dictionary list
Python code to convert dictionary list to pyspark dataframe.
Python3
# import the modules from pyspark.sql import SparkSession, Row # Create Spark session app name # is GFG and master name is local spark = SparkSession.builder.appName("GFG").master("local") .getOrCreate() # dictionary list of college data data = [{"Name": 'sravan kumar', "ID": 1, "Percentage": 94.29}, {"Name": 'sravani', "ID": 2, "Percentage": 84.29}, {"Name": 'kumar', "ID": 3, "Percentage": 94.29} ] # create dataframe using sql expression dataframe = spark.createDataFrame([Row(**variable) for variable in data]) dataframe.show() |
Output:
