How to generate Pyspark dynamic frame name dynamically

I have a table which has data as shown in the diagram . I want to create store results in dynamically generated data frame names.

For eg here in the below example I want to create two different data frame name
dnb_df and es_df and store the read result in these two frames and print structure of each data frame

When I am running the below code getting the error

> SyntaxError: can't assign to operator (TestGlue2.py, line 66)

import sys
import boto3
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from awsglue.dynamicframe import DynamicFrame
from pyspark.sql.functions import regexp_replace, col


args = getResolvedOptions(sys.argv, ['JOB_NAME'])





sc = SparkContext()
#sc.setLogLevel('DEBUG')
glueContext = GlueContext(sc)
spark = glueContext.spark_session

#logger = glueContext.get_logger()
#logger.DEBUG('Hello Glue')
job = Job(glueContext)
job.init(args["JOB_NAME"], args)



client = boto3.client('glue', region_name='XXXXXX')
response = client.get_connection(Name='XXXXXX')
connection_properties = response['Connection']['ConnectionProperties']
URL = connection_properties['JDBC_CONNECTION_URL']
url_list = URL.split("/")
host = "{}".format(url_list[-2][:-5])
new_host=host.split('@',1)[1]
port = url_list[-2][-4:]
database = "{}".format(url_list[-1])
Oracle_Username = "{}".format(connection_properties['USERNAME'])
Oracle_Password = "{}".format(connection_properties['PASSWORD'])

#print("Oracle_Username:",Oracle_Username)
#print("Oracle_Password:",Oracle_Password)
print("Host:",host)
print("New Host:",new_host)
print("Port:",port)
print("Database:",database)
Oracle_jdbc_url="jdbc:oracle:thin:@//"+new_host+":"+port+"/"+database
print("Oracle_jdbc_url:",Oracle_jdbc_url)
source_df = spark.read.format("jdbc").option("url", Oracle_jdbc_url).option("dbtable", "(select * from schema.table order by VENDOR_EXECUTION_ORDER) ").option("user", Oracle_Username).option("password", Oracle_Password).load()
vendor_data=source_df.collect()
for row  in vendor_data :
    vendor_query=row.SRC_QUERY
   row.VENDOR_NAME+'_df'= spark.read.format("jdbc").option("url", 
               Oracle_jdbc_url).option("dbtable", vendor_query).option("user", 
            Oracle_Username).option("password", Oracle_Password).load()
    print(row.VENDOR_NAME+'_df')

Added use case in picture

Update: As discussed in the comments, your requirement is to further join all with another dataframe

for row in vendor_data:
  rowAsDict=row.asDict()
  # Here you can use any variable as rowAsDict is not going to be used anywhere else anyway 
  rowAsDict[rowAsDict["VENDOR_NAME"]+"_df"] = spark.sql(rowAsDict["SOURCE_QUERY"])
  main_dataframe=main_dataframe.join(rowAsDict[rowAsDict["VENDOR_NAME"]+"_df"], "acc_id")

Input main_dataframe:

source_df :

View1 and View2:

Output main_dataframe

If I understood correctly, you need to generate the VENDOR_NAME_DF dynamically.

You won't be able to assign to the Row Object, neither it'll be useful to assign dataframe to a Row as you can't create a Dataframe with a column of type Dataframe.

Though, you can convert a row to a dict using asDict and use that instead.

This would work:

vendor_data=source_df.collect()

for row in vendor_data:
  rowAsDict=row.asDict()
  # Replace this with spark.read() or any way to create a Dataframe
  rowAsDict[rowAsDict["VENDOR_NAME"]+"_df"] = spark.sql(rowAsDict["SOURCE_QUERY"]) 
  rowAsDict[rowAsDict["VENDOR_NAME"]+"_df"].show() 

Input Source_DF:

Result of SOURCE_QUERY:

Output (of rowAsDict[rowAsDict["VENDOR_NAME"]+"_df"].show()):

Final rowAsDict:

{'VENDOR_NAME': 'Name1', 'SOURCE_QUERY': 'select * from view1', 'Name1_df': DataFrame[id: string, date: string, Code: string]}

Add the last two lines in your for loop, you should be able to get the results.
First one is creating a temp table using the dynamic df name
Second is to show the data in that temp table.

for row  in vendor_data :
    vendor_query=row.SRC_QUERY
    spark.read.format("jdbc").option("url", 
               Oracle_jdbc_url).option("dbtable", vendor_query).option("user", 
            Oracle_Username).option("password", Oracle_Password).load().createOrReplaceTempView(row.VENDOR_NAME+'_df')   
    spark.sql("select * from "+row.VENDOR_NAME+"_df").show()