Creating data frames columns from a list of dictionaries

I want to create 2 data frames out of the below list:-

results = [
         {'type': 'check_datatype',
          'kwargs': {'table': 'cars', 'columns': ['car_id','index'], 'd_type': 'str'},
          'datasource_path': '/cars_dataset_ok/',
          'Result': False},
        {'type': 'check_string_consistency',
          'kwargs': {'table': 'cars', 'columns': ['car_id'], 'string_length': 6},
          'datasource_path': '/cars_dataset_ok/',
          'Result': False}
        ]
    
    
    
    
1st dataframe should give type,id(it will be incremental id and unique for each type)

2nd dataframe should give argument details of each type e.g. for 'check_datatype',

1st data frame should have output like this -

type | id

check_datatype,1

check_string_consistency,2
    
2nd data frame should have output like this -

id|key|value|index

1,table,cars,1

1,columns,car_id,1

1,columns,index,2

1,dtype,str,1

2,table,cars,1

2,columns,car_id,1

2,string_length,6,1

What I have tried:

<pre>Somehow, i am able to create the first dataframe using below approach but not able to create second dataframe -

from pyspark.sql import functions as F
from pyspark.sql import Window
results = [[elt['type']] for elt in results]
checkColumns = ['type']
checkDF = spark.createDataFrame(data=results, schema=checkColumns)   
checkDF=checkDF.withColumn("id",F.row_number().over(Window.orderBy(F.monotonically_increasing_id())))
checkDF.printSchema()
checkDF.show(truncate=False)