[Python] which is the best way to convert json into a dataframe?

I have a question about the best way to convert this JSON to a Dataframe:

Json data:

{
"myschema": {
"accounts": {
"load_type": "daily",
"fields": {
"id": "nvarchar2",
"isdeleted": "number",
"master": "nvarchar2",
"name": "nvarchar2"
}
},
"customer": {
"load_type": "daily",
"fields": {
"id": "nvarchar2",
"accountid": "nvarchar2",
"usergroupid": "nvarchar2"
}
},
"resources": {
"load_type": "daily",
"fields": {
"id": "nvarchar2",
"isdeleted": "number",
"name": "nvarchar2",
"currency": "nvarchar2"
}
},
....
....
}
}


The result must be something like this:

TABLE |LOAD_TYPE |COLUMN |COLUMN_TYPE |
+-----------+-----------+-----------------+--------------+
| accounts |daily |id |NVARCHAR2 |
| accounts |daily |master |NVARCHAR2 |
| accounts |daily |name |NVARCHAR2 |
| customer |daily |id |NVARCHAR2 |
| customer |daily |accountid |NVARCHAR2 |
| customer |daily |usergroupid |NVARCHAR2 |
| resources |daily |id |NVARCHAR2 |
| resources |daily |name |NVARCHAR2 |
| resources |daily |currency |NVARCHAR2 |
+-----------+-----------+-----------------+--------------+


I tried the next code:

df2 = spark.read.option("multiLine", "true").json(json_s3_path)
df2.printSchema()

root
|-- mySchema: struct (nullable = true)
| |-- accounts: struct (nullable = true)
| | |-- FIELDS: struct (nullable = true)
.....
.....


and also the next code:

df3 = spark.read.format("json") \
.option("multiLine", True) \
.option("header",True) \
.option("inferschema",True) \
.load(json_s3_path) \


and the result for this is:

+----------------------------------------------------------------------------------------------------------------------------------------------------+
|mySchema |
+----------------------------------------------------------------------------------------------------------------------------------------------------+
|{{{NVARCHAR2, NUMBER, NVARCHAR2, NVARCHAR2}, Delta}, {{NVARCHAR2, NVARCHAR2, NVARCHAR2}, Delta}, {{NVARCHAR2, NVARCHAR2, NUMBER, NVARCHAR2}, Delta}}|
+----------------------------------------------------------------------------------------------------------------------------------------------------+

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