Use DataFrame.groupby with DataFrame.apply and DataFrame.to_dict all columns with no Col1 filtered by Index.difference, create DataFrame by DataFrame.reset_index and last use DataFrame.to_dict for dictionary output or DataFrame.to_json for json output:

cols = df.columns.difference(['Col1'])
d = (df.groupby('Col1')[cols]
        .apply(lambda x: x.to_dict('r'))
        .reset_index(name='Other_details')
        .to_dict(orient='records'))

cols = df.columns.difference(['Col1'])
d = (df.groupby('Col1')[cols]
        .apply(lambda x: x.to_dict('r'))
        .reset_index(name='Other_details')
        .to_json(orient='records'))

Use DataFrame.to_json with parameters orient='records' and lines=True:

df.to_json(file, orient='records', lines=True)

Use Series.to_json and if necessary change key value add rename:

print (k.set_index('A').rename(columns={'B':'index1'}).to_json())
{"index1":{"1":"a","2":"b","3":"c","4":"d"}}

If need export to file:

k.set_index('A').rename(columns={'B':'index1'}).to_json('file.json')

Pandas is equipped for this out of the box.

pandas.DataFrame.to_json

here is the example dataframe:

import json
df = pd.DataFrame(
    [["a", "b"], ["c", "d"]],
    index=["row 1", "row 2"],
    columns=["col 1", "col 2"],
)

Here is the result using to_json():

result = df.to_json(orient="split")
parsed = json.loads(result)
json.dumps(parsed, indent=4)  
{
    "columns": [
        "col 1",
        "col 2"
    ],
    "index": [
        "row 1",
        "row 2"
    ],
    "data": [
        [
            "a",
            "b"
        ],
        [
            "c",
            "d"
        ]
    ]
}

here is the link: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_json.html

Simple solution

a={'data': 
  [
    {'Region': 'West', 'Airport': 'LAX', 'Score': 3, 'index': 0},
    {'Region': 'West', 'Airport': 'SFO', 'Score': 6, 'index': 1},
    {'Region': 'East', 'Airport': 'YYZ', 'Score': 9, 'index': 2}
  ]
}
pd.DataFrame(a['data'])

Also you can read JSON data directly

pd.read_json(your_json,orient='split')

Use json.loads or ast.literal_eval for convert strings to list of dicts:

import ast, json

df = pd.DataFrame(rows) 
df['Sales_Plan_Details'] = df['Sales_Plan_Details'].apply(json.loads)
#alternative solution
#df['Sales_Plan_Details'] = df['Sales_Plan_Details'].apply(ast.literal_eval)

j = df.to_json(orient='records')
print (j)
[{"Sales_Plan_Details":[{"Month":"2019-1","Quantity":10,"Product_Gid":3}],
  "customer_name":"ABI2","employee_name":"ASU2","location_name":"Cherai2"},
{"Sales_Plan_Details":[{"Month":"2019-1","Quantity":10,"Product_Gid":3}],
 "customer_name":"ABI","employee_name":"ASU","location_name":"Cherai"}]

Setup:

rows= [{
                    "customer_name": "ABI2",
                    "location_name": "Cherai2",
                    "employee_name": "ASU2",
                    "Sales_Plan_Details": "[{\"Month\": \"2019-1\", \"Quantity\": 10, \"Product_Gid\": 3}]"

    },
{
                "customer_name": "ABI",
                "location_name": "Cherai",
                "employee_name": "ASU",
                "Sales_Plan_Details": "[{\"Month\": \"2019-1\", \"Quantity\": 10, \"Product_Gid\": 3}]"

}]

You can first convert the dataframe to a dictionary of records, then transform each record to your desired format. Finally, convert the list of records to JSON.

import json

records = df.to_dict('records')
for record in records:
    record['WeatherGood'] = {
        k: record.pop(k) for k in ('Good_Laden_Miles_Min', 'Good_Ballast_Miles_Min')
    }
    record['WeatherSevere'] = {
        k: record.pop(k) for k in ('Severe_Laden_Miles_Min', 'Severe_Ballast_Miles_Min')
    }
>>> json.dumps(records)
'[{"Vessel_id": 1, "Org_id": 5, "Vessel_name": "ABC", "WeatherGood": {"Good_Laden_Miles_Min": 10, "Good_Ballast_Miles_Min": 15}, "WeatherSevere": {"Severe_Laden_Miles_Min": 25, "Severe_Ballast_Miles_Min": 35}}]'

I think what you need is to use reset_index to make a DataFrame, then use orient="records":

x = pandas.Series([1, 2, 3], index=["A", "B", "C"])
x.index.name = "stuff"
x.name = "cruddo"

>>> print(x)
stuff
A    1
B    2
C    3
Name: cruddo, dtype: int64
>>> x.reset_index().to_json(orient='records')
[{"stuff":"A","cruddo":1},{"stuff":"B","cruddo":2},{"stuff":"C","cruddo":3}]'