json_str_list = [] 
for i in df.index:
    json_str = df.loc[i].to_json()
    json_str_list.append(json_str)

The above code will solve your question. If you want it to be a JSON object you can import json and just do,

json_obj = json.loads(json_str)

Although what I am writing is not the answer to the question asked, still I am providing a solution to a small problem I was facing which I googled and reached here.

Problem: how to create a dictionary from a panda data frame with a column as the key and constant value (1 in my case) as the, you guessed it, value.

Solution:

f = pd.Series(data = [1]*df.shape[0],index=df['col_name'])
x = f.to_json(orient='columns')

Output:

{"one":1, "two":1, "three": 1}

Why would I do that? Because search in the dictionary is highly optimized (Yeah I can use set as well)

P.S. Novice in Python so please be gentle with me :).

As per the function provided here @Parsa T. You can just change the column names and use the function to get the required result.

def set_for_keys(my_dict, key_arr, val):
    """
    Set value at the path in my_dict defined by the string (or serializable object) array key_arr
    """
    current = my_dict
    for i in range(len(key_arr)):
        key = key_arr[i]
        if key not in current:
            current[key] = val if i==len(key_arr)-1 else {}
        else:
            if type(current[key]) is not dict:
                print("Given dictionary is not compatible with key structure requested")
                raise ValueError("Dictionary key already occupied")

        current = current[key]

    return my_dict

def to_formatted_json(df, sep="."):
    result = []
    for _, row in df.iterrows():
        parsed_row = {}
        for idx, val in row.iteritems():
            keys = idx.split(sep)
            parsed_row = set_for_keys(parsed_row, keys, val)

        result.append(parsed_row)
    return result


df.columns = ['ID', 'PERSONAL.NAME', 'PERSONAL.LAST', 'GEO.ADDRESS', 'GEO.COUNTY']
#Where df was parsed from json-dict using json_normalize
print(to_formatted_json(df, sep="."))

OUTPUT:

[{'ID': '0',
  'PERSONAL': {'NAME': 'jimmy', 'LAST': 'neutron'},
  'GEO': {'ADDRESS': '101 ocean avenue', 'COUNTY': 'yellow card park'}},
 {'ID': '1',
  'PERSONAL': {'NAME': 'james', 'LAST': 'baxter'},
  'GEO': {'ADDRESS': '202 bubble gum county', 'COUNTY': 'candy kingdom'}},
 {'ID': '2',
  'PERSONAL': {'NAME': 'joben', 'LAST': 'segel'},
  'GEO': {'ADDRESS': '303 china town', 'COUNTY': 'universal studio'}}]

You can also use the built-in json_normalize in pandas.

pd.io.json.json_normalize(json1, 'data')

  Airport Region  Score  index
0     LAX   West      3      0
1     SFO   West      6      1
2     YYZ   East      9      2 

You can use list comprehensions to map the Sales_Plan_Details values.

You can use json.loads() to deserialize the list value from the string.

import json

dataframe_json = [
    {
                    "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}]"

    }]

# get the "Sales_Plan_Details" key value's from the list
sales_plan_details_nested_list = [sales_plan_details_dict for sales_plan_details_dict in json.loads(item("Sales_Plan_Details")) for item in dataframe_json]

# flatten the list
sales_plan_details_list = [item for sublist in sales_plan_details_nested_list for item in sublist]

# pretty print the list now
print(json.dumps(sales_plan_details_list, indent=True))

You need to do

df.to_json('file.json', orient='records')

Note that this will give you a array of objects:

[
        {
            "col1": 1,
            "col1": 4,
            "col1": 7
        },
        {
            "col2": 2,
            "col2": 5,
            "col2": 8
        },
        {
            "col3": 3,
            "col3": 6,
            "col3": 9
        }
]

You can also do

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

if you want output like:

{"col1":1,"col1":4,"col1":7},
{"col2":2,"col2":5,"col2":8},
{"col3":3,"col3":6,"col3":9}

To prettify output:

pip install jq
cat file.json | jq '.' > new_file.json