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 :).

The output that you get after DF.to_json is a string. So, you can simply slice it according to your requirement and remove the commas from it too.

out = df.to_json(orient='records')[1:-1].replace('},{', '} {')

To write the output to a text file, you could do:

with open('file_name.txt', 'w') as f:
    f.write(out)

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'}}]