populate pandas DataFrame from JSON file

Iterating through the json file is maybe not the best way to do this IMO. I'd look into pd.json_normalize if I were you:

>>> df = pd.json_normalize(json_data)[['name', 'age', 'address.street', 'address.city']] 
>>> df

         name age address.street address.city
0  Brad Green  35   Nicol St. 16     Manhatan
1  Sara Brown  30    Adam St. 66     New York

You can rename the columns as you see fit after this, e.g.

df.columns = ["name", "age", "street", "city"]

Fixing your code

I wouldn't recommend this method, but to address your specific question about iterating through the json: you could get the dataframe using something like:

df_new = pd.DataFrame(columns=['name','age','street','city'])

for kv in json_data2:
    df_new = df_new.append(
        {
            "name": kv.get("name", None),
            "age": kv.get("age", None),
            "street": kv.get("address", {}).get("street", None),
            "city": kv.get("address", {}).get("city", None),
        },
        ignore_index=True
    )

Note I'm using .get() with a default value of None so that this won't fail if you have an inconsistantly structured record in your json (e.g. missing one of the required keys)

Use collections.defaultdict to collate the data while iterating, before creating the dataframe, should be more efficient that using json.normalize:

from collections import defaultdict

df = defaultdict(list)

for entry in json_data:
     df['name'].append(entry['name'])
     df['age'].append(entry['age'])
     df['street'].append(entry['address']['street'])
     df['city'].append(entry['address']['city'])

df

defaultdict(list,
            {'name': ['Brad Green', 'Sara Brown'],
             'age': ['35', '30'],
             'street': ['Nicol St. 16', 'Adam St. 66'],
             'city': ['Manhatan', 'New York']})

Create dataframe:

pd.DataFrame(df)
 
         name age        street      city
0  Brad Green  35  Nicol St. 16  Manhatan
1  Sara Brown  30   Adam St. 66  New York

You could also use jmespath, but I think it is overkill, as the json here is not too nested; still I'll add it here, in case you ever need to traverse nested json data. Summary for using jmespath, if it is a key, access with a ., if it is an array, access with []:

import jmespath

df = jmespath.search("""{name:[].name, 
                         age: [].age, 
                         street: [].address.street, 
                         city: [].address.city}
                     """, 
                     json_data)

pd.DataFrame(df)
 
         name age        street      city
0  Brad Green  35  Nicol St. 16  Manhatan
1  Sara Brown  30   Adam St. 66  New York