flatten nested json data using pandas' json_normalize

normalize_json.py

column_names = ["restaurant.id", "restaurant.name", "restaurant.cuisines", "restaurant.location.city",
                "restaurant.location.address", "restaurant.location.latitude",
                "restaurant.location.longitude", "restaurant.user_rating.rating_text"]
new_col_names = ["id", "name", "cuisines", "city", "address", "latitude", "longitude", "rating_text"]         


if 'restaurants' in el and len(el['restaurants']):
    df = pd.concat([pd.DataFrame(json_normalize(restaurant)) for restaurant in el['restaurants']])
    restaurants_df = df[[col for col in column_names]]
    restaurants_df.rename(columns=dict(zip(column_names, new_col_names)), inplace=True)