這是我們要做專題所需要用到的資料
資料部分是用kaggle
https://www.kaggle.com/yelp-dataset/yelp-dataset?select=yelp_academic_dataset_business.json
使用其中的
yelp_academic_dataset_business.json"資料

想要把json轉成csv的格式，但也同時要拉第二層巢狀結構的資料上來
前3筆資料結構如下

[{'business_id': '6iYb2HFDywm3zjuRg0shjw',
  'name': 'Oskar Blues Taproom',
  'address': '921 Pearl St',
  'city': 'Boulder',
  'state': 'CO',
  'postal_code': '80302',
  'latitude': 40.0175444,
  'longitude': -105.2833481,
  'stars': 4.0,
  'review_count': 86,
  'is_open': 1,
  'attributes': {'RestaurantsTableService': 'True',
   'WiFi': "u'free'",
   'BikeParking': 'True',
   'BusinessParking': "{'garage': False, 'street': True, 'validated': False, 'lot': False, 'valet': False}",
   'BusinessAcceptsCreditCards': 'True',
   'RestaurantsReservations': 'False',
   'WheelchairAccessible': 'True',
   'Caters': 'True',
   'OutdoorSeating': 'True',
   'RestaurantsGoodForGroups': 'True',
   'HappyHour': 'True',
   'BusinessAcceptsBitcoin': 'False',
   'RestaurantsPriceRange2': '2',
   'Ambience': "{'touristy': False, 'hipster': False, 'romantic': False, 'divey': False, 'intimate': False, 'trendy': False, 'upscale': False, 'classy': False, 'casual': True}",
   'HasTV': 'True',
   'Alcohol': "'beer_and_wine'",
   'GoodForMeal': "{'dessert': False, 'latenight': False, 'lunch': False, 'dinner': False, 'brunch': False, 'breakfast': False}",
   'DogsAllowed': 'False',
   'RestaurantsTakeOut': 'True',
   'NoiseLevel': "u'average'",
   'RestaurantsAttire': "'casual'",
   'RestaurantsDelivery': 'None'},
  'categories': 'Gastropubs, Food, Beer Gardens, Restaurants, Bars, American (Traditional), Beer Bar, Nightlife, Breweries',
  'hours': {'Monday': '11:0-23:0',
   'Tuesday': '11:0-23:0',
   'Wednesday': '11:0-23:0',
   'Thursday': '11:0-23:0',
   'Friday': '11:0-23:0',
   'Saturday': '11:0-23:0',
   'Sunday': '11:0-23:0'}},
 {'business_id': 'tCbdrRPZA0oiIYSmHG3J0w',
  'name': 'Flying Elephants at PDX',
  'address': '7000 NE Airport Way',
  'city': 'Portland',
  'state': 'OR',
  'postal_code': '97218',
  'latitude': 45.5889058992,
  'longitude': -122.5933307507,
  'stars': 4.0,
  'review_count': 126,
  'is_open': 1,
  'attributes': {'RestaurantsTakeOut': 'True',
   'RestaurantsAttire': "u'casual'",
   'GoodForKids': 'True',
   'BikeParking': 'False',
   'OutdoorSeating': 'False',
   'Ambience': "{'romantic': False, 'intimate': False, 'touristy': False, 'hipster': False, 'divey': False, 'classy': False, 'trendy': False, 'upscale': False, 'casual': True}",
   'Caters': 'True',
   'RestaurantsReservations': 'False',
   'RestaurantsDelivery': 'False',
   'HasTV': 'False',
   'RestaurantsGoodForGroups': 'False',
   'BusinessAcceptsCreditCards': 'True',
   'NoiseLevel': "u'average'",
   'ByAppointmentOnly': 'False',
   'RestaurantsPriceRange2': '2',
   'WiFi': "u'free'",
   'BusinessParking': "{'garage': True, 'street': False, 'validated': False, 'lot': False, 'valet': False}",
   'Alcohol': "u'beer_and_wine'",
   'GoodForMeal': "{'dessert': False, 'latenight': False, 'lunch': True, 'dinner': False, 'brunch': False, 'breakfast': True}"},
  'categories': 'Salad, Soup, Sandwiches, Delis, Restaurants, Cafes, Vegetarian',
  'hours': {'Monday': '5:0-18:0',
   'Tuesday': '5:0-17:0',
   'Wednesday': '5:0-18:0',
   'Thursday': '5:0-18:0',
   'Friday': '5:0-18:0',
   'Saturday': '5:0-18:0',
   'Sunday': '5:0-18:0'}},
 {'business_id': 'bvN78flM8NLprQ1a1y5dRg',
  'name': 'The Reclaimory',
  'address': '4720 Hawthorne Ave',
  'city': 'Portland',
  'state': 'OR',
  'postal_code': '97214',
  'latitude': 45.5119069956,
  'longitude': -122.6136928797,
  'stars': 4.5,
  'review_count': 13,
  'is_open': 1,
  'attributes': {'BusinessAcceptsCreditCards': 'True',
   'RestaurantsPriceRange2': '2',
   'ByAppointmentOnly': 'False',
   'BikeParking': 'False',
   'BusinessParking': "{'garage': False, 'street': True, 'validated': False, 'lot': False, 'valet': False}"},
  'categories': 'Antiques, Fashion, Used, Vintage & Consignment, Shopping, Furniture Stores, Home & Garden',
  'hours': {'Thursday': '11:0-18:0',
   'Friday': '11:0-18:0',
   'Saturday': '11:0-18:0',
   'Sunday': '11:0-18:0'}}]

目前我的程式碼如下

import pandas as pd
import json
path = "yelp_academic_dataset_business.json"
reviewList = []
i = 0
with open(path, 'r', encoding='utf-8') as f:
    try:
        
        while i<100:
            line_data = f.readline()
            if line_data:
                data = json.loads(line_data)
                reviewList.append({
                    'business_id': data['business_id'],
                    'name': data['name'],
                    'address': data['address'],
                    'city': data['city'],
                    'state': data['state'],
                    'postal_code': data['postal_code'],
                    'latitude': data['latitude'],
                    'longitude': data['longitude'],
                    'stars': data['stars'],
                    'review_count': data['review_count'],
                    'is_open': data['is_open'],
                    'attributes': data['attributes'],
                    'RestaurantsTableService': data['attributes']['RestaurantsTableService'],
                    'WiFi': data['attributes']['WiFi'],
                    'BikeParking': data['attributes']['BikeParking'],
                    'attributes': data['attributes']['BusinessParking'],
                    'categories': data['categories'],
                    'hours': data['hours']
                })
                i+=1
            else:
                break
    except Exception as e:
        print(e)
        f.close()
i
df = pd.DataFrame(reviewList)
df.to_csv('dataset_business3.csv')

顯示結果如下
'RestaurantsTableService'
輸出的csv資料也只有一行
如下圖

其原因是第二筆資料的['attributes']底下沒有['RestaurantsTableService']這個key
所以造成中斷了資料轉成csv
如果我想讓其如果這筆資料沒有這個key那就補空值或缺失值跑完所有資料，我該怎麼做
想要呈現的結果如下

另外這是在第二層結構下每筆資料可能key不相同

我想要append這筆資料遇到沒有的key自動補缺值讓我能順利輸出每筆資料
先行感謝各位大神