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追記
test
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```python
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import requests
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from bs4 import BeautifulSoup
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import re
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import pandas as pd
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import time
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import csv
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class Tabelog:
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def __init__(self, base_url, test_mode=False, p_ward='東京都内', begin_page=1, end_page=50):
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#変数宣言
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self.store_id = ''
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self.store_id_num = 0
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self.store_name = ''
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self.score = 0
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self.address_name = ""
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self.columns = ['store_id', 'store_name', 'address_name','score']
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self.df = pd.DataFrame(columns=self.columns)
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self.__regexcomp = re.compile(r'\n|\s') # \nは改行、\sは空白
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page_num = begin_page # 店舗一覧ページ番号
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if test_mode:
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list_url = base_url + str(page_num) + '/?Srt=D&SrtT=rt&sort_mode=1' #食べログの点数ランキングでソートする際に必要な処理
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self.scrape_list(list_url, mode=test_mode)
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else:
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while True:
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list_url = base_url + str(page_num) + '/?Srt=D&SrtT=rt&sort_mode=1' #食べログの点数ランキングでソートする際に必要な処理
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if self.scrape_list(list_url, mode=test_mode) != True:
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break
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#INパラメータまでのページ数データを取得する
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if page_num >= end_page:
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break
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page_num += 1
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# dfの確認
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print(self.df)
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def scrape_list(self, list_url, mode):
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r = requests.get(list_url)
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if r.status_code != requests.codes.ok:
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return False
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soup = BeautifulSoup(r.content, 'html.parser')
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soup_a_list = soup.find_all('a', class_='list-rst__rst-name-target') # 店名一覧
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if len(soup_a_list) == 0:
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return False
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if mode:
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for soup_a in soup_a_list[:2]:
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item_url = soup_a.get('href') # 店の個別ページURLを取得
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self.store_id_num += 1
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self.scrape_item(item_url, mode)
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else:
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for soup_a in soup_a_list:
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item_url = soup_a.get('href') # 店の個別ページURLを取得
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self.store_id_num += 1
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self.scrape_item(item_url, mode)
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return True
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def scrape_item(self, item_url, mode):
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start = time.time()
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r = requests.get(item_url)
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if r.status_code != requests.codes.ok:
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#print(f'error:not found{ item_url }')
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return
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soup = BeautifulSoup(r.content, 'html.parser')
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store_name_tag = soup.find('h2', class_='display-name')
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store_name = store_name_tag.span.string
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#print('{}→店名：{}'.format(self.store_id_num, store_name.strip()), end='')
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self.store_name = store_name.strip()
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# ラーメン屋、つけ麺屋以外の店舗は除外
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store_head = soup.find('div', class_='rdheader-subinfo') # 店舗情報のヘッダー枠データ取得
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store_head_list = store_head.find_all('dl')
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store_head_list = store_head_list[1].find_all('span')
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#print('ターゲット：', store_head_list[0].text)
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if store_head_list[0].text not in {'ラーメン', 'つけ麺'}:
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#print('ラーメンorつけ麺のお店ではないので処理対象外')
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self.store_id_num -= 1
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return
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try:
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address_name = soup.find("p", class_="rstinfo-table__address").text
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#print(" 住所：{}".format(address_name), end="")
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self.address_name = address_name
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except AttributeError:
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href = ''
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rating_score_tag = soup.find('b', class_='c-rating__val')
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rating_score = rating_score_tag.span.string
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#print(' 評価点数：{}点'.format(rating_score), end='')
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self.score = rating_score
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#評価点数が存在しない店舗は除外
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if rating_score == '-':
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#print(' 評価がないため処理対象外')
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self.store_id_num -= 1
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return
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#評価が3.5未満店舗は除外
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if float(rating_score) < 3.5:
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#print(' 食べログ評価が3.5未満のため処理対象外')
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self.store_id_num -= 1
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return
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#データフレームの生成
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self.make_df()
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return
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def make_df(self):
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self.store_id = str(self.store_id_num).zfill(8) #0パディング
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se = pd.Series([self.store_id, self.store_name, self.address_name, self.score], self.columns) # 行を作成
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self.df = self.df.append(se, self.columns) # データフレームに行を追加
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print(self.address_name)
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print(self.score)
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print(self.store_name)
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print(self.store_id)
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print('df appended!')
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print('='*50)
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time.sleep(0.4)
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return
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if __name__ == '__main__':
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tokyo_ramen_address = Tabelog(base_url="https://tabelog.com/tokyo/rstLst/ramen/",test_mode=False)
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tokyo_ramen_address.df.to_csv("tokyo_ramen_address.csv", encoding='utf_8_sig')
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```
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### 追記
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作成者が違う様で、質問者様に言っても仕方がない事では有ると思いますが
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確認用のprintはscrape_item関数内で行うよりも
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make_df関数内で確認を行った方が[抽出出来ているか]と[正しい値であるか]を
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@@ -28,280 +312,14 @@
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import re
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import pandas as pd
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import time
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import csv
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class Tabelog:
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def __init__(self, base_url, test_mode=False, p_ward='東京都内', begin_page=1, end_page=50):
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#変数宣言
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self.store_id = ''
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self.store_id_num = 0
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self.store_name = ''
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self.score = 0
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self.address_name = ""
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self.columns = ['store_id', 'store_name', 'address_name','score']
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self.df = pd.DataFrame(columns=self.columns)
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self.__regexcomp = re.compile(r'\n|\s') # \nは改行、\sは空白
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75
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page_num = begin_page # 店舗一覧ページ番号
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if test_mode:
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list_url = base_url + str(page_num) + '/?Srt=D&SrtT=rt&sort_mode=1' #食べログの点数ランキングでソートする際に必要な処理
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self.scrape_list(list_url, mode=test_mode)
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else:
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while True:
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list_url = base_url + str(page_num) + '/?Srt=D&SrtT=rt&sort_mode=1' #食べログの点数ランキングでソートする際に必要な処理
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if self.scrape_list(list_url, mode=test_mode) != True:
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break
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#INパラメータまでのページ数データを取得する
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if page_num >= end_page:
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break
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page_num += 1
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# dfの確認
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print(self.df)
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def scrape_list(self, list_url, mode):
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r = requests.get(list_url)
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if r.status_code != requests.codes.ok:
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return False
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122
|
-
|
|
123
|
-
|
|
124
|
-
|
|
125
|
-
soup = BeautifulSoup(r.content, 'html.parser')
|
|
126
|
-
|
|
127
|
-
soup_a_list = soup.find_all('a', class_='list-rst__rst-name-target') # 店名一覧
|
|
128
|
-
|
|
129
|
-
|
|
130
|
-
|
|
131
|
-
if len(soup_a_list) == 0:
|
|
132
|
-
|
|
133
|
-
return False
|
|
134
|
-
|
|
135
|
-
|
|
136
|
-
|
|
137
|
-
if mode:
|
|
138
|
-
|
|
139
|
-
for soup_a in soup_a_list[:2]:
|
|
140
|
-
|
|
141
|
-
item_url = soup_a.get('href') # 店の個別ページURLを取得
|
|
142
|
-
|
|
143
|
-
self.store_id_num += 1
|
|
144
|
-
|
|
145
|
-
self.scrape_item(item_url, mode)
|
|
146
|
-
|
|
147
|
-
else:
|
|
148
|
-
|
|
149
|
-
for soup_a in soup_a_list:
|
|
150
|
-
|
|
151
|
-
item_url = soup_a.get('href') # 店の個別ページURLを取得
|
|
152
|
-
|
|
153
|
-
self.store_id_num += 1
|
|
154
|
-
|
|
155
|
-
self.scrape_item(item_url, mode)
|
|
156
|
-
|
|
157
|
-
|
|
158
|
-
|
|
159
|
-
return True
|
|
160
|
-
|
|
161
|
-
|
|
162
|
-
|
|
163
|
-
def scrape_item(self, item_url, mode):
|
|
164
|
-
|
|
165
|
-
start = time.time()
|
|
166
|
-
|
|
167
|
-
|
|
168
|
-
|
|
169
|
-
r = requests.get(item_url)
|
|
170
|
-
|
|
171
|
-
if r.status_code != requests.codes.ok:
|
|
172
|
-
|
|
173
|
-
#print(f'error:not found{ item_url }')
|
|
174
|
-
|
|
175
|
-
return
|
|
176
|
-
|
|
177
|
-
|
|
178
|
-
|
|
179
|
-
soup = BeautifulSoup(r.content, 'html.parser')
|
|
180
|
-
|
|
181
|
-
|
|
182
|
-
|
|
183
|
-
store_name_tag = soup.find('h2', class_='display-name')
|
|
184
|
-
|
|
185
|
-
store_name = store_name_tag.span.string
|
|
186
|
-
|
|
187
|
-
#print('{}→店名：{}'.format(self.store_id_num, store_name.strip()), end='')
|
|
188
|
-
|
|
189
|
-
self.store_name = store_name.strip()
|
|
190
|
-
|
|
191
|
-
|
|
192
|
-
|
|
193
|
-
# ラーメン屋、つけ麺屋以外の店舗は除外
|
|
194
|
-
|
|
195
|
-
store_head = soup.find('div', class_='rdheader-subinfo') # 店舗情報のヘッダー枠データ取得
|
|
196
|
-
|
|
197
|
-
store_head_list = store_head.find_all('dl')
|
|
198
|
-
|
|
199
|
-
store_head_list = store_head_list[1].find_all('span')
|
|
200
|
-
|
|
201
|
-
#print('ターゲット：', store_head_list[0].text)
|
|
202
|
-
|
|
203
|
-
|
|
204
|
-
|
|
205
|
-
if store_head_list[0].text not in {'ラーメン', 'つけ麺'}:
|
|
206
|
-
|
|
207
|
-
#print('ラーメンorつけ麺のお店ではないので処理対象外')
|
|
208
|
-
|
|
209
|
-
self.store_id_num -= 1
|
|
210
|
-
|
|
211
|
-
return
|
|
212
|
-
|
|
213
|
-
|
|
214
|
-
|
|
215
|
-
try:
|
|
216
|
-
|
|
217
|
-
address_name = soup.find("p", class_="rstinfo-table__address").text
|
|
218
|
-
|
|
219
|
-
#print(" 住所：{}".format(address_name), end="")
|
|
220
|
-
|
|
221
|
-
self.address_name = address_name
|
|
222
|
-
|
|
223
|
-
|
|
224
|
-
|
|
225
|
-
except AttributeError:
|
|
226
|
-
|
|
227
|
-
href = ''
|
|
228
|
-
|
|
229
|
-
|
|
230
|
-
|
|
231
|
-
rating_score_tag = soup.find('b', class_='c-rating__val')
|
|
232
|
-
|
|
233
|
-
rating_score = rating_score_tag.span.string
|
|
234
|
-
|
|
235
|
-
#print(' 評価点数：{}点'.format(rating_score), end='')
|
|
236
|
-
|
|
237
|
-
self.score = rating_score
|
|
238
|
-
|
|
239
|
-
|
|
240
|
-
|
|
241
|
-
#評価点数が存在しない店舗は除外
|
|
242
|
-
|
|
243
|
-
if rating_score == '-':
|
|
244
|
-
|
|
245
|
-
#print(' 評価がないため処理対象外')
|
|
246
|
-
|
|
247
|
-
self.store_id_num -= 1
|
|
248
|
-
|
|
249
|
-
return
|
|
250
|
-
|
|
251
|
-
|
|
252
|
-
|
|
253
|
-
#評価が3.5未満店舗は除外
|
|
254
|
-
|
|
255
|
-
if float(rating_score) < 3.5:
|
|
256
|
-
|
|
257
|
-
#print(' 食べログ評価が3.5未満のため処理対象外')
|
|
258
|
-
|
|
259
|
-
self.store_id_num -= 1
|
|
260
|
-
|
|
261
|
-
return
|
|
262
|
-
|
|
263
|
-
|
|
264
|
-
|
|
265
|
-
|
|
266
|
-
|
|
267
|
-
#データフレームの生成
|
|
268
|
-
|
|
269
|
-
self.make_df()
|
|
270
|
-
|
|
271
|
-
return
|
|
272
|
-
|
|
273
|
-
|
|
274
|
-
|
|
275
|
-
def make_df(self):
|
|
276
|
-
|
|
277
|
-
self.store_id = str(self.store_id_num).zfill(8) #0パディング
|
|
278
|
-
|
|
279
|
-
se = pd.Series([self.store_id, self.store_name, self.address_name, self.score], self.columns) # 行を作成
|
|
280
|
-
|
|
281
|
-
self.df = self.df.append(se, self.columns) # データフレームに行を追加
|
|
282
|
-
|
|
283
|
-
print(self.address_name)
|
|
284
|
-
|
|
285
|
-
print(self.score)
|
|
286
|
-
|
|
287
|
-
print(self.store_name)
|
|
288
|
-
|
|
289
|
-
print(self.store_id)
|
|
290
|
-
|
|
291
|
-
print('df appended!')
|
|
292
|
-
|
|
293
|
-
print('='*50)
|
|
294
|
-
|
|
295
|
-
time.sleep(0.4)
|
|
296
|
-
|
|
297
|
-
return
|
|
298
|
-
|
|
299
|
-
|
|
300
|
-
|
|
301
|
-
if __name__ == '__main__':
|
|
302
|
-
|
|
303
|
-
tokyo_ramen_address = Tabelog(base_url="https://tabelog.com/tokyo/rstLst/ramen/",test_mode=False)
|
|
304
|
-
|
|
305
|
-
tokyo_ramen_address.df.to_csv("tokyo_ramen_address.csv", encoding='utf_8_sig')
|
|
306
|
-
|
|
307
|
-
```
|
|
315
|
+
また`if store_head_list[0].text not in {'ラーメン', 'つけ麺'}:`の点についてですが
|
|
316
|
+
|
|
317
|
+
店舗情報のヘッダー枠のジャンルという項目の１個目が[ラーメン]ではない場合に
|
|
318
|
+
|
|
319
|
+
実際にラーメン屋であった場合でも除外されてしまっております。
|
|
320
|
+
|
|
321
|
+
この場合の処理をもう少し柔軟に対応してあげられると更に精度が高まると思います。
|
|
322
|
+
|
|
323
|
+
例:ジャンル: ラーメン▼担々麺▼ → 処理対象
|
|
324
|
+
|
|
325
|
+
ジャンル: 担々麺▼ラーメン▼ → 除外対象
|