麦当劳加拿大位置 Scrape，缺少电话号码答案

【问题标题】：McDonald's Canada location Scrape, missing phone number麦当劳加拿大位置 Scrape，缺少电话号码
【发布时间】：2021-07-20 18:14:39
【问题描述】：

所以下面是我对加拿大麦当劳的刮擦，我找回了我想要的大部分信息。但是，我遇到了我没有找回电话号码的问题。（把它拿出来或休息不起作用）此外，到创建我的 csv 文件时，它会在每个单元格之间留下间隙。根据我对电话号码未显示的猜测，它可能位于不同的标题或其他变体下。如果有人有一个很棒的解决方案，主要关心的是正在获取数字，并且次要在 csv 文件中没有间隙。

import requests
import csv
import json

url = "https://www.mcdonalds.com/googleapps/GoogleRestaurantLocAction.do?method=searchLocation&latitude=43.6936965&longitude=-79.2969938&radius=1000000&maxResults=1700&country=ca&language=en-ca&showClosed=&hours24Text=Open%2024%20hr"

payload={}
files={}
headers = {
  'authority': 'www.mcdonalds.com',
  'sec-ch-ua': '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"',
  'accept': '*/*',
  'x-requested-with': 'XMLHttpRequest',
  'sec-ch-ua-mobile': '?0',
  'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36',
  'sec-fetch-site': 'same-origin',
  'sec-fetch-mode': 'cors',
  'sec-fetch-dest': 'empty',
  'referer': 'https://www.mcdonalds.com/ca/en-ca/restaurant-locator.html',
  'accept-language': 'en-GB,en-US;q=0.9,en;q=0.8',
  'cookie': 'bm_sz=C04645E7F7A956C5F9D9C5A20DEAEC97~YAAQ1Cv2SEtfMBN6AQAAItxfEwwTVV2V2Tr7UWpPt1Ps7gl84FzQlmbWIm4kBBh5dxlK3w8RenwiEiKtvERE6dLmrwPwJUuy+14gU/LeEZvP+uxzyBr04oQXdcSEQuiOgdkAGasqnBrTw1mp5E5iehnRpvHBDdSqh8wRSgJV0eG4f8YwSz66BfntCBALtQNCAFK2; _abck=F05779F2345218EA4989FF467D897C5A~0~YAAQ1Cv2SExfMBN6AQAAItxfEwaIwCrBeP25JBhBb7TX+HmnLQgrj1TkosrB+oHSv9ctrxRukqEDUaHPL1KkjpqjY1XY1yyulQ0ZRhsEfhY968YVsTOqfiosAu3kykd3pJG/bQ37XHwWs5qXpIdhMXRwJwXmkYtl3ETG8kXK2iZ22Q31COaSjNVACLaa7s9tCk9ItgLvUj5x9Nldjnd8AdXR0pXicrQY1IaruJyNqwMcJv42AUHW7iH4Ex9ZOSYsgEjLMNd44mS525X/gSNUTSOzoqoWsnH4MU59vfgLTwc2hVncAv67LBViTLxbWw4eVAvz7Z5phQfCmvoIy0PD8gy5iwPDMaD3GASrK9xScDPAPUI2wquxmSJ+f2cQaxZQKhvJCeH9cz14OZfx8ksA2ss53E0l0kDvgmnw~-1~-1~-1; ak_bmsc=BA4817D8DEE20E92C1E6251C54FC124348F62BD48F5F00005F91C9608B679D5F~plUkbYfsvYr5dCayJ9dMGEJ3QDgkmkv2mLpE7pCY9vW0xrdawvmyxfSnupw/4F7C48Akdn8PKsBniqz+7F+RZb8v4AkvH3c0RuvnynqJoni+kJcDYtPOxdMvdtGdTlZGIkSQNfpcxHNQDVlzojdSBX0vyBh/8seKQv10U67M7m787olYzg9jnsUwk3/VHBrnMDogiWJT8rNV7saSXunN0pAgucZWo/XhCpTJL+tI9urt0=; MCDCountry_code=US; bm_mi=BEE06312635FD442995BC0237BAFDA7C~f/RxgMW/JJSUc/wB9ZRg9fPD/76+wq/TaoWEZR1/ttrAiVTO256xhDTsVYc/kdHIjWkxvfO4XDcBjqe4hQ4qXt8Anpfi09vna/zcC7l6OVWpWeRSoZNztl7h5VF407L3XG+9CpzjSHNcaqAPRk5d0J5gLMtL/KmR8XBkAC0Syim7ST97nxNrPfLdlkSPMGm4Oy86xvY5PH5Nu47zS/gwhanBFg69tAdrQdaZewE2eGuzoJPsZit3UsihTzhXc4LY92hfSdh3/kZRId+NE8Jp0w==; bm_sv=7CACE3495320A7C0A6CF8F41DFE0EB36~F9KzvznVNk/fE4+ijLD5H/szY7O161rWlemmShElumIW7HN49Gq2d9Sd2tqBjCa9sJOX4zoehAkc8WvsID5Idon/hDlDeLJZuqnEmff4PN4a9yst3R170rBCm1egzGvCBmB1jq9aCwQm5VgIJgloPOdpiIPfD3kDxFbKhqMuS5U=; JSESSIONID=64PZkBXhhpvNjM4NganzSZ0r1npIIaM7Fo84EsxN.eap7node7; _abck=F05779F2345218EA4989FF467D897C5A~-1~YAAQ1Cv2SExyMBN6AQAA5Et0EwZueCejZbKz1VDGCq2sB43Yx4dq0SiiGeUS6gVpXRIdw3rA3OdpNGHq7tVzQ+IvPpEKwLML9736x1qB5SQxV3jai89y2B2QF6K8nKtyrDAes0qbeTyIrHu0Rh1HLs7CjNxiLi0wswbCZfSsPI6fJZiEt+Itre3lfmua/HkhIRwpVTKqlVN5eQ8XIX+s1jJbINx/jUmMTW+jB5k4A5NARGChYH7rJQGYIT/oyZYpSbS3Yweqa4FRgGMW4gYZBN39+t2xSfewADLdpihfOnoZtakw9VhcvAKaf4mEzjB7WEfNJIZSjSE8DzvbJNIF41MGuAhhrnEBwBE8uVCZsA+2qjVPSADVp2Nn8JanJXCbucnLFOLsmPz3oVtGzentht1cHog4+eYOUlmw~0~-1~-1; bm_sv=7CACE3495320A7C0A6CF8F41DFE0EB36~F9KzvznVNk/fE4+ijLD5H/szY7O161rWlemmShElumIW7HN49Gq2d9Sd2tqBjCa9sJOX4zoehAkc8WvsID5Idon/hDlDeLJZuqnEmff4PN5ZCTzA250oKEeVeXaa6j4gEGJ9RRtrTXQdYXzzSx6fM9aLwif+We2vtIc1yLQgTt4=',
  'dnt': '1'
} 

response = requests.request("GET", url, headers = headers, data = payload, files = files)


stores = json.loads(response.text)

with open('McdonlocationWORK.csv', mode='w') as CSVFile:
    writer = csv.writer(CSVFile, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL)

    writer.writerow([
        "addressLine1",
        "addressLine2",
        "addressLine3",
        "subDivision",
        "postcode",
        ])

    for store in stores['features']:
        row = []
        Match_Address1 = store['properties']["addressLine1"]
        Match_Address2 = store['properties']["addressLine2"]
        Match_Address3 = store['properties']["addressLine3"]
        subDivision = store['properties']["subDivision"]
        Postalcode = store['properties']["postcode"]                      
        

        row.append(Match_Address1)
        row.append(Match_Address2)
        row.append(Match_Address3)
        row.append(subDivision)
        row.append(Postalcode)
        writer.writerow(row)

【问题讨论】：

标签： json ajax csv web-scraping

【解决方案1】：

您可以使用dict.get 获取电话号码（或者如果它不存在，请提供默认值，在我的示例中为“N/A”）：

import csv
import json
import requests

url = "https://www.mcdonalds.com/googleapps/GoogleRestaurantLocAction.do?method=searchLocation&latitude=43.6936965&longitude=-79.2969938&radius=1000000&maxResults=1700&country=ca&language=en-ca&showClosed=&hours24Text=Open%2024%20hr"

payload = {}
files = {}
headers = {
    "authority": "www.mcdonalds.com",
    "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"',
    "accept": "*/*",
    "x-requested-with": "XMLHttpRequest",
    "sec-ch-ua-mobile": "?0",
    "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36",
    "sec-fetch-site": "same-origin",
    "sec-fetch-mode": "cors",
    "sec-fetch-dest": "empty",
    "referer": "https://www.mcdonalds.com/ca/en-ca/restaurant-locator.html",
    "accept-language": "en-GB,en-US;q=0.9,en;q=0.8",
    "cookie": "bm_sz=C04645E7F7A956C5F9D9C5A20DEAEC97~YAAQ1Cv2SEtfMBN6AQAAItxfEwwTVV2V2Tr7UWpPt1Ps7gl84FzQlmbWIm4kBBh5dxlK3w8RenwiEiKtvERE6dLmrwPwJUuy+14gU/LeEZvP+uxzyBr04oQXdcSEQuiOgdkAGasqnBrTw1mp5E5iehnRpvHBDdSqh8wRSgJV0eG4f8YwSz66BfntCBALtQNCAFK2; _abck=F05779F2345218EA4989FF467D897C5A~0~YAAQ1Cv2SExfMBN6AQAAItxfEwaIwCrBeP25JBhBb7TX+HmnLQgrj1TkosrB+oHSv9ctrxRukqEDUaHPL1KkjpqjY1XY1yyulQ0ZRhsEfhY968YVsTOqfiosAu3kykd3pJG/bQ37XHwWs5qXpIdhMXRwJwXmkYtl3ETG8kXK2iZ22Q31COaSjNVACLaa7s9tCk9ItgLvUj5x9Nldjnd8AdXR0pXicrQY1IaruJyNqwMcJv42AUHW7iH4Ex9ZOSYsgEjLMNd44mS525X/gSNUTSOzoqoWsnH4MU59vfgLTwc2hVncAv67LBViTLxbWw4eVAvz7Z5phQfCmvoIy0PD8gy5iwPDMaD3GASrK9xScDPAPUI2wquxmSJ+f2cQaxZQKhvJCeH9cz14OZfx8ksA2ss53E0l0kDvgmnw~-1~-1~-1; ak_bmsc=BA4817D8DEE20E92C1E6251C54FC124348F62BD48F5F00005F91C9608B679D5F~plUkbYfsvYr5dCayJ9dMGEJ3QDgkmkv2mLpE7pCY9vW0xrdawvmyxfSnupw/4F7C48Akdn8PKsBniqz+7F+RZb8v4AkvH3c0RuvnynqJoni+kJcDYtPOxdMvdtGdTlZGIkSQNfpcxHNQDVlzojdSBX0vyBh/8seKQv10U67M7m787olYzg9jnsUwk3/VHBrnMDogiWJT8rNV7saSXunN0pAgucZWo/XhCpTJL+tI9urt0=; MCDCountry_code=US; bm_mi=BEE06312635FD442995BC0237BAFDA7C~f/RxgMW/JJSUc/wB9ZRg9fPD/76+wq/TaoWEZR1/ttrAiVTO256xhDTsVYc/kdHIjWkxvfO4XDcBjqe4hQ4qXt8Anpfi09vna/zcC7l6OVWpWeRSoZNztl7h5VF407L3XG+9CpzjSHNcaqAPRk5d0J5gLMtL/KmR8XBkAC0Syim7ST97nxNrPfLdlkSPMGm4Oy86xvY5PH5Nu47zS/gwhanBFg69tAdrQdaZewE2eGuzoJPsZit3UsihTzhXc4LY92hfSdh3/kZRId+NE8Jp0w==; bm_sv=7CACE3495320A7C0A6CF8F41DFE0EB36~F9KzvznVNk/fE4+ijLD5H/szY7O161rWlemmShElumIW7HN49Gq2d9Sd2tqBjCa9sJOX4zoehAkc8WvsID5Idon/hDlDeLJZuqnEmff4PN4a9yst3R170rBCm1egzGvCBmB1jq9aCwQm5VgIJgloPOdpiIPfD3kDxFbKhqMuS5U=; JSESSIONID=64PZkBXhhpvNjM4NganzSZ0r1npIIaM7Fo84EsxN.eap7node7; _abck=F05779F2345218EA4989FF467D897C5A~-1~YAAQ1Cv2SExyMBN6AQAA5Et0EwZueCejZbKz1VDGCq2sB43Yx4dq0SiiGeUS6gVpXRIdw3rA3OdpNGHq7tVzQ+IvPpEKwLML9736x1qB5SQxV3jai89y2B2QF6K8nKtyrDAes0qbeTyIrHu0Rh1HLs7CjNxiLi0wswbCZfSsPI6fJZiEt+Itre3lfmua/HkhIRwpVTKqlVN5eQ8XIX+s1jJbINx/jUmMTW+jB5k4A5NARGChYH7rJQGYIT/oyZYpSbS3Yweqa4FRgGMW4gYZBN39+t2xSfewADLdpihfOnoZtakw9VhcvAKaf4mEzjB7WEfNJIZSjSE8DzvbJNIF41MGuAhhrnEBwBE8uVCZsA+2qjVPSADVp2Nn8JanJXCbucnLFOLsmPz3oVtGzentht1cHog4+eYOUlmw~0~-1~-1; bm_sv=7CACE3495320A7C0A6CF8F41DFE0EB36~F9KzvznVNk/fE4+ijLD5H/szY7O161rWlemmShElumIW7HN49Gq2d9Sd2tqBjCa9sJOX4zoehAkc8WvsID5Idon/hDlDeLJZuqnEmff4PN5ZCTzA250oKEeVeXaa6j4gEGJ9RRtrTXQdYXzzSx6fM9aLwif+We2vtIc1yLQgTt4=",
    "dnt": "1",
}

response = requests.request(
    "GET", url, headers=headers, data=payload, files=files
)
stores = json.loads(response.text)

with open("data.csv", mode="w") as CSVFile:
    writer = csv.writer(
        CSVFile, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL
    )

    writer.writerow(
        [
            "address",
            "subdivision",
            "postcode",
            "telephone",
        ]
    )

    for store in stores["features"]:
        address = " ".join(
            [
                store["properties"]["addressLine1"],
                store["properties"]["addressLine2"],
                store["properties"]["addressLine3"],
                store["properties"]["addressLine4"],
            ]
        )
        sub_division = store["properties"]["subDivision"]
        post_code = store["properties"]["postcode"]
        telephone = store["properties"].get("telephone", "N/A")

        writer.writerow([address, sub_division, post_code, telephone])

创建data.csv（来自 Libre Office 的屏幕截图）：

【讨论】：

如何将细分与地址和可能的沃尔玛标签分开？我还看到您在输出 csv 中确实有单独的细分但空白？
@John_Muir 如果subdivision 在每一行都是空白，您可以删除它。此外，要提取(Wal-Mart) 标签，您可以使用re 模块。