[PYTHON] Scraping Gehen Sie zu EAT-Mitgliedsgeschäften in der Präfektur Osaka und konvertieren Sie zu CSV

Scraping Member Stores von Go To Eat Osaka Campaign

import time

import requests
from bs4 import BeautifulSoup

import pandas as pd

result = []

url = "https://goto-eat.weare.osaka-info.jp/?search_element_0_0=2&search_element_0_1=3&search_element_0_2=4&search_element_0_3=5&search_element_0_4=6&search_element_0_5=7&search_element_0_6=8&search_element_0_7=9&search_element_0_8=10&search_element_0_9=11&search_element_0_cnt=10&search_element_1_0=12&search_element_1_1=13&search_element_1_2=14&search_element_1_3=15&search_element_1_4=16&search_element_1_5=17&search_element_1_6=18&search_element_1_7=19&search_element_1_8=20&search_element_1_9=21&search_element_1_10=22&search_element_1_11=23&search_element_1_12=24&search_element_1_13=25&search_element_1_14=26&search_element_1_15=27&search_element_1_16=28&search_element_1_17=29&search_element_1_cnt=18&searchbutton=%E5%8A%A0%E7%9B%9F%E5%BA%97%E8%88%97%E3%82%92%E6%A4%9C%E7%B4%A2%E3%81%99%E3%82%8B&csp=search_add&feadvns_max_line_0=2&fe_form_no=0"

while True:

    r = requests.get(url)
    r.raise_for_status()

    soup = BeautifulSoup(r.content, "html.parser")

    for li in soup.select("div.search_result_box > ul > li"):

        data = {}
        data["Speichername"] = li.select_one("p.name").get_text(strip=True)
        data["Genre"], data["Bereich"] = li.select_one("ul.tag_list").stripped_strings

        for tr in li.table.select("tr"):

            k = tr.th.get_text(strip=True)

            if k == "Adresse":
                v = list(tr.td.stripped_strings)

                data["Postleitzahl"] = v[0]
                data[k] = " ".join(v[-1].split())
            else:
                data[k] = tr.td.get_text(strip=True)

        result.append(data)

    tag = soup.select_one("div.wp-pagenavi > a.nextpostslink")

    if tag:

        url = tag.get("href")

    else:
        break

    time.sleep(1)

df = pd.DataFrame(result).reindex(
    columns=["Speichername", "Genre", "Bereich", "Postleitzahl", "Adresse", "TEL", "Öffnungszeiten", "Regelmäßiger Urlaub"]
)

df.to_csv("osaka.csv", encoding="utf_8_sig")