Scraping Go To EAT membres magasins dans la préfecture d'Osaka et conversion au format CSV

Suppression des magasins membres de la Campagne Go To Eat Osaka

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["Nom du magasin"] = li.select_one("p.name").get_text(strip=True)
        data["Genre"], data["zone"] = 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 de rue":
                v = list(tr.td.stripped_strings)

                data["Code postal"] = 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=["Nom du magasin", "Genre", "zone", "Code postal", "adresse de rue", "TEL", "heures de travail", "Vacances régulières"]
)

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