[PYTHON] Konvertieren Sie das PDF des neuen Corona-Ausbruchs in der Präfektur Aichi in CSV

import datetime
import pathlib
import re
from urllib.parse import urljoin

import pandas as pd
import pdfplumber
import requests
from bs4 import BeautifulSoup


def fetch_file(url, dir="."):

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

    p = pathlib.Path(dir, pathlib.PurePath(url).name)
    p.parent.mkdir(parents=True, exist_ok=True)

    with p.open(mode="wb") as fw:
        fw.write(r.content)
    return p


def days2date(s):

    y = dt_now.year

    days = re.findall("[0-9]{1,2}", s)

    if len(days) == 2:
        m, d = map(int, days)
        return pd.Timestamp(year=y, month=m, day=d)
    else:
        return pd.NaT


def wareki2date(s):

    m = re.search("(Showa|Heisei|Reiwa)([ 0-9 Yuan]{1,2})Jahr(\d{1,2})Mond(\d{1,2})Tag", s)

    if m:

        year, month, day = [1 if i == "Ehemalige" else int(i.strip()) for i in m.group(2, 3, 4)]

        if m.group(1) == "Showa":
            year += 1925
        elif m.group(1) == "Heisei":
            year += 1988
        elif m.group(1) == "Reiwa":
            year += 2018

        return datetime.date(year, month, day)

    else:
        return dt_now.date


url = "https://www.pref.aichi.jp/site/covid19-aichi/"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko"
}

JST = datetime.timezone(datetime.timedelta(hours=+9), "JST")
dt_now = datetime.datetime.now(JST)

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

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

dfs = []
dt_text = ""

for tag in soup.find("span", text="▶ Fälle des Auftretens in der Präfektur Aichi").parent.find_all(
    "a", href=re.compile(".pdf$")
)[::-1]:

    link = urljoin(url, tag.get("href"))

    path_pdf = fetch_file(link)

    with pdfplumber.open(path_pdf) as pdf:

        for page in pdf.pages:

            if page.page_number == 1:

                dt_text = page.within_bbox((0, 80, page.width, 90)).extract_text()

            table = page.extract_table()

            df_tmp = pd.DataFrame(table[1:], columns=table[0])

            dfs.append(df_tmp)

df = pd.concat(dfs).set_index("No")

df["Ankündigungsdatum"] = df["Ankündigungsdatum"].apply(days2date)

df.dropna(subset=["Ankündigungsdatum"], inplace=True)

#Teilen Sie Alter und Geschlecht
df_ages = df["Alter Geschlecht"].str.extract("(.+)(männlich|Weiblich)").rename(columns={0: "Alter", 1: "Sex"})

df = df.join(df_ages)


dt_update = wareki2date(dt_text)

path_csv = pathlib.Path(dt_update.strftime("%Y%m%d") + ".csv")

df.to_csv(path_csv, encoding="utf_8_sig")