Convertir le PDF d'un nouveau cas d'épidémie corona dans la préfecture d'Aichi en 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 yuans]{1,2})An(\d{1,2})Lune(\d{1,2})journée", s)

    if m:

        year, month, day = [1 if i == "Ancien" 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="▶ Cas d'occurrence dans la préfecture d'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["Date de l'annonce"] = df["Date de l'annonce"].apply(days2date)

df.dropna(subset=["Date de l'annonce"], inplace=True)

#Divisez l'âge et le sexe
df_ages = df["Âge gendre"].str.extract("(.+)(Masculin|Femelle)").rename(columns={0: "Âge", 1: "sexe"})

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")