def normalize(df):
df["country"].fillna("", inplace=True)
df["countryCode"].fillna("", inplace=True)
df["province"].fillna("", inplace=True)
df["provinceCode"].fillna("", inplace=True)
df["city"].fillna("", inplace=True)
df["cityCode"].fillna("", inplace=True)
df["confirmed"] = df["confirmed"].fillna(0).astype(int)
df["suspected"] = df["suspected"].fillna(0).astype(int)
df["cured"] = df["cured"].fillna(0).astype(int)
df["dead"] = df["dead"].fillna(0).astype(int)
# 修正数据
df["countryCode"] = df.apply(lambda x: get_country_code(x.country), axis=1)
df["provinceCode"] = df.apply(
lambda x: get_china_province_code(x.province, x.provinceCode), axis=1)
df["province"] = df.apply(
lambda x: get_china_area_name(x.provinceCode, x.province), axis=1)
df["cityCode"] = df.apply(
lambda x: get_china_city_code(x.provinceCode, x.city, x.cityCode),
axis=1)
df["city"] = df.apply(lambda x: get_china_area_name(x.cityCode, x.city),
axis=1)
df.drop_duplicates(subset=["date", "country", "province", "city"],
keep="last",
inplace=True)
df.sort_values(["date", "countryCode", "provinceCode", "cityCode", "city"],
inplace=True)
return df
df = pd.concat([df, pd.DataFrame(data_list)], sort=False)
df["country"].fillna("", inplace=True)
df["countryCode"].fillna("", inplace=True)
df["province"].fillna("", inplace=True)
df["provinceCode"].fillna("", inplace=True)
df["city"].fillna("", inplace=True)
df["cityCode"].fillna("", inplace=True)
df["confirmed"] = df["confirmed"].fillna(0).astype(int)
df["suspected"] = df["suspected"].fillna(0).astype(int)
df["cured"] = df["cured"].fillna(0).astype(int)
df["dead"] = df["dead"].fillna(0).astype(int)
# fix data
df["countryCode"] = df.apply(
lambda x: get_country_code(x.country), axis=1)
df["provinceCode"] = df.apply(
lambda x: get_china_province_code(x.province, x.provinceCode), axis=1)
df["province"] = df.apply(
lambda x: get_china_area_name(x.provinceCode, x.province), axis=1)
df["cityCode"] = df.apply(
lambda x: get_china_city_code(x.provinceCode, x.city, x.cityCode), axis=1)
df["city"] = df.apply(
lambda x: get_china_area_name(x.cityCode, x.city), axis=1)
df.drop_duplicates(
subset=["date", "country", "province", "city"], keep="last", inplace=True)
df.sort_values(["date", "countryCode", "provinceCode", "cityCode", "city"], inplace=True)
df.to_csv(csv_file, index=False, encoding='utf-8')
df.to_json(json_file, orient="records", force_ascii=False)
df.to_excel(xlsx_file, index=False)
print(f"""{datetime.now().strftime("%Y-%m-%d %H:%M:%S")} : Data update completed. """)
def parse_report(report):
date = str(report["时间"])
province = report.get("省", "")
confirmed = parse_int(report.get("确诊"))
suspected = parse_int(report.get("疑似"))
cured = parse_int(report.get("治愈"))
dead = parse_int(report.get("死亡"))
area_key = "city" if province else "province"
confirmed_list = parse_list(report.get("确诊详情"), [area_key, "confirmed"])
suspected_list = parse_list(report.get("疑似详情"), [area_key, "suspected"])
cured_list = parse_list(report.get("治愈详情"), [area_key, "cured"])
dead_list = parse_list(report.get("死亡详情"), [area_key, "dead"])
foreign_confirmed_list = parse_list(report.get("国外确诊详情"), ["country", "confirmed"])
foreign_cured_list = parse_list(report.get("国外治愈详情"), ["country", "cured"])
provinceCode = get_china_province_code(province) if province else ""
province = get_china_area_name(provinceCode, province) if provinceCode else ""
data = {
"provinceCode": provinceCode,
"province": province,
"confirmed": confirmed,
"suspected": suspected,
"cured": cured,
"dead": dead
}
for data_list in [confirmed_list, suspected_list, cured_list, dead_list]:
if data_list:
for x in data_list:
if province:
x["provinceCode"] = provinceCode
x["province"] = province
x["cityCode"] = get_china_city_code(provinceCode, x["city"])
x["city"] = get_china_area_name(x["cityCode"], x["city"])
else:
x["provinceCode"] = get_china_province_code(x["province"])
x["province"] = get_china_area_name(x["provinceCode"], x["province"])
df_list = [pd.DataFrame(x) for x in [confirmed_list, suspected_list, cured_list, dead_list] if x]
df = None
for index, x in enumerate(df_list):
if df is None:
df = x
else:
df = pd.merge(df, x, on=area_key, how="outer", suffixes=["", f"""_{index}"""], sort=False, copy=False)
columns = [
"date",
"country",
"countryCode",
"province",
"provinceCode",
"city",
"cityCode",
"confirmed",
"suspected",
"cured",
"dead"
]
if df is None:
df = pd.DataFrame([data], columns=columns)
else:
df = pd.DataFrame(df, columns=columns)
df = df.append([data])
df["country"] = "中国"
df["countryCode"] = "CN"
df["province"].fillna(province, inplace=True)
df["provinceCode"].fillna(provinceCode, inplace=True)
df["city"].fillna("", inplace=True)
df["cityCode"].fillna("", inplace=True)
df["provinceCode"] = df["province"].map(get_china_province_code)
df["cityCode"] = df.apply(
lambda x: get_china_city_code(x.provinceCode, x.city), axis=1)
for data_list in [foreign_confirmed_list, foreign_cured_list]:
if data_list:
for x in data_list:
x["countryCode"] = get_country_code(x["country"])
foreign_df = None
foreign_df_list = [pd.DataFrame(x) for x in [foreign_confirmed_list, foreign_cured_list] if x]
for index, x in enumerate(foreign_df_list):
if foreign_df is None:
foreign_df = x
else:
foreign_df = pd.merge(foreign_df, x, on="country", how="outer", suffixes=["", f"""_{index}"""], sort=False, copy=False)
if foreign_df is not None:
df = pd.concat([df, pd.DataFrame(foreign_df, columns=columns)], sort=False)
df["date"] = date
df.sort_values(["date", "countryCode", "provinceCode", "cityCode", "city"], inplace=True)
return df