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import pandas as pd
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from datetime import datetime
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import numpy as np
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import re
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import logging
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# 获取日志记录器
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logger = logging.getLogger(__name__)
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def effective_cities(df):
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try:
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logger.info("开始判断是否81地市，先从地市判断，如果没有，则从县区判断")
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# ---------------------------------test---------------------------------------
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# df = pd.read_excel(
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# r'E:\work_data\work\三工单日报\三工单\20250309\20250309\南方电网话务及投诉统计表（3月9日）.xlsx',
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# sheet_name='投诉明细（供电类）',
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# skiprows=1,
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# )
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# # 将时间列转成字符串
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# df['time'] = df['受理时间'].astype(str)
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# # 通过字符串功能格式化时间
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# df['time'] = df['time'].str.replace('-', '/')
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# # 转成date方便比较
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# df['datetime'] = pd.to_datetime(df['time'])
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#
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# # 开始时间和结束时间
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# start_time = datetime(2025, 3, 8, 17, 0, 0)
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# end_time = datetime(2025, 3, 9, 17, 0, 0)
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# # 拿到供电质量在当天的数据
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# df = df[(df['datetime'] > start_time) & (df['datetime'] <= end_time) & (df['一级分类'] == '供电质量')]
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#
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# # 判断数据区里面是否有深圳
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# df['地市'] = df['地市'].astype(str)
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#
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# df.loc[df['地市'].str.contains('深圳'), '省'] = '深圳'
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# ---------------------------------test---------------------------------------
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# 判断是否81个地市，如果不是，则忽略
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# 省份正则
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# 81地市正则
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city_list = [
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"文昌",
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"丽江",
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"贺州",
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"澄迈",
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"迪庆",
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"玉林",
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"河池",
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"百色",
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"梧州",
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"崇左",
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"怒江",
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"贵港",
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"韶关",
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"琼中",
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"肇庆",
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"文山",
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"桂林",
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"都匀",
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"大理",
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"瑞丽",
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"南宁",
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"汕尾",
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"来宾",
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"防城港",
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"钦州",
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"柳州",
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"清远",
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"阳江",
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"六盘水",
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"梅州",
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"北海",
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"昆明",
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"兴义",
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"揭阳",
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"万宁",
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"红河",
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"定安",
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"潮州",
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"茂名",
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"海口",
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"云浮",
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"德宏",
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"汕头",
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"惠州",
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"湛江",
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"毕节",
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"铜仁",
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"江门",
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"凯里",
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"三亚",
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"楚雄",
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"儋州",
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"东莞",
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"河源",
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"中山",
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"珠海",
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"临高",
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"乐东",
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"遵义",
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"东方",
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"佛山",
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"安顺",
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"琼海",
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"贵阳",
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"广州",
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"陵水",
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"深圳",
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"保亭",
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"屯昌",
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"白沙",
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"昌江",
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"五指山",
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"贵安",
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"昭通",
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"临沧",
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"曲靖",
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"西双版纳",
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"普洱",
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"玉溪",
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"保山",
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"三沙",
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]
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# 深圳区正则
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sz_district_list = [
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"罗湖",
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"福田",
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"南山",
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"宝安",
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"龙岗",
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"盐田",
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"龙华",
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"坪山",
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"光明",
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"大鹏",
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]
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# 地市判断正则
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city_pattern1 = "|".join(city_list)
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# 深圳区判断正则
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sz_district_pattern = "|".join(sz_district_list)
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# 地市里需要将深圳的区改成深圳：“罗湖|福田|南山|宝安|龙岗|盐田|龙华|坪山|光明|大鹏”
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# df['地市'] = df['地市'].replace(sz_district_pattern, '深圳', regex=True)
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# df["地市"] = np.where(
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# df["地市"].fillna('').str.contains(sz_district_pattern, regex=True),
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# "深圳", # 是 → 替换成固定值
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# np.where(
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# df["县区"].fillna('').str.contains(sz_district_pattern, regex=True),
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# "深圳", # 是 → 替换成另一个固定值
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# df["地市"] # 否 → 保持 col1（或改为 "" / np.nan）
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# )
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# )
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# -------------------------------------看起来是深圳的区，不一定是深圳----------------------------------------------
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# 条件1：b 字段匹配正则
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mask_b = df["地市"].str.contains(sz_district_pattern, regex=True, na=False)
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# 条件2：b 不匹配，但 c 字段匹配正则
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mask_c = (~mask_b) & df["县区"].str.contains(
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sz_district_pattern, regex=True, na=False
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)
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# 结合 a 字段的条件（例如，仅当 a 是 1, 3, 5 时才允许替换）
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mask_a = df["省"].isin(["广东", "深圳"])
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# 最终替换条件：满足 (b匹配 或 c匹配) 且 a 在允许范围内
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final_mask = (mask_b | mask_c) & mask_a
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# 执行替换
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df["地市"] = np.where(final_mask, "深圳", df["地市"])
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logger.info(f"判断县区是否有深圳的信息{df['地市']}")
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# -------------------------------------看起来是深圳的区，不一定是深圳----------------------------------------------
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# 不包含在地市，就在区县取值
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df["地市"] = np.where(
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df["地市"].fillna("").str.contains(city_pattern1, regex=True),
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df["地市"], # 是 → 保留 col1
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np.where(
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df["县区"].fillna("").str.contains(city_pattern1, regex=True),
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df["县区"], # 是 → 取 col2
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df["地市"], # 否 → 保持 col1（或改为 "" / np.nan）
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),
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)
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logger.info(f"81地市通过地市及县区修正后的数据{df}")
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# df = df[df['地市'].str.contains(city_pattern1)]
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# 如果以上述地市开头，并且中间还有信息，则直接用“地市”加“供电局”
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# 遍历数组，然后一一替换
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for city in city_list:
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df["地市"] = df["地市"].apply(lambda x: city if re.search(city, x) else x)
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logger.info(f"81地市修改成功")
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return df
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except Exception as e:
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logger.info(f"81地市修改失败{e}")
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#
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# if __name__ == '__main__':
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#
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# df = effective_cities()
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# print(df.head())
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