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import pandas as pd
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import numpy as np
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import logging
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import re
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from .effective_cities import effective_cities
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# 获取日志记录器
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logger = logging.getLogger(__name__)
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# 获取省份统计结果及地市统计结果，仅统计个数情况
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def deal_excel(start_time, end_time, file_path):
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try:
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logger.info("开始分析停电excel")
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logger.info(f"开始分析：{start_time}")
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logger.info(f"结束时间：{end_time}")
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# 获取所有sheet页名称
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excel_file = pd.ExcelFile(file_path)
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sheet_names = excel_file.sheet_names
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pattern_sheet = r"(2025年?投诉明细|投诉明细)[（\(\s]*供电类[\)）\s]*"
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# 使用正则表达式进行模糊匹配（不区分大小写）
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matched_sheets = [
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sheet
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for sheet in sheet_names
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if re.fullmatch(pattern_sheet, sheet, re.IGNORECASE)
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]
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if len(matched_sheets) == 1:
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final_sheet = matched_sheets[0]
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else:
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logger.error("没有找到匹配的sheet页")
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return None
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df = pd.read_excel(
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file_path,
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sheet_name=final_sheet,
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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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# ---------------------------------------去点中间或两侧空格---------------------------------
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df["省"] = df["省"].str.strip().str.replace(r"\s+", "", regex=True)
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df["地市"] = df["地市"].str.strip().str.replace(r"\s+", "", regex=True)
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df["县区"] = df["县区"].str.strip().str.replace(r"\s+", "", regex=True)
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df["一级分类"] = df["一级分类"].str.strip().str.replace(r"\s+", "", regex=True)
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df["time"] = (
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df["time"]
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.str.strip()
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.str.replace(r"-|年|月|日|\.|时|分|秒|点", "/", regex=True)
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)
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# ---------------------------------------去点中间或两侧空格---------------------------------
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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(r'-|年|月|日|\.|时|分|秒|点', '/', regex=True)
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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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# 开始时间和结束时间
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# start_time = datetime(2025, 3, 5, 17, 0, 0)
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# end_time = datetime(2025, 3, 6, 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[
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(df["datetime"] > start_time)
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& (df["datetime"] <= end_time)
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& (df["一级分类"] == "供电质量")
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]
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print(f"只通过时间筛选的数据行数{len(df)}")
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# 对省份数据进行清洗
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province_list = ["广东", "广西", "云南", "贵州", "海南", "深圳"]
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# 省份正则
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province_pattern = "|".join(province_list)
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# 对省份数据进行清洗
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df["省"] = df["省"].apply(
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lambda x: re.search(province_pattern, x).group()
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if re.search(province_pattern, x)
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else ""
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)
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# 删除省份为空的值
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df = df[df["省"] != ""]
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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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# df.loc[df['地市'].str.contains('深圳|罗湖|福田|南山|宝安|龙岗|盐田|龙华|坪山|光明|大鹏'), '省'] = '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(
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"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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regex=True,
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na=False,
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)
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# 条件2：a 字段是特定值（例如 a=1 或 a=3）
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mask_a = df["省"].isin(["广东", "深圳"]) # 替换条件：a=1 或 a=3
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# 最终条件：b 匹配正则 且 a 在允许范围内
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final_mask = mask_b & mask_a
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# 执行替换
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df.loc[final_mask, "省"] = "深圳"
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# 对数据按照’省‘进行分类汇总
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group_province = df.groupby("省")
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province_statistics = {
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"广东": 0,
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"广西": 0,
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"云南": 0,
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"贵州": 0,
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"海南": 0,
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"深圳": 0,
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}
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# 保存省份统计的数据到字典
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province_temp = group_province.size().to_dict()
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# 最终当天省份的统计数据,利用update更新旧字典
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province_statistics.update(province_temp)
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# 地市处理逻辑
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# 81地市优化函数,非81地市的省份要不要算进去？
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df = effective_cities(df)
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# 对数据按照区进行分组汇总
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# 首先对省和地区进行拼接
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# 替换掉'供电局'字样
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df["地市"] = df["地市"].str.replace("供电局", "")
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# 对非深圳的地市，拼接省的信息
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df.loc[df["省"] != "深圳", "地市"] = df["省"] + "" + df["地市"]
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# 按照地市进行分组统计
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group_district = df.groupby("地市")
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# 将地市的统计数据保存到字典
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district_statistics = group_district.size().to_dict()
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# 对数据进行降序排列
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district_statistics = sorted(
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district_statistics.items(), key=lambda x: x[1], reverse=True
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)
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return province_statistics, district_statistics
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except Exception as e:
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logger.exception(f"对数据按照’省‘进行分类汇总{e}")
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def deal_excel_over_load(file_path):
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try:
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logger.info("开始分析配变过载excel")
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# 获取所有sheet页名称
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excel_file = pd.ExcelFile(file_path)
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sheet_names = excel_file.sheet_names
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pattern_sheet = r"重过载明细"
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# 使用正则表达式进行模糊匹配（不区分大小写）
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matched_sheets = [
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sheet
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for sheet in sheet_names
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if re.fullmatch(pattern_sheet, sheet, re.IGNORECASE)
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]
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if len(matched_sheets) == 1:
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final_sheet = matched_sheets[0]
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else:
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logger.error("没有找到匹配的sheet页")
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return None
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df = pd.read_excel(
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file_path,
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sheet_name=final_sheet,
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)
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values_to_include = ['严重过载', '一般过载']
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filtered_df = df[df['重过载情况'].isin(values_to_include)]
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grouped_df = filtered_df.groupby(['分子公司', '地市局']).size().reset_index(name='记录数')
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sorted_df = grouped_df.sort_values(by='记录数', ascending=False)
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top_5_results = sorted_df.head(5)
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# ---------------------------------------去点中间或两侧空格---------------------------------
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top_5_results["分子公司"] = top_5_results["分子公司"].str.strip().str.replace(r"\s+", "", regex=True)
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top_5_results["地市局"] = top_5_results["地市局"].str.strip().str.replace(r"\s+", "", regex=True)
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# ---------------------------------------去点中间或两侧空格---------------------------------
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top_5_results['公司地市'] = top_5_results['分子公司'] + top_5_results['地市局']
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# 最终选择需要返回的列：'公司_地市' 和 '记录数'
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final_output = top_5_results[['公司地市', '记录数']]
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return final_output
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except Exception as e:
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logger.exception(f"对数据按照’省‘进行分类汇总{e}")
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# 判断地市top5环比方法
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def top5_dod_analysis(top, district_stat_before):
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try:
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logger.info("开始分析地市top5环比")
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if top[0] in district_stat_before.keys():
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top_dod = top[1] - district_stat_before[top[0]]
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if top_dod > 0:
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top_dod = "+" + str(top_dod)
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return top_dod
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elif top_dod == 0:
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top_dod = "持平"
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return top_dod
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else:
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return top_dod
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else:
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top_dod = "+" + str(top[1])
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return top_dod
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except KeyError:
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logger.exception(f"判断地市top5环比{KeyError}")
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except AttributeError:
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logger.exception(f"判断地市top5环比{AttributeError}")
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except ArithmeticError:
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logger.exception(f"判断地市top5环比{ArithmeticError}")
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# 判断省份环比
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# def province_dod_analysis(before:dict, after:dict):
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#
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# dod = sum(after.values()) - sum(before.values())
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#
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# if dod > 0:
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# dod = '+' + str(dod)
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# return dod
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# elif dod == 0:
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# dod = '持平'
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# return dod
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# 将统计出来的省份数据或者排名前五的数据、环比组成列表，再转至（行和列互换），方便写入表格
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def transform_data(data):
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try:
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logger.info("开始将统计出来的数据转至（行和列互换），方便写入表格")
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# 使用 NumPy 实现行转列
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transposed_data = np.array(data).transpose().tolist()
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return transposed_data
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except Exception as e:
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logger.exception(f"将统计出来的数据转至（行和列互换），方便写入表格{e}")
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