# import pandas as pd
# import os
# import re
# file_dir1 = r'C:\Users\鸽子\Desktop\一版结果\电压等级电量预测结果\偏差率'
# file_dir2 = r'C:\Users\鸽子\Desktop\一版结果\电压等级电量预测结果\月底3天预测结果'
# file_dir3 = r'C:\Users\鸽子\Desktop\一版结果\行业电量预测结果\偏差'
# import numpy as np
# np.set_printoptions(threshold=np.inf)
#
# # print(os.listdir(file_dir3))
# # str1 = '丽水电压等级10kv以下月底偏差率:0.00229'
# #
# # print(re.split('电压等级|月底偏差率:',str1))
# # with open(os.path.join(file_dir3,'9月底偏差率.txt'),'r',encoding='utf-8') as f:
# #     lines = f.readlines()
# #     list_city = []
# #     list_industry = []
# #     list_loss = []
# #     for i in lines:
# #         i = re.split(':|：|其中', i)
# #         print(i)
# #         list_city.append(i[0][:2])
# #         list_industry.append(i[-2].replace(i[0][:2],''))
# #         list_loss.append(i[-1][:-2])
# # df_level = pd.DataFrame({'城市':list_city,'行业':list_industry,'偏差':list_loss})
# # # df_level.to_csv(os.path.join(file_dir3,'9月底偏差率.csv'),encoding='gbk')
# # print(df_level)
# file_dir = r'C:\python-project\pytorch3\浙江行业电量\浙江所有地市133行业数据'
# # print(os.listdir(file_dir))
# dict1 = {}
#
# for file in os.listdir(file_dir):
#
#     df = pd.read_excel(os.path.join(file_dir,file),index_col=' stat_date ')
#
#     col_list = df.drop(columns=[i for i in df.columns if (df[i] == 0).sum() / len(df) >= 0.5]).columns
#     dict1[file[:2]] = col_list
#     print(dict1)
#
#     # print(len(df.drop(columns=[i for i in df.columns if (df[i] == 0).sum() / len(df) >= 0.5]).columns))
#
# read_path = r'C:\Users\鸽子\Desktop\一版结果\行业电量预测结果\月底预测结果'
# list1 = []
# for i in os.listdir(read_path):
#     print(i)
#     data = pd.read_csv(os.path.join(read_path, i), sep='\t',header=None)
#     data = data[data.columns[1:]]
#
#
#     for j,step in enumerate(range(0, len(data), 4)):
#         df = data.iloc[step+1:step + 4, :]
#         df.columns = ['预测值', '实际值', '偏差率']
#         try:
#             df['行业'] = dict1[i[2:4]][j]
#         except:
#             pass
#         df['城市'] = i[2:4]
#         list1.append(df)
#         print(df)
# df = pd.concat(list1,ignore_index=True)
# df.to_csv('各市行业电量预测结果.csv',encoding='gbk')
# print(df)
import os
from openpyxl import Workbook
import pandas as pd
# df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江省11月分行业售电量预测v2.xlsx',sheet_name=1)
# print(df.head())
# print(df[df.columns[2:]].groupby(df['city_name']).sum().T)
# df2 = df[df.columns[2:]].groupby(df['city_name']).sum().T
# df2.to_excel(r'C:\Users\鸽子\Desktop\1.xlsx')

file_dir = r'C:\Users\鸽子\Desktop\11月区县分压预测'
for file in os.listdir(file_dir):
    city = file[:-5]
    wb = Workbook()
    wb.save(fr'C:\Users\鸽子\Desktop\11月区县分压汇总\{city}.xlsx')

for file in os.listdir(file_dir):
    city = file[:-5]
    excel_file = pd.ExcelFile(os.path.join(file_dir,file))
    sheet_names = excel_file.sheet_names[1:]
    for sheet in sheet_names:
        df = excel_file.parse(sheet)
        df_result = df[df.columns[1:]].sum()
        df_result = pd.DataFrame(df_result)
        df_result.columns = ['售电量']

        with pd.ExcelWriter(fr'C:\Users\鸽子\Desktop\11月区县分压汇总\{city}.xlsx', mode='a', engine='openpyxl',
                            if_sheet_exists='replace') as writer:
            df_result.to_excel(writer, sheet_name=f'{sheet}')