|
|
# 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}')
|