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# import pandas as pd
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# import os
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# import re
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# 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'
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# 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'
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# file_dir3 = r'C:\Users\鸽子\Desktop\一版结果\行业电量预测结果\偏差'
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# import numpy as np
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# np.set_printoptions(threshold=np.inf)
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#
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# # print(os.listdir(file_dir3))
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# # str1 = '丽水电压等级10kv以下月底偏差率:0.00229'
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# #
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# # print(re.split('电压等级|月底偏差率:',str1))
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# # with open(os.path.join(file_dir3,'9月底偏差率.txt'),'r',encoding='utf-8') as f:
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# # lines = f.readlines()
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# # list_city = []
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# # list_industry = []
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# # list_loss = []
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# # for i in lines:
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# # i = re.split(':|：|其中', i)
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# # print(i)
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# # list_city.append(i[0][:2])
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# # list_industry.append(i[-2].replace(i[0][:2],''))
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# # list_loss.append(i[-1][:-2])
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# # df_level = pd.DataFrame({'城市':list_city,'行业':list_industry,'偏差':list_loss})
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# # # df_level.to_csv(os.path.join(file_dir3,'9月底偏差率.csv'),encoding='gbk')
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# # print(df_level)
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# file_dir = r'C:\python-project\pytorch3\浙江行业电量\浙江所有地市133行业数据'
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# # print(os.listdir(file_dir))
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# dict1 = {}
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#
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# for file in os.listdir(file_dir):
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#
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# df = pd.read_excel(os.path.join(file_dir,file),index_col=' stat_date ')
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#
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# col_list = df.drop(columns=[i for i in df.columns if (df[i] == 0).sum() / len(df) >= 0.5]).columns
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# dict1[file[:2]] = col_list
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# print(dict1)
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#
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# # print(len(df.drop(columns=[i for i in df.columns if (df[i] == 0).sum() / len(df) >= 0.5]).columns))
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#
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# 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'
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# list1 = []
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# for i in os.listdir(read_path):
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# print(i)
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# data = pd.read_csv(os.path.join(read_path, i), sep='\t',header=None)
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# data = data[data.columns[1:]]
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#
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#
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# for j,step in enumerate(range(0, len(data), 4)):
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# df = data.iloc[step+1:step + 4, :]
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# df.columns = ['预测值', '实际值', '偏差率']
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# try:
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# df['行业'] = dict1[i[2:4]][j]
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# except:
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# pass
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# df['城市'] = i[2:4]
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# list1.append(df)
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# print(df)
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# df = pd.concat(list1,ignore_index=True)
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# df.to_csv('各市行业电量预测结果.csv',encoding='gbk')
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# print(df)
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import os
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from openpyxl import Workbook
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import pandas as pd
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# 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)
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# print(df.head())
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# print(df[df.columns[2:]].groupby(df['city_name']).sum().T)
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# df2 = df[df.columns[2:]].groupby(df['city_name']).sum().T
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# df2.to_excel(r'C:\Users\鸽子\Desktop\1.xlsx')
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file_dir = r'C:\Users\鸽子\Desktop\11月区县分压预测'
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# for file in os.listdir(file_dir):
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# city = file[:-5]
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# wb = Workbook()
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# wb.save(fr'C:\Users\鸽子\Desktop\11月区县分压汇总\{city}.xlsx')
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#
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# for file in os.listdir(file_dir):
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# city = file[:-5]
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# excel_file = pd.ExcelFile(os.path.join(file_dir,file))
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# sheet_names = excel_file.sheet_names[1:]
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# for sheet in sheet_names:
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# df = excel_file.parse(sheet)
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# df_result = df[df.columns[1:]].sum()
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# df_result = pd.DataFrame(df_result)
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# df_result.columns = ['售电量']
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#
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# with pd.ExcelWriter(fr'C:\Users\鸽子\Desktop\11月区县分压汇总\{city}.xlsx', mode='a', engine='openpyxl',
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# if_sheet_exists='replace') as writer:
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# df_result.to_excel(writer, sheet_name=f'{sheet}')
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df = pd.read_excel('C:\python-project\p1031\浙江行业电量\浙江各地市行业电量数据\台州.xlsx').set_index('stat_date')
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print(df.columns)
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import matplotlib.pyplot as plt
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import matplotlib as mpl
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import matplotlib.dates as mdates
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date_rng = pd.date_range(start=df['4.有色金属矿采选业'].index[0], end=df['4.有色金属矿采选业'].index[-1], freq='D')
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mpl.rcParams['font.sans-serif']=['kaiti']
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print(df['4.有色金属矿采选业'][:-1])
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plt.figure(figsize=(10, 6))
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plt.plot(df['4.有色金属矿采选业'].index[:-1],df['4.有色金属矿采选业'][:-1])
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plt.title(f'4.有色金属矿采选业')
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plt.gcf().autofmt_xdate()
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plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=120))
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plt.xticks(rotation=45)
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plt.xlabel('时间')
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plt.ylabel('数值')
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plt.show()
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