输出预测结果
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from prophet import Prophet
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import pandas as pd
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import os
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import numpy as np
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def normal(data):
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high = data.describe()['75%'] + 1.5 * (data.describe()['75%'] - data.describe()['25%'])
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low = data.describe()['25%'] - 1.5 * (data.describe()['75%'] - data.describe()['25%'])
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return (data<=high)&(data>=low)
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file_dir = 'C:\python-project\p1031\浙江电压等级电量\浙江各地市分电压日电量数据'
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for city in os.listdir(file_dir):
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df_city = pd.read_excel(os.path.join(file_dir, city))
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# df_city['stat_date'] = df_city['stat_date'].map(lambda x: str(x).strip()[:10])
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df_city['stat_date'] = pd.to_datetime(df_city['stat_date'])
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list_goal = []
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list_industry = []
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result_dict = {}
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for level in df_city.columns[2:]:
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s1 = df_city[['stat_date', level]]
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ds_train = s1[(s1['stat_date'] >= '2022-09-30') & (s1['stat_date'] <= '2023-11-27')].sort_values(by='stat_date')
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ds_train.rename(columns={'stat_date': 'ds', level: 'y'}, inplace=True)
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df_train = ds_train.copy()
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df_train['y'] = df_train['y'].where(normal(df_train['y']), other=np.nan).fillna(method='ffill')
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model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=True)
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model.add_country_holidays(country_name="CN")
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model.fit(df_train)
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future = model.make_future_dataframe(periods=3, freq='D')
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predict = model.predict(future)
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print(city[1:3],level)
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predict = predict[['ds', 'yhat']].set_index('ds').loc['2023-11'].rename(columns={'yhat':'售电量'})
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ds_train.rename(columns={'y':'售电量'},inplace=True)
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result = pd.concat((ds_train.set_index('ds').loc['2023-11'][:27],predict[-3:]))
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result_dict[level] = list(result['售电量'])
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with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\分压电量预测v1213.xlsx',mode='a',if_sheet_exists='replace',engine='openpyxl') as writer:
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pd.DataFrame(result_dict,index=pd.date_range(start=f'2023-11-01', end=f'2023-11-30', freq='D').strftime('%Y-%m-%d')).to_excel(writer,sheet_name=city[1:3])
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# df = predict.join(s1.set_index('ds')).loc['2023-8']
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# df['偏差率'] = (df['y'] - df['yhat']) / df['y']
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# df['goal'] = (df['y'] - df['yhat'])[-3:].sum() / df['y'].sum()
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# list_goal.append((df['y'] - df['yhat'])[-3:].sum() / df['y'].sum())
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# list_industry.append(industry)
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# df = pd.DataFrame({'industry': list_industry, 'goal': list_goal})
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# df.to_csv(fr'C:\Users\鸽子\Desktop\行业8月偏差\{city[:2]}_goal.csv', index=False, encoding='gbk')
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#
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# with open(r'C:\Users\鸽子\Desktop\goal_8.txt','a') as f:
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# f.write(f'{city[:2]}\n')
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# df['goal'].value_counts(bins=[-np.inf,-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.02, 0.05,np.inf], sort=False).to_csv(f,header=False,sep='\t')
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from prophet import Prophet
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import pandas as pd
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import os
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import numpy as np
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def normal(data):
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high = data.describe()['75%'] + 1.5 * (data.describe()['75%'] - data.describe()['25%'])
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low = data.describe()['25%'] - 1.5 * (data.describe()['75%'] - data.describe()['25%'])
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return (data<=high)&(data>=low)
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file_dir = 'C:\python-project\p1031\浙江电压等级电量\浙江各地市分电压日电量数据'
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for city in os.listdir(file_dir):
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df_city = pd.read_excel(os.path.join(file_dir, city))
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# df_city['stat_date'] = df_city['stat_date'].map(lambda x: str(x).strip()[:10])
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df_city['stat_date'] = pd.to_datetime(df_city['stat_date'])
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list_goal = []
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list_industry = []
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result_dict = {}
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for level in df_city.columns[2:]:
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s1 = df_city[['stat_date', level]]
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ds_train = s1[(s1['stat_date'] >= '2022-09-30') & (s1['stat_date'] <= '2023-11-27')].sort_values(by='stat_date')
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ds_train.rename(columns={'stat_date': 'ds', level: 'y'}, inplace=True)
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df_train = ds_train.copy()
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df_train['y'] = df_train['y'].where(normal(df_train['y']), other=np.nan).fillna(method='ffill')
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model = Prophet(yearly_seasonality=True, weekly_seasonality=True, daily_seasonality=True)
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model.add_country_holidays(country_name="CN")
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model.fit(df_train)
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future = model.make_future_dataframe(periods=3, freq='D')
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predict = model.predict(future)
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print(city[1:3],level)
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predict = predict[['ds', 'yhat']].set_index('ds').loc['2023-11'].rename(columns={'yhat':'售电量'})
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ds_train.rename(columns={'y':'售电量'},inplace=True)
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result = pd.concat((ds_train.set_index('ds').loc['2023-11'][:27],predict[-3:]))
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result_dict[level] = list(result['售电量'])
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with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\分压电量预测v1213.xlsx',mode='a',if_sheet_exists='replace',engine='openpyxl') as writer:
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pd.DataFrame(result_dict,index=pd.date_range(start=f'2023-11-01', end=f'2023-11-30', freq='D').strftime('%Y-%m-%d')).to_excel(writer,sheet_name=city[1:3])
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# df = predict.join(s1.set_index('ds')).loc['2023-8']
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# df['偏差率'] = (df['y'] - df['yhat']) / df['y']
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# df['goal'] = (df['y'] - df['yhat'])[-3:].sum() / df['y'].sum()
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# list_goal.append((df['y'] - df['yhat'])[-3:].sum() / df['y'].sum())
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# list_industry.append(industry)
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# df = pd.DataFrame({'industry': list_industry, 'goal': list_goal})
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# df.to_csv(fr'C:\Users\鸽子\Desktop\行业8月偏差\{city[:2]}_goal.csv', index=False, encoding='gbk')
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#
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# with open(r'C:\Users\鸽子\Desktop\goal_8.txt','a') as f:
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# f.write(f'{city[:2]}\n')
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# df['goal'].value_counts(bins=[-np.inf,-0.05, -0.01, -0.005, 0, 0.005, 0.01, 0.02, 0.05,np.inf], sort=False).to_csv(f,header=False,sep='\t')
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import pandas as pd
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df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江电量20231202.xlsx', sheet_name=1)
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df['pt_date'] = pd.to_datetime(df['pt_date'])
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# 移动平均
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dict_big = {}
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dict_ok = {}
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# for city in df['city_name'].drop_duplicates():
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#
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# df_city1 = df[(df['city_name'] == city) & (df['county_name'].isnull())].set_index('pt_date').loc['2023-11']
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# resut_df = pd.DataFrame({})
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# index_level = []
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# tq_list = []
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# pred_list = []
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# loss_list = []
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# rate_list = []
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# for level in df_city1.columns[2:]:
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#
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# index_level.append(level)
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#
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# df_moving_avg = pd.DataFrame(df_city1[:-3][level], index=df_city1[:-3].index)
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# future = pd.date_range(start=df_city1.index[-3], periods=3, freq='D')
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#
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# for date in future:
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# df_moving_avg.loc[date, level] = df_moving_avg[-3:].mean().values
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# loss = (df_city1[level].tail(-3).sum() - df_moving_avg.tail(-3).sum()) / df_city1[level].sum()
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# tq_list.append(df_city1[level].sum())
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# pred_list.append(df_moving_avg[level].sum())
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# loss_list.append(df_city1[level].sum()-df_moving_avg[level].sum())
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# rate_list.append((df_city1[level].sum()-df_moving_avg[level].sum())/df_city1[level].sum())
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# resut_df = pd.DataFrame({'同期电量':tq_list,'预测电量':pred_list,'偏差':loss_list,'偏差率':rate_list},index=index_level)
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# with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\11月移动平均分压.xlsx',mode='a',if_sheet_exists='replace',engine='openpyxl') as writer:
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# resut_df.to_excel(writer,sheet_name=f'{city[4:6]}')
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excel_file = pd.ExcelFile(r'C:\Users\鸽子\Desktop\11月移动平均分压.xlsx')
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df1 = pd.read_excel(excel_file,sheet_name=1)
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df1.set_index(df1.columns[0],inplace=True)
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for sheet in excel_file.sheet_names[2:]:
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df = pd.read_excel(excel_file,sheet_name=sheet)
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df.set_index(df.columns[0],inplace=True)
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df1 += df
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df1['偏差'] = df1['同期电量']-df1['预测电量']
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df1['偏差率'] = df1['偏差']/df1['同期电量']
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df1.to_excel('移动平均_11月分压汇总.xlsx')
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print(df1)
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import pandas as pd
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df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江电量20231202.xlsx', sheet_name=2)
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df['stat_date'] = pd.to_datetime(df['stat_date'])
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# 移动平均
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city = df['city_name'].iloc[0]
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print(city)
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df_city1 = df[df['city_name'] == city].set_index('stat_date').loc['2023-11']
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dict_big = {}
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dict_ok = {}
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resut_df = pd.DataFrame({})
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index_industry = []
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tq_list = []
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pred_list = []
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loss_list = []
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rate_list = []
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for industry in df_city1.columns[1:]:
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index_industry.append(industry)
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df_moving_avg = pd.DataFrame(df_city1[:-3][industry], index=df_city1[:-3].index)
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future = pd.date_range(start=df_city1.index[-3], periods=3, freq='D')
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for date in future:
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df_moving_avg.loc[date, industry] = df_moving_avg[-3:].mean().values
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loss = (df_city1[industry].tail(-3).sum() - df_moving_avg.tail(-3).sum()) / df_city1[industry].sum()
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tq_list.append(df_city1[industry].sum())
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pred_list.append(df_moving_avg[industry].sum())
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loss_list.append(df_city1[industry].sum()-df_moving_avg[industry].sum())
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rate_list.append((df_city1[industry].sum()-df_moving_avg[industry].sum())/df_city1[industry].sum())
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resut_df = pd.DataFrame({'同期电量':tq_list,'预测电量':pred_list,'偏差':loss_list,'偏差率':rate_list},index=index_industry)
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print(resut_df)
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resut_df.to_excel(r'C:\Users\鸽子\Desktop\移动平均_丽水_行业.xlsx')
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# if loss.values >= 0.005:
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# dict_big[industry] = loss.values[0]
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# else:
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# dict_ok[industry] = loss.values[0]
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# print(len(dict_ok))
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# print(len(dict_big))
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