输出预测结果
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71bc236f76
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@ -1,47 +1,55 @@
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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 = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江power1225.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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for city in df['city_name'].drop_duplicates():
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df_city1 = df[(df['city_name'] == city) & (df['county_name'].isnull())].set_index('pt_date').loc['2023-12']
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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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# 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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df_moving_avg = pd.DataFrame(df_city1[level], index=df_city1.index)
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future = pd.date_range(start='2023-12-26', periods=5, freq='D')
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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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resut_df = pd.concat([resut_df, df_moving_avg], axis=1)
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print(city[4:6])
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print(resut_df)
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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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# 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},
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# index=index_level)
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# resut_df = pd.DataFrame({'预测电量': pred_list},index=index_level)
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with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\市分压电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
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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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@ -1,42 +0,0 @@
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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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@ -0,0 +1,49 @@
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import pandas as pd
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df = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江power1225.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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for city in df['city_name'].drop_duplicates():
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print(city)
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df_city = df[df['city_name'] == city].set_index('stat_date').loc['2023-12']
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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_city.columns[2:]:
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# index_industry.append(industry)
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df_moving_avg = pd.DataFrame(df_city[industry], index=df_city.index)
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future = pd.date_range(start='2023-12-26', periods=5, 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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resut_df = pd.concat([resut_df, df_moving_avg], axis=1)
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print(city[4:6])
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print(resut_df)
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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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with pd.ExcelWriter(r'C:\Users\鸽子\Desktop\行业电量预测_1227.xlsx', mode='a', if_sheet_exists='replace',
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engine='openpyxl') as writer:
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resut_df.to_excel(writer, sheet_name=f'{city[4:6]}')
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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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