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56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
import pandas as pd
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df = pd.read_excel(r'C:\Users\鸽子\Desktop\zj20231228.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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df_city1 = df[(df['city_name'] == city) & (df['county_name'].isnull())].set_index('pt_date').loc['2023-12'].sort_index()
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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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df_moving_avg = pd.DataFrame(df_city1[level], index=df_city1.index)
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future = pd.date_range(start='2023-12-27', 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},
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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_1228.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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