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48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
import pandas as pd
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df = pd.read_excel(r'C:\Users\鸽子\Desktop\20231229.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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df_city = df[df['city_name'] == city].set_index('stat_date').loc['2023-12'].sort_index()
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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='2023-12-28', periods=4, 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_1229.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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