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60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
import pandas as pd
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pd.set_option('display.width',None)
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df = pd.read_excel(r'C:\python-project\p1031\北京安徽\北京安徽电量数据\北京安徽行业.xlsx', sheet_name=0)
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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)&(df['county_name'].isnull())].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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df_moving_avg = pd.DataFrame(df_city.iloc[:-3][industry], index=df_city.iloc[:-3].index)
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future = pd.date_range(start='2023-12-29', 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[df_moving_avg.values!=0][-3:].mean().values
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resut_df = pd.concat([resut_df, df_moving_avg], axis=1)
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"result_df为明细数据"
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print(city[-6:])
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final_df = resut_df.sum()
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final_df = pd.DataFrame(final_df,columns=['预测值'])
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final_df['真实值'] = df_city[df_city.columns[2:]].sum()
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final_df['偏差'] = final_df['真实值'] - final_df['预测值']
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final_df['偏差率'] = final_df['偏差'] / final_df['真实值']
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final_df['偏差率'] = final_df['偏差率'].apply(lambda x:"{:.5%}".format(x))
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print(final_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\移动平均_北京行业_12月.xlsx', mode='a', if_sheet_exists='replace',
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engine='openpyxl') as writer:
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final_df.to_excel(writer, sheet_name=f'{city[-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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