我需要计算以列Wgt
中的新值指示的某个频率重置的累积积。你知道吗
例如,在生成的数据帧中:
df = pd.DataFrame(np.random.lognormal(0, 0.01, 27), pd.date_range('2019-01-06', '2019-02-01'), columns=['Chg'])
df['Wgt'] = df['Chg'].asfreq('W')
df.loc[df.Wgt > 0, 'Wgt'] = np.random.uniform(0.5, 1, df.Wgt.count())
Chg Wgt
2019-01-06 1.014571 0.861546
2019-01-07 1.018993 NaN
2019-01-08 1.017461 NaN
2019-01-09 1.003788 NaN
2019-01-10 1.014106 NaN
2019-01-11 0.995758 NaN
2019-01-12 0.989058 NaN
2019-01-13 0.995897 0.602225
2019-01-14 1.007336 NaN
2019-01-15 1.004143 NaN
...
我要计算一个新列Agg
,其值为:
df.Wgt != np.nan
那么df.Agg = df.Wgt
df.Agg = df.Agg.shift() * df.Chg
也就是说,在这个例子中Agg
将是:
Chg Wgt Agg
1/6/2019 1.014571 0.861546 0.861546
1/7/2019 1.018993 NaN 0.877909343
1/8/2019 1.017461 NaN 0.893238518
1/9/2019 1.003788 NaN 0.896622106
1/10/2019 1.014106 NaN 0.909269857
1/11/2019 0.995758 NaN 0.905412734
1/12/2019 0.989058 NaN 0.895505708
1/13/2019 0.995897 0.602225 0.602225
1/14/2019 1.007336 NaN 0.606642923
1/15/2019 1.004143 NaN 0.609156244
...
什么是泛滥成灾的做法?你知道吗
与
cumprod
一起使用np.where
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