假設我們有資料集df（可以從
例如，對于2022 年 9月y_agr_last2 = ((1 3.85/100)*(1 1.81/100))^(1/2) -1，，，y_agr_last3 = ((1 3.85/100)*(1 1.81/100)*(1 1.6/100))^(1/3) -1。
我使用的代碼如下，比較重復和瑣碎：
import math
df['y_shift12'] = df['y'].shift(12)
df['y_shift24'] = df['y'].shift(24)
df['y_shift36'] = df['y'].shift(36)
df['y_agr_last2'] = pow(((1 df['y_shift12']/100) * (1 df['y_shift24']/100)), 1/2) -1
df['y_agr_last3'] = pow(((1 df['y_shift12']/100) * (1 df['y_shift24']/100) * (1 df['y_shift36']/100)), 1/3) -1
df.drop(['y_shift12', 'y_shift24', 'y_shift36'], axis=1, inplace=True)
df
如何更簡潔地達到預期的結果？
參考：
根據過去幾年該月的 y 平均值創建一些特征
uj5u.com熱心網友回復：
以下是對其進行概括的一種方法：
import functools
import operator
num_yrs = 3
for n in range(1, num_yrs 1):
df[f"y_shift{n*12}"] = df["y"].shift(n*12)
df[f"y_agr_last{n}"] = pow(functools.reduce(operator.mul, [1 df[f"y_shift{i*12}"]/100 for i in range(1, n 1)], 1), 1/n) - 1
df = df.drop(["y_agr_last1"] [f"y_shift{n*12}" for n in range(1, num_yrs 1)], axis=1)
輸出：
date y x1 x2 y_agr_last2 y_agr_last3
0 2018/1/31 -13.80 1.943216 3.135839 NaN NaN
1 2018/2/28 -14.50 0.732108 0.375121 NaN NaN
...
22 2019/11/30 4.00 -0.273262 -0.021146 NaN NaN
23 2019/12/31 7.60 1.538851 1.903968 NaN NaN
24 2020/1/31 -11.34 2.858537 3.268478 -0.077615 NaN
25 2020/2/29 -34.20 -1.246915 -0.883807 -0.249940 NaN
26 2020/3/31 46.50 -4.213756 -4.670146 0.221816 NaN
...
33 2020/10/31 -1.00 1.967062 1.860070 -0.035569 NaN
34 2020/11/30 12.99 2.302166 2.092842 0.041998 NaN
35 2020/12/31 5.54 3.814303 5.611199 0.030017 NaN
36 2021/1/31 -6.41 4.205601 4.948924 -0.064546 -0.089701
37 2021/2/28 -22.38 4.185913 3.569100 -0.342000 -0.281975
38 2021/3/31 17.64 5.370519 3.130884 0.465000 0.298025
...
54 2022/7/31 0.80 -6.259455 -6.716896 0.057217 0.052793
55 2022/8/31 -5.30 1.302754 1.412277 0.015121 -0.000492
56 2022/9/30 NaN -2.876968 -3.785964 0.028249 0.024150
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