使用累积总和超过特定阈值来迭代数据

Question

假设我有一个列X及其索引i如下：

index   X
0     0.007934
1     0.015535
2     0.000368
3     0.000212
4     0.000111
5    -0.001123
6    -0.000539
7    -0.000142
8    -0.000114
9    -0.002034
10   -0.041414
11    0.002792
12    0.016099
13    0.006480
14   -0.007141
15   -0.010191
16   -0.044790
17   -0.004887
18   -0.009217
19   -0.039200
20   -0.066433
21    0.021649
22    0.004331
23   -0.000393
24   -0.005410
25    0.006222
26    0.001342
27    0.065700
28    0.003055
29   -0.004560

我想迭代它以创建一个满足以下条件的新列Y： - 如果索引i是X_i的累积和超过阈值0.0006的第一个退出索引，则索引i处的Y值将记录为$ Y_i ='是' - 否则记录为$ Y_i ='否'

以下是前7个索引的说明：

abs(cumulative sum(X[:0]) = 0.007934 > 0.0006 then Y_0 = Yes.

abs(cumulative sum(X[1:1]) = 0.015535 > 0.0006 then Y_1 = Yes

abs(cumulative sum(X[2:2]) = 0.000368 < 0.0006 then Y_2 = No

abs(cumulative sum(X[2:3]) = 0.000580 < 0.0006 then Y_3 = No

abs(cumulative sum(X[2:4]) = 0.000691 > 0.0006 then Y_4 = Yes

abs(cumulative sum(X[5:5]) = 0.001123 > 0.0006 then Y_5 = Yes.

abs(cumulative sum(X[6:6]) = 0.000539 < 0.0006 then Y_6 = No

abs(cumulative sum(X[6:7]) = 0.000681 > 0.0006 then Y_7 = Yes

...

迭代直到样品耗尽。

你能帮我解决这个问题吗？非常感谢你。

Answer 1

更新我已根据您的评论更正了代码。如果我正确理解它，那么解决问题的代码可能如下所示：

def Y(x, x_history):
    x_history.append(abs(x))
    if sum(x_history) > 0.0006:
        return True, []
    return False, x_history

X = [0.007934, 0.015535, 0.000368, 0.000212, 0.000111, -0.001123, -0.000539,
     -0.000142, -0.000114, -0.002034, -0.041414, 0.002792, 0.016099, 0.006480,
     -0.007141, -0.010191, -0.044790, -0.004887, -0.009217, -0.039200,
     -0.066433, 0.021649, 0.004331, -0.000393, -0.005410, 0.006222, 0.001342,
     0.065700, 0.003055, -0.004560]

print('index   X    Y')
x_history = []
for i, x in enumerate(X):
    y, x_history = Y(x, x_history)
    print(i, x, 'Yes' if y else 'No')

输出是：

index   X    Y
0 0.007934 Yes
1 0.015535 Yes
2 0.000368 No
3 0.000212 No
4 0.000111 Yes
5 -0.001123 Yes
6 -0.000539 No
7 -0.000142 Yes
8 -0.000114 No
9 -0.002034 Yes
10 -0.041414 Yes
11 0.002792 Yes
12 0.016099 Yes
13 0.00648 Yes
14 -0.007141 Yes
15 -0.010191 Yes
16 -0.04479 Yes
17 -0.004887 Yes
18 -0.009217 Yes
19 -0.0392 Yes
20 -0.066433 Yes
21 0.021649 Yes
22 0.004331 Yes
23 -0.000393 No
24 -0.00541 Yes
25 0.006222 Yes
26 0.001342 Yes
27 0.0657 Yes
28 0.003055 Yes
29 -0.00456 Yes