這幾天我一直在努力對列進行一些修改。batt_status列的計算如下：
如果（batt_input) > 0：
batt_status[i] = batt_status[i-1] (batt_input[i] / 4)
否則：
batt_status[i] = batt_status[i-1] - (batt_output[i] / 4)。
batt_input batt_output batt_status
0 0.000000 0.000000 0.000000
1 2.739314 0.000000 0.684829
2 5.000000 0.000000 1.934829
3 5.000000 0.000000 3.184829
4 4.190054 0.000000 4.232342
5 4.627677 0.000000 5.389261
6 4.237302 0.000000 6.448587
7 1.251996 0.000000 6.761586
8 4.147673 0.000000 7.798504
9 2.921009 0.000000 8.528756
10 4.877213 0.000000 9.748060
11 5.000000 0.000000 10.998060
12 5.000000 0.000000 12.248060
13 5.000000 0.000000 13.498060
14 0.000000 3.120185 12.718013
15 0.000000 3.094523 11.944382
16 0.000000 3.711843 11.016422
17 0.000000 4.338085 9.931900
18 0.000000 4.173286 8.888579
19 0.000000 4.312411 7.810476
20 0.000000 4.345891 6.724003
21 0.000000 4.512739 5.595818
22 0.000000 4.543866 4.459852
23 0.000000 4.450718 3.347172
24 0.000000 4.511852 2.219209
25 0.000000 4.765721 1.027779
26 0.000000 4.713985 -0.150717
27 0.000000 4.604684 -1.301888
我使用了代碼：
df['batt_status'] = np.where(df['batt_input'] == 0, 0, np.nan)
for i in range(1, len(df)):
if(df.loc[i, 'batt_input'] > 0):
df.loc[i, 'batt_status'] = df.loc[i-1, 'batt_status'] (df.loc[i, 'batt_input']/4)
else:
df.loc[i, 'batt_status'] = df.loc[i-1, 'batt_status'] - (df.loc[i, 'batt_output']/4)
正如您在batt_status專欄中所指出的，可能會累積負值或強正值。現在我想限制列中的值，batt_status使它們達到最小值 0 和最大值 20。
然后，當達到零時，后續值應該重復零，直到可以添加其他值（batt_input > 0）。
一旦達到值 20，它也應該被復制，直到可以減去一些東西 ( batt_output > 0)。等等。
預期輸出（示例）：
batt_input batt_output batt_status
0 0.000000 0.000000 0.000000
1 2.739314 0.000000 0.684829
2 5.000000 0.000000 1.934829
3 5.000000 0.000000 3.184829
4 4.190054 0.000000 4.232342
5 4.627677 0.000000 5.389261
6 4.237302 0.000000 6.448587
7 1.251996 0.000000 6.761586
8 4.147673 0.000000 7.798504
9 2.921009 0.000000 8.528756
10 4.877213 0.000000 9.748060
11 5.000000 0.000000 10.998060
12 5.000000 0.000000 12.248060
13 5.000000 0.000000 13.498060
14 0.000000 3.120185 12.718013
15 0.000000 3.094523 11.944382
16 0.000000 3.711843 11.016422
17 0.000000 4.338085 9.931900
18 0.000000 4.173286 8.888579
19 0.000000 4.312411 7.810476
20 0.000000 4.345891 6.724003
21 0.000000 4.512739 5.595818
22 0.000000 4.543866 4.459852
23 0.000000 4.450718 3.347172
24 0.000000 4.511852 2.219209
25 0.000000 4.765721 1.027779
26 0.000000 4.713985 0.000000 # (here batt_status reaches lower than 0, so put the smallest possible i.e. 0)
27 0.000000 4.604684 0.000000 # repeat 0
28 0.000000 3.567943 0.000000 # repeat 0
29 0.000000 2.344556 0.000000 # repeat 0
30 2.739314 0.000000 0.684829 # can add (batt_input/4)
31 10.35678 0.000000 3.274024
32 65.03452 0.000000 19.53265
33 3.452341 0.000000 20.00000 # (here batt_status reaches value greater than 20, so put as much as possible, i.e. 20)
34 2.345566 0.000000 20.00000 # repeat 20
35 45.56677 0.000000 20.00000 # repeat 20
36 0.000000 25.45600 13.63600 # can substract (batt_output/4)
37 0.000000 2.445552 13.02462
你對此有什么想法嗎？
uj5u.com熱心網友回復：
min使用and修改代碼相當容易max；下面的實作加上一個小的變化
df['batt_change'] = np.where(df['batt_input']>0, df['batt_input']/4, -df['batt_output']/4)
df['batt_status'] = 0
for i in range(1, len(df)):
df.loc[i, 'batt_status'] = min(max(df.loc[i-1, 'batt_status'] df.loc[i, 'batt_change'],0),20)
引入專欄的原因'batt_change'是您的原始代碼可以簡單地實作
df['batt_status'] = df['batt_change'].cumsum()
但不幸的是，我們不能這樣做，因為您想限制和限制輸出。所以仍然需要那個回圈
df 看起來像這樣（對于你的第二個例子）
batt_input batt_output batt_change batt_status
-- ------------ ------------- ------------- -------------
0 0 0 -0 0
1 2.73931 0 0.684828 0.684828
2 5 0 1.25 1.93483
3 5 0 1.25 3.18483
4 4.19005 0 1.04751 4.23234
5 4.62768 0 1.15692 5.38926
6 4.2373 0 1.05933 6.44859
7 1.252 0 0.312999 6.76159
8 4.14767 0 1.03692 7.7985
9 2.92101 0 0.730252 8.52876
10 4.87721 0 1.2193 9.74806
11 5 0 1.25 10.9981
12 5 0 1.25 12.2481
13 5 0 1.25 13.4981
14 0 3.12019 -0.780046 12.718
15 0 3.09452 -0.773631 11.9444
16 0 3.71184 -0.927961 11.0164
17 0 4.33809 -1.08452 9.9319
18 0 4.17329 -1.04332 8.88858
19 0 4.31241 -1.0781 7.81048
20 0 4.34589 -1.08647 6.724
21 0 4.51274 -1.12818 5.59582
22 0 4.54387 -1.13597 4.45985
23 0 4.45072 -1.11268 3.34717
24 0 4.51185 -1.12796 2.21921
25 0 4.76572 -1.19143 1.02778
26 0 4.71399 -1.1785 0
27 0 4.60468 -1.15117 0
28 0 3.56794 -0.891986 0
29 0 2.34456 -0.586139 0
30 2.73931 0 0.684828 0.684828
31 10.3568 0 2.5892 3.27402
32 65.0345 0 16.2586 19.5327
33 3.45234 0 0.863085 20
34 2.34557 0 0.586391 20
35 45.5668 0 11.3917 20
36 0 25.456 -6.364 13.636
37 0 2.44555 -0.611388 13.0246
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