我有两个变量A和B，都是标量。以及具有1000列和86400行的DataFrame df1。为了简单起见，在下表中只有10列：
0 1 2 3 4 5 6 7 8 9 f
0 4.000000 23.000000 6.000000 36.000000 37.000000 33.000000 22.000000 28.000000 8.000000 14.000000 50.135
1 4.002361 23.002361 6.002361 36.002361 37.002361 33.002361 22.002361 28.002361 8.002361 14.002361 50.130
2 4.004722 23.004722 6.004722 36.004722 37.004722 33.004722 22.004722 28.004722 8.004722 14.004722 50.120
3 4.007083 23.007083 6.007083 36.007083 37.007083 33.007083 22.007083 28.007083 8.007083 14.007083 50.112
4 4.009444 23.009444 6.009444 36.009444 37.009444 33.009444 22.009444 28.009444 8.009444 14.009444 50.102
5 4.011806 23.011806 6.011806 36.011806 37.011806 33.011806 22.011806 28.011806 8.011806 14.011806 50.097
... ... ... ... ... ... ... ... ... ... ... ...
86387 207.969306 226.969306 209.969306 239.969306 240.969306 236.969306 225.969306 231.969306 211.969306 217.969306 49.920
86388 207.971667 226.971667 209.971667 239.971667 240.971667 236.971667 225.971667 231.971667 211.971667 217.971667 49.920
86389 207.974028 226.974028 209.974028 239.974028 240.974028 236.974028 225.974028 231.974028 211.974028 217.974028 49.920
86390 207.976389 226.976389 209.976389 239.976389 240.976389 236.976389 225.976389 231.976389 211.976389 217.976389 49.920
86391 207.978750 226.978750 209.978750 239.978750 240.978750 236.978750 225.978750 231.978750 211.978750 217.978750 49.917
86392 207.981111 226.981111 209.981111 239.981111 240.981111 236.981111 225.981111 231.981111 211.981111 217.981111 49.917
86393 207.983472 226.983472 209.983472 239.983472 240.983472 236.983472 225.983472 231.983472 211.983472 217.983472 49.915
86394 207.985833 226.985833 209.985833 239.985833 240.985833 236.985833 225.985833 231.985833 211.985833 217.985833 49.915
86395 207.988194 226.988194 209.988194 239.988194 240.988194 236.988194 225.988194 231.988194 211.988194 217.988194 49.915
86396 207.990556 226.990556 209.990556 239.990556 240.990556 236.990556 225.990556 231.990556 211.990556 217.990556 49.912
86397 207.992917 226.992917 209.992917 239.992917 240.992917 236.992917 225.992917 231.992917 211.992917 217.992917 49.915
86398 207.995278 226.995278 209.995278 239.995278 240.995278 236.995278 225.995278 231.995278 211.995278 217.995278 49.917
86399 207.997639 226.997639 209.997639 239.997639 240.997639 236.997639 225.997639 231.997639 211.997639 217.997639 49.917
我想逐行执行操作：
当f> 50时：将C = A / B / 3600添加到1-999列中的值。
当f <50时：将C = A / B / 3600减去1-999列中的值。
cols = df1.columns[df1.columns.isin(range(0, 999))]
df1[cols] = np.where(df1[cols] > 50,
df1[cols].values - np.arange(len(df1))[:, None] * C,
df1[cols].values + np.arange(len(df1))[:, None] * C)
可以看出，即使f <50，该值也继续增加。
有什么建议吗？ 预先谢谢你
答案 0 :(得分：1)
如果性能很重要，请使用numpy.where并按条件添加numpy.arange：
cols = df.columns[df.columns.isin(range(1, 1000))]
df[cols] = np.where(df[cols] > 50,
df[cols].values - np.arange(len(df))[:, None],
df[cols].values + np.arange(len(df))[:, None])
print (df)
0 1 2 3 4 5 6 7 8 9 991 992 993 994 995 996 \
0 18 8 9 5 38 11 26 25 2 30 23 18 34 1 29 34
1 18 9 10 6 39 12 27 26 3 31 24 19 35 2 30 35
2 18 10 11 7 40 13 28 27 4 32 25 20 36 3 31 36
3 18 11 12 8 41 14 29 28 5 33 26 21 37 4 32 37
4 18 12 13 9 42 15 30 29 6 34 27 22 38 5 33 38
5 18 13 14 10 43 16 31 30 7 35 28 23 39 6 34 39
6 18 14 15 11 44 17 32 31 8 36 29 24 40 7 35 40
86393 18 15 16 12 45 18 33 32 9 37 30 25 41 8 36 41
86394 18 16 17 13 46 19 34 33 10 38 31 26 42 9 37 42
86395 18 17 18 14 47 20 35 34 11 39 32 27 43 10 38 43
86396 18 18 19 15 48 21 36 35 12 40 33 28 44 11 39 44
86397 18 19 20 16 49 22 37 36 13 41 34 29 45 12 40 45
86398 18 20 21 17 50 23 38 37 14 42 35 30 46 13 41 46
86399 18 21 22 18 51 24 39 38 15 43 36 31 47 14 42 47
86400 18 22 23 19 52 25 40 39 16 44 37 32 48 15 43 48
997 998 999 f
0 25 15 2 50.135
1 26 16 3 50.130
2 27 17 4 50.120
3 28 18 5 50.112
4 29 19 6 50.102
5 30 20 7 50.097
6 31 21 8 50.095
86393 32 22 9 49.915
86394 33 23 10 49.915
86395 34 24 11 49.915
86396 35 25 12 49.912
86397 36 26 13 49.915
86398 37 27 14 49.917
86399 38 28 15 49.917
86400 39 29 16 49.915
编辑：
A = 360000
B = 5
C = A/B/3600
cols = df.columns[df.columns.isin(range(1, 1000))]
mask = df[cols] > 50
df[cols] = np.where(mask,
df[cols].values - mask.cumsum().sub(1) * C,
df[cols].values + (~mask).cumsum().sub(1) * C)
print (df)
0 1 2 3 4 5 \
0 4.000000 23.000000 6.000000 36.000000 37.000000 33.000000
1 4.002361 43.002361 26.002361 56.002361 57.002361 53.002361
2 4.004722 63.004722 46.004722 76.004722 77.004722 73.004722
3 4.007083 83.007083 66.007083 96.007083 97.007083 93.007083
4 4.009444 103.009444 86.009444 116.009444 117.009444 113.009444
5 4.011806 123.011806 106.011806 136.011806 137.011806 133.011806
86387 207.969306 226.969306 209.969306 239.969306 240.969306 236.969306
86388 207.971667 206.971667 189.971667 219.971667 220.971667 216.971667
86389 207.974028 186.974028 169.974028 199.974028 200.974028 196.974028
86390 207.976389 166.976389 149.976389 179.976389 180.976389 176.976389
86391 207.978750 146.978750 129.978750 159.978750 160.978750 156.978750
86392 207.981111 126.981111 109.981111 139.981111 140.981111 136.981111
86393 207.983472 106.983472 89.983472 119.983472 120.983472 116.983472
86394 207.985833 86.985833 69.985833 99.985833 100.985833 96.985833
86395 207.988194 66.988194 49.988194 79.988194 80.988194 76.988194
86396 207.990556 46.990556 29.990556 59.990556 60.990556 56.990556
86397 207.992917 26.992917 9.992917 39.992917 40.992917 36.992917
86398 207.995278 6.995278 -10.004722 19.995278 20.995278 16.995278
86399 207.997639 -13.002361 -30.002361 -0.002361 0.997639 -3.002361
6 7 8 9 f
0 22.000000 28.000000 8.000000 14.000000 50.135
1 42.002361 48.002361 28.002361 34.002361 50.130
2 62.004722 68.004722 48.004722 54.004722 50.120
3 82.007083 88.007083 68.007083 74.007083 50.112
4 102.009444 108.009444 88.009444 94.009444 50.102
5 122.011806 128.011806 108.011806 114.011806 50.097
86387 225.969306 231.969306 211.969306 217.969306 49.920
86388 205.971667 211.971667 191.971667 197.971667 49.920
86389 185.974028 191.974028 171.974028 177.974028 49.920
86390 165.976389 171.976389 151.976389 157.976389 49.920
86391 145.978750 151.978750 131.978750 137.978750 49.917
86392 125.981111 131.981111 111.981111 117.981111 49.917
86393 105.983472 111.983472 91.983472 97.983472 49.915
86394 85.985833 91.985833 71.985833 77.985833 49.915
86395 65.988194 71.988194 51.988194 57.988194 49.915
86396 45.990556 51.990556 31.990556 37.990556 49.912
86397 25.992917 31.992917 11.992917 17.992917 49.915
86398 5.995278 11.995278 -8.004722 -2.004722 49.917
86399 -14.002361 -8.002361 -28.002361 -22.002361 49.917