我有一个名为 df_expanded 的数据框，看起来像这样。关键列是'A'，这个问题的重要索引是29-31（这是一个修改版本，因为实际数据框很大）：
>>> display(df_expanded)
A C Cl Co D E F Fa G Ga H HW
index
...
2.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 11.0 2.0 8.0 0.0 0.0
2.0 0.0 0.0 0.0 0.0 0.0 4.2 0.0 11.8 2.4 8.6 0.0 0.0
2.0 0.0 0.0 0.0 0.0 0.0 4.4 0.0 12.6 2.8 9.2 0.0 0.0
2.0 0.0 0.0 0.0 0.0 0.0 4.6 0.0 13.4 3.2 9.8 0.0 0.0
2.0 0.0 0.0 0.0 0.0 0.0 4.8 0.0 14.2 3.6 10.4 0.0 0.0
3.0 0.0 0.0 0.0 0.0 0.0 5.0 0.0 15.0 4.0 11.0 0.0 0.0
3.0 0.4 0.0 0.0 0.0 0.0 5.2 0.0 16.0 4.0 11.6 0.0 0.0
3.0 0.8 0.0 0.0 0.0 0.0 5.4 0.0 17.0 4.0 12.2 0.0 0.0
3.0 1.2 0.0 0.0 0.0 0.0 5.6 0.0 18.0 4.0 12.8 0.0 0.0
3.0 1.6 0.0 0.0 0.0 0.0 5.8 0.0 19.0 4.0 13.4 0.0 0.0
4.0 2.0 0.0 0.0 0.0 0.0 6.0 0.0 20.0 4.0 14.0 0.0 0.0
4.0 2.0 0.0 0.0 0.0 0.0 6.0 0.0 21.2 4.0 14.4 0.0 0.0
4.0 2.0 0.0 0.0 0.0 0.0 6.0 0.0 22.4 4.0 14.8 0.0 0.0
4.0 2.0 0.0 0.0 0.0 0.0 6.0 0.0 23.6 4.0 15.2 0.0 0.0
4.0 2.0 0.0 0.0 0.0 0.0 6.0 0.0 24.8 4.0 15.6 0.0 0.0
5.0 2.0 0.0 0.0 0.0 0.0 6.0 0.0 26.0 4.0 16.0 0.0 0.0
5.0 2.0 0.0 0.0 0.0 0.0 6.2 0.0 27.4 4.0 16.6 0.0 0.0
5.0 2.0 0.0 0.0 0.0 0.0 6.4 0.0 28.8 4.0 17.2 0.0 0.0
5.0 2.0 0.0 0.0 0.0 0.0 6.6 0.0 30.2 4.0 17.8 0.0 0.0
5.0 2.0 0.0 0.0 0.0 0.0 6.8 0.0 31.6 4.0 18.4 0.0 0.0
6.0 2.0 0.0 0.0 0.0 0.0 7.0 0.0 33.0 4.0 19.0 0.0 0.0
6.0 2.0 0.0 0.0 0.0 0.0 7.0 1.0 33.4 4.0 19.2 0.0 0.0
6.0 2.0 0.0 0.0 0.0 0.0 7.0 2.0 33.8 4.0 19.4 0.0 0.0
6.0 2.0 0.0 0.0 0.0 0.0 7.0 3.0 34.2 4.0 19.6 0.0 0.0
6.0 2.0 0.0 0.0 0.0 0.0 7.0 4.0 34.6 4.0 19.8 0.0 0.0
7.0 2.0 0.0 0.0 0.0 0.0 7.0 5.0 35.0 4.0 20.0 0.0 0.0
7.0 2.0 0.0 0.0 0.0 0.0 7.0 5.0 36.2 4.0 20.4 0.0 0.0
7.0 2.0 0.0 0.0 0.0 0.0 7.0 5.0 37.4 4.0 20.8 0.0 0.0
7.0 2.0 0.0 0.0 0.0 0.0 7.0 5.0 38.6 4.0 21.2 0.0 0.0
7.0 2.0 0.0 0.0 0.0 0.0 7.0 5.0 39.8 4.0 21.6 0.0 0.0
8.0 2.0 0.0 0.0 0.0 0.0 7.0 5.0 41.0 4.0 22.0 0.0 0.0
8.0 2.0 0.0 0.0 0.0 0.0 7.2 5.0 41.0 4.0 22.2 0.0 0.0
8.0 2.0 0.0 0.0 0.0 0.0 7.4 5.0 41.0 4.0 22.4 0.0 0.0
8.0 2.0 0.0 0.0 0.0 0.0 7.6 5.0 41.0 4.0 22.6 0.0 0.0
8.0 2.0 0.0 0.0 0.0 0.0 7.8 5.0 41.0 4.0 22.8 0.0 0.0
9.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 41.0 4.0 23.0 0.0 0.0
9.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 41.6 4.0 23.4 0.0 0.0
9.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 42.2 4.0 23.8 0.0 0.0
9.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 42.8 4.0 24.2 0.0 0.0
9.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 43.4 4.0 24.6 0.0 0.0
10.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 44.0 4.0 25.0 0.0 0.0
10.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 45.2 4.0 25.6 0.0 0.0
10.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 46.4 4.0 26.2 0.0 0.0
10.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 47.6 4.0 26.8 0.0 0.0
10.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 48.8 4.0 27.4 0.0 0.0
11.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 50.0 4.0 28.0 0.0 0.0
11.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 50.4 4.4 28.4 0.2 0.0
11.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 50.8 4.8 28.8 0.4 0.0
11.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 51.2 5.2 29.2 0.6 0.0
11.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 51.6 5.6 29.6 0.8 0.0
12.0 2.0 0.0 0.0 0.0 0.0 8.0 5.0 52.0 6.0 30.0 1.0 0.0
12.0 2.0 0.0 0.0 0.0 0.0 8.0 5.8 52.6 6.0 30.6 1.0 0.0
12.0 2.0 0.0 0.0 0.0 0.0 8.0 6.6 53.2 6.0 31.2 1.0 0.0
12.0 2.0 0.0 0.0 0.0 0.0 8.0 7.4 53.8 6.0 31.8 1.0 0.0
12.0 2.0 0.0 0.0 0.0 0.0 8.0 8.2 54.4 6.0 32.4 1.0 0.0
13.0 2.0 0.0 0.0 0.0 0.0 8.0 9.0 55.0 6.0 33.0 1.0 0.0
13.0 2.0 0.0 0.0 0.0 0.0 8.0 10.0 55.4 6.0 33.6 1.0 0.0
13.0 2.0 0.0 0.0 0.0 0.0 8.0 11.0 55.8 6.0 34.2 1.0 0.0
13.0 2.0 0.0 0.0 0.0 0.0 8.0 12.0 56.2 6.0 34.8 1.0 0.0
13.0 2.0 0.0 0.0 0.0 0.0 8.0 13.0 56.6 6.0 35.4 1.0 0.0
14.0 2.0 0.0 0.0 0.0 0.0 8.0 14.0 57.0 6.0 36.0 1.0 0.0
14.0 2.0 0.0 0.0 0.0 0.0 8.0 15.6 57.0 6.4 36.2 1.0 0.0
14.0 2.0 0.0 0.0 0.0 0.0 8.0 17.2 57.0 6.8 36.4 1.0 0.0
14.0 2.0 0.0 0.0 0.0 0.0 8.0 18.8 57.0 7.2 36.6 1.0 0.0
14.0 2.0 0.0 0.0 0.0 0.0 8.0 20.4 57.0 7.6 36.8 1.0 0.0
15.0 2.0 0.0 0.0 0.0 0.0 8.0 22.0 57.0 8.0 37.0 1.0 0.0
15.0 2.0 0.0 0.2 0.0 0.0 8.0 22.6 58.0 8.4 37.0 1.0 0.0
15.0 2.0 0.0 0.4 0.0 0.0 8.0 23.2 59.0 8.8 37.0 1.0 0.0
15.0 2.0 0.0 0.6 0.0 0.0 8.0 23.8 60.0 9.2 37.0 1.0 0.0
15.0 2.0 0.0 0.8 0.0 0.0 8.0 24.4 61.0 9.6 37.0 1.0 0.0
16.0 2.0 0.0 1.0 0.0 0.0 8.0 25.0 62.0 10.0 37.0 1.0 0.0
16.0 2.0 0.0 1.0 0.0 0.0 8.2 25.0 63.2 10.0 37.0 1.0 0.0
16.0 2.0 0.0 1.0 0.0 0.0 8.4 25.0 64.4 10.0 37.0 1.0 0.0
16.0 2.0 0.0 1.0 0.0 0.0 8.6 25.0 65.6 10.0 37.0 1.0 0.0
16.0 2.0 0.0 1.0 0.0 0.0 8.8 25.0 66.8 10.0 37.0 1.0 0.0
17.0 2.0 0.0 1.0 0.0 0.0 9.0 25.0 68.0 10.0 37.0 1.0 0.0
17.0 2.0 0.0 1.2 0.0 0.0 9.0 26.2 68.4 10.4 37.2 1.0 0.0
17.0 2.0 0.0 1.4 0.0 0.0 9.0 27.4 68.8 10.8 37.4 1.0 0.0
17.0 2.0 0.0 1.6 0.0 0.0 9.0 28.6 69.2 11.2 37.6 1.0 0.0
17.0 2.0 0.0 1.8 0.0 0.0 9.0 29.8 69.6 11.6 37.8 1.0 0.0
18.0 2.0 0.0 2.0 0.0 0.0 9.0 31.0 70.0 12.0 38.0 1.0 0.0
18.0 2.0 0.0 2.0 0.0 0.0 9.0 31.8 70.0 12.0 38.0 1.0 0.0
18.0 2.0 0.0 2.0 0.0 0.0 9.0 32.6 70.0 12.0 38.0 1.0 0.0
18.0 2.0 0.0 2.0 0.0 0.0 9.0 33.4 70.0 12.0 38.0 1.0 0.0
18.0 2.0 0.0 2.0 0.0 0.0 9.0 34.2 70.0 12.0 38.0 1.0 0.0
19.0 2.0 0.0 2.0 0.0 0.0 9.0 35.0 70.0 12.0 38.0 1.0 0.0
19.0 2.0 0.6 2.0 0.0 0.0 9.4 35.2 70.0 12.4 38.0 1.0 0.0
19.0 2.0 1.2 2.0 0.0 0.0 9.8 35.4 70.0 12.8 38.0 1.0 0.0
19.0 2.0 1.8 2.0 0.0 0.0 10.2 35.6 70.0 13.2 38.0 1.0 0.0
19.0 2.0 2.4 2.0 0.0 0.0 10.6 35.8 70.0 13.6 38.0 1.0 0.0
20.0 2.0 3.0 2.0 0.0 0.0 11.0 36.0 70.0 14.0 38.0 1.0 0.0
20.0 2.0 3.4 2.0 0.0 0.0 11.4 36.0 70.0 14.2 38.4 1.0 0.0
20.0 2.0 3.8 2.0 0.0 0.0 11.8 36.0 70.0 14.4 38.8 1.0 0.0
20.0 2.0 4.2 2.0 0.0 0.0 12.2 36.0 70.0 14.6 39.2 1.0 0.0
20.0 2.0 4.6 2.0 0.0 0.0 12.6 36.0 70.0 14.8 39.6 1.0 0.0
21.0 2.0 5.0 2.0 0.0 0.0 13.0 36.0 70.0 15.0 40.0 1.0 0.0
21.0 2.0 5.6 2.0 0.0 0.0 13.2 36.6 70.0 15.4 40.0 1.0 0.0
21.0 2.0 6.2 2.0 0.0 0.0 13.4 37.2 70.0 15.8 40.0 1.0 0.0
21.0 2.0 6.8 2.0 0.0 0.0 13.6 37.8 70.0 16.2 40.0 1.0 0.0
21.0 2.0 7.4 2.0 0.0 0.0 13.8 38.4 70.0 16.6 40.0 1.0 0.0
22.0 2.0 8.0 2.0 0.0 0.0 14.0 39.0 70.0 17.0 40.0 1.0 0.0
22.0 2.0 8.2 2.2 0.0 0.0 14.0 40.2 70.4 17.0 40.4 1.0 0.0
22.0 2.0 8.4 2.4 0.0 0.0 14.0 41.4 70.8 17.0 40.8 1.0 0.0
22.0 2.0 8.6 2.6 0.0 0.0 14.0 42.6 71.2 17.0 41.2 1.0 0.0
22.0 2.0 8.8 2.8 0.0 0.0 14.0 43.8 71.6 17.0 41.6 1.0 0.0
23.0 2.0 9.0 3.0 0.0 0.0 14.0 45.0 72.0 17.0 42.0 1.0 0.0
23.0 2.0 9.0 3.0 0.0 0.0 14.2 45.6 72.0 17.6 42.4 1.0 0.0
23.0 2.0 9.0 3.0 0.0 0.0 14.4 46.2 72.0 18.2 42.8 1.0 0.0
23.0 2.0 9.0 3.0 0.0 0.0 14.6 46.8 72.0 18.8 43.2 1.0 0.0
23.0 2.0 9.0 3.0 0.0 0.0 14.8 47.4 72.0 19.4 43.6 1.0 0.0
24.0 2.0 9.0 3.0 0.0 0.0 15.0 48.0 72.0 20.0 44.0 1.0 0.0
24.0 2.0 9.0 3.0 0.0 0.0 15.2 48.0 72.0 20.6 44.2 1.0 0.0
24.0 2.0 9.0 3.0 0.0 0.0 15.4 48.0 72.0 21.2 44.4 1.0 0.0
24.0 2.0 9.0 3.0 0.0 0.0 15.6 48.0 72.0 21.8 44.6 1.0 0.0
24.0 2.0 9.0 3.0 0.0 0.0 15.8 48.0 72.0 22.4 44.8 1.0 0.0
25.0 2.0 9.0 3.0 0.0 0.0 16.0 48.0 72.0 23.0 45.0 1.0 0.0
25.0 2.0 9.4 3.0 0.0 0.0 16.4 48.0 72.0 23.2 45.0 1.0 0.4
25.0 2.0 9.8 3.0 0.0 0.0 16.8 48.0 72.0 23.4 45.0 1.0 0.8
25.0 2.0 10.2 3.0 0.0 0.0 17.2 48.0 72.0 23.6 45.0 1.0 1.2
25.0 2.0 10.6 3.0 0.0 0.0 17.6 48.0 72.0 23.8 45.0 1.0 1.6
26.0 2.0 11.0 3.0 0.0 0.0 18.0 48.0 72.0 24.0 45.0 1.0 2.0
26.0 2.0 11.6 3.0 0.2 0.0 18.2 48.0 72.0 24.0 45.0 1.0 2.6
26.0 2.0 12.2 3.0 0.4 0.0 18.4 48.0 72.0 24.0 45.0 1.0 3.2
26.0 2.0 12.8 3.0 0.6 0.0 18.6 48.0 72.0 24.0 45.0 1.0 3.8
26.0 2.0 13.4 3.0 0.8 0.0 18.8 48.0 72.0 24.0 45.0 1.0 4.4
27.0 2.0 14.0 3.0 1.0 0.0 19.0 48.0 72.0 24.0 45.0 1.0 5.0
27.0 2.0 14.4 3.0 1.0 0.0 19.4 48.0 72.4 24.4 45.2 1.0 5.4
27.0 2.0 14.8 3.0 1.0 0.0 19.8 48.0 72.8 24.8 45.4 1.0 5.8
27.0 2.0 15.2 3.0 1.0 0.0 20.2 48.0 73.2 25.2 45.6 1.0 6.2
27.0 2.0 15.6 3.0 1.0 0.0 20.6 48.0 73.6 25.6 45.8 1.0 6.6
28.0 2.0 16.0 3.0 1.0 0.0 21.0 48.0 74.0 26.0 46.0 1.0 7.0
28.0 2.0 16.6 3.0 1.0 0.0 21.2 48.2 74.0 26.6 46.0 1.0 7.2
28.0 2.0 17.2 3.0 1.0 0.0 21.4 48.4 74.0 27.2 46.0 1.0 7.4
28.0 2.0 17.8 3.0 1.0 0.0 21.6 48.6 74.0 27.8 46.0 1.0 7.6
28.0 2.0 18.4 3.0 1.0 0.0 21.8 48.8 74.0 28.4 46.0 1.0 7.8
29.0 2.0 19.0 3.0 1.0 0.0 22.0 49.0 74.0 29.0 46.0 1.0 8.0
29.0 2.0 19.2 3.4 1.0 0.0 22.0 50.0 74.0 29.0 46.4 1.0 8.0
29.0 2.0 19.4 3.8 1.0 0.0 22.0 51.0 74.0 29.0 46.8 1.0 8.0
29.0 2.0 19.6 4.2 1.0 0.0 22.0 52.0 74.0 29.0 47.2 1.0 8.0
29.0 2.0 19.8 4.6 1.0 0.0 22.0 53.0 74.0 29.0 47.6 1.0 8.0
30.0 2.0 20.0 5.0 1.0 0.0 22.0 54.0 74.0 29.0 48.0 1.0 8.0
30.0 1.6 16.0 4.2 0.8 0.0 17.8 44.2 59.6 23.8 38.4 0.8 6.8
30.0 1.2 12.0 3.4 0.6 0.0 13.6 34.4 45.2 18.6 28.8 0.6 5.6
30.0 0.8 8.0 2.6 0.4 0.0 9.4 24.6 30.8 13.4 19.2 0.4 4.4
30.0 0.4 4.0 1.8 0.2 0.0 5.2 14.8 16.4 8.2 9.6 0.2 3.2
31.0 0.0 0.0 1.0 0.0 0.0 1.0 5.0 2.0 3.0 0.0 0.0 2.0
31.0 0.0 0.0 1.0 0.0 0.0 1.2 5.4 2.2 3.6 0.0 0.0 2.8
31.0 0.0 0.0 1.0 0.0 0.0 1.4 5.8 2.4 4.2 0.0 0.0 3.6
31.0 0.0 0.0 1.0 0.0 0.0 1.6 6.2 2.6 4.8 0.0 0.0 4.4
31.0 0.0 0.0 1.0 0.0 0.0 1.8 6.6 2.8 5.4 0.0 0.0 5.2
...
在索引这个数据帧时，我觉得 df_expanded.iloc[31] 应该返回以下内容：
A C Cl Co D E F Fa G Ga H HW
index
31.0 0.0 0.0 1.0 0.0 0.0 1.0 5.0 2.0 3.0 0.0 0.0 2.0
31.0 0.0 0.0 1.0 0.0 0.0 1.2 5.4 2.2 3.6 0.0 0.0 2.8
31.0 0.0 0.0 1.0 0.0 0.0 1.4 5.8 2.4 4.2 0.0 0.0 3.6
31.0 0.0 0.0 1.0 0.0 0.0 1.6 6.2 2.6 4.8 0.0 0.0 4.4
31.0 0.0 0.0 1.0 0.0 0.0 1.8 6.6 2.8 5.4 0.0 0.0 5.2
但是，返回以下内容：
>>> print(df_expanded.iloc[31])
A 2.0
C 0.0
Cl 0.0
Co 0.0
D 0.0
E 7.0
F 1.0
Fa 33.4
G 4.0
Ga 19.2
H 0.0
HW 0.0
为什么索引第 31 个索引会为 A（以及其他列的累积值）返回 2.0，而不是显示 df_expanded 时显示的值？我不明白为什么它会这样工作，所以任何形式的帮助都将不胜感激！
答案 0 :(得分：-1)
df_expanded.iloc[31]
不会返回您期望的内容，因为它返回数据集中的第 31 行，您可以检查第 31 行，这正是您得到的。
想要什么就试试
df_expanded.loc[31]