我有以下df：
I_q_0_sub I_q_1_sub I_q_2_sub I_q_3_sub I_q_4_sub I_q_5_sub
q
0.016513 1.0 1.086586 0.396789 0.030419 0.167913 0.626752
0.017082 1.0 1.088389 0.397858 0.029408 0.166246 0.629824
0.017651 1.0 1.088213 0.398661 0.028985 0.167011 0.628466
0.018221 1.0 1.085454 0.396699 0.027980 0.165895 0.627416
0.018790 1.0 1.078595 0.395192 0.026815 0.165361 0.625276
0.019360 1.0 1.076327 0.393727 0.026964 0.166564 0.624980
0.019929 1.0 1.076141 0.392881 0.026499 0.166089 0.624884
0.020498 1.0 1.074617 0.391246 0.026023 0.164293 0.625018
0.021068 1.0 1.074573 0.389804 0.025534 0.165650 0.623080
0.021637 1.0 1.074772 0.390498 0.025619 0.166404 0.622398
0.022207 1.0 1.072407 0.389034 0.025418 0.165267 0.620503
0.022776 1.0 1.068778 0.389453 0.025364 0.165631 0.621173
0.023345 1.0 1.069125 0.388866 0.025222 0.165374 0.622733
0.023915 1.0 1.067703 0.389035 0.024862 0.164636 0.621182
0.024484 1.0 1.063856 0.387513 0.025124 0.164992 0.619048
0.025054 1.0 1.063000 0.388187 0.025446 0.164981 0.617603
0.025623 1.0 1.063995 0.387414 0.025752 0.165825 0.617720
0.026192 1.0 1.061866 0.387479 0.025579 0.165128 0.618729
0.026762 1.0 1.060178 0.384343 0.025603 0.165227 0.616478
0.027331 1.0 1.057169 0.384075 0.025644 0.164989 0.617416
0.027900 1.0 1.054479 0.384566 0.026249 0.164863 0.615285
0.028470 1.0 1.054914 0.383443 0.026397 0.166146 0.616100
0.029039 1.0 1.054963 0.383084 0.026302 0.165473 0.617631
0.029609 1.0 1.052284 0.382753 0.026824 0.164973 0.614430
0.030178 1.0 1.053644 0.383991 0.027040 0.166437 0.615252
0.030747 1.0 1.051703 0.384502 0.027135 0.166372 0.614781
0.031317 1.0 1.048446 0.383240 0.027762 0.165991 0.614492
0.031886 1.0 1.050411 0.382216 0.027915 0.167335 0.613784
0.032455 1.0 1.052862 0.383122 0.028400 0.167722 0.615104
0.033025 1.0 1.048664 0.384156 0.029077 0.167987 0.614716
0.033594 1.0 1.045783 0.384269 0.029518 0.166930 0.614234
0.034163 1.0 1.049077 0.384258 0.030929 0.168138 0.614413
0.034733 1.0 1.047248 0.384060 0.031300 0.168228 0.613657
0.035302 1.0 1.044294 0.385330 0.031312 0.168637 0.612413
0.035872 1.0 1.045500 0.384630 0.031975 0.169539 0.613903
0.036441 1.0 1.047008 0.385461 0.032721 0.169195 0.614401
0.037010 1.0 1.046601 0.386015 0.033526 0.171218 0.615378
0.037580 1.0 1.039578 0.385855 0.034593 0.170812 0.611779
0.038149 1.0 1.042296 0.386241 0.035050 0.170443 0.611111
0.038718 1.0 1.041640 0.385285 0.035902 0.171739 0.611083
0.039288 1.0 1.046594 0.388858 0.037150 0.174225 0.613183
0.039857 1.0 1.045652 0.390708 0.038682 0.173627 0.613125
0.040426 1.0 1.046337 0.392301 0.039181 0.174176 0.612989
0.040996 1.0 1.041239 0.392167 0.039861 0.175146 0.612321
0.041565 1.0 1.040595 0.393418 0.040991 0.174704 0.613320
当我为各个列和整个df应用nsmallest方法时，我得到了不同的值。正如您所看到的，对于各个列，我得到了这个：
df["I_q_0_sub"].iloc[15:60].nsmallest(n = 5).mean()
1.0
df["I_q_1_sub"].iloc[15:60].nsmallest(n = 5).mean()
1.041069402080646
df["I_q_2_sub"].iloc[15:60].nsmallest(n = 5).mean()
0.3828830431385227
df["I_q_3_sub"].iloc[15:60].nsmallest(n = 5).mean()
0.025197931800085817
df["I_q_4_sub"].iloc[15:60].nsmallest(n = 5).mean()
0.16474921466342365
df["I_q_4_sub"].iloc[15:60].nsmallest(n = 5).mean()
0.61174148613757
整个df：
df.iloc[15:60].nsmallest(n = 5, columns=formFactor_sub.columns).mean()
I_q_0_sub 1.000000
I_q_1_sub 1.041069
I_q_2_sub 0.388593
I_q_3_sub 0.037279
I_q_4_sub 0.172569
I_q_5_sub 0.611923
因此，各列的值与整个数据帧的值不同，但不可以。任何关于为什么会发生这种情况的提示都会受到赞赏。
答案 0 :(得分：0)
nsmallest仅适用于column参数，即必须找到特定列的最小行。如果您使用columns = formFactor_sub.columns完成所有列的传递，那么它所做的就是它返回第一列的nsmallest值。
考虑这个数据框，
[0]返回
a b c
0 1 7 1
1 10 9 2
2 8 3 3
3 11 6 4
4 -1 1 5
5 5 8 6
6 15 2 7
df.nsmallest(3, columns = df.columns)
与
相同 a b c
4 -1 1 5
0 1 7 1
5 5 8 6
但它与你得到的不同
df.nsmallest(3, columns = 'a')
或
df['b'].nsmallest(3)