python - 在 pandas 数据帧上应用条件来过滤数组时出现 FutureWarning

Question

我已将 PCA 应用于大约 1000 个观测值的数组，但只想将观测值保留在新数组中(如果原始数组中的某个特征 = 某些内容)。

我有一个 numpy 数组 df2 和一个数据帧 df。我想查找 df2 中 df.Position 为 CDM 的所有行。

我的实际数据:

df2

[[ -6.00987823e+00   4.46585005e+00]
 [ -7.09055159e+00   1.89437600e+00]
 [ -5.91044431e+00  -1.97888707e+00]
 [ -4.85698965e+00  -1.09936724e+00]
 [ -4.01780368e-01  -2.57178392e+00]
 [ -2.97351215e+00  -3.15940358e+00]
 [ -4.27973589e+00   2.82707326e+00]
 [  3.95086576e+00   1.08281922e+00]
 [ -2.94075361e+00  -1.95544661e+00]
 [ -4.83788056e+00   2.32369496e+00]
 [ -5.00473716e+00  -3.37680552e-01]
 [ -4.88905829e+00  -1.55527476e+00]
 [ -3.38202709e+00  -1.04402867e+00]
 [ -2.14261510e+00  -5.30757477e-01]
 [  3.00813803e-01  -2.11010985e+00]
 [ -2.67824986e+00  -1.83303905e+00]
 [ -1.64547049e+00  -2.48056250e+00]
 [ -2.92550543e+00  -3.02363170e+00]
 [ -4.01116933e+00   2.90363840e+00]
 [ -1.04571206e+00   7.58064433e-01]
 [  2.34068739e-01  -2.33981296e+00]
 [  3.15597517e+00   1.09429188e+00]
 [ -3.83828970e+00   1.14195305e-01]
 [ -7.33794066e-01  -3.70152816e+00]
 [  8.21789967e-01  -4.77818413e-01]
 [ -3.29257688e+00  -1.61887349e+00]
 [ -4.24297171e+00   2.27187714e+00]
 [  1.45714199e+00  -3.56024788e+00]
 [  1.79855738e+00  -3.71818328e-01]
 [  3.68171085e-01  -3.52961707e+00]
 [  3.77585412e+00  -3.01627595e-01]
 [ -4.21740128e+00  -1.30913719e+00]
 [ -3.85041585e+00  -1.05515969e+00]
 [ -5.01752378e+00   4.67348167e-01]
 [  3.65943448e+00   9.21016483e-01]
 [  3.12159896e+00  -1.25707872e-01]
 [ -4.50219722e+00  -4.06752784e+00]
 [ -3.92172250e+00  -2.88567430e+00]
 [ -2.68908475e-01  -2.17506629e+00]
 [ -1.13728112e+00  -2.66843007e+00]
 [ -8.73467957e-01  -1.24389494e+00]
 [  3.21966300e+00  -1.35271239e-01]
 [ -4.31060796e+00  -1.90505910e+00]
 [  3.73904981e+00   7.70228802e-01]
 [  1.02646986e+00  -5.91828676e-01]
 [  8.43840480e-01  -1.49636218e+00]
 [  1.54065978e+00  -1.65086030e+00]
 [  2.96602068e+00  -7.41024474e-01]
 [  6.53636345e-01   3.04647288e-01]
 [  2.59236989e+00  -6.70435261e-02]
 [  2.00184665e-01  -1.55230314e+00]
 [ -7.29533092e-01  -2.73390749e+00]
 [ -2.93578745e+00  -2.18118257e+00]
 [ -4.37481195e+00   1.02701222e+00]
 [  1.00713302e+00  -1.39943282e+00]
...]


df

(足球中的简单踢球位置 - FB、CB、CDM、CM、AM、FW)

Position
FW
FW
FW
FW
FB
AM
FW
CB
AM
FW
AM
FW
AM
CM
FB
AM
CM
CM
FW
CM
CDM
CB
AM
FB
CDM
FW
FW
CDM
FB
CDM
CB
AM
...
AM

<小时/>

过滤时，我得到以下输出(以及 FutureWarning):

我哪里出错了，如何正确过滤数据？

Answer 1

FutureWarning 可能是您的 numpy 和 pandas 版本过时的结果。您可以使用以下方法升级它们:

pip install --upgrade numpy pandas 

至于过滤，有很多选择。在这里，我用一些虚拟数据提到了每一个。

<小时/>

设置

df
    name colour  a  b  c  d  e  f
0   john    red  1  2  3  4  5  6
1  james    red  2  3  4  5  6  7
2   jane   blue  1  2  3  5  7  8

df2
       0      1
0  0.122  0.222
1  0.343  0.345
2  0.345  0.563

选项 1
bool 索引

df2[df.colour == 'red']
Out[726]: 
       0       1
0  0.122   0.222
1  0.343   0.345

选项 2
df.eval

df2[df.eval('colour == "red"')]
Out[732]: 
       0       1
0  0.122   0.222
1  0.343   0.345

请注意，即使 df2 是以下形式的 numpy 数组，这两个选项也有效:

array([[ 0.122,  0.222],
       [ 0.343,  0.345],
       [ 0.345,  0.563]])

<小时/>

对于您的实际数据，您需要执行相同的操作:

df2

array([[-6.01 ,  4.466],
       [-7.091,  1.894],
       [-5.91 , -1.979],
       [-4.857, -1.099],
       [-0.402, -2.572],
       [-2.974, -3.159],
       [-4.28 ,  2.827],
       [ 3.951,  1.083],
       [-2.941, -1.955],
       [-4.838,  2.324],
       [-5.005, -0.338],
       [-4.889, -1.555],
       [-3.382, -1.044],
       [-2.143, -0.531],
       [ 0.301, -2.11 ],
       [-2.678, -1.833],
       [-1.645, -2.481],
       [-2.926, -3.024],
       [-4.011,  2.904],
       [-1.046,  0.758],
       [ 0.234, -2.34 ],
       [ 3.156,  1.094],
       [-3.838,  0.114],
       [-0.734, -3.702],
       [ 0.822, -0.478],
       [-3.293, -1.619],
       [-4.243,  2.272],
       [ 1.457, -3.56 ],
       [ 1.799, -0.372],
       [ 0.368, -3.53 ],
       [ 3.776, -0.302],
       [-4.217, -1.309]])

df

   Position
0        FW
1        FW
2        FW
3        FW
4        FB
5        AM
6        FW
7        CB
8        AM
9        FW
10       AM
11       FW
12       AM
13       CM
14       FB
15       AM
16       CM
17       CM
18       FW
19       CM
20      CDM
21       CB
22       AM
23       FB
24      CDM
25       FW
26       FW
27      CDM
28       FB
29      CDM
30       CB
31       AM

df2[df.Position == 'CDM']

array([[ 0.234, -2.34 ],
       [ 0.822, -0.478],
       [ 1.457, -3.56 ],
       [ 0.368, -3.53 ]])