1

我已将 PCA 应用于大约 1000 个观察值的数组，但只想将观察结果保留在新数组中，如果原始数组中的一个特征 = 某物。

我有一个 numpy 数组df2和一个 dataframe df。我想找到df2where df.Positionis中的所有行CDM。

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

（只是在足球/足球中的位置 - 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

过滤时，我得到这个输出（连同 a FutureWarning）：

在此处输入图像描述

我哪里出错了，如何适当地过滤数据？

4

2 回答 2

1

这FutureWarning可能是您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每一个。

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
boolean indexing

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numpy以下形式的数组，这两个选项都有效：

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

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 ]])
于 2017-09-23T04:36:27.270 回答
1

我认为您需要布尔索引：

from sklearn.decomposition import PCA
import pandas as pd

d = {'d': [4, 5, 5],
     'a': [1, 2, 1], 
     'name': ['john', 'james', 'jane'], 
     'e': [5, 6, 7],
     'f': [6, 7, 8], 'c': [3, 4, 3], 
     'b': [2, 3, 2], 
     'colour': ['red', 'red', 'blue']}
cols = ['name', 'colour', 'a', 'b', 'c', 'd', 'e', 'f']
df = pd.DataFrame(d, columns = cols)
print (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

#create mask by condition
mask = df['colour'] == 'red'
#for multiple values
#mask = df['colour'].isin(['red', 'green', 'blue'])
print (mask)
0     True
1     True
2    False
Name: colour, dtype: bool

#filter only numeric values and convert to numpy array
arr = df.drop(['name','colour'], axis=1).values
print (arr)
[[1 2 3 4 5 6]
 [2 3 4 5 6 7]
 [1 2 3 5 7 8]]

pca = PCA(n_components=5)
pca.fit(arr)
print (pca.components_ )
[[-0.0463861  -0.0463861  -0.0463861  -0.35279184 -0.65919758 -0.65919758]
 [ 0.55515147  0.55515147  0.55515147  0.21897879 -0.11719389 -0.11719389]
 [ 0.62531284 -0.13184966 -0.136648   -0.71363037  0.17840759  0.17840759]]

#filter by condition
arr1 = pca.components_ [mask]
print (arr1)
[[-0.0463861  -0.0463861  -0.0463861  -0.35279184 -0.65919758 -0.65919758]
 [ 0.55515147  0.55515147  0.55515147  0.21897879 -0.11719389 -0.11719389]]
于 2017-09-23T04:40:37.320 回答