2017-07-10 52 views

回答

5

看来mat是包含形状(1831, 21)Xy与形状(1831, 1)的字典,和一些元数据。假设X是数据和y是同一标签,可以用np.hstack水平堆叠它们,并将它们加载到大熊猫:

In [1755]: mat = scipy.io.loadmat('cardio.mat') 

In [1758]: cardio_df = pd.DataFrame(np.hstack((mat['X'], mat['y']))) 

In [1759]: cardio_df.head() 
Out[1759]: 
     0   1   2   3   4   5   6 \ 
0 0.004912 0.693191 -0.203640 0.595322 0.353190 -0.061401 -0.278295 
1 0.110729 -0.079903 -0.203640 1.268942 0.396246 -0.061401 -0.278295 
2 0.216546 -0.272445 -0.203640 1.050988 0.148753 -0.061401 -0.278295 
3 0.004912 0.727346 -0.203640 1.212171 -0.683598 -0.061401 -0.278295 
4 -0.100905 0.363595 1.321366 1.027120 0.141359 -0.061401 -0.278295 

In [1760]: cardio_df.shape 
Out[1760]: (1831, 22) 
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@COLDSPEED如何转换为阵列状的形状与=(N_SAMPLES次,n_features )? – KouchakYazdi

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@KouchakYazdi你的意思是,你想先重塑它? –

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@COLDSPEED如果我想要一个数组中的mat文件,比如shape =(n_samples,n_features)格式,该怎么办? (不是数据框) – KouchakYazdi