Numpy - 从数组中切片2d行或列向量

Question

我正试图从2D数组中切出行/列并获取(col_size x 1)或(1 x row_size)的数组。

难道还有比每个切片后使用numpy.reshape()更简单的方法？

干杯， 斯蒂芬

Answer 1

您可以切片并插入一个单一操作新轴。例如，这里有一个2D阵列：

>>> a = np.arange(1, 7).reshape(2, 3) 
>>> a 
array([[1, 2, 3], 
     [4, 5, 6]]) 

切出单柱，切片（返回形状(2, 1)阵列）与None作为第三维度：

>>> a[:, 1, None] 
array([[2], 
     [5]]) 

切出一个单排排（排列形状为(1, 3)），以None作为第二维的切片：

>>> a[0, None, :] 
array([[1, 2, 3]]) 

Answer 2

赚了指数片，列表或数组

X[[0],:] 
    X[0:1,4] 

但没有什么错reshape比它需要打字的事实等。这并不慢。 [None,:]是一个很好的短手。

列表索引的使用可能是最短的，但它确实产生一个副本（正或负）并较慢

对于(100,100)整数数组：

In [487]: timeit x[[50],:] 
100000 loops, best of 3: 10.3 µs per loop # slowest 

In [488]: timeit x[50:51,:] 
100000 loops, best of 3: 2.24 µs per loop # slice indexing is fast 

In [489]: timeit x[50,:].reshape(1,-1) 
100000 loops, best of 3: 3.29 µs per loop # minimal time penalty 

In [490]: timeit x[50,:][None,:] 
100000 loops, best of 3: 3.55 µs per loop 

In [543]: timeit x[None,50,:]   # **best** 
1000000 loops, best of 3: 1.76 µs per loop 

一个用于复制测试是将数据缓冲区指针与原始数据进行比较。

In [492]: x.__array_interface__['data'] 
Out[492]: (175920456, False) 
In [493]: x[50,:].__array_interface__['data'] 
Out[493]: (175940456, False) 
In [494]: x[[50],:].__array_interface__['data'] 
Out[494]: (175871672, False) # different pointer 
In [495]: x[50:51,:].__array_interface__['data'] 
Out[495]: (175940456, False) 
In [496]: x[50,:][None,:].__array_interface__['data'] 
Out[496]: (175940456, False) 

Answer 3

这个很好又简单的方法怎么样？

In [73]: arr = (np.arange(5, 25)).reshape(5, 4) 

In [74]: arr 
Out[74]: 
array([[ 5, 6, 7, 8], 
     [ 9, 10, 11, 12], 
     [13, 14, 15, 16], 
     [17, 18, 19, 20], 
     [21, 22, 23, 24]]) 

# extract column 1 as a column vector 
In [79]: col1 = arr[:, [0]] 
In [80]: col1.shape 
Out[80]: (5, 1) 

In [81]: col1 
Out[81]: 
array([[ 5], 
     [ 9], 
     [13], 
     [17], 
     [21]]) 


# extract row 1 as a row vector 
In [82]: row1 = arr[[0], :] 

In [83]: row1.shape 
Out[83]: (1, 4) 

In [84]: row1 
Out[84]: array([[5, 6, 7, 8]])