感覺numpy.hstack()和numpy.column_stack()函數略有相似，numpy.vstack()與numpy.row_stack()函數也是挺像的。

stackoverflow上也有類似的討論，在這里numpy vstack vs. column_stack。

給一個相關函數的列表：

stack() ?? Join a sequence of arrays along a new axis.

hstack() ?? Stack arrays in sequence horizontally (column wise).

dstack() ?? Stack arrays in sequence depth wise (along third dimension).

concatenate() ??? Join a sequence of arrays along an existing axis.

vsplit () ? Split array into a list of multiple sub-arrays vertically.

一、numpy.stack()函數

函數原型：numpy.stack(arrays, axis=0)

程序實例：

>>> arrays = [np.random.randn(3, 4) for _ in range(10)] >>> np.stack(arrays, axis=0).shape (10, 3, 4)>>>>>> np.stack(arrays, axis=1).shape (3, 10, 4)>>>>>> np.stack(arrays, axis=2).shape (3, 4, 10)>>>>>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.stack((a, b)) array([[1, 2, 3],[2, 3, 4]])>>>>>> np.stack((a, b), axis=-1) array([[1, 2],[2, 3],[3, 4]])

二、numpy.hstack()函數

函數原型：numpy.hstack(tup)

其中tup是arrays序列，The arrays must have the same shape, except in the dimensioncorresponding to axis (the first, by default).

等價于：np.concatenate(tup, axis=1)

程序實例：

>>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.hstack((a,b)) array([[1, 2],[2, 3],[3, 4]])

三、numpy.vstack()函數

函數原型：numpy.vstack(tup)

等價于：np.concatenate(tup, axis=0）?if tup contains arrays thatare at least 2-dimensional.

程序實例：

>>> a = np.array([1, 2, 3]) >>> b = np.array([2, 3, 4]) >>> np.vstack((a,b)) array([[1, 2, 3],[2, 3, 4]])>>>>>> a = np.array([[1], [2], [3]]) >>> b = np.array([[2], [3], [4]]) >>> np.vstack((a,b)) array([[1],[2],[3],[2],[3],[4]])

四、numpy.dstack(）函數

函數原型：numpy.dstack(tup)

等價于：np.concatenate(tup, axis=2)

程序實例：

>>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.dstack((a,b)) array([[[1, 2],[2, 3],[3, 4]]])>>>>>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.dstack((a,b)) array([[[1, 2]],[[2, 3]],[[3, 4]]])

五、numpy.concatenate()函數

函數原型：numpy.concatenate((a1, a2, ...), axis=0)

程序實例：

>>> a = np.array([[1, 2], [3, 4]]) >>> b = np.array([[5, 6]]) >>> np.concatenate((a, b), axis=0) array([[1, 2],[3, 4],[5, 6]]) >>> np.concatenate((a, b.T), axis=1) array([[1, 2, 5],[3, 4, 6]])This function will not preserve masking of MaskedArray inputs. >>>>>> a = np.ma.arange(3) >>> a[1] = np.ma.masked >>> b = np.arange(2, 5) >>> a masked_array(data = [0 -- 2],mask = [False True False],fill_value = 999999) >>> b array([2, 3, 4]) >>> np.concatenate([a, b]) masked_array(data = [0 1 2 2 3 4],mask = False,fill_value = 999999) >>> np.ma.concatenate([a, b]) masked_array(data = [0 -- 2 2 3 4],mask = [False True False False False False],fill_value = 999999)

六、numpy.vsplit()函數

函數原型：numpy.vsplit(ary, indices_or_sections)

程序實例：

>>> x = np.arange(16.0).reshape(4, 4) >>> x array([[ 0., 1., 2., 3.],[ 4., 5., 6., 7.],[ 8., 9., 10., 11.],[ 12., 13., 14., 15.]]) >>> np.vsplit(x, 2) [array([[ 0., 1., 2., 3.],[ 4., 5., 6., 7.]]),array([[ 8., 9., 10., 11.],[ 12., 13., 14., 15.]])] >>> np.vsplit(x, np.array([3, 6])) [array([[ 0., 1., 2., 3.],[ 4., 5., 6., 7.],[ 8., 9., 10., 11.]]),array([[ 12., 13., 14., 15.]]),array([], dtype=float64)]With a higher dimensional array the split is still along the first axis. >>>>>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[[ 0., 1.],[ 2., 3.]],[[ 4., 5.],[ 6., 7.]]]) >>> np.vsplit(x, 2) [array([[[ 0., 1.],[ 2., 3.]]]),array([[[ 4., 5.],[ 6., 7.]]])]

參考：

numpy中的部分源碼

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