感觉numpy.hstack()和numpy.column_stack()函数略有相似，numpy.vstack()与numpy.row_stack()函数也是挺像的。
stackoverflow上也有类似的讨论，在这里numpy vstack vs. column_stack。
给一个相关函数的列表：
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()函数
函数原型：numpy.stack(arrays, axis=0)
程序实例：
[python] view plain copy print ? >>> 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]]) >>> 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()函数
函数原型：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).
等价于：np.concatenate(tup, axis=1)
程序实例：
[python] view plain copy print ? >>> 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]]) >>> 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()函数
函数原型：numpy.vstack(tup)
等价于：np.concatenate(tup, axis=0） if tup contains arrays thatare at least 2-dimensional.
程序实例：
[python] view plain copy print ? >>> 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]]) >>> 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(）函数
函数原型：numpy.dstack(tup)
等价于：np.concatenate(tup, axis=2)
程序实例：
[python] view plain copy print ? >>> 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]]]) >>> 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()函数
函数原型：numpy.concatenate((a1, a2, …), axis=0)
程序实例：
[python] view plain copy print ? >>> 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) >>> 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()函数
函数原型：numpy.vsplit(ary, indices_or_sections)
程序实例：
[python] view plain copy print ? >>> 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.]]])] >>> 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.]]])]
参考：
numpy中的部分源码
转自博客：http://blog.csdn.net/garfielder007/article/details/51378296
