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：
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- >>> 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：
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- >>> 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：
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- >>> 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：
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- >>> 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：
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- >>> 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：
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- >>> 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.]]])]
