Python numpy函数hstack() vstack() stack() dstack() vsplit() concatenate()

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numpy.stack()函数

• 函数原型：numpy.stack(arrays, axis=0)

程序实例：

>>> 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]])

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：

>>> 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]])

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：

>>> 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]])

numpy.dstack(）函数

• 函数原型：numpy.dstack(tup)

• 等价于：np.concatenate(tup, axis=2)

程序实例：

>>> 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]]])

numpy.concatenate()函数

• 函数原型：numpy.concatenate((a1, a2, ...), axis=0)

程序实例：

>>> 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)

numpy.vsplit()函数

• 函数原型：numpy.vsplit(ary, indices_or_sections)

程序实例：

>>> 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.]]])]