【python】numpy库数组拼接快捷方式horizontal&vertical、np.c_、np.column_stack等官方文档详解与实例

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vstack(tup) #给对象传入元组

    Parameters     #多个数组的元组
    ----------
    tup : sequence of ndarrays   
        Tuple containing arrays to be stacked. The arrays must have the same
        shape along all but the first axis.

    Returns       #拼接好的多维数组
    -------
    stacked : ndarray   
        The array formed by stacking the given arrays.

    Notes      #等价于np.concatenate(tup, axis=0)操作
    -----
    Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
    are at least 2-dimensional.

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

In [5]: a = np.array([[1], [2], [3]])
   ...: b = np.array([[2], [3], [4]])
   ...: np.vstack((a,b))
   ...:
Out[5]:
array([[1],
       [2],
       [3],
       [2],
       [3],
       [4]])

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help(np.hsplit) #进行查看详细功能

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In [6]: a = np.array([[1], [2], [3]])
   ...: b = np.array([[2], [3], [4]])
   ...: np.hstack((a,b))
   ...:
   ...:
Out[6]:
array([[1, 2],
       [2, 3],
       [3, 4]])

In [7]: a = np.array([1, 2, 3])
   ...: b = np.array([2, 3, 4])
   ...: np.hstack((a,b))
   ...:
   ...:
Out[7]: array([1, 2, 3, 2, 3, 4])

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column_stack(tup)   #如果传递的是一个一维数组，则会先将一维数组转化为2维数组，然后进一步进行拼接操作
    Stack 1-D arrays as columns into a 2-D array.

    Parameters
    ----------
    tup : sequence of 1-D or 2-D arrays.
        Arrays to stack. All of them must have the same first dimension.

    Returns
    -------
    stacked : 2-D array
        The array formed by stacking the given arrays.

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#如果开始传入的是一维数组，首先将一维数组转化为2维数组
In [13]: a = np.array((1,2,3))
    ...: b = np.array((2,3,4))
    ...: np.column_stack((a,b))
    ...:
Out[13]:
array([[1, 2],
       [2, 3],
       [3, 4]])
#如果开始传入的是多维数组，则直接进行拼接操作
In [15]: a = np.array(((1,2,3),(4,3,2)))
    ...: b = np.array(((2,3,4),(2,12,2)))
    ...: np.column_stack((a,b))
    ...:
    ...:
Out[15]:
array([[ 1,  2,  3,  2,  3,  4],
       [ 4,  3,  2,  2, 12,  2]])

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# np.c_  与np.column_stack 是一个意思，对数组的column列的数据进行横向操作
In [24]: a = np.array([1, 2, 3])

In [25]: b = np.array([2, 3, 4])

In [26]: np.c_[a,b]
Out[26]:
array([[1, 2],
       [2, 3],
       [3, 4]])

# np.r_  与np.row_stack 不完全等价，因为操作对象不一样

In [31]: a = np.array((1,2,3))
    ...: b = np.array((2,3,4))
    ...: c = np.row_stack((a,b))
    ...: d = np.r_[a,b]
    ...:

In [32]: c
Out[32]:
array([[1, 2, 3],
       [2, 3, 4]])

In [33]: d
Out[33]: array([1, 2, 3, 2, 3, 4])