Python——数组重组（flatten

一、numpy.flatten$一、numpy.flatten$$一、numpy.flatten$

ndarray.flatten(order='C')

    将数组变为一维

Parameters:     order : {‘C’, ‘F’, ‘A’, ‘K’}, optional
                        ‘C’ means to flatten in row-major (C-style) order.
                        ‘F’ means to flatten in column-major (Fortran- style) 
                        order. ‘A’ means to flatten in column-major order if 
                        a is Fortran contiguous in memory, row-major order 
                        otherwise. ‘K’ means to flatten a in the order the 
                        elements occur in memory. The default is ‘C’.
Returns:        y : ndarray
                        A copy of the input array, flattened to one
                        dimension.

>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()   # 默认参数为"C"，即按照行进行重组
array([1, 2, 3, 4])
>>> a.flatten('F') # 按照列进行重组
array([1, 3, 2, 4])

二、numpy.flat$二、numpy.flat$$二、numpy.flat$

>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
       [4, 5, 6]])
>>> x.flat[3] # 返回重组后的一维数组下标为3的元素
4
>>> x.T
array([[1, 4],
       [2, 5],
       [3, 6]])
>>> x.T.flat[3] # 返回x的转置重组后的一维数组下标为3的元素
5
>>> x.flat = 3 # 将数组的元素均变为3
>>> x
array([[3, 3, 3],
       [3, 3, 3]])
>>> x.flat[[1,4]] = 1 # 将数组重组后的一维数组小标为1,4的元素变为1
>>> x
array([[3, 1, 3],
       [3, 1, 3]])

三、numpy.ravel$三、numpy.ravel$$三、numpy.ravel$

numpy.ravel

numpy.ravel(a, order='C')

>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> y = np.ravel(x) # 默认order="C",按照行进行重组
>>> y
[1 2 3 4 5 6]
>>> y = np.ravel(x, order='F') # 按照列进行重组
>>> y
[1 4 2 5 3 6]
>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2)
>>> a
array([[[ 0,  2,  4],
        [ 1,  3,  5]],
       [[ 6,  8, 10],
        [ 7,  9, 11]]])
>>> a.ravel(order='C')
array([ 0,  2,  4,  1,  3,  5,  6,  8, 10,  7,  9, 11])
>>> a.ravel(order='K')
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

四、numpy.reshape(等价于ndarray.reshape)$四、numpy.reshape(等价于ndarray.reshape)$$四、numpy.reshape(等价于ndarray.reshape)$

numpy.reshape(a, newshape, order='C')

>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
       [2, 3],
       [4, 5]])
>>> np.reshape(a, (2, 3)) 
array([[0, 1, 2],
       [3, 4, 5]])
>>> a.reshape(-1)
array([0, 1, 2, 3, 4, 5])
>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])

五、resize$五、resize$$五、resize$

numpy.resize

>>> a=np.array([[0,1],[2,3]])
>>> np.resize(a,(2,3))
array([[0, 1, 2],
       [3, 0, 1]])
>>> np.resize(a,(1,4))
array([[0, 1, 2, 3]])
>>> np.resize(a,(2,4))
array([[0, 1, 2, 3],
       [0, 1, 2, 3]])

ndarray.resize

>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) 
>>> b
array([[0, 1, 2],
       [3, 0, 0]])
>>> b.resize(1,4)
array([[0, 1, 2, 3]])
>>> b.resize(2,4)
array([[0, 1, 2, 3],
       [0, 0, 0, 0]])

请注意上述两者之间的区别，numpy.resize重组数据不够时，使用原数据依次填补；ndarray.resize重组数据不够时，使用原数据第一个元素填补。