ndarray对象的基本操作 | 机器学习 |《python学习之路》| python 技术论坛-江南app体育官方入口

ndarray对象的基本操作

• 加减乘除四则运算

    >>> a = np.full((3,3),3)
    >>> a
    array([[3, 3, 3],
           [3, 3, 3],
           [3, 3, 3]])
    >>> a1
    array([[4, 4, 4],
           [4, 4, 4],
           [4, 4, 4]])
    >>> a-2
    array([[1, 1, 1],
           [1, 1, 1],
           [1, 1, 1]])
    >>> a*5
    array([[15, 15, 15],
           [15, 15, 15],
           [15, 15, 15]])
    >>> a/3
    array([[1., 1., 1.],
           [1., 1., 1.],
           [1., 1., 1.]])
    >>> a
    array([[3, 3, 3],
           [3, 3, 3],
           [3, 3, 3]])
    >>> b = np.full((3,3),3)
    >>> b
    array([[3, 3, 3],
           [3, 3, 3],
           [3, 3, 3]])
    >>> ab
    array([[6, 6, 6],
           [6, 6, 6],
           [6, 6, 6]])
    >>> a  = b
    >>> a
    array([[6, 6, 6],
           [6, 6, 6],
           [6, 6, 6]])
  

  注意：虽然可以对两个ndarray对象进行操作，但是如果没有赋值，不会改变原来的ndarray对象

当对两个ndarray对象数据类型精度不一样进行操作时，结果的精度为更精确的那个数据类型

>>> a = np.array([[0.1,0.2,0.3],[0.3,0.2,0.1]],dtype=np.float32)
>>> a
array([[0.1, 0.2, 0.3],
       [0.3, 0.2, 0.1]], dtype=float32)
>>> b = np.array([[0.3,0.2,0.1],[0.1,0.2,0.3]],dtype=np.float64)
>>> b
array([[0.3, 0.2, 0.1],
       [0.1, 0.2, 0.3]])
>>> c = ab
>>> c
array([[0.4       , 0.4       , 0.40000001],
       [0.40000001, 0.4       , 0.4       ]])
>>> c.dtype
dtype('float64')

• 常用数学函数sum、min、max等

  >>> a = np.arange(12).reshape(4,3) # reshape可以设置输出时的维度
  >>> a
  array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])
  >>> a.sum()
  66
  >>> a.sum(axis=0)   # axis=0表示求列的相关操作
  array([18, 22, 26]) # axis=1表示求行的相关操作
  >>> a.sum(axis=1)
  array([ 3, 12, 21, 30])
  >>> a.min()
  0
  >>> a.min(axis=0)   
  array([0, 1, 2])
  >>> a.min(axis=1)
  array([0, 3, 6, 9])
  >>> a.max()
  11
  >>> a.max(axis=0)
  array([ 9, 10, 11])
  >>> a.max(axis=1)
  array([ 2,  5,  8, 11])
  >>> np.sin(a)
  array([[ 0.        ,  0.84147098,  0.90929743],
       [ 0.14112001, -0.7568025 , -0.95892427],
       [-0.2794155 ,  0.6569866 ,  0.98935825],
       [ 0.41211849, -0.54402111, -0.99999021]])
  >>> np.cos(a)
  array([[ 1.        ,  0.54030231, -0.41614684],
       [-0.9899925 , -0.65364362,  0.28366219],
       [ 0.96017029,  0.75390225, -0.14550003],
       [-0.91113026, -0.83907153,  0.0044257 ]])
  >>> np.tan(a)
  array([[ 0.00000000e 00,  1.55740772e 00, -2.18503986e 00],
       [-1.42546543e-01,  1.15782128e 00, -3.38051501e 00],
       [-2.91006191e-01,  8.71447983e-01, -6.79971146e 00],
       [-4.52315659e-01,  6.48360827e-01, -2.25950846e 02]])

• 类似于python中列表的操作
  • 索引,一维数组的索引和列表一样，多维数组的索引需要根据维度索引

    >>> a = np.arange(12)
    >>> a
    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
    >>> a[0]
    0
    >>> a[11]
    11
    >>> b = np.arange(12).reshape(2,6)
    >>> b[0,0]
    0
    >>> b[5,5]
    >>> b[1,5]
    11

  • 切片，与python中的列表相似，也是左包含右不包含

    >>> b[:,5]
    array([ 5, 11])
    >>> b[1,1:2]
    array([7])
    >>> b[1,1:5]
    array([ 7,  8,  9, 10])

  多维数组切片时，一定要注意好维度，根据维度来切片
  • 迭代，与python的列表相似，都可以用for in 来遍历ndarray对象，一维数组遍历和列表一样，多维数组遍历会得到次维的数组

    >>> for i in b:
    ...     print(i)
    ...
    [0 1 2 3 4 5]
    [ 6  7  8  9 10 11]