矩阵数组互相转换,数组按列增加维度

骑猪看日落 2022-05-25 12:13 200阅读 0赞

>>> import numpy as np
    >>> a=np.random.rand(3,3)
    >>> a
    array([[0.34466754, 0.81649866, 0.49670435],
           [0.48517591, 0.29970508, 0.82610943],
           [0.09956289, 0.0032179 , 0.40870012]])
    >>> b=np.mat(a)#数组转换成矩阵
    >>> b
    matrix([[0.34466754, 0.81649866, 0.49670435],
            [0.48517591, 0.29970508, 0.82610943],
            [0.09956289, 0.0032179 , 0.40870012]])
    >>> c=b.tolist()#矩阵转换成列表
    >>> c
    [[0.3446675379532145, 0.816498659005646, 0.49670434900037175], [0.48517591424371664, 0.29970507744939645, 0.826109430164631], [0.09956289145143449, 0.0032179037025916735, 0.4087001225345782]]
    >>> c=np.array(c)
    >>> c
    array([[0.34466754, 0.81649866, 0.49670435],
           [0.48517591, 0.29970508, 0.82610943],
           [0.09956289, 0.0032179 , 0.40870012]])
    >>> #即实现了从数组到矩阵的转换
    >>> 
    >>> #计算数组矩阵的所有元素的均值
    >>> a
    array([[0.34466754, 0.81649866, 0.49670435],
           [0.48517591, 0.29970508, 0.82610943],
           [0.09956289, 0.0032179 , 0.40870012]])
    >>> np.mean(a)
    0.4200379872783978
    >>> #计算每列元素的均值
    >>> np.mean(a,axis=0)
    array([0.30980211, 0.37314055, 0.5771713 ])
    >>> #计算每行元素的均值
    >>> np.mean(a,axis=1)
    array([0.55262352, 0.53699681, 0.17049364])
    >>> #对每一列求标准差
    >>> np.std(a, axis=0)
    array([0.15934459, 0.33605652, 0.17965491])
    >>> #对每一行求标准差
    >>> np.std(a, axis=1)
    array([0.19664073, 0.21800526, 0.17296885])
    >>> a[:,0].min()#取第0列的最小值
    0.09956289145143449
    >>> a[0,:].max()#取第0行的最大值
    0.816498659005646
    >>> #
    KeyboardInterrupt
    >>> #向数组中添加列：np.hstack()
    >>> a
    array([[0.34466754, 0.81649866, 0.49670435],
           [0.48517591, 0.29970508, 0.82610943],
           [0.09956289, 0.0032179 , 0.40870012]])
    >>> b=np.array([1,2,3]).reshape(3,1)
    >>> b
    array([[1],
           [2],
           [3]])
    
    >>> c=np.hstack((a,b))
    >>> c
    array([[0.34466754, 0.81649866, 0.49670435, 1.        ],
           [0.48517591, 0.29970508, 0.82610943, 2.        ],
           [0.09956289, 0.0032179 , 0.40870012, 3.        ]])
    >>>