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python数据分析与机器学习实战-03.Numpy矩阵基础

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import numpy as np
print(np.arange(15))
a = np.arange(15).reshape(3,5)
print(a)
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[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]
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#结构a.shape
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(3, 5)
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#维度
a.ndim
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#数据类型
a.dtype
dtype('int64')
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#元素数量
a.size
15
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#初始化元素为0的矩阵
np.zeros((3,4))
array([[0., 0., 0., 0.],
       [0., 0., 0., 0.],
       [0., 0., 0., 0.]])
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#初始化元素为1的矩阵，数据类型为int32,结构为（2，3，4）
np.ones((2,3,4),dtype=np.int32)
array([[[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]],

       [[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]]], dtype=int32)
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#生成序列，从10到30，间隔为5
np.arange(10,30,5)
array([10, 15, 20, 25])
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#生成序列，从0到2，间隔为0.3
np.arange(0,2,0.3)
array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])
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#生成序列，并对序列进行结构操作，当arange()没有制定起始值时，默认从0开始
np.arange(12).reshape(3,4)
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
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#生成随机数
np.random.random((3,4))
array([[0.29086774, 0.66853514, 0.80893891, 0.4672383 ],
       [0.65005622, 0.433645  , 0.6389985 , 0.00900272],
       [0.49442838, 0.66853125, 0.57854652, 0.85672554]])
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from numpy import pi
#linspace()，从0到2*pi均匀取100个数
np.linspace(0,2*pi,100)
array([  0.        ,   1.01010101,   2.02020202,   3.03030303,
         4.04040404,   5.05050505,   6.06060606,   7.07070707,
         8.08080808,   9.09090909,  10.1010101 ,  11.11111111,
        12.12121212,  13.13131313,  14.14141414,  15.15151515,
        16.16161616,  17.17171717,  18.18181818,  19.19191919,
        20.2020202 ,  21.21212121,  22.22222222,  23.23232323,
        24.24242424,  25.25252525,  26.26262626,  27.27272727,
        28.28282828,  29.29292929,  30.3030303 ,  31.31313131,
        32.32323232,  33.33333333,  34.34343434,  35.35353535,
        36.36363636,  37.37373737,  38.38383838,  39.39393939,
        40.4040404 ,  41.41414141,  42.42424242,  43.43434343,
        44.44444444,  45.45454545,  46.46464646,  47.47474747,
        48.48484848,  49.49494949,  50.50505051,  51.51515152,
        52.52525253,  53.53535354,  54.54545455,  55.55555556,
        56.56565657,  57.57575758,  58.58585859,  59.5959596 ,
        60.60606061,  61.61616162,  62.62626263,  63.63636364,
        64.64646465,  65.65656566,  66.66666667,  67.67676768,
        68.68686869,  69.6969697 ,  70.70707071,  71.71717172,
        72.72727273,  73.73737374,  74.74747475,  75.75757576,
        76.76767677,  77.77777778,  78.78787879,  79.7979798 ,
        80.80808081,  81.81818182,  82.82828283,  83.83838384,
        84.84848485,  85.85858586,  86.86868687,  87.87878788,
        88.88888889,  89.8989899 ,  90.90909091,  91.91919192,
        92.92929293,  93.93939394,  94.94949495,  95.95959596,
        96.96969697,  97.97979798,  98.98989899, 100.        ])
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#矩阵数学运算
a  = np.array([20,30,40,50])
b =  np.arange(4)
print(a)
print(b)

[20 30 40 50]
[0 1 2 3]

c = a+b
print(c)
[20 31 42 53]
c = c-1
print(c)
[19 30 41 52]
print(b**2)
[0 1 4 9]
print(a<35)
[ True  True False False]
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#矩阵乘法
a = np.array((
    [1,5],
    [0,1]))
b = np.array((
    [2,0],
    [3,4]))
print(a)
print(b)
print("-------")
#对应位置相乘
print(a*b)
[[1 5]
 [0 1]]
[[2 0]
 [3 4]]
-------
[[2 0]
 [0 4]]
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print(a.dot(b))
print(np.dot(a,b))
[[17 20]
 [ 3  4]]
[[17 20]
 [ 3  4]]