05.其他创建numpy数组的方法

>>> import numpy as np
>>> np.zeros(10,dtype=int)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

>>> np.zeros((3,5))
array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])

>>> np.zeros(shape=(3,5),dtype=int)
array([[0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0]])

>>> np.ones(10)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])

>>> np.ones((3,5))
array([[1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.],
       [1., 1., 1., 1., 1.]])

>>> np.full(shape=(3,5),fill_value=666)
array([[666, 666, 666, 666, 666],
       [666, 666, 666, 666, 666],
       [666, 666, 666, 666, 666]])

>>> [i for i in range(0,20,2)]
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

>>> np.arange(0,20,2)
array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])

>>> np.arange(0,1,0.2)
array([0. , 0.2, 0.4, 0.6, 0.8])

>>> np.linspace(0,20,10)
array([ 0.        ,  2.22222222,  4.44444444,  6.66666667,  8.88888889,
       11.11111111, 13.33333333, 15.55555556, 17.77777778, 20.        ])

>>> np.linspace(0,20,11)
array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.])

>>> np.random.randint(10)
0

>>> np.random.randint(0,10,10)
array([1, 4, 7, 9, 0, 6, 1, 5, 2, 6])

>>> np.random.randint(2,10,size=10)
array([3, 3, 8, 9, 4, 3, 2, 7, 2, 4])

>>> np.random.randint(2,10,size=(3, 5))
array([[9, 8, 4, 2, 3],
       [6, 3, 5, 8, 6],
       [7, 8, 6, 7, 9]])

>>> np.random.seed(666)
>>> np.random.randint(2,10,size=(3, 5))
array([[6, 4, 7, 8, 8],
       [8, 3, 8, 6, 7],
       [5, 8, 5, 6, 9]])

>>> np.random.randint(2,10,size=(3, 5))
array([[6, 8, 3, 9, 2],
       [8, 2, 9, 7, 4],
       [6, 9, 7, 7, 7]])

>>> np.random.random()
0.7744794542685887

>>> np.random.random(10)
array([0.00510884, 0.11285765, 0.11095367, 0.24766823, 0.0232363 ,
       0.72732115, 0.34003494, 0.19750316, 0.90917959, 0.97834699])

>>> np.random.random((3, 5))
array([[0.53280254, 0.25913185, 0.58381262, 0.32569065, 0.88889931],
       [0.62640453, 0.81887369, 0.54734542, 0.41671201, 0.74304719],
       [0.36959638, 0.07516654, 0.77519298, 0.21940924, 0.07934213]])

>>> np.random.normal(10,100)
-110.99026554923134