numpy小结

import numpy as np
my_arr = np.arange(1000000)

my_list = list(range(1000000))

%time for _ in range(10): my_arr2 = my_arr * 2

CPU times: user 21.2 ms, sys: 11.2 ms, total: 32.4 ms
Wall time: 32.6 ms

%time for _ in range(10):  my_list = [x * 2 for x in my_list]

CPU times: user 1.21 s, sys: 252 ms, total: 1.47 s
Wall time: 1.71 s

data = np.random.randn(2,3)
data

array([[ 0.90479239, -3.06481532, -1.43044274],
       [ 0.77066475,  0.1601102 ,  0.48827119]])

data * 10

array([[  9.04792392, -30.64815316, -14.30442743],
       [  7.70664748,   1.60110198,   4.8827119 ]])

data + data

array([[ 1.80958478, -6.12963063, -2.86088549],
       [ 1.5413295 ,  0.3202204 ,  0.97654238]])

data.shape

(2, 3)

data.dtype

dtype('float64')

data1 = [1,3,54,23]
arr1 = np.array(data1)
arr1

array([ 1,  3, 54, 23])

data2 = [[1,3,2,2], [93,2,4,3]]
arr2 = np.array(data2)
arr2

array([[ 1,  3,  2,  2],
       [93,  2,  4,  3]])

arr2.ndim

2

arr2.shape

(2, 4)

np.size(arr2)

6

np.zeros(10)

array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

np.zeros((2,3,4))

array([[[0., 0., 0., 0.],
        [0., 0., 0., 0.],
        [0., 0., 0., 0.]],

       [[0., 0., 0., 0.],
        [0., 0., 0., 0.],
        [0., 0., 0., 0.]]])

np.empty((2,3,4))

array([[[0., 0., 0., 0.],
        [0., 0., 0., 0.],
        [0., 0., 0., 0.]],

       [[0., 0., 0., 0.],
        [0., 0., 0., 0.],
        [0., 0., 0., 0.]]])

np.arange(15)

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

arr1 = np.array([1,2,3], dtype=np.float64)
arr1.dtype

dtype('float64')

arr = np.array([1,2,3,4,5])
arr.dtype

dtype('int64')

float_arr = arr.astype(np.float64)
float_arr.dtype

dtype('float64')

arr = np.array([1, 3.8, 0.9, 8.3, 1.2, 2.4])
arr.astype(np.int32)

array([1, 3, 0, 8, 1, 2], dtype=int32)

numeric_strings = np.array(['1.2', '-9.6', '3.4'])
numeric_strings.astype(float)

array([ 1.2, -9.6,  3.4])

int_array = np.arange(10)
calibers = np.array([.22, .270, .357])

int_array.astype(calibers.dtype)

array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])

arr = np.array([[1.2, 2.0, 3.0], [4.0,5.0,6.]])
arr

array([[1.2, 2. , 3. ],
       [4. , 5. , 6. ]])

arr * arr

array([[ 1.44,  4.  ,  9.  ],
       [16.  , 25.  , 36.  ]])

arr ** 0.5

array([[1.09544512, 1.41421356, 1.73205081],
       [2.        , 2.23606798, 2.44948974]])

arr2 = np.array([[1.8, 4., 1.],[7., 2., 12.]])
arr2

array([[ 1.8,  4. ,  1. ],
       [ 7. ,  2. , 12. ]])

arr2 > arr

array([[ True,  True, False],
       [ True, False,  True]])

arr = np.arange(10)
arr

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

arr[3]

3

arr[4:8]

array([4, 5, 6, 7])

arr[4:8] = 12   # 切片是引用传递；改变切片数据，原数组中的数据也会改变

arr

array([ 0,  1,  2,  3, 12, 12, 12, 12,  8,  9])

arr_slice = arr[4:8]
arr_slice

array([12, 12, 12, 12])

arr_slice[1] = 12345

arr

array([    0,     1,     2,     3,    12, 12345,    12,    12,     8,
           9])

arr_slice

array([   12, 12345,    12,    12])

arr_slice[:] = 64

arr_slice

array([64, 64, 64, 64])

#### 高维数组

arr2d = np.array([[1,2,3],
                  [4,5,6],
                  [7,8,9]])
arr2d

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

arr2d[2]

array([7, 8, 9])

arr2d[0][2]

3

arr2d[0,2]   # axis=0表示行，axis=1表示列

3

arr3d = np.array([[[1,2,3],[4,5,6]],
                   [[7,8,9],[10,11,12]]])
arr3d

array([[[ 1,  2,  3],
        [ 4,  5,  6]],

       [[ 7,  8,  9],
        [10, 11, 12]]])

old_values = arr3d[0].copy()

arr3d[0] = 42   # 将整个第一维的数据进行赋值42

arr3d

array([[[42, 42, 42],
        [42, 42, 42]],

       [[ 7,  8,  9],
        [10, 11, 12]]])

arr3d[0] = old_values

arr3d

array([[[ 1,  2,  3],
        [ 4,  5,  6]],

       [[ 7,  8,  9],
        [10, 11, 12]]])

arr3d[1,0]

array([7, 8, 9])

x = arr3d[1]
x

array([[ 7,  8,  9],
       [10, 11, 12]])

x[0]

array([7, 8, 9])

arr

array([ 0,  1,  2,  3, 64, 64, 64, 64,  8,  9])

arr[1:6]

array([ 1,  2,  3, 64, 64])

arr2d

array([[1, 2, 3],
       [4, 5, 6],
       [7, 8, 9]])

arr2d[:2]

array([[1, 2, 3],
       [4, 5, 6]])

arr2d[:2, 1:]

array([[2, 3],
       [5, 6]])

arr2d[1, :2]

array([4, 5])

arr2d[:2, 2]

array([3, 6])

arr2d[:,:1]

array([[1],
       [4],
       [7]])

arr2d[:2,:] = 10

arr2d

array([[10, 10, 10],
       [10, 10, 10],
       [ 7,  8,  9]])

names = np.array(['Bob', 'Joe', 'Will', 'Bob','Will', 'Joe', 'Joe'])

data = np.random.randn(7,4)

data

array([[-0.10772198, -0.29184497,  1.3724022 ,  0.07641245],
       [-0.56546012, -0.21549842,  0.65314245,  2.03431214],
       [ 2.01365241, -1.14999284, -0.14336555, -2.08048877],
       [ 1.34347561, -0.35609267, -0.55492504, -1.40518892],
       [ 0.26469331,  0.61377631,  0.45934999,  0.63168711],
       [ 0.37609032,  1.593136  ,  1.95688825, -0.53681938],
       [ 1.21065416, -0.87146262, -1.26135673,  1.2322734 ]])

names == "Bob"

array([ True, False, False,  True, False, False, False])

data[names == "Bob"]  # 选择出Bob对应的行

array([[-0.10772198, -0.29184497,  1.3724022 ,  0.07641245],
       [ 1.34347561, -0.35609267, -0.55492504, -1.40518892]])

data[0]

array([-0.10772198, -0.29184497,  1.3724022 ,  0.07641245])

data[3]

array([ 1.34347561, -0.35609267, -0.55492504, -1.40518892])

data[names == 'Bob',2:]

array([[ 1.3724022 ,  0.07641245],
       [-0.55492504, -1.40518892]])

data[names != "Bob"]

array([[-0.56546012, -0.21549842,  0.65314245,  2.03431214],
       [ 2.01365241, -1.14999284, -0.14336555, -2.08048877],
       [ 0.26469331,  0.61377631,  0.45934999,  0.63168711],
       [ 0.37609032,  1.593136  ,  1.95688825, -0.53681938],
       [ 1.21065416, -0.87146262, -1.26135673,  1.2322734 ]])

data[~(names == "Bob")]   # 取反的另一种操作

array([[-0.56546012, -0.21549842,  0.65314245,  2.03431214],
       [ 2.01365241, -1.14999284, -0.14336555, -2.08048877],
       [ 0.26469331,  0.61377631,  0.45934999,  0.63168711],
       [ 0.37609032,  1.593136  ,  1.95688825, -0.53681938],
       [ 1.21065416, -0.87146262, -1.26135673,  1.2322734 ]])

cond = (names == "Bob")
data[~cond]

array([[-0.56546012, -0.21549842,  0.65314245,  2.03431214],
       [ 2.01365241, -1.14999284, -0.14336555, -2.08048877],
       [ 0.26469331,  0.61377631,  0.45934999,  0.63168711],
       [ 0.37609032,  1.593136  ,  1.95688825, -0.53681938],
       [ 1.21065416, -0.87146262, -1.26135673,  1.2322734 ]])

data[(names == "Bob") | (names == "Joe")]

array([[-0.10772198, -0.29184497,  1.3724022 ,  0.07641245],
       [-0.56546012, -0.21549842,  0.65314245,  2.03431214],
       [ 1.34347561, -0.35609267, -0.55492504, -1.40518892],
       [ 0.37609032,  1.593136  ,  1.95688825, -0.53681938],
       [ 1.21065416, -0.87146262, -1.26135673,  1.2322734 ]])

data[data < 0] = 0   # 将负值设为0
data

array([[0.        , 0.        , 1.3724022 , 0.07641245],
       [0.        , 0.        , 0.65314245, 2.03431214],
       [2.01365241, 0.        , 0.        , 0.        ],
       [1.34347561, 0.        , 0.        , 0.        ],
       [0.26469331, 0.61377631, 0.45934999, 0.63168711],
       [0.37609032, 1.593136  , 1.95688825, 0.        ],
       [1.21065416, 0.        , 0.        , 1.2322734 ]])

data[names != "Joe"] = 7   # 将不是Joe的行赋值为7
data

array([[7.        , 7.        , 7.        , 7.        ],
       [0.        , 0.        , 0.65314245, 2.03431214],
       [7.        , 7.        , 7.        , 7.        ],
       [7.        , 7.        , 7.        , 7.        ],
       [7.        , 7.        , 7.        , 7.        ],
       [0.37609032, 1.593136  , 1.95688825, 0.        ],
       [1.21065416, 0.        , 0.        , 1.2322734 ]])

arr = np.empty((8,4))
arr

array([[-2.31584178e+077, -2.31584178e+077,  1.33397724e-322,
         0.00000000e+000],
       [ 0.00000000e+000,  5.02034658e+175,  2.70088625e-056,
         3.69670980e-057],
       [ 4.88947235e-033,  5.18944342e+170,  1.47763641e+248,
         1.16096346e-028],
       [ 7.69165785e+218,  1.35617292e+248,  5.98432176e-067,
         4.01584398e-057],
       [ 1.09392309e-042,  6.28964548e-066,  4.30240881e-096,
         6.32299154e+233],
       [ 6.48224638e+170,  5.22411352e+257,  5.74020278e+180,
         8.37174974e-144],
       [ 1.41529402e+161,  9.16651763e-072,  4.95296723e+097,
         7.50274445e-067],
       [ 3.07800883e+126,  2.08600674e-308, -2.31584178e+077,
        -2.31584178e+077]])

for i in range(8):
    arr[i] = i

arr

array([[0., 0., 0., 0.],
       [1., 1., 1., 1.],
       [2., 2., 2., 2.],
       [3., 3., 3., 3.],
       [4., 4., 4., 4.],
       [5., 5., 5., 5.],
       [6., 6., 6., 6.],
       [7., 7., 7., 7.]])

arr[[4,3,0,6]]

array([[4., 4., 4., 4.],
       [3., 3., 3., 3.],
       [0., 0., 0., 0.],
       [6., 6., 6., 6.]])

arr = np.arange(32).reshape((8,4))
arr

array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15],
       [16, 17, 18, 19],
       [20, 21, 22, 23],
       [24, 25, 26, 27],
       [28, 29, 30, 31]])

arr[[1,5,7,2],[0,3,1,2]]

array([ 4, 23, 29, 10])

arr[[1,5,7,2]][:,[0,3,1,2]]

array([[ 4,  7,  5,  6],
       [20, 23, 21, 22],
       [28, 31, 29, 30],
       [ 8, 11,  9, 10]])

arr[[1,5,7,2]]

array([[ 4,  5,  6,  7],
       [20, 21, 22, 23],
       [28, 29, 30, 31],
       [ 8,  9, 10, 11]])

arr[[1,5,7,2]][:,[0,3,1,2]]

array([[ 4,  7,  5,  6],
       [20, 23, 21, 22],
       [28, 31, 29, 30],
       [ 8, 11,  9, 10]])

arr = np.arange(15).reshape((3,5))
arr

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

arr.T   # 求转置

array([[ 0,  5, 10],
       [ 1,  6, 11],
       [ 2,  7, 12],
       [ 3,  8, 13],
       [ 4,  9, 14]])

np.dot(arr.T, arr)

array([[125, 140, 155, 170, 185],
       [140, 158, 176, 194, 212],
       [155, 176, 197, 218, 239],
       [170, 194, 218, 242, 266],
       [185, 212, 239, 266, 293]])

arr

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

arr.swapaxes(1,0)

array([[ 0,  5, 10],
       [ 1,  6, 11],
       [ 2,  7, 12],
       [ 3,  8, 13],
       [ 4,  9, 14]])

arr

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

np.sqrt(arr)

array([[0.        , 1.        , 1.41421356, 1.73205081, 2.        ],
       [2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ],
       [3.16227766, 3.31662479, 3.46410162, 3.60555128, 3.74165739]])

np.exp(arr)

array([[1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,
        5.45981500e+01],
       [1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,
        8.10308393e+03],
       [2.20264658e+04, 5.98741417e+04, 1.62754791e+05, 4.42413392e+05,
        1.20260428e+06]])

x = np.random.randn(8)

y = np.random.randn(8)

np.maximum(x,y)

array([-0.53590291, -0.52641016,  1.55493034,  0.45145878,  0.56083064,
        0.98578306,  1.34527153,  0.23153891])

reminder, whole_apart = np.modf(x)
print(reminder)   # 小数部分
print(whole_apart)  # 整数部分

[-0.96304512 -0.52641016  0.55493034  0.40595377 -0.02314699  0.56459784
  0.34527153  0.04450806]
[-0. -0.  1.  0. -1.  0.  1.  0.]

points = np.arange(-5, 5, 0.01)

x, y = np.meshgrid(points,points)
print(x)
print(y)

[[-5.   -4.99 -4.98 ...  4.97  4.98  4.99]
 [-5.   -4.99 -4.98 ...  4.97  4.98  4.99]
 [-5.   -4.99 -4.98 ...  4.97  4.98  4.99]
 ...
 [-5.   -4.99 -4.98 ...  4.97  4.98  4.99]
 [-5.   -4.99 -4.98 ...  4.97  4.98  4.99]
 [-5.   -4.99 -4.98 ...  4.97  4.98  4.99]]
[[-5.   -5.   -5.   ... -5.   -5.   -5.  ]
 [-4.99 -4.99 -4.99 ... -4.99 -4.99 -4.99]
 [-4.98 -4.98 -4.98 ... -4.98 -4.98 -4.98]
 ...
 [ 4.97  4.97  4.97 ...  4.97  4.97  4.97]
 [ 4.98  4.98  4.98 ...  4.98  4.98  4.98]
 [ 4.99  4.99  4.99 ...  4.99  4.99  4.99]]

z = np.sqrt(x ** 2 + y ** 2)
z

array([[7.07106781, 7.06400028, 7.05693985, ..., 7.04988652, 7.05693985,
        7.06400028],
       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,
        7.05692568],
       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,
        7.04985815],
       ...,
       [7.04988652, 7.04279774, 7.03571603, ..., 7.0286414 , 7.03571603,
        7.04279774],
       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,
        7.04985815],
       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,
        7.05692568]])

import matplotlib.pyplot as plt

plt.imshow(z, cmap=plt.cm.gray)

<matplotlib.image.AxesImage at 0x119c72e10>

x = np.array([2.2, 1.3, 9.3, 6.2, 7.4])
y = np.array([8.2, 1.7, 5.3, 4.9, 9.2])

cond = np.array([True, False, True, False, True])

arr = np.where(cond, x, y)   # True的时候选择x，False的时候选择y
arr

array([2.2, 1.7, 9.3, 4.9, 7.4])

z = np.random.randn(4, 4)
z

array([[ 0.66304028,  1.56104402, -1.64553308,  1.38520089],
       [ 0.11778316,  0.73150194,  0.93620809, -1.04122122],
       [ 1.02459426,  2.17382769, -0.20349334, -0.58694299],
       [ 1.00603491, -1.44444792, -0.11128948, -0.69493726]])

z > 0

array([[ True,  True, False,  True],
       [ True,  True,  True, False],
       [ True,  True, False, False],
       [ True, False, False, False]])

np.where(z > 0, 2, -2)

array([[ 2,  2, -2,  2],
       [ 2,  2,  2, -2],
       [ 2,  2, -2, -2],
       [ 2, -2, -2, -2]])

np.where(z > 0, 2, z)

array([[ 2.        ,  2.        , -1.64553308,  2.        ],
       [ 2.        ,  2.        ,  2.        , -1.04122122],
       [ 2.        ,  2.        , -0.20349334, -0.58694299],
       [ 2.        , -1.44444792, -0.11128948, -0.69493726]])

arr

array([2.2, 1.7, 9.3, 4.9, 7.4])

print(np.mean(arr))
arr.mean()

5.1






5.1

arr.sum()

25.5

arr.mean(axis=0)   # 计算列的平均值

5.1

arr.cumsum(axis=0)

array([ 2.2,  3.9, 13.2, 18.1, 25.5])

np.argmin(arr)

1

np.argmax(arr)

2

arr = np.random.randn(100)
arr

array([-3.22314012e-01, -4.20983200e-01, -8.60895143e-01,  1.19762451e+00,
       -1.18101654e+00, -1.40988582e+00,  2.69878711e-01,  9.49317233e-01,
       -6.17494119e-01, -5.93705233e-01, -1.73454244e+00,  2.44835633e-02,
       -1.58724929e+00, -1.93150694e-01,  3.68590165e-01,  5.57850914e-02,
       -4.08251237e-01, -3.18700903e-01,  1.10942971e-01,  1.45641795e+00,
        1.57429326e+00, -7.83492435e-01, -2.17803238e+00,  7.95258546e-01,
       -1.86616031e-01,  8.36777476e-01,  2.80993789e-01,  1.32231167e+00,
        4.52074029e-01,  1.34281313e+00,  5.91006237e-01, -8.52972682e-01,
       -2.98589281e-01, -3.15472509e-01, -1.53592401e-01, -2.91603279e-01,
        2.00179770e-01,  8.93278122e-01, -1.92902599e+00, -6.42802967e-02,
       -2.26946105e-01, -4.14173881e-01, -5.61157264e-01,  5.21075788e-01,
        1.34999883e+00, -4.56088898e-01, -6.28535428e-02, -1.71763669e+00,
       -1.09604392e+00,  5.71857881e-01, -2.40084694e-02, -1.47217876e+00,
        2.03613785e+00,  2.58368897e-01,  5.21694750e-01,  3.57046139e-01,
       -8.90487809e-02,  1.05867421e+00, -3.73744876e-01, -1.44889846e+00,
        1.73627536e+00, -1.23398347e+00,  1.24276070e+00, -8.35275697e-01,
        3.95759416e-01, -1.41817016e+00, -1.13538227e-01, -5.51357114e-01,
       -1.22022368e+00,  6.42102384e-02,  4.05726450e-01, -9.20956259e-04,
        1.78650830e+00, -1.82602641e-01,  4.55384759e-01, -9.64795416e-01,
       -1.10006078e+00, -7.36474954e-01, -1.81803987e+00, -6.56197052e-01,
        2.33504470e-01,  5.16974528e-01,  4.59053845e-01,  4.65928281e-01,
        9.68000084e-01,  1.05966457e+00, -9.25582483e-01, -1.17524366e+00,
        3.16498243e-01, -3.36469567e-01,  3.79026924e-01, -3.63310587e-01,
        7.75914268e-01,  1.93695657e-01,  1.17826822e+00,  1.20396230e+00,
        1.53059394e+00,  7.45524766e-01,  3.62200452e-02,  2.34353279e-01])

(arr > 0).sum()

49

arr_bool = arr > 0
arr_bool

array([False, False, False,  True, False, False,  True,  True, False,
       False, False,  True, False, False,  True,  True, False, False,
        True,  True,  True, False, False,  True, False,  True,  True,
        True,  True,  True,  True, False, False, False, False, False,
        True,  True, False, False, False, False, False,  True,  True,
       False, False, False, False,  True, False, False,  True,  True,
        True,  True, False,  True, False, False,  True, False,  True,
       False,  True, False, False, False, False,  True,  True, False,
        True, False,  True, False, False, False, False, False,  True,
        True,  True,  True,  True,  True, False, False,  True, False,
        True, False,  True,  True,  True,  True,  True,  True,  True,
        True])

arr_bool.any()

True

arr_bool.all()

False

names

array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'], dtype='<U4')

np.unique(names)

array(['Bob', 'Joe', 'Will'], dtype='<U4')

sorted(set(names))   # python的方法，先去重再排序

['Bob', 'Joe', 'Will']

np.in1d(names, ["Joe", "Bob"])

array([ True,  True, False,  True, False,  True,  True])

x = np.array([[9,8,7],[6,5,4]])

y = np.array([[1,2],[3,4],[5,6]])
y

array([[1, 2],
       [3, 4],
       [5, 6]])

print(np.dot(x,y))
x.dot(y)

[[68 92]
 [41 56]]






array([[68, 92],
       [41, 56]])

np.dot(x, np.ones(3))

array([24., 15.])

x @ np.ones(3)

array([24., 15.])

# numpy.linalg
from numpy.linalg import inv, qr

x = np.random.randn(5,5)

mat = x.T.dot(x)

inv(mat)

array([[   3.97162611,   10.06047893,   20.33957725,   41.96110271,
         -13.90272187],
       [  10.06047893,   27.43032073,   55.05907039,  113.90833794,
         -37.73094005],
       [  20.33957725,   55.05907039,  111.1893652 ,  229.79046577,
         -76.19206977],
       [  41.96110271,  113.90833794,  229.79046577,  475.7391915 ,
        -157.59917145],
       [ -13.90272187,  -37.73094005,  -76.19206977, -157.59917145,
          52.39033219]])

mat.dot(inv(mat))

array([[ 1.00000000e+00, -4.07659961e-15,  9.20702620e-15,
        -4.41896636e-15, -1.84962408e-15],
       [-4.17848390e-15,  1.00000000e+00,  3.46416569e-15,
         3.80287405e-15, -7.28660199e-15],
       [-1.50445857e-14, -9.63178031e-15,  1.00000000e+00,
        -2.43789390e-13, -1.83785834e-15],
       [ 6.79492973e-15, -8.93413272e-15,  3.28937408e-14,
         1.00000000e+00,  1.59360368e-14],
       [-5.98805626e-15, -8.68880470e-15, -9.44099027e-14,
        -3.39755600e-14,  1.00000000e+00]])

q, r = qr(mat)

print("r:\n", r)
q

r:
 [[-4.54992004e+00 -9.08567164e-01  4.77266232e+00 -8.88857419e-01
   2.40794600e+00]
 [ 0.00000000e+00 -7.07679685e+00  2.64829180e+00  1.05836155e+00
   1.94029724e+00]
 [ 0.00000000e+00  0.00000000e+00 -5.97731101e+00  1.81171976e+00
  -3.24859061e+00]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 -1.85854206e+00
  -5.60796440e+00]
 [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00
   5.34488990e-03]]






array([[-0.91170893,  0.10377487, -0.38996846, -0.02050499, -0.07430852],
       [-0.02064984, -0.95935654, -0.16282362, -0.10966861, -0.20166772],
       [ 0.38363091,  0.18615893, -0.78064734,  0.20716887, -0.40723822],
       [-0.05534304,  0.16086956,  0.35220049, -0.37075471, -0.84235022],
       [ 0.1346287 ,  0.09127778, -0.2965765 , -0.89842882,  0.28002056]])

samples = np.random.normal(size=(4,4))
samples

array([[-0.91715594, -1.39832053,  1.59702221,  1.10642995],
       [-0.67526269,  0.54100686,  0.31399945, -0.94730476],
       [-0.74915956,  0.33955031,  1.09219803, -0.603868  ],
       [ 2.01827315,  0.80366446,  0.14261334,  0.99553088]])

# 随机种子
np.random.seed(1234)  # 全局种子

rng = np.random.RandomState(1234)
rng.randn(10)

array([ 0.47143516, -1.19097569,  1.43270697, -0.3126519 , -0.72058873,
        0.88716294,  0.85958841, -0.6365235 ,  0.01569637, -2.24268495])

# python
import random
position = 0
walk = [position]
steps = 1000
for i in range(steps):
    step = 1 if random.randint(0,1) else -1
    position += step
    walk.append(position)

plt.plot(walk[:100])

[<matplotlib.lines.Line2D at 0x11b0a6dd8>]

# numpy
nsteps = 1000

draws = np.random.randint(0,2,size=nsteps)

steps = np.where(draws > 0, 1, -1)    # 大于0填充1，小于0填充-1

walk = steps.cumsum()

walk.min()

-9

walk.max()

60