Github源碼In [1]:

import numpy as np

B = np.arange(3)

print(B)

print(np.exp(B)) #返回e的冪次方，e是一個常數(shù)為2.71828

print(np.sqrt(B))# 開根號

[0 1 2]

[1. 2.71828183 7.3890561 ]

[0. 1. 1.41421356]

In [2]:

# floor向下取整 3.5 向下取整為 3

a = np.floor(10*np.random.random((3,4)))

print(a)

print(a.shape)

# 把矩陣?yán)?#xff0c;從矩陣?yán)闪讼蛄?返回新的 不改變原來的數(shù)據(jù)

print(a.ravel())

print(7777777777777777777777)

print(a)

print(a.shape)

a.shape = (6, 2)

print(a)

print(a.T) # 轉(zhuǎn)置

print(a.resize((2,6)))

print(a)

#If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated:

#a.reshape(3,-1) # 3行 -1自動計算

[[5. 2. 7. 0.]

[3. 6. 4. 7.]

[4. 0. 7. 7.]]

(3, 4)

[5. 2. 7. 0. 3. 6. 4. 7. 4. 0. 7. 7.]

7777777777777777777777

[[5. 2. 7. 0.]

[3. 6. 4. 7.]

[4. 0. 7. 7.]]

(3, 4)

[[5. 2.]

[7. 0.]

[3. 6.]

[4. 7.]

[4. 0.]

[7. 7.]]

[[5. 7. 3. 4. 4. 7.]

[2. 0. 6. 7. 0. 7.]]

None

[[5. 2. 7. 0. 3. 6.]

[4. 7. 4. 0. 7. 7.]]

In [4]:

# hstack 矩陣橫拼接，hstack 矩陣縱拼接

a = np.floor(10*np.random.random((2,2)))

b = np.floor(10*np.random.random((2,2)))

print(a)

print('---')

print(b)

print('---')

print(np.hstack((a,b)) )#橫拼接

print('---')

print(np.vstack((a,b)) ) #豎拼接

[[4. 2.]

[7. 1.]]

---

[[8. 4.]

[2. 1.]]

---

[[4. 2. 8. 4.]

[7. 1. 2. 1.]]

---

[[4. 2.]

[7. 1.]

[8. 4.]

[2. 1.]]

In [5]:

a = np.floor(10*np.random.random((2,12)))

print(a)

print('---')

print(np.hsplit(a,3)) #橫著切分 3分

print('---')

# 傳元組 就相當(dāng)于指定了一個位置 在(3,4)位置切一刀

print(np.hsplit(a,(3,4)) )

a = np.floor(10*np.random.random((12,2)))

print(a)

np.vsplit(a,3) #縱切分

[[1. 7. 6. 4. 7. 7. 5. 6. 8. 0. 1. 6.]

[7. 2. 2. 8. 6. 4. 6. 9. 5. 5. 4. 2.]]

---

[array([[1., 7., 6., 4.],

[7., 2., 2., 8.]]), array([[7., 7., 5., 6.],

[6., 4., 6., 9.]]), array([[8., 0., 1., 6.],

[5., 5., 4., 2.]])]

---

[array([[1., 7., 6.],

[7., 2., 2.]]), array([[4.],

[8.]]), array([[7., 7., 5., 6., 8., 0., 1., 6.],

[6., 4., 6., 9., 5., 5., 4., 2.]])]

[[9. 0.]

[1. 8.]

[3. 5.]

[8. 2.]

[7. 3.]

[0. 8.]

[7. 7.]

[5. 9.]

[4. 2.]

[1. 9.]

[9. 6.]

[3. 7.]]

Out[5]:

[array([[9., 0.],

[1., 8.],

[3., 5.],

[8., 2.]]), array([[7., 3.],

[0., 8.],

[7., 7.],

[5., 9.]]), array([[4., 2.],

[1., 9.],

[9., 6.],

[3., 7.]])]

In [8]:

#Simple assignments make no copy of array objects or of their data.

a = np.arange(12)

b = a

# a and b are two names for the same ndarray object

print(b is a)

b.shape = 3,4

print(a.shape)

print(id(a))

print(id(b)) # 名字不同 指向的內(nèi)存地址是一樣的

True

(3, 4)

86084464

86084464

In [7]:

#The view method creates a new array object that looks at the same data.

c = a.view()# 相當(dāng)于是一個淺復(fù)制 id值不一樣 但是公用的是一套值

print(c is a)

c.shape = 2,6

#print a.shape

c[0,4] = 1234

a

False

Out[7]:

array([[ 0, 1, 2, 3],

[1234, 5, 6, 7],

[ 8, 9, 10, 11]])

In [9]:

#The copy method makes a complete copy of the array and its data.

d = a.copy() # 深復(fù)制 么有關(guān)系的

print(d is a)

d[0,0] = 9999

print(a)

print(b)

False

[[ 0 1 2 3]

[ 4 5 6 7]

[ 8 9 10 11]]

[[ 0 1 2 3]

[ 4 5 6 7]

[ 8 9 10 11]]

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