numpy入门-数组运算

对数组做基本的算术运算，将会对整个数组的所有元组进行逐一运算，并将运算结果保存在一个新的数组内，而不会破坏原始的数组

• 数组和向量之间的运算
• 数组和数组之间的运算
• 通用函数的使用

数组和向量之间的运算

import numpy as np

a = np.array([20,40,50,80])
b = np.arange(4)
print(a)
print(b)

[20 40 50 80]
[0 1 2 3]

c = a - b
c

array([20, 39, 48, 77])

b**2   # 每个元素进行平方

array([0, 1, 4, 9], dtype=int32)

10*np.sin(a)

array([ 9.12945251,  7.4511316 , -2.62374854, -9.93888654])

a < 40

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

a[a>45]

array([50, 80])

数组和数组之间的运算

A = np.array( [[1,1],
            [0,1]])
B = np.array( [[2,0],
            [3,4]])

print(A)
B

[[1 1]
 [0 1]]

array([[2, 0],
       [3, 4]])

四则运算

print(A - B)
A*B

[[-1  1]
 [-3 -3]]

array([[2, 0],
       [0, 4]])

向量点乘的实现

• dot
• @

A.dot(B)

array([[5, 4],
       [3, 4]])

np.dot(A,B)

array([[5, 4],
       [3, 4]])

A@B

array([[5, 4],
       [3, 4]])

自加和自乘

A += B
A

array([[3, 1],
       [3, 5]])

A *= B
A

array([[ 6,  0],
       [ 9, 20]])

聚合函数

A.mean()

8.75

A.max()

20

A.min()

0

指定行列的聚合

c = np.array(np.arange(12).reshape(3,4))
c

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

c.sum(axis=1)  # 行上求和

array([ 6, 22, 38])

c.mean(axis=0) # 列上求均值

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

c.cumsum(axis=1)  # 行上求累加

array([[ 0,  1,  3,  6],
       [ 4,  9, 15, 22],
       [ 8, 17, 27, 38]], dtype=int32)

sort函数

• Signature: np.sort(a, axis=-1, kind=‘quicksort’, order=None)
• axis : int or None, optional
  Axis along which to sort. If None, the array is flattened before
  sorting. The default is -1, which sorts along the last axis.
• kind : {‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional
  Sorting algorithm. Default is ‘quicksort’.

# 排序：默认是快排，从低到高
c.sort()
c

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

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

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

d.sort(axis=0)   # 在行index上进行排序
d

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

通用函数

• all
• any
• argmax
• argmin
• argsort
• average
• diff

c

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

np.exp(c)   # 求e的指数

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]])

np.sqrt(c)

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

np.add(c,c)   # 两个c相加

array([[ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

c * 2

array([[ 0,  2,  4,  6],
       [ 8, 10, 12, 14],
       [16, 18, 20, 22]])

np.argsort(c, axis=0)    # 返回的是排序后的索引：axis=0 行上进行排序
# c.argsort()

array([[0, 0, 0, 0],
       [1, 1, 1, 1],
       [2, 2, 2, 2]], dtype=int64)

np.argsort(c, axis=1)  # axis=1 : 列上进行排序

array([[0, 1, 2, 3],
       [0, 1, 2, 3],
       [0, 1, 2, 3]], dtype=int64)