[Python知识库]numpy

提示：对于黑马人工智能基础课程所做的笔记

import numpy as np

1. hello-numpy

1.1 ndarray与Python原生list运算效率对比

import random
import time
import numpy as np
a = []
for i in range(100000000):
    a.append(random.random())

# 通过%time魔法方法, 查看当前行的代码运行一次所花费的时间
%time sum1=sum(a)

b=np.array(a)

%time sum2=np.sum(b)

Wall time: 420 ms
Wall time: 111 ms

2. ndarray的属性

b.shape

(100000000,)

b.ndim

1

b.size

100000000

b.itemsize

8

b.dtype

dtype('float64')

创建数据时指定类型

b = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
b

array([[1., 2., 3.],
       [4., 5., 6.]], dtype=float32)

arr = np.array(["pythonI", "hello", "I"], dtype=np.string_)
arr

array([b'pythonI', b'hello', b'I'], dtype='|S7')