数据科学包-Day1-numpy（一）

最新推荐文章于 2025-08-27 20:22:07 发布

原创最新推荐文章于 2025-08-27 20:22:07 发布 · 118 阅读

0 ·

CC 4.0 BY-SA版权

机器学习专栏收录该内容

38 篇文章

订阅专栏

本文详细介绍Numpy库的安装方法及基础运算操作，包括数组创建、维度查看、数学运算、条件判断、矩阵运算、统计分析、转置、切片、数组连接与分割等核心功能。

文章目录

Numpy
- 安装
- numpy基础运算

Numpy

安装

Pycharm直接安装numpy包

Files->Setting->Project workspace->Project:，点击+按钮，搜索numpy包，点击安装即可。

numpy基础运算

import numpy as np
y = np.array([[11,4,2],[2,6,1],[32,6,42]])
print(y)
print('number of dim:',y.ndim)
print('shape:',y.shape)


[[11  4  2]
 [ 2  6  1]
 [32  6 42]]
number of dim: 2
shape: (3, 3)

import numpy as np
a = np.array([10,20,30,40])
b = np.arange(4)
c = 10*np.sin(a)
print(c)

[-5.44021111  9.12945251 -9.88031624  7.4511316 ]

import numpy as np
a = np.array([10,20,30,40])
b = np.arange(4)
print(b)
print(b==3)

[0 1 2 3]
[False False False  True]

import numpy as np
a = np.array([[1,1],[0,1]])
b = np.arange(4).reshape((2,2))

c = a*b
c_dot = np.dot(a,b)
print(c)
print(c_dot)

[[0 1]
 [0 3]]
[[2 4]
 [2 3]]

import numpy as np
a = np.random.random((2,4))

print(np.sum(a))
print(np.min(np.sum(a,axis=1)))#每一行
print(np.min(np.min(a,axis=0)))#每一列
print(np.min(np.max(a,axis=1)))


3.443379837630782
3.443379837630782
0.16345946606982198
0.7558721678310337

import numpy as np
A= np.arange(2,14).reshape((3,4))
print(np.argmin(A))
print(np.argmax(A))
print(np.mean(A))
print(np.median(A))
print(np.cumsum(A))
print(np.diff(A))

0
11
7.5
7.5
[ 2  5  9 14 20 27 35 44 54 65 77 90]
[[1 1 1]
 [1 1 1]
 [1 1 1]]

import numpy as np
A= np.arange(14,2,-1).reshape((3,4))
print(A)
print(np.transpose(A))
print(A.T)

[[14 13 12 11]
 [10  9  8  7]
 [ 6  5  4  3]]
[[14 10  6]
 [13  9  5]
 [12  8  4]
 [11  7  3]]
[[14 10  6]
 [13  9  5]
 [12  8  4]
 [11  7  3]]

import numpy as np
A= np.arange(14,2,-1).reshape((3,4))
print(A)
print(np.clip(A,5,9))

[[14 13 12 11]
 [10  9  8  7]
 [ 6  5  4  3]]
[[9 9 9 9]
 [9 9 8 7]
 [6 5 5 5]]

import numpy as np
A= np.arange(3,15).reshape((3,4))
print(A)
print(A[2][1])
print(A[1,1:2])

[[ 3  4  5  6]
 [ 7  8  9 10]
 [11 12 13 14]]
12
[8]

import numpy as np
A= np.array([1,1,1])
B= np.array([2,2,2])
C = np.vstack((A,B))
D = np.hstack((A,B))
print(D)
print(A.shape,D.shape)

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

import numpy as np
A = np.arange(12).reshape((3,4))
print(A)
print(np.split(A,2,axis=1))

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