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numpy模块学习

前言

Numpy库支持高级大量的维度数组与矩阵运算，Numpy同时也对数组运算提供大量的数学函数，对于大量计算运行效率极好，是大量机器学习框架的基础库

常用属性与方法

>>> import numpy as np

# 生成行向向量
>>> A = np.array([1, 2, 3, 4])
>>> print(A)
[1 2 3 4]

# 生成矩阵向量
>>> B = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [4, 3, 2, 1]])
>>> print(B)
[[1 2 3 4]
 [5 6 7 8]
 [4 3 2 1]]

# shape 属性 代表维度
# 如果是行向量，打印出来只有当前的向量中的元素个数
>>> print(A.shape)
(4,)
>>> print(B.shape)
(3, 4)

# dtype 属性 代表所有的数据类型
>>> print(A.dtype)
int32
>>> C = np.array([1, 2, 3.0, 4])
>>> print(C.dtype)
float64
>>> D = np.array([1, 2, 3.0, ‘4‘])
>>> print(D.dtype)
<U32

# 生成一个一维数组，并转化为 2行 x 5列 的数组
>>> a = np.arange(10)
>>> print(a)
[0 1 2 3 4 5 6 7 8 9]
>>> b = a.reshape(2, 5)
>>> print(b)
[[0 1 2 3 4]
 [5 6 7 8 9]]

# 初始化一个全零的 3行 x 4列 的数组
>>> c = np.zeros((3, 4), dtype=int)
>>> print(c)
[[0 0 0 0]
 [0 0 0 0]
 [0 0 0 0]]

# 在 [10, 25) 内以5为步长生成一维数组
>>> d = np.arange(10, 25, 5)
>>> print(d)
[10 15 20]

# 生成随机的 2行 x 3列 数组
>>> e = np.random.rand(2, 3)
>>> print(e)
[[0.84489957 0.05814669 0.68395074]
 [0.99549967 0.40004318 0.06304403]]

# 在 [0, 20] 内生成 5 个等差数列的一维数组
>>> f = np.linspace(0, 20, 5)
>>> print(f)
[ 0.  5. 10. 15. 20.]

# 排序 0表示按列排序，1表示按行排序
>>> g = np.random.rand(3, 4)
>>> print(np.sort(g, axis=1))
[[0.2781411  0.3905857  0.49844233 0.87326903]
 [0.20858033 0.38660401 0.49121198 0.78279587]
 [0.04344476 0.64196408 0.66657039 0.89382273]]
>>> print(np.sort(g, axis=0))
[[0.2781411  0.20858033 0.38660401 0.04344476]
 [0.64196408 0.49844233 0.66657039 0.3905857 ]
 [0.78279587 0.89382273 0.87326903 0.49121198]]

# 从小到大各个数的索引位置
>>> print(np.argsort(g))
[[0 3 1 2]
 [1 2 3 0]
 [3 0 2 1]]
>>> print(g)
[[0.2781411  0.49844233 0.87326903 0.3905857 ]
 [0.78279587 0.20858033 0.38660401 0.49121198]
 [0.64196408 0.89382273 0.66657039 0.04344476]]

# 矩阵对应元素相乘 相加
>>> h = np.array([[1, 2, 3], [4, 5, 6]])
>>> h2 = np.array([[1, 2, 3], [4, 5, 6]])
>>> h3 = h * h2
>>> print(h3)
[[ 1  4  9]
 [16 25 36]]
>>> h4 = h + h2
>>> print(h4)
[[ 2  4  6]
 [ 8 10 12]]

# 矩阵做内积
>>> print(h)
[[1 2 3]
 [4 5 6]]
>>> print(h2)
[[1 4]
 [2 5]
 [3 6]]
>>> print(np.dot(h, h2))
[[14 32]
 [32 77]]

# 矩阵拼接
>>> print(k)
[[1 2 3]
 [4 5 6]]
>>> print(k2)
[[1 2 3]
 [4 5 6]]
>>> k3 = np.hstack((k, k2)) # 列拼接
>>> print(k3)
[[1 2 3 1 2 3]
 [4 5 6 4 5 6]]
>>> k3 = np.vstack((k, k2)) # 行拼接
>>> print(k3)
[[1 2 3]
 [4 5 6]
 [1 2 3]
 [4 5 6]]

# 矩阵转向量
>>> l = np.array([[1, 2, 3, 4], [1, 2, 3, 4]])
>>> l2 = np.ravel(l)
>>> print(l2)
[1 2 3 4 1 2 3 4]

# 拆分矩阵
>>> print(l)
[[1 2 3 4]
 [1 2 3 4]]
>>> print(np.vsplit(l, 2)) # 按行拆分
[array([[1, 2, 3, 4]]), array([[1, 2, 3, 4]])]
>>> print(np.hsplit(l, 2)) # 按列拆分
[array([[1, 2],
       [1, 2]]), 
array([[3, 4],
       [3, 4]])]

# 读取文件 转换成矩阵或向量
# genfromtxt()
# delimiter 指的是用什么分割
# dtype 指定当前数据类型
# skip_header 消除前多少行的数据
D:\Document\LearningNotes>type test.txt
this a test page
this a test page
this a test page
this a test page
this a test page
this a test page
this a test page
this a test page
D:\Document\LearningNotes>python
Python 3.9.4 (tags/v3.9.4:1f2e308, Apr  6 2021, 13:40:21) [MSC v.1928 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy as np
>>> text = np.genfromtxt(‘test.txt‘, delimiter=‘.‘, dtype=str, skip_header=1)
>>> print(text)
[‘this a test page‘ ‘this a test page‘ ‘this a test page‘
 ‘this a test page‘ ‘this a test page‘ ‘this a test page‘
 ‘this a test page‘]
>>> text = np.genfromtxt(‘test.txt‘, delimiter=‘ ‘, dtype=str, skip_header=1)
>>> print(text)
[[‘this‘ ‘a‘ ‘test‘ ‘page‘]
 [‘this‘ ‘a‘ ‘test‘ ‘page‘]
 [‘this‘ ‘a‘ ‘test‘ ‘page‘]
 [‘this‘ ‘a‘ ‘test‘ ‘page‘]
 [‘this‘ ‘a‘ ‘test‘ ‘page‘]
 [‘this‘ ‘a‘ ‘test‘ ‘page‘]
 [‘this‘ ‘a‘ ‘test‘ ‘page‘]]

# 矩阵数据操作
>>> print(text[2, 0])
this
>>> print(text[3, 2])
test
>>> print(text[3, 1:3])
[‘a‘ ‘test‘]
>>> print(text[1:3, 1:3])
[[‘a‘ ‘test‘]
 [‘a‘ ‘test‘]]
>>> print(text.T) # 转置
[[‘this‘ ‘this‘ ‘this‘ ‘this‘ ‘this‘ ‘this‘ ‘this‘]
 [‘a‘ ‘a‘ ‘a‘ ‘a‘ ‘a‘ ‘a‘ ‘a‘]
 [‘test‘ ‘test‘ ‘test‘ ‘test‘ ‘test‘ ‘test‘ ‘test‘]
 [‘page‘ ‘page‘ ‘page‘ ‘page‘ ‘page‘ ‘page‘ ‘page‘]]

# 求最大值，最小值，平均值
>>> nums = np.array([[1, 0, 3], [1, 2, 7]])
>>> print(nums.max())
7
>>> print(nums.min())
0
>>> print(nums.mean())
2.3333333333333335
>>> print(nums[0:1, 1:2].max())
0

# 求和、行和、列和
>>> print(nums)
[[1 0 3]
 [1 2 7]]
>>> print(nums.sum())
14
>>> print(nums.sum(axis=1))
[ 4 10]
>>> print(nums.sum(axis=0))
[ 2  2 10]

参考

https://blog.csdn.net/nahanai/article/details/88874555

numpy模块

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原文地址：https://www.cnblogs.com/shivers0x72/p/14860882.html