Python_Numpy教程_2

最新推荐文章于 2025-05-17 23:18:14 发布

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这篇教程涵盖了Python Numpy的基本运算，包括通用函数ufunc、形状操作如索引、切片和迭代，以及如何使用stack组合数组和split进行数组分割。

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基本运算

import numpy as np

a = np.array([20,30,40,50])

b = np.arange(4)

c = a - b  #减法
d = b**2   #平方
e = 10 *np.sin(a)
print(a<35)

A = np.array([[1,1],[0,1]])
B = np.array([[2,0],[3,4]])
C = A*B  #元素相乘
E = np.dot(A,B)  #矩阵相乘
print(C)
print(E)

import numpy as np

a = np.ones((2,3),dtype = int)
b = np.random.random((2,3))
print(a)
print(b)
b += a
print(b)
#a += b   #error
       # Cannot cast ufunc add output from dtype('float64') to dtype('int32') with casting rule 'same_kind'
print(a)

print(a.sum())
print(a.min())
print(a.max())

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

d = c.sum(axis=0)  #sum of each column
print(d)
e = c.min(axis=1) # min of each row
print(e)
f = c.cumsum(axis=1)  #cumulative sum along each row
print(f)

输出

[[1 1 1]
 [1 1 1]]
[[ 0.33554238  0.2963108   0.91907985]
 [ 0.86329755  0.21385802  0.98444173]]
[[ 1.33554238  1.2963108   1.91907985]
 [ 1.86329755  1.21385802  1.98444173]]
[[1 1 1]
 [1 1 1]]
6
1
1
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
[12 15 18 21]
[0 4 8]
[[ 0  1  3  6]
 [ 4  9 15 22]
 [ 8 17 27 38]]

通用函数ufunc

import numpy as np

B = np.arange(3)
print(B)
print(np.exp(B)) 
print(np.sqrt(B))
C = np.array([2,1,3])
print(np.add(B,C))

索引、切片和迭代

a = np.arange(10)**3
print(a)
print(a[2])
print(a[2:5])
a[:6:2] = -1000
print(a)
print(a[: :-1])

def f(x,y):
    return 10*x+y

b = np.fromfunction(f,(5,4),dtype= int)
print(b)
print(b[2,3])
print(b[0:5,1])
print(b[:,1])
print(b[1:3,:])
print(b[-1])

形状操作

import numpy as np

#floor 计算各元素的floor值，即小于等于该值的最大正数
a = np.floor(10*np.random.random((3,4)))
print(a)
print(a.shape)
# ravel flatten实现功能是一致的，将多维数组将为一维
#两者的区别在于ravel返回视图，会影响原始矩阵
#flatten 返回一份拷贝，对拷贝修改不影响原始矩阵
print(a.ravel())
print(a.flatten())
print(a.transpose())

#reshape 函数改变参数形状并返回它
#resize 函数改变数组自身
b = np.array([[7,5],[9,3],[7,2],[7,8],[6,8],[3,2]])
print(b)
print(b.reshape(3,4))
b.resize((3,2))
print(b)

stack组合不同的数组

import numpy as np

a = np.array([1,2,3])
print(a) 
b = np.array([4,5,6])
print(b)
c = np.vstack((a,b))
print(c)
d = np.hstack((a,b))
print(d)
e = np.column_stack((a,b))
print(e)

输出

[1 2 3]
[4 5 6]
[[1 2 3]
 [4 5 6]]
[1 2 3 4 5 6]
[[1 4]
 [2 5]
 [3 6]]

将数组分割split成几个小数组

f = np.array([1,2,3,4,5,6,7,8,9,10,11,12]).reshape(2,6)
print(f)
print(np.hsplit(f,3))   #沿水平方向
print(np.vsplit(f,2))   #沿垂直方向