python开发笔记-ndarray方法属性详解

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Python中的数组ndarray是什么？

1、NumPy中基本的数据结构

2、所有元素是同一种类型

3、别名是array

4、利于节省内存和提高CPU计算时间

5、有丰富的函数

ndarray的创建：

import numpy as np
>>> aArray=np.array([1,2,3])
>>> aArray
array([1, 2, 3])
>>> bArray=np.array([(1,2,3),(4,5,6)])
>>> bArray
array([[1, 2, 3],
[4, 5, 6]])
>>> np.arange(1,5,0.5)
array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])
>>> np.random.random((2,2))
array([[0.15637741, 0.23650666],
[0.37523649, 0.4608882 ]])
>>> np.linspace(1,2,10,endpoint=False)
array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])

np.ones([2,3])
array([[1., 1., 1.],
[1., 1., 1.]])
>>> np.zeros((2,2))
array([[0., 0.],
[0., 0.]])
>>> np.fromfunction(lambda i,j:(i+1)*(j+1),(9,9))
array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9.],
[ 2., 4., 6., 8., 10., 12., 14., 16., 18.],
[ 3., 6., 9., 12., 15., 18., 21., 24., 27.],
[ 4., 8., 12., 16., 20., 24., 28., 32., 36.],
[ 5., 10., 15., 20., 25., 30., 35., 40., 45.],
[ 6., 12., 18., 24., 30., 36., 42., 48., 54.],
[ 7., 14., 21., 28., 35., 42., 49., 56., 63.],
[ 8., 16., 24., 32., 40., 48., 56., 64., 72.],
[ 9., 18., 27., 36., 45., 54., 63., 72., 81.]])

import numpy as np
>>> x = np.array([(1,2,3),(4,5,6)])
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.ndim
2
>>> x.shape
(2, 3)
>>> x.size
6

import numpy as np
>>> aArray=np.array([(1,2,3),(4,5,6)])
>>> print(aArray[1])
[4 5 6]
>>> print(aArray[0])
[1 2 3]
>>> print(aArray[0:2])
[[1 2 3]
[4 5 6]]
>>> print(aArray[:,[0,1]])
[[1 2]
[4 5]]
>>> print(aArray[1,[0,1]])
[4 5]
>>> for row in aArray:
print(row) [1 2 3]
[4 5 6]

ndarray的操作：

import numpy as np
>>> aArray=np.array([(1,2,3),(4,5,6)])
>>> aArray.shape
(2, 3)
>>> bArray=aArray.reshape(3,2)
>>> bArray
array([[1, 2],
[3, 4],
[5, 6]])
>>> aArray
array([[1, 2, 3],
[4, 5, 6]])

import numpy as np
>>> aArray=np.array([(1,2,3),(4,5,6)])
>>> aArray.resize(3,2)
>>> aArray
array([[1, 2],
[3, 4],
[5, 6]])
>>> bArray=np.array([1,3,7])
>>> cArray=np.array([3,5,8])
>>> np.vstack((bArray,cArray))
array([[1, 3, 7],
[3, 5, 8]])
>>> np.hstack((bArray,cArray))
array([1, 3, 7, 3, 5, 8])

ndarray的运算：

import numpy as np
>>> aArray=np.array([(5,5,5),(5,5,5)])
>>> bArray=np.array([(2,2,2),(2,2,2)])
>>> cArray=aArray*bArray
>>> cArray
array([[10, 10, 10],
[10, 10, 10]])
>>> aArray+=bArray
>>> aArray
array([[7, 7, 7],
[7, 7, 7]])

广播的思想：

a=np.array([1,2,3])
>>> b=np.array([[1,2,3],[4,5,6]])
>>> a+b
array([[2, 4, 6],
[5, 7, 9]])

统计运算：

import numpy as np
>>> aArray=np.array([(1,2,3),(4,5,6)])
>>> aArray.sum()
21
>>> aArray.sum(axis=0)
array([5, 7, 9])
>>> aArray.sum(axis=1)
array([ 6, 15])
>>> aArray.min()
1
>>> aArray.argmax()
5
>>> aArray.mean()
3.5
>>> aArray.var()
2.9166666666666665
>>> aArray.std()
1.707825127659933

ndarray的专门应用--线性代数：

>>> import numpy as np
>>> x=np.array([[1,2],[3,4]])
>>> r1=np.linalg.det(x)
>>> print(r1)
-2.0000000000000004
>>> r1
-2.0000000000000004
>>> r2=np.linalg.inv(x)
>>> r2
array([[-2. , 1. ],
[ 1.5, -0.5]])
>>> print(r2)
[[-2. 1. ]
[ 1.5 -0.5]]
>>> r3=np.dot(x,x)
>>> r3
array([[ 7, 10],
[15, 22]])
>>> print(r3)
[[ 7 10]
[15 22]]