Numpy入门（四）深入学习Numpy模块

川长思鸟来 2022-04-18 03:27 352阅读 0赞

### 1 linalg模块 ###

import numpy as np

A = np.mat("0 1 2; 1 0 3; 4 -3 8")

matrix([[ 0,  1,  2],
            [ 1,  0,  3],
            [ 4, -3,  8]])

# 矩阵的逆
    np.linalg.inv(A)

matrix([[-4.5,  7. , -1.5],
            [-2. ,  4. , -1. ],
            [ 1.5, -2. ,  0.5]])

A * np.linalg.inv(A)

matrix([[1., 0., 0.],
            [0., 1., 0.],
            [0., 0., 1.]])

A = np.mat("1 -2 1;0 2 -8;-4 5 9")

matrix([[ 1, -2,  1],
            [ 0,  2, -8],
            [-4,  5,  9]])

b = np.array([0,8,-9])

x = np.linalg.solve(A,b)

array([29., 16.,  3.])

np.dot(A,x)

matrix([[ 0.,  8., -9.]])

np.linalg.det(A)

1.9999999999999998

### 2 fft模块 ###

from matplotlib.pyplot import *

x = np.linspace(0,2*np.pi,30)
    wave = np.cos(x)

trans = np.fft.fft(wave)

plot(trans)
    show()

D:\Anaconda3-5\lib\site-packages\numpy\core\numeric.py:492: ComplexWarning: Casting complex values to real discards the imaginary part
      return array(a, dtype, copy=False, order=order)

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### 3 随机数模块 ###

N = 10000

normal_values = np.random.normal(size = N)

plot(normal_values)
    show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjM2Mzk5Nw_size_16_color_FFFFFF_t_70 1]

### 4 排序 ###

a = np.random.randint(0,100,size=10)

array([80,  5, 42,  5, 30, 34, 12, 37, 65,  2])

np.sort(a)

array([ 2,  5,  5, 12, 30, 34, 37, 42, 65, 80])

b = np.random.randn(10)

array([ 0.24423767, -0.17118507,  0.48413289, -0.10999024, -1.16224052,
           -0.59838132, -0.56166675,  0.51791264,  0.0882722 ,  0.01779734])

for each in zip(a ,b):
        print(each)

(80, 0.24423766862382038)
    (5, -0.17118507409164932)
    (42, 0.4841328947126019)
    (5, -0.1099902424565808)
    (30, -1.1622405164607184)
    (34, -0.5983813182583405)
    (12, -0.5616667452697109)
    (37, 0.5179126359900825)
    (65, 0.08827219948984628)
    (2, 0.017797342769532515)

np.random.random(5)

array([0.57029857, 0.37006366, 0.82282303, 0.87245183, 0.68276258])

### 5 搜索 ###

a = np.array([2,4,8])

np.argmax(a)

b = np.array([np.nan,2,4])

np.nanargmax(b)

np.argwhere(a<5)

array([[0],
           [1]], dtype=int64)

a=np.arange(7)

condition = (a%2) == 0

np.extract(condition,a)

array([0, 2, 4, 6])

np.nonzero(a)

(array([1, 2, 3, 4, 5, 6], dtype=int64),)

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