current position:Home>Numpy core syntax and code sorting summary!

Numpy core syntax and code sorting summary!

2022-05-15 07:44:28Xiaobai learns vision

Click on the above “ Xiaobai studies vision ”, Optional plus " Star standard " or “ Roof placement

 Heavy dry goods , First time delivery 

7a43680dff9fec3e7e854d561436ce49.png

Numpy Summary

Numpy It's a use python Implementation of scientific computing extension library , Include

  • A powerful N Dimensional array object Array;

  • More mature ( radio broadcast ) function library ;

  • For consolidation C/C++ and Fortran Code toolkit ;

  • Practical linear algebra 、 Fourier transform and random number generation function .numpy And sparse matrix operation package scipy With the use of more convenient .

NumPy(Numeric Python) Many advanced numerical programming tools are provided , Such as : Matrix data type 、 Vector processing , And a sophisticated computing library . Produced for rigorous digital processing . Used by many large financial companies , And the core scientific computing organization :Lawrence Livermore,NASA Use it to deal with some of the original use C++,Fortran or Matlab And so on .

This paper sorts out a Numpy A little meter reading , Sum up Numpy Common operations of , You can collect it. Take your time .

( You can click the big picture to view the picture ~)

1、 install Numpy

Can pass Pip perhaps Anaconda install Numpy:

$ pip install numpy

or

$ conda install numpy

 2、 Basics

NumPy One of the most common features is NumPy Array : List and NumPy The main difference between arrays is functionality and speed .

Lists provide basic operations , but NumPy Added FTTs、 Convolution 、 Quick search 、 Basic statistics 、 linear algebra 、 Histogram, etc .

The most important difference between the two data science is Able to use NumPy Array for element level computation .

  • axis 0: Usually refers to the line

  • axis 1: Usually refers to the column

10d296f37778a3b49c6612a645ed575e.png

1. Place holder

7ffc69f8583e56db0f075ac321de11f3.png

give an example :

import numpy as np

# 1 dimensional
x = np.array([1,2,3])
# 2 dimensional
y = np.array([(1,2,3),(4,5,6)])

x = np.arange(3)
>>> array([0, 1, 2])

y = np.arange(3.0)
>>> array([ 0., 1., 2.])

x = np.arange(3,7)
>>> array([3, 4, 5, 6])

y = np.arange(3,7,2)
>>> array([3, 5])

2. Array attribute

d78b4fc4ae785d7e873dd6b37253b628.png

3. Copy / Sort

c28cff0589fb790d3137d9d709fab468.png

give an example :

import numpy as np
# Sort sorts in ascending order
y = np.array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
y.sort()
print(y)
>>> [ 1  2  3  4  5  6  7  8  9  10]

4. Array manipulation routines

  Add or subtract elements  

0aa37d8cfc3c04a41447056fadd5a36b.png

give an example :

import numpy as np
# Append items to array
a = np.array([(1, 2, 3),(4, 5, 6)])
b = np.append(a, [(7, 8, 9)])
print(b)
>>> [1 2 3 4 5 6 7 8 9]

# Remove index 2 from previous array
print(np.delete(b, 2))
>>> [1 2 4 5 6 7 8 9]

  Combining arrays  

6ab91206e293c1235437e78b2b29c8fd.png

give an example :

import numpy as np
a = np.array([1, 3, 5])
b = np.array([2, 4, 6])

# Stack two arrays row-wise
print(np.vstack((a,b)))
>>> [[1 3 5]
     [2 4 6]]

# Stack two arrays column-wise
print(np.hstack((a,b)))
>>> [1 3 5 2 4 6]

  Split array  

e8268b53aff39a80669898a85c4bc08f.png

give an example :

# Split array into groups of ~3
a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
print(np.array_split(a, 3))
>>> [array([1, 2, 3]), array([4, 5, 6]), array([7, 8])]

  Array shape changes  

  • operation

2265697843e2c96dbca2bcfef389949f.png

  • other

2bad119063ac794f82c74bdaf7bcaa03.png

give an example :

# Find inverse of a given matrix
>>> np.linalg.inv([[3,1],[2,4]])
array([[ 0.4, -0.1],
       [-0.2, 0.3]])

5. Mathematical calculation

  operation  

36991eba2034490ad0c818b32d55440b.png

give an example :

# If a 1d array is added to a 2d array (or the other way), NumPy
# chooses the array with smaller dimension and adds it to the one
# with bigger dimension
a = np.array([1, 2, 3])
b = np.array([(1, 2, 3), (4, 5, 6)])
print(np.add(a, b))
>>> [[2 4 6]
     [5 7 9]]
     
# Example of np.roots
# Consider a polynomial function (x-1)^2 = x^2 - 2*x + 1
# Whose roots are 1,1
>>> np.roots([1,-2,1])
array([1., 1.])
# Similarly x^2 - 4 = 0 has roots as x=±2
>>> np.roots([1,0,-4])
array([-2., 2.])

  Compare  

ae4ed0164b81b57f215643d48e89b9d4.png

give an example :

# Using comparison operators will create boolean NumPy arrays
z = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
c = z < 6
print(c)
>>> [ True  True  True  True  True False False False False False]

  Basic statistics  

77bc380a518e2477731c712b15b9ac2f.png

give an example :

# Statistics of an array
a = np.array([1, 1, 2, 5, 8, 10, 11, 12])

# Standard deviation
print(np.std(a))
>>> 4.2938910093294167

# Median
print(np.median(a))
>>> 6.5

  more  

73cb04fc5101e2fb9bd18768a16a956b.png

6. Slices and subsets

abefa6049658933bd139a1284046e775.png

give an example :

b = np.array([(1, 2, 3), (4, 5, 6)])

# The index *before* the comma refers to *rows*,
# the index *after* the comma refers to *columns*
print(b[0:1, 2])
>>> [3]

print(b[:len(b), 2])
>>> [3 6]

print(b[0, :])
>>> [1 2 3]

print(b[0, 2:])
>>> [3]

print(b[:, 0])
>>> [1 4]

c = np.array([(1, 2, 3), (4, 5, 6)])
d = c[1:2, 0:2]
print(d)
>>> [[4 5]]

Slice for example :

import numpy as np
a1 = np.arange(0, 6)
a2 = np.arange(10, 16)
a3 = np.arange(20, 26)
a4 = np.arange(30, 36)
a5 = np.arange(40, 46)
a6 = np.arange(50, 56)
a = np.vstack((a1, a2, a3, a4, a5, a6))

Generate matrix and slice diagram

063cb5dc1fc3a25cac004757dceb2779.png

12c9138606668c636ada3c428682cf8e.png

7. Tips

  Boolean index  

# Index trick when working with two np-arrays
a = np.array([1,2,3,6,1,4,1])
b = np.array([5,6,7,8,3,1,2])

# Only saves a at index where b == 1
other_a = a[b == 1]
#Saves every spot in a except at index where b != 1
other_other_a = a[b != 1]
import numpy as np
x = np.array([4,6,8,1,2,6,9])
y = x > 5
print(x[y])
>>> [6 8 6 9]

# Even shorter
x = np.array([1, 2, 3, 4, 4, 35, 212, 5, 5, 6])
print(x[x < 5])
>>> [1 2 3 4 4]
 download 1:OpenCV-Contrib Chinese version of extension module 

 stay 「 Xiaobai studies vision 」 Official account back office reply : Extension module Chinese course , You can download the first copy of the whole network OpenCV Extension module tutorial Chinese version , Cover expansion module installation 、SFM Algorithm 、 Stereo vision 、 Target tracking 、 Biological vision 、 Super resolution processing and other more than 20 chapters .


 download 2:Python Visual combat project 52 speak 
 stay 「 Xiaobai studies vision 」 Official account back office reply :Python Visual combat project , You can download, including image segmentation 、 Mask detection 、 Lane line detection 、 Vehicle count 、 Add Eyeliner 、 License plate recognition 、 Character recognition 、 Emotional tests 、 Text content extraction 、 Face recognition, etc 31 A visual combat project , Help fast school computer vision .


 download 3:OpenCV Actual project 20 speak 
 stay 「 Xiaobai studies vision 」 Official account back office reply :OpenCV Actual project 20 speak , You can download the 20 Based on OpenCV Realization 20 A real project , Realization OpenCV Learn advanced .


 Communication group 

 Welcome to join the official account reader group to communicate with your colleagues , There are SLAM、 3 d visual 、 sensor 、 Autopilot 、 Computational photography 、 testing 、 Division 、 distinguish 、 Medical imaging 、GAN、 Wechat groups such as algorithm competition ( It will be subdivided gradually in the future ), Please scan the following micro signal clustering , remarks :” nickname + School / company + Research direction “, for example :” Zhang San  +  Shanghai Jiaotong University  +  Vision SLAM“. Please note... According to the format , Otherwise, it will not pass . After successful addition, they will be invited to relevant wechat groups according to the research direction . Please do not send ads in the group , Or you'll be invited out , Thanks for your understanding ~

copyright notice
author[Xiaobai learns vision],Please bring the original link to reprint, thank you.
https://en.chowdera.com/2022/131/202205102123000496.html

Random recommended