What to do with python programming-Python Tutorial-php.cn

Home

Backend Development

Python Tutorial

What to do with python programming

藏色散人

Jul 04, 2019 am 11:47 AM

python

What can you do with python programming?

1 WEB Development

In China, Douban has used Python as the basic language for web development from the beginning. Zhihu’s entire architecture is also based on the Python language. This makes web development develop very well in China.

Although Python is not currently the first choice for web development, it has always occupied a place that cannot be ignored. There are various web frameworks in Python, whether they are simple micro-frameworks that can be freely matched or full-featured large-scale MVC frameworks, which are also very advantageous in web projects that require agile development. Large-scale web services provided by Python that are widely used (or were widely used) include Zhihu, Douban, Dropbox and other websites. Coupled with the "glue" characteristics of Python itself, it is easy to integrate other languages when large-scale performance-level calculations are required, while retaining the portability and speed of web development.

In addition, there are a large number of "out-of-the-box" modules in Python for related functions such as docking with various other websites. If you want to develop functions related to WeChat public accounts, packages such as wechat-sdk/weixin-python can enable you to almost completely ignore the various server interaction details mentioned in the document and focus on the function implementation to complete the development.

Currently, domestic Python web development mainly has two technology stacks:

(1) Django

Django is an advanced agile web development If you learn the framework, you can build a website very quickly. Of course, if it is purely compared to the speed of the website, Ruby on rails based on ruby is obviously faster, but one advantage of Django is that it has excellent performance and is more suitable for the application scenarios of domestic websites. Pinterest, a well-known foreign picture community, was also developed based on Django in its early days and has withstood the impact of rapid user growth. So if you want to quickly develop a website and also take into account the API calling needs of the APP client, Django can be trusted.

(2) Flask

Compared with Django, Flask is a lightweight web framework. The biggest advantage of Flask is its superior performance and it is suitable for developing background API services with mobile clients. The domestic Restful API service based on Flask is very popular and is also in greatest demand. Well-known companies such as Baidu, NetEase, Xiaomi, Momo, etc. have deployed applications based on Flask. Of course, if you want to build a traditional web website, it is still recommended to use Django. The advantage of Flask is the backend and API, and it is not suitable for building a full-featured website.

2 Web crawler

Web crawler is a commonly used scenario in Python. Internationally, Google used Python language extensively in the early days as the basis for web crawlers, which led to Application development of the entire Python language. In the past, many people in China used collectors to search for content on the Internet. Now it is much easier to use Python to collect information on the Internet than before.

Python has accumulated many tools in this area, whether it is Requests for simulating HTTP requests, PyQuery/BeautifulSoup for HTML DOM parsing, Scrapy for automating distributed crawling tasks, or using Various ORMs that simplify database access have made Python one of the preferred languages for data crawling. In particular, data analysis and calculation after crawling are the areas that Python is best at and are very easy to integrate. Currently, the most popular web crawler framework in Python is the very powerful scrapy.

3 Artificial Intelligence and Machine Learning

Artificial intelligence is a very hot direction now, and the AI boom makes the future of the Python language full of unlimited potential. Most of the several very influential AI frameworks released now are implemented in Python. Why?

Because Python is dynamic enough and has sufficient performance, these are the technical characteristics required by AI technology. For example, some websites based on Python-based deep learning libraries, deep learning directions, machine learning directions, and natural language processing directions are basically implemented through Python. Most of the tool frameworks for machine learning, especially the popular deep learning, provide Python interfaces. Python has always had a good reputation in the field of scientific computing. Its concise and clear syntax and rich computing tools are deeply loved by developers in this field.

Long before deep learning and Tensorflow and other frameworks became popular, scikit-learn existed in Python, which can easily complete almost all machine learning models. It only takes a few simple lines to download classic data sets and build models. code. It can be easily adjusted with tools such as Pandas and matplotlib.

Deep learning frameworks such as Tensorflow, PyTorch, MXNet, and Keras have greatly expanded the possibilities of machine learning. Using Keras to write a deep learning network for handwritten digit recognition only requires a few dozen lines of code, and you can use the underlying implementation to easily call a large number of resources, including GPUs, to complete the work.

It is worth mentioning that no matter what framework, Python is only used as a language for front-end description, and the actual calculation is implemented through the underlying C/C. Because Python can easily introduce and use C/C projects and libraries to achieve expansion in functionality and performance, in such large-scale calculations, developers can focus more on the logic of the data itself, and focus less on complex work such as memory allocation. Liberation is an important reason why Python is widely used in the field of machine learning.

4 Data analysis and processing

In terms of data analysis and processing, Python has a very complete ecological environment. For distributed computing, data visualization, database operations, etc. involved in "big data" analysis, Python has mature modules that you can choose to complete its functions. For both Hadoop-MapReduce and Spark, Python can be used directly to complete the calculation logic. This is very convenient for both data scientists and data engineers.

5 Server operation and maintenance and other gadgets

Python is also very important for server operation and maintenance. Since almost all Linux distributions currently come with a Python interpreter, using Python scripts for batch file deployment and operation adjustments has become a very good choice on Linux servers. Python also contains many convenient tools, from paramiko for controlling ssh/sftp, to supervisor for monitoring services, to build tools such as bazel, and even package management tools for C such as conan, Python provides a full range of tools Collection, and on this basis, combined with the Web, it will become very simple to develop tools that facilitate operation and maintenance.

What’s more interesting is that developers in the Python community have also produced development toolkits such as itchat. You can use WeChat to manage servers or run various services. Think about it, a WeChat robot can report the server or program operation status when an exception occurs, or at a fixed time every day, and even include charts drawn with matplotlib/seaborn, which are clear at a glance, and you send a simple sentence to it , you can complete the adjustment of the server.

6 Desktop program

Python can also be used for desktop software development (such as sublime text, etc.), and even mobile development (see kivy). Python is simple and convenient, and its comprehensive toolkit environment can greatly reduce the burden on developers. The famous UI framework QT has a Python language implementation version PyQT. The simple and easy-to-use features of Python coupled with the elegance of QT make it easy to develop desktop programs with complex interfaces and achieve cross-platform features easily.

7 Multimedia applications

You can use PIL, Piddle, ReportLab and other modules in Python to process images, sounds, videos, animations, etc. You can also use Python Generate dynamic charts and statistical analysis charts. In addition, you can also use the PyOpenGl module to write three-dimensional scenes very quickly and effectively.

Related recommendations: "Python Tutorial"

The above is the detailed content of What to do with python programming. For more information, please follow other related articles on the PHP Chinese website!

Statement

The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

How to implement factory model in Python?May 16, 2025 pm 12:39 PM

Implementing factory pattern in Python can create different types of objects by creating a unified interface. The specific steps are as follows: 1. Define a basic class and multiple inheritance classes, such as Vehicle, Car, Plane and Train. 2. Create a factory class VehicleFactory and use the create_vehicle method to return the corresponding object instance according to the type parameter. 3. Instantiate the object through the factory class, such as my_car=factory.create_vehicle("car","Tesla"). This pattern improves the scalability and maintainability of the code, but it needs to be paid attention to its complexity

What does r mean in python original string prefixMay 16, 2025 pm 12:36 PM

In Python, the r or R prefix is used to define the original string, ignoring all escaped characters, and letting the string be interpreted literally. 1) Applicable to deal with regular expressions and file paths to avoid misunderstandings of escape characters. 2) Not applicable to cases where escaped characters need to be preserved, such as line breaks. Careful checking is required when using it to prevent unexpected output.

How to clean up resources using the __del__ method in Python?May 16, 2025 pm 12:33 PM

In Python, the __del__ method is an object's destructor, used to clean up resources. 1) Uncertain execution time: Relying on the garbage collection mechanism. 2) Circular reference: It may cause the call to be unable to be promptly and handled using the weakref module. 3) Exception handling: Exception thrown in __del__ may be ignored and captured using the try-except block. 4) Best practices for resource management: It is recommended to use with statements and context managers to manage resources.

Usage of pop() function in python list pop element removal method detailed explanation of theMay 16, 2025 pm 12:30 PM

The pop() function is used in Python to remove elements from a list and return a specified position. 1) When the index is not specified, pop() removes and returns the last element of the list by default. 2) When specifying an index, pop() removes and returns the element at the index position. 3) Pay attention to index errors, performance issues, alternative methods and list variability when using it.

How to use Python for image processing?May 16, 2025 pm 12:27 PM

Python mainly uses two major libraries Pillow and OpenCV for image processing. Pillow is suitable for simple image processing, such as adding watermarks, and the code is simple and easy to use; OpenCV is suitable for complex image processing and computer vision, such as edge detection, with superior performance but attention to memory management is required.

How to implement principal component analysis in Python?May 16, 2025 pm 12:24 PM

Implementing PCA in Python can be done by writing code manually or using the scikit-learn library. Manually implementing PCA includes the following steps: 1) centralize the data, 2) calculate the covariance matrix, 3) calculate the eigenvalues and eigenvectors, 4) sort and select principal components, and 5) project the data to the new space. Manual implementation helps to understand the algorithm in depth, but scikit-learn provides more convenient features.

How to calculate logarithm in Python?May 16, 2025 pm 12:21 PM

Calculating logarithms in Python is a very simple but interesting thing. Let's start with the most basic question: How to calculate logarithm in Python? Basic method of calculating logarithm in Python The math module of Python provides functions for calculating logarithm. Let's take a simple example: importmath# calculates the natural logarithm (base is e) x=10natural_log=math.log(x)print(f"natural log({x})={natural_log}")# calculates the logarithm with base 10 log_base_10=math.log10(x)pri

How to implement linear regression in Python?May 16, 2025 pm 12:18 PM

To implement linear regression in Python, we can start from multiple perspectives. This is not just a simple function call, but involves a comprehensive application of statistics, mathematical optimization and machine learning. Let's dive into this process in depth. The most common way to implement linear regression in Python is to use the scikit-learn library, which provides easy and efficient tools. However, if we want to have a deeper understanding of the principles and implementation details of linear regression, we can also write our own linear regression algorithm from scratch. The linear regression implementation of scikit-learn uses scikit-learn to encapsulate the implementation of linear regression, allowing us to easily model and predict. Here is a use sc

See all articles