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This article mainly introduces "what the new handwork of Python has". In the daily operation, I believe that many people have doubts about what the new handcraft of Python has. The editor consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful for you to answer the doubt of "what does the new handcraft of Python have?" Next, please follow the editor to study!

1 、 IDLE

IDLE literally means integrated development and learning environment, and IDLE is generally installed by default when you install Python. Each language can have its own IDLE. It makes it easy to get started with Python, and it's right for people who have no foundation. Its main functions include Python shell window (interactive interpreter), cross-platform (Windows, Linux, UNIX, Mac OS X), intelligent indentation, code coloring, automatic hint, basic integrated debugger that can realize breakpoint prompts, single-step execution and other debugging functions. IDLE is easy to learn because it is light and easy to use. But it is just a stepping stone to the programming world, allowing you to get started quickly, and you still have to find some other tools on the road.

2 、 Scikit-learn

Scikit-learn is a Python module for machine learning based on Scipy. Among them, scikit-learn is the most famous and open source, and anyone can use this library or carry out secondary development for free. It is a very powerful tool that can provide a high level of support and strict management for library development. It is also supported by many third-party tools and has a wealth of functionality suitable for a variety of use cases.

Scikit-learn has six basic functions, namely, classification, regression, clustering, data dimensionality reduction, model selection and data preprocessing. It also has a very active user community, and the basic features have very detailed documentation for you to consult. Read scikit-learn 's user guide and documentation, and you will have a more comprehensive understanding of the use of the algorithm.

3 、 Theano

Theano is one of the more established and stable machine learning python libraries, although the number of current users has declined. But after all, it is an ancestral existence, and it must have its advantages. Theano is good at dealing with multi-dimensional arrays based on Python, which is a relatively low-level framework. Theano was originally designed for the operation of large-scale artificial neural network algorithms in deep learning. We can use symbolic language to define the desired results and support GPU acceleration, which is very suitable for deep learning Python.

4 、 Selenium

Selenium is one of the best tools for automation. It belongs to the automation of Python testing. It is used in the automation framework in Web applications. Support a variety of mainstream browsers, provide a feature-rich API interface, often used as a crawler tool. With it, you can write test scripts in many programming languages, including Java, C #, python, ruby, and so on. You can also integrate uranium tools such as Junit and TestNG to manage test cases and generate reports.

5 、 Skulpt

Skulpt is an online Python execution environment implemented by Javascript, which completely depends on the browser-side simulation to realize the Python running tool. You don't need any preprocessing, plug-ins, or server-side support, just write python and reload it. Because the code runs entirely in the browser, you don't have to worry about server crashes.

6 、 Jupyter Notebook

Jupyter Notebook is like a draft book, which can assemble your text comments, mathematical equations, code, and visual content into an easy-to-share document and present it as a Web page. It is a necessary tool for data analysis and machine learning. It's a scientific computing platform that supports almost all languages, and it's out of date if you haven't used it yet.

7 、 PuLP

PuLP is an optimization type of Python tool for linear programming, which can maximize the objective function under some given constraints. You can also generate LP files and call highly optimized solvers GLPK, COIN CLP/CBC, CPLEX, and GUROBI to solve these linear problems. Beginners can use this tool to do regular research and use it at work if you like.

8 、 Anaconda

Anaconda absolutely admires you, it can help you install a lot of troublesome things, including: Python environment, pip package management tools, commonly used libraries, configured environment paths, and so on, is highly recommended. Some things that beginners do on their own will easily encounter problems and bring frustration and will back down. If you are using Python to do data things, you can install it, and it has developed a set of JIT interpreter Numba. With JIT, things that require high computational efficiency of online science can also be carried out.

At this point, the study of "what the new handicrafts of Python have" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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