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This article mainly explains "which Python tools are suitable for beginners". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn which Python tools are suitable for beginners.

1. IDLE

When you install Python, IDLE is also installed by default. This is one of the better Python tools. This makes it very easy to get started in Python. 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. However, it is not the best choice for large projects.

2. Scikit-learn

Scikit-learn is a Python module for machine learning based on Scipy. A large number of Scipy-based toolkits, collectively known as Scikits, have been exhibited in different application areas. Of all the branch versions, scikit-learn is the most famous and open source, and anyone can use the library or re-develop it for free.

Scikit-learn contains many top machine learning algorithms, which have six basic functions, namely, classification, regression, clustering, data dimensionality reduction, model selection and data preprocessing. Scikit-learn has a very active user community, and basically all of its functions have very detailed documentation for users to consult. You can study the user guide and documentation of scikit-learn to have a better understanding of the use of its algorithms.

3. Theano

Theano is one of the more established and stable machine learning python libraries. Theano based on Python is good at dealing with multi-dimensional arrays (tightly integrated Numpy), is a relatively low-level framework, theano is also designed for deep learning in large-scale artificial neural network algorithm design, we can use symbolic language to define the desired results, and then theano will compile our program to make it run efficiently in GPU or CPU, it is very suitable for deep learning Python.

4. Selenium

Selenium is one of the best python tools for automation. It belongs to the automation of Python testing. It is used in the automation framework in Web applications. With Selenium, we can write test scripts in many programming languages, including Java, C #, python, ruby, and many other languages that programmers and beginners will use. You can also integrate uranium tools such as Junit and TestNG to manage test cases and generate reports.

5. Test complete

It is one of the better automated Python tools that support Web, mobile, and desktop automated testing. It supports unit testing, functional testing, regression testing, distributed testing, data-driven testing, HTTP load testing, Web testing, manual testing, etc., supports five scripting languages to write VBScript, JScript, C++ Script, DelphiScript, C#Script, supports code control systems such as SVN and VSS, and supports defect tracking systems such as Bugzilla.

6. Beautiful soup

Beautiful Soup is a HTML/XML parser written in Python, which can well handle irregular tags and generate parsing trees (parse tree). It provides simple and commonly used navigating, search and modify profiling tree operations, it can use your favorite converter to achieve the usual way of document navigation, find, modify documents, in short, it can greatly save your programming time

7. Pandas

Pandas is a tool based on NumPy, which is created to solve data analysis tasks. Pandas incorporates a large number of libraries and some standard data models, providing the tools needed to manipulate large datasets efficiently. Provide a large number of functions and methods that enable us to process data quickly and easily, and provide the best, high-performance work and easy-to-use data structure and data analysis tools for the Python programming language.

8. Pulp

Linear programming is a mathematical theory and method to study the extremum problem of linear objective function under linear constraints. There are many third-party tools in Python that can solve such problems, and the common pulp toolkits are introduced here. Pulp can solve most linear programming problems, including integer programming, and provides a variety of solver, each solver has a better effect for different types of linear programming problems. And puLP can generate LP files and call highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI to solve these linear problems.

Thank you for your reading, the above is the content of "what are the Python tools for novices". After the study of this article, I believe you have a deeper understanding of what is suitable for novice Python tools, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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