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This article mainly explains "what Python tools are available". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's take you to learn "What Python tools are there"!

Python is an open source programming language used for Web programming, data science, artificial intelligence, and many scientific applications. Learning Python enables programmers to focus on problem solving rather than syntax, and its rich library gives it the power it needs to accomplish great tasks.

1) IDLE

When installing Python, IDLE is also installed by default. This is one of the better Python tools. This makes getting started in Python very easy. Its main features include Python shell window (interactive interpreter), cross-platform (Windows, Linux, UNIX, Mac OS X), intelligent indentation, code coloring, automatic prompt, can achieve breakpoint prompt, single step and other debugging functions of the basic integrated debugger. IDLE is easy to learn because it is lightweight and easy to use. However, it is not the best option for large projects.

2) Scikit-learn

scikit-learn is a Python module for machine learning built on Scipy. A number of Scipy-based toolkits have been developed in different application areas, collectively known as Scikits. Of all the fork versions, scikit-learn is the most famous, open source, and anyone can use the library for free or for secondary development.

Scikit-learn contains many top-level machine learning algorithms, mainly with six basic functions, namely classification, regression, clustering, data dimension, model selection and data preprocessing. Scikit-learn has a very active user community, and almost all of its features are well documented. You can read scikit-learn's user guide and documentation for a fuller understanding of how to use the algorithm.

3) Theano

Theano is one of the older and more stable machine learning Python libraries. Theano is based on Python and is good at processing multidimensional arrays (closely integrated with Numpy). It belongs to 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. Then theano will compile our program to run efficiently on 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 Python test automation. It is used in automation frameworks in Web applications. With Selenium, we can write test scripts in many programming languages, including Java, C#, Python, Ruby, and many others that programmers and novices will use. You can also integrate tools like JUnit and TestNG to manage test cases and generate reports.

5) Test complete

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

6) Beautiful soup

Beautiful Soup is an HTML/XML parser written in Python that handles irregular markup and generates parse trees. It provides simple and commonly used navigation (navigating), search and modify the parse tree operation, it can be achieved through your favorite converter customary document navigation, find, modify the document way, simply said, it can greatly save your programming time

7) Pandas

Pandas is a NumPy based tool created to solve data analysis tasks. Pandas incorporates a large number of libraries and standard data models, providing the tools needed to efficiently manipulate large data sets. It provides a large number of functions and methods that enable us to process data quickly and easily, providing the best, high-performance work and easy-to-use data structures 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 constraint conditions. Python has a number of third-party tools that can solve this problem, and here is a look at the commonly used pulp toolkit. Pulp can solve most linear programming problems, including integer programming, and provides multiple solvers, each of which has better results 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 linearity problems.

At this point, I believe that everyone has a deeper understanding of "what Python tools are available", so let's actually operate it! Here is the website, more related content can enter the relevant channels for inquiry, pay attention to us, continue to learn!

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