


5 essential Python development tools for beginners (recommended)
5 essential tools for Python development suitable for beginners (recommended)
Desire to be good at work To do anything, you must first sharpen your tools. A good tool can get twice the result with half the effort. Excellent tools can help developers realize some ideas more conveniently. The following tools have also brought a lot of convenience to my work. Recommended For you who pursue beautiful things.
1. Python Tutor
Python Tutor is a free educational tool developed by Philip Guo, which can help developers overcome basic obstacles in programming learning and understand every line. The process by which source code moves through a computer when a program is executed. Through this tool, developers can write Python code directly in the web browser and run the program visually step by step. If you don’t know how the code runs in memory, you might as well copy it to Tutor and execute it visually to deepen your understanding.
Address: www.pythontutor.com/
2. IPython
IPython is for Humans Python interactive shell. After using it, you will no longer want to use the built-in Python shell. IPython supports automatic variable completion, automatic indentation, supports bash shell commands, and has many built-in practical functions and functions. It is also a scientific computing tool. and the best platform for interactive visualization.
Address: ipython.org/
3. Jupyter Notebook
Jupyter Notebook is like A scratch pad that combines text notes, mathematical equations, code, and visual content into an easily shareable document, displayed as a Web page. It is an essential tool for data analysis and machine learning.
Address: jupyter.org/
4. Anaconda
Although Python is good, it can You will always encounter various package management and Python version problems, especially many packages on the Windows platform that cannot be installed normally. In order to solve these problems, Anoconda appeared. Anoconda includes a package management tool and a Python management environment, and also comes with a A large number of commonly used data science packages are also standard for data analysis.
Address: www.anaconda.com/
5. Skulpt
Skulpt is implemented in Javascript An online Python execution environment that allows you to easily run Python code in your browser. Use skulpt combined with the CodeMirror editor to achieve a basic online Python editing and running environment.
Address: www.skulpt.org/
Thank you for reading, I hope you can learn more about python.
This article is reproduced from: https://blog.csdn.net/EAPxUO/article/details/79029946
Recommended tutorial: "python tutorial"
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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