


Bret Victor’s Inventing on principle is one of the most exciting and shocking demonstrations I have ever seen. Although the former Apple UI guru made this demonstration as early as 2012, his influence has not diminished. Any changes in the process of writing programs should directly generate feedback so that programmers can see the results. , or in other words, creators need real-time feedback on what they create.
I have been using Python before and like IPython Notebook very much. It is very convenient to use IPython Notenook to quickly complete some prototypes. Now due to the needs of the project, I want to start using the Go language. I was wondering, is there an IPython environment that can use Go? There is also a related post on Zhihu, but unfortunately it does not give a valid answer.
I did some small homework, but the result is not perfect, so I will share it with you here.
Official version Go Playground
The best resource to start learning Go language is the official Tour. You can learn and run Go sample programs at the same time to get the running results directly. Perfectly embodies the concept of Inventing on principle.
This Tour has a Go Playgound embedded in it. You can find the code of the project on github.
This project contains a front-end and a containerized back-end Sandbox to ensure the security of program operation.
However, go playground has some limitations:
cannot import user-defined packages
The editor is weak, no syntax highlighting, no prompts, no undo...
No segmented interaction like Ipython
XIAM version of Go Playground
XIAM’s go playground has made significant improvements based on the official playground. Includes:
Supports user-defined packages
Supports unsafe sandbox, users can access the network, file system, etc.
Containerization of the front end
If you want to use a custom package, you need to modify the Dockerfile of the corresponding sandbox
FROM xiam/go-playground/unsafebox RUN go get github.com/myuser/mypackageRUN go get github.com/otheruser/otherpackage ENTRYPOINT ["/go/bin/sandbox"]
Then Just rebuild the container's Image.
Although we have solved the problem of custom packages, this editor is still too weak and lacks the segmented interaction of IPython. Is there anything better?
GopherNotes
Jupyter’s Notebook can actually support different language cores. The GopherNotes project provides the Go language core for Jupyter.
This project is inspired by Gore (based on igo kernel) which is no longer maintained.
The above is a test I did using Gophernotes. When I run a loop, if I write it in one line, In[7], everything is OK. But when I write three lines, In[8], the correct result cannot be output.
The error given in the background is:
Error running goimports: /tmp/979860191/func_proxy.go:4:4: expected declaration, found 'for' [I 08:18:56.621 NotebookApp] Saving file at /Untitled.ipynb

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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