This comment got me thinking “What do you notice that is incorrect or important but missing from this page?” I discovered two things: 1) I’m too shallow in Python to identify any such issues and 2) while GPT-4 and Sage struggled with that precise question, in about 3 seconds I got what appears to be a really useful critique from Claude 2 100k. I’ll share that in case anybody is interested:
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Here are a few things I noticed that could be improved on the Python cheatsheet page:
- The dictionary section is missing explanation of dict comprehensions. Dict comprehensions are an important and useful feature for constructing dictionaries in a concise way.
- In the section on modules, it would be good to mention virtual environments. Virtual environments are an important tool for Python dependency and package management.
- The examples in the Pandas section are useful, but more explanation or details could be provided on some of the core DataFrame operations like merging/joining, groupby, aggregations, etc.
- In the sections on concurrency and parallelism, async/await could be explained and demonstrated. Asyncio is commonly used for asynchronous programming in Python.
- The cheatsheet focuses mainly on built-in modules and functionality. It could be useful to also cover some widely used 3rd party libraries like NumPy, SciPy, Matplotlib, TensorFlow, etc.
- Sections on testing and debugging could be added - things like unittest, pytest, logging, debugging tools. Testing and debugging are key skills for Python developers.
- The cheatsheet is very text heavy. More visuals, diagrams, or tables could help make it more scannable and easier to navigate.
Overall it covers a lot of ground, but filling in some of those gaps would make it more comprehensive and useful as a reference. The content is excellent, just some ways it could be expanded on.
Eh. I think that critique misses more than it hits.
> The dictionary section is missing explanation of dict comprehensions. Dict comprehensions are an important and useful feature for constructing dictionaries in a concise way.
> In the sections on concurrency and parallelism, async/await could be explained and demonstrated. Asyncio is commonly used for asynchronous programming in Python.
> The cheatsheet focuses mainly on built-in modules and functionality. It could be useful to also cover some widely used 3rd party libraries like NumPy, SciPy, Matplotlib, TensorFlow, etc.
(It doesn't cover SciPy, Tensorflow. That said, I've been writing python for 23+ years, and can count on two fingers how many times I've needed either of those.)
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Some parts of the critique are correct, if not especially useful IMO. I'd say most of the parts of the critique that are correct seem to misunderstand the purpose of the cheatsheet. The best points the model generated, IMO, were the ones related to pandas and virtual environments.
Thanks for the drill down. I should just searched the cheatsheet for each purportedly missing item. And I got a good chuckle picturing you counting “on two fingers” how many times you’ve needed SciPy or TensorFlow in 23 years!
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Here are a few things I noticed that could be improved on the Python cheatsheet page:
- The dictionary section is missing explanation of dict comprehensions. Dict comprehensions are an important and useful feature for constructing dictionaries in a concise way.
- In the section on modules, it would be good to mention virtual environments. Virtual environments are an important tool for Python dependency and package management.
- The examples in the Pandas section are useful, but more explanation or details could be provided on some of the core DataFrame operations like merging/joining, groupby, aggregations, etc.
- In the sections on concurrency and parallelism, async/await could be explained and demonstrated. Asyncio is commonly used for asynchronous programming in Python.
- The cheatsheet focuses mainly on built-in modules and functionality. It could be useful to also cover some widely used 3rd party libraries like NumPy, SciPy, Matplotlib, TensorFlow, etc.
- Sections on testing and debugging could be added - things like unittest, pytest, logging, debugging tools. Testing and debugging are key skills for Python developers.
- The cheatsheet is very text heavy. More visuals, diagrams, or tables could help make it more scannable and easier to navigate.
Overall it covers a lot of ground, but filling in some of those gaps would make it more comprehensive and useful as a reference. The content is excellent, just some ways it could be expanded on.