100-pandas-puzzles
sourcery
| 100-pandas-puzzles | sourcery | |
|---|---|---|
| 6 | 13 | |
| 2,209 | 1,483 | |
| - | 0.7% | |
| 0.0 | 6.3 | |
| 7 days ago | 6 days ago | |
| Jupyter Notebook | ||
| MIT License | MIT License |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
100-pandas-puzzles
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What are the best Python libraries to learn for beginners?
#1: Welcome to df[pandas]! #2: 100 data puzzles for pandas, ranging from short and simple to super tricky | 3 comments #3: Happy Halloween, Pandas! 🎃🤓 | 0 comments
- 100 data puzzles for pandas, ranging from short and simple to super tricky
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pandas practice resources?
I remember someone sharing this with me earlier: https://github.com/ajcr/100-pandas-puzzles Let me know if you think it's comprehensive and a good resource.
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how important are learning the data manipulation libraries?
If you want to get better with pandas specifically you could work through the 100 pandas puzzles repo in your spare time, https://github.com/ajcr/100-pandas-puzzles
- Can anyone recommend resources to prepare for Pandas and Numpy interview questions?
- Is there anything AoC-like for Machine Learning or Data Science?
sourcery
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Ask HN: How do you get an open-source product noticed by developers?
In my experience, the developer tools that really catch on do so via word of mouth. For example, our whole team recently adopted https://sourcery.ai/ (not an ad) because one developer tried it and hyped it up to everyone else who also liked it.
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Google Python Style Guide
To those that wish to automate a subset of these conventions, there is a tool called Sourcery[1] that I, personally, am a huge fan of! Not only does it have a large set of default rules[2], but it can also allow you to write your own rules that may be specific to your team or organization, and as mentioned it can enable you to follow Google's Python style guide as well[3].
There are some refactorings that Sourcery suggest that I don't agree with myself, namely the usage of 'contextlib.suppress'[4] as I don't like to introduce an additional 'import' statement just to do something so trivial. I wish Sourcery would add the relevance of having possibly too many 'import' statements as a heuristic.
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[1]: https://sourcery.ai/
[2]: https://docs.sourcery.ai/Reference/Default-Rules/ (expand the sub-pages)
[3]: https://docs.sourcery.ai/Reference/Optional-Rules/gpsg/
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What are the best Python libraries to learn for beginners?
During development, tools like Sourcery could show you improvements for code quality.
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Quick wins in improving your Python codebase health
One of the first tools I install when setting up my Python dev environment is Sourcery. This still uses AI/ML to suggest code improvements to your Python code, but unlike GitHub's Copilot, it won't write code for you.
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git client for kde (gitklient)
"Sourcery" exists
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Making Python Code Idiomatic by Automatic Refactoring Non-Idiomatic Python Code with Pythonic Idioms
Looks downright wicked https://sourcery.ai/
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Create file if it doesn't exist, as well as its folders?
As a bit of trivia, https://sourcery.ai/ will replace
- Is there a linter which would suggest using elif rather than an else in an if clause?
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[lspconfig] The Authentication token must be provided
I guess you have to signup in their website sourcery.ai. I actually don't use sourcery, I don't know the details on how to get the token.
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Tools to write clean Go code
When I'm writing Python, one of my favorite tools is [Sourcery](https://sourcery.ai/). Are there any similar tools for Go? What else do you recommend?
What are some alternatives?
numpy-100 - 100 numpy exercises (with solutions)
jedi - Awesome autocompletion, static analysis and refactoring library for python
tempo - API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
pylsp-rope - Extended refactoring capabilities for python-lsp-server using Rope
pandas_exercises - Practice your pandas skills!
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
idx2numpy_array - Convert data in IDX format in MNIST Dataset to Numpy Array using Python
ruff - An extremely fast Python linter and code formatter, written in Rust.
RasgoQL - Write python locally, execute SQL in your data warehouse
yt-channels-DS-AI-ML-CS - A comprehensive list of 180+ YouTube Channels for Data Science, Data Engineering, Machine Learning, Deep learning, Computer Science, programming, software engineering, etc.
tempo - Grafana Tempo is a high volume, minimal dependency distributed tracing backend.
study-path - An organized learning path on Clean Code, Test-Driven Development, Legacy Code, Refactoring, Domain-Driven Design and Microservice Architecture