sourcery
Pandas
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sourcery | Pandas | |
---|---|---|
13 | 395 | |
1,481 | 41,983 | |
0.9% | 1.6% | |
6.3 | 10.0 | |
13 days ago | 1 day ago | |
Python | ||
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
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?
Pandas
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
What are some alternatives?
jedi - Awesome autocompletion, static analysis and refactoring library for python
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
pylsp-rope - Extended refactoring capabilities for python-lsp-server using Rope
tensorflow - An Open Source Machine Learning Framework for Everyone
pre-commit - A framework for managing and maintaining multi-language pre-commit hooks.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
ruff - An extremely fast Python linter and code formatter, written in Rust.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
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.
Keras - Deep Learning for humans
study-path - An organized learning path on Clean Code, Test-Driven Development, Legacy Code, Refactoring, Domain-Driven Design and Microservice Architecture
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration