rustfmt VS Pandas

The author provides very surface-level criticism of two Rust tools, but they don't look into why those choices were made.

With about five minutes of my time, I found out:

wrap_comments was introduced in 2019 [0]. There are bugs in the implementation (it breaks Markdown tables), so the option hasn't been marked as stable. Progress on the issue has been spotty.

--no-merge-sources is not trivial to re-implement [1]. The author has already explained why the flag no longer works -- Cargo integrated the command, but not all of the flags. This commit [2] explains why this functionality was removed in the first place.

Rust is open source, so the author of this blog post could improve the state of the software they care about by championing these issues. The --no-merge-sources error message even encourages you to open an issue, presumably so that the authors of Cargo can gauge the importance of certain flags/features.

You could even do something much simpler, like adding a comment to the related issues mentioning that you ran into these rough edges and that it made your life a little worse, or with a workaround that you found.

Alternatively, you can continue to write about how much free software sucks.

[0]: https://github.com/rust-lang/rustfmt/issues/3347

[1]: https://github.com/rust-lang/cargo/pull/10344

[2]: https://github.com/rust-lang/cargo/commit/3842d8e6f20067f716...

The new style still supports single line let-else, and there is a configuration parameter to make it be on one line also for longer lines.

It seems there is an issue about this dating all the way back from 2018 but yet it still hasn't been fixed.

However since 4179 recent versions should merge configuration files. Not sure what the details / specifics are but if just ignoring the file entirely is not good enough you might give it its own directory and rustfmt.toml file and see if that works.

Could this be be #3863 - Gives up on chains if any line is too long? It might not be, because I can't see a specific "line" that's too long to format, but there's more detail about the exact problem in the issue.

Rustfmt is a tool that formats Rust code in compliance with style guidelines. Its name precisely reflects its purpose. To install rustfmt, you can run rustup component add rustfmt. Once installed, you can execute cargo fmt to format Rust code in your workspace. If you require further information, you can visit rustfmt’s GitHub repository.

code must be formatted with rustfmt.

Yes, some cases are not yet supported (https://github.com/rust-lang/rustfmt/issues/4914).

Just FYI, the let-else suggestions will only work until rustfmt settles on a format for it. So you may be surprised when this happens and it suddenly changes.

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.

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.

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.

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

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.

pandas: A library for data analysis in Python

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.

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.

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:

Weirdly most of my time spent with data analysis was in the C layers in pandas.