cgmath-rs VS Pandas

Take a look into math libraries, like glam, nalgebra, and cgmath. I've only used these through game engines, though, so I can't offer per-basis reviews/advice.

But I am writing a Vulkan-based game engine and I use https://crates.io/crates/cgmath extensively. It has vector classes, all the math functions I need, and it even supports a version of swizzling if you activate the feature. Maybe this crate can do what you need?

There's also a good crate which you can use to quickly create the required matrices called cgmath: https://crates.io/crates/cgmath

One great thing about Rust is that the library ecosystem is surprisingly mature, especially considering how young the language is (1.0 was released in 2015). C# also has good libraries, but from my experience it's kinda fiddly to use most open source libraries with Unity, at least without modifications. Rust's ecosystem has some excellent libraries that help with game development, such as noise for procedural generation and cgmath for linear algebra.

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.