vim-win32-installer VS Pandas

There are official 64-bit Windows builds, you can find them here, for example: https://github.com/vim/vim-win32-installer/releases

I have no idea, but why wait? There's nothing tricky about installing nightly (I called them current in my original reply) versions. Here's a link to a Windows executable. https://github.com/vim/vim-win32-installer/releases

gVim

This is with gvim builds from vim-win32-installer?

This is already part of the official Vim (through the patches). The various Linux distributions and MacOS ship Vim with various patches. So this will be available whenever these distributions decide to include this patch. If you want to use the latest Vim without waiting for the official distribution, you can download it from: Windows: https://github.com/vim/vim-win32-installer/releases MacOS: https://github.com/macvim-dev/macvim/releases Linux: You can build the latest sources.

You can install the latest Vim release from https://github.com/vim/vim-win32-installer/releases. This installation includes vim.exe (terminal Vim for MS-Windows).

Yes, filesystem redirections are generally in place for 32-bit programs on 64-bit Windows. Another option instead of using sysnative is to install the 64-bit version of vim. That's what I do anyway. You can get it at https://github.com/vim/vim-win32-installer/releases and download the 64-bit versions.

Windows Releases: https://github.com/vim/vim-win32-installer/releases/tag/v9.0.0000

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.