sphinx VS Pandas

Sphinx is primarily known as a documentation generator, but it can also be used to create static websites. It excels in generating technical documentation, and its support for multiple output formats, including HTML and PDF, makes it a versatile tool. Sphinx uses reStructuredText for content creation and is highly extensible through plugins.

Sphinx a documentation generator or a tool that translates a set of plain text source files into various output formats, automatically producing cross-references, indices, etc. That is, if you have a directory containing a bunch of reStructuredText or Markdown documents, Sphinx can generate a series of HTML files, a PDF file (via LaTeX), man pages and much more.

Notable mentions to [Sphinx](https://www.sphinx-doc.org/). It's workflow is more tuned to the "book" format rather than the blog, forum or thread format.

Currently trying out Sphinx (https://www.sphinx-doc.org) and the trying to get the autodocgen feature to see what that can do.

So, I've documented my code and now I need a .PDF with this documentation. Is there any easy way to do it? Once I used Sphinx but it generated a not so easy .TeX.

AutoPyTabs allows you to write code examples in your documentation targeting a single version of Python and then generates examples targeting higher Python versions on the fly, presenting them in tabs, using popular tabs extensions. This all comes packaged as a markdown extension, MkDocs plugin and a Sphinx, so it can easily be integrated with your documentation workflow.

Honestly, I think it's just an issue of documentation. For example, if there was an easier way to document @overload functions, that would help (cf. https://github.com/sphinx-doc/sphinx/issues/7787)

We use sphynx for our pipeline documentation for all technical details Classes , packages and functions docstrings using reStructuredText (reST) format

I like Sphinx [0] with the MyST Markdown syntax [1]. There is a related project, Myst NB [2], which enables including Jupyter notebooks in your site. There is also a plugin for blogging [3].

[0]: https://www.sphinx-doc.org

Sphinx is the go-to tool for documentation. It took me a while to understand how to use Sphinx, but I now have a decent workflow with MyST which allows me to write all the docs in markdown. My sphinx-markdown-docs repo shows an example of what I do.

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.

Explore here.