SaltStack VS Pandas

In the new style, when the tag is longer than 20 characters, an end of tag string is appended to the tag given by the string constant TAGEND, that is, two line feeds '\n\n'. When the tag is less than 20 characters then the tag is padded with pipes "|" out to 20 characters as before. When the tag is exactly 20 characters no padded is done. source: https://github.com/saltstack/salt/blob/master/salt/utils/event.py

https://github.com/saltstack/salt https://github.com/chocolatey/choco https://github.com/nextcloud https://github.com/authelia/authelia https://github.com/grafana/grafana

SaltStack

One thing that really irks me about Salt, though, is that they are very slow to fix bugs. My Salt states are littered with workarounds for bugs that have been open for multiple years. Even in basic things, like ssh authorized_keys management. Other than bug velocity, though, I've been pretty pleased with Salt.

Here are several related GitHub issues: - https://github.com/saltstack/salt/issues/54791 - https://github.com/saltstack/salt/issues/57541 - https://github.com/saltstack/salt/issues/16089

For clarity, here's the issue: https://github.com/saltstack/salt/issues/64111

Nightly builds on supported branches & master running the full test suite, producing fully tested builds. https://github.com/saltstack/salt/actions/workflows/nightly.yml

It's definitely some sort of AI script. Not this exactly, but something working off Python or scripts of thar nature. https://github.com/saltstack/salt

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.