Mezzanine VS Pandas

I've recently started playing around with Mezzanine, a django-based CMS. I recently just managed to configure Fabric to get it uploading to my host, webfaction.com, as its a bit more involved automatically creating the website on the shared hosting, and I wanted to automate that process.

To give you a better idea of how Python-based applications work on our servers, we’ll show you how to install the Django framework-powered Mezzanine CMS on our platform via SSH.

There is also Mezzanine / Cartridge which is kinda like WordPress / WooCommerce in the PHP world, it’s primarily for a website that may have a shop added to it. Be aware that this is also somewhat legacy, last time I checked it was kinda in maintenance mode and the variant system for products was super limited.

Mezzanine / Cartridge is similar to WooCommerce in the WordPress world, if you are wanting to add a shop to an existing site then this is a decent option. The problem with it is that the main dev on it went off to work for Google so it’s more or less in maintenance mode and the product variant system is very basic.

mezzanine is probably a simpler one. it recently just got revived and their 5.0 release is now in rc1 state. There's also django-fiber which seems to be quite simple (not much code, one app to add only)

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.