WeasyPrint VS Pandas

Is there a reason you didn't consider something like Weasyprint?

https://weasyprint.org

I've gone through a number of systems to convert CV's, business cards, and other docs and it hasn't let me down yet.

You don't _have_ to use a browser. I had very good results with Weasyprint [0]. And there's also PrinceXML [1] if you're willing to pay.

[0]: https://weasyprint.org/

Thanks for your answer! I imagined you would be using PrinceXML behind the scenes since that is probably the gold standard in HTML+CSS rendering.

The only open source alternative I know of is WeasyPrint at https://weasyprint.org/. I'm not sure how well it fares against PrinceXML, though.

And thanks for the pointer to Taffy - I didn't know it before!

Flexbox support has been [included][1] since 2018, although my use case was the prototypical one - a single row w/ 3 columns - so YMMV with how it handles more complex layouts.

[1]: https://github.com/Kozea/WeasyPrint/pull/579

Performance is not the strength of WeasyPrint, meaning that heavy HTML files will increase generation time. You should always compress images before attaching them, as they are not compressed by default. Generating a 50-page-long PDF may take up to a minute in extreme cases, although multi-page documents generated on my project take fewer than 2 seconds to generate.

For Python there is Weasyprint: you prepare the invoice as an HTML document, and Weasyprint turns it into a PDF

https://weasyprint.org/

Well yes, but you can implement HTML+CSS. WeasyPrint did from scratch, and independent implementations of HTML+CSS are considerably more numerous than HTML+CSS+JS.

In a recent project I used https://github.com/Kozea/WeasyPrint/ it is written in python, so you will need to use it like so:

You should maybe check out weasyprint. https://weasyprint.org/

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.