PdfPig VS Pandas

Maybe (most likely) this is a problem of GitHub's terminology. For genuine bugs, e.g. here's the repro, the stack trace, the code to replicate it, it happens 100% of the time if you follow these steps, I'd agree that just having it open and in the backlog would be preferable.

The problem is those make up maybe at a generous estimate, 10-15% of issues in a projects backlog. In the interests of full disclosure here's mine (I don't use stalebot) https://github.com/UglyToad/PdfPig/issues?page=1&q=is%3Aissu.... As you can see from the backlog I close almost nothing. This was a deliberate choice to avoid closing things until the fix was confirmed by the reporter.

But equally that's the first time I've opened the repository in a couple of months and the amount of angst and dread I feel just from the size of that list means I'll probably find yet another excuse not to do anything on it this coming month.

Discussions on this topic feel a lot like "technical solutions to social problems"; by which I mean "well in the ideal world a perfectly logical person would do x, y, z so the system should reflect that". And while a stalebot is the archetypal technical solution to a social problem it at least works with how maintainers work. Sometimes in life you want to ignore a problem and have it go away. When you can't do that, e.g. government bureaucracy, work stuff, social obligations, that's where stress comes from. And asking volunteer maintainers to add a whole new source of stress in their life falls apart when people get busy, or their life circumstances change, or they get ill or tired or whatever.

Yes, in a perfect world the issue backlog would be sacrosanct and perfectly groomed/prioritized. But we're just fleshy sacks of chemicals and we're not perfect. Unrealistic expectations from users are the cause of maintainer burnout.

Because GitHub closed issues are still viewable and searchable (I'd guess most people search it through a search engine not the terrible inbuilt search) I'd disagree that they're deceiving users somehow.

What about PdfPig? It's under Apache 2.0.

You could try PdfPig. https://uglytoad.github.io/PdfPig/ I've used it for some small tasks and found it very useful. If you want to handle scanned pdfs you would need to use OCR instead.

PDF Pig is open source and allows you to read text and even extract images.

https://uglytoad.github.io/PdfPig/ https://github.com/empira/PDFsharp

Example of document creation API here https://github.com/UglyToad/PdfPig#document-creation-005 and wiki with more details here https://github.com/UglyToad/PdfPig/wiki/Document-Creation

Example with PDFpig https://github.com/UglyToad/PdfPig/blob/master/examples/GeneratePdfA2AFile.cs

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.