jabref VS Pandas

JabRef might work for you. Website link and GitHub link.

This issue https://github.com/JabRef/jabref/issues/5867 hints at JavaFX issue with the potential workaround of running with the environment variable GDK_DISPLAY=1.

I use JabRef for managing references, which allows you to generate entries via a DOI and saves to a local .bib file.

JabRef

If the town is "online reference managers," you are probably right, but I would argue that reference managers are one of those areas where you really want something offline:

- Offline ensures that you do not suffer an externally caused downtime just before a deadline

- Offline ensures that you have a path for keeping your database throughout your research career, and to do system updates when _you_ want to.

- Offline ensures that if you leave academia, you will always have access to local copies of the academic papers you have referenced.

My favorite offline/local reference manager is `jabRef` [0] which stores all metadata directly in a bibtex-file. The GUI has an excellent pdf-integration, and everything is local and super fast.

Case in point: after a decade in industry, I am looking to get back into my academic fief. All the papers I ever read are in my Dropbox, and all I had to do to pick up where I left was download a current version jabRef and point it to my database which it read without any issues.

[0]: https://www.jabref.org/

I used JabRef throughout my work. It's indeed too late for my PhD work, but Zotero does indeed look very nice.

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.