Azure-ttk-theme VS mercury

https://github.com/TomSchimansky/CustomTkinter https://github.com/israel-dryer/ttkbootstrap/ https://github.com/TkinterEP/ttkthemes https://github.com/RobertJN64/TKinterModernThemes https://github.com/rdbende/Sun-Valley-ttk-theme https://github.com/rdbende/Azure-ttk-theme https://github.com/rdbende/Forest-ttk-theme

If you would want to make tkinter app a little bit nicer looking you could use theme like this one.

You can make tkinter look good with this or this.

Surprisingly more than ever! There are a few libraries and themes that make it look very good. This one and this one are both amazing

Move to html/css for your interface or for desktop only Tkinter with some new looks. And if you need really complex stuff, there's PySide6.

Easily themable (some great themes here).

Matplotlib or Plotly will give you grafana style plots, the rest of what you want (Buttons, Entry Boxes, Labels) is all in Tkinter! If you don't like the look of tkinter, you can change it with this, this or this. (that last one is my favorite).

Also with the new tkinter themes provided here, tkinter is having a micro-resurgence thanks to its new look.

But I recently found this beautiful theme for ttk and some customizations for tkinter that made me look twice before going to the PySide. If your UI does not need complex interaction, tkinter can look really good.

I'm build dashboards in Jupyter Lab. My plotting libraries are Altair, matplotlib, seaborn, Plotly - all work well in notebook.

My favorite is Altair. It provides interactivity for charts, so you can move/zoom your plots and have tooltips. It is much lighter than Plotly after saving the notebook to ipynb file. Altair charts looks much better than in matplotlib. One drawback, that exporting to PDF doesn't work. To serve notebook as dashboard with code hidden, I use Mercury framework, you can check example https://runmercury.com/tutorials/vega-altair-dashboard/

disclaimer: I'm author of Mercury framework https://github.com/mljar/mercury

Web App is using two open-source packages that I've created:

- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised

- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury

You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.

The GitHub repository https://github.com/mljar/mercury

We are working on open-source web framework Mercury that converts Python notebooks to Web Apps.

It is very opinionated:

- it has no callbacks - we automatically re-execute cells below updated widget

- it has no layout widgets, all input widgets are always in the left sidebar

Thanks to above decisions you don't need to change notebook's code to have web app and fit to the framework.

The simplicity of the framework is very important to us. We also care about deployment simplicity. That's why we created a shared hosting service called Mercury Cloud. You can deploy notebook by uploading a file.

The GitHub repository https://github.com/mljar/mercury

Documentation https://RunMercury.com/docs/

Mercury Cloud https://cloud.runmercury.com

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.

Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.