vega VS dash

Hi HN – Excited to share a beta for Minard, a new data visualization toolkit we've been working on that lets you generate publication-quality charts with simple natural language (throw away your matplotlib docs and rejoice!).

Upload or import CSVs, Excel, and JSON, give it a spin, and please let us know what you think! (Long format data works best for now)

For those curious, the stack is a simple Django app with HTMX/Alpine and all of the charts are specified and rendered as Vega (https://vega.github.io/vega/). Lots of LLM function calling under the hood as well.

I recently added support for plotting XGBoost models using Vega (https://vega.github.io/vega/) into the XGBoost Elixir API (https://github.com/acalejos/exgboost).

Since EXGBoost supports loading trained models across different APIs, you can even train using the Python API and then plot using this Elixir API if you prefer.

The Data Source is from devjobsscanner (I am basically the owner, so I have the data) an the tool used to make the chart is Vega

It’s based on Vega https://vega.github.io/vega/ which means it’s an already matured backend. Vega-lite is the Javascript package and Altair is the Python.

Site:: https://vega.github.io/vega/

Eh, I have no reason to, and not much interest in PHP anymore. Things like Vega seem really cool.

This chart is make with Vega (and also all the charts from the article). Data source is from devjobsscanner and contains about 10 Million dev jobs offers, which only a small subset contain salary information thought.

Not that I'm working on it, but I'd love to see a vega stack in rust.

Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.

You could use something like Streamlit or Dash. In any case you will be accessing your app through the browser.

Useful list. Dash & bokeh as two more in the space

https://github.com/plotly/dash

I also heard of Dash which serves the same purpose I guess, but I think it has more to offer.

Plotly is a plotting library, and Dash is their open-source framework for building data apps with Python, R or Julia. (Dash also has an Enterprise version, but we'll focus on the open-source library here.)

Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally, we wanted to offer a gentle learning curve while still providing the ability to go all the way down to HTML, CSS, and JavaScript if needed.

looks like you can get it manually (albeit with a loss of interactivity) https://github.com/plotly/dash/issues/145

Shiny is a framework that makes it easy to build interactive web applications. Shiny was introduced 10 years ago as an R package. In his 10th anniversary keynote speech, Joe Cheng announced Shiny for Python at the 2022 RStudio Conference. Python programmers can now try out Shiny to create interactive data-driven web applications. Shiny comes as an alternative to other frameworks, like Dash, or Streamlit.