stylegan2-projecting-images VS vega

A Colab notebook.

To play with the dataset, we first must create a Jupyter notebook, a powerful and popular tool among data engineers. I created mine on Google Colab.

Please head over to the Google Colab

But regardless of what you want to do, you'll probably use Python. In this context, a good way to work with Python is using Jupyter Notebooks. So you should start with installing Python and Jupyter and go from there. If you want to get started without installing anything, Google Colab gives you a remote Jupyter Notebook which runs in the browser for free.

访问 Google Colab 。

Remember school days when you'd share notes with classmates? Jupyter takes that spirit and amplifies it. Once you've crafted your Notebook, you can share it with peers, collaborators, and the world. Platforms like GitHub and Google's Colab natively render Jupyter Notebooks. It's like penning an open letter to the world but in a delightful mix of code, text, and visuals.

Open your web browser and navigate to Google Colab.

Using Google Colab you can develop Python codes, similar to Jupyter Notebooks. You will have an environment prepared with various Python libraries. In addition you have tips on small codes for development, some tutorials, gihub connection, cloud -saved notebooks and more.

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.