Numpy.NET VS vega

I did find some wrappers - Numpy.NET for numpy, Emgu CV for OpenCV.

numpy in c#

## **Follow-up use case - building a query in a query language that the user may not know** This feature is useful when a user needs to query a tool with its own specific query language or with a structure that the user doesn’t know. AWS seems to be running an A/B test of a feature where you can generate a CloudWatch search query based on a natural language input.  **Use case - data visualization** Interesting use case - you allow users to pull report data (by e.g. querying an ElasticSearch index or a database). This is often combined with visualizing the data. Could we allow the user to also define how they’d like the data presented? Let’s say a manager needs a pie chart for their PowerPoint presentation. Fortunately, there’s a tool called [Vega](https://vega.github.io/vega/) which is a visualization grammar that allows you to define graphs using a JSON schema. It also comes with [Vega-Lite](https://vega.github.io/vega-lite/), which allows us to easily render these charts using JavaScript. There’s also an [interactive playground](https://vega.github.io/editor/#/examples/vega-lite/) that we can use for testing our diagrams. Let’s take [a sample CSV file from Vega-Lite’s demo](https://vega.github.io/editor/data/stocks.csv) that contains the stock prices history of a few tech companies. It will be easy for us to reference it on the playground. The file is a regular CSV that contains a few columns:

Document address: Vega Official Document

I feel like this is good plain js:

https://github.com/vega/vega/tree/main/packages/vega-view/sr...

This looks interesting but I’m pretty sure it’s not the first declarative charting tool. (Eg Vega https://vega.github.io/vega/)

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/