Plotly
D3.js
RAWGraphs
Tableau
Highcharts
Google Charts
Bokeh
Chart.js
pikaur
Yay
paru
Trizen
Pakku
pacaur
aurutils
Aura Soundscape Player
Plotly pikaurPlotly is recommended for data scientists, analysts, and developers who need to create interactive and visually appealing data visualizations. It's particularly useful for those who work with Python or R and want the ability to embed their visualizations in web applications or dashboards.
Based on our record, Plotly should be more popular than pikaur. It has been mentiond 34 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 4 months ago
Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago
Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago
In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / over 1 year ago
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago
Have a look here. Did you not search for the answer? That's part of the Arch(based) ethos. We tend to like to learn by reading whatever is required. :). Source: about 3 years ago
I was also looking for something nicer for Arch, but haven't found anything as nice as Nala. For now, I switched to pikaur, which at least displays updates in a much clearer way. Source: almost 4 years ago
Nice, but this definately needs a dependency resolver, otherwise it can only install a fraction of the available AUR packages. Since you're already using python, you may adapt your whole code on top a another python-based AUR helper like pikaur. You maybe also could take at the dep resolver of my ABS project. It's python, too, maybe not as clean as pikaur's code but simpler and not too integrated. Source: over 4 years ago
I've been using pikaur ever since pacaur became abandonware and I'm very happy with it, can't recommend it enough. Sure, it's not implemented in Rust or Go so it's certainly not as cool as yay or paru but that doesn't really matter much to me, being an end user. I don't really care as long as it does its job, as advertised. Source: about 5 years ago
D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.
Yay - Yay is an AUR helper written in go, based on the design of yaourt, apacman and pacaur.
RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...
paru - An AUR helper written in Rust and based on the design of yay. It aims to be your standard pacman wrapping AUR helper with minimal interaction.
Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Trizen - Trizen AUR Package Manager: A lightweight wrapper for AUR.