plotly
d3
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plotly | d3 | |
---|---|---|
65 | 277 | |
15,247 | 107,634 | |
2.3% | 0.3% | |
9.4 | 8.0 | |
7 days ago | 17 days ago | |
Python | Shell | |
MIT License | ISC License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
plotly
- Yes, Python and Matplotlib can make pretty charts
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/plotly/plotly.py
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How to Create a Pareto Chart 📐
First we need to install the Plotly. To create some very dynamic graphics, this tool helps a lot.
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For all you computational people: What’s your favorite plotting software?
my good dude wake up and smell the plotly. Knowing the ins and outs of matplotlib is helpful but doing interactive stuff with jupyter I always use plotly.
- What does Power BI offer?
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Other programing options?
Plotly documentation (https://plotly.com/python/)
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Advice on upgrading my Presentation template
I don´t know your workflow, but I use 2 markdown based presentations: obsidian advance slides and Quarto presentations. The former is a plugin for Obsidian, which is the software I use to take all my notes, write my thesis, etc., so It makes it extremely easy to make presentations since all my information is in Obsidian. In the other hand, Quarto is a publishing system (articles, presentations, websites books) that can be easily integrated with python and R. This makes it supper convenient for showing my data to my PI since I can analyze my data and at the same time make a presentation for the data. Besides this, Quarto also integrates with my Zotero library, so I can insert citations. Lastly, one thing that made my Quarto presentations infinitely better that the powerpoints, Is that I can insert interactive graphs with plotly, so when I'm showing my data, my PI is able to explore the data inside the presentation.
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[OC] Clustering Images with OpenAI CLIP, T-SNE, UMAP & Plotly
Plotly GitHub repository: https://github.com/plotly/plotly.py
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Could you recommend some graphing GitHub Repo. for JupyterLab?
I'm using plotly.py now. This is why I love this community.
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Anyone else feel ‘trapped’ in power bi?
Depending on the nature of your reporting requirements, you could output a formatted Excel document with Python and a library such as openpyxl, and shove that into your SharePoint environment. This would be less dynamic than PBI reports can be, but may be sufficient. If you want viz as well, you can use something like ggplot or Plotly. Again, less dynamic than PBI for the same effort.
d3
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A visual guide to Vision Transformer – A scroll story
Yes this was done with a combination of GSAP Scrolltrigger https://gsap.com/docs/v3/Plugins/ScrollTrigger/ and https://d3js.org/
- Ask HN: Tips to get started on my own server
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Full Stack Web Development Concept map
d3 - very power visualization library enabling dynamic visualizations. docs
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Observable 2.0, a static site generator for data apps
Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d say we’re trying to target data app developers more than data analysts; we offer a lot of power and expressiveness, and emphasize custom visualizations and interaction (leaning on Observable Plot or D3), as well as polyglot programming with data loaders written in any language (Python, R, not just SQL).
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Using Deno with Jupyter Notebook to build a data dashboard
D3.js: A robust library to visualize your data and create interactive data-driven visualizations.
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What is the technology stack used to create these live charts?
They are images so it could be any number of things, datawrapper, charts.js, d3.js to name a few options.
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Animated map showing frequency and location of births around the world [OC]
I made this interactive visualization that attempts to show the real-time frequency and location of births around the world. A country’s annual births (i.e. the country’s population times its birthrate) were distributed across all of the populated locations in each country, weighted by the population distribution (i.e. more populated areas got a greater fraction of the births). Data Sources and Tools Population and birthrate data for 2023 was obtained from Wikipedia (Population and birth rates). Population distribution across the globe was obtained from Socioeconomic Data and Applications Center (sedac) at Columbia University. Data is processed and visualized at a 1 degree x 1 degree resolution, each of which has a different probability of a birth occurring in a specific time period. D3.js was used to create the map elements and html, css and javascript were used to create the user interface.
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How do you implement library types?
When I go to the homepage of types/d3 the only hint for any kind of documentation is what seems to be the main github page of d3. It's highly possible I'm missing something here, so sorry if I am but I can't find any documentation of how you are supposed to type these library objects.
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The top 11 React chart libraries for data visualization
Website: D3.js official site
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Frontend development roadmap
D3js
What are some alternatives?
Altair - Declarative statistical visualization library for Python
echarts - Apache ECharts is a powerful, interactive charting and data visualization library for browser
bokeh - Interactive Data Visualization in the browser, from Python
GoJS, a JavaScript Library for HTML Diagrams - JavaScript diagramming library for interactive flowcharts, org charts, design tools, planning tools, visual languages.
matplotlib - matplotlib: plotting with Python
vis
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
d4 - A friendly reusable charts DSL for D3
folium - Python Data. Leaflet.js Maps.
svg.js - The lightweight library for manipulating and animating SVG
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
sigma.js - A JavaScript library aimed at visualizing graphs of thousands of nodes and edges