d3
seaborn
d3 | seaborn | |
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4 | 77 | |
100,996 | 11,969 | |
- | - | |
7.1 | 8.4 | |
about 2 years ago | 15 days ago | |
JavaScript | Python | |
ISC License | BSD 3-clause "New" or "Revised" License |
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d3
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D3, nested appends, and data flow
I found mention of nested selection on another post, which pointed to http://bost.ocks.org/mike/nest/. But is nested selection, and therefore breaking apart the appends into three chunks, appropriate/idiomatic for this situation? Or is there actually a well-constructed way to form this structure in one chain of declarations? It seems like there might be a way with subselections mentioned on https://github.com/mbostock/d3/wiki/Selections, but I'm not familiar enough with the language to test that hypothesis.
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How do you create a family tree in d3.js?
I've chosen d3.js for this because it looks like would be capable of doing the job. I just don't know how or even where to start. Tutorials about d3.js only cover standard charts like bar charts.
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How do I shut down a python simpleHTTPserver?
So I'm trying to learn d3, and the wiki suggested that
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D3.js: "Uncaught SyntaxError: Unexpected token ILLEGAL"?
D3 Sandbox But when I load this page, my console (in Chrome) is giving me this error:
Uncaught SyntaxError: Unexpected token ILLEGAL: line 2
It doesn't like the pi and e symbols at the start of the file. Errrr... what can I do about this? I am serving the file with python's SimpleHTTPServer.Update: yes I know I can just link to a CDN version, but I would prefer to serve the file locally.
Answer link : https://codehunter.cc/a/javascript/d3-js-uncaught-syntaxerror-unexpected-token-illegal
seaborn
- "No" is not an actionable error message
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Apache Superset
If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.
I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.
I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.
For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.
Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).
The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.
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Seaborn bug responsible for finding of declining disruptiveness in science
It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).
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Why Pandas feels clunky when coming from R
While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.
https://seaborn.pydata.org/
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.
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Seven Python Projects to Elevate Your Coding Skills
Matplotlib Seaborn Example data sets
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Seaborn - Statistical data visualization using Matplotlib.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/mwaskom/seaborn
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Best Portfolio Projects for Data Science
Seaborn Documentation
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[OC] Nationwide Public Transit Ridership is down 30% from pre-lockdown levels; San Francisco's BART ridership is down almost 70%
You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts
What are some alternatives?
visx - 🐯 visx | visualization components
bokeh - Interactive Data Visualization in the browser, from Python
Cesium - An open-source JavaScript library for world-class 3D globes and maps :earth_americas:
Altair - Declarative statistical visualization library for Python
GreenSock-JS - GSAP (GreenSock Animation Platform), a JavaScript animation library for the modern web
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
echarts - Apache ECharts is a powerful, interactive charting and data visualization library for browser
ggplot - ggplot port for python
react-motion - A spring that solves your animation problems.
plotnine - A Grammar of Graphics for Python
quasar-framework - Quasar Framework - Build high-performance VueJS user interfaces in record time [Moved to: https://github.com/quasarframework/quasar]
matplotlib - matplotlib: plotting with Python