VisPy
pygwalker
| VisPy | pygwalker | |
|---|---|---|
| 4 | 22 | |
| 3,228 | 10,213 | |
| 0.9% | 7.8% | |
| 8.6 | 9.6 | |
| 10 days ago | 4 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
VisPy
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
VisPy - High-performance scientific visualization based on OpenGL.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/vispy/vispy
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Seeking library recommendation for 3D visualization of crystal structure
Two similar alternatives you could look at are PyVista which is based on the same framework as Mayavi and VisPy. Mayavi is strongly dependent on the whole Enthought suite which can be a disadvantage if you don’t really use its abilities.
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Show HN: MPL Plotter – Python library to make technical plots more efficiently
2. I recommend Datashader (https://datashader.org/) (HoloViz is super cool) and Vispy (https://vispy.org/). I found Vispy's documentation a bit lacking some time ago, but they probably have improved it since then, and it's very capable. Lastly, check Taichi (https://taichi.graphics/), might not be a conventional data representation library (or rather, not only), but it's amazing and worth a look.
To add some more depth to the Seaborn comparison, and not being an expert Seaborn user, I'd say:
1. MPL Plotter is lighter (but also with less wide-ranging plot options)
pygwalker
- Show HN: Use an "eraser" to clean data on flight without breaking your workflow
- Show HN: Data Painter – different way to interact with data in Jupyter notebook
- PyGWalker: a Python library for data engineer that turns your dataframe into tableau-like data app.
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Top 10 growing data visualization libraries in Python in 2023
The most popular data visualization python library in 2023. It turns your dataframe into an interactive data exploration app like tableau/powerBI with one line of code. It provides simple drag-and-drop/chat interface for you to build charts. It can run in juypter notebook, which means you do not need to switch between your code and the visualization app. Besides, you can also build interactive spitial visualization on maps with it. And it also has Javascript and R version.
- Boost pygwalker's speed for visual analysis with duckDB
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Turn your data frame into a tableau-style interactive visualization interface in R
GWalkR is the R binding of Graphic-Walker, if you want to use it in python, check the python version: PyGWalker: https://github.com/Kanaries/pygwalker
- FLaNK Stack Weekly on 26 June 2023
- A blocky based CAD program
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Show HN: RATH – Open-Source Copilot and Autopilot for Data Analysis
+ Graphic Walker (https://github.com/Kanaries/graphic-walker): A lite embeddable component for visual analysis.
+ PyGWalker (https://github.com/Kanaries/pygwalker): turning your pandas dataframe into a Tableau-style User Interface for visual exploration.
RATH is a collection of interesting ideas that we think the next generation of data analysis software should be, so there might be many features that not well organized to be a united app. Tell me which feature you prefer and which is not. Looking forward for your ideas and advice.
- Converting a huge CSV file into a custom made table
What are some alternatives?
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
matplotlib - matplotlib: plotting with Python
Rath - Next generation of automated data exploratory analysis and visualization platform.
graphic-walker - An open source alternative to Tableau. Embeddable visual analytic
pyrender - Easy-to-use glTF 2.0-compliant OpenGL renderer for visualization of 3D scenes.
RasgoQL - Write python locally, execute SQL in your data warehouse
bokeh - Interactive Data Visualization in the browser, from Python
devpod - Codespaces but open-source, client-only and unopinionated: Works with any IDE and lets you use any cloud, kubernetes or just localhost docker.
Flask JSONDash - :snake: :bar_chart: :chart_with_upwards_trend: Build complex dashboards without any front-end code. Use your own endpoints. JSON config only. Ready to go.
ai - Build AI-powered applications with React, Svelte, Vue, and Solid