styles
Altair
| styles | Altair | |
|---|---|---|
| 1 | 43 | |
| 7 | 8,965 | |
| - | 1.3% | |
| 5.1 | 9.0 | |
| 10 days ago | 1 day ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" 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.
styles
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How do people package Altair themes?
To use any of the themes available in the three examples mentioned above globally, we need to alt.themes.register() and alt.themes.enable() them. In the case of the carbonplan-styles package, which provides a light theme and a dark theme for Altair and Matplotlib, we just need to enable the theme (and we don't need to import any extras). In other words, there is no need to register the theme, as it is added to the available themes "automatically" from the entry points defined in the setup.py file (or equivalent):
Altair
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Ask HN: What's the best charting library for customer-facing dashboards?
I like Vega-Lite: https://vega.github.io/vega-lite/
It’s built by folks from the same lab as D3, but designed as “a higher-level visual specification language on top of D3” [https://vega.github.io/vega/about/vega-and-d3/]
My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if you need to move to something besides Python to productionize, you can produce the same Vega-Lite definitions using the framework of your choice.
- FLaNK AI Weekly 18 March 2024
- FLaNK AI for 11 March 2024
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Vega-Altair: Declarative Visualization in Python
Feel free to open an issue to let us know which parts of the documentation you find obscure and if you have suggestions for how to improve them. We did a larger overhaul a few months back and are always open to feedback on how to improve it further! https://altair-viz.github.io/
(disclaimer: I'm a co-maintainer of Altair)
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Gnuplotlib: Non-Painful Plotting for NumPy
Vega-Altair is pretty great as well. It uses a grammar of graphics that’s slightly different from ggplot, but has most of the same advantages.
https://altair-viz.github.io/
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Altair - Declarative statistical visualization library for Python.
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Top 10 growing data visualization libraries in Python in 2023
Github: Altair
- What python library you are using for interactive visualisation?(other than plotly)
- Libs para gráficos
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If you had to pick a library from another language (Rust, JS, etc.) that isn’t currently available in Python and have it instantly converted into Python for you to use, what would it be?
Yeah, that's one of the main reasons I like altair. It has 10M downloads per month and the newest Git update is from two days ago.
What are some alternatives?
altair-latimes - A Los Angeles Times theme for Python's Altair statistical visualization library
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
cheatsheets - Official Matplotlib cheat sheets
bokeh - Interactive Data Visualization in the browser, from Python
hueniversitypy
seaborn - Statistical data visualization in Python
ggplot - ggplot port for python
plotnine - A Grammar of Graphics for Python
matplotlib - matplotlib: plotting with Python
folium - Python Data. Leaflet.js Maps.
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.
ggplot2 - An implementation of the Grammar of Graphics in R