panel
plotnine
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panel | plotnine | |
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38 | 36 | |
4,004 | 3,781 | |
6.4% | - | |
9.9 | 9.7 | |
2 days ago | about 20 hours ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT 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.
panel
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panel VS solara - a user suggested alternative
2 projects | 13 Oct 2023
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What python library you are using for interactive visualisation?(other than plotly)
https://panel.holoviz.org/ It's a web app framework for Python similar to what Dash does for plotly. It plays nicely with bokeh visuals and I think the front-end is built using bokeh css elements.
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FastAPI, Panel and Bokeh
I'm following the Panel FastAPI example here: https://github.com/holoviz/panel/blob/main/examples/apps/fastApi/main.py
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How to approach GIS and which language to use
If you want to build Python dashboards, look at the solara (react-style lib, https://solara.dev/) and panel (https://panel.holoviz.org/).
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Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
My suggestion is https://panel.holoviz.org/
Fully open sourced, makes it easy to make reactive apps with small changes, can even configured as a graphical REPL.
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Updating a page with MQTT
I am doing something like this in a [panel](https://panel.holoviz.org/) dashboard, which I am currently converting to nicegui. Maybe I can provide an example in some days.
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Mercury – Turn Python Notebooks to Web Apps
Ill have to check it out and see how it compares to voilà and holoviz panel. What I like about Holoviz panel is you can create a data web app from code that resides in a notebook or create a completely standalone app from just plain py scripts, and it supports many different visualization backends. I have found it to be the more flexible and generalizable data web app framework among the others I have come across (like Voilà, Dash, Plotly, and Streamlit).
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4 Streamlit Alternatives for Building Python Data Apps
Like the previous three alternatives, Panel is an open-source Python library for creating interactive dashboard web apps. Panel is extremely flexible, allowing you to use any plotting library you like. Like Gradio but unlike Streamlit, you can use Panel in Jupyter notebooks. Panel dashboards can also be deployed as standalone web apps, but like Plotly Dash, you'll need to set up a server to deploy it yourself.
- Sunday Daily Thread: What's everyone working on this week?
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I've updated the README of Panel. Let me know what you think. Thanks.
I've contributed an update to the README in attempt to better explain the WHY and WHAT of Panel.
plotnine
- FLaNK AI Weekly 18 March 2024
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A look at the Mojo language for bioinformatics
To your last point, have you tried plotnine? It's meant to be ggplot2 for python.
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
plotnine - A grammar of graphics for Python based on ggplot2.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/has2k1/plotnine
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Lets-Plot: An open-source plotting library by JetBrains
This seems quite similar to plotnine [0], which also provides a grammar of graphics interface for Python. That said, I love ggplot and I can't wait to use this in my research! I hope we can port/re-implement ggthemes, scientificplots [1], and other ggplot libraries for lets-plot.
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Every modeler is supposed to be a great Python programmer
> Python doesn’t yet have anything remotely close to ggplot for rapidly making exploratory graphics, for example.
Plug for plotnine (https://plotnine.readthedocs.io/en/stable/). I don't know R but use ggplot indirectly through this library for exploratory data analysis, and comparing the experience to any other python plotting library, I understand why R folks are usually so sad to be using Python.
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What's New in Matplotlib 3.6.0
Python is my daily driver, but I briefly experimented with R and had a delightful experience with ggplot2. The ‘grammar of graphics’ was hard to leave behind when I switched back to Python, until I heard about Plotnine [1], which brings much of the same grammar and functionality to Python. It’s built on Matplotlib and a few other common libraries like Pandas.
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Will Rust-based data frame library Polars dethrone Pandas? We evaluate on 1M+ Stack Overflow questions
The best one I've found is plotnine, which is just a reimplementation of the ggplot API.
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What to do next after learning basic python grammar
I will separate my answer on two parts: (1) fun stuff and (2) useful work-related things. As it relates to (1), it is obviously a function on what you're mainly interested in. Nonetheless, I definitely recommend reading parts of the book Think Python (available for free here), which includes many different examples on how to use Python for creating your own functions, and I've used it to build my own tweet scraper using Tweepy, create budget planners, etc. As it relates to (2), I believe it is useful to learn about data manipulation and visualization libraries (I have a job related to business development). For instance, knowing how to use Pandas to take a database of customers, group them by some useful variables (such as location, spending potential, etc.), and use visualization libraries (such as MatplotLib or Plotnine) to display your analysis can help you build sales report much more quickly. Hope this helps.
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Unpopular opinion: Matplotlib is a bad library
I think plotnine is one solution. It is implemented based on matplotlib, but it provides an almost complete ggplot syntax for matplotlib. The other solution is a next-generation seaborn interface. It is also `build on matplotlib and still in progress; however, the API would be really useful! And I have personally also developed a few libraries to solve the complex syntax of matplotlib. As an example, patchworkllib allows dynamic subplot layout on Jupyter-lab. Maybe the library can support handling matplotlib and seaborn plots.
What are some alternatives?
seaborn - Statistical data visualization in Python
matplotlib - matplotlib: plotting with Python
streamlit - Streamlit — A faster way to build and share data apps.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
Altair - Declarative statistical visualization library for Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
ggplot - ggplot port for python
gradio - Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
bokeh - Interactive Data Visualization in the browser, from Python
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust