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plotnine | ggplot | |
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36 | 3 | |
3,781 | 3,676 | |
- | 0.0% | |
9.7 | 0.0 | |
about 8 hours ago | about 1 year ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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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.
ggplot
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Best tools for good looking tables and piecharts
Seaborn is based on matplotlib and quite modern. Coming from R and used to ggplot (which is also available in python) I really like it.
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Which Python visualization module to use for research-quality graphs?
If you're familiar with R, there's always ggplot.
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Plotting in R's ggplot2 vs Python's Matplotlib: Is it just me or is ggplot2 WAY smoother of an experience than Matplotlib?
I'd agree in that it's a well-specified language for defining graphics; it's not very good with rendering performance. There are packages which try to achieve similar goals in Python as well (ggplot / ggpy) and packages like Seaborn. Though, like you, I use R for lots of EDA. Hard to beat data.table and R graphics for speed and expressiveness. I prefer base graphics though; ggplot2 tends to render too slowly for any data sets I work with.
What are some alternatives?
seaborn - Statistical data visualization in Python
matplotlib - matplotlib: plotting with Python
Altair - Declarative statistical visualization library for Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
bokeh - Interactive Data Visualization in the browser, from Python
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.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust