matplotlib
plotnine
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matplotlib | plotnine | |
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36 | 36 | |
19,056 | 3,781 | |
1.4% | - | |
10.0 | 9.7 | |
7 days ago | about 11 hours ago | |
Python | Python | |
Python License 2.0 | 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.
matplotlib
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How and where is matplotlib package making use of PySide?
However, when I look up the matplotlib source, I can't find pyside used anywhere in dependency list. Even a repo search for the term "pyside" gives mentions in the issue tracker but no actual use in the code.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/matplotlib/matplotlib
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The Python Packages That Gave Me Nightmares: A Guide to Overcoming Common Challenges
Matplotlib: Matplotlib is a 2D plotting library that allows you to create visualizations of your data. It's a powerful tool for data analysis, but the syntax can be complex and the customization options can be overwhelming. GitHub - https://github.com/matplotlib/matplotlib
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Where to find a dynamic charge density animation/simulation?
I will think more about what I want to say next, but for now, I would like to say that I need the super-particles and PIC methods as I think that is the way forward for me. Are there ways to implement these methods in matplotlib, Visit or Paraview? Do I take existing code and import it into those programs to visualize it? Or can I directly program/simulate something in those visualizion tools without needing to import any code?
Your choices are an n-body simulation (e.g., LAMMPS) with Coulomb interactions or, if your electrons are sufficiently sparse, a particle-in-cell (e.g., Starfish). Your best bets for visualization are going to be matplotlib or something more user-friendly like Visit or Paraview. Without a neutralizing background, however, your electrons are just going to repel each other, hit the walls, and disappear - there's not going to be much interesting to visualize. What are you actually trying to simulate? With more information maybe you could receive some more targeted advice.
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How to model the hanging chain PDE using numerical methods in Python?
There are plenty of data visualization tools in python, but probably the easiest to get started with is Matplotlib
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Best way to learn ML/AI hands-on as a developer?
An example of how I would do this is to just plot your data on a line graph (https://matplotlib.org/) . Are there any repeating trends? Next try splitting your data into day of the week, day of the month, months, etc. Look for any kind of seasonality (we're trying to use the past to predict the future, so if the future is not like the past our models will fail).
- Matplotlib - Visualization with Python
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SerpApi Demo Project: Walmart Coffee Exploratory Data Analysis
Install libraries and tell matplotlib to plot inline (inside notebook) with the help of % magic functions which sets the backend of matplotlib to the inline backend:
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?
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!
pygal - PYthon svg GrAph plotting Library
bqplot - Plotting library for IPython/Jupyter notebooks
bokeh - Interactive Data Visualization in the browser, from Python
seaborn - Statistical data visualization in Python
VisPy - Main repository for Vispy
Graphviz - Simple Python interface for Graphviz
Altair - Declarative statistical visualization library for Python
ggplot - ggplot port for python
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]