seaborn
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seaborn | PyQtGraph | |
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76 | 25 | |
11,836 | 3,636 | |
- | 1.8% | |
8.5 | 9.1 | |
10 days ago | 5 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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seaborn
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Apache Superset
If you are doing data analysis I don't think any of the 3 pieces of software you mentioned are going to be that helpful.
I see these products as tools for data visualization and reporting i.e. presenting prepared datasets to users in a visually appealing way. They aren't as well suited for serious analytics.
I can't comment on Superset or Tableau but I am familiar with Power BI (it has been rolled out across my org), the type of statistics you can do with it are fairly rudimentary. If you need to do any thing beyond summarizing (counts, averages, min, max etc). It is not particularly easy.
For data analysis I use SAS or R. This software allows you do things like multivariate regression, timeseries forecasting, PCA, Cluster analysis etc. There is also plotting capability.
Both these products are kind of old school, I've been using them since early 2000's, the "new school" seems to be Python. Pretty much all the recent data science people in my organization use Python. Particularly Pandas and libraries like Seaborn (https://seaborn.pydata.org/).
The "power" users of Power BI in my organization tend to be finance/HR people for use cases like drill down into cost figures or Interactively presenting KPI's and other headline figures to management things like that.
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Seaborn bug responsible for finding of declining disruptiveness in science
It's referring to the seaborn library (https://seaborn.pydata.org/), a Python library for data visualization (built on top of matplotlib).
The seaborn bug linked in the paper: “Treat binwidth as approximate to avoid dropping outermost datapoints” (https://github.com/mwaskom/seaborn/pull/3489)
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Why Pandas feels clunky when coming from R
While it’s not perfect and it’s not ggplot2, Seaborn is definitely a big improvement over bare matplotlib. You can still use matplotlib to modify the plots it spits out if you want to but the defaults are pretty good most of the time.
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Releasing The Force Of Machine Learning: A Novice’s Guide 😃
Seaborn: A statistical data visualization library based on Matplotlib, enhancing the aesthetics and visual appeal of statistical graphics.
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Seven Python Projects to Elevate Your Coding Skills
Matplotlib Seaborn Example data sets
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Seaborn - Statistical data visualization using Matplotlib.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/mwaskom/seaborn
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Best Portfolio Projects for Data Science
Seaborn Documentation
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Introducing seaborn-polars, a package allowing to use Polars DataFrames and LazyFrames with Seaborn
I'm sure that your package is great, but seaborn will soon support the interchange protocol and will work relatively seamlessly with polars. https://github.com/mwaskom/seaborn/pull/3340
PyQtGraph
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
PyQtGraph - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
- Lets-Plot: An open-source plotting library by JetBrains
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Use cases for PySide
Image, 3D, or data visualization applications using OpenCV and the SciPy ecosystem. The Graphics View Framework can display an image and let the user interact with it, and the Python ecosystem is very rich for image processing, data analysis, and visualization. For example, LabelMe for image labeling, PyQtGraph for scientific graphics, or custom QWidget integration in Maya.
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Microcontroller real time UART interface with PC data plotting (python code not working)
I did something like this recently but I used pyqtgraph: https://www.pyqtgraph.org/
- Unpopular opinion: Matplotlib is a bad library
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Current situation of library support for M1
Most of the issues are with GPU support. Some libraries still struggle with it AFAIU but I don't use GPU much. M1 native is mostly good everywhere, except Pyqt + Qt 5 (Qt6 is native, but some libraries don't support it yet), But for those you can just start a bash shell under intel emulation and all is fine. Performances do take a hit take hit, but are still better in M1+emulation intel than native on my old mac.
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Python and Qt Simplified. Create a Python GUI in Minutes
And for those who would need Python and Qt "complexified" :
> PyQtGraph is a pure-python graphics and GUI library built on PyQt / PySide and numpy. It is intended for use in mathematics / scientific / engineering applications. Despite being written entirely in python, the library is very fast due to its heavy leverage of NumPy for number crunching and Qt's GraphicsView framework for fast display. PyQtGraph is distributed under the MIT open-source license.
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> Packaging for Distribution
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(I think) I stress tested matplotlib for real-time graping.
Very impressive! Have you looked into pyqtgraph? It's based on the widely used PyQt/PySide packages. Using the included video speed test, I am able to process a 512 x 512 picture at nearly 1000 fps (though my monitor is capped at 144 Hz) with an RTX 2080 Ti. I encourage you to open up the examples and see what capabilities it has for your system.
- PyFlow – visual and modular block programming in Python
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Qt with Python
If you decide you like widgets, check out https://www.pyqtgraph.org/
What are some alternatives?
matplotlib - matplotlib: plotting with Python
bokeh - Interactive Data Visualization in the browser, from Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
VisPy - Main repository for Vispy
Altair - Declarative statistical visualization library for Python
ggplot - ggplot port for python
plotnine - A Grammar of Graphics for Python
folium - Python Data. Leaflet.js Maps.
pygal - PYthon svg GrAph plotting Library