Skeletron
seaborn
| Skeletron | seaborn | |
|---|---|---|
| 1 | 77 | |
| 103 | 11,969 | |
| - | - | |
| 0.0 | 8.4 | |
| almost 8 years ago | 11 days ago | |
| Python | Python | |
| - | BSD 3-clause "New" or "Revised" License |
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Skeletron
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Prettymaps: Small Python library to draw customized maps from OpenStreetMap data
There are tools to do that sort of feature generalization. https://github.com/migurski/Skeletron is one that comes to mind.
seaborn
- "No" is not an actionable error message
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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).
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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.
https://seaborn.pydata.org/
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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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[OC] Nationwide Public Transit Ridership is down 30% from pre-lockdown levels; San Francisco's BART ridership is down almost 70%
You've done a great job presenting this. Maybe you already know, but seaborne is an extension of matplotlib that makes it pretty easy to "beautify" matplotlib charts
What are some alternatives?
osm-renderer - OpenStreetMap raster tile renderer written in Rust
bokeh - Interactive Data Visualization in the browser, from Python
vsketch - Generative plotter art environment for Python
Altair - Declarative statistical visualization library for Python
prettymaps - A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
awesome-vector-tiles - Awesome implementations of the Mapbox Vector Tile specification
ggplot - ggplot port for python
tilemaker - Make OpenStreetMap vector tiles without the stack
plotnine - A Grammar of Graphics for Python
matplotlib - matplotlib: plotting with Python
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications