Cartopy
seaborn
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| Cartopy | seaborn | |
|---|---|---|
| 9 | 76 | |
| 1,348 | 11,946 | |
| 1.1% | - | |
| 8.8 | 8.5 | |
| 11 days ago | 6 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" 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.
Cartopy
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OSCAR 2022 sea surface velocity streamplot animation
Cartopy
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
Cartopy - A cartographic Python library with matplotlib support.
- How to plot latitude and longitude points on a world map, and choose what kind of projection to use? (I don't want the mercator projection)
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[OC] One year in life of ocean eddies
There is not really much to it, really. Everything is done in python, with use of standard libraries, of which probably most important in this case is cartopy https://scitools.org.uk/cartopy/docs/latest/ This is the latest cartopy tutorial I was able to find: https://www.youtube.com/watch?v=ivmd3RluMiw
- [OC] This animation shows how the amount of daylight throughout the year changes based on Latitude. Made with Python and the matplotlib library.
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Is there a good library for creating maps and altering them according to data?
Cartopy is a great lib that has a ton of utilities for those purposes. It is a little bit tricky to use at first but you can get really nice visualizations. I recommend installing it using with a conda env because its dependencies with other libraries.
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[OC] Active Covid-19 cases per Capita in USA. 1/21/2020 - 8/23/2021
- Cartopy: https://github.com/SciTools/cartopy
- Module for projecting flat earth map to full disc
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[OC] Surface wind for the first 3 months of 2005, with a 6 hours timestep
Made with: Cartopy, imageio and good 'ol Matplotlib (all Python libraries)
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).
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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
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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
What are some alternatives?
folium - Python Data. Leaflet.js Maps.
bokeh - Interactive Data Visualization in the browser, from Python
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
Altair - Declarative statistical visualization library for Python
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
ggplot - ggplot port for python
matplotlib - matplotlib: plotting with Python
plotnine - A Grammar of Graphics for Python
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.