prettymaps
seaborn
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| prettymaps | seaborn | |
|---|---|---|
| 50 | 76 | |
| 10,815 | 11,946 | |
| - | - | |
| 0.0 | 8.5 | |
| 6 months ago | 7 days ago | |
| Jupyter Notebook | Python | |
| GNU Affero General Public License v3.0 | 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.
prettymaps
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Show HN: Map2Image – Download Beautiful City Maps
These maps look great! Reminds me of a project I saw a long time ago [1]. Glad you made this downloadable for everyone who cannot write code.
P.S.: Now, I also have some (birthday) presents ;-)
[1] https://github.com/marceloprates/prettymaps
- A small set of Python functions to draw pretty maps from OpenStreetMap data
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Riddle me this: "Huku ni wapi?"
You can generate them yourself from Open Street Maps. Use this Google Colab. Source - prettymaps.
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Used Python to draw this map of Motijheel, Dhaka, Bangladesh.
Check out this repo
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Shapefiles for planet? Geofabrik only seems to have shapefiles for Antartica.
Check out Pretty Maps, or the online version, and /r/prettymaps_
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Geovisualization test with OSM map data and matplotib for styling
Great work! I love osmnx, it's such a nice library. I remember using it at school to work out distance from fire stations across my city. There's this nice library that uses it called prettymaps, I've been meaning to take some time with it to do some sweet posters.
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This Week In Python
prettymaps – A small set of Python functions to draw pretty maps from OpenStreetMap data
- prettymaps v1.0.0 released
- prettymaps 1.0.0 released
- Prettymaps: A minimal Python library to draw customized maps from OpenStreetMap
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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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?
vsketch - Generative plotter art environment for Python
bokeh - Interactive Data Visualization in the browser, from Python
awesome-vector-tiles - Awesome implementations of the Mapbox Vector Tile specification
Altair - Declarative statistical visualization library for Python
abstreet - Transportation planning and traffic simulation software for creating cities friendlier to walking, biking, and public transit
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
Openstreetmap - The Rails application that powers OpenStreetMap
ggplot - ggplot port for python
plotnine - A Grammar of Graphics for Python
owid-grapher - A platform for creating interactive data visualizations
matplotlib - matplotlib: plotting with Python