As a data scientist with a background in hard science, Kirk Borne often reminds me of the importance of robust data. His advice today is a great pro-tip for fellow materials researchers:

"Don't just plot your data; visualise the uncertainty! Using tools like #Seaborn can add confidence intervals to your graphs, making your findings more robust. This simple step adds credibility and clarity to your results."

How do skills acquired through DataCamp translate to a better understanding of biodiversity data?

For Ibrahim Abdel Basir Gomaa, who completed courses in #Pandas, #Seaborn, machine learning, #SQL and statistics, these skills enabled him to confidently query large biodiversity datasets from sources like GBIF.

“One of my proudest achievements was developing a predictive model to assess species distribution patterns based on environmental factors...."

Prepping for my sci-fi inspired data science projects live stream

We decided on the last call to do img classification #CNN w/ #NVIDIA GPUs and #RAPIDS #opencv #cuml #seaborn #cuDF #cupy and started a PRD. I'll share my finished PRD and get to building live for 2 hours.

Wed, July 23 1:15p ET / 12:15p CT / 5:15p GMT

Feel free to come by & say 'hello'

https://www.youtube.com/live/2IPZ35XpZaY?si=IDqW2EHAGNDed7jZ

Ever thought about learning new data skills?

Through our partnership with DataCamp Adel learned introductory skills in #Python, #Pandas and #Seaborn.

"Using DataCamp, I developed a biodiversity monitoring dashboard that streamlined data collection and reporting. This tool is now being used by my team to track species populations more effectively and make data-driven conservation decisions."

Apply for the 2025 programme: https://gbif.link/datacampdonates

Compare the Best Data Visualisation Libraries for Python and Pandas

Python Histogram Plotting: Visualizing Multiple Datasets with Seaborn
Master Python Histogram Plotting for effective data visualization! Learn to create clear histograms with multiple datasets using Matplotlib & Seaborn. Improve your data analysis skills today! #Python #DataVisualization #Matplotlib #Seaborn #Histograms #DataAnalysis
https://tech-champion.com/programming/python-programming/python-histogram-plotting-visualizing-multiple-datasets-with-seaborn/

How to create a pie chart with #Python and #seaborn

#DataViz Decision-Making Guide

"How do you decide between #Plotly and #Seaborn?
* If you need interactive and dynamic visualizations, especially for dashboards or 3D data, Plotly is the way to go.
* If you’re focused on statistical analysis, creating publication-ready visuals, or conducting exploratory data analysis, Seaborn is likely your best choice."
by Amit Yadav: https://medium.com/@amit25173/plotly-vs-seaborn-f7207dd3e642

Nom.

https://fediscience.org/@tlohde/113412191058419280
@tlohde - Croissant Distribution

or, where am I most likely to have my next croissant, assuming (something something) independence from my previous croissant. ^k * e^(-) / k!
--
2d kernel density estimation of the location of bakeries (blue) and farms (red) across France.

Locations taken from OpenStreetMap, with #osmnx & plotted with #matplotlib & #seaborn. font: Borel, by Rosalie Wagner.

#30DayMapChallenge Day 2: Lines

Croissant Distribution

or, where am I most likely to have my next croissant, assuming (something something) independence from my previous croissant. ^k * e^(-) / k!
--
2d kernel density estimation of the location of bakeries (blue) and farms (red) across France.

Locations taken from OpenStreetMap (considered paying attention to the size of the farm, but didn't), with #osmnx & plotted with #matplotlib & #seaborn. font: Borel, by Rosalie Wagner.

#30DayMapChallenge Day 2: Lines

Playing with #Kaggle today...
File similarity work bleeding over into my weekend mindspace.

https://www.kaggle.com/code/osunderdog/filesimilarityvisual

Hey #spatial #R folks, is it possible to extract geometries from #ggplot as files? That is, the point, line, and polygon geometries in the graphs to #geopackage or #shapefile etc.

It is possible with #seaborn and #matplotlib, but that's Python not R. There's a nice and lightning fast #EdgeBundling technique in the newest ggplot, which is very promising for the #visualization of massive #mobility data.

There is also something in #HoloViews but I do not understand that library at all.

I created a post on my #blog It's the first one on #datavisualization

I'm really liking #seaborn

https://blog.ddavo.me/posts/tutorials/seaborn-heatmap-marginal-distribution/

Late contribution to #TidyTuesday Canadian NHL Player Birth Dates. Trying to get back into the swing of #dataviz.

Settled on a combination of stripplot, scatter and line to explore the average age of players over time. Looks like the league is getting a bit older overall.

Made in #python using #matplotlib and #seaborn, custom legend and managed to display a discontinued y-axis.

Full code (not pretty but works): https://github.com/Lisa-Ho/small-data-projects/blob/main/2024/2401-canadian-nhl-players/canadian-nhl-players.ipynb