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#seaborn

0 posts0 participants0 posts today
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Eric Maugendre

#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: medium.com/@amit25173/plotly-v

Medium · Plotly vs Seaborn - Amit Yadav - MediumBy Amit Yadav
#dataDev#retrieval#dataMining
Public
Ruth Mottram

Nom.

fediscience.org/@tlohde/113412
@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

FediScience.orgtlohde (@tlohde@fediscience.org)Attached: 1 image 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
Public
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 (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

Minimalistic map of France, showing a series of nested contour lines in blue and red. Blue contours represented the density of bakeries; red farms. Above the main plot, the longitudinal distribution of bakeries and farms are shown (with a prominent bakery mode inline with Paris), and to the right the latitudinal distributions.
Public
Tuomas Väisänen 📼🧟‍♂️

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.

Public
Lisa Hornung

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): github.com/Lisa-Ho/small-data-

Is the Canadian National Hockey League getting older? Chart showing average age of players in the National Hockey League between 2079/80 and 2023/24. Trendline is going up slightly, reaching it’s highest point of 27 years in 2023/24. Each season there is usually quite a variation between teams which is visualised with dots (each dot=average age for 1 team per season, multiple teams=overlayed dots)
Public *
Christof Schöch

So far, I really like the (relatively) new #object-oriented interface for #Seaborn, one of my favorite #visualization libraries in #Python.

It feels much cleaner, more logical, more compact, and there is indeed less need to go down to #Matplotlib for many tweaks to a plot.

Documentation here: seaborn.pydata.org/tutorial/ob

Code and result for a very simple bar chart example below. Quite compact and very clean.

( so.Plot(data=coeditors, x="num", y="perc", text="perc") .add(so.Bar()) .add(so.Text(color="w", valign="top", offset=5)) .scale(x=so.Continuous().tick(every=1)) .label(x="Number of co-editors", y="Percentage") .save("coeditor-percentages.png", dpi=300) .show() )
Barplot resulting from the code shown in the first image. Seven light blue bars of differing height. Most prevalent number of co-editors is 2, with 37.9 percent.
Public *
Sam Harrison

#Python is now officially supported in #Excel!

As much as I like to pretend that I dislike anything #Microsoft related (except VS Code, of course), I often find myself turning to Excel for small tasks, particularly when collaborating with non-coder colleagues. This new Python support, in particular the ability to represent ranges of Excel cells as #Pandas DataFrames, and use #Matplotlib and #Seaborn for plotting, could be a game change for these applications.

techcommunity.microsoft.com/t5

TECHCOMMUNITY.MICROSOFT.COMAnnouncing%20Python%20in%20Excel%3A%20Combining%20the%20power%20of%20Python%20and%20the%20flexibility%20of%20Excel.Since its inception, Microsoft Excel has changed how people organize, analyze, and visualize their data, providing a basis for decision-making for the..
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