I really want to see this but sadly I have childcare duties at that time.
will it be available afterwards as video, perhaps on YouTube?
Here is my own beginner attempt at an animated visualisation using open source tools (python) ..
I really want to see this but sadly I have childcare duties at that time.
will it be available afterwards as video, perhaps on YouTube?
Here is my own beginner attempt at an animated visualisation using open source tools (python) ..
Pillow y Matplotlib tienen propósitos diferentes, pero complementarios
Comparar Imagen vs Gráfico:
#Python #Pillow #Matplotlib Imagen Imagenes curso #Anzoategui #Lecheria
Combinar los procedimientos STEM con la creatividad e innovación STEAM
Comparar STEM vs STEAM
#Python pillow #matplotlib #Anzoategui #Lecheria
If you’re interested in baseball in particular, or in any other sport where some data sources are available for which a variable needs to be followed over time over the central 50% quartile, this post covers both data reading, transformation, and plotting.
#DataManipulation #DataVisualization #CSV #Python #pandas #matplotlib https://fosstodon.org/@drdrang/115296285675549307
Відео, у якому демонструється процес створення анімованих графіків у #Python за допомогою #Matplotlib
https://www.youtube.com/watch?v=kjYM3dTKA2c
When I created my UV index charts back in the summer, I didn’t foresee the problem that the bars would all be yellow and therefore the white labels would be unreadable.
the cyclic #twilight #matplotlib #colormap https://matplotlib.org/stable/users/explain/colors/colormaps.html Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_09_16
More sketch-a-day: https://abav.lugaralgum.com/sketch-a-day
If you like this, support my work:
https://www.paypal.com/donate/?hosted_button_id=5B4MZ78C9J724
https://liberapay.com/Villares
https://wise.com/pay/me/alexandrev562 #Processing #Python #py5 #CreativeCoding
Update: Sorry folks, I messed up the links/date yesterday. I fixed it now (I hope)!
Using py5 #matplotlib #colormap integration: #viridis Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_09_12
(The 2015 talk about Viridis I posted earlier is great, check it out... https://www.youtube.com/watch?v=xAoljeRJ3lU)
More sketch-a-day: https://abav.lugaralgum.com/sketch-a-day
If you like this, support my work:
https://www.paypal.com/donate/?hosted_button_id=5B4MZ78C9J724
https://liberapay.com/Villares
https://wise.com/pay/me/alexandrev562 #Processing #Python #py5 #CreativeCoding
@hisold Citing a plain website or GitHub repo is kinda unprofessional. Many widespead software packages have a publication that is well citable, e.g. #sympy has this one with a proper DOI: https://doi.org/10.7717/peerj-cs.103, same for #numpy, #scipy, #matplotlib, etc. Some have at least a #Zenodo entry (with a DOI) to be properly citable. #PlatformIO apparently has none of those.
"A Better Default #Colormap for #Matplotlib | #SciPy 2015 | Nathaniel Smith and Stéfan van der Walt"
What if all munros (mountains in Scotland > 3000ft or 914.4m) were on a single munro?
Fun visual experiment for #TidyTuesday using #python, pandas, #matplotlib, pyfonts.
Code https://github.com/Lisa-Ho/small-data-projects?tab=readme-ov-file#082025-scottish-munros
#Matplotlib ma sporo testów opartych o "porównywaniu grafik", które często się sypią. Technicznie rzecz biorąc, większość z nich dopuszcza pewien odchył od obrazów referencyjnych, ale całkiem często dostałem większe wartości RMS. Tak więc przez długi czas musieliśmy utrzymywać spore łatki, które zwiększały tolerancję w tych testach, i regularnie wymagały aktualizacji.
W pewnym momencie autorzy zaczęli dodawać do testów warunki, dopuszczające większą tolerancję na platformach innych niż x86_64. Oczywiście, każda taka zmiana zmuszała mnie do aktualizacji naszej łatki. Co ciekawe, wartości, które poprzednio wprowadziłem, były zbliżone do tych, które teraz dodano dla platform innych niż x86_64.
Dziś do mnie w końcu dotarło, że zamiast znów aktualizować łatkę, mogę spróbować ją całkiem wywalić i podmienić sedem wszystkie instancje `platform.machine() == 'x86_64'` na `False` — i jak się okazuje, po tej zmianie poza zakresem zostały 3 testy (związane z TeΧ). I nie muszę już spędzać 15 minut ręcznie robiąc właściwie to samo.
Anybody have ideas why my bar plot is coming out with multiple vertical lines like this? The plot should look like the second one. But I was trying to fix some other errors and produced new problems instead.
Code: plot = = data.resample('YE').count().to_period('Y').plot.bar(legend=False, rot=0, grid=True, figsize=(12,6))
I move 1 along the x-axis, then rotate by an angle theta, I move 1/phi, rotate by theta, I move 1/phi², rotate by theta etc etc.
Made with #python #numpy #matplotlib
Mit den #opendata Wetterdaten des @DeutscherWetterdienst unter https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/solar/ kann man die Sonnenscheindauer gegen die Unfallzahlen plotten. #matplotlib #python #fedibikes
Want to level up your Python data visualization skills?
Join Stefanie Molin at #EuroSciPy2025 for a beginner-friendly tutorial:
"Beyond the Basics: Data Visualization in Python"
Learn Matplotlib
Create beautiful charts
Explore interactivity with HoloViz & Bokeh
Perfect for newcomers—no prior experience required!
Aug 18–22 in Kraków
https://euroscipy.org/tickets
#Python #DataViz #Matplotlib #ScientificPython #EuroSciPy
I usually do not ask chatgpt, but today after a long search for something on the internet I tried my luck. The result is the most crappy, useless thing I read in a while.
So, does anyone here know if I can embed a SVG/PDF plot as a #matplotlib axis, and how? :)
Finding all regions defined by a set of pseudorandom circles. Here, 8 input circles gave 37 output regions.
Python using pyclipper for 2d predicates & matplotlib for display. The (probably inefficient and maybe wrong) algorithm for ensuring all regions are found by me using trial and error.
pssst don't tell anyone but the circles are actually just 360-gons.
Oh dear, I'm still working on erosion. For quick experimentation, I've even broken out #Python, #Jupyter, #numpy and #matplotlib.
Here's a neat picture showing that something is working a little bit, although I think usually rivers are lower than the surrounding terrain, not higher.
Where will this end?
More of a general question about community. I want to draw a pie plot, in a package/rendering engine that is not #matplotlib . But I know that matplotlib does do the math I need.
Theoretically, the "correct" approach would be to isolate that math, make a new package and hook it in so that both matplotlib and my new package can now use the same math, same package. I can reuse the math I need without their rendering assumptions.
But I don't think they would do this. (1/2) ...