Introduction to Matplotlib | SciPy 2019 Tutorial | Hannah Aizenman, Thomas Caswell

Users will learn how to plot some of the most common types of data- such as discrete, continuous and categorical, in 1D and 2D - using Matplotlib. In doing so, this tutorial will unpack some of the fundamental concepts that underlie the architecture of Matplotlib. Using these concepts, attendees will learn how to change the "look and feel" of plots. The concepts taught in this tutorial will lay the foundation for creating effective visualizations using Matplotlib.

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[edterrell]

Great job on adding new material to this standard talk by enthought. If you've seen any of the previous tutorials from prior years, this one is not to be missed. It has a lot of new material (mostly more of it pertains to daily use cases). The talk really comes into its own about 1/2 way through, so pick and choose what's best for you. I came away with a lot of new ideas and ways of accomplishing the same thing more efficiently. I find its easy to get in way over my head with matplotlib but my comfort level has increased tremendously!

[richardwfr]

wow, Hannah, it is a long time since we met at NCAR in 2012. Nice tutorial.

[IrfanKhan-ygzu]

Nice, Hannah, i recently came to know about python for finance, i really like to learn python, i found this, thanks

[BiancaAguglia]

The tip at 95:00 about "the aesthetics of the plots being controlled by function parameters (in this case method parameters) and the set up / elements / artists of the plot being controlled by object methods" was useful. I'm a little embarrassed I hadn't noticed that pattern on my own before. 😊


I hope you don't mind that I have a bit of constructive criticism: in previous Matplotlib videos aimed to beginners you did a wonderful job explaining the difference between state dependent vs. object oriented plotting, explaining the anatomy of a figure and the meaning of artists (plus specific examples). I think those explanations are crucial for someone who's new to Matplotlib and this video didn't spend enough time on those topics.


Other than that, I have no criticism. I hesitated to say anything because you're in my top three favorite channels for data science tutorials, but I figured that open criticism is better than hidden grumbling. 😁 Anyway, I love your content and am very thankful you're make these SciPy tutorials available online. Keep up the great work. I'm getting back to binge watching your latest batch of videos. 😊

[victornoagbodji]

😊 🙏 😊
this is a great talk. thanks for sharing it with us!

[eltonrr]

Great talk, nicely instructional . Would be better if the questions from audience were reproduced in mic, and if less questions were posed to the public.

[Weijie]

The content that she is introducing can be compressed into 30 min if without too much random talk.

[unclestephen]

Terrible talk, wildly full of irrelevancies, asking stupid questions of audience, weird pronunciations, wonder what her native language is, takes ages to get anywhere, clearly the result of insufficient preparation. Compare with numpy talk, for example.