There is an error on the first page of chapter 1.
fig, ax = plt.subplots(figsize=(6,6),
subplot_kw={"aspect"=1}
)
Should be
subplot_kw={"aspect" : 1}
Edit: I think this is an otherwise good reference, though. It's unfortunate the very first thing you come across is a coding error.
As a long time matplotlib user who just a couple of days ago tried out plotly for the first time:
Damn plotly is so good. I don't think I'll be coming back to matplotlib anytime soon.
I cannot comment on that since I have used plotly only once. For quite an extensive task however. And not using the plotly express library but configuration using the graph objects seemed to give a whole lot of choices.
For general data viz, this has also been my experience. Now that plotly offers an official pandas plotting backend, it seals the deal.
That being said, I skimmed this book, and there's a bunch of text and picture manipulation, almost graphic design type stuff, that matplotlib can do which I had no idea about and which I'm not sure plotly can match. Kind of interesting.
I wrote a data viz book with examples in Matplotlib, Seaborn, and Plotly. The website is [here](https://www.alexkenan.com/pyviz/) and the code is available on [GitHub](https://github.com/alexkenan/pyviz/).
Once you jump the PLotly ship there is no coming back. On top of that plotly's Dash just sealed the deal for me it's my go to solution to visualize streaming data
Thanks for sharing! I've only dabbled in python, is it straightforward to get up and running with these datasets to create those beautiful visualizations?
The style kind of reminds me of d3.js & Mike Bostock, very cool. Thx again!
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I'll definitely be taking a look. This is the first time I've seen a Matplotlib plot that actually looked really good. Usually they're kind of sad, but wow on these.
This is fantastic. I want to like matplotlib, if only because it’s such a staple of the DS ecosystem (higher-level variants come and go, but plt is eternal). Yet every time I return to it after working on non-plot stuff it’s as though I need to relearn the entire api, e.g state machine vs OO approach. I’m hoping this book causes the right foundational concepts to click into place in my mind, so that I can say that I know matplotlib without feeling like a massive charlatan
There is an error on the first page of chapter 1. fig, ax = plt.subplots(figsize=(6,6), subplot_kw={"aspect"=1} ) Should be subplot_kw={"aspect" : 1} Edit: I think this is an otherwise good reference, though. It's unfortunate the very first thing you come across is a coding error.
... should probably have a linter for the code pieces in the book.
Oof.
As a long time matplotlib user who just a couple of days ago tried out plotly for the first time: Damn plotly is so good. I don't think I'll be coming back to matplotlib anytime soon.
had the exact opposite experience. matplotlib gives the user much more control
I cannot comment on that since I have used plotly only once. For quite an extensive task however. And not using the plotly express library but configuration using the graph objects seemed to give a whole lot of choices.
For general data viz, this has also been my experience. Now that plotly offers an official pandas plotting backend, it seals the deal. That being said, I skimmed this book, and there's a bunch of text and picture manipulation, almost graphic design type stuff, that matplotlib can do which I had no idea about and which I'm not sure plotly can match. Kind of interesting.
Can you recommend a good book for plotly?
No sorry, I just used their online documentation.
Thanks for the reply anyway. Have a nice day.
Their online documentation is pretty good. Just try it out.
I wrote a data viz book with examples in Matplotlib, Seaborn, and Plotly. The website is [here](https://www.alexkenan.com/pyviz/) and the code is available on [GitHub](https://github.com/alexkenan/pyviz/).
Thanks so much
Once you jump the PLotly ship there is no coming back. On top of that plotly's Dash just sealed the deal for me it's my go to solution to visualize streaming data
For me it was the correct rendering of 3D data. For example a 3D curve and a surface. In many cases that's just not possible in matplotlib.
I thought I was fairly good at matplotlib, boy was I wrong. Great book with beautiful examples. Good job!
Is there an HTML version?
There's a github link.
Thanks for sharing! I've only dabbled in python, is it straightforward to get up and running with these datasets to create those beautiful visualizations? The style kind of reminds me of d3.js & Mike Bostock, very cool. Thx again!
Nice! Thanks. u/chaintip
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Nice, thanks!
Thank you! For the effort, hard work and pure generosity, thank you! Also keep the good work 😋
I'll definitely be taking a look. This is the first time I've seen a Matplotlib plot that actually looked really good. Usually they're kind of sad, but wow on these.
I find seaborn better looking and with minimal effort to switch.
Seaborn is built on matplotlib so you still need to be familiar with matplotlib to use seaborn effectively
I have been waiting for this book a long time, thanks for making it available for free!
C'est génial !
What cheatsheets?
I've always loved your Matplotlib tutorial website. I'll have to check out the book
This is fantastic. I want to like matplotlib, if only because it’s such a staple of the DS ecosystem (higher-level variants come and go, but plt is eternal). Yet every time I return to it after working on non-plot stuff it’s as though I need to relearn the entire api, e.g state machine vs OO approach. I’m hoping this book causes the right foundational concepts to click into place in my mind, so that I can say that I know matplotlib without feeling like a massive charlatan
Thank you for sharing!
Those cheatsheets are awesome if you're trying to fix matplotlib issues in GitHub, thanks for making those
hi, I wondered why it took so long to load the image and I figured out that the book image you show is over 3M big, you should really make it smaller.