An Introduction to Scientific Python – Matplotlib | Hacker News Reader

An Introduction to Scientific Python – Matplotlib

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49 pointsJmoir17 comments

One of my biggest frustrations with python for data science is how bad the documentation for matplotlib is. Also the default settings leave a lot to be desired - look at the color map scatter plots to see what I mean. What is with all that white-space around the graph?

I fully agree with the crappy documentation. Which to be honest isn't consistent with the rest of the python sphere. Documentation tends to be pretty good generally. It's a shame.

I find pandas documentation verbose but ultimately not real world. Every one of them generates random values which aren't visually distinct. Makes it hard to follow operations.

I completely agree with this assessment. Demonstration of the functionality is technically all there. It's often just hard to parse.

(Admittedly, this is a bit of a nitpick for free software. Overall, I'm very happy with the package.)

For what it's worth, the Python version of ggplot seems to be making some headway.

http://ggplot.yhathq.com

tight_layout() gets rid of all that whitespace :)

There are some alternatives with pretty defaults - I personally love seaborn [1] which builds on top of matplotlib, but seaborn is even more "do it the way I like it or suffer the consequences" - however, the coming v2.0 of Matplotlib comes with a few API changes and a different default color scheme: http://matplotlib.org/style_changes.html or http://matplotlib.1069221.n5.nabble.com/matplotlib-v2-0-0b1-...

[1] http://stanford.edu/~mwaskom/software/seaborn/examples/index...

I needed pixel perfect plots for visualising raster data and did not manage to make it render that. Felt similar to LaTeX: Great if you like what it does by default but don't you dare want something reasonably different. :\

Similar sentiments here. I get slightly further with ggplot2 but still end up fixing stuff manually in Illustrator which adds significant time. Anyone know 1) a more customizable plotting library or 2) a way to apply manual changes to new input pdfs? Biggest sticking points for me are overlapping labels and compositing multiple figures

Buying a copy of Illustrator was pretty much the best thing that ever happened to my plotting.

How did illustrator help you?

The amount of basic formatting, changes, etc. that are trivial in a proper vector graphics editor but hard to cajole ggplot2, matplotlib or R's base graphics are...extensive.

I find google charts a nice balance between ease of use and good looking charts.

I find matplotlib far more flexible than ggplot. Which means 9 times out of 10 I can make the graph in about 5 lines of ggplot, and the more unusual takes about 50 lines of matplotlib.

Have you tried D3 for static plots? It looks very verbose, but offers a lot of fine control.

Is there a good introduction to using the object oriented interface to matplotlib?

I sometimes see it in examples and referenced in the documentation, but I can't really wrap my head around it.

Hmmm, I did a bit of searching and didn't find anything I particularly liked, I guess I'll add this to the list of posts I need to write. Nevertheless, I did find something. Check out these links:

http://matplotlib.org/users/artists.html

http://matplotlib.org/users/transforms_tutorial.html

http://matplotlib.org/users/path_tutorial.html

http://matplotlib.org/users/event_handling.html

Anatomy of MatPlotLib is informative and clear. But, alas, not the oop tutorial you're looking for. https://m.youtube.com/watch?v=MKucn8NtVeI&autoplay=1

Data to ink here is too low...

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