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 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
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.
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.
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: