What are ways to speed up seaborns pairplot
Rather than parallelizing, you could downsample your DataFrame to say, 1000 rows to get a quick peek, if the speed bottleneck is indeed occurring there. 1000 points is enough to get a general idea of what's going on, usually. 
i.e. sns.pairplot(df.sample(1000)).
I have a dataframe with 250.000 rows but 140 columns and I'm trying to construct a pair plot. of the variables.
I know the number of subplots is huge, as well as the time it takes to do the plots. (I'm waiting for more than an hour on an i5 with 3,4 GHZ and 32 GB RAM).
Remebering that scikit learn allows to construct random forests in parallel, I was checking if this was possible also with seaborn.
However, I didn't find anything. The source code seems to call the matplotlib plot function for every single image.
Couldn't this be parallelised? If yes, what is a good way to start from here?