Summary: The fit method of the distributions in SciPy does maximum likelihood fitting of parameters to data. This blog post gives a couple of nice quick examples.

To see what else is available, one would go here: http://docs.scipy.org/doc/scipy/reference/stats.html

I just want to point out that "we can observe that they are really similar" isn't likely to impress a statistician. It's possible to make a ballpark estimate of how close they actually are because there is a graph with marked axes, but the real derived parameters would be nice to know, and maybe even do the legwork and calculate the expected error for your sample size.

Thanks for the heads up on a "Rayleigh distribution" (from http://en.wikipedia.org/wiki/Rayleigh_distribution):

"One example where the Rayleigh distribution naturally arises is when wind speed is analyzed into its orthogonal 2-dimensional vector components. Assuming that the magnitude of each component is uncorrelated and normally distributed with equal variance, then the overall wind speed (vector magnitude) will be characterized by a Rayleigh distribution."

That is cool, I wonder if I will ever use it.

This is regularly done in R using the MASS library.