For a Gaussian distribution, the best fit is provided by the normal

distribution which has the same mean and stddev as your empirical data

(this is not true in general for other distributions). Once you have

the mean and stddev from the data, you can use normpdf to plot the

analytic density -- see for example

http://matplotlib.sourceforge.net/search.html?q=normpdf

For more powerful density fitting and sampling, see scipy.stats

functions, eg scipy.stats.norm.fit

JDH

## ···

On Mon, Nov 30, 2009 at 6:44 PM, William Carithers <wccarithers@...1352...> wrote:

I would like to fit a gaussian to a histogram and then overplot it. I can

write the code to do this but most plotting packages support such fitting.

However I can't find it for pyplot even after scanning documentation,

googling, etc. In fact, the only fitting functionality I could find was the

polynomial fitting for numpy that is layered underneath matplotlib, i.e.

Numpy.polyfit(...).

Does anyone know if/how this might be built into matplotlib?