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
For more powerful density fitting and sampling, see scipy.stats
functions, eg scipy.stats.norm.fit
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.
Does anyone know if/how this might be built into matplotlib?