# Fitting distribution in histogram using Python

I was surprised that I couldn't found this piece of code somewhere.

What I basically wanted was to fit some theoretical distribution to my graph. If you are lucky, you should see something like this:

```
from scipy import stats
import numpy as np
import matplotlib.pylab as plt
# create some normal random noisy data
ser = 50*np.random.rand() * np.random.normal(10, 10, 100) + 20
# plot normed histogram
plt.hist(ser, normed=True)
# find minimum and maximum of xticks, so we know
# where we should compute theoretical distribution
xt = plt.xticks()[0]
xmin, xmax = min(xt), max(xt)
lnspc = np.linspace(xmin, xmax, len(ser))
# lets try the normal distribution first
m, s = stats.norm.fit(ser) # get mean and standard deviation
pdf_g = stats.norm.pdf(lnspc, m, s) # now get theoretical values in our interval
plt.plot(lnspc, pdf_g, label="Norm") # plot it
# exactly same as above
ag,bg,cg = stats.gamma.fit(ser)
pdf_gamma = stats.gamma.pdf(lnspc, ag, bg,cg)
plt.plot(lnspc, pdf_gamma, label="Gamma")
# guess what :)
ab,bb,cb,db = stats.beta.fit(ser)
pdf_beta = stats.beta.pdf(lnspc, ab, bb,cb, db)
plt.plot(lnspc, pdf_beta, label="Beta")
plt.show()
```