Fitting a gamma distribution with (python) Scipy

Question

Can anyone help me out in fitting a gamma distribution in python? Well, I've got some data : X and Y coordinates, and I want to find the gamma parameters that fit this distribution... In the Scipy doc, it turns out that a fit method actually exists but I don't know how to use it :s.. First, in which format the argument "data" must be, and how can I provide the second argument (the parameters) since that's what I'm looking for?

Answer 1

Generate some gamma data:

import scipy.stats as stats     alpha = 5 loc = 100.5 beta = 22 data = stats.gamma.rvs(alpha, loc=loc, scale=beta, size=10000)     print(data) # [ 202.36035683  297.23906376  249.53831795 ...,  271.85204096  180.75026301 #   364.60240242] 

Here we fit the data to the gamma distribution:

fit_alpha, fit_loc, fit_beta=stats.gamma.fit(data) print(fit_alpha, fit_loc, fit_beta) # (5.0833692504230008, 100.08697963283467, 21.739518937816108)  print(alpha, loc, beta) # (5, 100.5, 22) 

Answer 2

I was unsatisfied with the ss.gamma.rvs-function as it can generate negative numbers, something the gamma-distribution is supposed not to have. So I fitted the sample through expected value = mean(data) and variance = var(data) (see wikipedia for details) and wrote a function that can yield random samples of a gamma distribution without scipy (which I found hard to install properly, on a sidenote):

import random import numpy  data = [6176, 11046, 670, 6146, 7945, 6864, 767, 7623, 7212, 9040, 3213, 6302, 10044, 10195, 9386, 7230, 4602, 6282, 8619, 7903, 6318, 13294, 6990, 5515, 9157]  # Fit gamma distribution through mean and average mean_of_distribution = numpy.mean(data) variance_of_distribution = numpy.var(data)  def gamma_random_sample(mean, variance, size):     """Yields a list of random numbers following a gamma distribution defined by mean and variance"""     g_alpha = mean*mean/variance     g_beta = mean/variance     for i in range(size):         yield random.gammavariate(g_alpha,1/g_beta)  # force integer values to get integer sample grs = [int(i) for i in gamma_random_sample(mean_of_distribution,variance_of_distribution,len(data))]  print("Original data: ", sorted(data)) print("Random sample: ", sorted(grs))  # Original data: [670, 767, 3213, 4602, 5515, 6146, 6176, 6282, 6302, 6318, 6864, 6990, 7212, 7230, 7623, 7903, 7945, 8619, 9040, 9157, 9386, 10044, 10195, 11046, 13294] # Random sample:  [1646, 2237, 3178, 3227, 3649, 4049, 4171, 5071, 5118, 5139, 5456, 6139, 6468, 6726, 6944, 7050, 7135, 7588, 7597, 7971, 10269, 10563, 12283, 12339, 13066]