Fitting a pareto distribution with (python) Scipy

I have a data set that I know has a Pareto distribution. Can someone point me to how to fit this data set in Scipy? I got the below code to run but I have no idea what is being returned to me (a,b,c). Also, after obtaining a,b,c, how do I calculate the variance using them?

import sc开发者_运维百科ipy.stats as ss 
import scipy as sp

a,b,c=ss.pareto.fit(data)

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Be very careful fitting power laws!! Many reported power laws are actually badly fitted by a power law. See Clauset et al. for all the details (also on arxiv if you don't have access to the journal). They have a companion website to the article which now links to a Python implementation. Don't know if it uses Scipy because I used their R implementation when I last used it.

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Here's a quickly written version, taking some hints from the Reference page that Rupert gave. This is currently work in progress in scipy and statsmodels and requires MLE with some fixed or frozen parameters, which is only available in the trunk versions. No standard errors on the parameter estimates or other result statistics are available yet.

'''estimating pareto with 3 parameters (shape, loc, scale) with nested
minimization, MLE inside minimizing Kolmogorov-Smirnov statistic

running some examples looks good
Author: josef-pktd
'''

import numpy as np
from scipy import stats, optimize
#the following adds my frozen fit method to the distributions
#scipy trunk also has a fit method with some parameters fixed.
import scikits.statsmodels.sandbox.stats.distributions_patch

true = (0.5, 10, 1.)   # try different values
shape, loc, scale = true
rvs = stats.pareto.rvs(shape, loc=loc, scale=scale, size=1000)

rvsmin = rvs.min() #for starting value to fmin


def pareto_ks(loc, rvs):
    est = stats.pareto.fit_fr(rvs, 1., frozen=[np.nan, loc, np.nan])
    args = (est[0], loc, est[1])
    return stats.kstest(rvs,'pareto',args)[0]

locest = optimize.fmin(pareto_ks, rvsmin*0.7, (rvs,))
est = stats.pareto.fit_fr(rvs, 1., frozen=[np.nan, locest, np.nan])
args = (est[0], locest[0], est[1])
print 'estimate'
print args
print 'kstest'
print stats.kstest(rvs,'pareto',args)
print 'estimation error', args - np.array(true)

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Let's say you data is formated like this

import openturns as ot
data = [
    [2.7018013],
    [8.53280352],
    [1.15643882],
    [1.03359467],
    [1.53152735],
    [32.70434285],
    [12.60709624],
    [2.012235],
    [1.06747063],
    [1.41394096],
]
sample = ot.Sample([[v] for v in data])

You can easily fit a Pareto distribution using ParetoFactory of OpenTURNS library:

distribution = ot.ParetoFactory().build(sample)

You can of course print it:

print(distribution)
>>> Pareto(beta = 0.00317985, alpha=0.147365, gamma=1.0283)

or plot its PDF:

from openturns.viewer import View

pdf_graph = distribution.drawPDF()
pdf_graph.setTitle(str(distribution))
View(pdf_graph, add_legend=False)

More details on the ParetoFactory are provided in the documentation.

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Before passing the data to build() function in OPENTURNS, make sure to convert it this way:

data = [[i] for i in data]

Because Sample() function may return an error.

FYI @Tropilio