How to check variable distribution in python?

Question

In uni testing, I need to verify that the distribution of array values ​​is uniform. For instance:

in array = [1, 0, 1, 0, 1, 1, 0, 0] there is a uniform distribution of values. Since there are four "1" and four "0"

For longer array lengths, the distribution is more “uniform”

How to prove that the array that is testing has a uniform distribution?

Note: an array is created using random.randint(min,max,len), fromnumpy.random

Answer 1

- . scipy.stats.kstest http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kstest.html#scipy.stats.kstest.

In [12]:

import scipy.stats as ss
import numpy as np
In [14]:

A=np.random.randint(0,10,100)
In [16]:

ss.kstest(A, ss.randint.cdf, args=(0,10))
#args is a tuple containing the extra parameter required by ss.randint.cdf, in this case, lower bound and upper bound
Out[16]:
(0.12, 0.10331653831438881)
#This a tuple of two values; KS test statistic, either D, D+ or D-. and p-value

P 0,1033, , A . P , , , (: ), , . KS , A . p 0,1033 , . P 0,05 0,01, null. p 0,05, , A .

scipy.stats.chisquare():

In [17]:

import scipy.stats as ss
import numpy as np
In [18]:

A=np.random.randint(0, 10, 100)
In [19]:

FRQ=(A==np.arange(10)[...,np.newaxis]).sum(axis=1)*1./A.size #generate the expect frequecy table.
In [20]:

ss.chisquare(FRQ) #If not specified, the default expected frequency is uniform across categories.
Out[20]:
(0.084000000000000019, 0.99999998822800984)

- chisquare, - P.