

In [1]:

    
# load packages
import pandas as pd
import numpy as np
import ineqpy as inq
%matplotlib inline

First-steps



In [2]:

    
# load data
data = pd.read_csv('eusilc.csv', index_col=0).dropna()
svy = inq.api.Survey(data, weights='rb050')



In [3]:

    
svy.gini('eqincome')









    Out[3]:





0.26516133165507139



In [4]:

    
svy.atkinson('eqincome')









    Out[4]:





0.060002757905598392



In [5]:

    
svy.theil('eqincome')









    Out[5]:





0.12064816023130914



In [6]:

    
svy.mean('eqincome')









    Out[6]:





20431.292738646902



In [7]:

    
svy.percentile('eqincome')









    Out[7]:





18658.461904761898



In [8]:

    
svy.kurt('eqincome')









    Out[8]:





13.28551976978007



In [9]:

    
svy.skew('eqincome')









    Out[9]:





2.1150515104443115



In [10]:

    
svy.lorenz('eqincome').plot(figsize=(5,5))









    Out[10]:





<matplotlib.axes._subplots.AxesSubplot at 0x119fcab70>



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