A Python Package To Quantitative Analysis Of Inequality
Project description
This package provides statistics to do a properly quantitative analysis of inequality. Among the estimators provided by this package you can find:
Atkinson Index
Gini Index
Kakwani Index
Lorenz curve
Variance
Mean
Kurtosis
Skewness
First-steps
Installation
Examples
Install
git clone https://github.com/mmngreco/IneqPy.git
cd IneqPy
pip install .
Examples
Some examples of how use this package: Data of example:
import pandas as pd
import numpy as np
import ineqpy
d
renta factor
0 -13004.12 1.0031
89900 141656.97 1.4145
179800 1400.38 4.4122
269700 415080.96 1.3295
359600 69165.22 1.3282
449500 9673.83 19.4605
539400 55057.72 1.2923
629300 -466.73 1.0050
719200 3431.86 2.2861
809100 423.24 1.1552
899000 0.00 1.0048
988900 -344.41 1.0028
1078800 56254.09 1.2752
1168700 60543.33 2.0159
1258600 2041.70 2.7381
1348500 581.38 7.9426
1438400 55646.05 1.2818
1528300 0.00 1.0281
1618200 69650.24 1.2315
1708100 -2770.88 1.0035
1798000 4088.63 1.1256
1887900 0.00 1.0251
1977800 10662.63 28.0409
2067700 3281.95 1.1670
Descriptive statistics
ineqpy.xbar(x=d.renta, weights=d.factor)
20444.700666031338
ineqpy.var(x=d.renta, weights=d.factor)
2982220948.7413292
x, w = d.renta.values, d.factor.values
Note that the standardized moment for order n, retrieve the value in that column:
n |
value |
---|---|
1 |
0 |
2 |
1 |
3 |
Skew |
4 |
Kurtosis |
A helpful table of interpretation of the moments
ineqpy.stdmoment(x, w, 1) # = 0
2.4624948200717338e-17
ineqpy.stdmoment(x, w, 2) # = 1
1.0
ineqpy.stdmoment(x, w, 3) # = skew
5.9965055750379426
ineqpy.skew(x, w)
5.9965055750379426
ineqpy.stdmoment(x, w, 4) # = kurtosis
42.319928851703004
ineqpy.kurt(x, w)
42.319928851703004
Inequality estimators
# pass a pandas.DataFrame and inputs as strings
ineqpy.gini(df=d, income='renta', weights='factor')
0.76739136365917116
# you can pass arrays too
ineqpy.gini(income=d.renta.values, weights=d.factor.values)
0.76739136365917116
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