concentrationMetrics 0.6.0

A python library for the computation of various concentration, inequality and diversity indices

The concentrationMetrics Library

concentrationMetrics is an MIT-licensed Python package aimed at becoming a reference implementation of indexes used in the analysis of concentration, inequality and diversity measures.

Overview of Main Features

• exhaustive collection of concentration and inequality indexes and metrics

• supports file input/output in both json and csv formats

• detailed mathematical documentation

• computation of confidence intervals via bootstraping

• visualization using matplotlib

Usage

Using the library is quite straightforward. For example calculating the Gini and the HHI indexes on randomly generated portfolio data:

import numpy as np
from concentrationMetrics import Index

# Create some data
a = 1.7
portfolio = np.random.zipf(a, 100)

# Calculate the desired indexes
indices = Index()
hhi = indices.hhi(portfolio)
gini = indices.gini(portfolio)

# Compute the confidence interval around the HHI index value
lower_bound, val, upper_bound = indices.compute(portfolio, index='hhi', ci=0.95, samples=10000)

# Calculate indexes on a dataframe
BCI = pd.read_json(dataset_path + "BCI.json")
y = BCI.values
myGroupIndex = cm.Index(data=y, index='simpson')
myGroupIndex.print(6)

Many more examples and tests are available in the examples and test directories.

File structure

• concentrationMetrics/model.py The library module

• datasets/ Contains a variety of datasets useful for getting started with the ConcentrationMetrics

• examples/ Variety of usage examples

• docs/ Sphinx generated documentation

• tests/ testing the implementation

All indexes are currently implemented in concentrationMetrics/model.py as methods of the Index object.

Dependencies

The main dependencies are the standard python datascience stack (numpy, pandas etc) and networkx. The full list is in requirements.txt

• matplotlib

• numpy

• pandas

• scipy

• networkx

Datasets

Version 0.5.0 includes datasets used primarily for testing and comparison with R implementations:

• hhbudget.csv

• Ilocos.csv

• BCI.json

Comparison with R packages

• atkinson_test.py compares the Atkinson function with the IC2/Atk function