Skip to main content

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

Project description

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


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')

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

File structure

  • concentrationMetrics/ 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/ as methods of the Index object.


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


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

  • compares the Atkinson function with the IC2/Atk function

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

concentrationMetrics-0.6.0.tar.gz (11.4 kB view hashes)

Uploaded source

Built Distribution

concentrationMetrics-0.6.0-py2.py3-none-any.whl (18.1 kB view hashes)

Uploaded py2 py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page