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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

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

Project details


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