Skip to main content

Inequality Coefficients:

This is small library with some implemented coefficients (or indices) intended to measure inequality or concentration of the values in a population.

Implemented coefficients

  • Gini Coefficient:
    • Ordinary
    • Corrected: Using a correction for small datasets based on Deltas, 2003.
  • Ratio top / rest

Installation

This library is hosted on PyPI, so installation is straightforward. The easiest way to install type this at the command line (Linux, Mac, or Windows):

pip install inequality_coefficients

This library also depends on numpy, but pip should take of that for you already.

Basic Usage

For the simplest, typical use cases, this tells you everything you need to know.:

import inequality_coefficients as ineq
data = array([1.7, 3.2 ...]) # data can be list of nums or numpy array
gini_coeff = ineq.gini_coeff(data)
ratio_top_rest = ineq.ratio_top10_rest(data)

Acknowledgements

Firstly, I was based on Felipe Ortega's wikixray code for implementing the gini coefficient, however, my code has changed so much and I'm using numpy as backend.

Anyway, I want to thank him for open sourcing that project.

Download files

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

Source Distribution

inequality_coefficients-1.1.0.linux-x86_64.tar.gz (4.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

inequality_coefficients-1.1.0-py3-none-any.whl (5.2 kB view details)

Uploaded Python 3

File details

Details for the file inequality_coefficients-1.1.0.linux-x86_64.tar.gz.

File metadata

  • Download URL: inequality_coefficients-1.1.0.linux-x86_64.tar.gz
  • Upload date:
  • Size: 4.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/40.6.3 requests-toolbelt/0.8.0 tqdm/4.19.6 CPython/3.5.2

File hashes

Hashes for inequality_coefficients-1.1.0.linux-x86_64.tar.gz
Algorithm Hash digest
SHA256 d957eda00373021a85536f0d89f41ae3ae6e9a665270820601b3abb8205b3e5b
MD5 baa80a83f83116a574c2d7a7e24941b3
BLAKE2b-256 3caecfe68ba0b8cc4b9a7471e4cc4217e6fe310712565f59e2cb5f66e511a3b3

See more details on using hashes here.

File details

Details for the file inequality_coefficients-1.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for inequality_coefficients-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c59ea66fc816bab457f15e8091f07d2ab3c9890a2adbdc22953978bd5c9ae0b9
MD5 6ae6e617737fcca9e8ff54c4eb18d1d6
BLAKE2b-256 c96d8dbdb5dd8cdc8a78b62a7f02bc817455130aa657ac75c272973aba301105

See more details on using hashes here.

Release history Release notifications | RSS feed

1.2.3

1 file

1.2.2

1 file

1.2.1

1 file

1.2.0

1 file

1.1.1

1 file

This release

1.1.0 This release

2 files

1.0.1

1 file

1.0.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page