Skip to main content

A series of classes to match mentors and mentees

Project description

Mentor Match

This is a package to help match mentees and mentors. It's specifically designed for a volunteer programme I support, but you could probably extend or alter it to suit whatever you're doing.

It uses this implementation of Munkres to find the most effective pairings. The Munkres algorithm works on a grid of scores.

Scoring

Full details of how the matches are calculated can be read in the code itself. Customisable configurations are on the roadmap but are not planned for any upcoming releases.

Installation

You can install this project with python -m pip install mentor-match

Use

To use this library, first install it (see above). You may need to munge your data for the system to be happy with it. Use the attached CSV files as guides for your mentor and mentee data, then put them together in the same folder.

The software will run three matching exercises. Participants who don't match in the first round are more heavily weighted in the next round. The aim is to improve the experience for everyone.

The weightings are as follows:

property First run Second run Third run
profession 4 4 0
grade 3 3 3
unmatched bonus 0 50 100

Here is a snippet that outlines a minimal use in a Python project:

from matching import process

data_folder = "Documents/mentoring-data"
mentors, mentees = process.conduct_matching_from_file(data_folder)

output_folder = data_folder / "output"
process.create_mailing_list(mentors, output_folder)
process.create_mailing_list(mentees, output_folder)

This creates a mailing list according to a set template, ready for processing by your favourite/enterprise mandated email solution

Alternatively, you can run this software from the command line as follows

python -m matching /path/to/participant/data

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

mentor-match-4.0.1.tar.gz (8.4 kB view hashes)

Uploaded source

Built Distribution

mentor_match-4.0.1-py3-none-any.whl (9.4 kB view hashes)

Uploaded 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