heat_helper 0.3.0

A python library to help users of the Higher Education Access Tracker (HEAT) process their data.

HEAT Helper

heat_helper is a Python utility library designed to streamline the cleaning and preparation of student data for use with the Higher Education Access Tracker (HEAT). It's available on PyPI here.

Preparing CSV or Excel files for HEAT often involves repetitive tasks like cleaning and formatting of names, year groups, and postcodes. This package automates these common data-cleaning tasks to ensure your imports are valid and consistent. It also provides functions to help you match students to your existing HEAT records and get their IDs to support you in reducing duplicate records uploaded to the database and with registering students to activities.

Features

heat_helper provides functions to support many common tasks:

• Text Cleaning: simple functions to normalise names (including removing numbers, converting diacritics to plain text, removing punctuation except for hyphens and apostrophes, cleaning extra white spaces, and casing), postcodes, and year groups.
• Working with Dates: reverse day/month in a date, calculate a year group from date of birth, or calculate a date of birth range from year group
• Student Matching: exact and fuzzy match students from external sources (e.g. registers) to your HEAT Students export to get their ID numbers for activity linking.
• Data Validation: derive the expected date-of-birth range for a year group so you can check students against it, and normalise postcodes to the UK format. Optionally install pydantic and generate error reports for your data.
• Bulk processing: get lists of Excel files in folders so you can process lots of files at once.
• Duplicates: find potential duplicates in a dataset based on name, date of birth and postcode.
• Compatibility: built for use with pandas for handling your data.

What can I use heat_helper for?

Common use cases for heat_helper include:

• Cleaning new data to be uploaded to the HEAT database
• Checking if 'new' students already have records in HEAT
• Checking which fields need updating if students already have records in HEAT
• Matching students from your activities to their records on HEAT, so you can use their IDs to bulk register student records to activity records within HEAT

Installation

You can install heat_helper from PyPI or directly from this repository. I recommend uv for easy package management.

with pip

You can install heat_helper with pip by typing the following into your terminal:

pip install heat_helper

or from this repo:

pip install git+https://github.com/hammezii/heat-helper.git

with uv

If have already initialised a project with uv, you can add heat_helper as a dependency:

uv add heat_helper

or from this repo:

uv add git+https://github.com/hammezii/heat-helper.git

Dependencies

heat_helper has the following dependencies which will also be installed:

• pandas - for DataFrames and dealing with spreadsheet data
• rapidfuzz - for fuzzy matching
• openpyxl - for processing Excel files
• Optional: pydantic - for validating your data and producing error reports

This means that in a new environment you can simply install heat_helper and have a complete setup for processing and manipulating CSV or Excel files. See documentation for information about optional dependencies and how to install them.

Documentation

You can access the documentation here.

The documentation is a work in progress and will be updated with more examples of how to use heat_helper.

Contributing

You are welcome to contribute to heat_helper. Please either submit an issue or a pull request.