hillmaker 0.4.2

Occupancy analysis tool for systems having time of day and day of week effects

hillmaker

hillmaker is a Python package that computes time of day and day of week specific occupancy statistics from transaction data containing arrival and departure timestamps. Typical use is for capacity planning problems in places like hospital emergency departments, surgical recovery rooms or any system in which entities arrive, occupy capacity for some amount of time, and then depart. It gets its name from the hill-like nature of plots based on temporal occupancy statistics.

• Takes a pandas DataFrame or csv file as the input data type
• Functions for computing arrival, departure and occupancy summary statistics by time of day, day of week, and entity category based on a pandas DataFrame containing one record per visit.
• Functions for computing arrival, departure and occupancy for each datetime bin in the analysis period, by zero or one category fields.
• Select any time bin size (minutes) that divides evenly into a day.
• Output statistics includes sample size, mean, min, max, standard deviation, coefficient of variation, standard error, skew, kurtosis, and percentiles.
• Output CSV files are written by default but can be supressed.
• Optionally capture outputs as a dictionary of pandas DataFrames for further post-processing (e.g. plot creation).
• Requires Python 3.7+ and pandas 1.2.0+
• MIT License

Where to get it

• Source code: http://github.com/misken/hillmaker
• Binary and source on PyPI: https://pypi.python.org/pypi/hillmaker
• Documentation: Coming soon

Quick Start

A companion repo, https://github.com/misken/hillmaker-examples/ contains Jupyter notebooks and Python scripts illustrating the use of hillmaker.

You can see an html version of the basic_usage_shortstay_unit notebook here.

The source and a binary wheel are available from PyPi. You can install using pip:

pip install hillmaker

Learn more about hillmaker

Hillmaker has been around in various forms for over 30 years. A few blog posts describing how it works are available though many things about the API as well as the computational guts of hillmaker have changed with this recent version.

• Computing occupancy statistics with Python - 1 of 3
• Computing occupancy statistics with Python - 1 of 3
• Plotting occupancy statistics with Python - 3 of 3
• Analyzing bike share usage
• Using hillmaker from R with reticulate
• Numpy speeds up hillmaker dramatically

I published a paper a long time ago regarding the use of hillmaker in practice.