Solution
Tasks specified in a tasks.csv are scheduled to workers specified in json form as below:
{"Cho Chang": "engineer",... , "Susan Bones": "architect"}
A high level handler function task_scheduler.schedule takes as arguments a pandas.DataFrame
of tasks and a dict of workers data as above and manages how the tasks are assigned
to individual workers through a task quota assigning function quota_func. Different
quota functions can be specified with optional arguments resulting in a task quota dict as
below:
{"Cho Chang": {"engineer": 76}, ..., "Susan Bones": {"architect": 124}}
Unassigned tasks are then assigned by a simple assign_tasks function according to the
quota data. The pandas.DataFrame tasks is returned modified in place with all unassigned
tasks assigned.
Install
virtualenv -p python3 .venv
source .venv/bin/activate
python3 -m pip install .
Test
Unit tests.
python3 -m unittest discover -s task_scheduler/tests
Code style tests.
python3 -m pip install flake8
python3 -m flake8 task_scheduler/ --ignore E501 && echo "Code clean." || echo "Code dirty!"
Usage
As package
from task_scheduler.schedule import schedule, schedule_uniform_by_type
from task_scheduler.io import load_csv_data, load_json_data
if __name__ == '__main__':
tasks_path = 'task_scheduler/tests/data/tasks.csv'
workers_path = 'task_scheduler/tests/data/employees.txt'
tasks = load_csv_data(tasks_path)
workers = load_json_data(workers_path)
tasks = schedule(tasks, workers, quota_func=schedule_uniform_by_type)
tasks.to_csv('./tasks-assigned.csv')