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The next-generation for loop and work tracker

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Programs often need to process a list of “items” of one sort or another. That’s what loops are for, right?

The problem: With the exception of the current loop value or loop index, programming languages rarely help keep track of how processing is going. How many items have been successfully processed? How far along the total job are we now? Which items had problems that might need to be looked at later? Developers are left to manage these almost ubiquitous situations pretty much on their own.

In other words: Bookkeeping tasks required in essentially every program are the developers’ responsibility. Not so high-level after all, huh?

“Here are some loops; have fun!” leads to the use of many ad hoc containers, counters, and status flags. Developers “reinvent the wheel” of task bookkeeping for every new program, needlessly introducing complexity and errors, and pointlessly consuming effort.

chores fights this, reducing effort and errors by providing a simple, repeatable pattern for processing items and tracking their status. It is an example of “cross-cutting”–dealing with several apparently different concerns in a concerted way because they are, in fact, connected.

The documentation can be found at Read the Docs.


from chores import Chores

chores = Chores('Jones able baker charlie 8348 Smith Brown Davis'.split())

for chore_id in chores:
    status = 'name' if chore_id.istitle() else 'other'
    chores.mark(chore_id, status)

print chores.count('name'), "names,", \
      chores.count(exclude='name'), "others"


4 names, 4 others

Or if you decide you actually want more information, change just the output statements:

print chores.count('name'), "names:", chores.marked('name')
print chores.count(exclude='name'), "others:", chores.marked(exclude='name')

Now you get:

4 names: ['Jones', 'Smith', 'Brown', 'Davis']
4 others: ['able', 'baker', 'charlie', '8348']


Many programs use lists, dictionaries, sets, counters, and status flags to track the status of items being processed, or the health of the overall process. chores might not seem a great advance on this at first, since the same kind of initialization and looping is present.

But it gets more interesting at the end of the processing loop, where the summary or report of what was processed, the disposition of each item worked on, what items yielded errors or other conditions, and what special cases were handled is produced.

In the examples above, we never had to keep a counter of how many names were found, or how many non-names. When we decided we wanted to change the output from summary counts to a full listing, we didn’t have go back and collect different information. We just differently displayed information already at at hand. Also note that the order of the results is nicely maintained. When we’re reviewing reports about “what transpired,” we don’t have to work very hard to correlate the results with the inputs; unlike when using dict and set structures, items are reported on in the same order they arrived.

Typically a developer will start with only a little thought about various dispositions for each item being processed. Over time, she’ll start to realize: “I need to count those cases, so I can report on them!” Or, “I kept an error counter, but I really should have been keeping a list of which items broke, because I now have to tell the user not just how many went wrong, but which ones in particular.” Or “I need to keep track of which ones failed the main processing so that I can do more intensive processing on just those special cases.” Then she’ll go back and add counters, collection lists, and so on–adding a fair amount of ad hoc code that must be built, tested, and debugged.

This is especially tricking for data that needs to move through multiple stages or phases of work. The developer then has to add structures to communicate from earlier processing steps to later ones.

With chores, there’s no need for such custom work. It takes over th tracking which items led to which outcomes. It’s always ready to render quality information, either for reporting or for managing subsequent processing stages. Bookkeeping information is readily available in a tidy, logical format, with no additional development effort.

And if the developer switches later from printing a count of errors to listing out or retrying the specific items that led to error cases, all that’s required is a simple change from s.count('error') (a statistical count) to s.marked('error') (a list of only those items that led to errors). Or get both. With no additional work, that information is at the ready.

Additional information can be found at Read the Docs.


  • I’ve successfully used chores in my own projects, and it has a real test suite. But realistically it should be considered “early beta” code. It’s explicitly part of experiment to up-level development tasks, so its API and mode of use will evolve.

  • Automated multi-version testing managed with the wonderful pytest and tox. Successfully packaged for, and tested against, all late-model versions of Python: 2.6, 2.7, 3.2, 3.3, and 3.4, as well as PyPy 2.6.0 (based on 2.7.9) and PyPy3 2.4.0 (based on 3.2.5). Should run fine on Python 3.5, though py.test is broken on its pre-release iterations.

  • The author, Jonathan Eunice or @jeunice on Twitter welcomes your comments and suggestions.


To install or upgrade to the latest version:

pip install -U chores

To easy_install under a specific Python version (3.3 in this example):

python3.3 -m easy_install --upgrade chores

(You may need to prefix these with sudo command to authorize installation. In environments without super-user privileges, you may want to use pip’s --user option, to install only for a single user, rather than system-wide.)

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