Tournament Data Importers

This page describes how to write your own tournament data importer. It is aimed at an audience that is familiar with programming in Python, and may be willing to get their head around the Django model if necessary.

The tournament data importer is the class that imports data from one or more files (usually CSV files) into the database. A base class BaseTournamentDataImporter is in importer/ An example of a data importer is in importer/


This page is incomplete. If you’re finding this information insufficient, please contact Chuan-Zheng using the contact details in the Authors & Acknowledgements section.

Why write your own?

While Tabbycat has standard import formats, you might find that none of them fit the data that you need to import.

It’s not possible to devise a single, universally-convenient import file format. Tabbycat supports way too many permutations of configurations for this to be workable. Instead, we provide the ones that have been useful before and are therefore likely to be useful again—but if your tournament has different needs, you might decide that it’s easier to write an importer to conform to you, rather than conform to the importer.

A base importer class abstracts away most of the nitty-gritty of parsing files, allowing new importers to focus on their interpretation with as little code as possible.

To allow new importers to be written with as little code as possible, most of the work is abstracted to the base class. The flipside of this abstraction is that it induces a learning curve.

Basic workflow

  1. Choose a name. We name importers after items of clothing in alphabetical order (starting at ‘Anorak’).
  2. Write a subclass of BaseTournamentDataImporter.
  3. Write the front-end interface. This will probably be a Django management command.

A basic example

It’s easiest to start with an example. Here’s a basic importer with just one import method, which imports adjudicators.

from .base import BaseTournamentDataImporter, make_lookup, make_interpreter
from participants.models import Person, Adjudicator

class ExampleTournamentDataImporter(BaseTournamentDataImporter):

    lookup_gender = make_lookup("gender", {
        ("male", "m"): Person.GENDER_MALE,
        ("female", "f"): Person.GENDER_FEMALE,
        ("other", "o"): Person.GENDER_OTHER,

    def import_adjudicators(self, f):
        """Imports adjudicators. `f` is a file object."""
        interpreter = make_interpreter(
        counts, errors = self._import(f, Adjudicator, interpreter)
        return counts, errors

Let’s break this down. The method import_adjudicators() takes a single argument, a file object representing the CSV file. Most of the work is passed off to self._import(). This helper method is defined in BaseTournamentDataImporter and is where most of the intelligence lies.

When called, self._import(f, model, interpreter) does the following:

  1. It reads the CSV file using a csv.DictReader. A DictReader iterates through the CSV file, yielding a dict for each line, whose keys are given by the column header names in the first row of the file.
  2. On each line:
  1. It passes the dict given by the DictReader to interpreter. The interpreter modifies the dict (or creates a new one) to prepare it for the model constructor, and returns it.
  2. The dict returned by interpreter is then passed as keyword arguments to the model constructor.

So in very simplified form, self._import(f, model, interpreter) does this:

def _import(self, f, model, interpreter):
    reader = csv.DictReader(f)
    for line in reader:
        kwargs = interpreter(line)
        inst = model(**kwargs)

(There’s a lot more to it than that, but that’s the basic idea.)


A consequence of relying on column headers to identify fields is that the header names in CSV files must match model field names exactly, unless they are deleted by the interpreter using the DELETE keyword (see below).


The main task of an importer, then, is to provide interpreters so that self._import knows how to interpret the data in a CSV file. An interpreter takes a dict and returns a dict. For example:

def interpreter(line):
    line['institution'] = Institution.objects.lookup(line['institution'])
    line['gender'] = self.lookup_gender(line['gender'])
    line['tournament'] = self.tournament
    return line

This interpreter does the following:

  • Replaces line['institution'] with an Institution object, by looking up the original value by name.
  • Replaces line['gender'] with a Person.GENDER_* constant. We’ll come back to how this works later.
  • Adds a new line['tournament'] entry to the dict, being the Tournament object represented by self.tournament, the tournament that was passed to the importer’s constructor.
  • Leaves all other entries in the dict unchanged.

This looks simple enough, but it’s very robust. What if a cell in the CSV file is blank, or what if the file omits a column? (For example, some tournaments might not collect information about participant gender, so Tabbycat doesn’t require it.) We could deal with these scenarios on a case-by-case basis, but that’s cumbersome.

Instead, we provide a make_interpreter method that returns an interpreter method which, in turn, takes care of all these details. This way, all you have to do is provide the functions that transform fields. So the following is equivalent to the above, but better:

interpreter = make_interpreter(

Notice that we provided a callable in two of these keyword arguments, and a (non-callable) Tournament object to the third. make_interpreter is smart enough to tell the difference, and treat them differently. What it does with each field depends on (a) whether a value exists in the CSV file and (b) what transformation function was provided, as summarised in the following table:

Value in CSV file Transformation Action
  provided and not callable populate model field with interpreter value
does not exist or blank callable or not provided do not pass to model constructor
exists and not blank callable call interpreter on column value, pass result to model constructor
exists and not blank not provided pass column value directly to model constructor


  • If a transformation isn’t an existing method, you might find lambda functions useful. For example: lambda x: Speaker.objects.get(name=x).
  • You shouldn’t check for mandatory fields. If a mandatory field is omitted, the model constructor will throw an error, and self._import() will catch the error and pass a useful message on to the caller. On the other hand, if it’s an optional field in the model, it should optional in the importer, too. Similarly, interpreters generally shouldn’t specify defaults; these should be left to model definitions.
  • You don’t need to include interpreter transformations for things like converting strings to integers, floats or booleans. Django converts strings to appropriate values when it instantiates models. So, for example, adding test_score=float to the above interpreter would be redundant.

More complicated interpreters

If you have a column in the CSV file that shouldn’t be passed to the model constructor, you can tell the interpreter to remove it by using the special DELETE argument:

interpreter = make_interpreter(
    DELETE=['unwanted_column_1', 'unwanted_column_2']

The make_interpreter can only deal with modifications where each field is modified separately of the others (or not at all). If you want to combine information from multiple fields, you need to write your interpreter the long way (perhaps calling a function returned by make_interpreter to do some of the work).

On the other hand, if you don’t need to do any transformations involving some sort of object or constant lookup, then you can just omit the interpreter argument of self._lookup(), and it’ll just leave the fields as-is.

Lookup functions

In the above example, we used a function self.lookup_gender to convert from the text in the CSV file to a Person.GENDER_* constant. To make this easier, the importer provides a convenience function to define such lookup functions. Let’s look at the relevant lines again:

lookup_gender = make_lookup("gender", {
    ("male", "m"): Person.GENDER_MALE,
    ("female", "f"): Person.GENDER_FEMALE,
    ("other", "o"): Person.GENDER_OTHER,

This should be a member of your subclass, in our case, ExampleTournamentDataImporter. It generates a function that looks something like:

def lookup_gender(val):
    if val in ("male", "m"):
        return Person.GENDER_MALE
    elif val in ("female", "m"):
        return Person.GENDER_FEMALE
    elif val in ("other", "o"):
        return Person.GENDER_OTHER
        raise ValueError("Unrecognised value for gender: %s" % val)

The make_lookup function takes two arguments. The first is a text description of what it’s looking up; this is used for the error message if the value in the CSV file isn’t recognised. The second is a dict mapping tuples of valid strings to constants.

Debugging output

The BaseTournamentDataImporter constructor accepts a loglevel argument:

importer = MyTournamentDataImporter(tournament, loglevel=logging.DEBUG)

If loglevel is set to logging.DEBUG, the importer will print information about every instance it creates.

You can also pass in a logger for it to use (instead of the default one) with the logger argument.