API

ddt.add_test(cls, test_name, func, *args, **kwargs)

Add a test case to this class.

The test will be based on an existing function but will give it a new name.

ddt.data(*values)

Method decorator to add to your test methods.

Should be added to methods of instances of unittest.TestCase.

ddt.ddt(cls)

Class decorator for subclasses of unittest.TestCase.

Apply this decorator to the test case class, and then decorate test methods with @data.

For each method decorated with @data, this will effectively create as many methods as data items are passed as parameters to @data.

The names of the test methods follow the pattern original_test_name_{ordinal}_{data}. ordinal is the position of the data argument, starting with 1.

For data we use a string representation of the data value converted into a valid python identifier. If data.__name__ exists, we use that instead.

For each method decorated with @file_data('test_data.json'), the decorator will try to load the test_data.json file located relative to the python file containing the method that is decorated. It will, for each test_name key create as many methods in the list of values from the data key.

ddt.feed_data(func, new_name, *args, **kwargs)

This internal method decorator feeds the test data item to the test.

ddt.file_data(value)

Method decorator to add to your test methods.

Should be added to methods of instances of unittest.TestCase.

value should be a path relative to the directory of the file containing the decorated unittest.TestCase. The file should contain JSON encoded data, that can either be a list or a dict.

In case of a list, each value in the list will correspond to one test case, and the value will be concatenated to the test method name.

In case of a dict, keys will be used as suffixes to the name of the test case, and values will be fed as test data.

ddt.mk_test_name(name, value, index=0)

Generate a new name for a test case.

It will take the original test name and append an ordinal index and a string representation of the value, and convert the result into a valid python identifier by replacing extraneous characters with _.

If hash randomization is enabled (a feature available since 2.7.3/3.2.3 and enabled by default since 3.3) and a “non-trivial” value is passed this will omit the name argument by default. Set PYTHONHASHSEED to a fixed value before running tests in these cases to get the names back consistently or use the __name__ attribute on data values.

A “trivial” value is a plain scalar, or a tuple or list consisting only of trivial values.

ddt.process_file_data(cls, name, func, file_attr)

Process the parameter in the file_data decorator.

ddt.unpack(func)

Method decorator to add unpack feature.