Use unittest.mock.patch to replace a dependency where the code under test looks it up, then configure the replacement with return_value or side_effect. Keep the patch scoped to the test, and use autospec when you want the mock to enforce the real API’s attributes and call signature.
A minimal example: patch the name the code uses
Suppose service.py imports a function directly from another module:
# service.py
from gateway import fetch_record
def label_for(record_id):
record = fetch_record(record_id)
return record["label"].upper()Test it by patching service.fetch_record, not gateway.fetch_record. After the direct import, label_for resolves the name in the service module.
# test_service.py
from unittest import TestCase
from unittest.mock import patch
from service import label_for
class LabelTests(TestCase):
@patch("service.fetch_record", autospec=True)
def test_label_for_uppercases_label(self, fetch_record):
fetch_record.return_value = {"label": "sample"}
result = label_for("r-17")
self.assertEqual(result, "SAMPLE")
fetch_record.assert_called_once_with("r-17")patch temporarily substitutes the target for the duration of the decorated test and restores it afterward. It can also be used as a context manager for a narrower scope.
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Choose a mock that fits the dependency
Mock for calls and configured attributes
Mock records how it is called and creates attributes as they are accessed. It is suitable when the dependency is called or when you explicitly configure the attributes the code needs.
MagicMock for Python protocols
MagicMock is a Mock variant with common magic methods pre-created. Use it when the replacement must behave like an iterable, support indexing, or respond to len() through methods such as __iter__, __getitem__, or __len__.
Use a fake when it makes the behavior clearer
If a small deterministic object can express the dependency behavior directly, a handwritten fake may be easier to understand than a configurable mock. Choose based on which approach makes the test’s intent most legible.
Set return values, errors, and changing outcomes
Return a fixed value
Assign return_value when the dependency should produce one predictable result:
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fetch_record.return_value = {"label": "sample"}Raise an exception
Set side_effect to an exception class or instance to test an error path:
fetch_record.side_effect = TimeoutError("gateway timed out")Calculate a result from arguments
A function assigned to side_effect can vary the result based on the call arguments:
def response_for(record_id):
return {"label": f"record {record_id}"}
fetch_record.side_effect = response_forProvide successive results
An iterable assigned to side_effect supplies one outcome per call:
fetch_record.side_effect = [
{"label": "first"},
{"label": "second"},
]If the code calls the mock more times than there are iterable values, the next call raises StopIteration.
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Choose the right patching surface
patch("package.module.name")replaces a name addressed by its import path. Patch the name in the module where the system under test looks it up.patch.object(obj, "attribute")replaces an attribute on an object you already hold.patch.dict(mapping, ...)temporarily changes a mapping’s contents.patch.multiplecan replace several attributes in one operation.
Use a decorator when the replacement is needed throughout a test method, or a context manager when only a portion of the test should see the substitution. The original target is restored when the patch scope ends.
Make mocks stricter with autospec
A bare mock is permissive: it can allow misspelled attributes and calls that the real dependency would reject. Use autospec=True with patch, or create_autospec(), to constrain available attributes and check function signatures. Use spec_set=True when setting attributes absent from the specification should also fail.
from unittest.mock import create_autospec
mock_fetch = create_autospec(fetch_record)Autospec relies on introspection. It may not fit objects that create attributes dynamically or whose attribute access has side effects; in those cases, use a more suitable specification or a simpler fake.
Mock asynchronous dependencies
When patch creates a replacement for an asynchronous function and no replacement is supplied, it uses AsyncMock by default. Async mocking details can vary by Python version, so consult the documentation for the version installed in your project.
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Assert behavior and meaningful interactions
Start with the observable result of the code under test. Add call assertions when the interaction is itself part of the contract—for example, ensuring the correct identifier is passed or that a second request is avoided. Avoid making a test depend on incidental implementation details that can change without changing the behavior users rely on.
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The real dependency still runs
The patch may target the definition rather than the lookup. If the module under test used from gateway import fetch_record, patch its local name, such as service.fetch_record.
A later test sees a patched dependency
Check that patching is scoped with a decorator or context manager rather than leaving a manual replacement active beyond the test. Scoped patches restore the original target when they end.
The mock accepts an impossible call
A plain mock does not enforce the dependency’s full interface. Try autospec or spec_set=True, unless dynamic attributes or unsafe introspection make those unsuitable.
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A later call raises StopIteration
An iterable side_effect has been exhausted. Add another outcome or use a function side effect if the number or arguments of calls are variable.
Magic-method behavior is missing
If the code iterates, indexes, or takes the length of a replacement, use MagicMock or provide an appropriate fake that implements the protocol.
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Cost and reliability notes
unittest.mock is part of Python’s standard library, and mocking dependencies avoids making a unit test depend on a live external service. It does not prove that the real service works or that its integration is correct; keep tests of real boundaries separate from isolated unit tests. For the installed Python version’s exact API behavior, use its matching official documentation rather than relying on development-version details.
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