Monkey patching changes what an object, class, module, or name does while a Python program is running, without changing its original source definition. It is a technique, not a Python keyword or one particular library. A common, relatively safe use is a narrowly scoped test that substitutes a dependency—such as an environment variable or API call—and restores the original state afterward.
What is monkey patching in Python?
A monkey patch changes or replaces behavior at runtime. You might assign a different function to a module attribute, replace a class method, alter a mapping, or bind a name to a substitute. The term describes the general technique; it is not limited to tests and does not refer to one specific Python feature.
For example, suppose a module reads the current working directory through an imported function:
# mymodule.py
from os import getcwd
def current_folder():
return getcwd()
A test can replace the name that mymodule looks up while the test runs. That replacement is a monkey patch. It need not modify mymodule.py on disk.
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When is monkey patching useful?
Its strongest everyday use is controlling dependencies in tests. A test can avoid a real network request, database connection, filesystem operation, or machine-specific setting by substituting predictable behavior. That makes the test faster or more isolated and lets it check a particular condition reliably.
- Set or remove an environment variable for one test.
- Replace a function or property with a controlled implementation.
- Change a dictionary or module attribute temporarily.
- Set a test-specific working directory or import path.
- Use a mock when the test also needs to assert how a dependency was called.
For production code, a global runtime patch is usually a poor way to make a durable customization. If you control the code, pass the dependency in explicitly where practical. That makes the dependency visible and replaceable without silently changing shared state.
Patch the name the code actually looks up
Python names can refer to the same original object without remaining the same binding. In the example above, mymodule.getcwd is a name imported into that module. Replacing os.getcwd later does not necessarily replace the already-imported name that current_folder() calls.
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Patch the lookup site used by the code under test:
import mymodule
def fake_getcwd():
return "/test/folder"
# Replace the name current_folder() resolves.
mymodule.getcwd = fake_getcwd
assert mymodule.current_folder() == "/test/folder"
This direct assignment demonstrates the idea, but it leaves the changed binding in place until something restores it. For tests, use a scoped tool so cleanup happens even if an assertion fails.
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Use pytest monkeypatch for scoped test changes
pytest provides a fixture named monkeypatch. pytest automatically undoes its changes at test teardown. Its methods cover common operations including setting attributes, dictionary entries, environment variables, the working directory, and the import path.
Replace a function attribute
def test_current_folder(monkeypatch):
import mymodule
monkeypatch.setattr(mymodule, "getcwd", lambda: "/test/folder")
assert mymodule.current_folder() == "/test/folder"
The patch targets mymodule.getcwd, the name the function uses. By default, setattr expects the attribute to exist; use raising=False only when creating a previously absent attribute is intentional.
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Set an environment variable
import os
def read_mode():
return os.environ.get("APP_MODE", "default")
def test_read_mode(monkeypatch):
monkeypatch.setenv("APP_MODE", "test")
assert read_mode() == "test"
pytest restores the prior environment after the test, including removing the variable if it did not exist before. To remove a variable for a test, use monkeypatch.delenv("APP_MODE", raising=False); the flag avoids an error if the variable is already absent.
Other common pytest operations
def test_configuration(monkeypatch, tmp_path):
settings = {"mode": "production"}
monkeypatch.setitem(settings, "mode", "test")
assert settings["mode"] == "test"
monkeypatch.chdir(tmp_path)
assert Path.cwd() == tmp_path
For this example, add from pathlib import Path. pytest also supports deleting attributes or mapping entries, changing sys.path, and temporarily changing the current directory. Use the fixture’s corresponding method so teardown can undo the change.
Limit a risky patch to a smaller block
If a patch should last for only part of a test, use monkeypatch.context():
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def test_one_operation(monkeypatch):
import mymodule
with monkeypatch.context() as patch:
patch.setattr(mymodule, "getcwd", lambda: "/temporary")
assert mymodule.current_folder() == "/temporary"
# The original binding has been restored here.
Use unittest.mock.patch when you need interaction assertions
unittest.mock.patch temporarily rebinds a target, usually to a mock, and restores it when its context exits or its decorated function finishes. A mock records calls, so the test can assert that the code used the dependency as expected.
from unittest.mock import patch
import mymodule
def test_current_folder_calls_getcwd():
with patch("mymodule.getcwd", return_value="/test/folder") as getcwd:
assert mymodule.current_folder() == "/test/folder"
getcwd.assert_called_once_with()
The target string names the object to patch where it is looked up. Like pytest’s fixture, patch should generally target the consumer’s namespace, not automatically the module where a function was first defined.
pytest monkeypatch or unittest.mock.patch?
| Need | Useful choice | Why |
|---|---|---|
| Change an attribute, mapping, environment variable, import path, or working directory and undo it automatically | pytest monkeypatch |
The fixture has methods for these common test changes and undoes them at teardown. |
| Replace a target with a mock and assert calls or arguments | unittest.mock.patch |
It can create a mock that records how tested code interacted with it. |
| Keep an unusual or risky patch limited to a small block | monkeypatch.context() or a patch() context manager |
Both bound the change and restore it when the block exits. |
These tools are not competing definitions of monkey patching: both can temporarily alter bindings. Choose according to the operation and whether you need call assertions. If a mock’s flexibility might hide a changed interface, use spec or autospec where appropriate and retain integration tests for the way components connect.
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How to keep patches safe
- Target the consumer’s namespace. Follow the name resolution used by the function under test, especially when code imported a name directly.
- Keep scope narrow. Use pytest’s automatic teardown, a smaller
monkeypatch.context()block, or apatch()context manager. - Avoid patching builtins casually. Replacing builtins such as
openorcompilecan disrupt pytest itself or libraries used by the test runner. If it is unavoidable, isolate it in the smallest practical block. - Prefer explicit dependencies in code you own. Passing a dependency into a function or object makes it easier to substitute deliberately than changing a global binding.
- Do not let mocks conceal API drift. A permissive mock can let a test pass after the real interface changes. Add an appropriate spec and test important component connections.
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ScreenshotNeo API documentation
import requests
r = requests.get(
"https://api.screenshotneo.com/v1/shot",
params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"},
timeout=90,
)
open("shot.webp", "wb").write(r.content)
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Frequently Asked Questions
Does monkey patching require changing a Python source file?
No. A patch changes runtime behavior or a binding while the program runs; it does not require editing the original source definition.
Can pytest monkeypatch and unittest.mock.patch be used in the same project?
Yes. They serve overlapping purposes, and a project can use either or both where each fits the test.
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