Python assert
last modified January 29, 2024
In this article we show how to work with assertions in Python. We define assertions, explain the difference between assertions and exceptions and show their relation to unit tests.
Assertions
Assertions are internal self-checks in the code; they are suited for developers to find bugs as soon as possible. The purpose of the assertions is to detect problems early in the program, where the cause is clear, and avoid them to pop up as a side-effect of some other operation.
Assertions should not be used for data processing or validation, because they are turned off in production. Some programming languages, such as Go, do not use assertions. Other languages have assert statements but they are used less often (Java).
Assertions vs exceptions
Assertions are boolean expressions that must be true for the code to be correct. Assertion failures result in code correction. Exceptions are indications about non-typical situations that can occur at runtime. Exceptions may not result in fixing the code. For instance, if the Internet connection is down, or the file permissions are not sufficient, the user has to deal with them. Assertions should not be used for handling runtime errors.
Design by contract
Design by contract, or programming by contract, is a software development practice in which developers define formal, precise and verifiable interface specifications for software components. The specifications are divided into preconditions, postconditions and invariants.
A precondition is a predicate that must always be true prior to the execution of a piece of code. A poscondition is a predicate that must always be true after the execution of a piece of code. An invariant is a property which remains unchanged after all operations.
The design by contract approach was developed in the 1980s and was inspired by business contracts.
Assertions and unit tests
Assertions and unit tests overlap in some areas, but there are some differences. Assertions are assumptions about the internal state of the program, while unit tests examine the external behavior of a module. Unit tests work with test data, while assertions do not.
Assert statements are used in unit tests. Classic assertions are assumptions about something which is true, while in unit tests, developers often test for false conditions.
Python assert
The assert statement is a convenient way to insert debugging
assertions into a Python program. The assert statements are removed with
the -O, -OO options and the PYTHONOPTIMIZE
variable.
The Python assert statement consists of a boolean condition and an
optional error message, separated by comma.
Python assert examples
The following examples show the usage of the Python assert statement.
#!/usr/bin/python
def info(age, name):
assert age >= 18 and age <= 65, 'age value must be between values 18 and 65'
print(f'{name} is {age} years old')
age = 84
name = 'Peter'
info(age, name)
In the example, we have a precondition about the age value.
$ ./precondition.py
Traceback (most recent call last):
File "./precondition.py", line 14, in <module>
info(age, name)
File "./precondition.py", line 6, in info
assert age >= 18 and age <= 65, 'age value must be between values 18 and 65'
AssertionError: age value must be between values 18 and 65
The program fails because the precondition was not valid.
#!/usr/bin/python
def do_discount(price, discount):
discounted_price = price - discount * price
assert 0 < discounted_price < price, 'discounted price must be greater than zero and lower than original price'
return discounted_price
price = 120
discount = 0.2
dis_price = do_discount(price, discount)
print(dis_price)
The second example has a postcondition about a discounted price. It has to be greater than zero and lower than the original price.
Python assertions in unit tests
Python has several popular unit test frameworks, including pytest,
unittest, and nose. While pytest and
nose use the assert statement, unittest
favours functions such as assertEqual and assertLess.
Pytest example
The following example shows the usage of the pytest framework.
def max(values):
_max = values[0]
for val in values:
if val > _max:
_max = val
return _max
def min(values):
_min = values[0]
for val in values:
if val < _min:
_min = val
return _min
We have two algorithms in a module.
#!/usr/bin/python
import algo
def test_min():
values = (2, 3, 1, 4, 6)
val = algo.min(values)
assert val == 1
def test_max():
values = (2, 3, 1, 4, 6)
val = algo.max(values)
assert val == 6
We pass test data to the tested algo functions.
We check the expected values with the assert statement.
$ pytest-3 min_max_test.py ============================================================= test session starts ============================================================= platform linux -- Python 3.7.6, pytest-4.6.9, py-1.8.1, pluggy-0.13.0 rootdir: /root/Documents/prog/python/assert collected 2 items min_max_test.py ..
We run the tests.
Unittest example
Now we test the same module with unittest.
#!/usr/bin/python
import unittest
import algo
class SimpleTest(unittest.TestCase):
def test_min(self):
values = (2, 3, 1, 4, 6)
val = algo.min(values)
self.assertEqual(val, 1)
def test_max(self):
values = (2, 3, 1, 4, 6)
val = algo.max(values)
self.assertEqual(val, 6)
if __name__ == '__main__':
unittest.main()
In the example, we use the assertEqual assertions.
$ ./min_max_test.py .. ---------------------------------------------------------------------- Ran 2 tests in 0.000s OK
We run the tests.
Source
Python assert statement - language reference
In this article we have worked with Python assert statement and explained assertions in general.
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