Pytest Fixtures and standard functions serve as effective tools for organizing test code and minimizing redundancy. The explanation given by George Udosen highlights this point.

However, the original poster inquired about the variances between a pytest.fixture and a regular Python function, revealing several distinctions:

Scope of Pytest Fixtures

By default, a pytest.fixture runs for every test function that utilizes it. In instances where the fixture setup is resource-intensive or time-consuming, such as setting up a database, the scope of a pytest.fixture can be expanded to encompass multiple tests within a module or even across an entire pytest run. The following example demonstrates how a module-scoped fixture can optimize test execution:

[Module-Scoped Fixture Example]

While varied scoping can be achieved with traditional function calls, the convenience of scoped fixtures is highly preferable.

Dependency Injection with Pytest Fixtures

Pytest registers all fixtures during the test collection phase. When a test function necessitates an argument corresponding to a registered fixture name, Pytest ensures that the fixture is instantiated and provided to the test function—a form of dependency injection. Contrastingly, referencing any pytest.fixture by name without explicit importation is an advantage over regular functions. For instance, the built-in tmp_path fixture simplifies working with temporary files in tests. This excerpt from the Pytest documentation exemplifies the streamlined process:

[tmp_path Fixture Example]

The seamless usage of fixtures without prior imports is notably convenient for users.

Furthermore, applying a fixture to a test function without direct request is feasible (see Autouse fixtures).

Parametrization of Pytest Fixtures

Similar to test parametrization, fixture parametrization allows for specifying multiple variations of a fixture, each with distinct return values. Consequently, every test employing that fixture will run multiple times, once for each variant. To illustrate, testing HTTP and HTTPS URLs separately is achievable through parametrizing the fixture, as shown in the subsequent example:

[Parametrized Fixture Example]

This approach enables executing each referencing test with every version of the fixture:

[Test Results]

In Summary

Pytest fixtures offer numerous advantages over conventional function calls. It is advisable to prioritize Pytest fixtures over regular functions, unless there is a necessity to directly invoke the fixture, which is not recommended and may result in failure.

Utilizing a fixture serves the following purposes:

• It provides a specific function that can be reused multiple times, simplifying testing processes.

• It executes actions before commencing and after completing a test using yield.

For instance, consider the functions test_get_username() and test_set_check_password() in Django as detailed below:

import pytest

from django.contrib.auth.models import User

@pytest.mark.django_db
def test_get_username():
    user = User.objects.create_user("test-user")
    user.username = "John"
    assert user.get_username() == "John"

@pytest.mark.django_db
def test_set_password_and_check_password():
    user = User.objects.create_user("test-user")
    user.set_password("new-password")
    assert user.check_password("new-password") == True

In this scenario, you can define and employ user() with @pytest.fixture to simplify the process of executing test_get_username() and

test_set_password_and_check_password()

, as shown below:

import pytest

from django.contrib.auth.models import User

@pytest.fixture # Here
def user():
    user = User.objects.create_user("test-user")
    return user

@pytest.mark.django_db
def test_get_username(user): # <- user
    user.username = "John"
    assert user.get_username() == "John"

@pytest.mark.django_db
def test_set_password_and_check_password(user): # <- user
    user.set_password("new-password")
    assert user.check_password("new-password") == True

Furthermore, consider the provided example with fixture_1(), utilizing yield, and test_1():

import pytest

@pytest.fixture
def fixture_1():
    print('Before Test')
    yield 6
    print('After Test')

def test_1(fixture_1):
    print('Running Test')
    assert fixture_1 == 6

The output displayed would be:

$ pytest -q -rP
.                               [100%]
=============== PASSES ===============
_______________ test_1 _______________
------- Captured stdout setup --------
Before Test
-------- Captured stdout call --------
Running Test
------ Captured stdout teardown ------
After Test
1 passed in 0.10s

Additionally, running multiple fixtures for test_1() and test_2() is feasible, exemplified through the following code snippet. *Refer to my answer for guidance on calling a fixture from another fixture in Pytest:

import pytest

@pytest.fixture
def fixture_1():
    return "fixture_1"

@pytest.fixture
def fixture_2():
    return "fixture_2"

def test_1(fixture_1, fixture_2):
    print(fixture_1, fixture_2)
    assert True

def test_2(fixture_1, fixture_2):
    print(fixture_1, fixture_2)
    assert True

The resultant output will be:

$ pytest -q -rP
..                               [100%]
=============== PASSES ================
_______________ test_1 ________________
-------- Captured stdout call ---------
fixture_1 fixture_2
_______________ test_2 ________________
-------- Captured stdout call ---------
fixture_1 fixture_2
2 passed in 0.33s

Although I am still new to using pytest, one key aspect that stands out is its ability to minimize the necessity of rewriting code that is utilized across multiple tests. In a scenario like yours, having to repeatedly rewrite the same function could be avoided with this snippet as a starting point:

Fixtures play a vital role in providing data for tests, such as database connections, URLs, and input data.

By attaching fixture functions to the tests, we eliminate the need to run identical code for each test, allowing the fixture function to provide the necessary data before executing each test.

-- Source: https://www.tutorialspoint.com/pytest/pytest_fixtures.htm

In essence, leveraging fixtures helps in sharing resources among tests, thus reducing redundancy. Furthermore, the return values from fixture functions can serve as 'input parameters' for individual tests, exemplified by the following code snippet:

@pytest.fixture
def input_value():
   input = 39
   return input

def test_divisible_by_3(input_value):
   assert input_value % 3 == 0

From my perspective, the primary distinction between functions and fixtures lies in the fact that fixtures enable you to prepare something for the test, potentially with some state, and then clean it up after the test concludes.

The information provided on Stack Overflow in response to the question What are fixtures in programming? sheds light on this topic.

If there is no need to establish and dismantle any state, using a fixture doesn't offer much advantage other than just adding some syntactic sugar.