Pytest Tutorial – Complete Guide

Enter the world of PyTest

Dive into the dynamic universe of Python testing with us. In this tutorial, we’ll explore PyTest, an exceptional tool that streamlines the testing process in Python.

What is PyTest?

PyTest is a testing framework that allows you to write test codes using Python. It’s a powerful tool, with a simple syntax for writing tests, automated discovery of test modules and functions, and detailed info on failures.

Why PyTest?

Well, testing is an integral part of building robust, bug-free applications. And PyTest makes this task easier and more efficient. Here’s why you should consider learning it:

  • PyTest’s plain syntax makes writing tests as simple as penning down python functions.
  • It enables you to check a large number of input combinations through parameterized testing.
  • PyTest is compatible with other Python testing tools like unittest and doctest, thereby providing flexibility.
  • It gives detailed information on failures that aids in debugging.

As we dive deeper into the beautiful, problem-solving world of PyTest, you might just find it to be the tool that revolutionizes your approach towards testing in Python. Let’s start this exciting journey together.

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Getting Started with PyTest

Let’s start with installing PyTest. With Python and pip already installed, it’s as easy as running:

pip install pytest

Now that we have PyTest installed, let’s write our first test. Create a new Python file and call it

def test_addition():
    assert 1 + 1 == 2

Here, we’ve defined a test function that checks if the result of 1 + 1 equals 2.

Running Tests

The simplicity of PyTest really shines when running tests. With a single command, we can run all the tests in a directory.


If everything is correct, PyTest will find our test and happily report that everything passed.

Testing for Exceptions

If a piece of code is supposed to throw an exception, we can test for that using PyTest. Assume we have a function divide(x, y) that is supposed to raise a ZeroDivisionError if y is 0. Here’s the code for the function:

def divide(x, y):
    if y == 0: raise ZeroDivisionError("Can't divide by zero!")
    return x / y

Let’s write a test case for this.

import pytest

def test_zero_division():
    with pytest.raises(ZeroDivisionError):
        divide(1, 0)

The test_zero_division function checks if the divide function raises a ZeroDivisionError when the second argument is 0.

Parameterizing Tests

PyTest allows us to run a test function with different sets of input data, and expect different results. We can use the @pytest.mark.parametrize decorator for that.

@pytest.mark.parametrize("num1, num2, result",
                            (1, 2, 3),
                            (2, 3, 5),
                            (3, 5, 8)
def test_add(num1, num2, result):
    assert num1 + num2 == result

This demonstrates the basic usage of PyTest, including parameterizing tests, testing for exceptions, and setting up and running tests. Go ahead and experiment by creating your tests using PyTest. Happy Testing!

Fixtures in PyTest

In PyTest, fixtures are functions that run before each test function to which it is applied. They are used to feed some data to the tests. Let’s create a fixture which will create some data for our tests.

def data():
    return "sample data"

We have created a fixture named “data” which returns “sample data”. Now, we can use this fixture in our test functions.

def test_data_length(data):
    assert len(data) == 11

Using Mocks and Spies with PyTest

Mocks are objects that simulate the behaviour of real objects. Using mocks, we can fake the output of a function and focus on the code being tested. Mocks can be spies as well, meaning we can check if certain methods were called on them and what the passed arguments were.

from unittest.mock import Mock

def test_mock_method():
    mock = Mock(return_value="mocked data")
    assert mock() == "mocked data"

In this simple example, we made a mock object that returns “mocked data” when called.

Using PyTest with Flask

It’s not just simple functions and methods, you can also use PyTest to test web applications written using Flask.

from flask import Flask

def create_app():
    app = Flask(__name__)

    def home():
        return "Hello, World!"

    return app

This simple Flask application just has a single route that returns “Hello, World!”. Now, let’s test the Flask application.

from create_app import create_app

def test_home():
    flask_app = create_app()
    client = flask_app.test_client()

    response = client.get("/")
    assert == "Hello, World!"

We created a client for testing, then we sent a GET request to our application and asserted that the response was equal to “Hello, World!”.

Testing Asynchronous Code with PyTest

PyTest can also test async code. Let’s say we have an async function that we want to test.

import asyncio

async def async_add(x, y):
    await asyncio.sleep(3)
    return x + y

We can write a test like this for the async function.

async def test_async_add():
    result = await async_add(1, 2)
    assert result == 3

This concludes our tutorial on PyTest. Exploring, understanding, and implementing PyTest in its true essence can radically help understand Python testing better and give your code the robustness and freedom from bugs it deserves.

Where to Go Next?

Congratulations on making it this far! You’ve just scratched the surface of what you can achieve with PyTest. The world of Python is vast and filled with opportunities waiting to be discovered.

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In conclusion, PyTest is a versatile and powerful tool that can greatly enhance your Python programming journey. As we have demonstrated, its wide range of features—from testing for exceptions and parameterizing tests to working with Flask and async code—make it an essential addition to your developer toolkit.

With the knowledge gained, it’s time to take the next step in your Python journey. Continue leveraging the power of Python by exploring our Python Mini-Degree. It’s a great way to solidify your skills and move towards becoming an accomplished Python developer. Remember, every line of code is a step closer to mastery. Happy coding!


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