Pytest With Eric

Learn to write production level Python Unit Tests with Pytest

Have you ever been stuck debugging a unit test, only to be overwhelmed by cryptic error messages? Maybe you can’t figure out which line of code is the culprit?

Debugging tests can be a real challenge, especially if you’re not very familiar with the testing framework.

What if there was a way to make debugging easier? A way to get more detailed information about test failures, so you could quickly pinpoint the problem?

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Testing code is paramount, it ensures your code behaves as expected.

If you write Python unit tests, you’re likely familiar with the MagicMock class, which enables the simulation of object behaviours for testing purposes.

One common testing scenario is checking how your code handles exceptions.

But how can you simulate an exception being raised from a mock object (like a mocked function)?

How can you leverage MagicMock to raise exceptions deliberately, mimicking error scenarios during testing?

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Running tests with Pytest is straightforward for simple programs. However, things get a bit tricky when your program relies on pre-setup tasks like initializing variables or declaring classes.

While these tasks are a breeze when you run the program as usual, handling them in your tests may seem less intuitive. So, how can you tackle this challenge in your testing process?

Pytest offers a clever solution to address this challenge through its setup and teardown methods. Let’s break it down further:

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Asynchronous task processing has become a cornerstone for building efficient and scalable applications in today’s fast-paced world.

Users perform actions and expect results near real-time. This can be efficiently handled with distributed computing and asynchronous task processing.

At the heart of this asynchronous revolution lies Celery, a powerful and popular distributed task queue framework for Python.

Celery empowers you to offload time-consuming tasks to the background, allowing applications to remain responsive and performant while handling resource-intensive operations.

While Celery simplifies the implementation of asynchronous workflows, testing them can be challenging.

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As a software developer, you’re no stranger to the importance of effective testing strategies, especially Test-Driven Development (TDD).

As projects become complex, so do the testing requirements, leading to repetitive and time-consuming test case creation. Is there a way to simplify test generation?

Fortunately, a game-changing solution exists!

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Have you explored passing command line arguments in Python? If you have, you’re likely familiar with the convenience it offers.

The capability to define and modify runtime behavior through command line arguments is a potent tool, and this is the reason, why it is widely used in the design of various libraries.

However, a question arises: How can you extend this versatility to your unit tests when using Pytest? How can you dynamically adapt the behavior of your tests during their execution?

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There’s no doubt that Pytest fixtures are incredibly useful and help you write clean and maintainable tests.

But what if you want to do something more dynamic?

Maybe set up a database connection or pass different data inputs to each test case?

Setting up and tearing down identical fixtures with very minor changes leads to code repetition and maintenance nightmare.

Maybe you want to parameterize your fixtures?

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As a Python developer striving for accurate and efficient testing, you will likely encounter scenarios where verifying floating-point values or approximate comparisons presents challenges.

In many real-world applications, especially with scientific computing, simulations, high-performance computing, financial calculations, and data analysis, you’ll often deal with floating-point numbers.

These numbers are represented in computers using a finite number of binary digits, which can lead to rounding errors and precision limitations.

So what do you and how do you test these floating-point values?

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You’ve written code and Unit tests, and want to make sure it works. You simply run the pytest command in your terminal to run them the tests. Boom! some tests fail.

How do you debug it?

To debug, it’s sometimes helpful to run one test, run tests in a specific module or class, or run tests based on a marker.

But how do you run just a single test?

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In our fast-paced world, every millisecond matters and user experience is paramount.

The importance of faster code faster cannot be overstated.

Beyond correct functioning, it’s imperative to ensure that it operates efficiently and consistently across varying workloads.

This is where performance testing and benchmarking step in to uncover bottlenecks, inefficiencies, and regressions.

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