Pytest

is a Python testing tool that can be used to test Python code.

There are many benefits to testing your code. This increases confidence in your code’s behavior and helps to prevent any code changes from causing regressions. It can be difficult to write and maintain tests. You should make the most of all your tools in order to make it as easy as possible. You can boost your testing productivity with pytest.

Learn all about pytest framework,from top instructors and experts

This tutorial will teach you:

What are the benefits of pytest?

make sure your tests are stateless

make repetitive tests easier to understand run subsets by name or custom groups

to make and maintain reusable testing tools

What makes pytest so useful?

You may have used Python’s unittest module if you’ve ever written unit tests for Python code. Although unittest is a solid foundation for building your test suites, it does have some limitations.

Many third-party frameworks exist to help address the problems with unittest. Among them, pytest is one of the most widely used. pytest, a plugin-based environment for testing Python code, is feature-rich. Learn pytest tutorial,for beginners to expets

You’re in for a treat if you haven’t yet tried pytest. The features and philosophy of pytest will make testing more enjoyable and productive. Common tasks are easier with pytest than complex tasks. Advanced tasks can be accomplished using a variety of time-saving commands or plugins. It can even run existing tests, including ones written with unit test.

Some development patterns that seem to make sense when you start using pytest may become problematic as your test suite grows. This tutorial will explain some of the features pytest offers to help keep your testing efficient as it grows.

There are fewer boilerplates

The Arrange-Act–Assert model is used for most functional tests.

Set up the conditions for the test.

Call a function or method to act

Assert the truth of some end condition

Test frameworks can hook into your test assertions to provide information when an assertion fails. Unittest is an example of a tool that provides many useful assertion utilities. Even a small number of tests can require a lot of boilerplate code.

Imagine that you would like to create a test suite to ensure unittest works properly in your project. One test might pass every time, while another one may fail.

One test passed, and one failed. Now that you have proved unittest to be effective, let’s take a look at what it took to get there.

Import the TestCase Class from unittest

Create TryTesting as a subclass in TestCase

For each test, write a method in TryTesting

To make assertions, use one of the self.assert* techniques from unittest.TestCase

This is a lot of code, and you would end up writing the exact same code repeatedly. Using Python’s assert keyword directly in pytest makes this easier.

The test results are presented differently by pytest than in the unit test. This report shows:

The system state includes which Python versions, pytest, and any plugins that you have installed

The root directory, also known as the directory where configuration and testing can be found

The number of runs the runner has completed

The output shows the status of each test, using a syntax similar in unittest.

A dot (..) signifies that the test passed. A dot (.) signifies that the test was passed.

A F indicates that the test was not passed.

A test has raised an unexpected exception if it is marked E.

The report provides a breakdown of failures in tests. The test failed because assert False is always failing. The report provides an overview of the test suite.

Check Filtering

You may want to test a small number of features and save the entire suite for later. There are several ways to do this with pytest:

Name-based filtering. You can restrict pytest from running tests whose names are fully qualified to match an expression. This can be done with the -k parameter.

Directory scoping: This will only run the tests that are within or under the current directory.

You can categorize tests by using the -m parameter. This allows you to include or exclude certain categories from which you have defined. This can be done with the -m parameter

Test Parametrization

After that, You’ll be writing many similar tests when you test functions that process data and perform generic transformations. These tests may only differ in the input and output of the code being tested. Duplicating test codes can lead to inconsistent behavior, which can sometimes be confusing.

Therefore, Unittest allows you to combine multiple tests into one test, but the results don’t show them as separate tests in your result reports. The entire group will still have a failing result if one test fails but all others pass. Each test can be passed or failed independently by pytest. This tutorial will show you how to parametrize tests using pytest.

Plugin-Based Architecture

One of the best features of pytest lies in its ability to allow customization and add new features. Nearly every part of the program can easily be cracked open and modified. The result is that pytest users have created a wealth of useful plugins.

In other words, While some plugins for pytest are focused on Django frameworks, others can be used with all test suites. This tutorial will provide more information on specific plugins.