Course Content

Python Advanced Concepts

1. Mastering Python Modules and Imports

Python Project Structure: Organizing Code for Scalability Understanding Python Modules Importing Modules in Python: Best Practices for Code Reusability Exploring Python Libraries: Built-in and Third-Party Solutions Python Standard Library: Essential Modules for Everyday Tasks Managing Python Packages

2. Mastering Error Handling in Python

Fundamentals of Error Handling in Python: Preventing Code Crashes Raising Exceptions in Python: Triggering and Managing Errors Executing Code After Exceptions: Handling Cleanup Operations Using Assertions in Python: Debugging and Ensuring Code Integrity Best Practices for Exception Handling

3. Mastering File Handling in Python

Introduction to File Handling in Python Reading and Writing Files: Handling Text and Data Efficiently Advanced File Operations: Modifying and Managing File Content Working with File Paths in Python Using Context Managers for File Handling in Python Creating Custom Context Managers Handling Binary Files in Python

4. Mastering Pytest Framework

Introduction to Testing in Python Pytest Framework: Setting Up and Running Automated Tests Using the Assert Statement in Pytest: Validating Test Conditions Test Parametrization in Pytest: Running Tests with Multiple Inputs

5. Mastering Unittest Framework

Introduction to Unittest in Python: Writing Structured Unit Tests Using Assert Methods in Unittest: Validating Test Results SetUp and TearDown in Unittest: Managing Test Environments Mocking Techniques in Unittest: Simulating Dependencies Advanced Mocking in Unittest

6. Mastering Iterators and Generators in Python

Python Iterables: Understanding Sequences and Data Structures Understanding Iterators in Python Lazy Evaluation in Python: Optimizing Memory and Performance Introduction to Generators: Yielding Values for Efficient Iteration Practical Applications of Generators: Real-World Use Cases

Test Parametrization in Pytest: Running Tests with Multiple Inputs

Parameterized testing is crucial for enhancing test coverage without writing numerous test functions for scenarios that only differ by their inputs and expected outputs. This approach not only saves time but also increases the clarity and maintainability of test suites by:

Reducing code duplication and the potential for errors in test code;
Enabling easy addition of new test cases by simply adding parameters;
Facilitating comprehensive testing across a wide range of inputs.

Let's consider a simple function that calculates the average of two numbers. There can be a logical error if to forget to put braces. We'll use @pytest.mark.parametrize to test this function with several pairs of numbers.

This example demonstrates how to run test_calculate_average with three different sets of numbers, checking if the function correctly computes their average.

Syntax with id

The id parameter is used to assign an identifier to each set of inputs, which helps to identify tests more easily in the test output:

Using pytest.param allows for more granular control over each test case, including setting individual IDs directly.

Syntax with ids

You can also provide a unique ID for each test case using ids, which makes test reports more readable:

In this code, each set of parameters has an associated ID that describes the test case, which will appear in the test output.

Here's what the output might look like:

1. Consider a function that multiplies two numbers. Complete the test case by filling in the missing parts:

2. What is the primary benefit of specifying identifiers using `id` or `ids` in `@pytest.mark.parametrize`?

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