Contenido del Curso

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

Using the Assert Statement in Pytest: Validating Test Conditions

The assert statement is used to verify that a condition is True. If the condition is False, the test fails. Pytest enhances the basic assert by providing detailed output when the assertion fails, making it easier to understand what went wrong.

Simple Assertions:

Check that the condition is evaluated as True. If it is evaluated as False, Pytest raises an AssertionError and marks the test as failed.


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In the next example, word.isupper() should return True if word is in uppercase. If not, the test fails.


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Comparative Assertions:


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To verify that the result of an operation (actual) matches the expected value.


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Here, the add function is tested to ensure it correctly adds two numbers. The message after the comma provides additional context if the test fails.

Assertions with Detailed Feedback:


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To provide a custom error message that displays when the assertion fails, offering more insight into the issue.


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This assertion checks whether the multiply function returns 10 when multiplying 2 and 5. The custom message clarifies the expected outcome versus the actual result if the test fails.

Assertions for Exceptions:


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To confirm that a certain exception is raised by a block of code.


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This test ensures that dividing by zero raises a ZeroDivisionError. It’s essential for testing error handling in your code.

Advanced Assertions:

Use assertions to check more complex conditions, such as contents of lists, dictionaries, or more nuanced states in your application.


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This test checks for the presence of an item in a list, providing clear feedback if the item is missing.

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