Kursusindhold

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

Lazy Evaluation in Python: Optimizing Memory and Performance

In this chapter, we introduce the concept of lazy evaluation, a technique where data is produced only when needed rather than being computed and stored upfront. Lazy evaluation is a key feature of iterators and is particularly useful for working with large datasets or infinite sequences.

Key Benefits:

Memory Efficiency: only one element is generated at a time;
Performance Optimization: computation occurs only when needed;
Support for Infinite Sequences: you can work with sequences of arbitrary size without running out of memory.

Let's create an infinite dice roller that generates random rolls on demand. This ensures we never need to store all the rolls in memory, no matter how many rolls we perform.


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import random

# Infinite dice roller
class InfiniteDiceRoller:
    def __iter__(self):
        return self

    def __next__(self):
        return random.randint(1, 6)

# Using the infinite dice roller
dice_roller = InfiniteDiceRoller()
for i, roll in enumerate(dice_roller):
    if i >= 10:  # Stop after 10 rolls
        break
    print(f"Roll {i + 1}: {roll}")

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