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

Introduction to Generators: Yielding Values for Efficient Iteration

Generators simplify lazy evaluation by providing a concise and readable way to create iterators. While an iterator is implemented as a class with the __iter__() and __next__() methods, a generator is implemented as a function that uses the yield keyword to produce values one at a time. Generators maintain their state automatically between calls, making them more intuitive and efficient for many use cases.

A generator is a special type of function that:

Uses the yield keyword instead of return;
Pauses execution and retains its state when yield is called;
Resumes execution from where it left off when the generator is called again.


              123456789
            
def example_generator():
    yield "First value"
    yield "Second value"
    yield "Third value"

gen = example_generator()
print(next(gen))  # Output: First value
print(next(gen))  # Output: Second value
print(next(gen))  # Output: Third value


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import random

def limited_dice_roller(num_rolls):
    for _ in range(num_rolls):
        yield random.randint(1, 6)

# Using the limited dice roller
print("Rolling the dice:")
for roll in limited_dice_roller(5):
    print(f"Rolled: {roll}")

Differences Between Iterator and Generator

Task

Swipe to start coding

In the previous task, you implemented an infinite dice roller using a custom iterator class. Now, you will simplify the same functionality by using a generator function. Generators provide a concise and readable way to produce values lazily using the yield keyword.

Define the generator function. Use the yield keyword inside the dice_roller function to produce random dice rolls between 1 and 6. Use the random.randint() function to simulate each roll.
Call the dice_roller() function to create a generator object and assign it to the variable dice_generator.
Use a for loop with enumerate() to iterate over the generator. Stop the iteration after 10 rolls using an if condition and the break statement.

Solution

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Section 6. Chapter 4

Introduction to Generators: Yielding Values for Efficient Iteration

A generator is a special type of function that:

Uses the yield keyword instead of return;
Pauses execution and retains its state when yield is called;
Resumes execution from where it left off when the generator is called again.


              123456789
            
def example_generator():
    yield "First value"
    yield "Second value"
    yield "Third value"

gen = example_generator()
print(next(gen))  # Output: First value
print(next(gen))  # Output: Second value
print(next(gen))  # Output: Third value


              12345678910
            
import random

def limited_dice_roller(num_rolls):
    for _ in range(num_rolls):
        yield random.randint(1, 6)

# Using the limited dice roller
print("Rolling the dice:")
for roll in limited_dice_roller(5):
    print(f"Rolled: {roll}")

Differences Between Iterator and Generator

Task

Swipe to start coding

Define the generator function. Use the yield keyword inside the dice_roller function to produce random dice rolls between 1 and 6. Use the random.randint() function to simulate each roll.
Call the dice_roller() function to create a generator object and assign it to the variable dice_generator.
Use a for loop with enumerate() to iterate over the generator. Stop the iteration after 10 rolls using an if condition and the break statement.

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 6. Chapter 4

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