Course Content

Python Advanced Concepts

1. Modules and Imports

Python Project Structure Understanding Modules Import Statements Understanding Libraries in Python Exploring Standard Libraries Package Management

2. Error Handling

Basics of Error Handling Raising Exceptions Handling Code After Exceptions Using Assertions Best Practices in Exception Handling

3. File Handling

Introduction to File Handling Reading and Writing Files Advanced File Operations Working with File Paths Context Managers Custom Context Managers Handling Binary Files

4. Pytest Framework

Introduction to Testing in Python Pytest Framework Assert Statement Test Parametrization

5. Unittest Framework

Introduction to Unittest Assert Methods SetUp and TearDown Steps Mocking Technique Mocking in Details

6. Iterators and Generators

Python Iterables Understanding Iterators Lazy Evaluation Introduction to Generators Practical Examples of Generators

Introduction to Generators

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.


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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 show code editor

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.

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Section 6. Chapter 4

Introduction to Generators

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 show code editor

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.

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

Introduction to Generators

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 show code editor

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.

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

Everything was clear?

Thanks for your feedback!

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 show code editor

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.

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

Section 6. Chapter 4

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