Contenido del Curso

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

Understanding Iterators

What Are Iterators?

Technically, in Python, an iterator is an object that implements the iterator protocol, which consists of the following methods:

__iter__(): returns the iterator object itself, making it compatible with loops like for;
__next__(): returns the next element in the sequence. When no elements remain, it raises a StopIteration exception to signal the end of traversal.

An iterator enables traversal of elements in an iterable (e.g., list or string) one at a time while keeping track of its position.

Note
An iterator is also an iterable because it implements the __iter__() method.
Not all iterables are iterators. For example, a list is iterable but not an iterator. When you pass it to the iter() function, you get an iterator that allows element-by-element traversal.

Building a Custom Iterator

Suppose you want to process only even IDs from a sequence. Instead of filtering the entire dataset upfront, you can create a custom iterator to yield only even numbers dynamically.


              12345678910111213141516171819202122
            
class EvenIDIterator:
    def __init__(self, ids):
        self.ids = ids
        self.index = 0

    def __iter__(self):
        return self

    def __next__(self):
        while self.index < len(self.ids):
            id = self.ids[self.index]
            self.index += 1
            if id % 2 == 0:
                return id
        raise StopIteration

# Using the custom iterator
ids = [101, 102, 103, 104, 105]
even_ids = EvenIDIterator(ids)

for id in even_ids:
    print(f"Even ID: {id}")

Explanation:

__next__() skips odd IDs and returns only even IDs;
The StopIteration exception is raised when all elements are processed.

Iterators vs. Iterables

Iterator Exhaustion

Once an iterator is exhausted, it cannot be reused without recreating it. For example:


              12345678910
            
numbers = [1, 2, 3, 4]
iterator = iter(numbers)

# First iteration
for num in iterator:
    print(num)  # Output: 1, 2, 3, 4

# Second iteration
for num in iterator:
    print(num)  # Output: Nothing, the iterator is exhausted.

Iterators can only be traversed once. To iterate again, a new iterator needs to be created.

Tarea

Swipe to show code editor

Complete the missing parts of the code to implement a custom iterator class for simulating an infinite die roller. The iterator should lazily generate random rolls of a six-sided die and stop after 10 rolls.

The __iter__() method allows an object to be used as an iterator.
The __next__() method produces the next random die roll (a number between 1 and 6).
Create an instance of the InfiniteDie class, which represents the die roller.
Use a for loop with enumerate() to roll the die lazily. Stop after 10 rolls using an if condition and the break statement.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 6. Capítulo 2

Understanding Iterators

What Are Iterators?

Technically, in Python, an iterator is an object that implements the iterator protocol, which consists of the following methods:

__iter__(): returns the iterator object itself, making it compatible with loops like for;
__next__(): returns the next element in the sequence. When no elements remain, it raises a StopIteration exception to signal the end of traversal.

An iterator enables traversal of elements in an iterable (e.g., list or string) one at a time while keeping track of its position.

Note
An iterator is also an iterable because it implements the __iter__() method.
Not all iterables are iterators. For example, a list is iterable but not an iterator. When you pass it to the iter() function, you get an iterator that allows element-by-element traversal.

Building a Custom Iterator

Suppose you want to process only even IDs from a sequence. Instead of filtering the entire dataset upfront, you can create a custom iterator to yield only even numbers dynamically.


              12345678910111213141516171819202122
            
class EvenIDIterator:
    def __init__(self, ids):
        self.ids = ids
        self.index = 0

    def __iter__(self):
        return self

    def __next__(self):
        while self.index < len(self.ids):
            id = self.ids[self.index]
            self.index += 1
            if id % 2 == 0:
                return id
        raise StopIteration

# Using the custom iterator
ids = [101, 102, 103, 104, 105]
even_ids = EvenIDIterator(ids)

for id in even_ids:
    print(f"Even ID: {id}")

Explanation:

__next__() skips odd IDs and returns only even IDs;
The StopIteration exception is raised when all elements are processed.

Iterators vs. Iterables

Iterator Exhaustion

Once an iterator is exhausted, it cannot be reused without recreating it. For example:


              12345678910
            
numbers = [1, 2, 3, 4]
iterator = iter(numbers)

# First iteration
for num in iterator:
    print(num)  # Output: 1, 2, 3, 4

# Second iteration
for num in iterator:
    print(num)  # Output: Nothing, the iterator is exhausted.

Iterators can only be traversed once. To iterate again, a new iterator needs to be created.

Tarea

Swipe to show code editor

The __iter__() method allows an object to be used as an iterator.
The __next__() method produces the next random die roll (a number between 1 and 6).
Create an instance of the InfiniteDie class, which represents the die roller.
Use a for loop with enumerate() to roll the die lazily. Stop after 10 rolls using an if condition and the break statement.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 6. Capítulo 2

Understanding Iterators

What Are Iterators?

Technically, in Python, an iterator is an object that implements the iterator protocol, which consists of the following methods:

__iter__(): returns the iterator object itself, making it compatible with loops like for;
__next__(): returns the next element in the sequence. When no elements remain, it raises a StopIteration exception to signal the end of traversal.

An iterator enables traversal of elements in an iterable (e.g., list or string) one at a time while keeping track of its position.

Note
An iterator is also an iterable because it implements the __iter__() method.
Not all iterables are iterators. For example, a list is iterable but not an iterator. When you pass it to the iter() function, you get an iterator that allows element-by-element traversal.

Building a Custom Iterator

Suppose you want to process only even IDs from a sequence. Instead of filtering the entire dataset upfront, you can create a custom iterator to yield only even numbers dynamically.


              12345678910111213141516171819202122
            
class EvenIDIterator:
    def __init__(self, ids):
        self.ids = ids
        self.index = 0

    def __iter__(self):
        return self

    def __next__(self):
        while self.index < len(self.ids):
            id = self.ids[self.index]
            self.index += 1
            if id % 2 == 0:
                return id
        raise StopIteration

# Using the custom iterator
ids = [101, 102, 103, 104, 105]
even_ids = EvenIDIterator(ids)

for id in even_ids:
    print(f"Even ID: {id}")

Explanation:

__next__() skips odd IDs and returns only even IDs;
The StopIteration exception is raised when all elements are processed.

Iterators vs. Iterables

Iterator Exhaustion

Once an iterator is exhausted, it cannot be reused without recreating it. For example:


              12345678910
            
numbers = [1, 2, 3, 4]
iterator = iter(numbers)

# First iteration
for num in iterator:
    print(num)  # Output: 1, 2, 3, 4

# Second iteration
for num in iterator:
    print(num)  # Output: Nothing, the iterator is exhausted.

Iterators can only be traversed once. To iterate again, a new iterator needs to be created.

Tarea

Swipe to show code editor

The __iter__() method allows an object to be used as an iterator.
The __next__() method produces the next random die roll (a number between 1 and 6).
Create an instance of the InfiniteDie class, which represents the die roller.
Use a for loop with enumerate() to roll the die lazily. Stop after 10 rolls using an if condition and the break statement.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

What Are Iterators?

Technically, in Python, an iterator is an object that implements the iterator protocol, which consists of the following methods:

__iter__(): returns the iterator object itself, making it compatible with loops like for;
__next__(): returns the next element in the sequence. When no elements remain, it raises a StopIteration exception to signal the end of traversal.

An iterator enables traversal of elements in an iterable (e.g., list or string) one at a time while keeping track of its position.

Note
An iterator is also an iterable because it implements the __iter__() method.
Not all iterables are iterators. For example, a list is iterable but not an iterator. When you pass it to the iter() function, you get an iterator that allows element-by-element traversal.

Building a Custom Iterator

Suppose you want to process only even IDs from a sequence. Instead of filtering the entire dataset upfront, you can create a custom iterator to yield only even numbers dynamically.


              12345678910111213141516171819202122
            
class EvenIDIterator:
    def __init__(self, ids):
        self.ids = ids
        self.index = 0

    def __iter__(self):
        return self

    def __next__(self):
        while self.index < len(self.ids):
            id = self.ids[self.index]
            self.index += 1
            if id % 2 == 0:
                return id
        raise StopIteration

# Using the custom iterator
ids = [101, 102, 103, 104, 105]
even_ids = EvenIDIterator(ids)

for id in even_ids:
    print(f"Even ID: {id}")

Explanation:

__next__() skips odd IDs and returns only even IDs;
The StopIteration exception is raised when all elements are processed.

Iterators vs. Iterables

Iterator Exhaustion

Once an iterator is exhausted, it cannot be reused without recreating it. For example:


              12345678910
            
numbers = [1, 2, 3, 4]
iterator = iter(numbers)

# First iteration
for num in iterator:
    print(num)  # Output: 1, 2, 3, 4

# Second iteration
for num in iterator:
    print(num)  # Output: Nothing, the iterator is exhausted.

Iterators can only be traversed once. To iterate again, a new iterator needs to be created.

Tarea

Swipe to show code editor

The __iter__() method allows an object to be used as an iterator.
The __next__() method produces the next random die roll (a number between 1 and 6).
Create an instance of the InfiniteDie class, which represents the die roller.
Use a for loop with enumerate() to roll the die lazily. Stop after 10 rolls using an if condition and the break statement.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 6. Capítulo 2

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones