Advanced Python Series: Episode 2

🎓 Advanced Python Series: Episode 2

⚙️ Topic: Generators and Iterators in Python

Welcome to Episode 2 of our Advanced Python series on DarchumsTech! In this session, we’re exploring the difference between generators and iterators, and how to use them for efficient looping and memory-saving data processing in Python.

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🧠 What You'll Learn (Explained):

• What iterators are:
  Any object with __iter__() and __next__() methods is an iterator. They allow you to loop through data one item at a time using next().
• How generators simplify iteration:
  Generators are functions that yield values using the yield keyword instead of returning all data at once. This makes them more memory-efficient.
• Benefits of using generators:
  They are lazy — generating values only when needed. This is perfect for large datasets, pipelines, or streaming data.
• Real-world examples:
  We’ll build a custom range generator and demonstrate reading large files line-by-line without loading them fully into memory.

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🧪 Code Examples:

🔁 Custom Iterator Class

class CountUp:
    def __init__(self, max):
        self.max = max
        self.current = 0

    def __iter__(self):
        return self

    def __next__(self):
        if self.current >= self.max:
            raise StopIteration
        self.current += 1
        return self.current

⚡ Generator Version

def count_up(max):
    current = 1
    while current <= max:
        yield current
        current += 1

for number in count_up(5):
    print(number)

This generator does the same job as the class above, but with much less code and more elegance!

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🚀 Bonus: Generator Expression

squares = (x*x for x in range(10))
print(next(squares))  # Outputs: 0

Just like list comprehensions, but with parentheses — this creates a generator expression that doesn't store all values at once.

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🧰 Tip:

Use generators to avoid loading everything into memory. Ideal for large files, infinite sequences, or APIs with lots of data.

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📬 Coming Next:

Episode 3 will focus on Context Managers & the 'with' Statement — essential tools for managing resources and avoiding messy clean-up code.

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