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Python Generators: Implementation and Usage

In Python, generators are a way to create iterators using the yield keyword. A generator produces values one at a time and remembers its state between each yield, allowing memory-efficient iteration over large datasets or infinite sequences.

Generator Function Example

def my_generator(n):
    """Generate numbers from 0 to n-1"""
    i = 0
    while i < n:
        yield i  # Return current value and pause
        i += 1

# Create generator object
gen = my_generator(5)

# Using generator in a for loop
print("Using for loop:")
for number in gen:
    print(number)
  

Accessing Generator Values Manually with next()

gen2 = my_generator(3)

print("Using next() function:")
print(next(gen2))  # Output: 0
print(next(gen2))  # Output: 1
print(next(gen2))  # Output: 2
# print(next(gen2))  # Raises StopIteration: no more values
  

Key Concepts

• yield keyword: Temporarily suspends function execution, returns a value, and resumes from the same point on the next call.
• Generator object: Calling a generator function returns a generator object, not a list.
• for loop: Automatically calls __next__() on the generator to get each value.
• next() function: Manually retrieve the next value using next(gen).

Advantages of Generators

• Memory efficiency: Values are generated on-the-fly instead of storing all values in memory.
• Supports infinite sequences: Combine while True and yield to produce values as needed without memory overload.

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Use generators in Python to efficiently iterate over large datasets or create infinite sequences while saving memory and maintaining performance.