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Generators Generators are a powerful feature in Python that allows you to create iterators in a concise and efficient way. Instead of creating a whole list in memory at once, generators produce values one at a time, only when requested. This makes them memory-efficient, especially when dealing with large datasets or infinite sequences.

Key characteristics:

• Memory Efficiency: Generates values on demand, avoiding storage of the entire sequence.
• Lazy Evaluation: Values are computed only when needed.
• Iterable: Can be used in for loops and other iteration contexts.

Creating Generators: Generators are defined using functions, but instead of a return statement, they use the yield keyword. yield pauses execution and returns a value, preserving the generator’s state. The next time the generator is called, it resumes from where it left off.

def my_generator(n):
    for i in range(n):
        yield i

gen = my_generator(5)
print(next(gen))  # Output: 0
print(next(gen))  # Output: 1
print(list(gen))  # Output: 2, 3, 4 #consuming the rest

#using for loop
for i in my_generator(3):
    print(i) #output 0,1,2

Generator Expressions:

Similar to list comprehensions, generator expressions offer a concise way to create generators. They use parentheses () instead of square brackets 1.

gen_expr = (i**2 for i in range(5))
print(list(gen_expr)) #Output: [0, 1, 4, 9, 16

Advantages over Lists:

• Memory Efficiency: Especially beneficial for large datasets or infinite sequences.
• Readability: Can make code cleaner and more concise than explicit iterator classes.
• Lazy Evaluation: Avoids unnecessary computations.

Related Notes:

• Iterators
• List Comprehensions
• Async Generators (for asynchronous operations)