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return
statement, generators use the yield
statement. When the generator function is called, it returns an iterator object but does not start execution immediately.yield
statement is executed, the state of the function is "paused" and saved, allowing the function to resume from where it left off on subsequent calls.next()
function, you can iterate over the generator object. Each call to next()
resumes the function and runs it until the next yield
statement is encountered.StopIteration
exception, signaling the end of the iteration.# A simple generator that yields square numbers
def square_numbers(nums):
for num in nums:
yield num * num
# Create a generator object
gen = square_numbers([1, 2, 3, 4, 5])
# Iterate over the generator object
for square in gen:
print(square)
Output:
1
4
9
16
25
squares = (x**2 for x in range(10))
Python generators offer a powerful tool for creating iterable sequences in a memory-efficient manner. They are particularly useful in scenarios where data needs to be processed incrementally, without loading the entire dataset into memory. Understanding how to implement and utilize generators can significantly enhance the performance and scalability of your Python applications.