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In Python, there are significant differences between generator functions and normal functions in terms of their behavior, execution, and the values they return. Here's a detailed explanation of the differences between generator functions and normal functions: Normal Functions: Normal functions in Python are defined using the`def` keyword and are executed sequentially. They perform a series of operations, possibly accepting arguments and returning a value or modifying data. Normal functions execute from start to finish, and their results are returned at the end of the function using the`return` statement.

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def square_numbers(numbers):
result = []
for num in numbers:
result.append(num ** 2)
return result

squared = square_numbers([1, 2, 3, 4, 5])

print(squared)  # Output: [1, 4, 9, 16, 25]
``````

In this example, the`square_numbers()` function takes a list of numbers and returns a new list containing the squares of those numbers. The function iterates over the input list, calculates the square of each number, and appends it to the`result` list. Finally, the`result` list is returned. Generator Functions: Generator functions, on the other hand, are defined using the`def` keyword and contain one or more`yield` statements. They generate a sequence of values that can be iterated over using a loop or other iterable methods. Generator functions use lazy evaluation, meaning they produce values on-the-fly as they are requested, saving memory and improving performance.

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``````
def square_numbers(numbers):
for num in numbers:
yield num ** 2

squared = square_numbers([1, 2, 3, 4, 5])

print(squared)  # Output: <generator object square_numbers at 0x...>

for num in squared:
print(num)
``````

In this example, the`square_numbers()` function is a generator function that yields the squares of numbers in the input list one at a time. When the function is called, it returns a generator object. To retrieve the values generated by the generator, you can iterate over it using a loop or other iterable methods. The key differences between generator functions and normal functions are: 1. Execution and Memory Efficiency: Normal functions execute all operations and store the entire result in memory before returning it. Generator functions, on the other hand, produce values lazily, one at a time, saving memory and improving performance, especially for large or infinite sequences. 2. Iterable vs. Return Value: Generator functions produce an iterable generator object that can be iterated over using loops or iterable methods. Normal functions typically return a value using the`return` statement. 3. State Preservation: Generator functions preserve their internal state between invocations, allowing them to resume execution from where they left off. Normal functions start from the beginning each time they are called. 4. Use Cases: Generator functions are useful when working with large datasets, infinite sequences, or situations where you want to generate values on-the-fly. Normal functions are more appropriate for performing a series of operations and returning a final result. In summary, generator functions and normal functions have distinct behaviors and use cases. Generator functions generate values lazily and produce an iterable sequence, while normal functions execute sequentially and typically return a final result. Choose the appropriate type of function based on your specific requirements and the nature of the problem you're solving.