How Does Python's `yield` Keyword Create Efficient Iterators in Generator Functions?

What does the "yield" keyword do in Python?

Python's "yield" keyword is used in generator functions, which are special functions that act as iterators.

Generator Functions and Iterators

• Iterators: Objects that produce a sequence of values one at a time.
• Generator Functions: Functions that return an iterator instead of a specific value. They use the "yield" keyword to generate values on demand.

Functionality of "yield"

When a generator function is called:

• The generator function does not run its code immediately.
• Instead, it returns a generator object.
• This generator object is the iterator that produces values.

When an iterator is used in a 'for' loop:

• The first time the loop encounters a "yield" statement, the generator function resumes execution and yields its first value.
• Subsequent iterations of the loop cause the generator function to resume and yield the next values until there are no more values left.
• The generator function terminates when it reaches the end of its logic or encounters a "return" statement.

Example Code Explanation

The given code snippet defines a generator function called _get_child_candidates within the node class.

• The function takes parameters distance, min_dist, and max_dist.
• It yields the left and right child nodes if their distances meet the criteria.
• If no more children meet the criteria, the generator function terminates.

In the caller code:

• result is initialized as an empty list.
• candidates is initialized as a list containing the current node object.
• The loop iterates over candidates.
• For each node, it checks the distance and adds its values to result if it meets the criteria.
• It then extends candidates with the child nodes obtained from the _get_child_candidates generator.
• The loop continues until all child nodes have been processed.

Benefits of Generators

• Memory efficiency: Generators do not store all values in memory, unlike lists.
• Lazy evaluation: Values are computed only when the generator is iterated over, reducing computation overhead.
• Advanced control over iteration: You can control the generation of values using the logic within the generator function.

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