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How to Use Yield Keyword for Memory Efficient Python Code

Last Updated : 18 Mar, 2024
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We are given a task to understand how to use the yield keyword for memory-efficient Python code with the help of different examples with their code. In this article, we will see how to use the yield keyword for memory-efficient Python code.

What is the Yield Keyword?

The yield keyword in Python is used in the context of generator functions and is fundamental to creating iterators efficiently. It allows a function to pause its execution and yield a value to the caller without losing its state. When the function is called again, it resumes execution from where it left off.

Use Yield Keyword for Memory-Efficient Python Code

Below are some of the examples by which we can understand how to use the yield keyword for memory-efficient Python code:

Memory-Efficient Even Number Filtering

In this example, filter_even_numbers function utilizes the yield keyword to lazily generate even numbers from the input data, resulting in memory-efficient iteration and printing of the filtered values.


Output
2
4

Generating Lazy Sequences Efficiently

In this example, in below code The lazy_sequence generator function produces a sequence of doubled values up to the specified limit, demonstrating memory-efficient iteration when consumed in a for loop.


Output
0
2
4
6
8

Memory-Efficient Fibonacci Sequence Generation

In this example, the fibonacci_sequence generator function yields Fibonacci sequence values up to the specified limit, showcasing memory-efficient iteration for generating and printing the sequence.


Output
0
1
1
2
3
5
8
13
21
34

Memory-Efficient Task Parallelism

In this example, the parallel_tasks generator yields lambda functions representing parallel tasks, enabling memory-efficient execution of each task on a given input value (data in this case).


Output
10
25

Memory Efficiency Comparison: Yield vs. Generator

In this example, below code compares memory usage between a list-based approach (without yield) and a generator-based approach (with yield) for generating a sequence of squares up to a specified limit. The generator-based approach demonstrates superior memory efficiency, especially for large limits, as it generates values on-the-fly without storing the entire sequence in memory.


Output
Memory usage without yield: 8697472 bytes
Memory usage with yield: 128 bytes

Conclusion

In conclusion, leveraging generators in Python is a powerful strategy for achieving memory-efficient code, particularly in situations where handling large datasets or dynamic data streams is essential. The lazy evaluation approach of generators allows for on-the-fly generation of values, reducing the overall memory footprint and enabling the processing of data in a scalable and efficient manner.


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