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Process Large Sequence Efficiently

Python Medium 0 views

Learn Python generator expressions to process large datasets memory-efficiently. Unlike list comprehensions that build entire lists in memory, generators produce values on-demand, making them ideal for processing large or infinite sequences.

Your task is to use generator expressions and the itertools module to create efficient data pipelines. This is crucial for processing large files or streams where loading everything into memory is impractical.

Example 1:

Input: numbers = range(1, 6)
Output: 55
Explanation: Sum of squares (1 + 4 + 9 + 16 + 25) using generator.

Example 2:

Input: numbers = [1, 2, 3, 4, 5]
Output: [1, 3, 5]
Explanation: Get first 3 odd numbers using generator.

Example 3:

Input: start=1, end=10
Output: 6
Explanation: Count even numbers in range without creating list.

Algorithm Flow

Recommendation Algorithm Flow for Process Large Sequence Efficiently - Budibadu
Recommendation Algorithm Flow for Process Large Sequence Efficiently - Budibadu

Best Answers

python - Approach 1
def process_large_data(items):
    for x in range(items):
        yield x * x