Python Generators Deep Dive Part 1: Lazy Evaluation & Memory Optimization🚀

Python Generators Deep Dive Part 1: Lazy Evaluation & Memory Optimization🚀 The Problem: Memory Bloat in Data Processing You've hit this before: processing a large dataset crashes your application with MemoryError . The culprit? Loading everything into memory at once. # Processing a 50GB log file def analyze_logs ( filename ): with open ( filename ) as f : lines = f . readlines () # Loads entire 50GB into RAM return [ line for line in lines if ' ERROR ' in line ] # Result: MemoryError (or system freeze) Root cause: Eager evaluation - computing all values before you need them. Solution: Lazy evaluation with generators. What Are Generators? Generators are iterators that produce values on-demand using lazy evaluation. Instead of computing all values upfront, they: Compute one value at a time Pause execution after yielding Preserve local state between calls Resume from the exact pause point Memory footprint: Constant O(1), regardless of data size. Eager vs Lazy Evaluation Eager (Lists) def