♾️ Python Generators — Memory-Efficient Iterators
Generators produce values on demand instead of storing everything in memory. Perfect for large datasets, streaming, and infinite sequences.
💡 Quick Tip:
Use generators when processing large files, streams, or infinite sequences. For small data, regular lists are easier.
💻 Code Example:
# 1. Generator function with yield def count_up(limit): """Generates numbers from 0 to limit — one at a time.""" n = 0 while n < limit: yield n # Pauses here and returns n n += 1 # Only ~48 bytes memory — regardless of how large limit is! gen = count_up(1_000_000) print(next(gen)) # 0 print(next(gen)) # 1 # 2. Generator expression (like list comp but lazy) large_squares = (x**2 for x in range(1_000_000)) # List would use 8MB; generator uses ~128 bytes! # 3. Real-world use: reading a huge file line by line def read_large_pynfinity_log(filepath): with open(filepath, 'r') as f: for line in f: yield line.strip() # for log_entry in read_large_pynfinity_log('app.log'): # process(log_entry) # 4. Infinite sequence generator def fibonacci(): a, b = 0, 1 while True: yield a a, b = b, a + b fib = fibonacci() first_10 = [next(fib) for _ in range(10)] print(first_10) # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
| Concept | Key Takeaway |
|---|---|
| yield | Pauses function and returns a value — resumes on next call |
| Generator expression | (x for x in iterable) — lazy version of list comp |
| Memory | Lists store all elements; generators compute one at a time |
| send() | Can SEND a value BACK into a generator (bidirectional) |
Keep exploring and happy coding! 🚀
pynfinity
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