Save memory with generators.

Basic Generator

```python def count_up_to(n): count = 1 while count <= n: yield count count += 1

for num in count_up_to(5): print(num) # 1, 2, 3, 4, 5 ```

Generator vs List

```python # List (loads all in memory) def get_squares_list(n): return [i ** 2 for i in range(n)]

Generator (one at a time) def get_squares_gen(n): for i in range(n): yield i ** 2

Memory efficient for square in get_squares_gen(1000000): print(square) ```

Generator Expression

```python # List comprehension squares_list = [i ** 2 for i in range(5)]

Generator expression (use parentheses) squares_gen = (i ** 2 for i in range(5))

print(list(squares_gen)) # [0, 1, 4, 9, 16] ```

Real-World Example

```python def read_large_file(file_path): with open(file_path) as file: for line in file: yield line.strip()

Process huge file without loading all for line in read_large_file("huge_data.txt"): process(line) ```

Remember

- yield returns one value at a time - Saves memory for large datasets - Can't access values by index