Memory Optimization Techniques

import sys

# 1. Use generators instead of lists
list_data = [x**2 for x in range(10000)]
gen_data = (x**2 for x in range(10000))
print(f"List: {sys.getsizeof(list_data)} bytes")
print(f"Generator: {sys.getsizeof(gen_data)} bytes")

# 2. Use __slots__ for memory-efficient classes
class PointRegular:
    def __init__(self, x, y):
        self.x = x
        self.y = y

class PointSlots:
    __slots__ = ['x', 'y']
    def __init__(self, x, y):
        self.x = x
        self.y = y

p1 = PointRegular(1, 2)
p2 = PointSlots(1, 2)
print(f"\nRegular: {sys.getsizeof(p1.__dict__)} bytes (dict)")
print(f"Slots: no __dict__ (much smaller!)")

# 3. Use appropriate data types
import array

py_list = [1, 2, 3, 4, 5]
py_array = array.array('i', [1, 2, 3, 4, 5])
print(f"\nList: {sys.getsizeof(py_list)} bytes")
print(f"Array: {sys.getsizeof(py_array)} bytes")

# 4. Delete large objects when done
large_data = list(range(100000))
print(f"\nBefore del: {sys.getsizeof(large_data)} bytes")
del large_data
# large_data is now freed

# 5. Use itertools for memory-efficient iteration
from itertools import islice, chain

# Instead of creating full lists:
first_10 = list(islice(range(1000000), 10))
print(f"First 10 of million: {first_10}")

# Tips summary
print("\n=== Memory Tips ===")
tips = [
    "Use generators for large sequences",
    "Use __slots__ for many small objects",
    "Delete large objects with 'del'",
    "Use array.array for numeric data",
    "Process files line-by-line, not all at once",
]
for tip in tips:
    print(f"  • {tip}")