Memory Management

Introduction

Understanding and optimizing Python memory usage for better performance.

Memory Profiling

# Basic memory usage
import sys

lst = [1, 2, 3]
print(sys.getsizeof(lst))  # Size in bytes

# Memory profiler
# pip install memory_profiler
from memory_profiler import profile

@profile
def my_function():
    data = [i ** 2 for i in range(100000)]
    return data

Garbage Collection

import gc

# Collect garbage
gc.collect()

# Get garbage collection stats
print(gc.get_stats())

# Find unreachable objects
print(gc.garbage)

# Disable automatic collection
gc.disable()
gc.enable()

Memory Efficient Patterns

# Use generators instead of lists
def get_lines(file):
    for line in open(file):
        yield line

# Use __slots__ in classes
class Point:
    __slots__ = ["x", "y"]
    
# Use arrays for numerical data
from array import array
arr = array("i", [1, 2, 3, 4, 5])

Practice Problems

1. Profile memory usage
2. Find memory leaks
3. Optimize data structures
4. Use generators for large files
5. Implement slots in classes