Integer Caching

CPython Behavior

1. Small Integers

CPython caches [-5, 256]:

# Cached range
a = 100
b = 100
print(a is b)           # True
print(id(a) == id(b))   # True

2. Outside Range

# Not cached
x = 1000
y = 1000
print(x is y)           # Usually False
print(x == y)           # True

Why Cache

1. Performance

# Without caching:
# Every loop creates new objects
for i in range(100):
    total += i          # 100 objects

# With caching:
# Reuses same 100 objects

2. Memory

# Without caching
numbers = [1, 2, 3, 1, 2, 3]
# 6 separate objects

# With caching
# Only 3 objects shared

Best Practices

1. Never Rely On

# Bad: assumes caching
def bad(x):
    if x is 42:         # Don't!
        return True

# Good: use ==
def good(x):
    if x == 42:         # Correct
        return True

2. Singletons Only

# Use 'is' only for:
if x is None:           # OK
    pass

if x is True:           # OK
    pass

# Not for integers:
# if x is 0:            # Bad!

Summary

1. CPython

• Caches [-5, 256]
• Automatic optimization
• Not part of language spec

2. Write Portable

# Always use ==
if count == 0:
    pass

# Only is for singletons
if result is None:
    pass