Integer caching in python

 Python (specifically the default CPython implementation) uses integer caching to optimize performance and memory usage by pre-allocating a range of commonly used small integer objects. 

How Integer Caching Works

• Cached Range: CPython pre-loads all integer objects in the range of -5 to 256 during interpreter startup.
• Singleton Instances: When you assign a variable an integer value within this range, Python simply returns a reference to the existing, pre-allocated object rather than creating a new one. This makes each integer in this range a singleton.
• Memory and Performance: This optimization saves significant memory and computation time because small integers are used very frequently (e.g., as loop counters or list indices).
• Immutability: Integer caching is safe because integers are immutable data types. Sharing the same object across multiple variables does not cause issues, as any operation that appears to modify an integer actually creates a new integer object. 

Key Behaviors and Examples

The effect of integer caching is most apparent when using the operator, which checks for object identity (same memory location) rather than value equality ( operator).

• Within the Cached Range:
• >>> a = 100
• >>> b = 100
• >>> a is b
• True
• >>> id(a) == id(b)
• True
• and point to the same memory location because 100 is within the range.
• Outside the Cached Range (in the Python REPL):
• >>> x = 257
• >>> y = 257
• >>> x is y
• False
• >>> id(x) == id(y)
• False
• and are different objects with different memory locations because 257 is outside the cached range. 

Compiler Optimization (Scripts vs. REPL)

Interestingly, the behavior for integers outside the range can differ between a Python interactive shell (REPL) and running code from a Python file (script).

• When running a whole script, the Python compiler can optimize and reuse identical integer literals even outside the standard range, making return for .
• In the REPL, code is compiled line-by-line, limiting this optimization and making the distinct object creation more apparent. 

Best Practices

Do not rely on the operator for integer value comparison, except when checking for (which is always a singleton). The behavior of for integers can vary across different Python implementations (e.g., CPython, PyPy) and even different versions of the same implementation. 

Always use the operator to check if two variables have the same value.