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The Python int is a built-in numeric data type representing signed, arbitrary-precision mathematical integers. Unlike integers in languages like C or Java, Python integers are not bound by fixed hardware memory limits (e.g., 32-bit or 64-bit). Instead, they dynamically scale in size and are constrained only by the available system memory.

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Instantiation and Syntax

Integers can be instantiated via numeric literals in multiple bases or through the int() constructor. 

# Base-10 (Decimal)
dec_int = 42


# Base-2 (Binary)
bin_int = 0b101010


# Base-8 (Octal)
oct_int = 0o52


# Base-16 (Hexadecimal)
hex_int = 0x2A


# Constructor casting and base conversion
cast_int = int(42.9)          # Truncates to 42
str_int = int("42")           # Parses string to base-10
base_int = int("2A", base=16) # Parses string using specified base

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Internal Representation (CPython)

In the standard CPython implementation, an int is not a primitive scalar. It is a full C struct (specifically PyLongObject). To achieve arbitrary precision, CPython represents the integer’s absolute value as an array of “digits.” On 64-bit systems, each digit in this array represents a base-2302^{30} value (stored in a 32-bit unsigned integer). As of Python 3.12, CPython utilizes a compact integer representation where the sign, the size (number of elements in the digit array), and memory allocation flags are stored in a dedicated tag field (lv_tag) within the integer struct, replacing the older approach of using the standard object header’s signed size field (ob_size). Because it is a full object, a Python int carries memory overhead. On a 64-bit system, a zero or single-digit integer typically consumes 24 to 28 bytes of memory, with the size growing by 4 bytes for every additional 2302^{30} magnitude.

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Immutability and Interning

Python integers are strictly immutable. Any arithmetic or bitwise operation performed on an int allocates and returns a completely new int object rather than modifying the existing one in place. To mitigate the performance overhead of object allocation for frequently used numbers, CPython implements integer interning. At startup, CPython pre-allocates an array of singleton integer objects for values ranging from -5 to 256. Any operation resulting in a value within this range returns a reference to the cached singleton rather than allocating a new object. Because the CPython compiler optimizes code blocks by reusing identical integer literals (constant folding/co-allocation), demonstrating the boundaries of this interned range requires dynamically parsing the integers to bypass the compiler’s static optimizations: 

# Within the interned range (-5 to 256)
a = 256
b = int("256")
print(a is b)  # True (Both reference the same pre-allocated singleton)


# Outside the interned range
x = 257
y = int("257")
print(x is y)  # False (Distinct objects are allocated in memory)

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Type Hierarchy

  • int implements the numbers.Integral abstract base class.
  • The Python bool type is a direct subclass of int. The boolean singletons True and False behave exactly as the integers 1 and 0 in all arithmetic contexts.

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Low-Level Methods

The int type exposes several methods for bitwise inspection and byte serialization, bypassing the need for external bit-manipulation libraries. 
n = 1024


# Returns the number of bits required to represent the integer in binary (excluding sign and leading zeros)
n.bit_length()  # 11


# Returns the number of ones in the binary representation of the absolute value (Python 3.10+)
n.bit_count()   # 1


# Serializes the integer to an array of bytes
byte_rep = (255).to_bytes(length=2, byteorder='big', signed=False) 

# Returns: b'\x00\xff'


# Deserializes an array of bytes back into an integer
int.from_bytes(b'\x00\xff', byteorder='big', signed=False) 

# Returns: 255
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