Python __sizeof__ method

The __sizeof__ method in Python is a special dunder method that returns an object’s size in bytes, offering insight into its memory footprint. Integral for memory optimization, it measures internal storage, excluding garbage collection overhead. This article explores its functionality, limitations, and practical uses in depth.

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1. What is the __sizeof__ Method?

The __sizeof__ method calculates the memory size of an object’s core structure, invoked via obj.__sizeof__().

• Syntax: def __sizeof__(self), returns an integer.
• Default: Provided by object for built-in types.
• Scope: Measures instance data, not referenced objects.

Technical Note: Introduced in Python 2.6, it’s distinct from sys.getsizeof(), which includes GC overhead.

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2. How __sizeof__ Works: A Basic Example

It reveals object size in bytes.

Script:

x = 100
print(x.__sizeof__())

Output:

28

Explanation: __sizeof__ reports the memory used by the integer 100 (typically 28 bytes on 64-bit systems).

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3. Comparing with sys.getsizeof()

It differs from sys.getsizeof() in scope.

Aspect	__sizeof__	sys.getsizeof()
Scope	Core object	Includes GC overhead
Use	Type-specific	General-purpose
Customizable	Yes	No

Example:

import sys

x = [1, 2, 3]
print(x.__sizeof__())
print(sys.getsizeof(x))

Output:

80
88

Note: sys.getsizeof() adds GC tracking overhead.

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4. Why Use __sizeof__?

It aids in memory analysis:

Benefit	Description
Insight	Reveals memory footprint.
Optimization	Guides data structure choice.
Comparison	Evaluates type efficiency.
Customization	Allows size overrides.

Analogy: __sizeof__ is like a tape measure—sizing up an object’s raw space requirements.

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5. Practical Applications

A. Built-in Types

Measure standard objects.

print((42).__sizeof__())
print((3.14).__sizeof__())
print(("hello").__sizeof__())

Output:

28
24
54

Use Case: Type comparison.

B. Collections Comparison

Optimize data structures.

lst = [1, 2, 3, 4, 5]
tup = (1, 2, 3, 4, 5)
print(lst.__sizeof__())
print(tup.__sizeof__())

Output:

96
80

Benefit: Tuples save memory due to immutability.

C. Custom Objects

Define size manually.

class Data:
    def __init__(self, x):
        self.x = x

    def __sizeof__(self):
        return 100  # Custom size

d = Data(42)
print(d.__sizeof__())

Output:

100

Use Case: Memory modeling.

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6. Advanced Insights

Aspect	Behavior	Notes
Exclusions	No references	Lists exclude element sizes.
Platform	Varies	Depends on architecture.
Recursion	Manual	Custom for nested objects.

Example (Nested Size):

class Container:
    def __init__(self, items):
        self.items = items

    def __sizeof__(self):
        return object.__sizeof__(self) + sum(item.__sizeof__() for item in self.items)

c = Container([1, 2, 3])
print(c.__sizeof__())

Output:

164  <!-- Varies; base + 3 * 28 -->

Tip: Use sys.getsizeof() for total size.

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7. Golden Rules for Using __sizeof__

• ✅ Know Limits: Excludes GC, references.
• ✅ Use Sys: Pair with sys.getsizeof().
• ✅ Compare: Test alternatives.
• ❌ Don’t Assume: Sizes vary by platform.
• ❌ Don’t Overuse: Simple checks suffice.

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8. Conclusion

The __sizeof__ method is a key tool for assessing an object’s memory footprint in Python, aiding in memory optimization and performance tuning. While limited to core size, it complements sys.getsizeof()—offering precision when customized. Mastering __sizeof__ sharpens your memory management skills.

Final Tip: "Think of __sizeof__ as your object’s weigh-in—measuring its bare essentials for the memory scale."