copium 0.1.0a1.dev84

Fast drop-in replacement for copy.deepcopy()

copium

Fast drop-in replacement for copy.deepcopy().

Benchmarked on GitHub Actions using tools/run_benchmark.py, charted with tools/generate_chart.py

Highlights

• ⚡ 4-28x faster than copy.deepcopy() on builtin types
• 🧠 uses ~44% less memory than copy.deepcopy() on average
• 🧪 passes all tests in CPython/Lib/test/test_copy.py
• 🎯 behaves exactly the same as copy.deepcopy() for all datamodelzoo.CASES
• 🛡️ tested for refcount, recursion, threading and memo edge cases
• 🐍 pre-built wheels for Python 3.10-3.14 on Linux/macOS/Windows (x64/ARM64)
• 🔓 passes all tesee-frts on threaded Pyth buildons

Installation

[!WARNING] This is an alpha version. Run your test suite with COPIUM_PATCH_DEEPCOPY=1 to verify compatibility in your environment.

pip install copium

Usage

Simply export COPIUM_PATCH_DEEPCOPY=1 before running your application. You don't have to change a single line of code:

cat example.py

from copy import deepcopy

assert deepcopy(x := []) is not x

COPIUM_PATCH_DEEPCOPY=1 python example.py

---

To enable the patch manually:

import copium.patch

copium.patch.enable()

---

To use manually:

from copium import deepcopy

assert deepcopy(x := []) is not x

The copium module includes all public declarations of stdlib copy module, so it's generally safe to:

- from copy import copy, deepcopy, Error
+ from copium import copy, deepcopy, Error

---

Speedups on Python 3.14

tuple

• () ~5.66x
• tuple[int, ...] ~12.46x (20 items)
• tuple[int, ...] ~28.28x (5000 items)
• tuple[tuple[str, tuple[tuple[str, tuple[tuple[str, tuple[tuple[str, tuple[int, ...]]]]]]]]]] ~14.54x

list

• [] ~4.97x
• list[int] ~9.58x (20 items)
• list[int] ~16.23x (5000 items)
• list[str | list[str | list[str | list[str | list[int]]]]] ~9.19x

dict

• {} ~4.59x
• dict[str, int] ~5.95x (20 items)
• dict[str, int] ~3.94x (5000 items)
• dict[str, dict[str, ...]] ~6.19x

set

• set() ~15.87x
• set[int] ~6.55x (20 items)
• set[int] ~3.69x (5000 items)
• set[frozenset[frozenset[int]]] ~13.72x

other

• int/str/bytes ~4.86x
• dataclasses ~2.74x

Expect much higher speedups on Python 3.13 and lower.