Benchmark list.append vs deque.append in Python

Measures and compares the performance of appending to a Python list versus a collections.deque using timeit.repeat, showing best and average timings.

Medium Python 3.10+ Aug 9, 2026 Concurrency & performance 12 views 0 copies

benchmark performance list deque timeit

Python code

23 lines

Python 3.10+

"""Benchmark list.append vs collections.deque.append."""

import timeit

def bench(stmt, setup, repeat=5, number=1_000_000):
    times = timeit.repeat(stmt, setup=setup, repeat=repeat, number=number)
    return min(times), sum(times) / len(times)

if __name__ == "__main__":
    number = 1_000_000
    list_best, list_avg = bench(
        "lst.append(1)",
        "lst = []",
        number=number,
    )
    deque_best, deque_avg = bench(
        "dq.append(1)",
        "from collections import deque; dq = deque()",
        number=number,
    )
    print(f"list.append  — best: {list_best:.4f}s, avg: {list_avg:.4f}s")
    print(f"deque.append — best: {deque_best:.4f}s, avg: {deque_avg:.4f}s")
    print(f"deque is {'faster' if deque_best < list_best else 'slower'} on best time")

Output

stdout

list.append  — best: 0.0345s, avg: 0.0350s
deque.append — best: 0.0512s, avg: 0.0530s
deque is slower on best time

How it works

This script uses timeit.repeat to run each append statement multiple times, collecting several timings to account for system variability. The min() result represents the fastest run, which is the most reliable indicator of performance, while the average gives a sense of typical behavior. Appending to a list is generally faster than appending to a deque because list append has a highly optimized path for adding to the end, while deque append has some overhead for maintaining the doubly-linked list structure. This benchmark highlights the importance of measuring before optimizing; while deques excel at appending to both ends, for simple end-append operations, lists are often sufficient and faster.

Common mistakes

Using `timeit.timeit` instead of `timeit.repeat` and not taking the minimum, which can overestimate times due to system noise
Not isolating setup code, causing the list or deque to be re-created each run, which skews results
Comparing operations with different data structures without considering their specific use cases (e.g., deque for both-end appends)

Variations

Use `timeit.default_timer()` and manual loops for more control over the benchmark setup
Benchmark other operations like `list.pop(0)` vs `deque.popleft()` to see where deque outperforms

Real-world use cases

Deciding between a list and deque when implementing a queue for a multi-threaded task scheduler.
Optimizing a high-frequency logging buffer where elements are appended and occasionally drained from the front.
Validating performance assumptions when building Python libraries that need predictable append-speed for large datasets.

Benchmark list.append vs deque.append in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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