Tuples vs. Lists in Python: When Does Performance Matter More?

Performance Comparison of Tuples and Lists in Python

In Python, the choice between tuples and lists for data storage often arises. This article examines the performance differences between these two data structures, focusing on instantiation and retrieval of elements.

Tuples Generally Outperform Lists

In most scenarios, tuples exhibit superior performance over lists. This performance advantage stems from several key factors:

Constant Folding: Tuples of constants are precomputed by Python's optimizer, while lists must be built from scratch.

Reusable Nature: Running tuple(some_tuple) simply returns the same tuple directly, avoiding unnecessary copies. In contrast, list(some_list) requires a new list to be created by copying the data.

Compactness: Tuples have a fixed size, enabling more compact storage compared to lists, which over-allocate to optimize append operations.

Direct Referencing: Tuples incorporate references to their elements directly, while lists have an extra layer of indirection to an external array of pointers. This provides a speed advantage for indexed lookups and unpacking.

Instantiation

When it comes to instantiation, both tuples and lists have similar performance:

>>> import timeit
>>> timeit.timeit("tuple(range(1000))")  # Tuples
0.11859810000000012
>>> timeit.timeit("list(range(1000))")  # Lists
0.11701059999999988

Retrieval

However, tuples exhibit faster retrieval speeds:

>>> a = (10, 20, 30)
>>> timeit.timeit("a[1]")  # Tuples
0.02905340000000018
>>> b = [10, 20, 30]
>>> timeit.timeit("b[1]")  # Lists
0.02982960000000023

Kesimpulan

While both tuples and lists serve their purpose in data storage, tuples generally offer better performance. Their constant folding capabilities, reusability, compactness, and direct element referencing provide significant advantages over lists for many applications.

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