Dictionaries vs. Lists vs. Sets: Which Python Data Structure is Best for Look-Up Tables?

Evaluating Python Data Structures for Efficient Look-up Tables

When dealing with large datasets, selecting the most efficient data structure for look-up operations is crucial. In Python, the two primary options for creating look-up tables are lists and dictionaries.

Speed Considerations

Dictionaries excel in fast look-up performance due to their hashing implementation. Lookups in dictionaries are amortized O(1), meaning the time complexity is nearly constant regardless of the number of items. On the other hand, lists require sequential searches, resulting in an O(n) time complexity, where n is the number of elements in the list.

Memory Usage

Both dictionaries and sets use hashing internally, which requires more memory than simply storing the objects themselves. According to A.M. Kuchling in "Beautiful Code," hashing is designed to keep the hash about 2/3 full, potentially resulting in memory overhead.

Optimal Choice for Look-Up Tables

If you do not need to associate values with the looked-up items (as implied by Edit 3 in the question), a set might be a more efficient choice. Sets provide O(1) look-up performance and consume less memory than lists or dictionaries.

If you must add new items to your look-up table on the fly, you could potentially sort the list and use binary search for O(log n) lookups. However, this approach may be slower for strings and impractical for objects without a natural ordering.

Ultimately, the choice between a list, dictionary, or set for your look-up table depends on the specific requirements of your application, particularly the size and lookup frequency of the data.

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