List vs. Dict: When Should You Use a Look-Up Table in Python?

Python: List vs Dict for Look Up Table

In Python, there are two common data structures for creating a look up table: lists and dictionaries. This article aims to explore the differences between the two and identify which one is more suitable for various scenarios.

Speed

One of the key factors to consider when choosing between a list and a dict is the lookup speed. Lookups in lists are performed sequentially, which means that the time complexity is O(n), where n is the number of elements in the list. On the other hand, lookups in dictionaries are amortized O(1) because they utilize a hash table to store key-value pairs, enabling direct access.

Memory

Both dictionaries and sets use hashing under the hood, which consumes more memory than just storing the object itself. The hash table implementation aims to keep its fill rate around 2/3, potentially resulting in memory overhead.

Suitability for Specific Scenarios

• If you need to associate values with keys: A dictionary is the best choice.
• If you do not have any values associated with keys: A set is a lightweight alternative to a dict, particularly if the number of elements is small.
• If you add new keys on the fly: A dictionary remains a suitable choice, as long as the dataset is not too large and you can accept the O(1) amortized lookup time.
• If you have a large dataset and you do not add new keys on the fly: Sorting the list and using binary search (O(log n)) can be a viable option, but it may be slower for strings and impossible for objects without a natural ordering.

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