How to Efficiently Check List Subset Membership in Performance-Critical Scenarios?

Verifying List Subset Membership for Performance-Critical Scenarios

Given two lists, determining whether one is a subset of the other is a common operation. To achieve optimal performance, selecting the most efficient approach is essential.

One method involves intersecting the sets derived from the lists and checking if the result is equal to one set. However, given the number of datasets involved, a more performant solution is necessary.

When one list is static and the other is a dynamic dictionary from which keys are extracted, an alternative approach is recommended. Consider the following solution:

<code class="python">def is_subset(lookup_table, dynamic_list):
    # Convert lookup table to a set for efficient lookups
    lookup_set = set(lookup_table)
    
    # Convert dynamic list to a set for faster comparisons
    dynamic_set = set(dynamic_list.keys())
    
    # Check if the dynamic list's set is a subset of the lookup table's set
    return dynamic_set <= lookup_set

By converting the static lookup table and dynamic list to sets, the lookup operations are significantly faster. Additionally, the use of the <= operator to compare sets is highly efficient.

Examples:

<code class="python">>>> lookup_table = [1, 3, 5]
>>> dynamic_list = [1, 3, 5, 8]
>>> is_subset(lookup_table, dynamic_list)
True

>>> lookup_table = ['yes', 'no', 'hmm']
>>> dynamic_list = ['sorry', 'no', 'hmm']
>>> is_subset(lookup_table, dynamic_list)
False</code>

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