Why are Sets in Python Displayed in a Seemingly Consistent Order?
While Python sets are indeed unordered, their displayed order might appear consistent. This order is not arbitrary but rather determined by the underlying hash algorithm and memory allocation.
Hashing and Memory Placement
Each element in a set is hashed, and the last N bits (where N depends on the set size) of the hash are used as an array index. The elements are then placed in memory at these indices. The order of elements in memory thus determines the order in which they are yielded.
Collision Resolution
However, when multiple elements have the same hash, collision resolution mechanisms come into play. These mechanisms distribute the elements into different memory locations (backup locations). The exact order in which this occurs is based on which elements arrived first.
Example with Integer Elements
Consider the example of set_1 and set_2:
set_1 = set([5, 2, 7, 2, 1, 88]) set_2 = set([5, 2, 7, 2, 1, 88])
The elements have unique last 3 bits in their hash, so collisions are avoided. The order of elements in both sets is preserved because they were added in the same order.
Example with String Elements
In the case of set_3 and set_4:
set_3 = set('abracadabra')
set_4 = set('abracadabra')Again, collisions are avoided due to unique last 3 bits in the hash. The elements are yielded in the order they were added, which happens to be the same order in both sets.
Insertion Order is Not Guaranteed
It's crucial to note that the order of elements in sets is not guaranteed. The order might differ if the input list is reordered, especially when collisions occur.
Performance Implications
The hashing and memory allocation process can impact set performance. For example, when the number of elements with similar hash values increases, collision resolution becomes more complex, affecting set lookup and insertion operations.
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