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Returns a unique list of tuples created by zipping multiple iterables together, removing any duplicate tuples while preserving order.

Purpose

This function combines multiple iterables using zip() and then filters out duplicate tuples, returning only unique combinations. It's useful when you need to pair elements from multiple sequences and ensure no duplicate pairings exist in the result. The function maintains the order of first occurrence for each unique tuple.

Source Code

def unique_zip(*args):
    """
    Returns a unique list of zipped values.
    """
    return list(unique_iterator(zip(*args)))

Parameters

Parameter Details

*args: Variable number of iterable arguments (lists, tuples, arrays, etc.) to be zipped together. Each iterable should ideally have the same length, though zip() will truncate to the shortest iterable. Can accept any number of iterables (0 or more).

Return Value

Returns a list of unique tuples, where each tuple contains one element from each input iterable at the corresponding position. Duplicates are removed based on the entire tuple comparison. If no arguments are provided, returns an empty list. The order of unique tuples corresponds to their first appearance in the zipped sequence.

Dependencies

• itertools

Required Imports

The function requires 'unique_iterator' to be available in the same module or imported

Usage Example

# Assuming unique_iterator is defined elsewhere in the module
# Example implementation of unique_iterator for demonstration:
def unique_iterator(seq):
    seen = set()
    for item in seq:
        if item not in seen:
            seen.add(item)
            yield item

# Using unique_zip
list1 = [1, 2, 3, 1, 2]
list2 = ['a', 'b', 'c', 'a', 'b']
list3 = [10, 20, 30, 10, 20]

result = unique_zip(list1, list2, list3)
print(result)
# Output: [(1, 'a', 10), (2, 'b', 20), (3, 'c', 30)]

# With two lists
result2 = unique_zip([1, 2, 2, 3], ['x', 'y', 'y', 'z'])
print(result2)
# Output: [(1, 'x'), (2, 'y'), (3, 'z')]

Best Practices

• Ensure all input iterables have compatible lengths or be aware that zip() will truncate to the shortest iterable
• The function depends on 'unique_iterator' being available in scope - verify this dependency exists
• For large datasets, be aware that this creates a full list in memory rather than returning an iterator
• The uniqueness check is based on the entire tuple, not individual elements
• Elements within the tuples must be hashable for the unique_iterator to work with set-based deduplication
• Consider using a generator version if memory efficiency is important for large datasets

Tags

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