# 5 Best Ways to Remove Duplicate Tuples From a Python List

π‘ Problem Formulation: When working with lists of tuples in Python, it is common to encounter duplicates that can skew data analysis or processing. The aim is to remove these duplicates while preserving the original order of elements wherever required. Given input like `[(1,2), (3,4), (1,2), (5,6)]`, the desired output is `[(1,2), (3,4), (5,6)]`.

## Method 1: Using a Loop

This method involves iterating through each item in the original list and adding it to a new list if it’s not already present, preserving order.

Here’s an example:

```original_list = [(1,2), (3,4), (1,2), (5,6)]
new_list = []

for a_tuple in original_list:
if a_tuple not in new_list:
new_list.append(a_tuple)

print(new_list)```

Output: `[(1, 2), (3, 4), (5, 6)]`

By looping through the list and checking for the existence of each tuple in a new list, we ensure that only unique tuples are added. This method is straightforward and preserves the order of elements.

## Method 2: Using Set with List Comprehension

This method converts tuples to a set to eliminate duplicates and then builds a list while preserving the original order using list comprehension.

Here’s an example:

```original_list = [(1,2), (3,4), (1,2), (5,6)]
unique_items = set()
deduped_list = [x for x in original_list if not (x in unique_items or unique_items.add(x))]

print(deduped_list)```

Output: `[(1, 2), (3, 4), (5, 6)]`

This concise approach uses list comprehension and a set to keep track of seen tuples. It is both efficient and preserves the order.

## Method 3: Using a Dictionary

Dictionaries in Python store unique keys, so we can use them to remove duplicates by turning the list of tuples into dictionary keys and then extracting them back into a list.

Here’s an example:

```original_list = [(1,2), (3,4), (1,2), (5,6)]
# Tuples as keys ensure uniqueness
unique_dict = dict.fromkeys(original_list)
# Extract keys back to a list
deduped_list = list(unique_dict)

print(deduped_list)```

Output: `[(1, 2), (3, 4), (5, 6)]`

This method uses the fact that dictionary keys are unique, so when we convert our list to a dictionary and back, we drop duplicates. It’s a fast operation but doesn’t preserve the order prior to Python 3.7.

## Method 4: Using itertools and OrderedSet

Using the `itertools` library in conjunction with `collections.OrderedDict` (before Python 3.8) or `collections.OrderedSet` (Python 3.8+), you can remove duplicates while preserving order.

Here’s an example:

```from collections import OrderedDict
original_list = [(1,2), (3,4), (1,2), (5,6)]

# Convert list of tuples into an OrderedDict
deduped_list = list(OrderedDict.fromkeys(original_list))

print(deduped_list)```

Output: `[(1, 2), (3, 4), (5, 6)]`

This approach makes use of the `OrderedDict.fromkeys()` method which removes duplicates and maintains the order of elements. It’s effective for ordered deduplication.

## Bonus One-Liner Method 5: Using functools and reduce

A functional programming one-liner using `functools.reduce` can accumulate unique tuples, preserving order.

Here’s an example:

```from functools import reduce
original_list = [(1,2), (3,4), (1,2), (5,6)]

deduped_list = reduce(lambda l, x: l.append(x) or l if x not in l else l, original_list, [])

print(deduped_list)```

Output: `[(1, 2), (3, 4), (5, 6)]`

Using `functools.reduce()`, this one-liner cumulatively builds up a list that includes only the first occurrence of each tuple. It’s a clever one-liner but may be less readable to those not familiar with functional paradigms.

## Summary/Discussion

• Method 1: Loop with Conditional Append. Simple and preserves order. However, can be slower for large lists.
• Method 2: Set with List Comprehension. Efficient and order-preserving. It’s a more Pythonic one-liner but requires understanding of how sets work in comprehension.
• Method 3: Dictionary Keys. Very fast, but order preservation is only guaranteed in Python 3.7 and later.
• Method 4: OrderedDict. Efficient and guaranteed order preservation. Useful for Python versions before 3.7 but slightly more complex to implement than other methods.
• Method 5: Reduce and Lambda. Clever and concise but poor in readability and potentially slower due to the nature of lambda functions.