# 5 Best Ways to Check if Two Lists of Tuples Are Identical in Python

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π‘ Problem Formulation: In Python, verifying if two lists of tuples are identical involves comparing each corresponding tuple from both lists for equality. This means they must contain the same elements, in the same order, and with the same types. For example, the lists `[(1, 2), (3, 4)]` and `[(1, 2), (3, 4)]` are identical, which is the desired output of the methods discussed.

## Method 1: Using the Equality Operator

The equality operator `==` is the most straightforward way to determine if two lists of tuples are identical. It compares the corresponding elements and the structure of both lists and evaluates to True only if both are completely equal.

Here’s an example:

```list1 = [(1, 2), (3, 4)]
list2 = [(1, 2), (3, 4)]
print(list1 == list2)```

Output:

`True`

This code snippet directly compares two lists using the equality operator. It prints `True` if every corresponding tuple in both lists is equal, and `False` otherwise.

## Method 2: Using a for Loop

A for loop can be used to iterate through each tuple of the lists and compare them individually. This method is useful if you need to perform additional operations during the comparison or make a more complex evaluation.

Here’s an example:

```list1 = [('apple', 'banana'), ('cherry', 'date')]
list2 = [('apple', 'banana'), ('cherry', 'date')]
identical = True
for tup1, tup2 in zip(list1, list2):
if tup1 != tup2:
identical = False
break
print(identical)```

Output:

`True`

This code uses a for loop to iterate over the zipped tuples of both lists and checks if they are not equal. If they are not, it sets `identical` to `False` and breaks the loop.

## Method 3: Using the all() Function

The `all()` function in conjunction with a generator expression provides an efficient, Pythonic way to determine if all corresponding tuples in both lists are equal. It returns True if all items in an iterable are true.

Here’s an example:

```list1 = [(5, 6), (7, 8)]
list2 = [(5, 6), (7, 8)]
print(all(tup1 == tup2 for tup1, tup2 in zip(list1, list2)))```

Output:

`True`

The code example uses `all()` to verify that every comparison of tuples in the zipped list is True. It is a compact and efficient method to check for equality of two lists.

## Method 4: Comparing Sorted Lists

If the order of the tuples within the lists is not important, but their content is, you can compare sorted versions of the lists. This method makes use of the `sorted()` function, which sorts each list prior to comparison.

Here’s an example:

```list1 = [(2, 3), (1, 2)]
list2 = [(1, 2), (2, 3)]
print(sorted(list1) == sorted(list2))```

Output:

`True`

The provided code snippet sorts both lists of tuples and then compares them using the equality operator. This ensures that the order of the tuples within the lists does not affect the equality check.

## Bonus One-Liner Method 5: Using Comparison with map()

The `map()` function can be combined with the `==` operator to produce a one-liner that checks if two lists of tuples are identical. It applies the comparison operator to each pair of corresponding tuple elements.

Here’s an example:

```list1 = [(9, 10), (11, 12)]
list2 = [(9, 10), (11, 12)]
print(all(map(lambda tup: tup[0] == tup[1], zip(list1, list2))))```

Output:

`True`

This one-liner uses `map()` to apply a lambda function that compares tuples from both lists. It is a concise way to perform the check, leveraging a functional programming style.

## Summary/Discussion

• Method 1: Equality Operator. Simple and straightforward. Works best when the lists are expected to be of the same length and order.
• Method 2: For Loop. More verbose but allows additional logic during iteration. Can be less efficient if lists are large.
• Method 3: all() Function. Concise and Pythonic. Use when you want a one-liner that is easy to read and understand.
• Method 4: Comparing Sorted Lists. Useful when order within the list does not matter. Involves additional overhead of sorting.
• Bonus Method 5: Comparison with map(). Compact and functional. May be less intuitive for those not familiar with functional programming concepts.