**π‘ Problem Formulation:** In Python, finding the intersection of iterables means identifying the common elements across two or more collections, such as lists, sets, or tuples. For instance, given two lists `list1 = [1, 2, 3, 4]`

and `list2 = [3, 4, 5, 6]`

, we seek an efficient method to obtain the intersection `[3, 4]`

, the elements present in both lists.

## Method 1: Using Set Intersection

The set intersection method is a straightforward approach that first converts the iterables into sets and then calls the `intersection()`

method or uses the `&`

operator to retrieve the common elements. It’s efficient for large datasets, as the average time complexity is O(min(len(s1), len(s2))).

Here’s an example:

{3, 4}

This example demonstrates the ability to chain iterable lists into a single set, before finding the intersection with another set. Although concise, this method is similar to Method 1 and may introduce complexity without any performance benefits.

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

import itertools list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(itertools.chain(list1)) & set(list2) print(intersection)

Output:

{3, 4}

This example demonstrates the ability to chain iterable lists into a single set, before finding the intersection with another set. Although concise, this method is similar to Method 1 and may introduce complexity without any performance benefits.

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

This code snippet finds an intersection considering elements’ counts, creating a `Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

For a bonus one-liner solution, we can utilize the `itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

import itertools list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(itertools.chain(list1)) & set(list2) print(intersection)

Output:

{3, 4}

This example demonstrates the ability to chain iterable lists into a single set, before finding the intersection with another set. Although concise, this method is similar to Method 1 and may introduce complexity without any performance benefits.

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

from collections import Counter list1 = [1, 2, 2, 3, 4] list2 = [2, 3, 3, 4, 5, 6] intersection = list((Counter(list1) & Counter(list2)).elements()) print(intersection)

Output:

[2, 3, 4]

This code snippet finds an intersection considering elements’ counts, creating a `Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

For a bonus one-liner solution, we can utilize the `itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

import itertools list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(itertools.chain(list1)) & set(list2) print(intersection)

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

Using `filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

The `collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

from collections import Counter list1 = [1, 2, 2, 3, 4] list2 = [2, 3, 3, 4, 5, 6] intersection = list((Counter(list1) & Counter(list2)).elements()) print(intersection)

Output:

[2, 3, 4]

This code snippet finds an intersection considering elements’ counts, creating a `Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

For a bonus one-liner solution, we can utilize the `itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = list(filter(lambda x: x in list2, list1)) print(intersection)

Output:

[3, 4]

Using `filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

The `collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

from collections import Counter list1 = [1, 2, 2, 3, 4] list2 = [2, 3, 3, 4, 5, 6] intersection = list((Counter(list1) & Counter(list2)).elements()) print(intersection)

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

This snippet generates a list of elements that are present in both `list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

The `filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = list(filter(lambda x: x in list2, list1)) print(intersection)

Output:

[3, 4]

Using `filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

The `collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = [item for item in list1 if item in list2] print(intersection)

Output:

[3, 4]

This snippet generates a list of elements that are present in both `list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

The `filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = list(filter(lambda x: x in list2, list1)) print(intersection)

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

The example shows converting two lists to sets and then finding their intersection using the `&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

List comprehension provides a concise way to create a new list by iterating over an iterable and selecting elements that meet specific conditions. This method is particularly useful when working with list-like iterables and can be tailored for various scenarios.

Here’s an example:

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = [item for item in list1 if item in list2] print(intersection)

Output:

[3, 4]

This snippet generates a list of elements that are present in both `list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

The `filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

The example shows converting two lists to sets and then finding their intersection using the `&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

List comprehension provides a concise way to create a new list by iterating over an iterable and selecting elements that meet specific conditions. This method is particularly useful when working with list-like iterables and can be tailored for various scenarios.

Here’s an example:

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = [item for item in list1 if item in list2] print(intersection)

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

The example shows converting two lists to sets and then finding their intersection using the `&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

List comprehension provides a concise way to create a new list by iterating over an iterable and selecting elements that meet specific conditions. This method is particularly useful when working with list-like iterables and can be tailored for various scenarios.

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.

list1 = [1, 2, 3, 4] list2 = [3, 4, 5, 6] intersection = set(list1) & set(list2) print(intersection)

Output:

{3, 4}

`&`

operator. The resulting set contains only the common elements. This code is concise and works best with hashable items, as sets do not support unhashable elements like lists or dictionaries.

## Method 2: Using List Comprehension

Here’s an example:

Output:

[3, 4]

`list1`

and `list2`

through a list comprehension. While flexible, the efficiency can be lower for large lists, as this approach has a time complexity of O(n*m), where n and m are the lengths of the lists.

## Method 3: Using filter() and lambda

`filter()`

function along with a lambda can be employed to filter elements in one iterable that are also present in another. This functional programming technique gives a more expressive and concise way to achieve the intersection without explicit loops.

Here’s an example:

Output:

[3, 4]

`filter()`

with a lambda function tests each element from `list1`

to see if it is also in `list2`

, yielding a filtered list with only the intersecting elements. This method enhances readability but may not be as efficient as set-based approaches for larger datasets.

## Method 4: Using Collections Module

`collections`

module provides specialized container datatypes and the `Counter`

class can be leveraged to find intersections of elements occurring in two lists, taking into account the number of occurrences of each element.

Here’s an example:

Output:

[2, 3, 4]

`Counter`

object for each list and employing the `&`

operator to get the intersection. The result includes duplicates based on minimum occurrences in both lists. The `elements()`

method is then used to get the expanded list from the `Counter`

object.

## Bonus One-Liner Method 5: Using itertools.chain()

`itertools.chain()`

function to create a single iterable from multiple iterables. Combined with set operations, it can swiftly yield the intersection.

Here’s an example:

Output:

{3, 4}

## Summary/Discussion

**Method 1: Set Intersection.**Fast and efficient. Requires elements to be hashable.**Method 2: List Comprehension.**Simple and Pythonic. Less efficient for large lists.**Method 3: filter() with lambda.**Functional programming approach. Not the most efficient but improves readability.**Method 4: Collections Counter.**Considers element counts. Useful for multisets, but a bit more complex.**Bonus Method 5: itertools.chain().**Convenient for chaining iterables, but offers no clear advantage over simple set intersection.