Common elements comparison between 2 lists

Diverse scenarios and solutions

Peeking under the hood, Python offers a range of strategies for diverse scenarios, from preserving order or finding intersections for immutable lists to optimizing operations for numerical data.

Set in stone: Immutable list intersections

 # When your data is as unchangeable as your partner's movie preferences
immutable_common_elements = list(frozenset(list1).intersection(list2))
print(immutable_common_elements)  # Fulfills its duty without altering the lists
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Numbers only: Numerical efficiency with NumPy

import numpy as np

numeric_list1 = np.array(list1)
numeric_list2 = np.array(list2)
 # Like a superhero for numerical lists!
common_numbers = np.intersect1d(numeric_list1, numeric_list2)
print(common_numbers)  # NP (No Problem) for NumPy!
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Practical cornerstones and pro-tips

Dive deeper with practical considerations: tackling duplicates, type-specific comparisons, large lists, and much more to become a list-slaying Pythonista.

Dealing with duplicates

from collections import Counter

counter1 = Counter(list1)
counter2 = Counter(list2)
 # Keeps the peace among duplicates!
common_elements = list((counter1 & counter2).elements())
print(common_elements)  # Huzzah! Handles duplicates
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Set-free solution

common_elements_no_set = [e for e in list1 if e in list2]
 # Who needs sets when you got skills!
print(common_elements_no_set)
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Type filter: Mixed type comparison:

common_elements_str = [str(element) for element in list1 if element in list2 and isinstance(element, str)]
 # Separates humans from robots, string from ints. 
print(common_elements_str)
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Work smart, not hard: Performance considerations

Python's methods like set intersection are generally more efficient than list comprehensions when working with large lists, primarily due to hash table implementations in Python sets.