GeeksforGeeks
geeksforgeeks.org › python › time-complexity-of-a-list-to-set-conversion-in-python
Time Complexity of A List to Set Conversion in Python - GeeksforGeeks
July 23, 2025 - The time to convert small list to set : 0.0 The set is : {1, 2, 3, 4, 5} The time to convert large list to set : 0.21737 · The time complexity of list to set conversion is O(n) where n is the number of element in the list.
GeeksforGeeks
geeksforgeeks.org › python › time-complexity-for-adding-element-in-python-set-vs-list
Time Complexity for Adding Element in Python Set vs List - GeeksforGeeks
July 23, 2025 - O(1) for initializing a set is constant time and adding an elements. O(n) for printing the list, as it requires iterating through all elements. When we add an element to a list using the append() method, Python directly adds the element to the end.
data structures - Time complexity of python set operations? - Stack Overflow
What is the the time complexity of each of python's set operations in Big O notation? I am using Python's set type for an operation on a large number of items. I want to know how each operation's More on stackoverflow.com
Time complexity of python set operations?
Python sets are powerful data ... tables, similar to dictionaries in Python but only storing keys without associated values. Due to their hash table implementation, most of the common operations on sets have efficient time complexities.... More on designgurus.io
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June 21, 2024Time complexity of sets
Ok, so sets/dictionaries work by hashing the index value. So that it's a constant time to find the item. You don't iterate through the set/dictionary. You just simply ask what is the value at this address? Let's say there are a number of people living on a street, everyone lives at the address that matches the length of their last name, and I told you got go to "smith" You wouldn't spend time checking houses to find smith, you would immediately go to house 5. The constant time spent was converting smith to 5. It would take you the same constant time to find where Scot or Johnson lived. That's how a hash works, it converts whatever value you have into an address in memory. It gets a bit more complex than just "length" and there is code in place to handle collisions (smith and jones are not at the same address). But that's the simple version of it. I understand that sets are data structures where all its elements are sorted They're not sorted. They're unordered. In recent version of python dictionaries maintain "insertion order". More on reddit.com
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March 17, 2021What's the time complexity of set.add()?
when I was reading the python docs at this part click here I had a slight doubt that whenever we perform any set.add(item) does this happens to traverse the entire set for item equality with the elements already inside the set. Code: class Foo: def __eq__(self, other): print("Called me.") return ... More on discuss.python.org
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March 27, 2024Videos
14:26
Python Sets Tutorial #1 & Time Complexity (BIG O) - YouTube
Time Complexity of Set operations | Lecture 20.1 | Python ...
17:41
Time & Space Complexity - Big O Notation - DSA Course in Python ...
29:58
Big-O Runtime Complexity List and Set Operations - YouTube
01:16
PYTHON : Time complexity of python set operations? - YouTube
08:50
Get Minimum Element from Stack in Constant O(1) time complexity ...
GeeksforGeeks
geeksforgeeks.org › python › internal-working-of-set-in-python
Internal working of Set in Python - GeeksforGeeks
July 11, 2025 - It's important to note that an ... exists in a set, you can use the in keyword. The average time complexity for this operation is O(1), but in the worst case, it can become O(n)....
GeeksforGeeks
geeksforgeeks.org › python › time-complexity-to-convert-a-set-to-list-in-python
Time Complexity to Convert a Set to List in Python - GeeksforGeeks
July 23, 2025 - Let's denote the size of the set as n. The time complexity to convert a set to a list generally takes O(n) time. where n is the n of elements in the set. There are various methods to convert a set to a list in Python.
GeeksforGeeks
geeksforgeeks.org › python › complexity-cheat-sheet-for-python-operations
Complexity Cheat Sheet for Python Operations - GeeksforGeeks
July 12, 2025 - Note: Defaultdict has operations same as dict with same time complexity as it inherits from dict. Python’s set is another hash-based collection, optimized for membership checks and set operations:
Python
wiki.python.org › moin › TimeComplexity
TimeComplexity - Python Wiki
As seen in the source code the complexities for set difference s-t or s.difference(t) (set_difference()) and in-place set difference s.difference_update(t) (set_difference_update_internal()) are different! The first one is O(len(s)) (for every element in s add it to the new set, if not in t).
DNMTechs
dnmtechs.com › time-complexity-of-python-set-operations-in-python-3-programming
Time Complexity of Python Set Operations in Python 3 Programming – DNMTechs – Sharing and Storing Technology Knowledge
Adding elements to a set is a fast operation in Python. The time complexity of adding a single element to a set is O(1) on average. This means that regardless of the size of the set, the time it takes to add an element remains constant.
Top answer 1 of 4
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According to Python wiki: Time complexity, set is implemented as a hash table. So you can expect to lookup/insert/delete in O(1) average. Unless your hash table's load factor is too high, then you face collisions and O(n).
P.S. for some reason they claim O(n) for delete operation which looks like a mistype.
P.P.S. This is true for CPython, pypy is a different story.
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The other answers do not talk about 2 crucial operations on sets: Unions and intersections. In the worst case, union will take O(n+m) whereas intersection will take O(min(x,y)) provided that there are not many element in the sets with the same hash. A list of time complexities of common operations can be found here: https://wiki.python.org/moin/TimeComplexity
GeeksforGeeks
geeksforgeeks.org › sets-in-python
Sets in Python - GeeksforGeeks
Time Complexity of this is O(min(len(s1), len(s2)) where s1 and s2 are two sets whose union needs to be done. ... # Python program to # demonstrate intersection # of two sets set1 = set() set2 = set() for i in range(5): set1.add(i) for i in ...
Published February 4, 2025
Reddit
reddit.com › r/learnpython › time complexity of sets
r/learnpython on Reddit: Time complexity of sets
March 17, 2021 -
I understand that sets are data structures where all its elements are sorted and it doesn't contain any duplicate values, but why is their time complexity just O(1)?
How can it be a constant value, even if the set contains millions of elements?
I thought that the complexity was O(n*log(n)) due to a binary search, but looks like it's even faster and I can't really understand how.
Thanks in advance for any answer!
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Ok, so sets/dictionaries work by hashing the index value. So that it's a constant time to find the item. You don't iterate through the set/dictionary. You just simply ask what is the value at this address? Let's say there are a number of people living on a street, everyone lives at the address that matches the length of their last name, and I told you got go to "smith" You wouldn't spend time checking houses to find smith, you would immediately go to house 5. The constant time spent was converting smith to 5. It would take you the same constant time to find where Scot or Johnson lived. That's how a hash works, it converts whatever value you have into an address in memory. It gets a bit more complex than just "length" and there is code in place to handle collisions (smith and jones are not at the same address). But that's the simple version of it. I understand that sets are data structures where all its elements are sorted They're not sorted. They're unordered. In recent version of python dictionaries maintain "insertion order".
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As others have pointed out, these are implemented with hash tables. Hashing is when you generate some pseudorandom number from some input data. In a hash table, that number is clipped (modulo) so as to fit inside the table. Ideally, different data will always get you a different number so you end up in the right spot of the hash table in constant time, but that's obviously not always going to happen and you will get so-called hash collisions. When those happen, some sort of strategy is necessary to deal with them and since you'd ideally design your hash table so they don't happen very often, that strategy tends to just be to use the next spot in the table, and then just linearly search. In that sense, it's not exactly a constant-time algorithm, but you really should only be searching a very small potion of the full table, so it's close. As the table fills up (its "load factor" increases), this cost generally grows, although that is not universally true (e.g., when perfect hashing is an option). It can also happen that the hash table needs to be grown, which will generally not be a constant-time operation. There are all sorts of strategies for that. More often than not, though, the hashing step will not lead to a collision, and you get O(1) performance.
GitHub
gist.github.com › Gr1N › 60b346b5e91babb5efac
Complexity of Python Operations · GitHub
Complexity of Python Operations. GitHub Gist: instantly share code, notes, and snippets.
Quora
quora.com › Why-do-sets-in-Python-have-an-algorithmic-complexity-of-O-1
Why do sets in Python have an algorithmic complexity of O(1)? - Quora
That is, a 10x increase in key size led to a 10x increase in the time taken to do set membership. ... ), because it requires a comparison. But sometimes we just ignore this or say that the membership check is O(k) ... ) expected time complexity assumes a non-adversarial model in which keys are distributed randomly (or randomly after hashing), not chosen by an opponent with knowledge of the hash function. See Generating 64 bit hash collisions to DOS Python – Robert Grosse – Medium.
Python.org
discuss.python.org › python help
What's the time complexity of set.add()? - Python Help - Discussions on Python.org
March 27, 2024 - when I was reading the python docs at this part click here I had a slight doubt that whenever we perform any set.add(item) does this happens to traverse the entire set for item equality with the elements already inside the set. Code: class Foo: def __eq__(self, other): print("Called me.") return id(self) == id(other) def __hash__(self): return 1 def __repr__(self): return "Dummy()" s = {Foo(), Foo(), Foo(), Foo()} print("==========") s.add(Foo...
Medium
binarybeats.medium.com › python-set-data-structure-methods-use-time-and-space-complexity-366b8c408345
Python Set Data Structure: Methods, Use, Time, and Space Complexity | by Binary Beats | Medium
April 22, 2023 - By using sets, we can solve this problem efficiently in O(n) time complexity, where n is the total number of elements in both lists.
Reddit
reddit.com › r/learnpython › don't understand time complexity of this easy leetcode question
r/learnpython on Reddit: Don't understand time complexity of this easy leetcode question
June 21, 2023 -
class Solution:
def containsDuplicate(self, nums: List[int]) -> bool:
hashset = set()
for n in nums:
if n in hashset:
return True
hashset.add(n)
return FalseThis is the solution provided and they say its O(n). The for loop is O(n) and the "if n in hashset" is O(n) and they're nested so how isn't this O(n2)?
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I think for Python sets, the average case of set lookup is O(1) since there are no duplicates. So O(1) nested in O(n) is linear time.
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set is implemented in python with a hash table so looking up if in a element is in the set is generally a O(1) operation (unless it’s really big and you start getting hash collisions) https://wiki.python.org/moin/TimeComplexity
Python Morsels
pythonmorsels.com › time-complexities
Python Big O: the time complexities of different data structures in Python - Python Morsels
April 16, 2024 - For example, sets are faster at key lookups than lists, but they have no ordering. Dictionaries are just as fast at key lookups as sets and they maintain item insertion order, but they require more memory. In day-to-day Python usage, time complexity tends to matter most for avoiding loops within loops.
GeeksforGeeks
geeksforgeeks.org › dsa › understanding-time-complexity-simple-examples
Time Complexity with Simple Examples - GeeksforGeeks
Therefore, the auxiliary space complexity of the program is O(nm + 1), which simplifies to O(n*m). So from the above examples, we can conclude that the time of execution increases with the type of operations we make using the inputs.
Published 1 month ago
UCI
ics.uci.edu › ~pattis › ICS-33 › lectures › complexitypython.txt
Complexity of Python Operations
------ 3) Algorithm 3: A list is ... O(1): 2 len (each O(1)) and == ints O(1) The complexity class for executing the entire function is O(N) + O(1) = O(N + 1) = O(N). So the complexity class for this algorithm/function is lower than both the first and second algorithms/...
DEV Community
dev.to › iihsan › time-complexity-analysis-of-python-methods-bigo-notations-for-list-tuple-set-and-dictionary-methods-47l9
Time Complexity Analysis of Python Methods: Big(O) Notations for List, Tuple, Set, and Dictionary Methods - DEV Community
January 15, 2024 - Whether you're working on real-world software or tackling problems in interviews, it's not just about writing code; it's all about writing efficient and scalable code. So, understanding the time complexity of your code becomes essential. In this article, we'll break down the Python methods for lists, tuples, sets, and dictionaries.