Python
wiki.python.org › moin › TimeComplexity
TimeComplexity - Python Wiki
[3] = For these operations, the worst case n is the maximum size the container ever achieved, rather than just the current size. For example, if N objects are added to a dictionary, then N-1 are deleted, the dictionary will still be sized for N objects (at least) until another insertion is made.
Stack Overflow
stackoverflow.com › questions › 49732932 › python-list-del-insert-no-of-assignments-and-time-complexity
Python list - del, insert, no. of assignments and time complexity - Stack Overflow
If I have a list containing 100000 elements and I delete the first, does python need 99999 assignments for the shifts or 100000? ... Do you really care if it's 79999 or 80000? If you want to be accurate, the complexity is actually O(n-i+1), ...
Why is removing elements from a list so slow, and is there a faster way?
Each time you remove an element from the list, the elements "shift down" in position. This takes time, as you are essentially recreating (large portions of) the list after each removal. When you create a new list, you build it once. A more Pythonic way to do this is to use a list comprehension. clw = [word for word in words if len(word) == length] More on reddit.com
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April 21, 2024python - Speed of del vs remove on list - Stack Overflow
Assume that I have a list, and I have an element that I want to remove from it. Now suppose I have the index of that element, and I also know exactly what element it is. Would it be faster to do del More on stackoverflow.com
Explain time and space complexity of two python codes. Which one is the best? (Non- subjective) - Stack Overflow
Do you want the most memory efficient, or time efficient? You should update your question to be non-subjective. ... I am new to python, i am not familiar using keywords in stack overflow, "subjective" or "non-subjective". Here for this question, the objective to know the complexities of both codes where i can write code with out using loop to sum even integers in a list. if there is any mistake in framing question, apologize me. ... According to the Python wiki, deleting ... More on stackoverflow.com
how much time will it take to remove a key, value pair from a dictionary?
Fixed time (O(1)). If you need the exact value on your computer, run a benchmark. More on reddit.com
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May 22, 2023GeeksforGeeks
geeksforgeeks.org › complexity-cheat-sheet-for-python-operations
Complexity Cheat Sheet for Python Operations - GeeksforGeeks
December 13, 2024 - Python’s set is another hash-based collection, optimized for membership checks and set operations: Tuples are immutable sequences, making them lighter but with limited operations compared to lists: Strings are immutable and behave similarly to tuples in terms of time complexities: ... Amortized complexity applies to append() because resizing happens occasionally. Insertions and deletions at arbitrary positions require shifting elements.
GeeksforGeeks
geeksforgeeks.org › python › time-complexities-of-python-dictionary
Time Complexities of Python Dictionary - GeeksforGeeks
July 23, 2025 - Example: Python · # Create a dictionary dict = {'a': 1, 'b': 2, 'c': 3} # Access value by key val = dict['b'] print(val) Why O(1)? Hash tables use the hash of the key to find the memory location which enables direct access. Let's explore the time complexities of other dictionary operations: Table of Content · Adding or Updating an Element (O(1)) Deleting an Element (O(1)) Checking if a Key Exists (O(1)) Iterating Over a Dictionary (O(n)) Getting All Keys or Values (O(n)) Copying a Dictionary (O(n)) Clearing a Dictionary (O(1)) Adding a new key-value pair or updating an existing key is also O(1) in average cases as the dictionary directly places the value in the hash table.
GitHub
gist.github.com › Gr1N › 60b346b5e91babb5efac
Complexity of Python Operations · GitHub
Complexity of Python Operations. GitHub Gist: instantly share code, notes, and snippets.
UCI
ics.uci.edu › ~pattis › ICS-33 › lectures › complexitypython.txt
Complexity of Python Operations
In fact, we could also simplify ... O(N Log N) - for fast Python sorting to just copy = sorted(alist) O(N Log N) - for fast Python sorting because sorted will create a list of all the values in its iterable argument, and return it after mutating (sorting) it. So we don't have to explicitly create such a copy in our code. This change will speed up the code, but it won't change the complexity analysis because O(N + N Log N) = O (N Log N)....
LinkedIn
linkedin.com › pulse › demystifying-python-data-structure-time-space-complexity-deepak-s
Demystifying Python Data Structure Time & Space Complexity: A Comprehensive Guide
July 19, 2023 - The average and worst-case time complexities for dictionary operations are as follows: Accessing an element by key: O(1) average case, O(n) worst case · Inserting or deleting an element: O(1) average case, O(n) worst case
DEV Community
dev.to › williams-37 › understanding-time-complexity-in-python-functions-5ehi
Understanding Time Complexity in Python Functions - DEV Community
October 25, 2024 - Python’s built-in sorting algorithm (Timsort) has a time complexity of O(n log n) in the average and worst cases. ... Retrieving a value by key in a dictionary is a constant time operation due to the underlying hash table implementation.
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.
Quora
quora.com › What-are-the-time-complexity-considerations-of-lists-in-Python
What are the time complexity considerations of lists in Python? - Quora
In CPython (the main implementation of Python) the time complexity of the find() function is O((n-m)*m) where n is the size of the string in which you search, and m is the size of the string which you search. For example let to search string ’a’*m+’b’ in string ‘a’*n (m < n).
Towards Data Science
towardsdatascience.com › home › latest › recursion vs dynamic programming – fibonacci(leetcode 509)
Recursion vs Dynamic Programming - Fibonacci(Leetcode 509) | Towards Data Science
March 5, 2025 - And the space complexity would be O(N) since we need to store all intermediate values into our dp_list. So the space we need is the same as n given. ... The red line represents the time complexity of recursion, and the blue line represents dynamic programming.
Reddit
reddit.com › r/learnpython › why is removing elements from a list so slow, and is there a faster way?
r/learnpython on Reddit: Why is removing elements from a list so slow, and is there a faster way?
April 21, 2024 -
I was trying to write a simple application, which is ao supposed to filter a list of words down to a list of words of a certain length. For that I could either remove the words of the wrong length, or create a new list of words with the correct length.
I had a list of around 58000 words, and wanted to filter out all the 6 letter words, which are around 6900.
with open('words.txt') as f:
words = f.readlines()
for i in range(len(words)):
words[i] = words[i].strip()
length = int(input("Desired word length "))
for i in reversed(words):
if len(i) != length:
words.remove(i)This took 22 seconds.
Another way is to just create a new list with words of the correct length. I did this as follows:
with open('words.txt') as f:
words = f.readlines()
for i in range(len(words)):
words[i] = words[i].strip()
length = int(input("Desired word length "))
clw = []
for i in words:
if len(i) == length:
clw.append(i)This only took 0.03 seconds. How can it be that creating a list of 6900 words takes 0.03 seconds, but removing 51100 words takes 22? It's only 7 times as many words, but takes 700 times as long. And is there a better and faster way to quickly remove list elements?
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Each time you remove an element from the list, the elements "shift down" in position. This takes time, as you are essentially recreating (large portions of) the list after each removal. When you create a new list, you build it once. A more Pythonic way to do this is to use a list comprehension. clw = [word for word in words if len(word) == length]
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Other comments explain why the remove approach isn't efficient. I also want to note that right now, you're reading through the words three times: once with readlines, once with your for loop that strips words, and once with your for loop that filters them. Instead, you can just get the words you need as you read the file for the first time. length = int(input("Desired word length: ")) words = [] with open("words.txt", "r") as f for line in f: word = line.strip() if len(word) == length: words.append(word) Another note, if words.txt has duplicates, so will your list. You could use a set instead to avoid this if that's undesirable.
DEV Community
dev.to › global_codess › time-complexities-of-python-data-structures-3bja
Time Complexities Of Python Data Structures - DEV Community
February 19, 2020 - Every data structure performs various operations when implementing an algorithm. Some of the key and... Tagged with python, computerscience, datastructures, timecomplexity.
Python Morsels
pythonmorsels.com › time-complexities
Python Big O: the time complexities of different data structures in Python - Python Morsels
April 16, 2024 - Well, as Brandon Rhodes noted in his PyCon 2014 talk, many of the classic CS data structures don't really make sense in Python because data structures in Python don't actually contain data but instead contain references to data (see variables and objects in Python). When you do need a data structure that's optimized for specific operations, you can always lookup an implementation online or find a PyPI module (such as sortedcollections). Note that time complexity can really compound when you're performing operations within a loop. For example, this code has an O(n²) time complexity because it contains a loop inside a loop:
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If you know the index already, you'd use del.
Otherwise, remove first needs to traverse the list, find the (first) index for the element, then del it. This would, therefore, make it slower.
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Without any knowledge on how del or remove() performs, we can write a test using the timeit library to determine which one is faster. In this example, I simulate using both methods 10,000 times and print the average time required:
import timeit
num_runs = 10000
del_method = 'lst = [1, 2, 3]; del lst[i]'
del_setup = 'i = 0'
print(timeit.Timer(del_method, setup=del_setup).timeit(number=num_runs))
remove_method = 'lst = [1, 2, 3]; lst.remove(ele)'
remove_setup = 'ele = 1'
print(timeit.Timer(remove_method, setup=remove_setup).timeit(number=num_runs))
Ouput:
0.0005947000000000036
0.0007260000000000044
As we can see, del performs faster in this simple scenario. This makes sense knowing that remove() performs a search before removing the element. I can imagine with an even larger list the difference between the times would only grow.
YourBasic
yourbasic.org › algorithms › time-complexity-arrays
Time complexity of array/list operations [Java, Python] · YourBasic
The worst-case time complexity is linear. Similarly, searching for an element for an element can be expensive, since you may need to scan the entire array. In this Python code example, the linear-time pop(0) call, which deletes the first element of a list, leads to highly inefficient code:
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According to the Python wiki, deleting an item from a list takes linear time proportional to the number of elements in the list. Since you delete every item in the list, and each deletion takes linear time, the overall runtime is proportional to the square of number of items in the list.
In your second code snippet, both sum as well as map take linear time. So the overall complexity is linear proportional to the number of elements in the list. Interestingly, sum_of_elements isn't used at all (but it doesn't sum all even elements either).
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what about the following?
import numpy as np
a = np.arange(20)
print np.sum(a[a%2==0])
It seems to be much more lightweight compared to your two code snippets.
Small timings with an np.arange(998):
Pure numpy:
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0.0
Class recursion:
248502
0.00399994850159
List/Numpy one:
248502
0.00200009346008
And, if there's a 999 element array, your class runs in failure, because the maximum recursion depth is reached.
Quora
quora.com › Why-is-the-time-complexity-of-deleting-an-item-from-an-array-linear-O-n-and-not-constant
Why is the time complexity of deleting an item from an array linear - O(n) and not constant? - Quora
This is simply not true: the complexity may be constant depending on your assumptions. What are you planning to accomplish? What is allowed and what is not? What is purposeful and what is accidental? For instance, deleting the last item is most certainly [math]\,O(1)\,[/math] however you choose to do it: maybe you just decrement the length of the array, or you replace the value with a sentinel, etc.
Codefinity
codefinity.com › courses › v2 › 212d3d3e-af15-4df9-bb13-5cbbb8114954 › 58324ed0-9644-4e88-ba8c-e93d15b8697a › 7272fc45-26bf-4235-9167-fb4f46e618f6
Learn Basic List Operations Time Complexity | List and Array
Just like in inserting operation we need toupdate the pointers of the adjacent nodes to bypass the deleted node. As a result we have O(1) time complexity for deleting operation.
Medium
medium.com › data-science › understanding-time-complexity-with-python-examples-2bda6e8158a7
Understanding time complexity with Python examples | by Kelvin Salton do Prado | TDS Archive | Medium
February 15, 2020 - Suppose we have the following unsorted list [1, 5, 3, 9, 2, 4, 6, 7, 8] and we need to find the index of a value in this list using linear search. best-case: this is the complexity of solving the problem for the best input. In our example, the best case would be to search for the value 1....