In Python, Set is an unordered collection of data type that is iterable, mutable and has no duplicate elements.
There are numerous ways that can be used to iterate over a Set. Some of these ways provide faster time execution as compared to others. Some of these ways include, iterating using for/while loops, comprehensions, iterators and their variations. Let’s see all the different ways we can iterate over a set in Python.
Analysis of each method:
For explaining the working of each way/technique, time per set(randomly generated set) has been calculated for 5-times to get a rough estimate on how much time every technique takes for iterating over a given set. random.seed(21) has been added to each script to fixate over the random numbers that are generated every time the program is executed. Using constant seed helps us to determine which technique is best for a given particular randomly generated set.
Method #1: Iterating over a set using simple for loop.
Python3
# Creating a set using stringtest_set = set("geEks")# Iterating using for loopfor val in test_set: print(val) |
Output:
k s e g E
Analysis:
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): for val in test_set: _ = val# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.06303901899809716 0.06756918999963091 0.06692574200133095 0.067220498000097 0.06748137499744189
Time complexity: O(n), where n is the number of elements in the set generated.
Auxiliary space: O(n), as the set of random numbers is stored in memory.
Method #2: Iterating over a set using enumerated for loop.
Python3
# Creating a set using stringtest_set = set("geEks")# Iterating using enumerated for loopfor id,val in enumerate(test_set): print(id, val) |
Output:
0 E 1 e 2 k 3 g 4 s
Time complexity: O(n).
Auxiliary space: O(n).
Analysis:
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): for id, val in enumerate(test_set): _ = val# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.1306622320007591 0.13657568199778325 0.13797824799985392 0.1386374360008631 0.1424286179972114
Method #3: Iterating over a set as indexed list.
Python3
# Creating a set using stringtest_set = set("geEks")test_list = list(test_set)# Iterating over a set as a indexed listfor id in range(len(test_list)): print(test_list[id]) |
Output:
g k E s e
Analysis:
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): test_list = list(test_set) for id in range(len(test_list)): _ = test_list[id]# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.20036015100049553 0.2557020290005312 0.4601482660000329 0.2161413249996258 0.18769703499856405
Method #4: Iterating over a set using comprehension and list constructor/initializer.
Python3
# Creating a set using stringtest_set = set("geEks")# Iterating using list-comprehensioncom = list(val for val in test_set)print(*com) |
Output:
k s e g E
Analysis:
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): list(val for val in test_set)# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.1662169310002355 0.1783527520019561 0.21661155100082397 0.19131610199838178 0.19931397800246486
Method #5: Iterating over a set using comprehension.
Python3
# Creating a set using stringtest_set = set("geEks")# Iterating using list-comprehensioncom = [print(val) for val in test_set] |
Output:
e E g s k
Analysis:
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): [val for val in test_set]# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.11386321299869451 0.111869686999853 0.1092844699996931 0.11223735699968529 0.10928539399901638
Method #6: Iterating over a set using map, lambda and list comprehension
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): [map(lambda val: val, test_set)]# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
1.0756000847322866e-05 1.310199877480045e-05 1.269100175704807e-05 1.1588999768719077e-05 1.2522999895736575e-05
Method #7: Iterating over a set using iterator.
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): for val in iter(test_set): _ = val# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.0676155920009478 0.07111633900058223 0.06994135700006154 0.0732101009998587 0.08668379899972933
Method #8: Iterating over a set using iterator and while loop.
Python3
# Creating a set using stringtest_set = set("geEks")iter_gen = iter(test_set)while True: try: # get the next item print(next(iter_gen)) ''' do something with element ''' except StopIteration: # if StopIteration is raised, # break from loop break |
Output:
E s e k g
Analysis:
Python3
# importing librariesfrom timeit import default_timer as timerimport itertoolsimport random# Function under evaluationdef test_func(test_set): iter_gen = iter(test_set) while True: try: # get the next item next(iter_gen) # do something with element except StopIteration: # if StopIteration is raised, break from loop break# Driver functionif __name__ == '__main__': random.seed(21) for _ in range(5): test_set = set() # generating a set of random numbers for el in range(int(1e6)): el = random.random() test_set.add(el) start = timer() test_func(test_set) end = timer() print(str(end - start)) |
Output:
0.2136418699992646 0.1952157889973023 0.4234208280031453 0.255840524998348 0.24712910099697183
Conclusion:
Among all the looping techniques, simple for loop iteration and looping over iterators works best, while comparing all the techniques, using map with lambda over set or iterator of set works best giving a performance of a million set iterations under 10 milliseconds. It is quite noticeable that above examples only have single access of set components per iteration, whereas if we increase the number of times a set component is accessed per iteration, it may change the time taken per iteration.
Note: Values mentioned above in the example output are bound to vary. The reason behind the variation of time consumption is machine dependency of processing power of individual’s system processor.
