Make your Big O smaller, complexity explained

Ever heard of big O and it sounded chinese/greek? Programmers that do not come from computer science do not know what this is. In this talk big O complexity is explained and displayed with examples in Python language how it can affect code execution. I give tips of how to make python code have better performance and why they they work.

Transcript

1. Make your Big O smaller complexity explained Meili Triantafyllidi - @meilitr
4. set or dict or list or tuple...?
5. set or dict or list or tuple...? How it will perform with more data?
6. Notation O(n), O(n2), O(nlogn), O(1) ..
7. Time complexity
8. Time complexity problem size ? time
9. O(n) - Linear complexity • 1 elements X time • 2 elements 2X time • 10 elements 10X time • 100 elements 100X time
10. O(n) - Linear complexity N Time N Time N Time Time = 3N Time = 0.5N Time = N
11. O(n) - Linear complexity for i in range(n): print(i)
12. O(n) - Linear complexity sum(lst)
13. O(n2) - Quadratic complexity • 1 elements X time • 2 elements 4X time • 10 elements 100X time • 100 elements 10000X time
14. O(n2) - Quadratic complexity • 1 elements X time • 2 elements 4X time • 10 elements 100X time • 100 elements 10000X time ←100 slower than O(n)
15. O(n2) - Quadratic complexity # Sequences of 2 for i in range(n): for k in range(n): print(i, k)
16. O(n2) - Quadratic complexity # Sequences of 2 for i in range(n): for k in range(n): print(i, k) O(n)
17. O(n2) - Quadratic complexity # Sequences of 2 for i in range(n): for k in range(n): print(i, k) O(n2)
18. O(1) - constant complexity • 1 elements X time • 2 elements X time • 10 elements X time • 100 elements X time
19. O(1) - constant complexity • 1 elements X time • 2 elements X time • 10 elements X time • 100 elements X time O(1)
20. O(1) - constant complexity # d: dict or set key in d
21. O(1) - constant complexity # d: dict or set key in d dict
22. Common pitfall key in d # O(1)
23. Common pitfall key in lst # O(n) instead of key in d # O(1)
24. Context matters for i in range(n): if i in lst: print(i) vs for i in range(n): if i in d: print(i)
25. Context matters for i in range(n): if i in lst: print(i) vs for i in range(n): if i in d: print(i) O(n)
26. O(n2) Context matters for i in range(n): if i in lst: print(i) vs for i in range(n): if i in d: print(i)
27. O(n2) Context matters for i in range(n): if i in lst: print(i) vs for i in range(n): if i in d: print(i) O(1)
28. O(n) O(n2) Context matters for i in range(n): if i in lst: print(i) vs for i in range(n): if i in d: print(i)
29. O(n) O(n2) Context matters for i in range(n): if i in lst: print(i) vs for i in range(n): if i in d: print(i) n=100 -> 100 slower !!!!!
30. Context matters s = set(lst) # O(n) key in s # O(1)
31. Context matters s = set(lst) # O(n) key in s # O(1) O(n)
32. do_a() # O(n) do_b() # O(n) do_c() # O(n2) Context matters
33. O(n2) Context matters do_a() # O(n) or O(1) do_b() # O(n) or O(1) do_c() # O(n2) No difference
34. Common pitfall a += "text" # O(len(a))
35. Common pitfall for name in lst: a += name ''.join(lst)
36. Common pitfall # list add/remove intermediate lst.pop(1) # O(n) lst.insert(10) # O(n)
37. Common pitfall # list add/remove intermediate lst.pop(1) # O(n) lst.insert(10) # O(n) dict, set, frozenset, collections.deque, priority queue (heapq)
38. https://wiki.python.org/moin/TimeComplexity
39. https://wiki.python.org/moin/TimeComplexity
40. Summary • Know your data structures ◦ set, dictionaries are our friends for lookups ◦ Lists are good for appends ◦ Search python complexity • Look for O(n) → O(1) where it reduces total complexity • Beware of hidden complexity in assignments
41. Thank you @meilitr