Most efficient way of making an if-elif-elif-else statement when the else is done the most?

by

The code…

options.get(something, doThisMostOfTheTime)()

…looks like it ought to be faster, but it’s actually slower than the if … elif … else construct, because it has to call a function, which can be a significant performance overhead in a tight loop.

Consider these examples…

1.py

something = 'something'

for i in xrange(1000000):
    if something == 'this':
        the_thing = 1
    elif something == 'that':
        the_thing = 2
    elif something == 'there':
        the_thing = 3
    else:
        the_thing = 4

2.py

something = 'something'
options = {'this': 1, 'that': 2, 'there': 3}

for i in xrange(1000000):
    the_thing = options.get(something, 4)

3.py

something = 'something'
options = {'this': 1, 'that': 2, 'there': 3}

for i in xrange(1000000):
    if something in options:
        the_thing = options[something]
    else:
        the_thing = 4

4.py

from collections import defaultdict

something = 'something'
options = defaultdict(lambda: 4, {'this': 1, 'that': 2, 'there': 3})

for i in xrange(1000000):
    the_thing = options[something]

…and note the amount of CPU time they use…

1.py: 160ms
2.py: 170ms
3.py: 110ms
4.py: 100ms

…using the user time from time(1).

Option #4 does have the additional memory overhead of adding a new item for every distinct key miss, so if you’re expecting an unbounded number of distinct key misses, I’d go with option #3, which is still a significant improvement on the original construct.

More Related Contents:

  1. What is the fastest and pythonic way to generate a list [(0,0), (0,1), (0,2)…(0,100)]? [closed]
  2. Keyword If Statements [closed]
  3. Get difference between two lists
  4. Which Python memory profiler is recommended? [closed]
  5. What is the most efficient way to loop through dataframes with pandas?
  6. A faster strptime?
  7. Compare two columns using pandas
  8. Link ATLAS/MKL to an installed Numpy
  9. Fastest way to grow a numpy numeric array
  10. What is the fastest way to parse large XML docs in Python?
  11. What is the time complexity of popping elements from list in Python?
  12. Pythonic way to combine for-loop and if-statement
  13. Poor scaling of multiprocessing Pool.map() on a list of large objects: How to achieve better parallel scaling in python?
  14. Fast arbitrary distribution random sampling (inverse transform sampling)
  15. Mass string replace in python?
  16. Python: get a dict from a list based on something inside the dict
  17. Why is variable1 += variable2 much faster than variable1 = variable1 + variable2?
  18. Why is `np.sum(range(N))` very slow?
  19. Python built-in sum function vs. for loop performance
  20. Too many if statements
  21. Are NumPy’s math functions faster than Python’s?
  22. Creating an empty list in Python
  23. Python: iterating over list vs over dict items efficiency
  24. First items in inner list efficiently as possible [duplicate]
  25. Select the max row per group – pandas performance issue
  26. Improving performance of very large dictionary in Python
  27. How to create an ’empty if statement’ in python
  28. What is the best approach in python: multiple OR or IN in if statement?
  29. numpy faster than numba and cython , how to improve numba code
  30. Why doesn’t Python’s mmap work with large files?

Leave a Comment