5 ms·

Depends on how you want to program: imperative vs functional. Personally I think list comprehensions are the most beautiful part of Python, though sometimes I

Depends on how you want to program: imperative vs functional. Personally I think list comprehensions are the most beautiful part of Python, though sometimes I use map() when I'm trying to be explicitly functional (I realize it's allegedly slower, etc). Generally I think list comprehensions are cleaner and allow you to write purer functions with fewer mutable variables. I disagree that deeply nested for loops are necessarily more readable.

existencebox 12y ago
Deeply nested I'd usually agree, but if you end up with deeply nested comprehensions as a result, in either case, you're probably better off with a little bit of restructuring/splitting things more functions to at least make the nesting cleaner to read. However, I think he was referring to if the conditionals/additional modifications needed to build your list get a bit excessive, so you'd have a like... [dostuffto(A) for A in alsodostuffto(LIST) if conditional(A)] (but with more complex operations at each step). Granted at that point you can argue that you should do just as my example shows and put the "dostuffto" into more encapsulated functions, but sometimes that doesn't seem like the right choice.
SEJeff 12y ago
Map is not allegedly slower, it is demonstrably slower. $ python -mtimeit -s'nums=range(10)' 'map(lambda i: i + 3, nums)' 1000000 loops, best of 3: 1.61 usec per loop $ python -mtimeit -s'nums=range(10)' '[i + 3 for i in nums]' 1000000 loops, best of 3: 0.722 usec per loop Function calls have overhead in python, list comprehensions are implemented knowing this fact and avoiding it so the heavy lifting ultimately happens in C code.
- bkeroack 12y ago
  Fair enough. I said "allegedly" because I had never personally measured the performance difference. Even though you could construe map as "half as fast" (or twice as slow) as the equivalent comprehension, I don't see a difference of ~1 usec making any difference in my code thus far. Good to know, though.
  - SEJeff 12y ago
    Yup, for very large calculations, or certain use cases, it can make much larger differences. It all depends on your use case.
- colcarroll 12y ago
  As you point out though, it depends on where the function is coming from: $ python -mtimeit -s'nums=range(10)' '[str(i) for i in nums]' 100000 loops, best of 3: 2.57 usec per loop $ python -mtimeit -s'nums=range(10)' 'map(str, nums)' 1000000 loops, best of 3: 1.88 usec per loop $ python -mtimeit -s'nums=range(10)' 'import math' '[math.sqrt(i) for i in nums]' 100000 loops, best of 3: 3.25 usec per loop $ python -mtimeit -s'nums=range(10)' 'import math' 'map(math.sqrt, nums)' 100000 loops, best of 3: 2.55 usec per loop

existencebox 12y ago

Deeply nested I'd usually agree, but if you end up with deeply nested comprehensions as a result, in either case, you're probably better off with a little bit of restructuring/splitting things more functions to at least make the nesting cleaner to read. However, I think he was referring to if the conditionals/additional modifications needed to build your list get a bit excessive, so you'd have a like... [dostuffto(A) for A in alsodostuffto(LIST) if conditional(A)] (but with more complex operations at each step). Granted at that point you can argue that you should do just as my example shows and put the "dostuffto" into more encapsulated functions, but sometimes that doesn't seem like the right choice.

SEJeff 12y ago

Map is not allegedly slower, it is demonstrably slower. $ python -mtimeit -s'nums=range(10)' 'map(lambda i: i + 3, nums)' 1000000 loops, best of 3: 1.61 usec per loop $ python -mtimeit -s'nums=range(10)' '[i + 3 for i in nums]' 1000000 loops, best of 3: 0.722 usec per loop Function calls have overhead in python, list comprehensions are implemented knowing this fact and avoiding it so the heavy lifting ultimately happens in C code.

bkeroack 12y ago
Fair enough. I said "allegedly" because I had never personally measured the performance difference. Even though you could construe map as "half as fast" (or twice as slow) as the equivalent comprehension, I don't see a difference of ~1 usec making any difference in my code thus far. Good to know, though.
- SEJeff 12y ago
  Yup, for very large calculations, or certain use cases, it can make much larger differences. It all depends on your use case.
colcarroll 12y ago
As you point out though, it depends on where the function is coming from: $ python -mtimeit -s'nums=range(10)' '[str(i) for i in nums]' 100000 loops, best of 3: 2.57 usec per loop $ python -mtimeit -s'nums=range(10)' 'map(str, nums)' 1000000 loops, best of 3: 1.88 usec per loop $ python -mtimeit -s'nums=range(10)' 'import math' '[math.sqrt(i) for i in nums]' 100000 loops, best of 3: 3.25 usec per loop $ python -mtimeit -s'nums=range(10)' 'import math' 'map(math.sqrt, nums)' 100000 loops, best of 3: 2.55 usec per loop