Sometimes I find myself writing code like this:
def analyse(somedata):
result = bestapproach(somedata)
if result:
return result
else:
orWhen the number of total functions is a) known, and b) small, and the test condition is based entirely on the truth value of the return, it's possible to simply use or as Grapsus suggested:
d = 'somedata'
result = f1(d) or f2(d) or f3(d) or f4(d)
Because Python's boolean operators short-circuit, the functions are executed from right to left until one of them produces a return value evaluated as True, at which point the assignment is made to result and the remaining functions are not evaluated; or until you run out of functions, and result is assigned False.
When the number of total functions is a) unknown, or b) very large, a one-liner generator comprehension method works, as Bitwise suggested:
result = (r for r in (f(somedata) for f in functions) if ).next()
This has the additional advantage over option #1 that you can use any you wish, instead of relying only on truth value. Each time .next() is called:
f from functions and tries to evaluate f(somedata)f is a function and somedata is a valid arugment), the inner generator yields the return value of f(somedata) to the outer generator is satisfied, the outer generator yields the return value of f(somedata) and we assign it as result was not satisfied in step 5, repeat steps 2-4A weakness of this method is that nested comprehensions can be less intuitive than their multi-line equivalents. Also, if the inner generator is exhausted without ever satisfying the test condition, .next() raises a StopIteration which must be handled (in a try-except block) or prevented (by ensuring the last function will always "succeed").
Since we can place callable functions in a list, one option is to explicitly list the functions you want to "try" in the order they should be used, and then iterate through that list:
def analyse(somedata):
analysis_functions = [best, okay, poor]
for f in analysis_functions:
result = f(somedata)
if result:
return result
Advantages: Fixes the problem of repeated code, it's more clear that you're engaged in an iterative process, and it short-circuits (doesn't continue executing functions after it finds a "good" result).
This could also be written with Python's for ... else syntax:*
def analyse(somedata):
analysis_functions = [best, okay, poor]
for f in analysis_functions:
result = f(somedata)
if result:
break
else:
return None
return result
The advantage here is that the different ways to exit the function are identified, which could be useful if you want complete failure of the analyse() function to return something other than None, or to raise an exception. Otherwise, it's just longer and more esoteric.
*As described in "Transforming Code into Beautiful, Idiomatic Python", starting @15:50.