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Looking before you leap with processor-intensive function

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A program can easily waste time by calling a processor-intensive function multiple times.

For example, take a function which looks like this: it returns an integer if the input value can produce one, else None: 

def intensive_f(value): # int -> Optional[int]
   # complex, and time-consuming code
   if process_has_failed:
       return None
   return integer_output

And it could be used in the following way: 

x = 5
if intensive_f(x) is not None:
    print(intensive_f(x) / 2)
else:
    print(x, "could not be processed")

print(x)

Whilst this will work, it has the problem of calling intensive_f, which doubles the length of time for the code to run. A better solution would be to get the return value of the function beforehand. 

x = 5
result = intensive_f(x)
if result is not None:
    print(result / 2)
else:
    print(x, "could not be processed")

However, a clearer and possibly more pythonic way is to use exceptions, for example: 

x = 5
try:
    print(intensive_f(x) / 2)
except TypeError: # The exception raised if None + 1 is attempted
    print(x, "could not be processed")

Here no temporary variable is needed. It may often be preferable to use a assert statement, and to catch the AssertionError instead.

Dictionary keys

A common example of where this may be found is accessing dictionary keys. For example compare: 

bird_speeds = get_very_long_dictionary()

if "european swallow" in bird_speeds:
    speed = bird_speeds["european swallow"]
else:
    speed = input("What is the air-speed velocity of an unladen swallow?")

print(speed)

with: 

bird_speeds = get_very_long_dictionary()

try:
    speed = bird_speeds["european swallow"]
except KeyError:
    speed = input("What is the air-speed velocity of an unladen swallow?")

print(speed)

The first example has to look through the dictionary twice, and as this is a long dictionary, it may take a long time to do so each time. The second only requires one search through the dictionary, and thus saves a lot of processor time.

An alternative to this is to use dict.get(key, default), however many circumstances may require more complex operations to be done in the case that the key is not present

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Python Anti-Patterns:
* Overzealous except clause
* Looking before you leap with processor-intensive function
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