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6 Functions and Functional Programming
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The lambda Operator
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Anonymous functions in the form of an expression can be created using the lambda statement:
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lambda args : expression
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args is a comma-separated list of arguments, and expression is an expression involv-
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ing those arguments Here s an example:
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a = lambda x,y : x+y r = a(2,3) # r gets 5
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The code defined with lambda must be a valid expression Multiple statements and other non-expression statements, such as for and while, cannot appear in a lambda statement lambda expressions follow the same scoping rules as functions The primary use of lambda is in specifying short callback functions For example, if you wanted to sort a list of names with case-insensitivity, you might write this:
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namessort(key=lambda n: nlower())
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Recursive functions are easily defined For example:
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def factorial(n): if n <= 1: return 1 else: return n * factorial(n - 1)
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However, be aware that there is a limit on the depth of recursive function callsThe function sysgetrecursionlimit() returns the current maximum recursion depth, and the function syssetrecursionlimit() can be used to change the valueThe default value is 1000 Although it is possible to increase the value, programs are still limited by the stack size limits enforced by the host operating systemWhen the recursion depth is exceeded, a RuntimeError exception is raised Python does not perform tailrecursion optimization that you often find in functional languages such as Scheme Recursion does not work as you might expect in generator functions and coroutines For example, this code prints all items in a nested collection of lists:
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def flatten(lists): for s in lists: if isinstance(s,list): flatten(s) else: print(s) items = [[1,2,3],[4,5,[5,6]],[7,8,9]] flatten(items) # Prints 1 2 3 4 5 6 7 8 9
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However, if you change the print operation to a yield, it no longer worksThis is because the recursive call to flatten() merely creates a new generator object without actually iterating over it Here s a recursive generator version that works:
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def genflatten(lists): for s in lists: if isinstance(s,list): for item in genflatten(s): yield item else: yield item
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Care should also be taken when mixing recursive functions and decorators If a decorator is applied to a recursive function, all inner recursive calls now get routed through the decorated version For example:
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@locked def factorial(n): if n <= 1: return 1 else: return n * factorial(n - 1)
# Calls the wrapped version of factorial
If the purpose of the decorator was related to some kind of system management such as synchronization or locking, recursion is something probably best avoided
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It is common practice for the first statement of function to be a documentation string describing its usage For example:
def factorial(n): """Computes n factorial For example: >>> factorial(6) 120 >>> """ if n <= 1: return 1 else: return n*factorial(n-1)
The documentation string is stored in the _ _doc_ _ attribute of the function that is commonly used by IDEs to provide interactive help If you are using decorators, be aware that wrapping a function with a decorator can break the help features associated with documentation strings For example, consider this code:
def wrap(func): call(*args,**kwargs): return func(*args,**kwargs) return call @wrap def factorial(n): """Computes n factorial"""
If a user requests help on this version of factorial(), he will get a rather cryptic explanation:
>>> help(factorial) Help on function call in module _ _main_ _: call(*args, **kwargs) (END) >>>
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To fix this, write decorator functions so that they propagate the function name and documentation string For example:
def wrap(func): call(*args,**kwargs): return func(*args,**kwargs) call_ _doc_ _ = func_ _doc_ _ call_ _name_ _ = func_ _name_ _ return call
Because this is a common problem, the functools module provides a function wraps that can automatically copy these attributes Not surprisingly, it is also a decorator:
from functools import wraps def wrap(func): @wraps(func) call(*args,**kwargs): return func(*args,**kwargs) return call
The @wraps(func) decorator, defined in functools, propagates attributes from func to the wrapper function that is being defined