Functional programming in python

AN OVERVIEW OF
FUNCTIONAL
PROGRAMMING IN PYTHON
Edward D. Weinberger, Ph.D, F.R.M.
Adjunct Professor
NYU Tandon School of Engineering
edw@wpq-inc.com

ULTIMATE PURPOSE
To avoid
dysfunctional
programming!

OUTLINE
• Functional programming: definition and
advantages
• Functional programming: key concepts
• Python support for functional programming:
the basics and beyond
• Potential pitfalls and best practices

FUNCTIONAL
PROGRAMMING DEFINED
“A programming paradigm that
treats computation as the
evaluation of mathematical
functions, avoiding changes of
state and mutable data”

IMPLICATIONS OF
DEFINITION
• Output values of functions must depend
only on inputs, not on internal state
variables
• Calling a function twice with the same
inputs results in the same output

“IMPERATIVE
PROGRAMMING” NO-NO’s
• For/While Loops
• Internal “state” variables (e.g. variables
assigned in __init__() and updated by other
methods)
• Re-assignment (mutability) of variables

OBVIOUS QUESTION:
Why bother???

IMPERATIVE VERSION OF
QUICKSORT
def quicksort(array):
indexes_stack = list()
idx = (0, len(array) - 1)
indexes_stack.append(idx)
for idx in indexes_stack:
elem_idx = idx[0]
pivot_idx = idx[1]
while pivot_idx > elem_idx:
pivot = array[pivot_idx]
elem = array[elem_idx]
if elem > pivot:
array[pivot_idx] = elem
array[elem_idx] = array[pivot_idx - 1]
array[pivot_idx - 1] = pivot
pivot_idx -= 1
else:
elem_idx += 1
boundar_low = idx[0]
boundar_high = idx[1]
if pivot_idx > 1 and boundar_low < pivot_idx - 1:
indexes_stack.append((boundar_low, pivot_idx - 1))
if pivot_idx < len(array) -1 and pivot_idx + 1 < boundar_high:
indexes_stack.append((pivot_idx + 1, boundar_high))
return array

FUNCTIONAL VERSION OF
QUICKSORT
def quicksort(lst):
if len(lst) == 0:
return lst
pivots = list(filter(lambda x: x == lst[0], lst))
small = quicksort(list(filter(lambda x: x < lst[0], lst)))
large = quicksort(list(filter(lambda x: x > lst[0], lst)))
return small + pivots + large

OTHER ADVANTAGES
(besides ease of reading/debugging)
• May facilitate
• Compiler optimization
• Parallelization
• Faster execution via memorization
• Lazy evaluation
• Academic studies of formal proofs of
program correctness have made
progress under functional programming
assumptions

FUNCTIONAL
PROGRAMMING: A FEW
KEY CONCEPTS

PURE FUNCTIONS
• Functions that
• always return the same result, given the same
argument, so sin(x) is pure, today() is impure
• do not have “side effects”
• Cannot use internal state to determine
what result to return
• Python functions may or may not be pure

REFERENTIAL
TRANSPARENCY
• Describes a function/expression that can be
replaced by its value without changing the
behavior of a program in which it is
embedded (e.g. sin(x)+5)
• Generally, an expression can only be
referentially transparent if any functions
used are pure

“HIGHER ORDER”
FUNCTIONS
• Functions that take other functions as
inputs and/or outputs
• Python examples:
• Python’s sorted function takes a key parameter,
the name of a function that returns a sort key
• A GUI button object’s __init__() might be
passed the name of the function to be executed
when the button is clicked

PYTHON SUPPORT FOR
FUNCTIONAL
PROGRAMMING

SUPPORT FOR FP IN
“EVERY DAY” PYTHON
• Recursion
• Lambda expressions
• All functions are “first class functions”, i.e.
function names can be
• passed as arguments
• assigned to variables
• appear as elements of lists
• etc.

PYTHON ITERABLES
AND ITERATORS
• An iterable is an object that defines either a
__getitem__() or an __iter__() method
• The built-in function iter(iterable) returns
an iterator (also returned by generators)
• Iterators define a (private) __next__()
method, called by the built-in function
next(), raising the StopIteration error
when nothing is next

ITERABLE/ITERATOR
EXAMPLE
>>> aList = [1, 2] #need not be a list
>>> aListIter = iter(aList) #possibly
>>> next(aListIter)# lazy evaluation
1
>>> next(aListIter)#
2
>>> next(aListIter)
StopIteration Traceback …

Returns an iterator which is the result of
applying <function> (of one argument) to
successive individual items supplied by
<iterable>
EXAMPLE:
>>> list(map(lambda x: x*x,[1,3,5,7]))
[1, 9, 25, 49]
map(<function>, <iterable>)

MAP RETURNS AN
ITERATOR
>>> seq = map(lambda x: x*x, [1, 3, 5, 7])
>>> seq
<map at 0x…>
>>> next(seq)
1
>>> next(seq)
9

applies <function> (of two arguments) to the
first two items supplied by <iterable>, thus
“reducing” them to a single value. This result
is supplied, along with the third item from
<iterable> thus “reducing” all three items to
a single value
Note: reduce must be imported from the
built-in library functools
reduce(<function>, <iterable>)

WORKING WITH reduce
>>> from functools import reduce
>>> reduce(lambda x,y: x*y, range(1, 6))
120 #The product of ((1*2)*3)*4)*5

Constructs an iterator for those elements of
<iterable> for which <function> returns True
Equivalent to the generator expression
(for item in <iterable>
item if <function>(item)
)
filter(<function>, <iterable>)

WORKING WITH filter
>>> isEven = lambda x: (x%2 == 0)
>>> yetAnotherList = [12, 43, 33, 32, 31]
>>> it = filter(isEven, yetAnotherList)
>>> next(it)
12
>>> next(it)
32
>>> list(filter(isEven, yetAnotherList))
[12, 32]

AN EXTENDED EXAMPLE
min_order = 100
invoice_totals = list(
map(lambda x: x if x[1] >= min_order
else (x[0], x[1] + 10),
map(lambda x: (x[0],x[2] * x[3]), orders)))

WHAT ELSE IS THERE TO
KNOW?
• More jargon (see Wikipedia)
• The operator, functools and itertools
libraries
• Various ideas about pitfalls/best practices

PITFALLS/BEST
PRACTICES IN
FUNCTIONAL
PROGRAMMING

BIGGEST PITFALL:
“It’s the new kid on the block!
• After 60+ years,
• Imperative programming (IP) is well understood
• Lots of hardware and compiler support for IP
• IP code has large code base

RECURSION IS
PROBLEMATIC
• Recursion is always relatively slow and
memory intensive because of the many
subroutine calls usually required
• Python, in particular, only grudgingly
supports recursion
• Implementation not particularly fast
• Finite recursion depth, even for tail recursion

FP “Update as you copy”
PARADIGM PROBLEMATIC
• The alternative to updating mutable
variables is “update as you copy”
• “Update as you copy” leads to memory
bloat
• Can be difficult to keep track of what’s
going on if nested objects are being
copied/updated

PURE FUNCTIONS AND I/O
DON’T REALLY MIX
• Python functions such as input()are not
even close to referentially transparent!
• Internal state variables needed for buffered
reads/writes

BEST PRACTICES SPECIFIC TO
FUNCTIONAL PROGRAMMING
• Use map, reduce, filter, and other library
functions in place of recursion if possible
• Cleanly separate pure functions from
intrinsically impure functions (such as I/O)
• Combine related functions in a class and
use inheritance (yes, FP and OO do mix!)

BEST PRACTICES APPLICABLE TO
ALL PYTHON PROGRAMMING
• “short and sweet” functions, doing one
thing well (1 or 2 ISO/ANSI 24x80 screens)
• Take advantage of Python’s polymorphism
• Don’t pack too much into a single line of
code
• Use descriptive variable names

BEST PRACTICES PER “THE ZEN
OF PYTHON”
• “… practicality beats purity”
• “Readability counts”
• “If an implementation is hard to explain, it’s
a bad idea”
• “If an implementation is easy to explain, it
might be a good idea”

RECAP
• Functional programming: definition and
advantages
• Functional programming: key concepts
• Python support for functional programming:
the basics and beyond
• Potential pitfalls and best practices

Functional programming in python

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