Introduction to Functional Programming in Python David Jones - - PowerPoint PPT Presentation

Introduction to Functional Programming in Python

David Jones drj@ravenbrook.com

Python and Functional Programming Beginner Journeyman Master

Functional Programming Object Oriented Programming Imperative Programming

Programming: Modes of Operation

FP versus Imperative

a spectrum of possibilities Java

more functional more imperative

Common Lisp Python ML Haskell Scheme C

A Function

double

in

• ut

double

in

• ut

5 10 double

in

• ut

'foo' 'foofoo'

def double(x) : return x * 2

>>> double(5) 10 >>> double('foo') 'foofoo'

First Class Functions

Can be... stored in variables; passed as arguments to

• ther functions;

returned as results from functions; created at runtime.

def hello() : return 'hello'

>>> hello() 'hello' >>> y = hello >>> y() 'hello' >>> type(y) <type 'function'>

Variables refer to Values hello y

The hello function

>>> y() 'hello' >>> del hello >>> hello()

Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'hello' is not defined

>>> help(y)

Help on function hello in module __main__: hello()

Factorial

def factorial(x) : # Imperative y = 1 for i in range(1,x+1) : y *= i return y def factorial(x) : # Functional if x <= 1 : return 1 else : return x * factorial(x-1)

Lists for a in l : ... map(..., l)

>>> r=[] >>> for x in range(10) : ... r.append(math.sqrt(x)) ... >>> r

[0.0, 1.0, 1.4142135623730951, 1.7320508075688772, 2.0, 2.2360679774997898, 2.4494897427831779, 2.6457513110645907, 2.8284271247461903, 3.0]

>>> map(math.sqrt, range(10))

[0.0, 1.0, 1.4142135623730951, 1.7320508075688772, 2.0, 2.2360679774997898, 2.4494897427831779, 2.6457513110645907, 2.8284271247461903, 3.0]

Imperative Functional

>>> posts = blog.metaWeblog.getRecentPosts (apiKey, 'drj11', password, 999) >>> len(posts) 59 >>> def postid(p) : return int(p['postid']) ... >>> map(postid, posts)

[97, 96, 94, 93, 92, 91, 90, 89, 87, 83, 77, 74, 80, 79, 78, 75, 73, 72, 70, 64, 30, 66, 65, 63, 60, 42, 57, 54, 56, 55, 52, 53, 38, 43, 40, 39, 37, 29, 28, 27, 25, 20, 19, 12, 9, 7, 5, 26, 41, 67, 68, 71, 76, 81, 82, 84, 86, 88, 95]

>>> map(lambda p: int(p['postid']), posts)

Lambda

def fibi(x) : # Imperative s = 0 ; t = 1 for i in range(x) : s,t = t,s+t return s def fibf(x) : # Functional if x <= 1 : return x return fibf(x-1) + fibf(x-2)

def timeit(f) : import time def t(*l) : t0 = time.time() x = f(*l) t1 = time.time() print "Time: %f" % (t1 - t0) return x return t

Timing

>>> timefibi = timeit(fibi) >>> timefibf = timeit(fibf) >>> timefibi(30) Time: 0.000026 832040 >>> timefibf(30) Time: 1.042492 832040

Timing Fibonacci

def memo(f) : cache = {} def m(*l) : if not cache.has_key(l) : cache[l] = f(*l) return cache[l] return m

Rembering Computations

>>> timefibf(30) Time: 1.036864 832040 >>> fibf = memo(fibf) >>> timefibf = timeit(fibf) >>> timefibf(30) Time: 0.000012 832040

Faster Fibonacci

Filter

>>> filter(lambda x : x % 2 == 0, range(11)) [0, 2, 4, 6, 8, 10] >>> filter(lambda x : x == int(math.sqrt(x))**2, range(40)) [0, 1, 4, 9, 16, 25, 36]

>>> l = ['food', 'python', 'pyfoo'] >>> r = re.compile('fo+') >>> filter(lambda x : r.search(x), l) ['food', 'pyfoo']

Object Methods and Functions

>>> filter(r.search, l) ['food', 'pyfoo']

Further Reading

Colophon

Slides were prepared using Omnigrae and Apple's OS X. Python code was generally executed in the CPython implementation version 2.5.1. Presentation was performed using PNG graphics and Apple Preview. The fonts used were Lucida Grande and Monaco, both designed by Bigelow and Holmes. The coee bean image is public domain and was created by Mark Sweep. The idea for improving the running time of Fibonacci using memo was suggested by Rici Lake on the Lua mailing list.