This is a re-work of one example from the first article, found under Eliminating Side-effects
# Examples of using functional programming in place of loops/control statements
# Reference: http://gnosis.cx/publish/programming/charming_python_13.html
# A larger example of converting IMPERATIVE program to FP
# Goal:
# list pairs of numbers whose products are > 25
#
# Standard imperative approach, the 'more stuff' lines
# represent spots where other processing might occur in
# a full program and represent spots where the key variables
# may take on different values; as well, when this section
# of code is complete, all the variables may have values
# that may or not be expected, or wanted, in the remaining code
xs = (1,2,3,4) # list of numbers
ys = (10, 15, 3, 22) # second list of numbers
bigmuls = [] # list to hold results
# ... more stuff ....
for x in xs:
for y in ys:
# ... more stuff ....
if x * y > 25:
bigmuls.append( (x,y) )
print('Imperative Programming result:', bigmuls)
# the same using an FP approach
# bigmuls = lambda xs,ys: filter(lambda (x,y):x*y > 25, combine(xs,ys))
# combine = lambda xs,ys: map(None, xs*len(ys), dupelms(ys,len(xs)))
# dupelms = lambda lst,n: reduce(lambda s,t:s+t, map(lambda l,n=n: [l]*n, lst))
#
# Note:
# the methods given in the article can be reduced to one line
# by using itertools.product() which produces 'all pairs' from two given lists
# essentially, this was the purpose of the 'dupelms() and 'combine()' functions
# in the original article.
# A key advantage of this approach:
# the original variable value NEVER change, so no possible side-effects
from itertools import product
bigmuls = lambda xs,ys: filter(lambda pair: pair[0]*pair[1] > 25, product(xs,ys) )
print('Functional Programming result:', list(bigmuls(xs,ys)))
# Or an even better example, just use a 'list comprehension' as a one-off need
res = [a for a in product(xs,ys) if a[0]*a[1] > 25]
print('List Comprehension result :', res)
