1. The building blocks of functional computing data, sequences conditionals recursion CS 121 today List Comprehensions map and applications

2. functional programming >>> 'fun' in 'functional' True Key ideas in functional programming create small building blocks (functions) leverage self-similarity (recursion) representation via list structures (data) Compose these together to solve or investigate problems. elegant and concise not maximally efficient for the computer… vs.

3. return to recursion Composing functions into specific applications Creating general functions that will be useful everywhere (or almost…)

4. return to recursion Composing functions into specific applications Creating general functions that will be useful everywhere (or almost…) building blocks with which to compose…

5. sum, range def sum(L): """ input: a list of numbers, L output: L's sum """

6. sum, range def sum(L): """ input: a list of numbers, L output: L's sum """ if len(L) == 0: return 0.0 else: return L[0] + sum(L[1:]) Base Case if the input has no elements, its sum is zero Recursive Case if L does have an element, add that element's value to the sum of the REST of the list… This input to the recursive call must be "smaller" somehow…

7. sum, range def range(low,hi): """ input: two ints, low and hi output: int list from low up to hi """ excluding hi

8. sum, range def range(low,hi): """ input: two ints, low and hi output: int list from low up to hi """ if hi <= low: return [] else: return excluding hi

9. sum, range def range(low,hi): """ input: two ints, low and hi output: int list from low up to hi """ if hi <= low: return [] else: return [low] + range(low+1,hi) excluding hi

10. Recursion: Good News/Bad News Recursion is common (fundamental) in functional programming def dblList(L): """ Doubles all the values in a list. input: L, a list of numbers """ if L == []: return L else: return [L[0]*2] + dblList(L[1:]) But you can sometimes hide it away!

11. Map: The recursion "alternative" def dbl(x): return 2*x def sq(x): return x**2 def isana(x): return x=='a’ >>> map( dbl, [0,1,2,3,4,5] ) [0, 2, 4, 6, 8, 10] >>> map( sq, range(6) ) [0, 1, 4, 9, 16, 25] >>> map( isana, 'go away!' ) [0, 0, 0, 1, 0, 1, 0, 0] Hey… this looks a bit False to me! (1) map always returns a list (2) map(f,L) calls f on each item in L

12. Map ! def dblList(L): """ Doubles all the values in a list. input: L, a list of numbers """ if L == []: return L else: return [L[0]*2] + dblList(L[1:]) Without map def dbl(x): return x*2 def dblList(L): """ Doubles all the values in a list. input: L, a list of numbers """ return map(dbl, L) With map!

13. Map: a higher-order function In Python, functions can take other functions as input… def map( f, L ): Key Concep t Functions ARE data!

14. Why use map ?

15. Faster execution in Python – map optimized for operations in lists More elegant / shorter code, “functional in style” Avoid rewriting list recursion (build once, use lots)

16. Mapping without map : List Comprehensions >>> [ dbl(x) for x in [0,1,2,3,4,5] ] [0, 2, 4, 6, 8, 10] >>> [ x**2 for x in range(6) ] [0, 1, 4, 9, 16, 25] >>> [ c == 'a' for c in 'go away!' ] [0, 0, 0, 1, 0, 1, 0, 0] Anything you want to happen to each element of a list output input name that takes on the value of each element in turn the list (or string) any name is OK!

17. Mapping without map : List Comprehensions >>> [ dbl(x) for x in [0,1,2,3,4,5] ] [0, 2, 4, 6, 8, 10] >>> [ x**2 for x in range(6) ] [0, 1, 4, 9, 16, 25] >>> [ c == 'a' for c in 'go away!' ] [0, 0, 0, 1, 0, 1, 0, 0] def dbl(x): return 2*x def sq(x): return x**2 def isana(x): return x=='a’ >>> map( dbl, [0,1,2,3,4,5] ) [0, 2, 4, 6, 8, 10] >>> map( sq, range(6) ) [0, 1, 4, 9, 16, 25] >>> map( isana, 'go away!' ) [0, 0, 0, 1, 0, 1, 0, 0]

18. List Comprehensions def len(L): if L == []: return 0 else: return 1 + len(L[1:]) len(L) implemented via raw recursion sScore(s) sajak(s) def sajak(s): if len(s) == 0: return 0 else: if s[0] not in 'aeiou': return sajak(s[1:]) else: return 1+sajak(s[1:]) def sScore(s): if len(s) == 0: return 0 else: return letScore(s[0]) + \ sScore(s[1:]) scrabble score

19. List Comprehensions LC = [1 for x in L] return sum( LC ) len(L)

20. List Comprehensions LC = [1 for x in L] return sum( LC ) len(L) sajak(s) LC = [c in 'aeiou' for c in s] return sum( LC ) # of vowels

21. List Comprehensions LC = [1 for x in L] return sum( LC ) len(L) sScore(s) sajak(s) LC = [c in 'aeiou' for c in s] return sum( LC ) scrabble score # of vowels LC = [ letScore(c) for c in s] return sum( LC )

22. Write each of these functions concisely using list comprehensions… Write def count(e,L): Write def lotto(Y,W): input: e, any element L, any list or string output: the # of times L contains e example: count('f', 'fluff') == 3 input: Y and W, two lists of lottery numbers (ints) output: the # of matches between Y & W example: lotto([5,7,42,44],[3,5,7,44]) == 3 Y are your numbers W are the winning numbers Remember True == 1 and False == 0 Extra! Write def divs(N): input: N, an int >= 2 output: the number of positive divisors of N example: divs(12) == 6 (1,2,3,4,6,12)

24. LC = [x==e for x in L] return sum( LC ) count(e,L)

25. lotto(Y,W) LC = [c in Y for c in W] return sum( LC )

26. divs(N) LC = [ N%c==0 for c in range(1,N+1)] return sum( LC )

27. LC = [x==e for x in L] return sum( LC ) count(e,L) divs(N) lotto(Y,W) LC = [c in Y for c in W] return sum( LC ) LC = [ N%c==0 for c in range(1,N+1)] return sum( LC )