1. EECS 110: Lec 13: Dictionaries Aleksandar Kuzmanovic Northwestern University http://cs.northwestern.edu/~akuzma/classes/EECS110-s10/

2. EECS 110 Today Today: Next week: Python's objects & Classes filesdictionaries Hw 5 Due this Sunday, 5/16

3. More Practice with Mutation What are the values of A, B, C and D at the indicated points? def mystery1(L1, N, C): for i in L1: if i == N: C += 1 return C def mystery2(C): for i in range(len(C)): C[i] *= 2 >>> A = [22, 10, 30] >>> B = 22 >>> C = 0 >>> D = mystery1(A, B, C) >>> mystery2(A) 1 2 1) 2)

4. More Practice with Mutation What are the values of A, B, C and D at the indicated points? def mystery1(L1, N, C): for i in L1: if i == N: C += 1 return C def mystery2(C): for i in range(len(C)): C[i] *= 2 >>> A = [22, 10, 30] >>> B = 22 >>> C = 0 >>> D = mystery1(A, B, C) >>> mystery2(A) >>> A 1 1) 2) A = [22, 10, 30] B = 22 C = 0 D = 1 2

5. More Practice with Mutation What are the values of A, B, C and D at the indicated points? def mystery1(L1, N, C): for i in L1: if i == N: C += 1 return C def mystery2(C): for i in range(len(C)): C[i] *= 2 >>> A = [22, 10, 30] >>> B = 22 >>> C = 0 >>> D = mystery1(A, B, C) >>> mystery2(A) >>> A 1 2 1) 2) A = [22, 10, 30] B = 22 C = 0 D = 1 A = [44, 20, 60]

6. Name that author… ?

7. HW5 Pr 2: Read some text and automatically generate new (reasonable?) text

8. WMSCI 2005 Randomly-generated submission accepted to WMSCI 2005 http://pdos.csail.mit.edu/scigen/ No end to the WMSCI emails…

9. Markov Model The text file: I like spam. I like toast and spam. I eat ben and jerry's ice cream too. Technique for modeling any sequence of natural data Each item depends on only the item immediately before it. The Model: 1st-order Markov Model For each word, keep track of the words that can follow it (and how often) I: like, like, eat like: spam, toast spam.: $ $: I, I, I toast: and eat: ben and: spam, jerry's ben: and jerry's: ice ice: cream cream: too. too.: $ We can repeat words to indicate frequency $ indicates beginning of a sentence

10. Generative Markov Model Technique for modeling any sequence of natural data Each item depends on only the item immediately before it. A key benefit is that the model can generate feasible data! I like spam. I like spam. I like toast and jerry's ice cream too. Generating text: 1) start with the '$' string 2) choose a word following '$', at random. Call it w 3) choose a word following w, at random. And so on… 4) If w ends a sentence, '$' becomes the next word.

11. HW5 Pr 2: Need to be able to… Read text from a file Compute and store the model Generate the new text

12. Reading Files >>> f = file( 'a.txt' ) >>> text = f.read() >>> text 'This is a file.\nLine 2\nLast line!\n' >>> f.close() In Python reading files is no problem…

13. Files >>> f = file( 'a.txt' ) >>> text = f.read() >>> text 'This is a file.\nLine 2\nLast line!\n' >>> f.close() In Python reading files is no problem… opens the file and calls it f reads the whole file and calls it text text is a single string containing all the text in the file closes the file (closing Python does the same) But how to process the text from here…?

14. String Manupulation >>> text 'This is a file.\nLine 2\nLast line!\n' >>> print text This is a file. Line 2 Last line! >>> text.split() ['This', 'is', 'a', 'file.', 'Line', '2', 'Last', 'line!'] >>> text 'This is a file.\nLine 2\nLast line!\n' >>> lines = text.split('\n') >>> lines ['This is a file.', 'Line 2', 'Last line!', ''] Returns a list of the words in the string (splitting at spaces, tabs and newlines) Returns a list of the lines in the string (splitting at newlines)

15. Objects, objects, everywhere! >>> L = [] # create a list, L >>> dir(L) # see all of L's methods >>> help(L.sort) # I wonder… all list methods appear -- lots of them including append, index, remove, and sort >>> help(L.index) # What does this do?

16. List methodsString methods but there's a fundamental difference… what and why? append count extend index insert pop remove reverse sort capitalize center count find index isalpha lower replace split strip title upper and ~10 more… >>> dir([]) help can help >>> dir('')

17. Mutable vs. immutable objects Lists are mutable objects. Strings are immutable objects. (So are numbers.) >>> L = [2,1,3] >>> L.sort() >>> L [1,2,3] L has changed. >>> s = 'string' >>> s.replace('st','') 'ring' >>> s 'string' returns a NEW string no return value s has NOT changed

18. HW5 Pr 2: Need to be able to… Read text from a file Compute and store the model Generate the new text

19. Lists vs. Dictionaries Lists are not perfect… L L[0]L[1] reference 5 42

20. Lists vs. Dictionaries Lists are not perfect… You can't choose what to name data. L[0], L[1], … L L[0]L[1] reference 5 42

21. Lists vs. Dictionaries Lists are not perfect… L[1988] = 'dragon' You can't choose what to name data. You have to start at 0. L[0], L[1], … L L[0]L[1] reference 5 42 L[1989] = 'snake'

22. Lists vs. Dictionaries Lists are not perfect… L[1988] = 'dragon' You can't choose what to name data. You have to start at 0. Some operations can be slow for big lists … L[0], L[1], … L L[0]L[1] reference 5 42 L[1989] = 'snake' if 'dragon' in L:

23. Lists vs. Dictionaries In Python a dictionary is a set of key - value pairs. It's a list where the index can be any immutable-type key. >>> d = {} >>> d[1988] = 'dragon' >>> d[1989] = 'snake' >>> d {1988: 'dragon', 1989: 'snake'} >>> d[1988] 'dragon' >>> d[1987] key error

24. Lists vs. Dictionaries In Python a dictionary is a set of key - value pairs. It's a list where the index can be any immutable-type key. >>> d = {} >>> d[1988] = 'dragon' >>> d[1989] = 'snake' >>> d {1988: 'dragon', 1989: 'snake'} >>> d[1988] 'dragon' >>> d[1987] key error creates an empty dictionary, d 1988 is the key 'dragon' is the value 1989 is the key 'snake' is the value Anyone seen this before? Retrieve data as with lists… or almost !

25. More on dictionaries Dictionaries have lots of built-in methods: >>> d = {1988: 'dragon', 1989: 'snake'} >>> d.keys() [ 1989, 1988 ] >>> d.has_key( 1988 ) True >>> d.has_key( 1969 ) False >>> d.pop( 1988 ) 'dragon' delete a key (and its value) check if a key is present get all keys

26. A family dictionary?

27. A family dictionary… T = {'abe' :['homer','herb'], 'jackie':['marge','patty','selma'], 'homer' :['hugo','bart','lisa','maggie'], 'marge' :['hugo','bart','lisa','maggie']} keys can be any immutable type values can be any type at all… T['abe'] How to get 'selma' from T ?

28. A family dictionary… T = {'abe' :['homer','herb'], 'jackie':['marge','patty','selma'], 'homer' :['hugo','bart','lisa','maggie'], 'marge' :['hugo','bart','lisa','maggie']} keys can be any immutable type values can be any type at all… T['abe'] How to get 'selma' from T ? ['homer','herb']

29. A family dictionary… T = {'abe' :['homer','herb'], 'jackie':['marge','patty','selma'], 'homer' :['hugo','bart','lisa','maggie'], 'marge' :['hugo','bart','lisa','maggie']} keys can be any immutable type values can be any type at all… T['abe'] How to get 'selma' from T ? ['homer','herb'] T['jackie'][2] (T['jackie'] is a list)

30. A functional family? def favChild( person, Tree ): """ person is a name (a string) Tree is a dictionary of children returns person's favorite child """ if Tree.has_key( person ): Kids = Tree[person] Kids.sort() return Kids[0] else: return 'no children' Who is favored ? Side effects ?

31. A functional family? def addChild( person, Tree, jr ): """ adds person's new child to Tree ""“ For example, >>> addChild( 'lisa', T, 'abejr' )

32. A functional family? def addChild( person, Tree, jr ): """ adds person's new child to Tree ""“ if Tree.has_key(person): kids = Tree[person] kids += [jr] For example, >>> addChild( 'lisa', T, 'abejr' )

33. A challenge… def provinceChallenge( prov ): """ prov is a dictionary of Canada's provinces -- the challenge is to name them all! """ while 0 in prov.values(): guess = raw_input("Name a province: ") if prov.has_key( guess ) == False: print 'Try again...' elif prov[guess] == 0: print 'Yes!' prov[guess] += 1 else: print 'Already guessed...' print 'Phew!' prov = { 'BC': 0, 'AB': 0, … } help?!

34. “Quiz” Change this code so that it keeps track of how many times you've guessed each item. Based on favChild, write favGChild to return the first grandchild alphabetically - or return 'no one' if there are none. def provinceChallenge( prov ): while 0 in prov.values(): guess = raw_input("Guess: ") if prov.has_key( guess ) == False: print 'Try again...' elif prov[guess] == 0: print 'Yes!' prov[guess] += 1 else: print 'Already guessed...' def favChild( person, Tree ): if Tree.has_key( person ): Kids = Tree[person] Kids.sort() return Kids[0] else: return 'no children' def favGChild( person, Tree ): first, for real provinces then, for incorrect guesses…

35. Change this code so that it tells you how many times you've guessed the same province… def provinceChallenge( prov ): while 0 in prov.values(): guess = raw_input("Guess: ") if prov.has_key( guess ) == False: print 'Try again... ' elif prov[guess] == 0: print 'Yes!' prov[guess] += 1 else: print 'Already guessed...‘ first, for real provinces then, for incorrect guesses…

36. Change this code so that it tells you how many times you've guessed the same province… def provinceChallenge( prov ): prov['incorrect']=0 while '0' in prov.values(): guess = raw_input("Guess: ") if prov.has_key( guess ) == False: print 'Try again... ' prov['incorrect'] += 1 elif prov[guess] == 0: print 'Yes!' prov[guess] += 1 else: print 'Already guessed...‘ prov[guess] += 1 first, for real provinces then, for incorrect guesses…

37. Based on favChild, write favGChild to return the first grandchild alphabetically - or return 'no one' if there are none. def favChild( person, Tree ): if Tree.has_key( person ): Kids = Tree[person] Kids.sort() return Kids[0] else: return 'no children'

38. Based on favChild, write favGChild to return the first grandchild alphabetically - or return 'no one' if there are none. def favGChild( person, Tree ): gChildren = [] if Tree.has_key( person ): for child in Tree[person]: if Tree.has_key( child ): gChildren += Tree[child] if gChildren == []: return 'no one' else: gChildren.sort() return gChildren[0] def favChild( person, Tree ): if Tree.has_key( person ): Kids = Tree[person] Kids.sort() return Kids[0] else: return 'no children'

39. Markov Model { 'toast': ['and'], 'and' : ['spam.', "jerry's"], 'like' : ['spam.', 'toast'], 'ben' : ['and'], 'I' : ['like', 'like', 'eat'], '$' : ['I', 'I', 'I'], The text file: I like spam. I like toast and spam. I eat ben and jerry's ice cream too. Technique for modeling any sequence of natural data Each item depends on only the item immediately before it. The Model: