OOP and Scripting in Python Advanced Features

Transcription

1 OOP and Scripting in Python Advanced Features Giuliano Armano Emanuele Tamponi

2 Advanced Features Structure of a Python Script More on Defining Functions Default Argument Values Keyword Arguments Arbitrary Argument Lists Lambda Forms Exercises More on Lists Functional Programming Tools List Comprehensions Exercises Looping Techniques Iterators Generators Generator Expressions

3 Structure of a Python Script Every Python file (extension.py) is called a module 1 #!/ usr / bin / python 2 3 # Import other modules 4 5 # Define your function, classes, variables and so on 6 7 if name == main : 8 print Hello, World!

4 Structure of a Python Script Every Python file (extension.py) is called a module 1 #!/ usr / bin / python 2 3 # Import other modules 4 5 # Define your function, classes, variables and so on 6 7 if name == main : 8 print Hello, World! To run a Python module from console, python must be in your path

5 Structure of a Python Script Every Python file (extension.py) is called a module 1 #!/ usr / bin / python 2 3 # Import other modules 4 5 # Define your function, classes, variables and so on 6 7 if name == main : 8 print Hello, World! To run a Python module from console, python must be in your path If so, you can open a prompt a write: 1 $ python program_name. py 2 Hello, World!

6 Structure of a Python Script Every Python file (extension.py) is called a module 1 #!/ usr / bin / python 2 3 # Import other modules 4 5 # Define your function, classes, variables and so on 6 7 if name == main : 8 print Hello, World! To run a Python module from console, python must be in your path If so, you can open a prompt a write: 1 $ python program_name. py 2 Hello, World!... otherwise, try with: 1 $ C:\ Python27 \ python. exe program_name. py

7 Default arguments values (1) 1 def ask_ok ( prompt, retries =4, complaint = Yes or no, please! ): 2 while True : 3 ok = raw_input ( prompt ) 4 if ok in ( y, ye, yes ): 5 return True 6 if ok in ( n, no, nop, nope ): 7 return False 8 retries = retries if retries < 0: 10 raise IOError ( refusenik user ) 11 print complaint How can you call this function?

8 Default arguments values (1) 1 def ask_ok ( prompt, retries =4, complaint = Yes or no, please! ): 2 while True : 3 ok = raw_input ( prompt ) 4 if ok in ( y, ye, yes ): 5 return True 6 if ok in ( n, no, nop, nope ): 7 return False 8 retries = retries if retries < 0: 10 raise IOError ( refusenik user ) 11 print complaint How can you call this function? ask_ok( Do you really want to quit? )

9 Default arguments values (1) 1 def ask_ok ( prompt, retries =4, complaint = Yes or no, please! ): 2 while True : 3 ok = raw_input ( prompt ) 4 if ok in ( y, ye, yes ): 5 return True 6 if ok in ( n, no, nop, nope ): 7 return False 8 retries = retries if retries < 0: 10 raise IOError ( refusenik user ) 11 print complaint How can you call this function? ask_ok( Do you really want to quit? ) ask_ok( OK to continue?, 2)

10 Default arguments values (1) 1 def ask_ok ( prompt, retries =4, complaint = Yes or no, please! ): 2 while True : 3 ok = raw_input ( prompt ) 4 if ok in ( y, ye, yes ): 5 return True 6 if ok in ( n, no, nop, nope ): 7 return False 8 retries = retries if retries < 0: 10 raise IOError ( refusenik user ) 11 print complaint How can you call this function? ask_ok( Do you really want to quit? ) ask_ok( OK to continue?, 2) ask_ok( OK to continue?, 2, Only yes or no. )

11 Default Argument Values (2) Default values are evaluated at the point of function definition in the defining scope. 1 >>> i = 5 2 >>> def f( arg =i): 3... print arg 4 >>> i = 6 5 >>> f()

12 Default Argument Values (2) Default values are evaluated at the point of function definition in the defining scope. 1 >>> i = 5 2 >>> def f( arg =i): 3... print arg 4 >>> i = 6 5 >>> f() 1 5

13 Default Argument Values (2) Default values are evaluated at the point of function definition in the defining scope. 1 >>> i = 5 2 >>> def f( arg =i): 3... print arg 4 >>> i = 6 5 >>> f() 1 5 Important warning: default values are evaluated only once. 1 def f(a, L =[]): 2 L. append (a) 3 return L 4 5 >>> print f (1) 6 >>> print f (2) 7 >>> print f (3)

14 Default Argument Values (2) Default values are evaluated at the point of function definition in the defining scope. 1 >>> i = 5 2 >>> def f( arg =i): 3... print arg 4 >>> i = 6 5 >>> f() 1 5 Important warning: default values are evaluated only once. 1 def f(a, L =[]): 2 L. append (a) 3 return L 4 5 >>> print f (1) 6 >>> print f (2) 7 >>> print f (3) 1 [1] 2 [1, 2] 3 [1, 2, 3]

15 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments.

16 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls:

17 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls: opera( Romeo and Juliet, Shakespeare, Tragedy )

18 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls: opera( Romeo and Juliet, Shakespeare, Tragedy ) opera(title= Romeo and Juliet,type= Tragedy,author= Shakespeare )

19 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls: opera( Romeo and Juliet, Shakespeare, Tragedy ) opera(title= Romeo and Juliet,type= Tragedy,author= Shakespeare ) opera( Romeo and Juliet,type= Tragedy,author= Shakespeare )

20 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls: opera( Romeo and Juliet, Shakespeare, Tragedy ) opera(title= Romeo and Juliet,type= Tragedy,author= Shakespeare ) opera( Romeo and Juliet,type= Tragedy,author= Shakespeare ) Invalid function calls:

21 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls: opera( Romeo and Juliet, Shakespeare, Tragedy ) opera(title= Romeo and Juliet,type= Tragedy,author= Shakespeare ) opera( Romeo and Juliet,type= Tragedy,author= Shakespeare ) Invalid function calls: opera(title= Romeo and Juliet, Shakespeare, Tragedy )

22 Keyword Arguments Functions can also be called using keyword arguments in the form kwarg=value. 1 def opera ( title, author, type ): 2 print title 3 print " -- a ", type, " written by", author Keyword arguments can only be used after positional arguments. Valid function calls: opera( Romeo and Juliet, Shakespeare, Tragedy ) opera(title= Romeo and Juliet,type= Tragedy,author= Shakespeare ) opera( Romeo and Juliet,type= Tragedy,author= Shakespeare ) Invalid function calls: opera(title= Romeo and Juliet, Shakespeare, Tragedy ) opera( Romeo and Juliet, Shakespeare, Tragedy,year=1596)

23 **keywords Arguments When a final formal parameter of the form **name is present, it receives a dictionary containing all keyword arguments except for those corresponding to another formal parameter. 1 def cheeseshop ( kind, ** info ): 2 print " -- Do you have any ", kind, "?" 3 print " -- I am sorry, we are all out of", kind 4 print "-" * 40 5 keys = sorted ( info. keys ()) 6 for kw in keys : 7 print kw, ":", info [kw]

24 **keywords Arguments When a final formal parameter of the form **name is present, it receives a dictionary containing all keyword arguments except for those corresponding to another formal parameter. 1 def cheeseshop ( kind, ** info ): 2 print " -- Do you have any ", kind, "?" 3 print " -- I am sorry, we are all out of", kind 4 print "-" * 40 5 keys = sorted ( info. keys ()) 6 for kw in keys : 7 print kw, ":", info [kw] It could be called like this: 1 >>> cheeseshop (" Limburger ", shopkeeper = Michael Palin, 2 client =" John ", sketch =" Cheese Shop Sketch ")

25 **keywords Arguments When a final formal parameter of the form **name is present, it receives a dictionary containing all keyword arguments except for those corresponding to another formal parameter. 1 def cheeseshop ( kind, ** info ): 2 print " -- Do you have any ", kind, "?" 3 print " -- I am sorry, we are all out of", kind 4 print "-" * 40 5 keys = sorted ( info. keys ()) 6 for kw in keys : 7 print kw, ":", info [kw] It could be called like this: 1 >>> cheeseshop (" Limburger ", shopkeeper = Michael Palin, 2 client =" John ", sketch =" Cheese Shop Sketch ") 1 -- Do you have any Limburger? 2 -- I am sorry, we are all out of Limburger client : John 5 shopkeeper : Michael Palin 6 sketch : Cheese Shop Sketch

26 Arbitrary Argument Lists (and unpacking) The *name syntax is used to specify that a function can be called with an arbitrary number of arguments (zero or more). 1 def write_multiple_items ( file, separator, * args ): 2 file. write ( separator. join ( args )) 3 4 >>> write_multiple_item (f,, Hello, world )

27 Arbitrary Argument Lists (and unpacking) The *name syntax is used to specify that a function can be called with an arbitrary number of arguments (zero or more). 1 def write_multiple_items ( file, separator, * args ): 2 file. write ( separator. join ( args )) 3 4 >>> write_multiple_item (f,, Hello, world ) You can also do the reverse: if you have all your arguments in a list or tuple, you can unpack them using the *-operator. 1 >>> range (3, 6) 2 [3, 4, 5] 3 >>> args = [3, 6] 4 >>> range (* args ) 5 [3, 4, 5]

28 Arbitrary Argument Lists (and unpacking) The *name syntax is used to specify that a function can be called with an arbitrary number of arguments (zero or more). 1 def write_multiple_items ( file, separator, * args ): 2 file. write ( separator. join ( args )) 3 4 >>> write_multiple_item (f,, Hello, world ) You can also do the reverse: if you have all your arguments in a list or tuple, you can unpack them using the *-operator. 1 >>> range (3, 6) 2 [3, 4, 5] 3 >>> args = [3, 6] 4 >>> range (* args ) 5 [3, 4, 5] If you have a dictionary, you can use the **-operator. 1 >>> d = { author : Shakespeare, 2 title : Romeo and Juliet, type : Tragedy } 3 >>> opera (** d)

29 Lambda Forms With the lambda keyword, small anonymous functions can be created. 1 def make_incrementor ( n): 2 return lambda x: x + n 3 4 >>> f = make_incrementor (42) 5 >>> f (0) >>> f (5) 8 47

30 Lambda Forms With the lambda keyword, small anonymous functions can be created. 1 def make_incrementor ( n): 2 return lambda x: x + n 3 4 >>> f = make_incrementor (42) 5 >>> f (0) >>> f (5) 8 47 Any number of arguments: lambda a,b,c: a+b+c

31 Lambda Forms With the lambda keyword, small anonymous functions can be created. 1 def make_incrementor ( n): 2 return lambda x: x + n 3 4 >>> f = make_incrementor (42) 5 >>> f (0) >>> f (5) 8 47 Any number of arguments: lambda a,b,c: a+b+c Only a single expression

32 Lambda Forms With the lambda keyword, small anonymous functions can be created. 1 def make_incrementor ( n): 2 return lambda x: x + n 3 4 >>> f = make_incrementor (42) 5 >>> f (0) >>> f (5) 8 47 Any number of arguments: lambda a,b,c: a+b+c Only a single expression Can reference variables from the containing scope

33 Exercises Define a function max(), that returns the maximum value among those received as parameters. It can receive either values as list or a list of values 1 >>> l = [1, 2, 3, 4, 3, 2, 1] 2 >>> max (l) >>> max (3, 4, 5, 6, 2, 3, 4) 5 6 Define a function histogram() that takes a string as first parameter, then an arbitrary number of parameters in the form bin_label=frequency and prints the relative histogram on the screen 1 >>> histogram (" Population of Pincoland ", =10, 2005=15, 2010=12) 3 Population of Pincoland ********** *************** ************

34 Methods of list Objects list.append(x), adds x as last element list.extend(l), the same as list + L list.insert(i, x), inserts x before the element at position i list.remove(x), removes the first occurrence of x list.pop([i]), removes the last (or i-th) element list.index(x), returns the position of x (first occurrence) list.count(x), counts the occurrences of x list.sort(), sorts the list in place list.reverse(), reverses the elements of the list, in place

35 Methods of list Objects list.append(x), adds x as last element list.extend(l), the same as list + L list.insert(i, x), inserts x before the element at position i list.remove(x), removes the first occurrence of x list.pop([i]), removes the last (or i-th) element list.index(x), returns the position of x (first occurrence) list.count(x), counts the occurrences of x list.sort(), sorts the list in place list.reverse(), reverses the elements of the list, in place Reminder: lists (square brackets) are mutable objects, tuples (standard brackets) are immutable objects.

36 filter, reduce...

37 filter, reduce... filter(function, sequence), returns a sequence consisting of those items from the sequence for which function(item) is true 1 def f(x): 2 return x % 2!= 0 and x % 3!= 0 3 >>> filter (f, range (2, 25)) 4 [5, 7, 11, 13, 17, 19, 23]

38 filter, reduce... filter(function, sequence), returns a sequence consisting of those items from the sequence for which function(item) is true 1 def f(x): 2 return x % 2!= 0 and x % 3!= 0 3 >>> filter (f, range (2, 25)) 4 [5, 7, 11, 13, 17, 19, 23] reduce(function, sequence), returns a single value constructed by calling the binary function on the first two items of the sequence, then on the result and the next item, and so on 1 def add (x, y): 2 return x + y 3 4 >>> reduce ( add, range (1, 11)) 5 55

39 filter, reduce... filter(function, sequence), returns a sequence consisting of those items from the sequence for which function(item) is true 1 def f(x): 2 return x % 2!= 0 and x % 3!= 0 3 >>> filter (f, range (2, 25)) 4 [5, 7, 11, 13, 17, 19, 23] reduce(function, sequence), returns a single value constructed by calling the binary function on the first two items of the sequence, then on the result and the next item, and so on 1 def add (x, y): 2 return x + y 3 4 >>> reduce ( add, range (1, 11)) 5 55 reduce(function, sequence, start_value), same as before, but use start_value as first parameter for the first run

40 ... and map map(function, sequence), calls function(item) for each of the sequence s items and returns a list of the return values 1 def cube (x): 2 return x* x* x 3 >>> map (cube, range (1, 11)) 4 [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

41 ... and map map(function, sequence), calls function(item) for each of the sequence s items and returns a list of the return values 1 def cube (x): 2 return x* x* x 3 >>> map (cube, range (1, 11)) 4 [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000] More than one sequence may be passed; the function must then have as many arguments as there are sequences, and the sequences must have the same length 1 def add (x, y): 2 return x + y 3 4 >>> map (add, range (8), range (8,16)) 5 [8, 10, 12, 14, 16, 18, 20, 22]

42 List Comprehensions 1 >>> squares = [] 2 >>> for x in range (10): 3... squares. append (x **2) >>> squares 6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

43 List Comprehensions 1 >>> squares = [] 2 >>> for x in range (10): 3... squares. append (x **2) >>> squares 6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] List comprehensions provide a concise way to create lists. 1 >>> squares = [ x **2 for x in range (10)]

44 List Comprehensions 1 >>> squares = [] 2 >>> for x in range (10): 3... squares. append (x **2) >>> squares 6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] List comprehensions provide a concise way to create lists. 1 >>> squares = [ x **2 for x in range (10)] Can you do it with map?

45 List Comprehensions 1 >>> squares = [] 2 >>> for x in range (10): 3... squares. append (x **2) >>> squares 6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] List comprehensions provide a concise way to create lists. 1 >>> squares = [ x **2 for x in range (10)] Can you do it with map? 1 >>> squares = map ( lambda x: x**2, range (10))

46 List Comprehensions 1 >>> squares = [] 2 >>> for x in range (10): 3... squares. append (x **2) >>> squares 6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] List comprehensions provide a concise way to create lists. 1 >>> squares = [ x **2 for x in range (10)] Can you do it with map? 1 >>> squares = map ( lambda x: x**2, range (10)) A listcomp consists of square brackets containing an expression followed by a for clause, then zero or more for or if clauses. 1 >>> [( x, y) for x in [1, 2, 3] for y in [3, 1, 4] if x!= y]

47 List Comprehensions 1 >>> squares = [] 2 >>> for x in range (10): 3... squares. append (x **2) >>> squares 6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] List comprehensions provide a concise way to create lists. 1 >>> squares = [ x **2 for x in range (10)] Can you do it with map? 1 >>> squares = map ( lambda x: x**2, range (10)) A listcomp consists of square brackets containing an expression followed by a for clause, then zero or more for or if clauses. 1 >>> [( x, y) for x in [1, 2, 3] for y in [3, 1, 4] if x!= y] 1 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

48 Matrix transpose with Nested List Comprehensions Provide a function transpose(matrix) that returns the transpose of the matrix received as parameter, represented as an array of rows. 1 >>> a_matrix = [ 2... [1, 2, 3, 4], 3... [5, 6, 7, 8], 4... [9, 10, 11, 12], 5... ]

49 Matrix transpose with Nested List Comprehensions Provide a function transpose(matrix) that returns the transpose of the matrix received as parameter, represented as an array of rows. 1 >>> a_matrix = [ 2... [1, 2, 3, 4], 3... [5, 6, 7, 8], 4... [9, 10, 11, 12], 5... ] 1 def transpose ( matrix ): 2 transposed = [] 3 for i in range ( len ( matrix [0])): 4 transposed_row = [] 5 for row in matrix : 6 transposed_row. append ( row [i]) 7 transposed. append ( transposed_row ) 8 return transposed

50 Matrix transpose with Nested List Comprehensions Provide a function transpose(matrix) that returns the transpose of the matrix received as parameter, represented as an array of rows. 1 >>> a_matrix = [ 2... [1, 2, 3, 4], 3... [5, 6, 7, 8], 4... [9, 10, 11, 12], 5... ] 1 def transpose ( matrix ): 2 transposed = [] 3 for i in range ( len ( matrix [0])): 4 transposed_row = [] 5 for row in matrix : 6 transposed_row. append ( row [i]) 7 transposed. append ( transposed_row ) 8 return transposed 1 def transpose ( matrix ): 2 cols = len ( matrix [0]) 3 return [[ row [ i] for row in matrix ] for i in range ( cols )]

51 Exercises Using reduce(), write a function max() that takes a list of numbers and returns the largest one Write a program that maps a list of words into a list of integers representing the lengths of the corresponding words Write a function find_longest_word() that takes a list of words and returns the length of the longest one Write a function remove_odd_words() that takes a list of words and returns a list of those that have an even length (use filter())

52 Looping Techniques (1)

53 Looping Techniques (1) enumerate(l), gets the position index and corresponding value 1 >>> for i, v in enumerate ([ tic, tac, toe ]): 2... print i, v tic 5 1 tac 6 2 toe

54 Looping Techniques (1) enumerate(l), gets the position index and corresponding value 1 >>> for i, v in enumerate ([ tic, tac, toe ]): 2... print i, v tic 5 1 tac 6 2 toe zip(), pairs entries of two or more sequences 1 >>> primes = [2, 3, 5, 7, 11, 13, 17] 2 >>> squares = [ x **2 for x in primes ] 3 >>> for p, s in zip ( primes, squares ): 4... print {0} squared is {1}.. format (p, s) squared is squared is

55 Looping Techniques (2) reversed(), reverses the order of the items 1 >>> for i in reversed ( range (1, 6, 2)): 2... print i

56 Looping Techniques (2) reversed(), reverses the order of the items 1 >>> for i in reversed ( range (1, 6, 2)): 2... print i sorted(), returns a new sorted list 1 >>> basket = [ apple, orange, apple, 2... pear, orange, banana ] 3 >>> for f in sorted ( set ( basket )): 4... print f apple 7 banana 8 orange 9 pear

57 Looping Techniques (3) dict.iteritems(), accesses keys and values of a dictionary at the same time 1 >>> knights = { gallahad : pure, robin : brave } 2 >>> for k, v in knights. iteritems (): 3... print k, the, v gallahad the pure 6 robin the brave

58 Looping Techniques (3) dict.iteritems(), accesses keys and values of a dictionary at the same time 1 >>> knights = { gallahad : pure, robin : brave } 2 >>> for k, v in knights. iteritems (): 3... print k, the, v gallahad the pure 6 robin the brave It is recommended that you first make a copy of a list if you want to modify it during iteration. Slices are very convenient for this 1 >>> words = [ cat, window, defenestrate ] 2 >>> for w in words [:]: 3... if len (w) > 6: 4... words. insert (0, w) >>> words 7 [ defenestrate, cat, window, defenestrate ]

59 Iterators 1 for x in l: 2 print x

60 Iterators 1 for x in l: 2 print x 1 it = iter ( l) 2 while True : 3 try : 4 x = it. next () 5 print x 6 except StopIteration : 7 break 8 del it

61 Iterators 1 for x in l: 2 print x 1 it = iter ( l) 2 while True : 3 try : 4 x = it. next () 5 print x 6 except StopIteration : 7 break 8 del it The iter() function creates an iterator: an object that has a next() method, which returns the next element in the list if it exists, otherwise it raises a StopIteration exception.

62 Iterators 1 for x in l: 2 print x 1 it = iter ( l) 2 while True : 3 try : 4 x = it. next () 5 print x 6 except StopIteration : 7 break 8 del it The iter() function creates an iterator: an object that has a next() method, which returns the next element in the list if it exists, otherwise it raises a StopIteration exception. Fully customizable using classes: you can decide how your object will iterate in a for loop!

63 Generators

64 Generators Simple and powerful tool for creating iterators

65 Generators Simple and powerful tool for creating iterators Written like regular functions but use yield instead of return 1 def reverse ( data ): 2 for index in range ( len ( data )-1, -1, -1): 3 yield data [ index ]

66 Generators Simple and powerful tool for creating iterators Written like regular functions but use yield instead of return 1 def reverse ( data ): 2 for index in range ( len ( data )-1, -1, -1): 3 yield data [ index ] Iterators are automatically created from function definition 1 >>> for char in reverse ( golf ): 2... print char f 5 l 6 o 7 g

67 Generators Simple and powerful tool for creating iterators Written like regular functions but use yield instead of return 1 def reverse ( data ): 2 for index in range ( len ( data )-1, -1, -1): 3 yield data [ index ] Iterators are automatically created from function definition 1 >>> for char in reverse ( golf ): 2... print char f 5 l 6 o 7 g Local variables and execution state are saved between subsequent calls of next()

68 Generator Expressions Generators created like list comprehensions, but without square brackets. 1 >>> sum ( i* i for i in range (10)) 2 285

69 Generator Expressions Generators created like list comprehensions, but without square brackets. 1 >>> sum ( i* i for i in range (10)) >>> xvec = [10, 20, 30] 2 >>> yvec = [7, 5, 3] 3 >>> sum (x*y for x,y in zip (xvec, yvec )) 4 260

70 Generator Expressions Generators created like list comprehensions, but without square brackets. 1 >>> sum ( i* i for i in range (10)) >>> xvec = [10, 20, 30] 2 >>> yvec = [7, 5, 3] 3 >>> sum (x*y for x,y in zip (xvec, yvec )) >>> from math import pi, sin 2 >>> sine_table = dict (( x, sin ( x* pi /180)) 3... for x in range (0, 91))

71 Generator Expressions Generators created like list comprehensions, but without square brackets. 1 >>> sum ( i* i for i in range (10)) >>> xvec = [10, 20, 30] 2 >>> yvec = [7, 5, 3] 3 >>> sum (x*y for x,y in zip (xvec, yvec )) >>> from math import pi, sin 2 >>> sine_table = dict (( x, sin ( x* pi /180)) 3... for x in range (0, 91)) 1 >>> unique_words = set ( word for line in page 2... for word in line. split ())

72 Generator Expressions Generators created like list comprehensions, but without square brackets. 1 >>> sum ( i* i for i in range (10)) >>> xvec = [10, 20, 30] 2 >>> yvec = [7, 5, 3] 3 >>> sum (x*y for x,y in zip (xvec, yvec )) >>> from math import pi, sin 2 >>> sine_table = dict (( x, sin ( x* pi /180)) 3... for x in range (0, 91)) 1 >>> unique_words = set ( word for line in page 2... for word in line. split ()) 1 >>> data = golf 2 >>> list ( data [i] for i in range ( len ( data ) -1, -1, -1)) 3 [ f, l, o, g ]