OOP and Scripting in Python Advanced Features · Structure of a Python Script I Every Python le...

OOP and Scripting in PythonAdvanced Features

Giuliano Armano Emanuele Tamponi

Advanced Features

Structure of a Python Script

More on Defining FunctionsDefault Argument ValuesKeyword ArgumentsArbitrary Argument ListsLambda FormsExercises

More on ListsFunctional Programming ToolsList ComprehensionsExercises

Looping TechniquesIteratorsGeneratorsGenerator Expressions

I Every Python file (extension .py) is called a module

1 #!/usr/bin/python

3 # Import other modules

5 # Define your function , classes , variables and so on

7 if __name__ == ’__main__ ’:

8 print ’Hello , World!’

I To run a Python module from console, python must be inyour path

I If so, you can open a prompt a write:

1 $ python program_name.py

2 Hello , World!

I ... otherwise, try with:

1 $ C:\ Python27\python.exe program_name.py

1 #!/usr/bin/python

7 if __name__ == ’__main__ ’:

2 Hello , World!

1 #!/usr/bin/python

7 if __name__ == ’__main__ ’:

2 Hello , World!

1 #!/usr/bin/python

7 if __name__ == ’__main__ ’:

2 Hello , World!

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 - 1

9 if retries < 0:

10 raise IOError(’refusenik user’)

11 print complaint

How can you call this function?

I ask_ok(’Do you really want to quit?’)

I ask_ok(’OK to continue?’, 2)

I ask_ok(’OK to continue?’, 2, ’Only yes or no.’)

2 while True:

5 return True

7 return False

9 if retries < 0:

11 print complaint

How can you call this function?I ask_ok(’Do you really want to quit?’)

2 while True:

5 return True

7 return False

9 if retries < 0:

11 print complaint

2 while True:

5 return True

7 return False

9 if retries < 0:

11 print complaint

Default Argument Values (2)

Default values are evaluated at the point of function definition inthe defining scope.

1 >>> i = 5

2 >>> def f(arg=i):

3 ... print arg

4 >>> i = 6

5 >>> f()

Important warning: default values are evaluated only once.

1 def f(a, L=[]):

2 L.append(a)

3 return L

5 >>> print f(1)

6 >>> print f(2)

7 >>> print f(3)

2 [1, 2]

3 [1, 2, 3]

1 >>> i = 5

2 >>> def f(arg=i):

3 ... print arg

4 >>> i = 6

5 >>> f()

1 def f(a, L=[]):

2 L.append(a)

3 return L

5 >>> print f(1)

6 >>> print f(2)

7 >>> print f(3)

2 [1, 2]

3 [1, 2, 3]

1 >>> i = 5

2 >>> def f(arg=i):

3 ... print arg

4 >>> i = 6

5 >>> f()

1 def f(a, L=[]):

2 L.append(a)

3 return L

5 >>> print f(1)

6 >>> print f(2)

7 >>> print f(3)

2 [1, 2]

3 [1, 2, 3]

1 >>> i = 5

2 >>> def f(arg=i):

3 ... print arg

4 >>> i = 6

5 >>> f()

1 def f(a, L=[]):

2 L.append(a)

3 return L

5 >>> print f(1)

6 >>> print f(2)

7 >>> print f(3)

2 [1, 2]

3 [1, 2, 3]

Keyword Arguments

Functions can also be called using keyword arguments in the formkwarg=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:

I opera(’Romeo and Juliet’, ’Shakespeare’,’Tragedy’)

I opera(title=’Romeo and Juliet’,type=’Tragedy’,author=’Shakespeare’)

I opera(’Romeo and Juliet’,type=’Tragedy’,author=’Shakespeare’)

Keyword Arguments

2 print title

Keyword arguments can only be used after positional arguments.Valid function calls:

Keyword Arguments

2 print title

Keyword Arguments

2 print title

Keyword Arguments

2 print title

Keyword Arguments

2 print title

Invalid function calls:

I opera(title=’Romeo and Juliet’,’Shakespeare’,’Tragedy’)

I opera(’Romeo and Juliet’, ’Shakespeare’,’Tragedy’,year=1596)

Keyword Arguments

2 print title

Keyword Arguments

2 print title

**keywords Arguments

When a final formal parameter of the form **name is present, itreceives a dictionary containing all keyword arguments except forthose 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

3 ----------------------------------------

4 client : John

5 shopkeeper : Michael Palin

6 sketch : Cheese Shop Sketch

4 print "-" * 40

6 for kw in keys:

3 ----------------------------------------

4 client : John

4 print "-" * 40

6 for kw in keys:

3 ----------------------------------------

4 client : John

Arbitrary Argument Lists (and unpacking)

The *name syntax is used to specify that a function can be calledwith an arbitrary number of arguments (zero or more).

1 def write_multiple_items(file , separator , *args):

2 file.write(separator.join(args))

4 >>> write_multiple_item(f, ’ ’, ’Hello’, ’world’)

You can also do the reverse: if you have all your arguments in a listor 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)

1 >>> range(3, 6)

2 [3, 4, 5]

3 >>> args = [3, 6]

4 >>> range(*args)

5 [3, 4, 5]

3 >>> opera (**d)

1 >>> range(3, 6)

2 [3, 4, 5]

3 >>> args = [3, 6]

4 >>> range(*args)

5 [3, 4, 5]

3 >>> opera (**d)

Lambda Forms

With the lambda keyword, small anonymous functions can becreated.

1 def make_incrementor(n):

2 return lambda x: x + n

4 >>> f = make_incrementor (42)

5 >>> f(0)

7 >>> f(5)

I Any number of arguments: lambda a,b,c: a+b+c

I Only a single expression

I Can reference variables from the containing scope

Lambda Forms

5 >>> f(0)

7 >>> f(5)

Lambda Forms

5 >>> f(0)

7 >>> f(5)

Lambda Forms

5 >>> f(0)

7 >>> f(5)

Exercises

I Define a function max(), that returns the maximum valueamong those received as parameters. It can receive eithervalues as list or a list of values

1 >>> l = [1, 2, 3, 4, 3, 2, 1]

2 >>> max(l)

4 >>> max(3, 4, 5, 6, 2, 3, 4)

I Define a function histogram() that takes a string as firstparameter, then an arbitrary number of parameters in theform bin_label=frequency and prints the relative histogram onthe screen

1 >>> histogram("Population of Pincoland",

2 2000=10 , 2005=15 , 2010=12)

3 Population of Pincoland

4 2000 **********

5 2005 ***************

6 2010 ************

Methods of list Objects

I list.append(x), adds x as last element

I list.extend(L), the same as list + L

I list.insert(i, x), inserts x before the element at position i

I list.remove(x), removes the first occurrence of x

I list.pop([i]), removes the last (or i-th) element

I list.index(x), returns the position of x (first occurrence)

I list.count(x), counts the occurrences of x

I list.sort(), sorts the list in place

I list.reverse(), reverses the elements of the list, in place

Reminder: lists (square brackets) are mutable objects, tuples(standard brackets) are immutable objects.

Methods of list Objects

I list.append(x), adds x as last element

I list.extend(L), the same as list + L

I list.insert(i, x), inserts x before the element at position i

I list.remove(x), removes the first occurrence of x

I list.pop([i]), removes the last (or i-th) element

I list.index(x), returns the position of x (first occurrence)

I list.count(x), counts the occurrences of x

I list.sort(), sorts the list in place

I list.reverse(), reverses the elements of the list, in place

Reminder: lists (square brackets) are mutable objects, tuples(standard brackets) are immutable objects.

filter, reduce...

I filter(function, sequence), returns a sequence consisting ofthose 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]

I reduce(function, sequence), returns a single value constructedby calling the binary function on the first two items of thesequence, then on the result and the next item, and so on

1 def add(x, y):

2 return x + y

4 >>> reduce(add , range(1, 11))

I reduce(function, sequence, start_value), same as before, butuse start_value as first parameter for the first run

filter, reduce...

1 def f(x):

2 return x % 2 != 0 and x % 3 != 0

4 [5, 7, 11, 13, 17, 19, 23]

1 def add(x, y):

2 return x + y

filter, reduce...

1 def f(x):

2 return x % 2 != 0 and x % 3 != 0

4 [5, 7, 11, 13, 17, 19, 23]

1 def add(x, y):

2 return x + y

filter, reduce...

1 def f(x):

2 return x % 2 != 0 and x % 3 != 0

4 [5, 7, 11, 13, 17, 19, 23]

1 def add(x, y):

2 return x + y

... and map

I map(function, sequence), calls function(item) for each of thesequence’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]

I More than one sequence may be passed; the function mustthen have as many arguments as there are sequences, and thesequences must have the same length

1 def add(x, y):

2 return x + y

4 >>> map(add , range(8), range (8 ,16))

5 [8, 10, 12, 14, 16, 18, 20, 22]

... and map

I map(function, sequence), calls function(item) for each of thesequence’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]

I More than one sequence may be passed; the function mustthen have as many arguments as there are sequences, and thesequences must have the same length

1 def add(x, y):

2 return x + y

4 >>> map(add , range(8), range (8 ,16))

5 [8, 10, 12, 14, 16, 18, 20, 22]

List Comprehensions

1 >>> squares = []

2 >>> for x in range (10):

3 ... squares.append(x**2)

5 >>> 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 expressionfollowed 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)]

List Comprehensions

1 >>> squares = []

5 >>> squares

6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

1 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

List Comprehensions

1 >>> squares = []

5 >>> squares

6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

1 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

List Comprehensions

1 >>> squares = []

5 >>> squares

6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

1 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

List Comprehensions

1 >>> squares = []

5 >>> squares

6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

1 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

List Comprehensions

1 >>> squares = []

5 >>> squares

6 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

1 [(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]

Matrix transpose with Nested List Comprehensions

Provide a function transpose(matrix) that returns the transpose ofthe 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

2 cols = len(matrix [0])

3 return [[row[i] for row in matrix] for i in range(cols)]

1 >>> a_matrix = [

2 ... [1, 2, 3, 4],

3 ... [5, 6, 7, 8],

4 ... [9, 10, 11, 12],

5 ... ]

2 transposed = []

8 return transposed

1 >>> a_matrix = [

2 ... [1, 2, 3, 4],

3 ... [5, 6, 7, 8],

4 ... [9, 10, 11, 12],

5 ... ]

2 transposed = []

8 return transposed

Exercises

I Using reduce(), write a function max() that takes a list ofnumbers and returns the largest one

I Write a program that maps a list of words into a list ofintegers representing the lengths of the corresponding words

I Write a function find_longest_word() that takes a list of wordsand returns the length of the longest one

I Write a function remove_odd_words() that takes a list of wordsand returns a list of those that have an even length (usefilter())

Looping Techniques (1)

I enumerate(L), gets the position index and corresponding value

1 >>> for i, v in enumerate ([’tic’, ’tac’, ’toe’]):

2 ... print i, v

4 0 tic

5 1 tac

6 2 toe

I 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)

6 2 squared is 4

7 3 squared is 9

2 ... print i, v

4 0 tic

5 1 tac

6 2 toe

1 >>> primes = [2, 3, 5, 7, 11, 13, 17]

6 2 squared is 4

7 3 squared is 9

2 ... print i, v

4 0 tic

5 1 tac

6 2 toe

1 >>> primes = [2, 3, 5, 7, 11, 13, 17]

6 2 squared is 4

7 3 squared is 9

I reversed(), reverses the order of the items

1 >>> for i in reversed(range(1, 6, 2)):

2 ... print i

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

6 apple

7 banana

8 orange

9 pear

I reversed(), reverses the order of the items

1 >>> for i in reversed(range(1, 6, 2)):

2 ... print i

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

6 apple

7 banana

8 orange

9 pear

I dict.iteritems(), accesses keys and values of a dictionary atthe same time

1 >>> knights = {’gallahad ’: ’pure’, ’robin’: ’brave’}

2 >>> for k, v in knights.iteritems ():

3 ... print k, ’the’, v

5 gallahad the pure

6 robin the brave

I It is recommended that you first make a copy of a list if youwant to modify it during iteration. Slices are very convenientfor this

1 >>> words = [’cat’, ’window ’, ’defenestrate ’]

2 >>> for w in words [:]:

3 ... if len(w) > 6:

4 ... words.insert(0, w)

6 >>> words

7 [’defenestrate ’, ’cat’, ’window ’, ’defenestrate ’]

I dict.iteritems(), accesses keys and values of a dictionary atthe same time

1 >>> knights = {’gallahad ’: ’pure’, ’robin’: ’brave’}

2 >>> for k, v in knights.iteritems ():

3 ... print k, ’the’, v

5 gallahad the pure

6 robin the brave

I It is recommended that you first make a copy of a list if youwant to modify it during iteration. Slices are very convenientfor this

1 >>> words = [’cat’, ’window ’, ’defenestrate ’]

2 >>> for w in words [:]:

3 ... if len(w) > 6:

4 ... words.insert(0, w)

6 >>> words

7 [’defenestrate ’, ’cat’, ’window ’, ’defenestrate ’]

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

I The iter() function creates an iterator: an object that has anext() method, which returns the next element in the list if itexists, otherwise it “raises” a StopIteration exception.

I Fully customizable using classes: you can decide how yourobject will “iterate” in a for loop!

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

7 break

8 del it

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

7 break

8 del it

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

7 break

8 del it

Generators

I Simple and powerful tool for creating iterators

I 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]

I Iterators are automatically created from function definition

1 >>> for char in reverse(’golf’):

2 ... print char

I Local variables and execution state are saved betweensubsequent calls of next()

Generators

3 yield data[index]

2 ... print char

Generators

3 yield data[index]

2 ... print char

Generators

3 yield data[index]

2 ... print char

Generators

3 yield data[index]

2 ... print char

Generator Expressions

Generators created like list comprehensions, but without squarebrackets.

1 >>> sum(i*i for i in range (10))

1 >>> xvec = [10, 20, 30]

2 >>> yvec = [7, 5, 3]

3 >>> sum(x*y for x,y in zip(xvec , yvec))

1 >>> 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’]

1 >>> xvec = [10, 20, 30]

2 >>> yvec = [7, 5, 3]

3 ... for x in range(0, 91))

3 [’f’, ’l’, ’o’, ’g’]

1 >>> xvec = [10, 20, 30]

2 >>> yvec = [7, 5, 3]

3 ... for x in range(0, 91))

3 [’f’, ’l’, ’o’, ’g’]

1 >>> xvec = [10, 20, 30]

2 >>> yvec = [7, 5, 3]

3 ... for x in range(0, 91))

3 [’f’, ’l’, ’o’, ’g’]

1 >>> xvec = [10, 20, 30]

2 >>> yvec = [7, 5, 3]

3 ... for x in range(0, 91))

3 [’f’, ’l’, ’o’, ’g’]

OOP and Scripting in Python Advanced Features · Structure of a Python Script I Every Python le...

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