Python lecture 05

Python & Perl
Lecture 06

Department of Computer Science
Utah State University

Recap
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Types in Python
Built-in Sequences
List: Python's Workhorse
Function parameters
Scoping

Outline
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Object Comparison

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Loops

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Dictionaries

Object Comparison
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The operators <, >, ==, >=, <=, and != compare the values of
two objects
The objects need not have the same type

Object Comparison
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Numbers are compared arithmetically
Strings are compared lexicographically using the numeric
equivalents of their characters
Tuples and lists are compared lexicographically using
comparison of corresponding elements
Sequences, to compare equal, must be of the same type and
have the same length and the corresponding elements must
compare equal
Mappings (dictionaries) compare equal if and only if their sorted
(key, value) lists compare equal

cmp(x, y)
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cmp(x, y) is a built-in function

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cmp(x, y) returns


if

x < y



1

if

x > y


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-1
0

if

x == y

cmp(x, y) provides the default comparison function for
sort()

cmp(x, y)
>>> cmp(1, 2)
-1
>>> cmp(2, 1)
1
>>> cmp(1, 1)
0
>>> cmp('a', 'b')
-1
>>> cmp('b', 'a')

1

Customizing sort()
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It is possible to customize sort()

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Customization requires two steps:


Define a new comparison function that takes two arguments and returns three integers according to
the cmp() convention



Pass the function object (just the function name with
no parenthesis) to sort()

Example 01: Customizing sort()
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Define a comparator function:
def neg_cmp(x, y):
return -cmp(x,y)

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Pass the comparator function to sort():
>>> z = [1, 2, 3, 4, 5]
>>> z.sort(neg_cmp)
>>> z
[5, 4, 3, 2, 1]

C/C++ while vs. Python while
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Common C/C++ idiom:
while ((x = next()) != NULL) {
// Some code
}

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Python while loops may not have assignments in the
test statements:
x = next()
while x:
## some code
x = next()

Python while Example
## print numbers from 1 upto 100
x = 1
while x <= 100:
print x
x += 1

Problem

Write a Python script that asks the user for his/her
name until the user actually enters a string

Coding Solution 1
### keep asking for name until the user types
### something; not '' evaluates to True

name = ''
while not name:
name = raw_input('Please enter your name: ')
print 'Hello, %s!' % name

Coding Solution 2
### keep asking for name until the user types
### something that does not consist only of white
### spaces; not '' evaluates to True

name=''
while not name.strip():
name=raw_input('Please enter your name: ')
print 'Hello, %s!' % name

for Loops
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Syntax
for targetList in IterableObject:
code block

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IterableObject is (usually) a sequence and
returns its elements one at a time in order
targetList is one or more comma separated
reference names

for Loop Example
## prints (1, 2) and (3, 4) on separate
## lines
for x in ((1, 2), (3, 4)):
print x
## prints 1 2 and 3 4 on separate lines
for x,y in ((1, 2), (3, 4)):
print x, y

Loop Else
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Both for loops and while loops have an extended form not usually seen in
other languages
while expression:
code block
else:
code block
for targetList in IterableObject:
code block
else:
code block

Using Loop Else
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The else block of a loop is run if the loop exits normally

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Use of continue allows the loop to exit normally

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If the loop exits because of a break, the else block is
not run

Builtin Functions Useful in Loops

range()
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range() generates a list with values of an arithmetic progression

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Syntax pattern 1: range(stop)
>>> range(5)

## start defaults to 0, step defaults to 1

[0, 1, 2, 3, 4]
>>> range(10) ## start defaults to 0, step defaults to 1
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

range()
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Syntax pattern 2: range(start,stop )
>>> range(5, 10)

## step defaults to 1

[5, 6, 7, 8, 9]
>>> range(10, 20) ## step defaults to 1
[10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

range()
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Syntax pattern 3: range(start,stop,step)
>>> range(1, 11, 2)

## odd numbers in [1, 10]

[1, 3, 5, 7, 9]
>>> range(9, -1, -2)

## odd numbers in [1, 10] from

## largest to smallest
[9, 7, 5, 3, 1]
>>> range(10, 0, -2)
[10, 8, 6, 4, 2]

## even numbers from 10 down to 1
## note 0 is not included but 10 is

xrange()
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range() creates the entire list that contains each element of the
arithmetic progression

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If the range is too large, this requires a large amount of memory

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xrange() is an alternative to range() for dealing large ranges

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xrange() uses the same arguments as range() but returns an iterable object that supports lazy iteration: generates each element
in the range one at a time
Bottom line: size of xrange() object is independent of the size
of the range

xrange()
>>> rng = xrange(10, 1000000)
>>> for x in rng:
if x > 999998:
print x
999999

Parallel Iteration
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If you want to iterate over two sequences at the same
time
names=['a','b','c','d']
ages=[12,15,25,17]

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To print the names with corresponding ages
for i in range(len(names)):
print names[i], 'is', ages[i], 'years old.'

Zip
>>> names=['a','b','c','d']
>>> ages=[12,15,25,17]
>>> zip(names,ages)
[('a', 12), ('b', 15), ('c', 25), ('d', 17)]
>>> for name,age in zip(names,ages):
print name, 'is', age, 'years old'

zip()
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zip() combines one or more sequences into a list of tuples where the n-th element of each tuple is the n-th element in the n-th sequence
>>> zip([1, 2], [3, 4])
[(1, 3), (2, 4)]
>>> zip([1, 2])
[(1,), (2,)]
>>> zip('ab', [3, 4])
[('a', 3), ('b', 4)]

zip()
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The length of the list returned by zip() is the length of the
shortest sequence
>>> zip([1, 2], [3, 4, 5])
[(1, 3), (2, 4)]
>>> zip('abc', [3, 4])
[('a', 3), ('b', 4)]

zip()
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zip() can handle ranges
>>> zip(xrange(1, 10), xrange(11, 20))
[(1, 11), (2, 12), (3, 13), (4, 14), (5, 15), (6, 16), (7, 17), (8,
18), (9, 19)]
>>> zip(range(1, 5), range(6, 10), range(11, 15))
[(1, 6, 11), (2, 7, 12), (3, 8, 13), (4, 9, 14)]

zip()
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zip() can be used in sequence unpacking
>>> x, y = [zip(range(1, 5), range(6, 10)), zip(range(-5, 0),
range(-10, -5))]
>>> x
[(1, 6), (2, 7), (3, 8), (4, 9)]
>>> y
[(-5, -10), (-4, -9), (-3, -8), (-2, -7), (-1, -6)]

enumerate()
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enumerate() returns an enumeration object
Enumeration objects support the next() method that returns the
tuple (i, j) where i is the number of element j in the enumeration
object
>>> en01 = enumerate(['a', 'b'])
>>> en01.next()
(0, 'a')
>>> en01.next()
(1, 'b')
>>> en01.next()

## error

Dictionary
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Dictionary is a set of key-value pairs

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Dictionaries are mutable

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Dictionary is the only type built-in mapping data
structure
The keys are not ordered

Basic Syntax
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Dictionary is defined by { }
emptyDict = {} ## defines empty dictionary

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A key-value pair is defined as key colon value
dict = {'one' : 1}

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Multiple key-value pairs are separated by commas
dict = {'one' : 1, 'two' : 2, 'three' : 3}

Keys and Values
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Keys can be any immutable objects: numbers,
characters, tuples, and strings

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Lists and dictionaries cannot be keys

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Values can be any objects

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Dictionaries can be nested, i.e., there can be a
dictionary within another dictionary

Example
box = {'size'

: {'height' : 10,
'width' : 20},

'isCardboard' : True,
'color'
'contents'

: 'red',
: ['nail',
'hammer',
'screw']}

Access
>>> box['size'] # must retrieve on key that exists
{'width': 20, 'height': 10}
>>> box['size']['width']
20
>>> box['contents']
['nail', 'hammer', 'screw']
>>> box['contents'][-1]
'screw'

Access
>>> box.hasKey('size')
True
>>> box.items() # do it only on small dictionaries
[('color', 'red'), ('isCardboard', True), ('contents', ['nail',
'hammer', 'screw']), ('size', {'width': 20, 'height': 10})]
>>> box.items()[0]
('color', 'red')
>>> box.items()[0][-1]
'red'

Adding Key-Value Pairs
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You can add new key value pairs to the previously
created dictionary
my_dict = {}
for c in 'abcdefg':
my_dict[c.upper()] = c
>>> my_dict
{'A': 'a', 'C': 'c', 'B': 'b', 'E': 'e', 'D': 'd', 'G': 'g', 'F': 'f'}
>>> my_dict[(1, 2)] = [1, 2]
>>> my_dict[(1, 2)]
[1, 2]

Construction
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The constructor dict() can be used to create dictionaries from a
sequence of two-item sequences
>>> my_dict2 = dict([['A', 'a'], ['B', 'b'], ['C', 'c']])
>>> my_dict2
{'A': 'a', 'C': 'c', 'B': 'b'}
>>> my_dict3 = dict([('A', 'a'), ('B', 'b'), ('C', 'c')])
>>> my_dict3
{'A': 'a', 'C': 'c', 'B': 'b'}
>>> my_dict4 = dict((('A', 'a'), ('B', 'b'), ('C', 'c')))
>>> my_dict4
{'A': 'a', 'C': 'c', 'B': 'b'}

Construction
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The constructor dict() can be used to create
dictionaries from keyword-value pairs
The keyword-value pair has the following syntax:
keyword = value
Keywords are converted into strings
>>> my_dict5 = dict(first_name = "John",
last_name = "Nicholson")
>>> my_dict5
{'first_name': 'John', 'last_name': 'Nicholson'}

Checking for Keys
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Option 1: key in dictionary
>>> my_dict = dict(first_name = "John", last_name =
"Nicholson")
>>> "first_name" in my_dict
True
>>> "middle_name" not in my_dict
True

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Option 2: dictionary.has_key(<key>)
>>> my_dict.has_key('first_name')
True

Safe Access with get()
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dict.get() is similar to use dict[] but safer

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Syntax: dict.get(key[, default])

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dict[key] returns the value of key in dict or throws
error if key is not in dict
dict.get(key) returns value of key in dict or None if
key is not in dict
dict.get(key, default) returns value of key in dict
or default if key is not in dict

Example
>>> my_dict = {'one' : 1, 'two' : ['a', 'b', 'c']}
>>> my_dict['one']
1
>>> my_dict['three']

## error

>>> my_dict.get('three') ## None (nothing) returned
>>> my_dict.get('three', 'not in dict')
'not in dict'

## default 'not in dict' returned

Clearing Dictionaries
>>> my_dict = { }
>>> my_dict['one'] = [1, 2, 3]
>>> my_dict['two'] = ['a', 'b', 'c']
>>> my_dict
{'two': ['a', 'b', 'c'], 'one': [1, 2, 3]}
>>> my_dict.clear()
>>> my_dict
{}

Shallow Copying
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The method copy() returns a shallow copy of the dictionary
my_dict = {'one' : 1, 'two' : ['a', 'b', 'c']}
my_dict2 = my_dict.copy()

print 'shallow copying:', my_dict, my_dict2
del my_dict2['two'][-1]
print 'shallow copying:', my_dict, my_dict2

Output:
shallow copying: {'two': ['a', 'b', 'c'], 'one': 1} {'two': ['a', 'b', 'c'], 'one': 1}
shallow copying: {'two': ['a', 'b'], 'one': 1} {'two': ['a', 'b'], 'one': 1}

Deep Copying
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The method deepcopy() returns a deep copy of the dictionary
from copy import deepcopy
my_dict3 = {'one' : 1, 'two' : ['a', 'b', 'c']}
my_dict4 = deepcopy(my_dict3)

print 'deep copying:', my_dict3, my_dict4
del my_dict4['two'][-1]
print 'deep copying:', my_dict3, my_dict4

Output:
deep copying: {'two': ['a', 'b', 'c'], 'one': 1} {'two': ['a', 'b', 'c'], 'one': 1}
deep copying: {'two': ['a', 'b', 'c'], 'one': 1} {'two': ['a', 'b'], 'one': 1}

Dictionary Creation From Keys
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The method fromkeys() creates a dictionary from keys each of
which maps to None
>>> d = {}.fromkeys(['key1', 'key2'])
>>> d
{'key2': None, 'key1': None}
>>> d['key1'] = 'value1'
>>> d['key2'] = 'value2'
>>> d
{'key2': 'value2', 'key1': 'value1'}

Deleting Keys with del()
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You can use del() to delete key-value pairs from
dictionaries
>>> my_dict
{0: 0, 1: 1, 2: 4, 3: 9}
>>> del my_dict[2]
>>> my_dict
{0: 0, 1: 1, 3: 9}

Reading & References
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www.python.org
Ch 03 (Strings), M. L. Hetland. Beginning Python From
nd
Novice to Professional, 2 Ed., APRESS
Ch 05 (Object Comparison, Loops), M. L. Hetland. Beginning Python From Novice to Professional, 2nd Ed., APRESS

Python lecture 05

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