Transcript Introduction-to

Introduction to Python
Based
on material for the course
Programming with Python by Chad Haynes.
Modified at Kasetsart University by Chaiporn Jaokaew and James Brucker.
Outline
 Overview
 Built-in objects
 Functions and scopes
 Object-oriented programming
 Functional programming
 Exercise
Python At First Glance
import math
Import a library module
def showArea(shape):
print("Area = ", shape.area() )
Function definition
def widthOfSquare(area):
return math.sqrt(area)
class Rectangle(object):
def __init__(self, width, height):
self.width = width
self.height = height
Class definition
def area(self):
return self.width * self.height
###### Main Program ######
r = Rectangle(10, 20)
showArea(r)
Comment
Object instantiation
Calling a function
Why use Python?
 Simple syntax: easy to learn and remember
 Portable
 Flexible
 Large standard library
 Short development time
 Lots of 3rd party tools/add-ons
 Many good implementations:
CPython, PyPy, IronPython, Jython
 Active open-source community
Similarities to Java
 Everything inherits from "object"

Has numbers, functions, classes, …

Everything is first-class
 Large standard library
 Garbage collection
 Introspection, serialization, threads, net,…
Python vs. Java/C++/C
 Typing: strong, but dynamic
Names have no type
 Objects have type
 No name declarations

 Sparse syntax


No { } for blocks, just indentation
No ( ) for if/while conditions
 Interactive interpreter
 # for comments (like Perl)
// this is Java
if (x < 10)
{
x = x + tmp;
y = y * x;
}
System.out.println(y);
Java
# this is Python
if x < 10:
x = x + tmp
y = y * x
print( y )
Python
Getting Started
 Download from: http://python.org/
Add python to PATH to run scripts from command line
 Python is available for most platforms, even mobile.
 Most Linux distributions have Python as package(s)
Python 3: print( "hello" )
There are some differences between Python 2.x and
Python 3 syntax.
 print is a function in Python 3, which uses
parenthesis:
Python 3.x:
print("hello")
Python 2.x:
print "hello"
Hello, World!
C#
using System;
class Hello
{
static void Main()
{
Console.WriteLine("Hello, World");
}
}
Python
print("Hello, World")
Variables
name x means 23
now it means 'foo'
x is undefined
>>> x = 23
>>> print(x)
23
>>> x = 'foo'
>>> print(x)
foo
>>> del x
>>> print(x)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'x' is not defined
>>>
Variables
Variable is a reference to an object
 not a value
 more than one variable can refer to the same
object
Var1
Var1_copy
Var2
Numeric Types
 Integers

Generally signed, 32-bit
 Long Integers

Unlimited size

Format: <number>L
Example: 4294967296L

 Float

Platform dependant “double” precision
 Complex


Format: <real>+<imag>j
Example: 6+3j
Strings
A sequence of characters enclosed in quotes
3 ways to quote strings:
'Single Quotes'
"Double Quotes"
"""Triple Quotes""" or '''triple quotes'''
– Triple quotes can span multiple lines
Examples
>>> print('This string may contain a "')
This string may contain a "
>>> print("A ' is allowed")
A ' is allowed
>>> print("""Either " or ' are OK""")
Either " or ' are OK
raw_input for reading input
raw_input( [prompt] )


Print prompt and return user's input as a string
a built-in function
Example
>>> reply = raw_input('Are you awake? ')
Are you awake? not sure
>>> print( reply )
not sure
Arithmetic Operations
operators: +
-
*
/
//
**
%
abs
Examples:
>>> 5 + 3
# Addition
8
>>> 2 ** 8
# Exponentiation
256
>>> 13 / 4
# Integer (Truncating) Division*
3
>>> float(13) / 4 # Float Division
3.25
>>> 13 % 4
# Remainder
1
>>> abs(-3.5)
# Absolute Value
3.5
* 13/4 performs float division in Python 3.x
Boolean comparisons
Comparison: <
<=
>
Results in 1 (true) or 0 (false)
Example
>>>
1
>>>
1
>>>
0
>>>
0
>>>
1
4 > 1.5
'this' != 'that'
4+3j == 4-2j
'5' == 5
0 < 10 < 20
>=
==
!=
<>
Boolean Operations
Operators: and
or
not
Standard Boolean Algebra
i1
i2
i1 and i2
i1 or i2
i1
not i1
1
1
1
1
1
0
1
0
0
1
0
1
0
1
0
1
0
0
0
0
Boolean values
True:
any non-zero, non-null value.
False:
None (null)
empty string
empty list
0
s = "hello"
if s:
print("true")
lst = []
if lst:
print("list is not empty")
Boolean Expressions
>>> 1 == 1 and 2 >= 3
False
>>> 1 == 1 or 2 >= 3
True
>>> not 5.3 != 2.2
# same as: not (5.3 != 2.2)
False
>>> 2 and '23' > '11' or 0
True
Building strings
Concatenation (+): string1 + string2
Example:
>>> 'Rockefeller' + 'University'
'RockefellerUniversity'
Repetition (*): string * number
Example:
>>> 'dog' * 5
'dogdogdogdogdog'
String Formatting
C-Style formatting (extended printf):
"format string" % (arg1, arg2, ...)
>>> "%i %s in the basket" % (2, "eggs")
'2 eggs in the basket'
>>> x = 2.0/9.0
>>> "%f to 2 dec places is %.2f" % (x, x)
'0.222222 to 2 decimal places is 0.22'
>>> length = 5
>>> obj = "fence"
>>> "Length of %(obj)s is %(length)i" % vars()
'Length of the fence is 5'
String Format Codes
Format codes begin with "%":
import math
x = 10
"x is %f" % x
"pi is %.8f" % math.pi
"pi is %12.6f" % math.pi
eps = 1.0E-17
"eps is %f (%g)" % (eps, eps)
String Formatting using .format
>>> "{0} {1}
in the basket".format(2, "eggs")
'2 eggs in the basket'
>>> x = 2.0/9.0
>>> "{0} to 2 dec places is {0:.2f}".format(x)
'0.222222 to 2 decimal places is 0.22'
"{0} to {1} dec places is {0:.{1}f}".format(x,3)
'0.222222 to 3 decimal places is 0.222'
>>> name = "James Bond"
>>> id = 7
>>> "{0:12s} is {1:03d}".format(name,id)
'James Bond
is 007'
Python format mini-language reference:
http://docs.python.org/3.1/library/string.html#format-specification-mini-language
String functions
s = '''Now is the time
for all good men'''
Multi-line strings (triple quote)
list = s.splitlines()
return list of lines in string
s.lower()
to lowercase
s.upper()
to uppercase
s.title()
title case
s.index('me')
index of first occurrence, throw
exception if substring not found
s.count('me')
count occurrences
s[1:10]
slice, just like list slice
s.replace("men","people") replace substring.
String format functions
>>> "Hello".ljust(8)
"Hello
"
>>> "Hello".rjust(8)
"
Left justify to given length.
Right justify.
Hello"
>>> "Hello".center(8)
" Hello
Center, of course.
"
>>> u = "Bird"
>>> "Hello {0}".format(u)
'Hello Bird'
Format using template.
type determines type of Object
Determine the type of an object
Syntax: type(object)
Examples
>>> type(2.45)
<type 'float'>
>>> type('x')
<type 'str'>
>>> type(2**34)
<type 'long'>
>>> type(3+2j)
<type 'complex'>
Testing the type
if type(x) is int:
print("x is an integer")
Type Conversions
Functions to convert between types:
str()
int()
float()
complex()
bool()
>>> str(0.5)
'0.5'
>>> float('-1.32e-3')
-0.00132
>>> int('0243')
243
>>> int(2**100)
1267650600228229401496703205376
>>> bool('hi')
True
>>> bool('False')
# any non-zero, non-null is true
True
Built-in Data Structures

List
l = [ 2, 3, 5, 8 ]

Tuple (read-only list)
t = ( 2, 3, 5, 8 )

Set
s = { 2, 5, 3, 8 }

Dictionary (key-value map) d = {"two":2, "three": 3, ...}
Lists
Syntax: [elem1, elem2, ...]
 Ordered sequence of any type (mixed types ok)
 Mutable
>>> list1 = [1, 'hello', 4+2j, 123.12]
>>> list1
[1, 'hello', (4+2j), 123.12]
>>> list1[0] = 'a'
>>> list1
['a', 'hello', (4+2j), 123.12]
Joining and Repeating Lists
Concatenation: list1 + list2
>>> [1, 'a', 'b'] + [3, 4, 5]
[1, 'a', 'b', 3, 4, 5]
Repetition: list * count
>>> [23, 'x'] * 4
[23, 'x', 23, 'x', 23, 'x', 23, 'x']
Adding Elements to a List
>>> list = [ "apple", "banana" ]
Append item to end
>>> list.append( "durian" )
Append another list
>>> list.extend( list2 )
-
Same as list + list2
Insert item anywhere
>>> list.insert( 0, "artichoke" )
>>> list.insert( 2, "carrot" )
Like Java list.add( n, item)
Not so object-oriented len( )
len( ) returns length of a list
>>> list = [ "a", "b", "c" ]
>>> len( list )
3
Removing Elements from a List
>>> list = [ "a" "b", "c", "b" ]
• Remove a matching element (w/o returning it)
>>> list.remove( "b" )
Throws exception if argument is not in the list
• Remove last element and return it
>>> list.pop( )
'b'
• Remove by position
>>> list.pop( 1 )
'c'
# 'b' removed already
Indexing
Syntax: list[n]
• Positive indices count from the left: list[0]
• Negative indices count from the right: list[-1]
0
1
2
3
4
5
6
a
b
c
d
e
f
g
-7
-6
-5
-4
-3
-2
-1
list[0] == a
sequence[-1] == g
list[2] == c
sequence[-2] == f
list[6] == g
sequence[-7] == a
List Slicing: get a sublist
list[m:n] return elements m up to n (exclusive)
syntax for both strings and lists
>>> x = [0, 1, 2, 3, 4, 5, 6, 7]
>>> x[1:4]
[1, 2, 3]
>>> x[2:-1]
[2, 3, 4, 5, 6]
# Missing Index means start or end of list
>>> x[:2]
[0, 1]
>>> "Hello nerd"[3:]
lo Nerd
Sorting a list
List.sort( [comparator] )
Sort List in place. Result is applied to the list!
Example:
>>> list3 = [4, 12, 3, 9]
>>> list3.sort()
>>> list3
[3, 4, 9, 12]
Count or find elements in a list
list.count( element )
count number of occurences of element.
n = list.index( element )
return index of first occurence of element.
Throws ValueError if element is not in list.
Tuples
Immutable list
Syntax: (elem1, elem2, …)
A tuple cannot be changed.
Example:
>>> tuple1 = (1, 5, 10)
>>> tuple1[2] = 2
Traceback (most recent call last):
File "<pyshell#136>", line 1, in ?
tuple1[2] = 2
TypeError: object doesn't support item
assignment
Converting between list and tuple
>>> list1 = ['a', 'b', 'c']
>>> tuple1 = tuple( list1 )
>>> type( tuple1 )
<class 'tuple'>
>>> tuple2 = ('cat', 'dog')
>>> list2 = list(tuple2)
len - Length of an object
n = len(object)
len is not object-oriented.
Return the length of object
Examples
>>> list1 = [1, 2, 3, 4, 5]
>>> len(list1)
5
>>> string1 = "length of a string"
>>> len(string1)
18
Multiple assignment using tuples
(a,b,c) = (10, 20, 50)
>>> b
20
This can be used in for loops.
points = [ (1,0), (0.2,0.9), (1,2) ]
for (x,y) in points:
r = math.hypot(x,y)
print("radius of (%f,%f) is %f" % (x,y,r) )
Dictionary: mapping key to value
A mapping of keys to values
Associate a key with a value
Each key must be unique
keys
'z'
'ab'
values
10
[2]
2.1
(3,8)
3
'hello'
Using Dictionaries
Syntax: dict = {key1: value1, key2: value2, ...}
>>> dict = {'a': 1, 'b': 2}
>>> dict
{'a': 1, 'b': 2}
>>> dict['a']
1
>>> dict['b']
2
>>> dict[3] = 'three'
>>> dict
{'a': 1, 'b': 2, 3: 'three'}
Set
An unordered collection, without duplicates (like Java).
Syntax is like dictionary, but no ":" between key-value.
>>> aset = { 'a', 'b', 'c' }
>>> aset
{'a', 'c', 'b'}
>>> aset.add('c")
>>> aset
{'a', 'c', 'b'}
# no effect, 'c' already in set
Set Methods
set.discard('cat')
remove cat. No error if not in set.
set.remove('cat')
remove cat. Error if not in set.
set3 = set1.union(set2)
doesn't change set1.
set4 =
set1.intersection(set2)
doesn't change set1.
set2.issubset( set1 )
set2.issuperset( set1 )
set1.difference( set2 )
element in set1 not set2
set1.symmetric_difference( xor
set2)
set1.clear( )
remove everything
Test for element in Set
item in set
>>> aset = { 'a', 'b', 'c' }
>>> 'a' in aset
True
>>> 'A' in aset
False
Flow Control
if condition :
body
elif condition :
body
else:
body
if x%2 == 0:
y = y + x
else:
y = y - x
while condition:
body
while count < 10:
count = 2*count
for name in iterable:
body
for x in [1,2,3]:
sum = sum + x
range: create a sequence
range([start,] stop[, step])
Generate a list of numbers from start to stop
stepping every step
start defaults to 0, step defaults to 1
Example
>>> range(5)
[0, 1, 2, 3, 4]
>>> range(1, 9)
[1, 2, 3, 4, 5, 6, 7, 8]
>>> range(2, 20, 5)
[2, 7, 12, 17]
for loop using range( )
Use range to generate values to use in for loop
>>> for i in range(1,4):
print i
1
2
3
dir: show all methods & attributes
dir returns all methods for a class or object
>>> lst = [1, 3, 2]
>>> dir(lst)
['__add__', '__class__', '__contains__', '__delattr__',
'__delitem__', '__delslice__', '__doc__', '__eq__', '__ge__',
'__getattribute__', '__getitem__', '__getslice__', ...
'__setitem__', '__setslice__', '__str__', 'append', 'count',
'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort'
>>> dir( math )
# import math first
['__doc__', '__name__', '__package__', 'acos', 'asin", 'atan',
'atan2', 'ceil', 'copysoign', 'cos', 'degrees', 'e', 'exp',
'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'hypot', ...
Getting Help
The help function gives help for a module or function.
>>> help(str)
Help on class str in module __builtin__:
class str(basestring)
| str(object) -> string
|
| Return a nice string representation of the object.
|
| Method resolution order:
|
str
|
basestring
|
object
|
| Methods defined here:
| ...
Functions and Scope
Defining Functions
Syntax: def func(arg1, …):
body
 Body of function must be indented
 If no value is returned explicitly, function will return None
def average(num1, num2, num3):
sum = num1 + num2 + num3
avg = sum / 3.0
return avg
Function Parameters
• Parameters can be any type
- A function can take any number of parameters or none at
all
def usage(programName, version):
print("%s Version %i" % (programName, version))
print("Usage: %s arg1 arg2" % programName)
>>> usage('Test', 1.0)
Test Version 1.0
Usage: Test arg1 arg2
Function Default Parameter values
 Parameters can be given a default values
split(string, substr=' ')
 The function can be called with fewer arguments than
there are parameters
 Parameters with default values must come last
>>> def printName(last, first, mi=""):
print("%s, %s %s" % (last, first, mi))
>>> printName("Smith", "John")
Smith, John
>>> printName("Smith", "John", "Q")
Smith, John Q
Keyword Arguments
Functions can be invoked using the name of the parameter
and a value
func(param=value, ...)
 The order of values passed by keyword does not matter
def fun(key1="X", key2="X", key3="X", key4="X"):
'''function with keywords and default values'''
print(key1, key2, key3, key4)
>>>
X O
>>>
X X
fun(key3="O", key2="O")
O X
fun(key4='Z')
X Z
Functions at Values
 Functions can be used just like any other data type
 Functions can be assigned to variables
def sub(a, b):
return a-b
>>> op = sub
>>> print op(3, 5)
-2
>>> type(op)
<type 'function'>
Functions as Parameters
Functions can be passed to other functions
def convert(data, convertFunc):
for i in range(len(data)):
data[i] = convertFunc(data[i])
return data
>>> convert(['1', '5', '10', '53'], int)
[1, 5, 10, 53]
>>> convert(['1', 'nerd', '10', 'hi!'], len)
[1.0, 5.0, 10.0, 53.0]
>>> convert(['1', '5', '10', '53'], complex)
[(1+0j), (5+0j), (10+0j), (53+0j)]
Functions can return multiple values
Return a tuple of values.
Example: string.split( ) returns a tuple of substrings.
def separate(text, size=3):
'''divide a string into two parts'''
head = text[:size]
tail = text[size:]
return (head,tail)
# ok to omit parens: start,last = separate(...)
>>> (start,last) = separate('GOODBYE', 4)
>>> start
GOOD
>>> last
BYE
Generators
 Generators are functions that generate a sequence of
items, but only when requested
 Generated sequence can be infinite
def fibonacci():
a = b = 1
while True:
yield a
a, b = b, a+b
for rabbits in fibonacci():
print( rabbits )
if rabbits > 100: break
1 1 2 3 5 8 13 21 34 55 89 144
Generators
 Generator invokes yield to "yield" control.
 Receiver calls next(generator) to invoke generator.
def filereader( filename ):
with open(filename) as infile:
line = infile.read()
yield line
gen = filereader( "readme.txt" )
s = next(gen)
process_line(s)
s = next(gen)
...
Namespaces and Scopes
Namespace

A mapping from names to objects

(Currently) implemented as Python dictionaries
Scope

A region of program where a namespace is
accessible

Name references search at most 3 scopes:
local, global, built-in

Assignments create or change local names by
default

Can force names to be global with global
command
Scope Example
X = 99
def func(Y):
Z = X+Y
# X not assigned, so it's global
return Z
>>> func(1)
X = 99
def func(Y):
X = 10
Z = X+Y
# X is local
return Z
>>> func(1)
>>> X
# still 99
Modules
A file containing Python definitions and statements
 Modules can be “imported”
 Module file name must end in .py
 Used to divide code between files
math.py
string.py
import math
import string
…
import Statement
import <module name>
 module name is the file name without .py extension
 You must use the module name to call functions
>>> import math
>>> dir(math)
['__doc__', '__name__', 'acos', 'asin', 'atan',
'atan2', 'ceil', 'cos', 'cosh', 'e', 'exp', 'fabs',
'floor', 'fmod', 'frexp', ...]
>>> math.e
2.71828182846
>>> math.sqrt(2.3)
1.51657508881
import specific names
from <module> import <name>
 Import a specific name from a module into global namespace
 Module name is not required to access imported name(s)
>>> from math import sqrt
>>> sqrt(16)
4
>>> dir(math)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'math' is not defined
import all names from module
from <module> import *
 Import everything into global namespace
>>> dir()
['__builtins__', '__doc__', '__name__']
>>> from time import *
>>> dir()
['__builtins__', '__doc__', '__name__',
'accept2dyear', 'altzone', 'asctime', 'clock',
'ctime', 'daylight',
'gmtime', 'localtime',
'mktime', 'sleep', 'strftime', 'time', ... ]
>>> time()
1054004638.75
Python Standard Libraries
sys
System-specific parameters and functions
time
Time access and conversions
thread
Multiple threads of control
re
Regular expression operations
email
Email and MIME handling
httplib
HTTP protocol client
tkinter
GUI package based on TCL/Tk (in Python
2.x this is named Tkinter)
See http://docs.python.org/library/index.html
The os Module
Operating System related functions
import os
os.getcwd( )
Get current working directory
os.chdir("/temp/somedir") Change dir. Use forward slash on
MS Windows.
os.getenv('JAVA_HOME')
Get environment variable.
os.unlink('filename')
Remove regular file
os.removedirs('dirname')
Remove directory(s)
stats = os.stat('file')
Get file metadata: ctime, mtime,
atime, uid, gid
os.path
Module for manipulating paths
> (dir, file) = os.path.split("/some/path/test.py")
> dir
'/some/path'
> file
'test.py'
> (name,ext) = os.path.splitext(file)
> ext
'py'
# Test absolute path to file
> os.chdir('/Temp/examples')
> os.path.realpath('readme.txt')
C:\\Temp\\examples\\readme.txt
glob
Module for getting files in directory
> import glob
> glob.glob('*.txt')
['help.txt', 'install.txt', 'readme.txt', 'zzz.txt']
with to define a block for open
 File is automatically closed at end of "with" block, even
if an exception occurs.
with open("students.csv",'r') as student_file:
# read each line of file
for line in student_file:
(id,name,email) = split(',', 3)
print("%s has id %s" % (name,id))
List Comprehensions
[ expression for var in list ]
Apply an expression to every element of a list
 Can simultaneously map and filter
>>> import math
>>> [math.pow(2,x) for x in range(1,7)]
[2.0, 4.0, 8.0, 16.0, 32.0, 64.0]
List Comprehension with filter
result = [ expr for var in list if expr2 ]
 apply to elements of list where expr2 is true
Example: Remove smallest element from list
>>> lst1 = [5, 10, 3, 9]
>>> [x for x in lst1 if x != min(lst1)]
[5, 10, 9]
Example: Sum all lists of size greater than 2
>>> lst1 = [[1, 2, 4], [3, 1], [5, 9, 10, 11]]
>>> [reduce(operator.add, x) for x in lst1 if len(x) > 2]
[7, 35]
List Comprehension with nested for
[expr for x in list1 for y in list2]
The loops will be nested
>>> vowels = ['a','e','i','o','u']
>>> const = ['b','s']
>>> [c+v for c in const for v in vowels]
['ba', 'be', 'bi', 'bo', 'bu', 'sa', 'se', 'si',
'so', 'su']
List Comprehension for files
 Find all files in directory larger than 1MB
 Return the real path to file
import os, glob
[os.path.realpath(f) for f in glob.glob('*.*')
if os.stat(f).st_size >= 1000000]
Dictionary Comprehensions
dict = {expr for var in list if expr2}
Like list comprehension but generates a dictionary.
• expr must be key:value pair (of course)
# Create dictionary of .exe filenames and sizes.
import os, glob
os.chdir('/windows/system32')
files = {fname:os.stat(fname).st_size
for fname in glob.glob('*.exe') }
# print them
for (key,val) in files.items():
print("%-20s %8d" % (key,val))
OO Programming
Defining a Class
Syntax:
class name[(base_class)]:
body
Create a class with default superclass
# old style
class name:
body
# new style
class name(object):
body
class MyClass(object):
myvar = 30
>>> MyClass.myvar
30
Defining Constructor and Methods
 All instance methods must must have explicit object
reference (self) as the first parameter
self is the conventional name (like Java "this")
class Rectangle(object):
def __init__(self, width, height):
self.width = width
self.height = height
def area(self):
return self.width * self.height
>>> r = Rectangle(10, 20)
>>> Rectangle.area(r)
200
>>> r.area()
200
No "new".
constructor
Weak Encapsulation
Everything is public.
Instance methods can be used in static way.
class Person:
def __init__(self, name):
self.name = name
def greet(self):
print("Hello "+self.name)
>>>
>>>
>>>
>>>
p = Person("Me")
p.name
p.greet()
Person.greet(p)
# attributes are public
# O-O style method call
# imperative style
Hiding by name mangling
Any attribute name that begins with "_ _" is mangled to
"_Class_ _name".
class Person:
def __init__(self, name):
self.__name = name
# Mangled to _Person__name
@property
def name(self):
return self.__name
#auto demangle
>>> p = Person("Lone Ranger")
>>> p.name
# returns the name
Lone Ranger
>>> p.name = "Zoro"
builtins.AttributeError: can't set attribute
Only One Constructor
Python classes can only have one __init__.
You can overload it using params with default values.
class Point:
'''Point with x,y coordinates'''
def __init__(self, x=0, y=0):
self.x = x
self.y = y
>>> p = Point(3,4)
>>> q = Point( )
# same as Point(0,0)
Static Methods
To define a static method, prefix with @staticmethod
and no self parameter.
class Point:
'''create a point using polar coordinates.'''
@staticmethod
def makePoint(r,a):
return Point(r*math.cos(a), r*math.sin(a))
>>> p = Point.makePoint( 10, math.pi/4 )
Static factory methods can help overcome limitation of
only one constructor.
Properties
Properties are like synthetic attributes. Used like "get"
properties in C#. Only parameter is self.
class Point:
def __init__(self,x=0,y=0):
self.x = x
self.y = y
@property
def length(self):
return math.hypot(self.x, self.y)
>>> p = Point(3,4)
>>> p.length
5
"Set" properties
Can use @property and @x.setter to protect
attributes.
class Person:
def __init__(self,name):
self.__name = name # hide the name
@property
def name(self):
# name accessor
return self.__name
@name.setter
def name(self,value): # set the name
if not isinstnace(value, str):
raise TypeError("Name must be str")
self.__name = value
Restricting Keys in the Object dict
 You can restrict the allowed attribute names.
 Use a tuple named __slots__
class Point:
__slots__ = ('x','y','name')
f.name = "weak, weak"
def __init__(x=0,y=0):
self.x, self.y = x, y
>>> p = Point(2,3)
>>> p.junk = "ha ha"
builtins.AttributeError: 'Point' object has no attribute 'junk'
Inheritance
Subclass must invoke parent's constructor explicitly
class Square(Rectangle):
def __init__(self, width):
Rectangle.__init__(self, width, width)
>>> s = Square(100)
>>> s.area()
10000
accessing superclass
Python 3 has a super() method to access superclass.
class Square(Rectangle):
def __init__(self, width):
super().__init__( width, width)
>>> s = Square(100)
>>> s.area()
10000
Polymorphism
 All methods are virtual
import math
class Circle(object):
def __init__(self, radius):
self.radius = radius
def area(self):
return math.pi*self.radius*self.radius
>>> shapes = [Square(5), Rect(2,8), Circle(3)]
>>> for x in shapes:
...
print x.area()
25
16
28.2743338823
Operator Overloading
 Objects can implement infix operators by defining
specialy named methods, such as __add__

operators: +, -, *, /, **, &, ^, ~, !=
class Point:
def __init__(self, x, y):
this.x, this.y = x, y
def __add__(self, other):
return Point(self.x+other.x, self.y+other.y);
>>> a = Point(1,2)
>>> b = Point(3,7)
>>> print(a + b)
# invokes Point.__add__(a,b)
(4, 9)
# assuming we also define Point.__str__
Python Object Hooks
 Objects can support built-in operators by implementing
special methods

operators: +, -, *, /, **, &, ^, ~, !=

Indexing (like sequences): obj[idx]

Calling (like functions): obj(args,...)

Iteration and containment tests
for item in obj: ...
if item in obj: ...
Object Data is Stored as a Dictionary
 Attributes are stored in a dictionary (__dict__).
 You can add attributes to existing object!
f = Fraction(2,3)
f.name = "weak, weak"
f.__dict__
{'num': 2, 'denom':3, 'name':'weak, weak'}
f.name = value invokes f.__setattr__("name",value)
del f.name
invokes f.__delattr__("name")
Testing object type
Get the type of a variable:
>>> p = Point( 1, 3)
>>> type(p)
<class '__main__.Point'>
Test the type using isinstance:
>>> p = Point( 1, 3)
>>> isinstance(p, Point)
True
>>> isinstance(p, float)
False
Functional Programming
Functional Approaches
• In Function Programming, functions can be used in
the same way as other data types.
• Python borrows from functional languages:
Lisp/Scheme
Haskell
• Python built-in functions for functional style:
map()
filter()
reduce()
zip()
map: "Apply to all" operation
result = map(func, list)
-
func is applied to each element of the list
-
result is a new list, same length as original list
-
the original list is not altered
>>> list = [2, 3, 4, 5]
>>> roots = map( math.sqrt, list )
>>> for x in roots:
print(x)
Function as
1.41421356237
argument.
1.73205080757
2.0
2.2360679775
map in Action
y1
func
ŷ1
y2
y3
y4
y5
y6
y7
y8
map in Action
y1
y2
func
ŷ1
ŷ2
y3
y4
y5
y6
y7
y8
map in Action
y1
y2
y3
func
ŷ1
ŷ2
ŷ3
y4
y5
y6
y7
y8
map in Action
y1
y2
y3
y4
func
ŷ1
ŷ2
ŷ3
ŷ4
y5
y6
y7
y8
map in Action
y1
y2
y3
y4
y5
y6
y7
y8
func
ŷ1
ŷ2
ŷ3
ŷ4
ŷ5
ŷ6
ŷ7
ŷ8
map with multiple lists
What if the function requires more than one argument?
result = map(func, list1, ..., listn)
 All lists must be of same length (not really)
 Number of lists (n) must match #args needed by func
# powers of 2
pows = map( math.pow, [2]*5, [1,2,3,4,5] )
# same thing, using a range
pows = map( math.pow, [2]*5, range(1,6) )
Use map to reduce coding
lst1 = [0, 1, 2, 3]
lst2 = [4, 5, 6, 7]
lst3 = []
for k in range(len(lst1)):
lst3.append( add2(lst1[k],lst2[k]) )
lst1 = [0, 1, 2, 3]
lst2 = [4, 5, 6, 7]
lst3 = map(add2, lst1, lst2)
Benefits
The map function can be used like an expression
 Can be used as a parameter to a function
>>> lst1 = [1, 2, 3, 4]
>>> string.join(lst1) # Error: lst1 contains ints
…
TypeError: sequence item 0: expected string, int
found
>>> string.join( map(str, lst1) )
'1 2 3 4'
# Correct
filter elements of a list
sublist = filter(func, list)
 return a sublist from list containing only elements that
"pass" func (func returns True or non-zero for element)
 the result has length less than or equal to the list
 list is not altered
def isEven(x):
return x%2 == 0
lst = [2, 7, 9, 8, -12, 11]
even = filter(isEven, lst) # even = [2, 8, -12]
reduce accumulate a result
result = reduce(func, list)
 Apply func cumulatively to a sequence
 func must take 2 parameters
 For each element in list, previous func result is used as
1st arg.
def multiply(x, y):
return x*y
lst = [1,2,3,4,5]
result = reduce(multiply, lst) # result = 1*2*3*4*5
reduce with initial value
reduce(func, initial_value, list)
 Use initial_value as parameter in first call to func
lst = [2,3,4,5]
result = reduce(multiply, 10, lst)
# result = 10 * 2 * 3 * 4 * 5
Code Comparison
lst1 = [ [2, 4], [5, 9], [1, 7] ]
result = operator.add([100], lst1[0])
for element in lst1[1:]:
result = operator.add(sum, element)
lst1 = [ [2, 4], [5, 9], [1, 7] ]
result = reduce(operator.add, lst1, [100])
Join lists element-by-element
zip(list1, …, listn)
Example: Combine two lists
>>> lst1 = [1, 2, 3, 4]
>>> lst2 = ['a', 'b', 'c', 'd', 'e']
>>> result = zip(lst1, lst2)
>>> result
[(1, 'a'), (2, 'b'), (3, 'c'), (4, 'd')]
The ‘e’ element was truncated since lst1 only has 4 elements
The result is a list of tuples
Uses for zip
• Create a dictionary using zip() and dict()
>>> produce = ['apples', 'oranges', 'pears']
>>> prices = [0.50, 0.45, 0.55]
>>> priceDict = dict(zip(produce, prices))
>>> print priceDict
{'pears': 0.55, 'apples': 0.5, 'oranges': 0.45}
Lambda Functions
Anonymous functions. Can be used assigned to variable.
Syntax : lambda p1[,…,pn]: expression
- expression should not use return
Example: create a square function
>>> sqr = lambda x: x*x
>>> print sqr(5)
25
Example: average of 2 values
>>> avg = lambda x,y: (x+y)/2
>>> avg(2,7)
4.5
Function can return a Lambda
Lambda's can be return values or function arguments.
# define a power function: only x is "bound" to the lambda
define power(n):
return lambda x: math.pow(x, n)
cube = power(3)
cube(10)
1000.0
list( map( cube, range(1,10) ) )
[1.0, 8.0, 27.0, ..., 729.0]
Generator and Iterators
Any function that uses yield is a generator.
The return value is a generator type.
def fibonacci(max):
'''compute all fibonacci numbers < max'''
a, b = 0, 1
while b < max:
yield b
# the next() result
a, b = b, a+b
>>> f = fibonacci(100)
>>> f
<generator object fibonacci at 0x00E52210>
Using Generator as Iterator
Each call to f.__next__( ) gets next return value.
>>> f = fibonacci(100)
>>> f.__next__( )
1
>>> f.__next__( )
1
>>> f.__next__( )
2
>>> f.__next__( )
3
Using Generator in context
Generators can be used in loops and as list generators
for x in fibonacci(40):
print( x )
1
1
2
3
5
8
13
21
34
lst = list(fibonacci(40))
[1,1,2,3,5,8,13,21,34]
Infinite Series
Lazily generate values in a series.
import Fraction
def harmonic():
'''return elements of harmonic series'''
n = 1
while True:
yield Fraction(1,n)
# the next() result
n = n + 1
# Print harmonic series until the sum > 5
sum = Fraction(0)
for f in harmonic():
Iterators
 Iterators implement the Iterator pattern.
 In Python, no "hasNext" method.
obj.__iter__() returns an iterator (usually self)
obj.__next__() returns next item. Raise a
StopIteration exception if no next
Note: in Python 2.x, __next__() was named next()
References
Python Documentation
http://docs.python.org
Videos
 Python for Programmers by Alex Martelli
http://video.google.com/videoplay?docid=1135114630744003385
 Advanced Python (Understanding Python) by Thomas
Wouters
http://video.google.com/videoplay?docid=7760178035196894549
References
Books
 Practical Programming: an Introduction ... by Jennifer
Campbell, et al. (Pragmatic Programmers, 2009)
Good book for novice programmer.
 Python Essential Reference, 4E. (Addison Wesley)
Recommended by CPE/SKE students. Has tutorial and language
description. More than just a reference.
Exercise
Write a Python program freq.py to:


Fetch a text file from the web
Report the most frequently used words in file
$ python freq.py http://www.cpe.ku.ac.th/~cpj/gpl.txt
345: the
221: of
192: to
184: a
151: or
128: you
102: license
98: and
97: work
91: that
$
Exercise Hints
 Accessing command-line arguments
import sys
url = sys.argv[1]
 Read a webpage - don't do this, use an iterator
import urllib
contents = urllib.openurl(url).readlines()
 Extracting all English words from text
import re
words = re.findall('[A-Za-z]+', text)