1 Introduction to Python Materials based on contents from the course Programming with Python by Chad...

1

Introduction to PythonIntroduction to Python

Materials based on contents from the course Programming with Python by Chad Haynes

2

OutlineOutline

• Overview

• Built-in objects

• Functions and scopes

• Object-oriented programming

• Functional programming

• Exercise

3

Python At First GlancePython At First Glanceimport math

def showArea(shape): print "Area = %d" % shape.area()

def widthOfSquare(area): return math.sqrt(area)

class Rectangle(object):

def __init__(self, width, height):self.width = widthself.height = height

def area(self):return self.width * self.height

###### Main Program ######r = Rectangle(10, 20)

showArea(r)

Function definition

Class definition

Import a library module

Calling a function

Comment

Object instantiation

4

Why use Python?Why use Python?

• Simple, clean syntax• Portable• Flexible• Large standard library• Short development time• Lots of 3rd-party tools/add-ons• Many good implementations

– CPython, PyPy, IronPython, Jython

• Strong support from open-source community

5

Similarities to JavaSimilarities to Java

• Everything inherits from "object"– Also numbers, functions, classes, …– Everything is first-class

• Vast, powerful standard library

• Garbage collection

• Introspection, serialization, threads, net,…

6

Similarities to C++Similarities to C++

• Multi-paradigm– OOP, procedural, generic, functional (a little)

• Multiple inheritance

• Operator overloading

7

Python vs. Java/C++/CPython vs. Java/C++/C

• Typing: strong, but dynamic– Names have no type– Objects have types

• No declarations• Sparse syntax

– No { } for blocks, just indentation– No ( ) for if/while conditions

• Interactive interpreter• # for comments

if (x < 10){ x = x + tmp; y = y * x;}System.out.println(y);

Java

if x < 10:x = x + tmpy = y * x

print y

Python

8

Getting StartedGetting Started

• Python already included in most Linux distributions

• Windows users can download from:– http://python.org/download– Add python to PATH to run scripts from

command line

http://python.org/download



9

using System;class Hello{

static void Main(){

Console.WriteLine("Hello, World");}

}

Hello, World!Hello, World!

• C#

• Pythonprint "Hello, World!"

10

VariablesVariables

>>> x = 23>>> print x23>>> x = 'foo'>>> print xfoo>>> del x>>> print xTraceback (most recent call last): File "<stdin>", line 1, in <module>NameError: name 'x' is not defined>>>

name x means 23

now it means 'foo'

x becomes undefined

11

Var1

Var1_copy

Var2

VariablesVariables

• Reference Model– Variables refer to an object– More than one variable can refer to the same

object

12

Numeric TypesNumeric Types

• Integers– Generally 32 signed bits

• Long Integers– Unlimited size– Format: <number>L– Example: 4294967296L

• Float– Platform dependant “double” precision

• Complex– Format: <real>+<imag>j– Example: 6+3j

13

StringsStrings

• A sequence of characters enclosed with quotes• 3 quoting styles

– 'Single Quotes'– "Double Quotes"– """Triple Quotes"""

• Examples>>> print 'This may contain a "'This may contain a ">>> print "A ' is allowed"A ' is allowed>>> print """Either " or ' are OK"""Either " or ' are OK

14

>>> info = raw_input('-> ')-> Here is info>>> print infoHere is info

Built-in Function: raw_inputBuilt-in Function: raw_input

• Syntax: raw_input([prompt])– Use prompt to ask user to input a string– Example

15

• Arithmetic

- + - * // / ** % abs

- Example

>>> 5 + 3 # Addition8>>> 2 ** 8 # Exponentiation256>>> 13 / 4 # Integer (Truncating) Division*3>>> float(13) / 4 # Float Division3.25>>> 13 % 4 # Remainder1>>> abs(-3.5) # Absolute Value3.5

Basic OperationsBasic Operations

* Becomes float division in version 3.x

16

• Comparison

- < <= > >= == != <>

- Results in 1 (true) or 0 (false)

- Example

>>> 4 > 1.51>>> 'this' != 'that'1>>> 4+3j == 4-2j0>>> '5' == 50>>> 0 < 10 < 201


17

• Boolean

- and or not

- Based on Boolean Algebra


i1 i2 i1 and i2

1

1

1

0

0

0

1

0

1

0

0

0

i1 or i2

1

1

1

0

i1 not i1

1

0 1

0

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• Boolean

- Example

>>> 1 == 1 and 2 >= 30>>> 1 == 1 or 2 >= 31>>> not 5.3 != 2.2 # same as: not (5.3 != 2.2)0>>> 2 and '23' > '11' or 01


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• Concatenation (+)- Syntax: string1 + string2

- Example:

>>> 'Rockefeller' + 'University'

'RockefellerUniversity'

• Repetition (*)- Syntax: string * number

- Example:

>>> 'dog' * 5

'dogdogdogdogdog'

Strings - OperationsStrings - Operations

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• C-Style formatting (extended printf)- Examples:

>>> "%i %s in the basket" % (2, "eggs")

'2 eggs in the basket'

>>> "%f to 2 decimal places: %.2f" %(2.0/9.0, 2.0/9.0)

'0.222222 to 2 decimal places: 0.22'

>>> length = 5

>>> obj = "fence"

>>> "Length of the %(obj)s is %(length)i" % vars()

'Length of the fence is 5'

Strings - FormattingStrings - Formatting

21

• Syntax: type(object)- Used to determine the type of an object

- Example

>>> type(2.45)

<type 'float'>

>>> type('x')

<type 'str'>

>>> type(2**34)

<type 'long'>

>>> type(3+2j)

<type 'complex'>

Built-in Function: typeBuilt-in Function: type

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• Use built-in functions to convert between types- str() int() float() long() complex() bool()

- Example>>> str(42.3)'42.3'>>> float('-1.32e-3')-0.00132>>> int('0243')243>>> int(2**34)Traceback (most recent call last): File "<pyshell#12>", line 1, in ? int(2**34)OverflowError: long int too large to convert to int>>> long(2**34)17179869184L

Type ConversionsType Conversions

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• Lists

• Tuples

• Dicts

Data StructuresData Structures

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• Construction- Syntax: [elem1, elem2, …]

- Heterogeneous, ordered sequence

- Mutable

- Example:

>>> list1 = [1, 'hello', 4+2j, 123.12]

>>> list1

[1, 'hello', (4+2j), 123.12]

>>> list1[0] = 'a'

>>> list1

['a', 'hello', (4+2j), 123.12]

ListsLists

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• Concatenation (+)- Syntax: list1 + list2

- Example:

>>> [1, 'a', 'b'] + [3, 4, 5]

[1, 'a', 'b', 3, 4, 5]

• Repetition (*)- Syntax: list * number

- Example:

>>> [23, 'x'] * 4

[23, 'x', 23, 'x', 23, 'x', 23, 'x']

Lists - OperationsLists - Operations

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• Indexing operator: [ ]

• Positive indices count from the left

• Negative indices count from the right

IndexingIndexing

0 1 2 3 4 5 6

-7 -6 -5 -4 -3 -2 -1

a b c d e f g

sequence[0] == a sequence[-7] == a

sequence[6] == g sequence[-1] == g

sequence[2] == c sequence[-5] == c

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• Two indices separated by a colon- Available for both strings and lists

- Example

>>> sequence = [0, 1, 2, 3, 4, 5, 6, 7]

>>> sequence[1:4]

[1, 2, 3]

>>> sequence[2:-1]

[2, 3, 4, 5, 6]

- Missing Index implies end point

>>> sequence[:2]

[0, 1]

>>> sequence[3:]

[3, 4, 5, 6, 7]

List SlicingList Slicing

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• Immutable version of list- Syntax: (elem1, elem2, …)

- Items in tuple can not be altered

- 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

TuplesTuples

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• Syntax: len(object)- Return the length of object

- Example

>>> list1 = [1, 2, 3, 4, 5]

>>> len(list1)

5

>>> string1 = "length of a string"

>>> len(string1)

18

Built-in Function: lenBuilt-in Function: len

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• Mapping- Associate a key with a value

- Each key must be unique

DictionariesDictionaries

'z' 'ab' 2.1 3keys

10 [2] (3,8) 'hello'values

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• Construction- Syntax: {key1: value1, key2: value2 …}

- Unordered map

- Example:

>>> dict1 = {'a': 1, 'b': 2}

>>> dict1

{'a': 1, 'b': 2}

>>> dict1['a']

1

>>> dict1['b']

2

DictionariesDictionaries

32

Control FlowControl Flow

if condition:body

elif condition:body

else:body

Examples

if x%2 == 0:y = y + x

else:y = y - x

while i < 0:count = count + 1

for x in [1,2,3]:sum = sum + x

while condition:body

for name in iterable:body

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• Syntax: 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]

Built-in Function: rangeBuilt-in Function: range

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• Using range with for- Generate list used by for with range

- Example

>>> for i in range(4):

print i

0

1

2

3

Controlling FlowControlling Flow

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• Data structures also have methods

• Use built-in function dir to list all available methods

• Example>>> lst = [1, 3, 2]

>>> dir(lst)

['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__delslice__', '__doc__', '__eq__', '__ge__', '__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__repr__', '__rmul__', '__setattr__', '__setitem__', '__setslice__', '__str__', 'append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']

Using Data StructuresUsing Data Structures

40

• append- Syntax: list.append(element)

- Add element to end of list

- Example:

>>> list1 = [3, '10', 2]

>>> list1.append('new')

>>> list1

[3, '10', 2, 'new']

Lists - MethodsLists - Methods

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• insert- Syntax: list.insert(index, element)

- Insert element into list at position index

- Example:

>>> list2 = [0, 1, 2, 3, 4, 5]

>>> list2.insert(3, 'new')

>>> list2

[0, 1, 2, 'new', 3, 4, 5]


44

• sort- Syntax: list.sort([cmpfunc])

- Sort list in place

- Example:

>>> list3 = [4, 12, 3, 9]

>>> list3.sort()

>>> list3

[3, 4, 9, 12]


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Functions and ScopesFunctions and Scopes

51

• Syntax: def func(arg1, …):

body- Body of function must be indented

- If no value is returned explicitly, function will return None

- Example:

>>> def average(num1, num2, num3):

sum = num1 + num2 + num3

avg = sum / 3.0

return avg

Defining FunctionsDefining Functions

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• Parameters- Parameters can be any type

- A function can take any number of parameters (or none at all)

- Example:

>>> 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

FunctionsFunctions

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• Default Parameters- One or more parameters can be given a default value

- The function can be called with fewer arguments than there are parameters

- All non-default (required) parameters must precede default parameters

- Example:

>>> def printName(last, first, mi=""):

print "%s, %s %s" % (last, first, mi)

>>> printName("Smith", "John")

Smith, John

>>> printName("Smith", "John", "Q")

Smith, John Q

FunctionsFunctions

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• Calling functions- Syntax: func(arg1, arg2, … argn)

- Order of arguments must match order of declared parameters

- No type checking is done

- Example

>>> def display(arg1, arg2, arg3):

print arg1

print arg2

print arg3

>>> display(1, 'x', 4.3)

1

x

4.3

FunctionsFunctions

55

• Keyword arguments- Functions can be called using the keyword of the argument

- Syntax: func(keyword=value, …)

- The order of the values passed by keyword does not matter

- Example

def keywords(key1="X", key2="X", key3="X",key4="X"):

print key1, key2, key3, key4

>>> keywords(key3="O", key2="O")

X O O X

>>> keywords()

X X X X

FunctionsFunctions

56

• Functions as variables- Functions can be assigned

- Example

def sub(a, b):

return a-b

>>> op = sub

>>> print op(3, 5)

-2

>>> type(op)

<type 'function'>

FunctionsFunctions

57

• Functions as parameters- Functions can be passed to other functions

- Example

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', '5', '10', '53'], float)

[1.0, 5.0, 10.0, 53.0]

>>> convert(['1', '5', '10', '53'], complex)

[(1+0j), (5+0j), (10+0j), (53+0j)]

FunctionsFunctions

58

• Returning multiple values- Return a tuple containing the values to return

- Example

def separate(text, size=3):

start = text[:size]

end = text[-size:]

return (start, end)

>>> separate('sample text')

('sam', 'ext')

>>> start, end = separate('sample text')

>>> print start

sam

>>> print end

ext

FunctionsFunctions

59

GeneratorsGenerators

• Generators are functions that generate sequence of items– Generated sequence can be infinite

def fibonacci():i = j = 1while True:

r, i, j = i, j, i+jyield r

for rabbits in fibbonacci():print rabbitsif rabbits > 100: break

1 1 2 3 5 8 13 21 34 55 89 144

60

Namespaces and ScopesNamespaces and Scopes

• Namespace– A mapping from names to objects– (Currently) implemented as Python dictionaries

• Scope– A region of program where a namespace is directly

accessible– Name references search at most 3 scopes: local,

global, built-in– Assignments create or change local names by default– Can force arguments to be global with global

command

61

Scope ExampleScope Example

x = 99def func(Y):

Z = X+Y # X is not assigned, so it's globalreturn Z

func(1)

62

• A file containing Python definitions and statements- Modules can be “imported”

- Module file name must end in .py

- Used to divide code between files

ModulesModules

import mathimport string…

math.py string.py

63

• Syntax 1: import <module name>- Module name is the file name without the .py extension

- You must use the module name to call the functions

- Example

>>> import math

>>> dir(math)

['__doc__', '__name__', 'acos', 'asin', 'atan', 'atan2', 'ceil', 'cos', 'cosh', 'e', 'exp', 'fabs', 'floor', 'fmod', 'frexp', 'hypot', 'ldexp', 'log', 'log10',

'modf', 'pi', 'pow', 'sin', 'sinh', 'sqrt', 'tan', 'tanh']

>>> print math.e

2.71828182846

>>> print math.sqrt(2.3)

1.51657508881

importimport Statement Statement

64

• Syntax 2: from <module> import <name>- Import only a specific name from a module into global namespace

- Module name is not required to access imported name

- Example

>>> 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


65

• Syntax 2a: from <module> import *- Import everything from a module into global namespace

- Example

>>> dir()

['__builtins__', '__doc__', '__name__']

>>> from time import *

>>> dir()

['__builtins__', '__doc__', '__name__', 'accept2dyear', 'altzone', 'asctime', 'clock', 'ctime', 'daylight', 'gmtime', 'localtime', 'mktime', 'sleep', 'strftime', 'struct_time', 'time', 'timezone', 'tzname']

>>> time()

1054004638.75


66

Python Standard LibrariesPython Standard Libraries

• Some examplessys - System-specific parameters and functions

time - Time access and conversions

thread - Multiple threads of control

re - Regular expression operations

email - Email and MIME handling package

httplib - HTTP protocol client

Tkinter - GUI package based on Tcl/Tk

• See http://docs.python.org/library/index.html

http://docs.python.org/library/index.html

http://docs.python.org/library/index.html

80

OO ProgrammingOO Programming

81

Defining a ClassDefining a Class

• Syntax:

• Creating a class with no superclass

• Basically, classes are simply namespacesclass MyClass(object):

myvar = 30

>>> MyClass.myvar30

class name[(base)]:body

class name:body

Old-style class New-style class

class name(object):body

82

Class ExampleClass Example

• All instance methods must explicitly take an instance as the first parameter– self is a commonly used name

class Rectangle(object):def __init__(self, width, height):

self.width = widthself.height = height

def area(self):return self.width * self.height

>>> r = Rectangle(10, 20)>>> Rectangle.area(r)200>>> r.area()200

Constructor

83

InheritanceInheritance

• 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

84

PolymorphismPolymorphism

• 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), Rectangle(2,8), Circle(3)]>>> for x in shapes:... print x.area()...251628.2743338823

85

Python Object HooksPython Object Hooks

• Objects can support built-in operators by implementing certain special methods– The usual operators: +, -, *, /, **, &, ^, ~, !=– Indexing (like sequences): obj[idx]– Calling (like functions): obj(args,...)– Iteration and containment tests

•for item in obj:...•if item in obj:...

86

Functional ProgrammingFunctional Programming

87

• Taken from functional languages- Lisp/Scheme

- Haskell

• Added to Python as built-in functions- map()

- filter()

- reduce()

- zip()

Functional ApproachesFunctional Approaches

88

• Perform an operation on each element of a list- A function is applied to each element

- The results of each function call are used to generate a new list

- The resulting list is always the same length as the original list

- The original list is not altered

Built-in function: Built-in function: mapmap

89


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ1

90


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ2ŷ1

91


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ3ŷ1 ŷ2

92


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ4ŷ1 ŷ2 ŷ3

93


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ5ŷ1 ŷ2 ŷ3 ŷ4

94


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ6ŷ1 ŷ2 ŷ3 ŷ4 ŷ5

95


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ7ŷ1 ŷ2 ŷ3 ŷ4 ŷ5 ŷ6

96


func

y1 y2 y3 y4 y5 y6 y7 y8

ŷ8ŷ1 ŷ2 ŷ3 ŷ4 ŷ5 ŷ6 ŷ7

97

• Syntax 1: map(func, list)- Example: Convert a list of integers to strings

>>> lst1 = [1, 2, 3, 4]

>>> lst2 = map(str, lst1)

>>> print lst2

['1', '2', '3', '4']

- The function (str) takes one argument

- The result (lst2) is the same length as the original (lst1)


98

• What if the function requires more than one argument?- Multiple lists can be passed to the map function

• Syntax 2: map(func, list1, …, listn)

- All lists must be of same length

- The function must take n parameters


99

• Example: adding numbersdef add2(a, b):

return a+b

>>> lst1 = [0, 1, 2, 3]

>>> lst2 = [4, 5, 6, 7]

>>> print map(add2, lst1, lst2)

[4, 6, 8, 10]


100

Code ComparisonCode Comparison

lst1 = [1, 2, 3, 4]

lst2 = []

for element in lst1:

lst2.append(str(element))

lst1 = [1, 2, 3, 4]

lst2 = map(str, lst1)

101


lst1 = [0, 1, 2, 3]

lst2 = [4, 5, 6, 7]

lst3 = []

for index in range(len(lst1)):

lst3.append(add2(lst1[index], lst2[index]))

lst1 = [0, 1, 2, 3]

lst2 = [4, 5, 6, 7]

lst2 = map(add2, lst1, lst2)

102

• The map function can be used like an expression- Can be used as a parameter to a function

- Example:

>>> lst1 = [1, 2, 3, 4]

>>> string.join(lst1) # Error because lst1 contains ints

…

TypeError: sequence item 0: expected string, int found

>>> string.join( map(str, lst1) ) # Correct

'1 2 3 4'

BenefitsBenefits

103

• Remove elements of a list based on a condition- Each element of a list is tested, those that fail are removed

- The resulting list is the same length or shorter than the original


Built-in function: Built-in function: filterfilter

104

• Syntax: filter(func, list)- Example: Remove all negative numbers

def removeNegative(number):

return number >= 0

>>> lst1 = [1, 2, -3, 4]

>>> lst2 = filter(removeNegative, lst1)

>>> print lst2

[1, 2, 4]

- The function (str) takes one argument and returns 0 or 1

- The result (lst2) is shorter than the original (lst1)

Built-in function: Built-in function: filterfilter

105


lst1 = [1, 2, -3, 4]

lst2 = []

for element in lst1:

if removeNegative(element):

lst2.append(element)

lst1 = [1, 2, -3, 4]

lst2 = filter(removeNegative, lst1)

106

• Apply a function cumulatively to a sequence- The function must take 2 parameters

- Function is applied to parameters from left to right

- The list is reduced to a single value


Built-in function: Built-in function: reducereduce

107


func

y1 y2 y3 y4

t1

func

t2

func

t3

108

Example: Reduce by AddingExample: Reduce by Adding

1+10

1 10 20 30

11

11+20

31

31+30

61

109

• Syntax 1: reduce(func, list)- Example: Find the sum of a list of integers

>>> lst1 = [1, 2, 3, 4]

>>> sum = reduce(operator.add, lst1)

>>> print sum

10

- The function (operator.add) takes two arguments

- The result is a single value


110

• Syntax 2: reduce(func, list, initialValue)- The initialValue is applied to func with the first value in the list

- If the list is empty, the initialValue is returned

- Example: Concatenating lists

>>> lst1 = [ [2, 4], [5, 9], [1, 7] ]

>>> result = reduce(operator.add, lst1, [100])

>>> print result

[100, 2, 4, 5, 9, 1, 7]


111


lst1 = [1, 2, 3, 4]

sum = operator.add(lst1[0], lst1[1])

for element in lst1[2:]:

sum = operator.add(sum, element)

lst1 = [1, 2, 3, 4]

sum = reduce(operator.add, lst1)

112


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])

113

• Combine multiple lists into one- Elements are paired by index

- The resulting list is the same length as the shortest list supplied

- Each element of resulting list contains a tuple

- The original lists are not altered

Built-in function: Built-in function: zipzip

114


y1 y2 y3 y4x1 x2 x3 x4

(x1, y1) (x2, y2) (x4, y4)(x3, y3)

115

• Syntax: zip(list1, …, listn)- Example: Combine two lists

>>> lst1 = [1, 2, 3, 4]

>>> lst2 = ['a', 'b', 'c', 'd', 'e']

>>> result = zip(lst1, lst2)

>>> print 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


116


lst1 = [1, 2, 3, 4]

lst2 = ['a', 'b', 'c', 'd', 'e']

lst3 = []

for index in range(min(len(lst1), len(lst2)):

lst3.append( (lst1[index], lst2[index]) )

lst1 = [1, 2, 3, 4]

lst2 = ['a', 'b', 'c', 'd', 'e']

lst3 = zip(lst1, lst2)

117

• Iterate over two lists simultaneously>>> produce = ['apples', 'oranges', 'pears']

>>> prices = [0.50, 0.45, 0.55]

>>> for fruit, cost in zip(produce, prices):

print '%s cost $%.2f'%(fruit, cost)

apples cost $0.50

oranges cost $0.45

pears cost $0.55

Uses for Uses for zipzip

118

• Create a dictionary using 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}

Uses for Uses for zipzip

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• Generate new lists from old ones- Can simultaneously map and filter

- More flexible than the built-in functions

List ComprehensionsList Comprehensions

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• Basic Syntax: [<exp> for <var> in <list>]- The resulting list is the result of exp evaluated for each var in list

- Example: Increase each element by 1

>>> [x+1 for x in range(5)]

[1, 2, 3, 4, 5]

- Example: Convert each element to a string

>>> [str(x) for x in range(5)]

['0', '1', '2', '3', '4']

Basic List ComprehensionsBasic List Comprehensions

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• Syntax: [<ex1> for <var> in <list> if <ex2>]- ex1 is only evaluated if ex2 is true

- Example: Remove smallest element

>>> 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]

More List ComprehensionsMore List Comprehensions

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• Multiple for loops can be included- The loops will be nested

- Example: Generate all possible combinations of letters a, c, g, t

>>> nucleo = ['a', 'g', 'c', 't']

>>> [a+b for a in nucleo for b in nucleo]

['aa', 'ag', 'ac', 'at', 'ga', 'gg', 'gc', 'gt', 'ca', 'cg', 'cc', 'ct', 'ta', 'tg', 'tc', 'tt']

More List ComprehensionsMore List Comprehensions

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nucleo = ['a', 'g', 'c', 't']

results = []

for a in nucleo:

for b in nucleo:

results.append(a+b)

nucleo = ['a', 'g', 'c', 't']

results = [a+b for a in nucleo for b in nucleo]

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• Anonymous functions- Body can consist of only a single expression

- Execute slightly slower than normal functions

- Can take any number of parameters

- In general, lambda functions should be avoided

Lambda FunctionsLambda Functions

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• Syntax : lambda p1[,…,pn]: <exp>

- Expression should not use return

- Example: adding two numbers

>>> add2 = lambda a,b: a+b

>>> print add2(1, 5)

6

- Example: simple factorial

>>> fac = lambda x: reduce(lambda a,b: a*b, range(1, x+1))

>>> print fac(5)

120

Lambda FunctionsLambda Functions

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ReferencesReferences

• Python Documentation http://docs.python.org

• Python for Programmers by Alex Martelli

• Advanced Python (Understanding Python) by Thomas Wouters

http://docs.python.org/

http://video.google.com/videoplay?docid=1135114630744003385

http://video.google.com/videoplay?docid=7760178035196894549

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ExerciseExercise

• Write a Python program freq.py to:– Fetch a text file from the web– Then report the most 10 frequently used words

$ 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$

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Exercise – HintsExercise – Hints

• Accessing command-line arguments

• Reading a webpage

• Extracting all English words from text

import urllibcontents = urllib.urlopen(url).read()

import sysurl = sys.argv[1]

import rewords = re.findall('[A-Za-z]+', text)

1 Introduction to Python Materials based on contents from the course Programming with Python by Chad...

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