Python Another Handout

7/30/2019 Python Another Handout

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Introduction Python Tutorial Numerics & Plotting Standard library

An introduction to the Python programming

language

Prabhu Ramachandran

Department of Aerospace Engineering

IIT Bombay

16, July 2007

Prabhu Ramachandran Introduction to Python


Outline

1 Introduction

Introduction to Python

2 Python Tutorial

Preliminaries

Data types

Control flow, functions

Modules, exceptions, classes

Miscellaneous

3 Numerics & Plotting

NumPy Arrays

Plotting: Matplotlib

SciPy4 Standard library

Quick Tour



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Introduction

Creator and BDFL: Guido van RossumBDFL == Benevolent Dictator For Life

Conceived in December 1989

The name Python: Monty Pythons Flying Circus

Current stable version of Python is 2.5.x

PSF license (like BSD: no strings attached)

Highly cross platform

Runs on the Nokia series 60!


Introduction Python Tutorial Numerics & Plotting Standard libraryIntroduction to Python

Resources

Available as part of any sane GNU/Linux distribution

Web: http://www.python.org

Documentation: http://www.python.org/doc

Free Tutorials:

Official Python tutorial:

http://docs.python.org/tut/tut.html

Byte of Python: http://www.byteofpython.info/

Dive into Python: http://diveintopython.org/

http://www.python.org/http://www.python.org/dochttp://docs.python.org/tut/tut.htmlhttp://www.python.org/http://www.python.org/dochttp://docs.python.org/tut/tut.htmlhttp://www.byteofpython.info/http://diveintopython.org/http://diveintopython.org/http://www.byteofpython.info/http://docs.python.org/tut/tut.htmlhttp://www.python.org/dochttp://www.python.org/


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Why Python?

High level, interpreted, modular, OO

Easy to learn

Easy to read code

Much faster development cycle

Powerful interactive interpreter

Rapid application development

Powerful standard library

Interfaces well to C++, C and FORTRAN libraries

In short: there is little you cant do with it



A quote

I came across Python and its Numerical extension in

1998 . . . I quickly fell in love with Python programming

which is a remarkable statement to make about a

programming language. If I had not seen others with the

same view, I might have seriously doubted my sanity.

Travis Oliphant (creator of NumPy)



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Why not ***lab?

Open Source, Free

Portable

Python is a real programming language: large and small

programs

Can do much more than just array and math

Wrap large C++ codes

Build large code bases via SCons

Interactive data analysis/plotting

Parallel applicationJob scheduling on a custom cluster

Miscellaneous scripts



Why not Python?

Can be slow for high-performance applications

This can be fairly easily overcome by using C/C++/FORTRAN

extensions



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Use cases

NASA: Python Streamlines Space Shuttle Mission DesignAstraZeneca Uses Python for Collaborative Drug Discovery

ForecastWatch.com Uses Python To Help Meteorologists

Industrial Light & Magic Runs on Python

Zope: Commercial grade CMS

RedHat: install scripts, sys-admin tools

Numerous success stories:

http://www.pythonology.com/success



Before we begin

This is only an introduction

Python is a full-fledged programming language

Please read the tutorial

It is very well written and can be read in one afternoon

http://www.pythonology.com/successhttp://www.pythonology.com/success


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Preliminaries Data types Control flow, functions Modules, exceptions, classes Miscellaneous

Preliminaries: The Interpreter

Python is interpretedInterpreted vs. Compiled

Interpreted languages allow for rapid testing/prototyping

Dynamically typed

Full introspection of code at runtime

Did I say dynamic?

Does not force OO or a particular programming paradigm

down your throat!


Introduction Python Tutorial Numerics & Plotting Standard libraryPreliminaries Data types Control flow, functions Modules, exceptions, classes Miscellaneous

Preliminaries . . .

Will follow the Official Python tutorial

No lexical blocking

Indentation specifies scope

Leads to easier to read code!



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Using the interpreter

Starting up: python or ipython

Quitting: Control-D or Control-Z (on Win32)

Can use it like a calculator

Can execute one-liners via the -c option: python -c

"print hello world"

Other options via python -h



IPython

Recommended interpreter, IPython:

http://ipython.scipy.org

Better than the default Python shell

Supports tab completion by defaultEasier object introspection

Shell access!

Command system to allow extending its own behavior

Supports history (across sessions) and logging

Can be embedded in your own Python code

Support for macros

A flexible framework for your own custom interpreter

Other miscellaneous conveniences

Well get back to this later

http://ipython.scipy.org/http://ipython.scipy.org/


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Basic IPython features

Startup: ipython [options] files

ipython [-wthread|-gthread|-qthread]:

Threading modes to support wxPython, pyGTK and Qt

ipython -pylab: Support for matplotlib

TAB completion:

Type object_name. to see list of options

Also completes on file and directory names

object? shows docstring/help for any Python object

object?? presents more docs (and source if possible)

Debugging with %pdb magic: pops up pdb on errors

Access history (saved over earlier sessions also)Use : move up history

Use string: search history backwards

Use Esc >: get back to end of history

%run [options] file[.py] lets you run Python code



Basic concepts

Dynamically typed

Assignments need not specify a type

a = 1a = 1. 1

a = " foo "

a = SomeClass ( )

Comments:

a = 1 # Inl i n e comments

# Comment i n a l i n e t o i t s e l f .

a = " # T hi s i s n o t a comment ! "



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Basic concepts

No lexical scoping

Scope determined by indentation

f o r i i n r an ge ( 1 0 ) :

p r i n t " i n s i d e l o op : " ,

# Do s ome th in g i n t h e l o op .

p r i n t i , i i

p r i n t " l o op i s done ! " # T hi s i s o ut si de t h e l oo p !

Assignment to an object is by referenceEssentially, names are bound to objects



Basic types

Basic objects: numbers (float, int, long, complex), strings,

tuples, lists, dictionaries, functions, classes, types etc.Types are of two kinds: mutable and immutable

Immutable types: numbers, strings, None and tuples

Immutables cannot be changed in-place

Mutable types: lists, dictionaries, instances, etc.

Mutable objects can be changed



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What is an object?

A loose and informal but handy descriptionAn object is a particular instance of a general class of things

Real world example

Consider the class of cars made by Honda

A Honda Accord on the road, is a particular instance of the

general class of cars

It is an object in the general sense of the term



Objects in a programming language

The object in the computer follows a similar idea

An object has attributes (or state) and behavior

Object contains or manages data (attributes) and hasmethods (behavior)

Together this lets one create representation of real things on

the computer

Programmers then create objects and manipulate them

through their methods to get things done

In Python everything is essentially an object you dont have

to worry about it



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Strings

s = t h i s i s a s t r i n g s = T hi s one has " q uo te s " i n s i d e !

s = " The r ev er se w i t h s i n g l eq uo te s i n s i d e ! "

l = " A l on g s t r i n g spanning s ev er al l i n e s \

one more l i n e \

y e t a n o th e r "

t = " " " A t r i p l e quoted s t r i n g

does n o t need t o be escaped a t t h e end and

" can have n e st e d q u ot e s " and whatnot . " " "



Strings

>>> word = " h e l l o "

>>> 2word + " w o rl d " # A r i t h me t i c on s t r i n g s

h e l l o h e l l o wo rl d

>>> p r i n t word [ 0 ] + word [ 2 ] + word [1]

h l o>>> # S t r i n g s are " immutable "

. . . word [ 0 ] = H

Traceback ( most r ec en t c a l l l a s t ) :

F i l e " < s t d i n > " , l i n e 1 , i n ?

T yp eE rr or : o b j e c t d oe sn t s u pp o r t i t em a ss ig nm en t

>>> len ( word ) # The l en gt h o f t h e s t r i n g

5> > > s = u Unicode s t r i n g s ! # unicode s t r i n g

>>> # Raw s t r i n g s ( no te t he l e ad i ng r )

. . . raw_s = r A \ LaTeX s t r i n g $ \ a lp ha \ nu$



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More on strings> > > a = h e l l o w or l d

>>> a . s t a r t s w i t h ( h e l l )

True

>>> a . endswith ( ld )

True>>> a . upper ( )

HELLO WORLD

>>> a . upper ( ) . lower ( )

h e l l o w or l d

>>> a . s p l i t ( )

[ h e l l o , world ]

>>> . j o i n ( [ a , b , c ] )

abc

>>> x , y = 1 , 1. 23 4>>> x i s %s , y i s %s %(x , y )

x i s 1 , y i s 1.234

>>> # c o ul d a ls o do : x i s %d , y i s %f %( x , y )

See http://docs.python.org/lib/typesseq-strings.html



Lists

Lists are mutable

Items are indexed at 0

Last element is -1

Length: len(list)

http://docs.python.org/lib/typesseq-strings.htmlhttp://docs.python.org/lib/typesseq-strings.html


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List: examples

>>> a = [ spam , eggs , 100 , 12 34 ]

> > > a [ 0 ]

spam > > > a [ 3 ]

1234

>>> a[2]

100

> > > a[ 1:1 ]

[ eggs , 100 ]

>>> a [ : 2 ] + [ bacon , 2

2][ spam , eggs , bacon , 4 ]

>>> 2a [ : 3 ] + [ Boe! ]

[ spam , eggs , 100 , spam , eggs , 100 , Boe! ]



Lists are mutable

>>> a = [ spam , eggs , 100 , 12 34 ]

>>> a [ 2 ] = a [ 2 ] + 23

>>> a[ spam , eggs , 123 , 12 34 ]

>>> a = [ spam , eggs , 100 , 12 34 ]

>>> a [ 0 : 2 ] = [ 1 , 1 2] # Replace some i t ems

>>> a

[ 1 , 12 , 123 , 1234]

>>> a [ 0 : 2 ] = [ ] # Remove some it em s

>>> a

[ 1 23 , 12 34 ]



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List methods

>>> a = [ spam , eggs , 100 , 12 34 ]

>>> l e n ( a )4

>>> a . revers e ( )

>>> a

[ 12 34 , 100 , eggs , spam ]

>>> a . append ( [ x , 1 ] ) # L i s t s can c o n t a i n l i s t s .

>>> a

[ 12 34 , 100 , eggs , spam , [ x , 1 ] ]

>>> a . e x te n d ( [ 1 , 2 ] ) # E xt en d t h e l i s t .>>> a

[ 12 34 , 100 , eggs , spam , [ x , 1 ] , 1 , 2 ]



Tuples

>>> t = ( 0 , 1 , 2 )

>>> p r i n t t [ 0 ] , t [ 1 ] , t [ 2 ] , t [

1 ] , t [

2 ] , t [

3]0 1 2 2 1 0

>>> # T u pl e s a r e i mm ut ab le !

. . . t [ 0 ] = 1



T y pe E rr o r : o b j e c t doesn t s u pp o r t i t em a ss ig nm en t



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Dictionaries

Associative arrays/mappings

Indexed by keys (keys must be immutable)

dict[key] = value

keys() returns all keys of the dict

values() returns the values of the dict

has_key(key) returns if key is in the dict



Dictionaries: example

> > > t e l = { ja ck : 4098 , sape : 41 39}

>>> t e l [ g u id o ] = 4127

>>> t e l

{ sape : 4139 , g u id o : 4127 , ja ck : 40 98}>>> t e l [ ja ck ]

4098

>>> del t e l [ sape ]

>>> t e l [ i r v ] = 4127

>>> t e l

{ g u id o : 4127 , i r v : 4127 , ja ck : 40 98}

>>> t e l . keys ( )

[ g u id o , i r v , ja ck ]>>> t e l . h as_ ke y ( g u id o )

True



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Control flow

Control flow is primarily achieved using the following:

if/elif/else

for

while

break, continue, else may be used to further control

pass can be used when syntactically necessary but nothing

needs to be done



If example

x = i n t ( r a w_ i n pu t ( " Please e n te r an i n t e g e r : " ) )

# r a w_ in pu t asks t h e user f o r i n p u t .

# i n t ( ) t y p e c a s t s t h e r e s u l t i n g s t r i n g i n t o an i n t .

i f x < 0 :

x = 0

p r i n t N e ga t iv e changed t o z er o

e l i f x = = 0 :

p r i n t Zero

e l i f x = = 1 :

p r i n t Sin gle

else :p r i n t More



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If example

>>> a = [ ca t , window , d e f e n e s t r a t e ]

>>> i f ca t i n a :

. . . p r i n t "meaw"

. . .

meaw

>>> p et s = { ca t : 1 , dog : 2 , cro c : 10}

>>> i f cro c i n pets :

. . . p r i n t pets [ cro c ]

. . .10



for example

>>> a = [ ca t , window , d e f e n e s t r a t e ]

>>> f o r x i n a :

. . . p r i n t x , l en ( x )

. . .c a t 3

window 6

d e f e n e st r a t e 12

> > > k n i g h t s = { gal lah ad : t h e p ur e ,

. . . r o b i n : t he brave }

>>> f o r k , v i n k n i g ht s . i t e r i t e m s ( ) :

. . . p r i n t k , v

. . .g a ll a ha d t he pure

r o b i n t he brave



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for and range example

>>> f o r i i n r an ge ( 5 ) :

. . . p r i n t i , i i

. . .

0 0

1 1

2 4

3 9

4 16

>>> a = [ a , b , c ]

>>> f o r i , x i n e n u m e r a t e ( a ) :

. . . p r i n t i , x

. . .

0 a

1 b

2 c



while example

>>> # F ib on ac ci s e r i e s :

. . . # t he sum o f two el emen ts d e f i ne s t he n ex t

. . . a , b = 0 , 1

>>> while b < 1 0 :. . . p r i n t b

. . . a , b = b , a+b

. . .

1

1

2

3

5

8



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Functions

Support default and keyword argumentsScope of variables in the function is local

Mutable items are passed by reference

First line after definition may be a documentation string

(recommended!)

Function definition and execution defines a name bound to the

function

You canassign a variable to a function!



Functions: examples

>>> def f i b ( n ) : # w r i t e F i b o n a c c i s er ie s up t o n

. . . " " " P r i n t a F ib on ac ci s e r i e s up t o n . " " "

. . . a , b = 0 , 1

. . . while b < n :

. . . p r i n t b ,

. . . a , b = b , a+b

. . .

>>> # Now c a l l t h e f u n c t i on we j u s t d ef in ed :

. . . f i b ( 2 00 0 )

1 1 2 3 5 8 1 3 2 1 3 4 5 5 8 9 1 4 4 2 3 3 3 7 7 6 1 0 9 8 7 1 5 9 7

> > > f = f i b # Assign a v a r i ab l e t o a f u n c ti o n

>>> f (1 0)

1 1 2 3 5 8



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Functions: default arguments

def ask_ok ( prompt , r e t r i e s =4 , c o m pl a i n t = Yes o r no ! ) :

while True :ok = r a w _ i np u t ( p ro mp t )

i f ok i n ( y , ye , yes ) :

r et u r n True

i f ok i n ( n , no , nop , nope ) :

r et u r n False

r e t r i e s = r e t r i e s 1

i f r e t r i e s < 0 :

r a i s e I OError , bad u s e r

p r i n t c o m p l a i n t



Functions: keyword arguments

def p a r r o t ( v o lt a g e , s t a t e = a s t i f f ,

a c t i o n = voom , t yp e= Norwegian Blue ) :

p r i n t "

T h i s p a r r o t woul dn t " , a ct io n ,p r i n t " i f you p u t " , v ol ta g e , " V o l t s t h ro u gh i t . "

p r i n t " Lovely plumage , t he " , t yp e

p r i n t " I t s " , s ta te , " ! "

p a r r o t (1 0 0 0)

p a r r o t ( a c t i o n = VOOOOOM , v o l t a g e = 1000000)

p a r r o t ( a t housand , s t a t e = p us hi ng up t he d a i s i e s )

p a r r o t ( a m i l l i o n , b e r e f t o f l i f e , jump )



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Modules

Define variables, functions and classes in a file with a .py

extension

This file becomes a module!Modules are searched in the following:

Current directory

Standard: /usr/lib/python2.3/site-packages/ etc.

Directories specified in PYTHONPATH

sys.path: current path settings (from the sys module)

The import keyword loads a module

One can also use:

from module import name1, name2, name2

where name1 etc. are names in the module, module

from module import * imports everything from

module, use only in interactive mode



Modules: example

# f oo . py

some_var = 1

def f i b ( n ) : # w r i t e F i b o n a c c i s er ie s up t o n

" " " P r i n t a F ib on ac ci s e r i e s up t o n . " " "

a , b = 0 , 1while b < n :

p r i n t b ,

a , b = b , a+b

# EOF

>>> import f o o

>>> f o o . f i b ( 1 0 )1 1 2 3 5 8

>>> fo o . some_var

1



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Namespaces

A mapping from names to objects

Modules introduce a namespace

So do classes

The running scripts namespace is __main__

A modules namespace is identified by its name

The standard functions (like len) are in the __builtin__

namespace

Namespaces help organize different names and their bindingsto different objects



Exceptions

Pythons way of notifying you of errorsSeveral standard exceptions: SyntaxError, IOError etc.

Users can also raise errors

Users can create their own exceptions

Exceptions can be caught via try/except blocks



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Exception: examples

>>> 10 ( 1 / 0 )

Traceback ( most r ec en t c a l l l a s t ) :F i l e " < s t d i n > " , l i n e 1 , i n ?

Z e ro D iv i s io n Er r o r : i n t e g e r d i v i s i o n or modulo by zero

>>> 4 + spam3



NameError : name spam i s not d e f i n e d

>>> 2 + 2

Traceback ( most r ec en t c a l l l a s t ) :F i l e " < s t d i n > " , l i n e 1 , i n ?

T y p e Er r o r : c a nn o t c o n c at e n a te s t r and i n t o b j e c t s



Exception: examples

>>> while True :

. . . t r y :

. . . x = i n t ( ra w_ i n p u t ( " E n t er a number : " ) )

. . . break

. . . except V a l u e E r r o r :

. . . p r i n t " I n v a l i d number , t r y a ga in . . . "

. . .

>>> # To r a i s e e xc ep ti on s

. . . r a i s e ValueError , " y ou r e r r o r message "


F i l e " < s t d i n > " , l i n e 2 , i n ?V a l u e E r r o r : y ou r e r r o r message



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Classes: the big picture

Lets you create new data types

Class is a template for an object belonging to that classNote: in Python a class is also an object

Instantiating a class creates an instance (an object)

An instance encapsulates the state (data) and behavior

(methods)

Allows you to define an inheritance hierarchy

A Honda car is a car.

A car is an automobile.A Python is a reptile.

Programmers need to think OO



Classes: whats the big deal?

Lets you create objects that mimic a real problem being

simulated

Makes problem solving more natural and elegant

Easier to create code

Allows for code-reuse

Polymorphism



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Class definition and instantiation

Class definitions when executed create class objects

Instantiating the class object creates an instance of the class

class Foo ( o b j e c t ) :

pass

# c l as s o b j e c t c re at ed .

# C re at e an i n st a nc e o f Foo .

f = Foo ( )

# Can a s si gn an a t t r i b u t e t o t he i ns ta nc e

f . a = 100

p r i n t f . a

100



Classes . . .

All attributes are accessed via the object.attribute

syntax

Both class and instance attributes are supported

Methodsrepresent the behavior of an object: crudely think of

them as functions belonging to the object

All methods in Python are virtual

Inheritance through subclassing

Multiple inheritance is supported

No special public and private attributes: only goodconventions

object.public(): public

object._private() & object.__priv(): non-public



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Classes: examples

class MyClass( ob je ct ) :

" " " Example c l a ss ( t h i s i s t he c l a ss d o c s t r i n g ) . " " "

i = 12345 # A c la s s a t t r i b u t e def f ( s e l f ) :

" " " T h i s i s t h e method d o c s t r i n g " " "

r et u r n h e l l o w or ld

>>> a = MyClass ( ) # c re at es an i n st an c e

>>> a . f ( )

h e l l o w or ld

>>> # a . f ( ) i s e q u i v al e n t t o MyClass . f ( a ). . . # T hi s a ls o e x pl a i ns why f has a s e l f argument .

. . . MyClass . f ( a )

h e l l o w or ld



Classes (continued)

self is conventionally the first argument for a method

In previous example, a.f is a method object

When a.f is called, it is passed the instance a as the first

argument

If a method called __init__exists, it is called when the

object is created

If a method called __del__exists, it is called before the

object is garbage collected

Instance attributes are set by simply setting them in self

Other special methods (by convention) like __add__ let you

define numeric types:http://docs.python.org/ref/specialnames.html

http://docs.python.org/ref/numeric-types.html

http://docs.python.org/ref/specialnames.htmlhttp://docs.python.org/ref/numeric-types.htmlhttp://docs.python.org/ref/specialnames.htmlhttp://docs.python.org/ref/numeric-types.htmlhttp://docs.python.org/ref/numeric-types.htmlhttp://docs.python.org/ref/specialnames.html


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Classes: examples

class Bag ( MyClass ) : # Shows how t o d e r i v e c l a s s e s

def _ _ i n i t _ _ ( s e l f ) : # c a l l e d on o b j e c t c r ea t i o n .

s e l f . data = [ ] # an i ns ta n ce a t t r i b u t e def add ( s e l f , x ) :

s e l f . dat a . append ( x )

def a d dt wi c e ( s e l f , x ) :

s e l f . add ( x )

s e l f . add ( x )

> > > a = B a g ( )

>>> a . f ( ) # I n h e r i t e d method

h e l l o w or ld >>> a . add ( 1 ) ; a . a dd tw ic e ( 2 )

>>> a . data

[ 1 , 2 , 2 ]



Derived classes

Call the parents __init__ if needed

If you dont need a new constructor, no need to define it in

subclass

Can also use the super built-in function

class AnotherBag(Bag):

def _ _ i n i t _ _ ( s e l f ) :

# Must c a l l p a r e n t s _ _ i n i t _ _ e x p l i c i t l y

Bag . _ _ i n i t _ _ ( s e l f )

# A l t e r n a t i v e l y use t h i s :

s up er ( AnotherBag , s e l f ) . _ _ i n i t _ _ ( )

# Now s et up any more dat a .

s e l f . more_data = [ ]



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Classes: polymorphism

class Drawable ( ob je ct ) :

def draw ( s e l f ) :

# J u s t a s p e c i f i c a t i o n .

pass

class Square ( Drawable ) :


# draw a s qu ar e .

class C i r c l e ( D ra wa bl e ) :


# draw a c i r c l e .

class A r t i s t ( Drawa ble ) :


f o r o b j i n s e l f . drawables :

ob j . draw ( )



Stand-alone scripts

Consider a file f.py:

# ! / us r / bin / env py t hon

" " " Module l e v e l document ation . " " "

# F i r s t l i n e t e l l s t h e s h e l l t h a t i t should use P ython # t o i n t e r p r e t t h e code i n t h e f i l e .

def f ( ) :

p r i n t " f "

# Check i f we a re r u nn i n g s ta n da l on e o r as module .

# When impor t ed , __name__ w i l l not be __main__

i f __name__ == __main__ :

# T hi s i s n ot exe cuted when f . py i s i mp or te d .

f ( )



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More IPython features

Input and output caching:

In: a list of all entered input

Out: a dict of all output

_, __, __are the last three results as is _N

%hist [-n] macro shows previous history, -n suppresses

line number information

Log the session using %logstart, %logon and %logoff

%run [options] file[.py] running Python code

%run -d [-b]: debug script with pdb N is the line

number to break at (defaults to 1)

%run -t: time the script

%run -p: Profile the script

%prun runs a statement/expression under the profiler

%macro [options] macro_name n1-n2 n3-n4 n6

save specified lines to a macro with name macro_name



More IPython features . . .

%edit [options] [args]: edit lines of code or file

specified in editor (configure editor via $EDITOR)

%cd changes directory, see also

%pushd, %popd, %dhist

Shell access

!command runs a shell command and returns its output

files = %sx ls or files = !ls sets files to all

result of the ls command

%sx is quiet

!ls $files passes the files variable to the shell

command

%alias alias_name cmd creates an alias for a system

command

%colors lets you change the color scheme to

NoColor, Linux, LightBG



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More IPython features . . .

Use ; at the end of a statement to suppress printing output

%who, %whos: print information on variables%save [options] filename n1-n2 n3-n4: save

lines to a file

%time statement: Time execution of a Python statement

or expression

%timeit [-n -r [-t|-c]] statement: time

execution using Pythons timeit module

Can define and use profiles to setup IPython differently:math, scipy, numeric, pysh etc.

%magic: Show help on all magics



File handling

>>> # Reading f i l e s :

. . . f = open ( / path / to / f i le _n am e )

>>> d a ta = f . r ea d ( ) # Read e n t i r e f i l e .

>>> l i n e = f . r e a d l i n e ( ) # Read one l i n e .

>>> # Read e n t i r e f i l e appending each l i n e i n t o a l i s t

. . . l i n e s = f . r e a d l i ne s ( )

>>> f . c los e ( ) # c l os e t he f i l e .

>>> # W r it in g f i l e s :

. . . f = open ( / path / to / f i le _n am e , w )

>>> f . w r i t e ( h e l l o w o rl d \ n )

tell(): returns int of current positionseek(pos): moves current position to specified byte

Call close() when done using a file



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Math

math module provides basic math routines for floats

cmath module provides math routies for complex numbers

random: provides pseudo-random number generators for

various distributions

These are always available and part of the standard library

More serious math is provided by the NumPy/SciPy modules

these are not standard and need to be installed separately



Timing and profiling

Timing code: use the time module

Read up on time.time() and time.clock()

timeit: is a better way of doing timing

IPython has handy time and timeit macros (type

timeit? for help)

IPython lets you debug and profile code via the run macro

(type run? on the prompt to learn more)



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Odds and ends

dir([object]) function: attributes of given object

type(object): returns type informationstr(), repr(): convert object to string representation

isinstance, issubclass

assert statements let you do debugging assertions in code

csv module: reading and writing CSV files

pickle: lets you save and load Python objects (serialization)

os.path: common path manipulationsCheck out the Python Library reference:

http://docs.python.org/lib/lib.html


Introduction Python Tutorial Numerics & Plotting Standard libraryNumPy Arrays Plotting: Matplotlib SciPy

The numpy module

Manipulating large Python lists for scientific computing is slow

Most complex computations can be reduced to a few standard

operationsThe numpy module provides:

An efficient and powerful array type for various common data

types

Abstracts out the most commonly used standard operations on

arrays

Numeric was the first, then came numarray. numpy is the

latest and is the future

This course uses numpy and only covers the absolute basics

http://docs.python.org/lib/lib.htmlhttp://docs.python.org/lib/lib.htmlhttp://docs.python.org/lib/lib.htmlhttp://docs.python.org/lib/lib.htmlhttp://docs.python.org/lib/lib.html


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NumPy Arrays Plotting: Matplotlib SciPy

Basic concepts

numpy arrays are of a fixed size (arr.size) and have the

same type (arr.dtype)

numpy arrays may have arbitrary dimensionalityThe shape of an array is the extent (length) of the array along

each dimension

The rank(arr) of an array is the dimensionality of the

array

The arr.itemsize is the number of bytes (8-bits) used for

each element of the array

Note: The shape and rank may change as long as the

size of the array is fixed

Note: len(arr) != arr.size in general

Note: By default array operations are performed elementwise

Indices start from 0



Examples of numpy

# S im pl e a r r a y math exampl e

>>> from numpy import

>>> a = a r r a y ( [ 1 , 2 , 3 , 4 ] )

>>> b = a r r a y ( [ 2 , 3 , 4 , 5 ] )

> > > a + b # Element w ise a d d i t i o n ! a rr ay ( [ 3 , 5 , 7 , 9 ] )

>>> p r i n t p i , e # P i and e are d ef in e d .

3.14159265359 2.71828182846

# Create a rr ay from 0 t o 10

>>> x = arange ( 0 . 0 , 1 0 .0 , 0 . 0 5)

>>> x

= 2

pi / 10 # m u l t i p l y a rr ay by s c a l ar v a l ue a r ra y ( [ 0 . , 0 . 0 3 1 4 , . . . , 6 . 2 5 2 ] )

# a pp ly f u nc t io n s t o a rr ay .

>>> y = s i n ( x )



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More examples of numpy

# S iz e , shape , r an k , t y p e e t c .

>>> x = a rr ay ( [ 1 . , 2 , 3 , 4 ] )

>>> s i z e ( x )

4>>> x . dtype # o r x . d t yp e . c ha r

d

>>> x . shape

( 4 , )

>>> p r i n t r an k ( x ) , x . i t e m s i z e

1 8

>>> x . t o l i s t ( )

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

# A rr ay i n de x in g

>>> x [ 0 ] = 10

>>> p r i n t x [ 0 ] , x [1]

1 0. 0 4 .0Prabhu Ramachandran Introduction to Python


Multi-dimensional arrays

>>> a = a rr ay ( [ [ 0 , 1 , 2 , 3 ] ,

. . . [ 1 0 , 1 1 , 1 2 , 1 3 ] ] )

>>> a . shape # ( rows , c olu mn s )

( 2 , 4 )

# Acc ess in g and s e t t i n g v al ue s

>>> a [ 1 , 3 ]

13

>>> a [ 1 , 3 ] = 1

> > > a [ 1 ] # The second row

ar ra y ([1 0 ,1 1 ,12 ,1 ] )

# F l a t t e n / r a v e l a rr a ys t o 1D a rr a ys >>> a . f l a t # o r r a v e l ( a )

ar ra y ([ 0 ,1 ,2 ,3 ,10 ,11 ,12 ,1 ] )

# Note : f l a t r ef er en ce s o r i g i n a l memory



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Slicing arrays

>>> a = a r r ay ( [ [ 1 , 2 , 3 ] , [ 4 , 5 , 6 ] , [ 7 , 8 , 9 ] ] )

>>> a [ 0 , 1 : 3 ]

a r ra y ( [ 2 , 3 ] )>>> a [ 1 : , 1 : ]

a r ra y ( [ [ 5 , 6 ] ,

[ 8 , 9 ] ] )

>>> a [ : , 2 ]

a rr ay ( [ 3 , 6 , 9 ] )

# S t r i d i n g . . .

>>> a [ 0 : : 2 , 0 : : 2 ]a r ra y ( [ [ 1 , 3 ] ,

[ 7 , 9 ] ] )

# A l l t he se s l i c e s a re r e fe r e nc e s t o t he same memory !



Array creation functions

array(object, dtype=None,

copy=1,order=None, subok=0,ndmin=0)

arange(start, stop=None, step=1,

dtype=None)

linspace(start, stop, num=50,

endpoint=True, retstep=False)

ones(shape, dtype=None, order=C)

zeros((d1,...,dn),dtype=float,order=C)

identity(n)

empty((d1,...,dn),dtype=float,order=C)

ones_like(x), zeros_like(x), empty_like(x)



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Array math

Basic elementwise math (given two arrays a, b):

a + b add(a, b)

a - b , subtract(a, b)

a * b, multiply(a, b)

a / b , divide(a, b)

a % b , remainder(a, b)

a ** b, power(a, b)

Inplace operators: a + = b, or add(a, b, a) etc.

Logical operations: equal (==), not_equal (!=),

less () etc.

Trig and other functions:sin(x), arcsin(x),

sinh(x), exp(x), sqrt(x) etc.

sum(x, axis=0), product(x, axis=0): sum and

product of array elements

dot(a, b)



Advanced

Only scratched the surface of numpy

Ufunc methods: reduce, accumulate, outer,

reduceat

Typecasting

More functions: take, choose, where, compress,

concatenate

Array broadcasting and None



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About matplotlib

Easy to use, scriptable, Matlab-like 2D plotting

Publication quality figures and interactive capabilitiesPlots, histograms, power spectra, bar charts, errorcharts,

scatterplots, etc.

Also does polar plots, maps, contours

Support for simple TEX markup

Multiple output backends (images, EPS, SVG, wx, Agg, Tk,

GTK)

Cross-platform: Linux, Win32, Mac OS X

Good idea to use via IPython: ipython -pylab

From scripts use: import pylab



More information

More information here: http://matplotlib.sf.net

http://matplotlib.sf.net/tutorial.html

http://matplotlib.sf.net/screenshots.html

http://matplotlib.sf.net/tutorial.htmlhttp://matplotlib.sf.net/screenshots.htmlhttp://matplotlib.sf.net/screenshots.htmlhttp://matplotlib.sf.net/screenshots.htmlhttp://matplotlib.sf.net/http://matplotlib.sf.net/tutorial.htmlhttp://matplotlib.sf.net/screenshots.htmlhttp://matplotlib.sf.net/screenshots.htmlhttp://matplotlib.sf.net/tutorial.htmlhttp://matplotlib.sf.net/


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Basic plotting with matplotlib

>>> x = arange ( 0 , 2p i , 0 . 05 )

>>> p l o t ( x , s i n ( x ) ) # Same as p l o t ( x , s i n ( x ) , b

)>>> p l o t ( x , s i n ( x ) , ro )

>>> axi s ( [ 0 , 2 p i , 1 , 1 ] )

>>> x l a b e l ( r $ \ c h i $ , c o l o r = g )

>>> y l a b e l ( r s i n ( $ \ chi $ ) , c o l o r = r )

>>> t i t l e ( A s i mpl e f i g u r e , f o n t s i z e =20)

>>> s a v e f i g ( / tmp / t e s t . eps )

# M u l t i p l e p l o t s i n one f i g u r e

>>> t = arange ( 0 . 0 , 5 . 2 , 0 . 2 )# r ed dashes , b l ue sq ua re s and gr een t r i a n g l e s

>>> p l o t ( t , t , r , t , t 2 , bs , t , t 3 , g^ )



Basic plotting . . .

# Set p r op e r t i e s o f o bj e c ts :

>>> p l o t ( x , s i n ( x ) , l i n e w i d t h = 2. 0 , c o l o r = r )

>>> l , = p l o t ( x , s i n ( x ) )

>>> s et p ( l , l i n e w i d t h = 2. 0 , c o l o r = r )

>>> l . s e t _ l i n e w i d t h ( 2 . 0 ) ; l . s e t _ c o l o r ( r )

>>> draw ( ) # Redraws c u r r e n t f i g u r e .

>>> set p ( l ) # P r i n t s a v ai l a b l e p r o p e r t i e s

>>> c l o s e ( ) # Closes t he f i g u r e .

# M u l t i p l e f i g u r e s :

>>> f i g u r e ( 1 ) ; p l o t ( x , s i n ( x ) )

>>> f i g u r e ( 2 ) ; p l o t ( x , tanh ( x ) )>>> f i g u r e ( 1 ) ; t i t l e ( Easy as 1 , 2 , 3 )



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Basic plotting . . .

>>> f i g u r e ( 1 )

>>> s u b p l o t ( 2 1 1 ) # Same as s u b p l o t ( 2 , 1 , 1 )

>>> p l o t ( x , cos ( 5x )exp(x ) )

>>> s u b p lo t ( 2 , 1 , 2 )

>>> p l o t ( x , cos ( 5x ) , r , l a b e l = cosi ne )

>>> p l o t ( x , s i n ( 5x ) , g , l a b e l = si ne )

>>> legend ( ) # Or l eg en d ( [ c o si n e , s i n e ] )

>>> t e x t (1 , 0 , (1 , 0) )

>>> axes = gca ( ) # C ur re nt a x i s >>> f i g = g cf ( ) # C u r r e n t f i g u r e



X-Y plot

Example code

t 1 = a ra nge ( 0 . 0 , 5 . 0 , 0 . 1 )t 2 = a ra ng e ( 0 . 0 , 5 . 0 , 0 . 0 2 )t 3 = a ra ng e ( 0 . 0 , 2 . 0 , 0 . 0 1 )su b p lo t (2 1 1 )p l o t ( t 1 , c os ( 2 p it 1 )exp( t1 ) , bo ,

t 2 , c os ( 2 p it 2 )exp( t2 ) , k )g r i d ( T r u e )t i t l e ( A t a l e o f 2 s ub pl ot s )y l a b e l ( Damped )su b p lo t (2 1 2 )p l o t ( t 3 , c os ( 2 p it3 ) , r )g r i d ( T r u e )x l a b e l ( t i m e ( s ) )y l a b e l ( Undamped )



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Errorbar

Example code

t = a r ang e ( 0 . 1 , 4 , 0 . 1 )s = e x p ( t )e = 0 . 1a bs ( r a nd n ( l e n ( s ) ) )f = 0 . 1a bs ( r a nd n ( l e n ( s ) ) )g = 2eh = 2 fe r r o r b ar ( t , s , [ e , g ] , f , f m t= o )x l a b e l ( Dista n ce (m) )y l a b e l ( He ig h t (m) )t i t l e ( Mean an d s t a n d a rd e r r o r \

as a f u n c ti o n o f d is t an c e )



Semi-log and log-log plots

Example code

d t = 0 . 0 1

t = a ra ng e ( d t , 2 0 . 0 , d t )su b p lo t (3 1 1 )semilogy ( t , exp( t / 5 . 0 ) )y l a b e l ( se milo g y )g r i d ( T r u e )su b p lo t (3 1 2 )s e m i lo g x ( t , s i n ( 2 p i t ) )y l a b e l ( se milo g x )g r i d ( T r u e )# m i no r g r i d on t o o

g ca ( ) . xa xis . g r i d (Tru e , wh ich = min o r )su b p lo t (3 1 3 )l o g l o g ( t , 20exp( t / 1 0 . 0 ) , b as ex = 4 )g r i d ( T r u e )y l a b e l ( l o g l o g b as e 4 on x )



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Histogram

Example code

mu , s ig ma = 1 0 0 , 15x = mu + sigmarandn(10000)# t h e h is t og ra m o f t he d at a

n , b i ns , p at c he s = h i s t ( x , 1 00 , normed = 1)# add a b es t f i t l i n e

y = n or mp df ( b i n s , mu , s ig ma )l = p l o t ( b i ns , y , r , l i n e w i d t h = 2)xl im (4 0 , 1 6 0 )x l a b e l ( Smarts )y l a b e l ( P )t i t l e ( r $ \rm{ IQ : } \ / \mu = 1 0 0 ,\ / \ s igma =1 5$ )



Bar charts

Example code

N = 5menMeans = ( 2 0 , 3 5 , 3 0 , 3 5 , 2 7 )menStd = ( 2 , 3 , 4 , 1 , 2 )

# t h e x l o c a ti o n s f o r t he gr ou ps i n d = a ra ng e ( N )# t h e w id th o f t he b ar s

w i d t h = 0 . 35p1 = bar ( ind , menMeans, width ,

c o l o r = r , ye r r= me nS td )womenMeans = (2 5 , 3 2 , 3 4 , 2 0 , 2 5 )womenStd = ( 3 , 5 , 2 , 3 , 3 )p2 = bar ( ind +width , womenMeans, width ,

c o l o r = y , ye rr =womenStd)y l a b e l ( Scores )t i t l e ( S core s b y g ro up a nd g e nd e r )x t i c k s ( i n d + w i dt h ,

( G1 , G2 , G3 , G4 , G5 ) )x l i m (width , le n ( ind ) )y t i c k s ( a r an ge ( 0 , 4 1 , 1 0 ) )l eg en d ( ( p1 [ 0 ] , p2 [ 0 ] ) ,

( Men , Women ) , shadow=True )



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Pie charts

Example code

# m ake a s q ua r e f i g u r e an d a x es

f i g u r e ( 1 , f i g s i z e =( 8 , 8 ) )ax = ax es ( [ 0 . 1 , 0 . 1 , 0 . 8 , 0 . 8 ] )l a b e l s = Fro gs , Hogs , Dogs , Logs f r a cs = [ 1 5 , 3 0 , 45 , 1 0]e xp lo de = ( 0 , 0 . 05 , 0 , 0 )p i e ( f r a c s , e x pl o de = ex p lo d e , l a b e l s = l a b e l s ,

a u t o p c t = %1.1 f%% , shadow=True)t i t l e ( Raining Hogs and Dogs ,

bbox={ f a c e c o l o r : 0.8 , pad : 5 } )



Scatter plots

Example code

N = 3 0x = 0 . 9 ra n d (N)y = 0 . 9 ra n d (N)# 0 t o 10 p o in t r ad iu se s

a re a = p i(1 0 rand (N))2volume = 4 00 + ra n d (N)450sc a t te r (x , y , s= are a , ma rke r= o , c=volume ,

a lp h a = 0 .7 5 )x l a b e l ( r $ \ D e l t a _ i $ , s i ze = xl a r g e )y l a b e l ( r $ \ Delta_ { i +1}$ , s i ze = xl a r g e )t i t l e ( r Volume and perc ent change )g r i d ( T r u e )c o l o r b a r ( )s a v e f i g ( s c a t t e r )



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Polar

Example code

f i g u r e ( f i g s i z e = ( 8 , 8 ) )a x = a xe s ( [ 0 . 1 , 0 . 1 , 0 . 8 , 0 . 8 ] , p o l a r =T ru e ,

a xisb g = #d5de9c )r = a ra ng e ( 0 , 1 , 0 . 0 0 1 )t h e t a = 22p irp o l a r ( t h e t a , r , c o l o r = #ee8d18 , l w =3 )# t he r ad iu s o f t he g r id l a be l s

s e t p ( a x . t h e t a g r i d l a b e l s , y = 1 . 07 5 )t i t l e ( r " $ \ t h e t a = 4 \ p i r " , f o n t s i z e = 20 )



Contours

Example code

x = a r a n g e (3 .0 , 3 . 0 , 0 . 0 25 )y = a r a n g e (2 .0 , 2 . 0 , 0 . 0 25 )X , Y = m es hg ri d ( x , y )

Z1 = b i v a ri a t e _n o r ma l ( X , Y , 1 . 0 , 1 . 0 , 0 . 0 , 0 . 0 )Z2 = b i v a ri a t e _n o r ma l ( X , Y , 1 . 5 , 0 . 5 , 1 , 1 )# d i f f e r e n c e o f G au ss ia ns

Z = 1 0 . 0 ( Z2 Z1 )im = imshow ( Z , i n t e r p o l a t i o n = b i l i n e a r ,

o r i g i n = lower ,cmap=cm. gray , ext ent =(3 , 3 ,2,2))

l e v e l s = a r an ge (1 .2 , 1 . 6 , 0 . 2 )# l a b e l e ve ry s ec ond l e v e l

c l a be l ( CS, l e v e l s [ 1 : : 2 ] , i n l i n e =1 ,f m t = %1.1f , f o n t s i z e = 14 )

CS = c o n to u r ( Z , l e v e l s ,o r i g i n = lower ,l i n e w i d t h s =2 ,e x t e n t = (3 , 3 ,2,2))

# make a c o l o rb a r f o r t h e c o nt o ur l i n e s CB = c o l o r b a r ( CS , s h r i n k = 0 . 8 , e x t en d = b o t h )t i t l e ( L i ne s w i t h c o l o r b a r )h ot ( ) ; f l a g ( )



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Velocity vectors

Example code

X , Y = m e sh g ri d ( a ra ng e ( 0 , 2 p i , . 2 ) ,arange(0,2 p i , . 2 ) )

U = c o s ( X )V = s i n ( Y )Q = q ui ve r ( X[ : : 3 , : : 3 ] , Y [ : : 3 , : : 3 ] ,

U [ : : 3 , : : 3 ] , V [ : : 3 , : : 3 ] ,c o l o r = r , u n it s = x ,l i n e w i d t h s = ( 2 , ) ,e d g e co lo rs= ( k ) ,h e a d a x i s l e n g t h =5 )

qk = q u iv e rk e y ( Q, 0 . 5 , 0 . 03 , 1 , 1 m / s ,f o n t p r o p e r t i e s ={ weight : bold } )

a x i s ( [

1 , 7 ,

1, 7 ] )t i t l e ( t r i a n g u l a r head ; s c al e \

w i t h x v i ew ; b l a c k e dg es )



Maps

For details see http://matplotlib.sourceforge.net/screenshots/plotmap.py

http://matplotlib.sourceforge.net/screenshots/plotmap.pyhttp://matplotlib.sourceforge.net/screenshots/plotmap.py


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Using SciPy

SciPy is Open Source software for mathematics, science, and

engineering

import scipy

Built on NumPy

Provides modules for statistics, optimization, integration, linear

algebra, Fourier transforms, signal and image processing,

genetic algorithms, ODE solvers, special functions, and more

Used widely by scientists world overDetails are beyond the scope of this tutorial


Introduction Python Tutorial Numerics & Plotting Standard libraryQuick Tour

Standard library

Very powerful

Batteries included

Example standard modules taken from the tutorial

Operating system interface: osSystem, Command line arguments: sys

Regular expressions: re

Math: math, random

Internet access: urllib2, smtplib

Data compression: zlib, gzip, bz2, zipfile, and

tarfile

Unit testing: doctest and unittest

And a whole lot more!Check out the Python Library reference:

http://docs.python.org/lib/lib.html

http://docs.python.org/lib/lib.htmlhttp://docs.python.org/lib/lib.html


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Quick Tour

Stdlib: examples

>>> import os

>>> os . system ( d at e )F r i Jun 10 2 2 :1 3 :0 9 IST 2005

0

>>> os . getcwd ( )

/ home / pra bhu

>>> os . c h d i r ( / tmp )

>>> import os

>>> d i r ( os )

< r e t u r ns a l i s t o f a l l module f u n ct i o ns >>>> help ( os )

< ex t en siv e manual page from module s d o c st r i ng s >



Stdlib: examples

>>> import sys

>>> # P r i n t t he l i s t o f command l i n e a rg s t o Python

. . . p r i n t sys . ar gv[ ]

>>> import r e # R eg ul ar e x p r es s i o ns

>>> r e . f i n d a l l ( r \ bf [ az ] ,

. . . which f o o t o r hand f e l l f a s t e s t )

[ f o o t , f e l l , f a s t e s t ]

>>> re . sub ( r ( \ b [ az ] + ) \ 1 , r \1 ,

. . . c at i n t h e t h e h at )

c at i n t h e h at



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Quick Tour

Stdlib: examples

>>> import math>>> math . cos ( math . p i / 4 . 0 )

0.70710678118654757

>>> math . l o g ( 1 0 2 4 , 2 )

10.0

>>> import random

>>> random . cho ice ( [ a p pl e , p ea r , banana ] )

p ea r



Stdlib: examples

>>> import u r l l i b 2>>> f = u r l l i b 2 . u rl op en ( h t t p : / / www. pyt hon . or g / )

>>> p r i n t f . read (100 )


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Quick Tour

Stdlib: examples

>>> import z l i b

> > > s = w i t c h wh ich has wh ich w i tc h e s w r i s t watch >>> l e n ( s )

41

>>> t = z l i b . compress ( s )

>>> le n ( t )

37

>>> z l i b . decompress ( t )

w i t c h wh ich has wh ich w i tc h e s w r i s t watch

>>> z l i b . crc32 ( t )

1438085031



Summary

Introduced Python

Basic syntax

Basic types and data structures

Control flow

Functions

Modules

Exceptions

Cl

Python Another Handout

Documents

Transcript of Python Another Handout