The document discusses object oriented programming concepts in Python including classes, objects, instances, methods, inheritance, and class attributes. It provides examples of defining classes, instantiating objects, using methods, and the difference between class and instance attributes. Key concepts covered include defining classes with the class keyword, creating object instances, using the __init__() method for initialization, and allowing derived classes to inherit from base classes.
import in python is similar to #include header_file in C/C++. Python modules can get access to code from another module by importing the file/function using import. The import statement is the most common way of invoking the import machinery, but it is not the only way. import module_name .When the import is used, it searches for the module initially in the local scope by calling __import__() function. The value returned by the function is then reflected in the output of the initial code.
در این جلسه به بررسی بحث برنامه نویسی شی گرا و کلاس ها در پایتون پرداختیم
PySec101 Fall 2013 J7E1 By Mohammad Reza Kamalifard
Talk About:
Object oriented programming and Classes in Python
import in python is similar to #include header_file in C/C++. Python modules can get access to code from another module by importing the file/function using import. The import statement is the most common way of invoking the import machinery, but it is not the only way. import module_name .When the import is used, it searches for the module initially in the local scope by calling __import__() function. The value returned by the function is then reflected in the output of the initial code.
در این جلسه به بررسی بحث برنامه نویسی شی گرا و کلاس ها در پایتون پرداختیم
PySec101 Fall 2013 J7E1 By Mohammad Reza Kamalifard
Talk About:
Object oriented programming and Classes in Python
Abstract: This PDSG workshop covers the basics of OOP programming in Python. Concepts covered are class, object, scope, method overloading and inheritance.
Level: Fundamental
Requirements: One should have some knowledge of programming.
Python Advanced – Building on the foundationKevlin Henney
This is a two-day course in Python programming aimed at professional programmers. The course material provided here is intended to be used by teachers of the language, but individual learners might find some of this useful as well.
The course assume the students already know Python, to the level taught in the Python Foundation course: http://www.slideshare.net/Kevlin/python-foundation-a-programmers-introduction-to-python-concepts-style)
The course is released under Creative Commons Attribution 4.0. Its primary location (along with the original PowerPoint) is at https://github.com/JonJagger/two-day-courses/tree/master/pa
An object oriented concept in python is detailed for the students or anyone who aspire to learn more powerful concept that helps in developing software or any web development to the persons who work in a tech filed
Abstract: This PDSG workshop covers the basics of OOP programming in Python. Concepts covered are class, object, scope, method overloading and inheritance.
Level: Fundamental
Requirements: One should have some knowledge of programming.
Python Advanced – Building on the foundationKevlin Henney
This is a two-day course in Python programming aimed at professional programmers. The course material provided here is intended to be used by teachers of the language, but individual learners might find some of this useful as well.
The course assume the students already know Python, to the level taught in the Python Foundation course: http://www.slideshare.net/Kevlin/python-foundation-a-programmers-introduction-to-python-concepts-style)
The course is released under Creative Commons Attribution 4.0. Its primary location (along with the original PowerPoint) is at https://github.com/JonJagger/two-day-courses/tree/master/pa
An object oriented concept in python is detailed for the students or anyone who aspire to learn more powerful concept that helps in developing software or any web development to the persons who work in a tech filed
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
Chap 3 Python Object Oriented Programming - Copy.ppt
1. Object Oriented Programming language
Python Object Oriented Programming Concept of class, object and instances, method call
Constructor, class attributes and destructors
Real time use of class in live projects
Inheritance, super class and overloading operators,
Static and class methods
Adding and retrieving dynamic attributes of classes
Programming using OOPS Deligation and container
Extra Readings: Integrating GUI framework with OOP
By Dr. Sharada Patil
2. • .
Concept of class, object and instances, method call
By Dr. Sharada Patil
3. • .
Concept of class, object and instances, method call
By Dr. Sharada Patil
4. • Classes introduce a little bit of new syntax, three new object types, and some
new semantics.
• 9.3.1. Class Definition Syntax
• The simplest form of class definition looks like this:
• Class definitions, like function definitions must be executed before they have
any effect.
Concept of class, object and instances, method call
By Dr. Sharada Patil
class ClassName:
<statement-1>
.
.
.
<statement-N>
5. • Class objects support two kinds of operations: attribute references and
instantiation.
• Attribute references use the standard syntax used for all attribute references in
Python:
• Obj.name. Valid attribute names are all the names that were in the class’s
namespace when the class object was created. So, if the class definition looked
like this:
• MyClass.i and MyClass.f are valid attribute references,returning an integer and
a function object, respectively. Class attributes can also be assigned to, so you
can change the value of MyClass.i by assignment. _doc_ is also a valid
attribute, returning the docstring belonging to the class “A simple example
class”
Concept of class, object and instances, method call
By Dr. Sharada Patil
class MyClass:
"""A simple example class"""
i = 12345
def f(self):
return 'hello world'
6. • Class instantiation uses function notation. Just pretend that the class object is a
parameterless function that returns a new instance of the class. For example
(assuming the above class):.
• creates a new instance of the class and assigns this object to the local variable
x
• The instantiation operation (“calling” a class object) creates an empty object.
Many classes like to create objects with instances customized to a specific
initial state. Therefore a class may define a special method named _init_() like
this:
• When a class defines an _init_() method, class instantiation automatically
invokes it for the newly-created class instance. So in this example, a new,
initialized instance can be obtained by:
Concept of class, object and instances, method call
By Dr. Sharada Patil
x = MyClass()
def __init__(self):
self.data = []
x = MyClass()
7. • Of course, the _init_. method may have arguments for greater flexibility. In that
case, arguments given to the class instantiation operator are passed on to
_init_() for example
Concept of class, object and instances, method call
By Dr. Sharada Patil
>>> class Complex:
... def __init__(self, realpart, imagpart):
... self.r = realpart
... self.i = imagpart
...
>>> x = Complex(3.0, -4.5)
>>> x.r, x.i
(3.0, -4.5)
8. • Instance Objects
– Now what can we do with instance objects? The only operations understood by instance
objects are attribute references. There are two kinds of valid attribute names: data attributes
and methods.
– data attributes correspond to “instance variables” in Smalltalk, and to “data members” in C++.
Data attributes need not be declared; like local variables, they spring into existence when they
are first assigned to. For example, if x is the instance of MyClass created above, the following
piece of code will print the value 16, without leaving a trace:
• .
Concept of class, object and instances, method call
By Dr. Sharada Patil
x.counter = 1
while x.counter < 10:
x.counter = x.counter * 2
print(x.counter)
del x.counter
9. • Method Objects
•
Concept of class, object and instances, method call
By Dr. Sharada Patil
Usually, a method is called right after it is bound:
x.f()
In the MyClass example, this will return the string 'hello world'. However,
it is not necessary to call a method right away: x.f is a method object, and can
be stored away and called at a later time. For example:
xf = x.f
while True:
printf(xf())
will continue to print hello world until the end of time.
What exactly happens when a method is called?
You may have noticed that x.f() was called without an argument above,
even though the function definition for f() specified an argument.
What happened to the argument? Surely Python raises an exception
when a function that requires an argument is called without any — even
if the argument isn’t actually used…
10. • Class and Instance Variables
Concept of class, object and instances, method call
By Dr. Sharada Patil
Generally speaking, instance variables are for data unique to each instance and class
variables are for attributes and methods shared by all instances of the class:
class Dog:
kind = 'canine' # class variable shared by all instances
def __init__(self, name):
self.name = name
# instance variable unique to each instance
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.kind # shared by all dogs
'canine'
>>> e.kind # shared by all dogs
'canine' >>> d.name # unique to d
'Fido'
>>> e.name # unique to e
'Buddy'
As discussed in A Word About Names and Objects, shared data can have possibly
surprising effects with involving mutable objects such as lists and dictionaries.
11. • .
By Dr. Sharada Patil
class Dog:
tricks = [] # mistaken use of a class variable
def __init__(self, name):
self.name = name
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks # unexpectedly shared by all dogs
['roll over', 'play dead']
Correct design of the class should use an instance variable instead:
class Dog:
def __init__(self, name):
self.name = name
self.tricks = [] # creates a new empty list for each dog
def add_trick(self, trick):
self.tricks.append(trick)
>>> d = Dog('Fido')
>>> e = Dog('Buddy')
>>> d.add_trick('roll over')
>>> e.add_trick('play dead')
>>> d.tricks ['roll over']
>>> e.tricks
12. • .
Random Remarks
By Dr. Sharada Patil
If the same attribute name occurs in both an instance and in a class, then attribute lookup
prioritizes the instance:
>>>
>>> class Warehouse:
purpose = 'storage'
region = 'west'
>>> w1 = Warehouse()
>>> print(w1.purpose, w1.region)
storage west
>>> w2 = Warehouse()
>>> w2.region = 'east'
>>> print(w2.purpose, w2.region)
storage east
Data attributes may be referenced by methods as well as by ordinary users (“clients”) of an object.
In other words, classes are not usable to implement pure abstract data types. In fact, nothing in
Python makes it possible to enforce data hiding — it is all based upon convention. (On the other
hand, the Python implementation, written in C, can completely hide implementation details and
control access to an object if necessary; this can be used by extensions to Python written in C.)
Clients should use data attributes with care — clients may mess up invariants maintained by the
methods by stamping on their data attributes. Note that clients may add data attributes of their
own to an instance object without affecting the validity of the methods, as long as name conflicts
are avoided — again, a naming convention can save a lot of headaches here.
13. • .
By Dr. Sharada Patil
There is no shorthand for referencing data attributes (or other methods!) from within
methods. I find that this actually increases the readability of methods: there is no chance
of confusing local variables and instance variables when glancing through a method.
Often, the first argument of a method is called self. This is nothing more than a
convention: the name self has absolutely no special meaning to Python. Note, however,
that by not following the convention your code may be less readable to other Python
programmers, and it is also conceivable that a class browser program might be written
that relies upon such a convention.
Any function object that is a class attribute defines a method for instances of that class.
It is not necessary that the function definition is textually enclosed in the class definition:
assigning a function object to a local variable in the class is also ok. For example:
# Function defined outside the class
def f1(self, x, y):
return min(x, x+y)
class C:
f = f1
def g(self):
return 'hello world'
h = g
Now f, g and h are all attributes of class C that refer to function objects, and
consequently they are all methods of instances of C — h being exactly
equivalent to g. Note that this practice usually only serves to confuse the reader of a
program.
14. • .
By Dr. Sharada Patil
Methods may call other methods by using method attributes of the self argument:
class Bag:
def __init__(self):
self.data = []
def add(self, x):
self.data.append(x)
def addtwice(self, x):
self.add(x)
self.add(x)
Methods may reference global names in the same way as ordinary functions. The global
scope associated with a method is the module containing its definition. (A class is never
used as a global scope.) While one rarely encounters a good reason for using global
data in a method, there are many legitimate uses of the global scope: for one thing,
functions and modules imported into the global scope can be used by methods, as
well as functions and classes defined in it. Usually, the class containing the method is
itself defined in this global scope, and in the next section we’ll find some good reasons
why a method would want to reference its own class.
Each value is an object, and therefore has a class (also called its type). It is stored as
object.__class__.
15. • .
Inheritance
By Dr. Sharada Patil
Of course, a language feature would not be worthy of the name “class” without supporting
inheritance. The syntax for a derived class definition looks like this:
class DerivedClassName(BaseClassName):
<statement-1>
. . .
<statement-N>
The name BaseClassName must be defined in a scope containing the derived class
definition. In place of a base class name, other arbitrary expressions are also allowed.
This can be useful, for example, when the base class is defined in another module:
class DerivedClassName(modname.BaseClassName):
Execution of a derived class definition proceeds the same as for a base class. When the
class object is constructed, the base class is remembered. This is used for resolving
attribute references: if a requested attribute is not found in the class, the search proceeds
to look in the base class. This rule is applied recursively if the base class itself is derived
from some other class.
16. • .
By Dr. Sharada Patil
There’s nothing special about instantiation of derived classes: DerivedClassName()
creates a new instance of the class. Method references are resolved as follows: the
corresponding class attribute is searched, descending down the chain of base classes if
necessary, and the method reference is valid if this yields a function object.
Derived classes may override methods of their base classes. Because methods have no
special privileges when calling other methods of the same object, a method of a base clas
that calls another method defined in the same base class may end up calling a method of
derived class that overrides it. (For C++ programmers: all methods in Python are
effectively virtual.)
An overriding method in a derived class may in fact want to extend rather than simply
replace the base class method of the same name. There is a simple way to call the base
class method directly: just call BaseClassName.methodname(self, arguments).
This is occasionally useful to clients as well. (Note that this only works if the base class is
accessible as BaseClassName in the global scope.)
Python has two built-in functions that work with inheritance:
•Use isinstance() to check an instance’s type: isinstance(obj, int) will be
True only if obj.__class__ is int or some class derived from int.
•Use issubclass() to check class inheritance: issubclass(bool, int) is True
since bool is a subclass of int. However, issubclass(float, int) is False since
float is not a subclass of int.
17. • .
Multiple Inheritance
By Dr. Sharada Patil
Python supports a form of multiple inheritance as well. A class definition with multiple base classes
looks like this:
class DerivedClassName(Base1, Base2, Base3):
<statement-1>
. . .
<statement-N>
For most purposes, in the simplest cases, you can think of the search for attributes inherited from a
parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap
in the hierarchy. Thus, if an attribute is not found in DerivedClassName, it is searched for in Base1,
then (recursively) in the base classes of Base1, and if it was not found there, it was searched for in
Base2, and so on.
In fact, it is slightly more complex than that; the method resolution order changes dynamically to
support cooperative calls to super(). This approach is known in some other multiple-inheritance
languages as call-next-method and is more powerful than the super call found in single-inheritance
languages.
Dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond
relationships (where at least one of the parent classes can be accessed through multiple paths from
the bottommost class). For example, all classes inherit from object, so any case of multiple
inheritance provides more than one path to reach object. To keep the base classes from being
accessed more than once, the dynamic algorithm linearizes the search order in a way that preserves
the left-to-right ordering specified in each class, that calls each parent only once, and that is
monotonic
18. • .
Private Variables
By Dr. Sharada Patil
“Private” instance variables that cannot be accessed except from inside an object don’t
exist in Python. However, there is a convention that is followed by most Python code: a
name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of
the API (whether it is a function, a method or a data member). It should be considered an
implementation detail and subject to change without notice.
Since there is a valid use-case for class-private members (namely to avoid name clashes
of names with names defined by subclasses), there is limited support for such a
mechanism, called name mangling. Any identifier of the form __spam (at least two
leading underscores, at most one trailing underscore) is textually replaced with
_classname__spam, where classname is the current class name with leading
underscore(s) stripped. This mangling is done without regard to the syntactic position of
the identifier, as long as it occurs within the definition of a class.
19. • .
By Dr. Sharada Patil
Name mangling is helpful for letting subclasses override methods without breaking
intraclass method calls. For example:
class Mapping:
def __init__(self, iterable):
self.items_list = []
self.__update(iterable)
def update(self, iterable):
for item in iterable:
self.items_list.append(item)
__update = update # private copy of original update() method
class MappingSubclass(Mapping):
def update(self, keys, values):
# provides new signature for update()
# but does not break __init__()
for item in (keys, values):
self.items_list.append(item)
20. • .
By Dr. Sharada Patil
The above example would work even if MappingSubclass were to introduce a
__update identifier since it is replaced with _Mapping__update in the Mapping class
and _MappingSubclass__update in the MappingSubclass class respectively.
Note that the mangling rules are designed mostly to avoid accidents; it still is possible to
access or modify a variable that is considered private. This can even be useful in special
circumstances, such as in the debugger.
Notice that code passed to exec() or eval() does not consider the classname of the
invoking class to be the current class; this is similar to the effect of the global statement
the effect of which is likewise restricted to code that is byte-compiled together.
The same restriction applies to getattr(), setattr() and delattr(), as well as
when referencing __dict__ directly.
21. • .
Odds and Ends
By Dr. Sharada Patil
Sometimes it is useful to have a data type similar to the Pascal “record” or C “struct”,
bundling together a few named data items. An empty class definition will do nicely:
class Employee:
pass
john = Employee() # Create an empty employee record
# Fill the fields of the record
john.name = 'John Doe'
john.dept = 'computer lab'
john.salary = 1000
A piece of Python code that expects a particular abstract data type can often be passed
a class that emulates the methods of that data type instead. For instance, if you have a
function that formats some data from a file object, you can define a class with methods
read() and readline() that get the data from a string buffer instead, and pass it as
an argument.
Instance method objects have attributes, too: m.__self__ is the instance object with
the method m(), and m.__func__ is the function object corresponding to the method.
23. • .
Iterators
By Dr. Sharada Patil
By now you have probably noticed that most container objects can be looped over using
a for statement:
for element in [1, 2, 3]:
print(element)
for element in (1, 2, 3):
print(element)
for key in {'one':1, 'two':2}:
print(key)
for char in "123":
print(char)
for line in open("myfile.txt"):
print(line, end='')
This style of access is clear, concise, and convenient. The use of iterators pervades and
unifies Python. Behind the scenes, the for statement calls iter() on the container
object. The function returns an iterator object that defines the method __next__()
which accesses elements in the container one at a time.
24. • .
By Dr. Sharada Patil
When there are no more elements, __next__() raises a StopIteration exception which tells
the for loop to terminate. You can call the __next__() method using the next() built-in function;
this example shows how it all works:
>>>
>>> s = 'abc‘
>>> it = iter(s)
>>> it
<str_iterator object at 0x10c90e650>
>>> next(it)
'a'
>>>next(it)
'b'
>>>next(it)
'c'
>>>next(it)
Traceback (most recent call last):
File, line 1, in <module>
next(it) StopIteration
Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your
classes. Define an __iter__() method which returns an object with a __next__() method.
If the class defines __next__(), then __iter__() can just return self:
25. • .
By Dr. Sharada Patil
class Reverse:
"""Iterator for looping over a sequence backwards."""
def __init__(self, data):
self.data = data
self.index = len(data)
def __iter__(self):
return self
def __next__(self):
if self.index == 0:
raise StopIteration
self.index = self.index - 1
return self.data[self.index]
>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
... print(char)
...
m
a
p
s
26. • .
Generators
By Dr. Sharada Patil
Generators are a simple and powerful tool for creating iterators. They are written like
regular functions but use the yield statement whenever they want to return data.
Each time next() is called on it, the generator resumes where it left off (it remembers
all the data values and which statement was last executed). An example shows that
generators can be trivially easy to create:
def reverse(data):
for index in range(len(data)-1, -1, -1): yield data[index]
>>>
>>> for char in reverse('golf'):
... print(char)
...
f
L
o
g
27. • .
Generator
By Dr. Sharada Patil
Anything that can be done with generators can also be done with class-based iterators
as described in the previous section. What makes generators so compact is that the
__iter__() and __next__() methods are created automatically.
Another key feature is that the local variables and execution state are automatically
saved between calls. This made the function easier to write and much more clear than
an approach using instance variables like self.index and self.data.
In addition to automatic method creation and saving program state, when generators
terminate, they automatically raise StopIteration. In combination, these features
make it easy to create iterators with no more effort than writing a regular function.
28. • Some simple generators can be coded succinctly as expressions using a syntax
similar to list comprehensions but with parentheses instead of square brackets.
These expressions are designed for situations where the generator is used right
away by an enclosing function. Generator expressions are more compact but
less versatile than full generator definitions and tend to be more memory
friendly than equivalent list comprehensions.
Generator Expressions
By Dr. Sharada Patil
29. • .
Examples:
By Dr. Sharada Patil
>>> sum(i*i for i in range(10)) # sum of squares
285
>>> xvec = [10, 20, 30]
>>> yvec = [7, 5, 3]
>>> sum(x*y for x,y in zip(xvec, yvec)) # dot product
260
>>> unique_words = set(word for line in page for word in line.split())
>>> valedictorian = max((student.gpa, student.name) for student in graduates)
>>> data = 'golf'
>>> list(data[i] for i in range(len(data)-1, -1, -1))
['f', 'l', 'o', 'g']