This document discusses object-oriented programming concepts in Python including:
- Classes define templates for objects with attributes and methods. Objects are instances of classes.
- The __init__ method initializes attributes when an object is constructed.
- Classes can make attributes private using double underscores. Encapsulation hides implementation details.
- Objects can be mutable, allowing state changes, or immutable like strings which cannot change.
- Inheritance allows subclasses to extend and modify parent class behavior through polymorphism.
Python programming computer science and engineeringIRAH34
Python supports object-oriented programming (OOP) through classes and objects. A class defines the attributes and behaviors of an object, while an object is an instance of a class. Inheritance allows classes to inherit attributes and methods from parent classes. Polymorphism enables classes to define common methods that can behave differently depending on the type of object. Operator overloading allows classes to define how operators like + work on class objects.
This document discusses classes and objects in Python. It defines what a class and object are, how to create classes and objects in Python, and provides an example Employee class with two employee objects. A class defines the blueprint for an object, containing attributes and methods. An object is an instance of a class. To create a class in Python, the class keyword is used followed by the class name and contents in a suite. Objects are created by calling the class and passing arguments to its __init__ method. Methods can then be accessed using dot notation on the object.
Class, object and inheritance in pythonSantosh Verma
The document discusses object-oriented programming concepts in Python, including classes, objects, methods, inheritance, and the built-in __init__ method. Classes are created using the class keyword and contain attributes and methods. Methods must have a self parameter, which refers to the instance of the class. The __init__ method is similar to a constructor and is called when an object is instantiated. Inheritance allows one class to inherit attributes and methods from another class.
Anton Kasyanov, Introduction to Python, Lecture5Anton Kasyanov
Classes allow users to create custom types in Python. A class defines the form and behavior of a custom type using methods. Objects or instances are created from a class and can have their own state and values. Common class methods include __init__() for initializing objects and setting initial values, and other methods like set_age() for modifying object attributes. Classes can inherit behaviors from superclasses and override existing methods.
This document discusses Python modules, classes, inheritance, and properties. Some key points:
- Modules allow the organization of Python code into reusable libraries by saving code in files with a .py extension. Modules can contain functions, variables, and be imported into other code.
- Classes are templates that define the properties and methods common to all objects of a certain kind. The __init__() method initializes new objects. Inheritance allows child classes to inherit properties and methods from parent classes.
- Properties provide a way to control access to class attributes, allowing them to be accessed like attributes while hiding the implementation details behind getter and setter methods.
Python classes allow for the creation of object-oriented programming through defining blueprints for objects with shared attributes and behaviors. Classes are created using the class keyword and contain attributes like variables and methods like functions. Objects are instantiated from classes and can access both class level and instance level attributes. Key concepts covered include inheritance, encapsulation, polymorphism, and special methods.
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
Python classes allow for the creation of object-oriented programming in Python. Classes define blueprints for objects with shared attributes and behaviors. Key aspects of classes include defining attributes and methods, constructing objects from classes, inheritance that allows subclasses to extend parent classes, and special methods that enable built-in behaviors. Classes are a fundamental part of Python that enable code reuse and organization.
This document provides an overview of object-oriented programming concepts in Python including classes, objects, inheritance, polymorphism and data hiding. It defines key OOP terms like class, object, method, and inheritance. It also demonstrates how to define classes with attributes and methods, create object instances, and extend functionality via inheritance. The document shows how operators and methods can be overloaded in classes.
The document discusses a lecture on object-oriented programming. It covers key topics like classes, objects, fields, methods, constructors, and creating objects from classes. It provides examples of how to define classes with fields, methods, and constructors. It also explains how to compile and run a simple Java program with a main method.
The document introduces Python modules and importing. It discusses three formats for importing modules: import somefile, from somefile import *, and from somefile import className. It describes commonly used Python modules like sys, os, and math. It also covers defining your own modules, directories for module files, object-oriented programming in Python including defining classes, creating and deleting instances, methods and self, accessing attributes and methods, attributes, inheritance, and redefining methods.
A class defines a data structure that can contain both data and functions as members. An object is an instance of a class that allocates memory for the class's data members. Classes allow the declaration of multiple objects that each have their own copies of the class's data members and can access the class's member functions. Constructors initialize an object's data members when it is created, while destructors perform cleanup tasks when an object is destroyed.
This document discusses classes in Python. It defines a class as a blueprint for objects, with variables and methods. An example Bike class is provided with name and gear variables. An Employee class is also shown with an id and name variable and a display method using self. The document then discusses classes as abstract data types (ADTs), where the behavior is defined but not the implementation. It provides syntax to create an abstract class in Python. Finally, it introduces data classes in Python 3.7 for storing structured data, with attributes declared using type hints and equality checking using ==.
Here is a Python class with the specifications provided in the question:
class PICTURE:
def __init__(self, pno, category, location):
self.pno = pno
self.category = category
self.location = location
def FixLocation(self, new_location):
self.location = new_location
This defines a PICTURE class with three instance attributes - pno, category and location as specified in the question. It also defines a FixLocation method to assign a new location as required.
Reflection in Java allows programs to inspect and modify classes, fields, methods, and constructors at runtime without knowing the names or types of said classes, fields, methods, or constructors at compile time. The lecture discusses the core reflection classes like Class, Field, Method, Constructor, and AccessibleObject. It demonstrates how to use reflection to get information about a class like its fields and methods, as well as how to invoke methods, get/set field values, and create new instances of classes using reflection on an example ATM class. Arrays can also be manipulated using reflection through the Array class.
The document discusses object-oriented programming in Python. It defines key OOP concepts like classes, objects, and methods. It provides examples of defining classes and methods in Python. It also covers inheritance, polymorphism, and data abstraction in OOP. Database programming in Python is also discussed, including connecting to databases and performing CRUD operations using the Python DB API.
The document provides an overview of object-oriented programming concepts in Python including defining classes, inheritance, methods, and data structures. Some key points:
- Classes define user-created data types that bundle together data (attributes) and functions (methods) that work with that data. Objects are instances of classes.
- Methods are defined within classes and must have "self" as the first argument to access attributes. The __init__ method serves as a constructor.
- Inheritance allows subclasses to extend existing classes, redefining or calling parent methods.
- Python supports lists, tuples, dictionaries, sets and other data structures that can be used to store and organize data. Lists are mutable while tuples are immutable.
This document discusses Python classes and objects. It defines a class as a user-defined prototype that defines attributes and methods to characterize objects of that class. Classes contain data members like class and instance variables that are accessed via dot notation. The document also covers creating classes, class variables, inheritance between classes, and calling parent and child methods.
The document discusses classes and objects in Python programming. It covers key concepts like defining classes, creating objects, assigning attributes to objects, passing objects as arguments and returning objects from functions. It provides examples to illustrate these concepts like defining a Point class to represent coordinate points, creating Rectangle class with a Point object as one of its attributes. The document also discusses concepts like aliasing of objects and how to create a copy of an object instead of alias.
The document discusses classes and objects in C++. It defines what a class is - a blueprint for an object that contains data members and member functions. An object is an instance of a class that allocates memory. The document explains how to define a class with public and private members, create objects of a class, and access class members through objects using dot operators. It also covers constructors and how they initialize objects automatically upon creation.
In modern aerospace engineering, uncertainty is not an inconvenience — it is a defining feature. Lightweight structures, composite materials, and tight performance margins demand a deeper understanding of how variability in material properties, geometry, and boundary conditions affects dynamic response. This keynote presentation tackles the grand challenge: how can we model, quantify, and interpret uncertainty in structural dynamics while preserving physical insight?
This talk reflects over two decades of research at the intersection of structural mechanics, stochastic modelling, and computational dynamics. Rather than adopting black-box probabilistic methods that obscure interpretation, the approaches outlined here are rooted in engineering-first thinking — anchored in modal analysis, physical realism, and practical implementation within standard finite element frameworks.
The talk is structured around three major pillars:
1. Parametric Uncertainty via Random Eigenvalue Problems
* Analytical and asymptotic methods are introduced to compute statistics of natural frequencies and mode shapes.
* Key insight: eigenvalue sensitivity depends on spectral gaps — a critical factor for systems with clustered modes (e.g., turbine blades, panels).
2. Parametric Uncertainty in Dynamic Response using Modal Projection
* Spectral function-based representations are presented as a frequency-adaptive alternative to classical stochastic expansions.
* Efficient Galerkin projection techniques handle high-dimensional random fields while retaining mode-wise physical meaning.
3. Nonparametric Uncertainty using Random Matrix Theory
* When system parameters are unknown or unmeasurable, Wishart-distributed random matrices offer a principled way to encode uncertainty.
* A reduced-order implementation connects this theory to real-world systems — including experimental validations with vibrating plates and large-scale aerospace structures.
Across all topics, the focus is on reduced computational cost, physical interpretability, and direct applicability to aerospace problems.
The final section outlines current integration with FE tools (e.g., ANSYS, NASTRAN) and ongoing research into nonlinear extensions, digital twin frameworks, and uncertainty-informed design.
Whether you're a researcher, simulation engineer, or design analyst, this presentation offers a cohesive, physics-based roadmap to quantify what we don't know — and to do so responsibly.
Key words
Stochastic Dynamics, Structural Uncertainty, Aerospace Structures, Uncertainty Quantification, Random Matrix Theory, Modal Analysis, Spectral Methods, Engineering Mechanics, Finite Element Uncertainty, Wishart Distribution, Parametric Uncertainty, Nonparametric Modelling, Eigenvalue Problems, Reduced Order Modelling, ASME SSDM2025
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Python classes allow for the creation of object-oriented programming through defining blueprints for objects with shared attributes and behaviors. Classes are created using the class keyword and contain attributes like variables and methods like functions. Objects are instantiated from classes and can access both class level and instance level attributes. Key concepts covered include inheritance, encapsulation, polymorphism, and special methods.
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
Python classes allow for the creation of object-oriented programming in Python. Classes define blueprints for objects with shared attributes and behaviors. Key aspects of classes include defining attributes and methods, constructing objects from classes, inheritance that allows subclasses to extend parent classes, and special methods that enable built-in behaviors. Classes are a fundamental part of Python that enable code reuse and organization.
This document provides an overview of object-oriented programming concepts in Python including classes, objects, inheritance, polymorphism and data hiding. It defines key OOP terms like class, object, method, and inheritance. It also demonstrates how to define classes with attributes and methods, create object instances, and extend functionality via inheritance. The document shows how operators and methods can be overloaded in classes.
The document discusses a lecture on object-oriented programming. It covers key topics like classes, objects, fields, methods, constructors, and creating objects from classes. It provides examples of how to define classes with fields, methods, and constructors. It also explains how to compile and run a simple Java program with a main method.
The document introduces Python modules and importing. It discusses three formats for importing modules: import somefile, from somefile import *, and from somefile import className. It describes commonly used Python modules like sys, os, and math. It also covers defining your own modules, directories for module files, object-oriented programming in Python including defining classes, creating and deleting instances, methods and self, accessing attributes and methods, attributes, inheritance, and redefining methods.
A class defines a data structure that can contain both data and functions as members. An object is an instance of a class that allocates memory for the class's data members. Classes allow the declaration of multiple objects that each have their own copies of the class's data members and can access the class's member functions. Constructors initialize an object's data members when it is created, while destructors perform cleanup tasks when an object is destroyed.
This document discusses classes in Python. It defines a class as a blueprint for objects, with variables and methods. An example Bike class is provided with name and gear variables. An Employee class is also shown with an id and name variable and a display method using self. The document then discusses classes as abstract data types (ADTs), where the behavior is defined but not the implementation. It provides syntax to create an abstract class in Python. Finally, it introduces data classes in Python 3.7 for storing structured data, with attributes declared using type hints and equality checking using ==.
Here is a Python class with the specifications provided in the question:
class PICTURE:
def __init__(self, pno, category, location):
self.pno = pno
self.category = category
self.location = location
def FixLocation(self, new_location):
self.location = new_location
This defines a PICTURE class with three instance attributes - pno, category and location as specified in the question. It also defines a FixLocation method to assign a new location as required.
Reflection in Java allows programs to inspect and modify classes, fields, methods, and constructors at runtime without knowing the names or types of said classes, fields, methods, or constructors at compile time. The lecture discusses the core reflection classes like Class, Field, Method, Constructor, and AccessibleObject. It demonstrates how to use reflection to get information about a class like its fields and methods, as well as how to invoke methods, get/set field values, and create new instances of classes using reflection on an example ATM class. Arrays can also be manipulated using reflection through the Array class.
The document discusses object-oriented programming in Python. It defines key OOP concepts like classes, objects, and methods. It provides examples of defining classes and methods in Python. It also covers inheritance, polymorphism, and data abstraction in OOP. Database programming in Python is also discussed, including connecting to databases and performing CRUD operations using the Python DB API.
The document provides an overview of object-oriented programming concepts in Python including defining classes, inheritance, methods, and data structures. Some key points:
- Classes define user-created data types that bundle together data (attributes) and functions (methods) that work with that data. Objects are instances of classes.
- Methods are defined within classes and must have "self" as the first argument to access attributes. The __init__ method serves as a constructor.
- Inheritance allows subclasses to extend existing classes, redefining or calling parent methods.
- Python supports lists, tuples, dictionaries, sets and other data structures that can be used to store and organize data. Lists are mutable while tuples are immutable.
This document discusses Python classes and objects. It defines a class as a user-defined prototype that defines attributes and methods to characterize objects of that class. Classes contain data members like class and instance variables that are accessed via dot notation. The document also covers creating classes, class variables, inheritance between classes, and calling parent and child methods.
The document discusses classes and objects in Python programming. It covers key concepts like defining classes, creating objects, assigning attributes to objects, passing objects as arguments and returning objects from functions. It provides examples to illustrate these concepts like defining a Point class to represent coordinate points, creating Rectangle class with a Point object as one of its attributes. The document also discusses concepts like aliasing of objects and how to create a copy of an object instead of alias.
The document discusses classes and objects in C++. It defines what a class is - a blueprint for an object that contains data members and member functions. An object is an instance of a class that allocates memory. The document explains how to define a class with public and private members, create objects of a class, and access class members through objects using dot operators. It also covers constructors and how they initialize objects automatically upon creation.
In modern aerospace engineering, uncertainty is not an inconvenience — it is a defining feature. Lightweight structures, composite materials, and tight performance margins demand a deeper understanding of how variability in material properties, geometry, and boundary conditions affects dynamic response. This keynote presentation tackles the grand challenge: how can we model, quantify, and interpret uncertainty in structural dynamics while preserving physical insight?
This talk reflects over two decades of research at the intersection of structural mechanics, stochastic modelling, and computational dynamics. Rather than adopting black-box probabilistic methods that obscure interpretation, the approaches outlined here are rooted in engineering-first thinking — anchored in modal analysis, physical realism, and practical implementation within standard finite element frameworks.
The talk is structured around three major pillars:
1. Parametric Uncertainty via Random Eigenvalue Problems
* Analytical and asymptotic methods are introduced to compute statistics of natural frequencies and mode shapes.
* Key insight: eigenvalue sensitivity depends on spectral gaps — a critical factor for systems with clustered modes (e.g., turbine blades, panels).
2. Parametric Uncertainty in Dynamic Response using Modal Projection
* Spectral function-based representations are presented as a frequency-adaptive alternative to classical stochastic expansions.
* Efficient Galerkin projection techniques handle high-dimensional random fields while retaining mode-wise physical meaning.
3. Nonparametric Uncertainty using Random Matrix Theory
* When system parameters are unknown or unmeasurable, Wishart-distributed random matrices offer a principled way to encode uncertainty.
* A reduced-order implementation connects this theory to real-world systems — including experimental validations with vibrating plates and large-scale aerospace structures.
Across all topics, the focus is on reduced computational cost, physical interpretability, and direct applicability to aerospace problems.
The final section outlines current integration with FE tools (e.g., ANSYS, NASTRAN) and ongoing research into nonlinear extensions, digital twin frameworks, and uncertainty-informed design.
Whether you're a researcher, simulation engineer, or design analyst, this presentation offers a cohesive, physics-based roadmap to quantify what we don't know — and to do so responsibly.
Key words
Stochastic Dynamics, Structural Uncertainty, Aerospace Structures, Uncertainty Quantification, Random Matrix Theory, Modal Analysis, Spectral Methods, Engineering Mechanics, Finite Element Uncertainty, Wishart Distribution, Parametric Uncertainty, Nonparametric Modelling, Eigenvalue Problems, Reduced Order Modelling, ASME SSDM2025
"Boiler Feed Pump (BFP): Working, Applications, Advantages, and Limitations E...Infopitaara
A Boiler Feed Pump (BFP) is a critical component in thermal power plants. It supplies high-pressure water (feedwater) to the boiler, ensuring continuous steam generation.
⚙️ How a Boiler Feed Pump Works
Water Collection:
Feedwater is collected from the deaerator or feedwater tank.
Pressurization:
The pump increases water pressure using multiple impellers/stages in centrifugal types.
Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
https://www.infopitaara.in/
How to Buy Snapchat Account A Step-by-Step Guide.pdfjamedlimmk
Scaling Growth with Multiple Snapchat Accounts: Strategies That Work
Operating multiple Snapchat accounts isn’t just a matter of logging in and out—it’s about crafting a scalable content strategy. Businesses and influencers who master this can turn Snapchat into a lead generation engine.
Key strategies include:
Content Calendars for Each Account – Plan distinct content buckets and themes per account to avoid duplication and maintain variety.
Geo-Based Content Segmentation – Use location-specific filters and cultural trends to speak directly to a region's audience.
Audience Mapping – Tailor messaging for niche segments: Gen Z, urban youth, gamers, shoppers, etc.
Metrics-Driven Storytelling – Use Snapchat Insights to monitor what type of content performs best per account.
Each account should have a unique identity but tie back to a central brand voice. This balance is crucial for brand consistency while leveraging the platform’s creative freedoms.
How Agencies and Creators Handle Bulk Snapchat Accounts
Digital agencies and creator networks often manage dozens—sometimes hundreds—of Snapchat accounts. The infrastructure to support this requires:
Dedicated teams for each cluster of accounts
Cloud-based mobile device management (MDM) systems
Permission-based account access for role clarity
Workflow automation tools (Slack, Trello, Notion) for content coordination
This is especially useful in verticals such as music promotion, event marketing, lifestyle brands, and political outreach, where each campaign needs targeted messaging from different handles.
The Legality and Risk Profile of Bulk Account Operations
If your aim is to operate or acquire multiple Snapchat accounts, understand the risk thresholds:
Personal Use (Low Risk) – One or two accounts for personal and creative projects
Business Use (Medium Risk) – Accounts with aligned goals, managed ethically
Automated Bulk Use (High Risk) – Accounts created en masse or used via bots are flagged quickly
Snapchat uses advanced machine learning detection for unusual behavior, including:
Fast switching between accounts from the same IP
Identical Snap stories across accounts
Rapid follower accumulation
Use of unverified devices or outdated OS versions
To stay compliant, use manual operations, vary behavior, and avoid gray-market account providers.
Smart Monetization Through Multi-Account Snapchat Strategies
With a multi-account setup, you can open doors to diversified monetization:
Affiliate Marketing – Niche accounts promoting targeted offers
Sponsored Content – Brands paying for story placement across multiple profiles
Product Launch Funnels – Segment users by interest and lead them to specific landing pages
Influencer Takeovers – Hosting creators across multiple themed accounts for event buzz
This turns your Snapchat network into a ROI-driven asset instead of a time sink.
Conclusion: Build an Ecosystem, Not Just Accounts
When approached correctly, multiple Snapchat accounts bec
6th International Conference on Big Data, Machine Learning and IoT (BMLI 2025)ijflsjournal087
Call for Papers..!!!
6th International Conference on Big Data, Machine Learning and IoT (BMLI 2025)
June 21 ~ 22, 2025, Sydney, Australia
Webpage URL : https://inwes2025.org/bmli/index
Here's where you can reach us : [email protected] (or) [email protected]
Paper Submission URL : https://inwes2025.org/submission/index.php
RICS Membership-(The Royal Institution of Chartered Surveyors).pdfMohamedAbdelkader115
Glad to be one of only 14 members inside Kuwait to hold this credential.
Please check the members inside kuwait from this link:
https://www.rics.org/networking/find-a-member.html?firstname=&lastname=&town=&country=Kuwait&member_grade=(AssocRICS)&expert_witness=&accrediation=&page=1
In tube drawing process, a tube is pulled out through a die and a plug to reduce its diameter and thickness as per the requirement. Dimensional accuracy of cold drawn tubes plays a vital role in the further quality of end products and controlling rejection in manufacturing processes of these end products. Springback phenomenon is the elastic strain recovery after removal of forming loads, causes geometrical inaccuracies in drawn tubes. Further, this leads to difficulty in achieving close dimensional tolerances. In the present work springback of EN 8 D tube material is studied for various cold drawing parameters. The process parameters in this work include die semi-angle, land width and drawing speed. The experimentation is done using Taguchi’s L36 orthogonal array, and then optimization is done in data analysis software Minitab 17. The results of ANOVA shows that 15 degrees die semi-angle,5 mm land width and 6 m/min drawing speed yields least springback. Furthermore, optimization algorithms named Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithm (GA) are applied which shows that 15 degrees die semi-angle, 10 mm land width and 8 m/min drawing speed results in minimal springback with almost 10.5 % improvement. Finally, the results of experimentation are validated with Finite Element Analysis technique using ANSYS.
We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a standalone surrogate modeling tool. We first briefly present the key mathematical tools on the basis of GP modeling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and user-friendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a well-known analytical benchmark function; a hierarchical Kriging example applied to wind turbine aero-servo-elastic simulations and a more complex geotechnical example that requires a non-stationary, user-defined correlation function. The GP-module, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
Efficient Algorithms for Isogeny Computation on Hyperelliptic Curves: Their A...IJCNCJournal
We present efficient algorithms for computing isogenies between hyperelliptic curves, leveraging higher genus curves to enhance cryptographic protocols in the post-quantum context. Our algorithms reduce the computational complexity of isogeny computations from O(g4) to O(g3) operations for genus 2 curves, achieving significant efficiency gains over traditional elliptic curve methods. Detailed pseudocode and comprehensive complexity analyses demonstrate these improvements both theoretically and empirically. Additionally, we provide a thorough security analysis, including proofs of resistance to quantum attacks such as Shor's and Grover's algorithms. Our findings establish hyperelliptic isogeny-based cryptography as a promising candidate for secure and efficient post-quantum cryptographic systems.
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PRIZ Academy - Functional Modeling In Action with PRIZ.pdfPRIZ Guru
This PRIZ Academy deck walks you step-by-step through Functional Modeling in Action, showing how Subject-Action-Object (SAO) analysis pinpoints critical functions, ranks harmful interactions, and guides fast, focused improvements. You’ll see:
Core SAO concepts and scoring logic
A wafer-breakage case study that turns theory into practice
A live PRIZ Platform demo that builds the model in minutes
Ideal for engineers, QA managers, and innovation leads who need clearer system insight and faster root-cause fixes. Dive in, map functions, and start improving what really matters.
2. Overview
A class in python is the blueprint from which specific objects are created. It lets you structure
your software in a particular way. Here comes a question how? Classes allow us to logically
group our data and function in a way that it is easy to reuse and a way to build upon if need to
be. Consider the below image.
(a) (b)
3. Overview
In the first image (a), it represents a blueprint of a house that can be considered as Class.
With the same blueprint, we can create several houses and these can be considered
as Objects. Using a class, you can add consistency to your programs so that they can be used
in cleaner and efficient ways. The attributes are data members (class variables and instance
variables) and methods which are accessed via dot notation.
Class variable is a variable that is shared by all the different objects/instances of a class.
Instance variables are variables which are unique to each instance. It is defined inside a
method and belongs only to the current instance of a class.
Methods are also called as functions which are defined in a class and describes the
behaviour of an object.
4. Overview
Now, let us move ahead and see how it works in Spyder. To get started, first have a look at the
syntax of a python class
Syntax:
class Class_name:
statement-1
.
.
statement-N
Here, the “class” statement creates a new class definition.The name of the class immediately
follows the keyword “class” in python which is followed by a colon.
5. To create a class in python, consider the below example:
class student:
pass
#no attributes and methods
s_1=student()
s_2=student()
#instance variable can be created manually
s_1.first="yogesh"
s_1.last="kumar"
s_1.email="[email protected]"
s_1.age=35
s_2.first="anil"
s_2.last="kumar"
s_2.email="[email protected]"
s_2.age=36
print(s_1.email)
print(s_2.email)
Output:
[email protected][email protected]
Now, what if we don’t want to manually set these variables. You will
see a lot of code and also it is prone to error. So to make it
automatic, we can use “init” method. For that, let’s understand what
exactly are methods and attributes in a python class.
6. Methods and Attributes in a Python Class
Creating a class is incomplete without some functionality. So functionalities can be defined by
setting various attributes which acts as a container for data and functions related to those
attributes. Functions in python are also called as Methods.
init method, it is a special function which gets called whenever a new object of that class is
instantiated.You can think of it as initialize method or you can consider this as constructors if
you’re coming from any another object-oriented programming background such as C++, Java
etc.
Now when we set a method inside a class, they receive instance automatically.
Let’s go ahead with python class and accept the first name, last name and fees using this
method.
7. class student:
def __init__(self, first, last, fee):
self.fname=first
self.lname=last
self.fee=fee
self.email=first + "." + last + "@yahoo.com"
s_1=student("yogesh","kumar",75000)
s_2=student("anil","kumar",100000)
print(s_1.email)
print(s_2.email)
print(s_1.fee)
print(s_2.fee)
Output:
[email protected][email protected]
75000
100000
“init” method, we have set these instance variables (self, first, last,
fee). Self is the instance which means whenever we write
self.fname=first, it is same as s_1.first=’yogesh’. Then we have
created instances of employee class where we can pass the values
specified in the init method. This method takes the instances as
arguments.
Instead of doing it manually, it will be done automatically now.
Methods and Attributes in a Python Class
8. Next, we want the ability to perform some kind of action. For that, we will add a method to this class.
Suppose I want the functionality to display the full name of the employee. So let’s us implement this
practically
class student:
def __init__(self, first, last, fee):
self.fname=first
self.lname=last
self.fee=fee
self.email=first + "." + last + "@yahoo.com"
def fullname(self):
return '{}{}'.format(self.fname,self.lname)
s_1=student("yogesh", "kumar", 75000)
s_2=student("anil", "kumar", 100000)
print(s_1.email)
print(s_2.email)
print(s_1.fullname())
print(s_2.fullname())
Output:
[email protected][email protected]
yogeshkumar
anilkumar
As you can see above, we have created a method called “full
name” within a class. So each method inside a python class
automatically takes the instance as the first argument. Now within
this method, we have written the logic to print full name and
return this instead of s_1 first name and last name. Next, we have
used “self” so that it will work with all the instances. Therefore to
print this every time, we use a method.
Methods and Attributes in a Python Class
9. Moving ahead with Python classes, there are variables which are shared among all the instances of a
class. These are called as class variables. Instance variables can be unique for each instance like
names, email, fee etc. Complicated? Let’s understand this with an example. Refer the code below to find
out the annual rise in the fee.
class student:
perc_raise =1.05
def __init__(self, first, last, fee):
self.fname=first
self.lname=last
self.fee=fee
self.email=first + "." + last + "@yahoo.com"
def fullname(self):
return "{}{}".format(self.fname,self.lname)
def apply_raise(self):
self.fee=int(self.fee*1.05)
s_1=student("yogesh","kumar",350000)
s_2=student("anil","kumar",100000)
print(s_1.fee)
s_1.apply_raise()
print(s_1.fee)
Output:
350000
367500
As you can see above, we have printed the fees first and then
applied the 1.5% increase. In order to access these class
variables, we either need to access them through the class or
an instance of the class.
Methods and Attributes in a Python Class
10. Attributes in a Python Class
Attributes in Python defines a property of an object, element or a file. There are two types of attributes:
Built-in Class Attributes: There are various built-in attributes present inside Python classes. For
example _dict_, _doc_, _name _, etc. Let us take the same example where we want to view all the key-
value pairs of student1. For that, we can simply write the below statement which contains the class
namespace:
print(s_1.__dict__)
After executing it, you will get output such as: {‘fname’:‘yogesh’,‘lname’:‘kumar’,‘fee’: 350000, ’email’:
‘[email protected]’}
11. Attributes defined by Users: Attributes are created inside the class definition. We can dynamically
create new attributes for existing instances of a class. Attributes can be bound to class names as well.
Next, we have public, protected and private attributes. Let’s understand them in detail:
Naming Type Meaning
Name Public These attributes can be freely used inside or outside of a class definition
_name Protected
Protected attributes should not be used outside of the class definition, unless
inside of a subclass definition
__name Private
This kind of attribute is inaccessible and invisible. It’s neither possible to read
nor to write those attributes, except inside of the class definition itself
Next, let’s understand the most important component in a python class i.e Objects.
Attributes in a Python Class
12. What are objects in a Python Class?
As we have discussed above, an object can be
used to access different attributes. It is used to
create an instance of the class. An instance is an
object of a class created at run-time.
To give you a quick overview, an object basically
is everything you see around. For eg: A dog is an
object of the animal class, I am an object of the
human class. Similarly, there can be different
objects to the same phone class. This is quite
similar to a function call which we have already
discussed.
13. Objects in a Python
class MyClass:
def func(self):
print('Hello')
# create a new MyClass
ob = MyClass()
ob.func()
14. Programmer-defined types
We have many of Python’s built-in types; now we are going to define a new type. As an example, we will
create a type called Point that represents a point in two-dimensional space.
(0, 0) represents the origin, and (x, y) represents the point x units to the right and y units up from the
origin. A programmer-defined type is also called a class. A class definition looks like this:
class Point:
"""Represents a point in 2-D space."""
The header indicates that the new class is called Point.The body is a docstring (documentation strings)
that explains what the class is for. Defining a class named Point creates a class object.
>>> Point
<class ' main .Point'>
Because Point is defined at the top level, its “full name” is main .Point.The class object is like a factory
for creating objects.To create a Point, we call Point as if it were a function.
15. Point
>>> blank = Point()
>>> blank
< main .Point object at 0xb7e9d3ac>
The return value is a reference to a Point object, which we assign to blank. Creating a new object is
called instantiation, and the object is an instance of the class.
When we print an instance, Python tells you what class it belongs to and where it is stored in
memory.
Every object is an instance of some class, so “object” and “instance” are interchangeable.
But in this chapter I use “instance” to indicate that I am talking about a programmer defined type.
16. Attributes
We can assign values to an instance using dot notation:
>>> blank.x = 3.0
>>> blank.y = 4.0
In this case, though, we are assigning values to named elements of an object.These elements are called attributes.
The variable blank refers to a Point object, which contains two attributes. Each attribute refers to a floating-point
number.We can read the value of an attribute using the same syntax:
>>> blank.y
4.0
>>> x = blank.x
>>> x
3.0
The expression blank.x means, “Go to the object blank refers to and get the value of x.” In the example, we
assign that value to a variable named x.
There is no conflict between the variable x and the attribute x.You can use dot notation as part of any expression.
18. Rectangles
We are designing a class to represent rectangles.There are at least two possibilities
You could specify one corner of the rectangle (or the center), the width, and the height
You could specify two opposing corners. At this point it is hard to say whether either is better than
the other, so we’ll implement the first one, just as an example.
Here is the class definition:
class Rectangle:
"""Represents a rectangle.
attributes: width, height, corner. """
19. Rectangle
The expression box.corner.x means, “Go to the object box refers to and select the attribute named
corner; then go to that object and select the attribute named x.”
The docstring lists the attributes: width and height are numbers; corner is a Point object that specifies
the lower-left corner.
To represent a rectangle, we have to instantiate a Rectangle object and assign values to the
attributes:
box = Rectangle( )
box.width = 100.0
box.height = 200.0
box.corner = Point( )
box.corner.x = 0.0
box.corner.y = 0.0
An object that is an attribute of another object is embedded.