This presentation covers a detailed overview of python advanced concepts. it covers the below aspects.
Comprehensions
Lambda with (map, filter and reduce)
Context managers
Iterator, Generators, Decorators
Python GIL and multiprocessing and multithreading
Python WSGI
Python Unittests
Machine Learning With Python From India’s Most Advanced Learner’s Community. 200+ High-Quality Lectures. 4 Months Live Mentor-ship. 15+ Projects. Industry Insights.
Visit- https://insideaiml.com/course-details/Machine-Learning-with-Python-Statistics
The document provides an introduction to Python programming. It discusses key concepts like variables, data types, operators, and sequential data types. Python is presented as an interpreted programming language that uses indentation to indicate blocks of code. Comments and documentation are included to explain the code. Various data types are covered, including numbers, strings, booleans, and lists. Operators for arithmetic, comparison, assignment and more are also summarized.
Here are the answers to the exercises:
1. The len() method is used to find the length of a string.
2. To get the first character of the string txt, it would be:
txt="hello"
x=txt[0]
3. The strip() method removes any whitespace from the beginning or the end of a string.
https://www.dmdiploma.com/studymaterial?id=5/python-for-data-science
This Python course provides a beginner-friendly introduction to Python for Data Science.
First in the series of slides for python programming, covering topics like programming language, python programming constructs, loops and control statements.
This document provides an introduction to the Python programming language. It describes Python as a multi-purpose, object-oriented language that is interpreted, dynamically typed and focuses on readability. It lists several major organizations that use Python. It then provides examples of basic Python programs and covers key Python concepts like variables, data types, strings, comments, functions and more in under 3 sentences each.
Machine Learning With Python From India’s Most Advanced Learner’s Community. 200+ High-Quality Lectures. 4 Months Live Mentor-ship. 15+ Projects. Industry Insights.
Visit- https://insideaiml.com/course-details/Machine-Learning-with-Python-Statistics
The document provides an introduction to Python programming. It discusses key concepts like variables, data types, operators, and sequential data types. Python is presented as an interpreted programming language that uses indentation to indicate blocks of code. Comments and documentation are included to explain the code. Various data types are covered, including numbers, strings, booleans, and lists. Operators for arithmetic, comparison, assignment and more are also summarized.
Here are the answers to the exercises:
1. The len() method is used to find the length of a string.
2. To get the first character of the string txt, it would be:
txt="hello"
x=txt[0]
3. The strip() method removes any whitespace from the beginning or the end of a string.
https://www.dmdiploma.com/studymaterial?id=5/python-for-data-science
This Python course provides a beginner-friendly introduction to Python for Data Science.
First in the series of slides for python programming, covering topics like programming language, python programming constructs, loops and control statements.
This document provides an introduction to the Python programming language. It describes Python as a multi-purpose, object-oriented language that is interpreted, dynamically typed and focuses on readability. It lists several major organizations that use Python. It then provides examples of basic Python programs and covers key Python concepts like variables, data types, strings, comments, functions and more in under 3 sentences each.
A program is a sequence of instructions that are run by the processor. To run a program, it must be compiled into binary code and given to the operating system. The OS then gives the code to the processor to execute. Functions allow code to be reused by defining operations and optionally returning values. Strings are sequences of characters that can be manipulated using indexes and methods. Common string methods include upper() and concatenation using +.
Operators and Control Statements in PythonRajeswariA8
This document discusses operators and conditional statements in Python programming. It defines operators as symbols that instruct the computer to perform tasks on operands or values. The main types of operators covered are arithmetic, comparison, assignment, logical, bitwise, membership and identity operators. Conditional statements like if, else if and else are described as ways to control program flow based on conditions. Examples of using different operators and conditional statements are provided.
This document provides an introduction to the Python programming language. It covers Python's background, syntax, types, operators, control flow, functions, classes, tools, and IDEs. Key points include that Python is a multi-purpose, object-oriented language that is interpreted, strongly and dynamically typed. It focuses on readability and has a huge library of modules. Popular Python IDEs include Emacs, Vim, Komodo, PyCharm, and Eclipse.
Python is a versatile, object-oriented programming language that can be used for web development, data analysis, and more. It has a simple syntax and is easy to read and learn. Key features include being interpreted, dynamically typed, supporting functional and object-oriented programming. Common data types include numbers, strings, lists, dictionaries, tuples, and files. Functions and classes can be defined to organize and reuse code. Regular expressions provide powerful string manipulation. Python has a large standard library and is used widely in areas like GUIs, web scripting, AI, and scientific computing.
After the end of lesson you will be able to learn Python basics-What Python is? Its releases. Where we can use Python? Python Features. Tokens, comments variables etc... In out next PPT you will learn how to input and get output in Python
Python Session - 4
if
nested-if
if-else
elif (else if)
for loop (for iterating_var in sequence: )
while loop
break
continnue
pass
What is a function in Python?
Types of Functions
How to Define & Call Function
Scope and Lifetime of variables
lambda functions(anonymous functions)
The document provides an overview of the Python programming language. It discusses what Python is, its history and naming, features like being dynamically typed and interpreted, popular applications like web development, machine learning, and its architecture. It also covers Python constructs like variables, data types, operators, and strings. The document compares Python to other languages and provides examples of common Python concepts.
The document provides an introduction to programming in Python. It discusses how Python can be used for web development, desktop applications, data science, machine learning, and more. It also covers executing Python programs, reading keyboard input, decision making and loops in Python, standard data types like numbers, strings, lists, tuples and dictionaries. Additionally, it describes functions, opening and reading/writing files, regular expressions, and provides examples of SQLite database connections in Python projects.
Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Functions tutorial covers all the important aspects of functions in Python right from the introduction to what functions are, all the way till checking out the major functions and using the code-first approach to understand them better.
Agenda
Why use Functions?
What are the Functions?
Types of Python Functions
Built-in Functions in Python
User-defined Functions in Python
Python Lambda Function
Conclusion
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
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Provides an introductory level understanding of the Python Programming Language and language features. Serves as a guide for beginners and a reference to Python basics and language use cases.
The document discusses dictionaries in Python. It explains that dictionaries are a mapping type that store key-value pairs, with keys being immutable types and values being any type. It provides examples of creating, accessing, updating, removing entries from, and accessing properties of dictionaries. It also covers functions, control flow statements like if/else and while loops, and list comprehensions.
Introduction To Programming with PythonSushant Mane
The document provides an introduction to the Python programming language. It discusses Python's core features like being an interpreted, object-oriented, and dynamic language. It covers basic Python concepts like data types, variables, operators, control flow, functions, modules, file handling, and object-oriented programming. The document contains examples and explanations of built-in types like numbers, strings, lists, tuples, and dictionaries. It also discusses control structures, functions, modules, and classes in Python.
1. Python can be used to automate repetitive tasks like data entry, file processing, report generation etc. This saves time and reduces human errors.
2. Python has many libraries for machine learning, data analysis and visualization which can be used to analyze patent data, identify trends, cluster similar technologies etc.
3. Web scraping and web development frameworks like Django can be used to build internal tools and dashboards to manage workflows more efficiently.
4. Python scripts can be written to extract and process data from various sources, perform calculations, format reports in a standardized way reducing manual efforts.
Python supports four main numerical types - integers, long integers, floating point numbers, and complex numbers. It provides various functions for mathematical, random number, trigonometric operations and constants like pi and e. Numbers are immutable and created using literals or by assigning values. The del statement can delete single or multiple number references.
This Presentation Helps for the beginners to understand easily Python Programming Language, because i had given an snapshot of each concepts. Those who are knowing C,C++ and Java they can easily understand my presentation.
Functions allow programmers to organize and structure their code by splitting it into reusable blocks. There are two types of functions: built-in functions that are predefined in Python, and user-defined functions that programmers create. Functions make code easier to debug, test and maintain by dividing programs into separate, reusable parts. Functions can take arguments as input and return values. Function definitions do not alter the normal flow of a program's execution, but calling a function causes the program flow to jump to the function code and then return to pick up where it left off.
Functions, Exception, Modules and Files
Functions: Difference between a Function and a Method, Defining a Function, Calling a Function, Returning Results from a Function, Returning Multiple Values from a Function, Functions are First Class Objects, Pass by Object Reference, Formal and Actual Arguments, Positional Arguments, Keyword Arguments, Default Arguments, Variable Length Arguments, Local and Global Variables, The Global Keyword, Passing a Group of Elements to a Function, Recursive Functions, Anonymous Functions or Lambdas (Using Lambdas with filter() Function, Using Lambdas with map() Function, Using Lambdas with reduce() Function), Function Decorators, Generators, Structured Programming, Creating our Own Modules in Python, The Special Variable __name__
Exceptions: Errors in a Python Program (Compile-Time Errors, Runtime Errors, Logical Errors),Exceptions, Exception Handling, Types of Exceptions, The Except Block, The assert Statement, UserDefined Exceptions, Logging the Exceptions
20%
Files: Files, Types of Files in Python, Opening a File, Closing a File, Working with Text Files Containing Strings, Knowing Whether a File Exists or Not, Working with Binary Files, The with Statement, Pickle in Python, The seek() and tell() Methods, Random Accessing of Binary Files, Random Accessing of Binary Files using mmap, Zipping and Unzipping Files, Working with Directories, Running Other Programs from Python Program
A program is a sequence of instructions that are run by the processor. To run a program, it must be compiled into binary code and given to the operating system. The OS then gives the code to the processor to execute. Functions allow code to be reused by defining operations and optionally returning values. Strings are sequences of characters that can be manipulated using indexes and methods. Common string methods include upper() and concatenation using +.
Operators and Control Statements in PythonRajeswariA8
This document discusses operators and conditional statements in Python programming. It defines operators as symbols that instruct the computer to perform tasks on operands or values. The main types of operators covered are arithmetic, comparison, assignment, logical, bitwise, membership and identity operators. Conditional statements like if, else if and else are described as ways to control program flow based on conditions. Examples of using different operators and conditional statements are provided.
This document provides an introduction to the Python programming language. It covers Python's background, syntax, types, operators, control flow, functions, classes, tools, and IDEs. Key points include that Python is a multi-purpose, object-oriented language that is interpreted, strongly and dynamically typed. It focuses on readability and has a huge library of modules. Popular Python IDEs include Emacs, Vim, Komodo, PyCharm, and Eclipse.
Python is a versatile, object-oriented programming language that can be used for web development, data analysis, and more. It has a simple syntax and is easy to read and learn. Key features include being interpreted, dynamically typed, supporting functional and object-oriented programming. Common data types include numbers, strings, lists, dictionaries, tuples, and files. Functions and classes can be defined to organize and reuse code. Regular expressions provide powerful string manipulation. Python has a large standard library and is used widely in areas like GUIs, web scripting, AI, and scientific computing.
After the end of lesson you will be able to learn Python basics-What Python is? Its releases. Where we can use Python? Python Features. Tokens, comments variables etc... In out next PPT you will learn how to input and get output in Python
Python Session - 4
if
nested-if
if-else
elif (else if)
for loop (for iterating_var in sequence: )
while loop
break
continnue
pass
What is a function in Python?
Types of Functions
How to Define & Call Function
Scope and Lifetime of variables
lambda functions(anonymous functions)
The document provides an overview of the Python programming language. It discusses what Python is, its history and naming, features like being dynamically typed and interpreted, popular applications like web development, machine learning, and its architecture. It also covers Python constructs like variables, data types, operators, and strings. The document compares Python to other languages and provides examples of common Python concepts.
The document provides an introduction to programming in Python. It discusses how Python can be used for web development, desktop applications, data science, machine learning, and more. It also covers executing Python programs, reading keyboard input, decision making and loops in Python, standard data types like numbers, strings, lists, tuples and dictionaries. Additionally, it describes functions, opening and reading/writing files, regular expressions, and provides examples of SQLite database connections in Python projects.
Python Functions Tutorial | Working With Functions In Python | Python Trainin...Edureka!
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on Python Functions tutorial covers all the important aspects of functions in Python right from the introduction to what functions are, all the way till checking out the major functions and using the code-first approach to understand them better.
Agenda
Why use Functions?
What are the Functions?
Types of Python Functions
Built-in Functions in Python
User-defined Functions in Python
Python Lambda Function
Conclusion
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Provides an introductory level understanding of the Python Programming Language and language features. Serves as a guide for beginners and a reference to Python basics and language use cases.
The document discusses dictionaries in Python. It explains that dictionaries are a mapping type that store key-value pairs, with keys being immutable types and values being any type. It provides examples of creating, accessing, updating, removing entries from, and accessing properties of dictionaries. It also covers functions, control flow statements like if/else and while loops, and list comprehensions.
Introduction To Programming with PythonSushant Mane
The document provides an introduction to the Python programming language. It discusses Python's core features like being an interpreted, object-oriented, and dynamic language. It covers basic Python concepts like data types, variables, operators, control flow, functions, modules, file handling, and object-oriented programming. The document contains examples and explanations of built-in types like numbers, strings, lists, tuples, and dictionaries. It also discusses control structures, functions, modules, and classes in Python.
1. Python can be used to automate repetitive tasks like data entry, file processing, report generation etc. This saves time and reduces human errors.
2. Python has many libraries for machine learning, data analysis and visualization which can be used to analyze patent data, identify trends, cluster similar technologies etc.
3. Web scraping and web development frameworks like Django can be used to build internal tools and dashboards to manage workflows more efficiently.
4. Python scripts can be written to extract and process data from various sources, perform calculations, format reports in a standardized way reducing manual efforts.
Python supports four main numerical types - integers, long integers, floating point numbers, and complex numbers. It provides various functions for mathematical, random number, trigonometric operations and constants like pi and e. Numbers are immutable and created using literals or by assigning values. The del statement can delete single or multiple number references.
This Presentation Helps for the beginners to understand easily Python Programming Language, because i had given an snapshot of each concepts. Those who are knowing C,C++ and Java they can easily understand my presentation.
Functions allow programmers to organize and structure their code by splitting it into reusable blocks. There are two types of functions: built-in functions that are predefined in Python, and user-defined functions that programmers create. Functions make code easier to debug, test and maintain by dividing programs into separate, reusable parts. Functions can take arguments as input and return values. Function definitions do not alter the normal flow of a program's execution, but calling a function causes the program flow to jump to the function code and then return to pick up where it left off.
Functions, Exception, Modules and Files
Functions: Difference between a Function and a Method, Defining a Function, Calling a Function, Returning Results from a Function, Returning Multiple Values from a Function, Functions are First Class Objects, Pass by Object Reference, Formal and Actual Arguments, Positional Arguments, Keyword Arguments, Default Arguments, Variable Length Arguments, Local and Global Variables, The Global Keyword, Passing a Group of Elements to a Function, Recursive Functions, Anonymous Functions or Lambdas (Using Lambdas with filter() Function, Using Lambdas with map() Function, Using Lambdas with reduce() Function), Function Decorators, Generators, Structured Programming, Creating our Own Modules in Python, The Special Variable __name__
Exceptions: Errors in a Python Program (Compile-Time Errors, Runtime Errors, Logical Errors),Exceptions, Exception Handling, Types of Exceptions, The Except Block, The assert Statement, UserDefined Exceptions, Logging the Exceptions
20%
Files: Files, Types of Files in Python, Opening a File, Closing a File, Working with Text Files Containing Strings, Knowing Whether a File Exists or Not, Working with Binary Files, The with Statement, Pickle in Python, The seek() and tell() Methods, Random Accessing of Binary Files, Random Accessing of Binary Files using mmap, Zipping and Unzipping Files, Working with Directories, Running Other Programs from Python Program
This document provides an overview of functions and file handling in Python. It discusses defining user-defined functions with the def keyword, including passing arguments, default arguments, keyword arguments, and variable number of arguments. It also covers recursion, anonymous functions, and attributes of file objects. For file handling, it explains opening, reading, writing, and appending files, as well as the different file modes.
Data Structures and Algorithms in PythonJakeLWright
Classes allow programmers to implement abstract data types (ADTs) to provide logical descriptions of data objects. A class defines an object's state via attributes and behaviors via methods. The Fraction class example demonstrates defining state as numerator and denominator attributes initialized in the __init__() constructor. Methods like __add__() and __eq__() allow Fraction objects to behave like numeric values and compare for equality. Exceptions must be handled to prevent program crashes from errors. List comprehensions provide a concise way to build lists through iteration and filtering.
Introduction to Python 01-08-2023.pon by everyone else. . Hence, they must be...DRVaibhavmeshram1
Python
Language
is uesd in engineeringStory adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
Story adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
Story adapted from Stephen Covey (2004) “The Seven Habits of Highly Effective People” Simon & Schuster).
“Management is doing things right, leadership is doing the right things”
(Warren Bennis and Peter Drucker)
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they should believe the change is really going to happen.
The decision maker:
Leaders usually control resources such as people, budgets, and equipment, and thus have the authority to make decisions (as per their span of control) that affect the initiative.
During change, leaders must leverage their decision-making authority and choose the options that will support the initiative.
The Decision-Maker is decisive and sets priorities that support change.
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they should believe the change is really going to happen.
The decision maker:
Leaders usually control resources such as people, budgets, and equipment, and thus have the authority to make decisions (as per their span of control) that affect the initiative.
During change, leaders must leverage their decision-making authority and choose the options that will support the initiative.
The Decision-Maker is decisive and sets priorities that support change.
The Sponsor:
Champion and advocates for the change at their level in the organization.
A Sponsor is the person who won’t let the change initiative die from lack of attention, and is willing to use their political capital to make the change happen
The Role model:
Behaviors and attitudes demonstrated by them are looked upon by everyone else. . Hence, they must be willing to go first.
Employees watch leaders for consistency between words and actions to see if they s
A for loop is probably the most common type of loop in Python. A for loop will select items from any iterable. In Python an iterable is any container (list, tuple, set, dictionary), as well as many other important objects such as generator function, generator expressions, the results of builtin functions such as filter, map, range and many other items.
The document outlines an advanced Python course covering various Python concepts like object orientation, comprehensions, extended arguments, closures, decorators, generators, context managers, classmethods, inheritance, encapsulation, operator overloading, and Python packages. The course agenda includes how everything in Python is an object, comprehension syntax, *args and **kwargs, closures and decorators, generators and iterators, context managers, staticmethods and classmethods, inheritance and encapsulation, operator overloading, and Python package layout.
This document discusses functions in Python. It begins by defining what a function is and provides examples of built-in functions and functions defined in modules. It then lists some advantages of using functions such as code reusability and readability. The document discusses the different types of functions - built-in functions, functions defined in modules, and user-defined functions. It provides examples of each type. The document also covers topics such as function parameters, return values, variable scope, lambda functions, and using functions from libraries.
MATLAB stands for Matrix Laboratory. MATLAB was written originally
to provide easy access to matrix software developed by the LINPACK (linear system package) and matlab 2012a manual pdf
Python is an interpreted, object-oriented programming language that uses indentation to identify blocks of code. It is dynamically typed and strongly typed, with objects determining types at runtime rather than requiring explicit type declaration. Common data types include mutable types like lists and dictionaries as well as immutable types like strings and tuples.
i. The linear convolution of two sequences was calculated using the conv command in MATLAB. The input sequences, individual sequences, and convolved output were plotted.
ii. Linear convolution was also calculated using the DFT and IDFT. The sequences were padded with zeros and transformed to the frequency domain using FFT. The transformed sequences were multiplied and inverse transformed using IFFT to obtain the circular convolution result.
iii. The circular convolution result using DFT/IDFT was the same as the linear convolution using the conv command, demonstrating the equivalence between linear and circular convolution in the frequency domain.
This lab manual covers MATLAB and digital signal processing concepts. It includes:
1) An introduction to MATLAB including basic commands, functions, vectors, matrices and operations.
2) Digital signal processing concepts like sampling, discrete time signals, linear convolution using the conv command are explained.
3) Experiments are included to verify the sampling theorem and study linear convolution of sequences.
This document discusses functions in Python. It defines a function as a named sequence of statements that performs a computation. Functions allow code to be reused by calling the function by name. Python has many built-in functions for tasks like type conversion and mathematics. Functions can take parameters and return values. Defining functions helps make programs easier to read, understand, debug and maintain.
The document discusses Python interview questions and answers related to Python fundamentals like data types, variables, functions, objects and classes. Some key points include:
- Python is an interpreted, interactive and object-oriented programming language. It uses indentation to identify code blocks rather than brackets.
- Python supports dynamic typing where the type is determined at runtime. It is strongly typed meaning operations inappropriate for a type will fail with an exception.
- Common data types include lists (mutable), tuples (immutable), dictionaries, strings and numbers.
- Functions use def, parameters are passed by reference, and variables can be local or global scope.
- Classes use inheritance, polymorphism and encapsulation to create
These questions will be a bit advanced level 2sadhana312471
These questions will be a bit advanced(Intermediate) in terms of Python interview.
This is the continuity of Nail the Python Interview Questions.
The fields that these questions will help you in are:
• Python Developer
• Data Analyst
• Research Analyst
• Data Scientist
The document provides information about a JavaScript course including:
1. The course consists of 5 lectures and 5 labs and is evaluated based on projects, assignments, labs and quizzes.
2. The lecture outline covers introduction to JavaScript, syntax, built-in objects and functions.
3. JavaScript was invented by Brendan Eich at Netscape and first appeared in the Netscape Navigator browser in 1995.
The document discusses different types of functions in MATLAB:
1) Functions allow grouping code to perform tasks and operate in their own workspace separately from the base workspace. They can accept multiple inputs and outputs.
2) Anonymous functions can be defined inline without a file using the @ syntax.
3) Primary functions must be in a file but can call sub-functions defined there as well.
4) Nested functions are defined within another function and share its workspace. Private functions reside in a private subfolder and are only visible locally.
5) Global variables can be shared between functions by declaring them globally at the start of relevant files.
Slides for Lecture 1 of the course: Introduction to Programming with Python offered at ICCBS.
It covers the following topics:
1.) Variables, Statements and Expressions
2.) Functions
3.) Flow Control
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
When deliberating between CodeIgniter vs CakePHP for web development, consider their respective strengths and your project requirements. CodeIgniter, known for its simplicity and speed, offers a lightweight framework ideal for rapid development of small to medium-sized projects. It's praised for its straightforward configuration and extensive documentation, making it beginner-friendly. Conversely, CakePHP provides a more structured approach with built-in features like scaffolding, authentication, and ORM. It suits larger projects requiring robust security and scalability. Ultimately, the choice hinges on your project's scale, complexity, and your team's familiarity with the frameworks.
Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...kalichargn70th171
A dynamic process unfolds in the intricate realm of software development, dedicated to crafting and sustaining products that effortlessly address user needs. Amidst vital stages like market analysis and requirement assessments, the heart of software development lies in the meticulous creation and upkeep of source code. Code alterations are inherent, challenging code quality, particularly under stringent deadlines.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
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- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
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2. Agenda
Comprehensions
Lambda with (map, filter and reduce)
Context managers
Iterator, Generators, Decorators
Python GIL and multiprocessing and multithreading
Python WSGI
Python Unittests
3. Comprehensions
Comprehensions in Python provide us with a short and concise way to construct new
sequences (such as lists, set, dictionary etc.) using sequences which have been already
defined. Python supports the following 4 types of comprehensions.
List Comprehensions
Dictionary Comprehensions
Set Comprehensions
Generator Comprehensions
4. List Comprehensions
For example, if we want to create an output list which contains only the even
numbers which are present in the input list.
input_list = [1, 2, 3, 4, 4, 5, 6, 7, 7]
list_comp = [var for var in input_list if var % 2 == 0]
print("Output List:", list_comp)
Note: that list comprehension may or may not contain an if condition. List
comprehensions can contain multiple for (nested list comprehensions).
5. Dictionary Comprehensions
For example, if we want to create an output dictionary which contains only the odd
numbers that are present in the input list as keys and their cubes as values.
input_list = [1,2,3,4,5,6,7]
dict_comp = {var:var ** 3 for var in input_list if var % 2 != 0}
print("Output Dictionary:", dict_comp)
6. Set Comprehensions
Set comprehensions are pretty similar to list comprehensions.The only difference
between them is that set comprehensions use curly brackets { }.
For example, if we want to create an output set which contains only the even
numbers that are present in the input list.
input_list = [1, 2, 3, 4, 4, 5, 6, 6, 6, 7, 7]
set_comp = {var for var in input_list if var % 2 == 0}
print("Output Set:", set_comp)
7. Generator Comprehensions
Generator Comprehensions are very similar to list comprehensions. One difference
between them is that generator comprehensions use circular brackets whereas list
comprehensions use square brackets.
The major difference between them is that generators don’t allocate memory for the
whole list. Instead, they generate each value one by one which is why they are memory
efficient.
input_list = [1, 2, 3, 4, 4, 5, 6, 7, 7]
output_gen = (var for var in input_list if var % 2 == 0)
print("generator object :", output_gen)
for var in output_gen:
print(var, end = ' ')
8. Lambda
In Python, Lambda functions are anonymous functions which means function
without a name. Lambda definition does not include a “return” statement, it always
contains an expression which is returned.
cube = lambda x: x*x*x
print(cube(3))
Note: Lambda functions can be used along with built-in functions like filter(), map()
and reduce().
9. Use of lambda with filter
The filter() function in Python takes in a function and a list as arguments.This
offers an elegant way to filter out all the elements of a sequence “sequence”, for
which the function returnsTrue.
li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61]
final_list = list(filter(lambda x: (x%2 != 0) , li))
print(final_list)
10. Use of lambda with map
The map() function in Python takes in a function and a list as argument.The
function is called with a lambda function and a list and a new list is returned which
contains all the lambda modified items returned by that function for each item.
li = [5, 7, 22, 97, 54, 62, 77, 23, 73, 61]
final_list = list(map(lambda x: x*2 , li))
print(final_list)
11. Use of lambda with reduce
The reduce() function in Python takes in a function and a list as argument.
The function is called with a lambda function and a list and a new reduced result is
returned.This performs a repetitive operation over the pairs of the list.This is a part
of functools module.
from functools import reduce
li = [5, 8, 10, 20, 50, 100]
sum = reduce((lambda x, y: x + y), li)
print (sum)
12. Context Manager
Managing Resources
In any programming language, the usage of resources like file operations or database
connections is very common. But these resources are limited in supply. Therefore, the
main problem lies in making sure to release these resources after usage. If they are not
released then it will lead to resource leakage and may cause the system to either slow
down or crash. It would be very helpful if user have a mechanism for the automatic
setup and teardown of resources.
In Python, it can be achieved by the usage of context managers which facilitate the
proper handling of resources.
13. Error scenario for resources
Let’s take the example of file management. When a file is opened, a file descriptor is consumed which
is a limited resource. Only a certain number of files can be opened by a process at a time.
file_descriptors = []
for x in range(100000):
file_descriptors.append(open('test.txt', 'w'))
Output:
Traceback (most recent call last):
File "context.py", line 3, in OSError: [Errno 24] Too many open files:
'test.txt‘
An error message saying that too many files are open. The above example is a case of file descriptor
leakage. It happens because there are too many open files and they are not closed.
14. Managing Resources using Context Manager
Python provides an easy way to manage resources i.e Context Managers.
The ”with”keyword is used. When it gets evaluated it should result in an object that
performs context management.
with open("test.txt") as f:
data = f.read()
15. Creating a Context Manager
When creating context managers using classes, user need to ensure that the class has the
methods: __enter__() and __exit__(). The __enter__() returns the resource
that needs to be managed and the __exit__() does not return anything but performs
the cleanup operations.
class FileManager():
def __init__(self, filename, mode):
self.filename = filename
self.mode = mode
self.file = None
def __enter__(self):
self.file = open(self.filename, self.mode)
return self.file
def __exit__(self, exc_type, exc_value, exc_traceback):
self.file.close()
16. Loading a file with context manager
loading a file with context manager created in previous slide
with FileManager('test.txt', 'w') as f:
f.write('Test')
print(f.closed)
17. Iterator
An iterator is an object that contains a countable number of values.
An iterator is an object that can be iterated upon, meaning that you can traverse
through all the values.
Technically, in Python, an iterator is an object which implements the iterator
protocol, which consist of the methods __iter__() and __next__()
18. Iterator vs Iterable
Lists, tuples, dictionaries, and sets are all iterable objects. They are
iterable containers which you can get an iterator from.
All these objects have a iter() method which is used to get an iterator:
For example:
mytuple = ("apple", "banana", "cherry")
myit = iter(mytuple)
print(next(myit))
print(next(myit))
print(next(myit))
19. Creating a Iterator
To create an object/class as an iterator you have to implement the
methods __iter__() and __next__() to your object.
The __iter__() method acts similar, you can do operations (initializing etc.),
but must always return the iterator object itself.
The __next__() method also allows you to do operations, and must return
the next item in the sequence.
To prevent the iteration to go on forever, we can use
the StopIteration statement.
20. Creating a Iterator
Below is a simple Python program that creates iterator type that iterates from 10 to given
limit. For example, if limit is 15, then it prints 10 11 12 13 14 15. And if limit is 5, then it
prints nothing.
class Test:
def __init__(self, limit):
self.limit = limit
def __iter__(self):
self.x = 10
return self
def next(self):
x = self.x
if x > self.limit:
raise StopIteration
self.x = x + 1;
return x
21. Creating a Iterator
Using iterators created in previous slide…
Prints numbers from 10 to 15
for i in Test(15):
print(i)
Prints nothing
for i in Test(5):
print(i)
22. Generator
There is a lot of overhead in building an iterator. We have to implement a class
with __iter__() and __next__() method, keep track of internal states,
raise StopIteration when there was no values to be returned etc.
This is both lengthy and counter intuitive. Generator comes into rescue in such
situations.
Python generators are a simple way of creating iterators. All the overhead we
mentioned above are automatically handled by generators in Python.
Simply speaking, a generator is a function that returns an object (iterator) which we
can iterate over (one value at a time).
23. Creating a Generator
It is fairly simple to create a generator in Python. It is as easy as defining a normal
function with yield statement instead of a return statement.
If a function contains at least one yield statement (it may contain
other yield or return statements), it becomes a generator function.
Both yield and return will return some value from a function.
The difference is that, while a return statement terminates a function entirely, yield
statement pauses the function saving all its states and later continues from there on
successive calls.
24. Generator example
For example:
def my_gen():
n = 1
print('This is printed first')
yield n
n += 1
print('This is printed second')
yield n
n += 1
print('This is printed at last')
yield n
25. Generator example
Try previous example on interpreter by calling one by one.
Or try it with for loop e.g.
for item in my_gen():
print(item)
26. Generator example2
Generator example that reverse a string
def rev_str(my_str):
length = len(my_str)
for i in range(length - 1,-1,-1):
yield my_str[i]
for char in rev_str("hello"):
print(char)
27. When to use yield instead of return
Generator Return sends a specified value back to its caller whereas Yield can
produce a sequence of values. We should use yield when we want to iterate over a
sequence, but don’t want to store the entire sequence in memory.
28. Decorator
Decorators are very powerful and useful tool in Python since it allows
programmers to modify the behavior of function or class.
Decorators allow us to wrap another function in order to extend the behavior of
wrapped function, without permanently modifying it.
In Decorators, functions are taken as the argument into another function and then
called inside the wrapper function.
Python allows you to use decorators in a simpler way with the @ symbol,
sometimes called the “pie” syntax.
29. Decorator example
For example
def my_decorator(func):
def wrapper():
print("Something is happening before the function is
called.")
func()
print("Something is happening after the function is
called.")
return wrapper
@my_decorator
def say_hello():
print(“Hellloooo!”)
30. Decorator example2
For example, timer decorator to count time of execution.
import functools
import time
def timer(func):
"""Print the runtime of the decorated function""“
@functools.wraps(func)
def wrapper_timer(*args, **kwargs):
start_time = time.perf_counter() # 1
value = func(*args, **kwargs)
end_time = time.perf_counter() # 2
run_time = end_time - start_time # 3
print(f"Finished {func.__name__!r} in {run_time:.4f} secs")
return value
return wrapper_timer
@timer
def waste_some_time(num_times):
for _ in range(num_times):
sum([i**2 for i in range(10000)])
31. Decorator example2
Execute the timer decorator with diff-2 values.
waste_some_time(1)
waste_some_time(999)
Check the output…
32. Decorator Appendix
The @functools.wrapsdecorator uses the
function functools.update_wrapper()to update
special attributes like __name__and __doc__ that are used in the
introspection.
Try below on interpriter.
>>> waste_some_time
>>> waste_some_time.__name__
>>> help(waste_some_time)
33. Python GIL
What is GIL
Python Global Interpreter Lock (GIL) is a type of process lock which is used by python
whenever it deals with processes. Generally, Python only uses one thread to execute
the set of written statements. This means that in python only one thread will be
executed at a time.
34. Python GIL
What problem did the GIL solve for Python
Python uses reference counting for memory management. It means that objects created in Python have
a reference count variable that keeps track of the number of references that point to the object. When
this count reaches zero, the memory occupied by the object is released.
For example:
import sys
a = []
b = a
sys.getrefcount(a)
NOTE: output of above program is 3, because the list object was referenced by a, b and the argument
passed to sys.getrefcount()
This reference counter variable needed to be protected, because sometimes two threads increase or
decrease its value simultaneously by doing that it may lead to memory leaked so in order to protect
thread we add locks to all data structures that are shared across threads but sometimes by adding locks
there exists a multiple locks which lead to another problem that is deadlock. In order to avoid memory
leaked and deadlocks problem, we used single lock on the interpreter that is Global Interpreter
Lock(GIL).
36. How to deal with GIL
Multi-processing vs multi-threading
The most popular way is to use a multi-processing approach where you use multiple processes instead
of threads. Each Python process gets its own Python interpreter and memory space so the GIL won’t be a
problem. Python has a multiprocessing module which lets us create processes easily
Alternative Python interpreters
Python has multiple interpreter implementations. CPython, Jython, IronPython and PyPy, written in C,
Java, C# and Python respectively, are the most popular ones. GIL exists only in the original Python
implementation that is CPython.
Just wait it out
While many Python users take advantage of the single-threaded performance benefits of GIL. The
multi-threading programmers don’t have to fret as some of the brightest minds in the Python
community are working to remove the GIL from CPython. One such attempt is known as the Gilectomy.
37. Python WSGI
WSGI is not a server, a python module, a framework, an API or any kind of software. It
is just an interface specification by which server and application communicate. Both
server and application interface sides are specified in the PEP 3333.
Beneath Django, Flask, Bottle and every other Python web framework, lies the Web
Server Gateway Interface or WSGI for short. WSGI is to Python what Servlets are to
Java — a common specification for web servers that allows different web servers and
application frameworks to interact based on a common API. However, as with most
things, the Python version is considerably simpler.
38. Python Unittests
Unit Testing is the first level of software testing where the smallest testable parts of a
software are tested. This is used to validate that each unit of the software performs as
designed. For example.
import unittest
class SimpleTest(unittest.TestCase):
def test(self):
self.assertTrue(True)
if __name__ == '__main__':
unittest.main()
39. Python Unittests
There are three types of possible test outcomes :
OK – This means that all the tests are passed.
FAIL – This means that the test did not pass and an AssertionError exception is
raised.
ERROR – This means that the test raises an exception other than AssertionError.
40. Python Unittests
Basic assertions used in the code
assertEqual() – This statement is used to check if the result
obtained is equal to the expected result.
assertTrue() / assertFalse() – This statement is
used to verify if a given statement is true or false.
assertRaises() – This statement is used to raise a specific
exception.