Python provides numerous built-in functions that are readily available to us at the Python prompt. Some of the functions like input() and print() are widely used for standard input and output operations respectively.
Exception handling in Python allows programmers to handle errors and exceptions that occur during runtime. The try/except block handles exceptions, with code in the try block executing normally and code in the except block executing if an exception occurs. Finally blocks ensure code is always executed after a try/except block. Programmers can define custom exceptions and raise exceptions using the raise statement.
The document discusses Python data types. It describes the numeric data types integer, float, and complex which are used to represent numbers. Integer is a whole number without decimals, float has decimals, and complex numbers have real and imaginary parts. None is described as a null value. Strings are arrays of characters and can be indexed. Tuples and lists are ordered collections that can hold heterogeneous data types. Sets are unordered collections of unique items. Dictionaries are unordered collections of key-value pairs that allow accessing values via keys.
This document discusses abstract classes and interfaces in Python. It provides examples of using abstract methods and abstract classes to define common behavior for subclasses while allowing subclasses to provide their own specific implementations. Interfaces are defined as abstract classes that contain only abstract methods, allowing subclasses to fully implement the interface. Concrete methods can also be defined in abstract classes to provide shared behavior across subclasses.
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
The document discusses strings in Python. It describes that strings are immutable sequences of characters that can contain letters, numbers and special characters. It covers built-in string functions like len(), max(), min() for getting the length, maximum and minimum character. It also discusses string slicing, concatenation, formatting, comparison and various string methods for operations like conversion, formatting, searching and stripping whitespace.
The document discusses Python exception handling. It describes three types of errors in Python: compile time errors (syntax errors), runtime errors (exceptions), and logical errors. It explains how to handle exceptions using try, except, and finally blocks. Common built-in exceptions like ZeroDivisionError and NameError are also covered. The document concludes with user-defined exceptions and logging exceptions.
This document provides an overview of different number systems, including positional and non-positional systems. It describes the binary, decimal, octal, and hexadecimal systems, explaining their bases and symbols. Methods are presented for converting between these systems, such as using binary as an intermediary. Conversions include changing number values, as well as fractional representations. The objective is to understand number systems and perform conversions between binary, octal, decimal, and hexadecimal formats.
The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.
Functions allow programmers to organize and reuse code. They take in parameters and return values. Parameters act as variables that represent the information passed into a function. Arguments are the actual values passed into the function call. Functions can have default parameter values. Functions can return values using the return statement. Python passes arguments by reference, so changes made to parameters inside functions will persist outside the function as well. Functions can also take in arbitrary or keyword arguments. Recursion is when a function calls itself within its own definition. It breaks problems down into sub-problems until a base case is reached. The main types of recursion are direct, indirect, and tail recursion. Recursion can make code more elegant but uses more memory than iteration.
All data values in Python are encapsulated in relevant object classes. Everything in Python is an object and every object has an identity, a type, and a value. Like another object-oriented language such as Java or C++, there are several data types which are built into Python. Extension modules which are written in C, Java, or other languages can define additional types.
To determine a variable's type in Python you can use the type() function. The value of some objects can be changed. Objects whose value can be changed are called mutable and objects whose value is unchangeable (once they are created) are called immutable.
The document describes programs to implement various operations on singly linked lists including insertion, deletion, counting nodes, creating a list, traversing a list, and copying a list. It provides functions for insertion at the beginning, end, and before/after a given node. Deletion functions remove from the beginning, end, or by item value. Counting returns the total nodes or occurrences of a value. Traversal and copying print or duplicate the list.
This document discusses tuples in Python. Some key points:
- Tuples are ordered sequences of elements that can contain different data types. They are defined using parentheses.
- Elements can be accessed using indexes like lists and strings. Tuples are immutable - elements cannot be changed.
- Common tuple methods include count, index, sorting, finding min, max and sum.
- Nested tuples can store related data like student records with roll number, name and marks.
- Examples demonstrate swapping numbers without a temporary variable, returning multiple values from a function, and finding max/min from a user-input tuple.
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.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This document discusses different types of sorting algorithms. It describes internal sorting and external sorting, with internal sorting handling all data in memory and external sorting requiring external memory. Bubble sort, selection sort, and insertion sort are briefly explained as examples of sorting methods. Bubble sort works by comparing adjacent elements and swapping if out of order, selection sort finds the minimum element and selection sort inserts elements into the sorted position. Pseudocode and examples are provided for each algorithm.
This document provides information about dictionaries in Python. It defines dictionaries as mutable containers that store key-value pairs, with keys being unique and values being of any type. It describes dictionary syntax and how to access, update, delete and add elements. It notes that dictionary keys must be immutable like strings or numbers, while values can be any type. Properties of dictionary keys like no duplicate keys and keys requiring immutability are also summarized.
The document discusses various concepts related to functions in Python including defining functions, passing arguments, default arguments, arbitrary argument lists, lambda expressions, function annotations, and documentation strings. Functions provide modularity and code reusability. Arguments can be passed by value or reference and default values are evaluated once. Keyword, arbitrary and unpacked arguments allow flexible calling. Lambda expressions define small anonymous functions. Annotations provide type metadata and docstrings document functions.
A stack is a data structure where items can only be inserted and removed from one end. The last item inserted is the first item removed (LIFO). Common examples include stacks of books, plates, or bank transactions. Key stack operations are push to insert, pop to remove, and functions to check if the stack is empty or full. Stacks can be used to implement operations like reversing a string, converting infix to postfix notation, and evaluating arithmetic expressions.
This document discusses loops in Python. It introduces loops as a way to repeat instructions multiple times until a condition is met. The two main types of loops in Python are for loops, which iterate over a sequence, and while loops, which execute statements as long as a condition is true. It provides examples of for and while loops and covers else statements, loop control statements like break and continue, and some key points about loops in Python.
This document discusses Python variables and data types. It defines what a Python variable is and explains variable naming rules. The main Python data types are numbers, strings, lists, tuples, dictionaries, booleans, and sets. Numbers can be integer, float or complex values. Strings are sequences of characters. Lists are mutable sequences that can hold elements of different data types. Tuples are immutable sequences. Dictionaries contain key-value pairs with unique keys. Booleans represent True and False values. Sets are unordered collections of unique elements. Examples are provided to demonstrate how to declare variables and use each of the different data types in Python.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
This document discusses Python namespaces and modules. It explains that namespaces prevent name conflicts and modules are the basic unit of code reuse. Functions, classes, and modules each have their own namespace. Importing modules adds their names to the global namespace. The document recommends importing modules using 'import' to avoid potential name conflicts or namespace pollution. It also describes how scopes resolve which definition to use when the same name is defined in multiple scopes.
Arrays in Python can hold multiple values and each element has a numeric index. Arrays can be one-dimensional (1D), two-dimensional (2D), or multi-dimensional. Common operations on arrays include accessing elements, adding/removing elements, concatenating arrays, slicing arrays, looping through elements, and sorting arrays. The NumPy library provides powerful capabilities to work with n-dimensional arrays and matrices.
The document discusses various aspects of structures in C programming language. It defines a structure as a collection of variables of different data types grouped together under a single name. Structures allow grouping of related data and can be very useful for representing records. The key points discussed include:
- Defining structures using struct keyword and accessing members using dot operator.
- Declaring structure variables and initializing structure members.
- Using arrays of structures to store multiple records.
- Nested structures to group related members together.
- Pointers to structures for dynamic memory allocation.
- Passing structures, structure pointers and arrays of structures to functions.
The document discusses various ways to perform input and output operations in Python including:
1. Using print() to output strings, variables, and formatted strings. Print can add newlines, tabs, or concatenate multiple values.
2. Taking input using input() or formatted input prompts. Input returns a string that can be cast to other types like int or float.
3. Parsing command line arguments using sys.argv to access arguments passed when running a Python program from the command line. The argparse module can also be used to define and access arguments in a user-friendly way.
Helpmeinhomework Experts provides the most trusted and reliable online Programming assignment help . Programming is one of the most widely taught subjects across the universities. The complexity of subjects make students seek for quality and affordable online guidance. We at helpmeinhomework.com Experts cater to such needs of the students. Our programming experts provide assignment help to students across UK, USA and Australia for multiple programming languages i.e. Java, Python, HTML, PHP, Assembly language, C ,Linux ,Unix etc.
The document discusses various Python datatypes. It explains that Python supports built-in and user-defined datatypes. The main built-in datatypes are None, numeric, sequence, set and mapping types. Numeric types include int, float and complex. Common sequence types are str, bytes, list, tuple and range. Sets can be created using set and frozenset datatypes. Mapping types represent a group of key-value pairs like dictionaries.
Functions allow programmers to organize and reuse code. They take in parameters and return values. Parameters act as variables that represent the information passed into a function. Arguments are the actual values passed into the function call. Functions can have default parameter values. Functions can return values using the return statement. Python passes arguments by reference, so changes made to parameters inside functions will persist outside the function as well. Functions can also take in arbitrary or keyword arguments. Recursion is when a function calls itself within its own definition. It breaks problems down into sub-problems until a base case is reached. The main types of recursion are direct, indirect, and tail recursion. Recursion can make code more elegant but uses more memory than iteration.
All data values in Python are encapsulated in relevant object classes. Everything in Python is an object and every object has an identity, a type, and a value. Like another object-oriented language such as Java or C++, there are several data types which are built into Python. Extension modules which are written in C, Java, or other languages can define additional types.
To determine a variable's type in Python you can use the type() function. The value of some objects can be changed. Objects whose value can be changed are called mutable and objects whose value is unchangeable (once they are created) are called immutable.
The document describes programs to implement various operations on singly linked lists including insertion, deletion, counting nodes, creating a list, traversing a list, and copying a list. It provides functions for insertion at the beginning, end, and before/after a given node. Deletion functions remove from the beginning, end, or by item value. Counting returns the total nodes or occurrences of a value. Traversal and copying print or duplicate the list.
This document discusses tuples in Python. Some key points:
- Tuples are ordered sequences of elements that can contain different data types. They are defined using parentheses.
- Elements can be accessed using indexes like lists and strings. Tuples are immutable - elements cannot be changed.
- Common tuple methods include count, index, sorting, finding min, max and sum.
- Nested tuples can store related data like student records with roll number, name and marks.
- Examples demonstrate swapping numbers without a temporary variable, returning multiple values from a function, and finding max/min from a user-input tuple.
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.
This slide is used to do an introduction for the matplotlib library and this will be a very basic introduction. As matplotlib is a very used and famous library for machine learning this will be very helpful to teach a student with no coding background and they can start the plotting of maps from the ending of the slide by there own.
This document discusses different types of sorting algorithms. It describes internal sorting and external sorting, with internal sorting handling all data in memory and external sorting requiring external memory. Bubble sort, selection sort, and insertion sort are briefly explained as examples of sorting methods. Bubble sort works by comparing adjacent elements and swapping if out of order, selection sort finds the minimum element and selection sort inserts elements into the sorted position. Pseudocode and examples are provided for each algorithm.
This document provides information about dictionaries in Python. It defines dictionaries as mutable containers that store key-value pairs, with keys being unique and values being of any type. It describes dictionary syntax and how to access, update, delete and add elements. It notes that dictionary keys must be immutable like strings or numbers, while values can be any type. Properties of dictionary keys like no duplicate keys and keys requiring immutability are also summarized.
The document discusses various concepts related to functions in Python including defining functions, passing arguments, default arguments, arbitrary argument lists, lambda expressions, function annotations, and documentation strings. Functions provide modularity and code reusability. Arguments can be passed by value or reference and default values are evaluated once. Keyword, arbitrary and unpacked arguments allow flexible calling. Lambda expressions define small anonymous functions. Annotations provide type metadata and docstrings document functions.
A stack is a data structure where items can only be inserted and removed from one end. The last item inserted is the first item removed (LIFO). Common examples include stacks of books, plates, or bank transactions. Key stack operations are push to insert, pop to remove, and functions to check if the stack is empty or full. Stacks can be used to implement operations like reversing a string, converting infix to postfix notation, and evaluating arithmetic expressions.
This document discusses loops in Python. It introduces loops as a way to repeat instructions multiple times until a condition is met. The two main types of loops in Python are for loops, which iterate over a sequence, and while loops, which execute statements as long as a condition is true. It provides examples of for and while loops and covers else statements, loop control statements like break and continue, and some key points about loops in Python.
This document discusses Python variables and data types. It defines what a Python variable is and explains variable naming rules. The main Python data types are numbers, strings, lists, tuples, dictionaries, booleans, and sets. Numbers can be integer, float or complex values. Strings are sequences of characters. Lists are mutable sequences that can hold elements of different data types. Tuples are immutable sequences. Dictionaries contain key-value pairs with unique keys. Booleans represent True and False values. Sets are unordered collections of unique elements. Examples are provided to demonstrate how to declare variables and use each of the different data types in Python.
Pandas is an open source Python library that provides data structures and data analysis tools for working with tabular data. It allows users to easily perform operations on different types of data such as tabular, time series, and matrix data. Pandas provides data structures like Series for 1D data and DataFrame for 2D data. It has tools for data cleaning, transformation, manipulation, and visualization of data.
This document discusses Python namespaces and modules. It explains that namespaces prevent name conflicts and modules are the basic unit of code reuse. Functions, classes, and modules each have their own namespace. Importing modules adds their names to the global namespace. The document recommends importing modules using 'import' to avoid potential name conflicts or namespace pollution. It also describes how scopes resolve which definition to use when the same name is defined in multiple scopes.
Arrays in Python can hold multiple values and each element has a numeric index. Arrays can be one-dimensional (1D), two-dimensional (2D), or multi-dimensional. Common operations on arrays include accessing elements, adding/removing elements, concatenating arrays, slicing arrays, looping through elements, and sorting arrays. The NumPy library provides powerful capabilities to work with n-dimensional arrays and matrices.
The document discusses various aspects of structures in C programming language. It defines a structure as a collection of variables of different data types grouped together under a single name. Structures allow grouping of related data and can be very useful for representing records. The key points discussed include:
- Defining structures using struct keyword and accessing members using dot operator.
- Declaring structure variables and initializing structure members.
- Using arrays of structures to store multiple records.
- Nested structures to group related members together.
- Pointers to structures for dynamic memory allocation.
- Passing structures, structure pointers and arrays of structures to functions.
The document discusses various ways to perform input and output operations in Python including:
1. Using print() to output strings, variables, and formatted strings. Print can add newlines, tabs, or concatenate multiple values.
2. Taking input using input() or formatted input prompts. Input returns a string that can be cast to other types like int or float.
3. Parsing command line arguments using sys.argv to access arguments passed when running a Python program from the command line. The argparse module can also be used to define and access arguments in a user-friendly way.
Helpmeinhomework Experts provides the most trusted and reliable online Programming assignment help . Programming is one of the most widely taught subjects across the universities. The complexity of subjects make students seek for quality and affordable online guidance. We at helpmeinhomework.com Experts cater to such needs of the students. Our programming experts provide assignment help to students across UK, USA and Australia for multiple programming languages i.e. Java, Python, HTML, PHP, Assembly language, C ,Linux ,Unix etc.
Python is a general purpose programming language that can be used for both programming and scripting. It is an interpreted language, meaning code is executed line by line by the Python interpreter. Python code is written in plain text files with a .py extension. Key features of Python include being object-oriented, using indentation for code blocks rather than brackets, and having a large standard library. Python code can be used for tasks like system scripting, web development, data analysis, and more.
This document provides an overview of the Python programming language. It discusses Python's history and evolution, its key features like being object-oriented, open source, portable, having dynamic typing and built-in types/tools. It also covers Python's use for numeric processing with libraries like NumPy and SciPy. The document explains how to use Python interactively from the command line and as scripts. It describes Python's basic data types like integers, floats, strings, lists, tuples and dictionaries as well as common operations on these types.
The document provides an introduction to programming with Python. It discusses key concepts like code, syntax, output, and consoles. It also covers compiling vs interpreting languages, with Python being an interpreted language. The document explains expressions, variables, basic math operations, and functions in Python like print and input. It introduces control structures like if/else statements, for loops, and while loops. It also covers different data types in Python including numbers, strings, lists, and dictionaries.
The document provides an introduction to programming with Python. It discusses key concepts like code, syntax, output, and consoles. It also covers compiling vs interpreting languages, with Python being an interpreted language. The document explains expressions, variables, basic math operations, and functions in Python like print and input. It introduces control structures like if/else statements and for/while loops. It also covers different data types in Python including numbers, strings, lists, and dictionaries.
Python is an interpreted programming language that can be used to perform calculations, handle text, and control program flow. It allows variables to store values that can later be used in expressions. Common operations include arithmetic, printing output, accepting user input, and repeating tasks using for loops and conditional statements like if/else. The interpreter executes Python code directly without a separate compilation step required by other languages.
This document provides an introduction and overview of the Python programming language. It describes Python as a general-purpose, object-oriented programming language with features like high-level programming capabilities, an easily understandable syntax, portability, and being easy to learn. It then discusses Python's characteristics like being an interpreted language, supporting object-oriented programming, being interactive and easy to use, having straightforward syntax, being portable, extendable, and scalable. The document also outlines some common uses of Python like for creating web and desktop applications, and provides examples of using Python's interactive and script modes.
This document provides an overview of the Python programming language. It discusses Python's history, features, and why it is a good programming language. Key points covered include:
- Python was created in the late 1980s and draws from many other languages.
- It is an open source, interpreted, object-oriented, and portable language with a large online community and library support.
- Python code is compiled to bytecode for performance. It has dynamic typing, automatic memory management, and is powerful yet easy to learn.
- The document reviews Python statements, expressions, variables, basic data types, functions, modules and exceptions. It provides examples of Python code.
This document provides information about the Python programming language. It discusses the features of Python, including that it is object-oriented, open source, portable, powerful, and easy to learn. It also covers Python syntax, statements, functions, modules, exception handling, and how to run Python programs. The outcomes of learning these Python concepts are also listed.
Python is an interpreted, general-purpose, high-level programming language. It allows programmers to define functions for reusing code and scoping variables within functions. Key concepts covered include objects, expressions, conditionals, loops, modules, files, and recursion. Functions can call other functions, allowing for modular and reusable code.
This document provides an introduction to Python programming using PyCharm. It discusses downloading and installing Python and PyCharm, creating and running simple Python scripts that use print statements and variables, taking user input, and introducing conditional logic using if/else statements and while loops. Examples include printing ASCII art, basic math operations, and building a text-based choose your own adventure game. Further exercises are suggested to improve the game by adding dice rolls and more options.
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.
Introduction to Python and Basic Syntax
Understand the basics of Python programming.
Set up the Python environment.
Write simple Python scripts
Python is a high-level, interpreted programming language known for its readability and versatility(easy to read and easy to use). It can be used for a wide range of applications, from web development to scientific computing
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Excellence Academy Is The Renowned & Best Python Training Institute In Chandigarh That Provides 100% Job-Oriented .Python Training Institute In Mohali Our Course Is Designed Especially For Students, Housewives & Other Who Are Looking For Python Training In Chandigarh.
The document provides an overview of the Python programming language. It discusses that Python is an easy to learn, high-level, open-source programming language. It describes Python's design philosophy of code readability and how it allows programmers to express concepts in fewer lines of code compared to languages like C++ and Java. The document also discusses Python's powerful libraries, wide use across industries, and how to get started with Python programming using the IDLE integrated development environment.
The document provides an overview of the basics of the Python programming language. It discusses that Python is an interpreted, interactive, object-oriented scripting language. It also covers Python's history and describes it as being easy to learn and read, easy to maintain, portable, and extensible. The document then details Python's core data types including numbers, strings, lists, tuples, and dictionaries. It provides examples of how to define and manipulate variables of each data type in Python.
Python Programming | JNTUK | UNIT 1 | Lecture 3FabMinds
The document discusses the syllabus for a Python programming unit. It covers topics like conceptual introductions to computer science and algorithms, modern computer systems, installing Python, basic syntax, interactive shells, editing, saving and running scripts, data types, variables, numerical types, arithmetic operators, and understanding error messages. It also provides a brief history of Python releases from 1991 to 2008 and highlights that Python is free, portable, simple to learn, has extensive libraries, is extensible and embeddable, and supports object-oriented programming.
The document discusses implementing a function to check if a character is a hexadecimal digit. It explains that a hexadecimal digit ranges from 0-9, A-F, a-f in the ASCII table. It provides examples of inputting different characters and checking if they are hexadecimal digits or not. The sample execution section is empty. It lists functions as the prerequisite for understanding how to create a custom function to check for hexadecimal digits.
The document provides an example program to implement a student record system using an array of structures. It involves reading the number of students and subjects, student names and marks for each subject, calculating averages and grades. The program displays menus to view all student details or a particular student's details based on roll number or name. It demonstrates declaring a structure for student records, reading input into an array of structures, calculating averages and grades, and printing the student records with options to search by roll number or name.
This document discusses writing a macro called swap(t,x,y) that swaps two arguments of any data type t. It asks the user to input a data type and two values of that type, then swaps the values and displays the output. It explains how to swap two integers by using a temporary variable and applying the same concept to arguments of any type t by using macros. The objective is to understand macro preprocessing in C.
This document discusses defining a macro called SIZEOF to return the size of a data type without using the sizeof operator. It explains that by taking the difference of the addresses of a variable and the variable plus one, cast to char pointers, you can get the size in bytes. An example is provided using an integer variable x, showing how taking the difference of (&x+1) and &x after casting to char pointers returns the size of an int, which is 4 bytes. Background on macros and pointers is provided. The objective is stated as understanding macro usage in preprocessing.
The document describes a C program to multiply two matrices. It explains that the program takes input of rows and columns for Matrix A and B, reads in the element values, and checks that the column of the first matrix equals the row of the second before calculating the product. An example is provided where the matrices can be multiplied, producing the output matrix, and another where they cannot due to mismatched dimensions. Requirements for the program include pointers, 2D arrays, and dynamic memory allocation.
The document describes an assignment to read in an unspecified number (n) of names of up to 20 characters each, sort the names alphabetically, and print the sorted list. It provides examples of reading in 3 names ("Arunachal", "Bengaluru", "Agra"), sorting them using a custom string comparison function, and printing the sorted list ("Agra", "Arunachal", "Bengaluru"). Pre-requisites for the assignment include functions, dynamic arrays, and pointers. The objective is to understand how to use functions, arrays and pointers to complete the task.
This document provides instructions for an assignment to implement fragments using an array of pointers. It asks the student to write a program that reads the number of rows and columns for each row, reads the elements for each row, calculates the average for each row, sorts the rows based on average, and prints the results. It includes examples that show reading input values, storing them in an array using pointers, calculating averages, sorting rows, and sample output. The prerequisites are listed as pointers, functions, and dynamic memory allocation, and the objective is stated as understanding dynamic memory allocation and arrays of pointers.
The document describes an algorithm to generate a magic square of size n×n. It takes the integer n as input from the user and outputs the n×n magic square. A magic square is an arrangement of distinct numbers in a square grid where the sum of each row, column and diagonal is equal. The algorithm uses steps like starting from the middle of the grid and moving element by element in a pattern, wrapping around when reaching the boundaries.
This document discusses endianness and provides an example program to convert between little endian and big endian formats. It defines endianness as the order of bytes in memory, and describes little endian as having the least significant byte at the lowest memory address and big endian as the opposite. An example shows inputting a 2-byte number in little endian format and outputting it in big endian. Pre-requisites of pointers and the objective of understanding endianness representations are also stated.
The document provides steps to calculate variance of an array using dynamic memory allocation in C. It explains what variance is, shows an example to calculate variance of a sample array by finding the mean, deviations from mean, squaring the deviations and calculating the average of squared deviations. The key steps are: 1) Read array size and elements, 2) Calculate mean, 3) Find deviations from mean, 4) Square the deviations and store in another array, 5) Calculate average of squared deviations to get variance.
This document provides examples for an assignment to create a menu-driven program that stores and manipulates different data types (char, int, float, double) in dynamically allocated memory. It allocates 8 consecutive bytes to store the variables and uses flags to track which data types are stored. The menu allows the user to add, display, and remove elements as well as exit the program. Examples demonstrate initializing the flags, adding/removing elements, updating the flags, and displaying only elements whose flags are set. The objective is to understand dynamic memory allocation using pointers.
The document discusses generating non-repetitive pattern strings (NRPS) of length n using k distinct characters. It explains that an NRPS has a pattern that is not repeated consecutively. It provides steps to check if a string is an NRPS, including comparing characters and resetting a count if characters do not match. It also describes how to create an NRPS by starting with an ordered pattern and then copying subsequent characters to generate new patterns without repetition until the string reaches the desired length n. Sample inputs and outputs are provided.
The document discusses how to check if a string is a pangram, which is a sentence containing all 26 letters of the English alphabet. It provides an example of implementing the algorithm to check for a pangram by initializing an array to track letter occurrences, iterating through the input string to mark letters in the array, and checking if all letters are marked to determine if it is a pangram.
The document explains how to print all possible combinations of a given string by swapping characters. It provides an example of generating all six combinations of the string "ABC" through a step-by-step process of swapping characters. It also lists the prerequisites as strings, arrays, and pointers and the objective as understanding string manipulations.
The document describes an assignment to write a program that squeezes characters from one string (s1) that match characters in a second string (s2). It provides examples of input/output and step-by-step demonstrations of the program removing matching characters from s1. It also lists prerequisites of functions, arrays, and pointers and the objective of understanding these concepts as they relate to strings.
The document discusses implementing the strtok() string tokenization function. It explains that strtok() breaks a string into tokens based on delimiters. The document then provides pseudocode to implement a custom strtok() function by iterating through the string, overwriting delimiter characters with null terminators to create tokens, and returning a pointer to each token. Sample input/output is provided. The objective is stated as understanding string functions, with prerequisites of strings, storage classes, and pointers.
The document provides details on an assignment to write a program that recursively reverses a given string without using static variables, global variables, or loops. It includes the input, output, and examples of reversing the strings "Extreme" and "hello world". It also provides sample execution and pre-requisites of strings and recursive functions, with the objective being to understand reversing a string recursively.
The document provides code and examples for reversing a string using an iterative method in C++. It explains taking in a string as input, declaring output and input strings of the same length, and swapping the first and last characters, second and second to last, and so on through multiple iterations until the string is reversed. Examples show reversing the strings "Extreme" to "emertxE" and "hello world" to "dlrow olleh" through this iterative swap process. Pre-requisites of strings and loops are noted, with the objective stated as understanding string reversal using an iterative approach.
Protecting Your Sensitive Data with Microsoft Purview - IRMS 2025Nikki Chapple
Session | Protecting Your Sensitive Data with Microsoft Purview: Practical Information Protection and DLP Strategies
Presenter | Nikki Chapple (MVP| Principal Cloud Architect CloudWay) & Ryan John Murphy (Microsoft)
Event | IRMS Conference 2025
Format | Birmingham UK
Date | 18-20 May 2025
In this closing keynote session from the IRMS Conference 2025, Nikki Chapple and Ryan John Murphy deliver a compelling and practical guide to data protection, compliance, and information governance using Microsoft Purview. As organizations generate over 2 billion pieces of content daily in Microsoft 365, the need for robust data classification, sensitivity labeling, and Data Loss Prevention (DLP) has never been more urgent.
This session addresses the growing challenge of managing unstructured data, with 73% of sensitive content remaining undiscovered and unclassified. Using a mountaineering metaphor, the speakers introduce the “Secure by Default” blueprint—a four-phase maturity model designed to help organizations scale their data security journey with confidence, clarity, and control.
🔐 Key Topics and Microsoft 365 Security Features Covered:
Microsoft Purview Information Protection and DLP
Sensitivity labels, auto-labeling, and adaptive protection
Data discovery, classification, and content labeling
DLP for both labeled and unlabeled content
SharePoint Advanced Management for workspace governance
Microsoft 365 compliance center best practices
Real-world case study: reducing 42 sensitivity labels to 4 parent labels
Empowering users through training, change management, and adoption strategies
🧭 The Secure by Default Path – Microsoft Purview Maturity Model:
Foundational – Apply default sensitivity labels at content creation; train users to manage exceptions; implement DLP for labeled content.
Managed – Focus on crown jewel data; use client-side auto-labeling; apply DLP to unlabeled content; enable adaptive protection.
Optimized – Auto-label historical content; simulate and test policies; use advanced classifiers to identify sensitive data at scale.
Strategic – Conduct operational reviews; identify new labeling scenarios; implement workspace governance using SharePoint Advanced Management.
🎒 Top Takeaways for Information Management Professionals:
Start secure. Stay protected. Expand with purpose.
Simplify your sensitivity label taxonomy for better adoption.
Train your users—they are your first line of defense.
Don’t wait for perfection—start small and iterate fast.
Align your data protection strategy with business goals and regulatory requirements.
💡 Who Should Watch This Presentation?
This session is ideal for compliance officers, IT administrators, records managers, data protection officers (DPOs), security architects, and Microsoft 365 governance leads. Whether you're in the public sector, financial services, healthcare, or education.
🔗 Read the blog: https://nikkichapple.com/irms-conference-2025/
UiPath Community Zurich: Release Management and Build PipelinesUiPathCommunity
Ensuring robust, reliable, and repeatable delivery processes is more critical than ever - it's a success factor for your automations and for automation programmes as a whole. In this session, we’ll dive into modern best practices for release management and explore how tools like the UiPathCLI can streamline your CI/CD pipelines. Whether you’re just starting with automation or scaling enterprise-grade deployments, our event promises to deliver helpful insights to you. This topic is relevant for both on-premise and cloud users - as well as for automation developers and software testers alike.
📕 Agenda:
- Best Practices for Release Management
- What it is and why it matters
- UiPath Build Pipelines Deep Dive
- Exploring CI/CD workflows, the UiPathCLI and showcasing scenarios for both on-premise and cloud
- Discussion, Q&A
👨🏫 Speakers
Roman Tobler, CEO@ Routinuum
Johans Brink, CTO@ MvR Digital Workforce
We look forward to bringing best practices and showcasing build pipelines to you - and to having interesting discussions on this important topic!
If you have any questions or inputs prior to the event, don't hesitate to reach out to us.
This event streamed live on May 27, 16:00 pm CET.
Check out all our upcoming UiPath Community sessions at:
👉 https://community.uipath.com/events/
Join UiPath Community Zurich chapter:
👉 https://community.uipath.com/zurich/
Create Your First AI Agent with UiPath Agent BuilderDianaGray10
Join us for an exciting virtual event where you'll learn how to create your first AI Agent using UiPath Agent Builder. This session will cover everything you need to know about what an agent is and how easy it is to create one using the powerful AI-driven UiPath platform. You'll also discover the steps to successfully publish your AI agent. This is a wonderful opportunity for beginners and enthusiasts to gain hands-on insights and kickstart their journey in AI-powered automation.
Agentic AI - The New Era of IntelligenceMuzammil Shah
This presentation is specifically designed to introduce final-year university students to the foundational principles of Agentic Artificial Intelligence (AI). It aims to provide a clear understanding of how Agentic AI systems function, their key components, and the underlying technologies that empower them. By exploring real-world applications and emerging trends, the session will equip students with essential knowledge to engage with this rapidly evolving area of AI, preparing them for further study or professional work in the field.
Measuring Microsoft 365 Copilot and Gen AI SuccessNikki Chapple
Session | Measuring Microsoft 365 Copilot and Gen AI Success with Viva Insights and Purview
Presenter | Nikki Chapple 2 x MVP and Principal Cloud Architect at CloudWay
Event | European Collaboration Conference 2025
Format | In person Germany
Date | 28 May 2025
📊 Measuring Copilot and Gen AI Success with Viva Insights and Purview
Presented by Nikki Chapple – Microsoft 365 MVP & Principal Cloud Architect, CloudWay
How do you measure the success—and manage the risks—of Microsoft 365 Copilot and Generative AI (Gen AI)? In this ECS 2025 session, Microsoft MVP and Principal Cloud Architect Nikki Chapple explores how to go beyond basic usage metrics to gain full-spectrum visibility into AI adoption, business impact, user sentiment, and data security.
🎯 Key Topics Covered:
Microsoft 365 Copilot usage and adoption metrics
Viva Insights Copilot Analytics and Dashboard
Microsoft Purview Data Security Posture Management (DSPM) for AI
Measuring AI readiness, impact, and sentiment
Identifying and mitigating risks from third-party Gen AI tools
Shadow IT, oversharing, and compliance risks
Microsoft 365 Admin Center reports and Copilot Readiness
Power BI-based Copilot Business Impact Report (Preview)
📊 Why AI Measurement Matters: Without meaningful measurement, organizations risk operating in the dark—unable to prove ROI, identify friction points, or detect compliance violations. Nikki presents a unified framework combining quantitative metrics, qualitative insights, and risk monitoring to help organizations:
Prove ROI on AI investments
Drive responsible adoption
Protect sensitive data
Ensure compliance and governance
🔍 Tools and Reports Highlighted:
Microsoft 365 Admin Center: Copilot Overview, Usage, Readiness, Agents, Chat, and Adoption Score
Viva Insights Copilot Dashboard: Readiness, Adoption, Impact, Sentiment
Copilot Business Impact Report: Power BI integration for business outcome mapping
Microsoft Purview DSPM for AI: Discover and govern Copilot and third-party Gen AI usage
🔐 Security and Compliance Insights: Learn how to detect unsanctioned Gen AI tools like ChatGPT, Gemini, and Claude, track oversharing, and apply eDLP and Insider Risk Management (IRM) policies. Understand how to use Microsoft Purview—even without E5 Compliance—to monitor Copilot usage and protect sensitive data.
📈 Who Should Watch: This session is ideal for IT leaders, security professionals, compliance officers, and Microsoft 365 admins looking to:
Maximize the value of Microsoft Copilot
Build a secure, measurable AI strategy
Align AI usage with business goals and compliance requirements
🔗 Read the blog https://nikkichapple.com/measuring-copilot-gen-ai/
Jira Administration Training – Day 1 : IntroductionRavi Teja
This presentation covers the basics of Jira for beginners. Learn how Jira works, its key features, project types, issue types, and user roles. Perfect for anyone new to Jira or preparing for Jira Admin roles.
Nix(OS) for Python Developers - PyCon 25 (Bologna, Italia)Peter Bittner
How do you onboard new colleagues in 2025? How long does it take? Would you love a standardized setup under version control that everyone can customize for themselves? A stable desktop setup, reinstalled in just minutes. It can be done.
This talk was given in Italian, 29 May 2025, at PyCon 25, Bologna, Italy. All slides are provided in English.
Original slides at https://slides.com/bittner/pycon25-nixos-for-python-developers
Exploring the advantages of on-premises Dell PowerEdge servers with AMD EPYC processors vs. the cloud for small to medium businesses’ AI workloads
AI initiatives can bring tremendous value to your business, but you need to support your new AI workloads effectively. That means choosing the best possible infrastructure for your needs—and many companies are finding that the cloud isn’t right for them. According to a recent Rackspace survey of IT executives, 69 percent of companies have moved some of their applications on-premises from the cloud, with half of those citing security and compliance as the reason and 44 percent citing cost.
On-premises solutions provide a number of advantages. With full control over your security infrastructure, you can be certain that all compliance requirements remain firmly in the hands of your IT team. Opting for on-premises also gives you the ability to design your infrastructure to the precise needs of that team and your new AI workloads. Depending on the workload, you may also see performance benefits, along with more predictable costs. As you start to build your next AI initiative, consider an on-premises solution utilizing AMD EPYC processor-powered Dell PowerEdge servers.
Multistream in SIP and NoSIP @ OpenSIPS Summit 2025Lorenzo Miniero
Slides for my "Multistream support in the Janus SIP and NoSIP plugins" presentation at the OpenSIPS Summit 2025 event.
They describe my efforts refactoring the Janus SIP and NoSIP plugins to allow for the gatewaying of an arbitrary number of audio/video streams per call (thus breaking the current 1-audio/1-video limitation), plus some additional considerations on what this could mean when dealing with application protocols negotiated via SIP as well.
Dev Dives: System-to-system integration with UiPath API WorkflowsUiPathCommunity
Join the next Dev Dives webinar on May 29 for a first contact with UiPath API Workflows, a powerful tool purpose-fit for API integration and data manipulation!
This session will guide you through the technical aspects of automating communication between applications, systems and data sources using API workflows.
📕 We'll delve into:
- How this feature delivers API integration as a first-party concept of the UiPath Platform.
- How to design, implement, and debug API workflows to integrate with your existing systems seamlessly and securely.
- How to optimize your API integrations with runtime built for speed and scalability.
This session is ideal for developers looking to solve API integration use cases with the power of the UiPath Platform.
👨🏫 Speakers:
Gunter De Souter, Sr. Director, Product Manager @UiPath
Ramsay Grove, Product Manager @UiPath
This session streamed live on May 29, 2025, 16:00 CET.
Check out all our upcoming UiPath Dev Dives sessions:
👉 https://community.uipath.com/dev-dives-automation-developer-2025/
Neural representations have shown the potential to accelerate ray casting in a conventional ray-tracing-based rendering pipeline. We introduce a novel approach called Locally-Subdivided Neural Intersection Function (LSNIF) that replaces bottom-level BVHs used as traditional geometric representations with a neural network. Our method introduces a sparse hash grid encoding scheme incorporating geometry voxelization, a scene-agnostic training data collection, and a tailored loss function. It enables the network to output not only visibility but also hit-point information and material indices. LSNIF can be trained offline for a single object, allowing us to use LSNIF as a replacement for its corresponding BVH. With these designs, the network can handle hit-point queries from any arbitrary viewpoint, supporting all types of rays in the rendering pipeline. We demonstrate that LSNIF can render a variety of scenes, including real-world scenes designed for other path tracers, while achieving a memory footprint reduction of up to 106.2x compared to a compressed BVH.
https://arxiv.org/abs/2504.21627
Jeremy Millul - A Talented Software DeveloperJeremy Millul
Jeremy Millul is a talented software developer based in NYC, known for leading impactful projects such as a Community Engagement Platform and a Hiking Trail Finder. Using React, MongoDB, and geolocation tools, Jeremy delivers intuitive applications that foster engagement and usability. A graduate of NYU’s Computer Science program, he brings creativity and technical expertise to every project, ensuring seamless user experiences and meaningful results in software development.
Introducing FME Realize: A New Era of Spatial Computing and ARSafe Software
A new era for the FME Platform has arrived – and it’s taking data into the real world.
Meet FME Realize: marking a new chapter in how organizations connect digital information with the physical environment around them. With the addition of FME Realize, FME has evolved into an All-data, Any-AI Spatial Computing Platform.
FME Realize brings spatial computing, augmented reality (AR), and the full power of FME to mobile teams: making it easy to visualize, interact with, and update data right in the field. From infrastructure management to asset inspections, you can put any data into real-world context, instantly.
Join us to discover how spatial computing, powered by FME, enables digital twins, AI-driven insights, and real-time field interactions: all through an intuitive no-code experience.
In this one-hour webinar, you’ll:
-Explore what FME Realize includes and how it fits into the FME Platform
-Learn how to deliver real-time AR experiences, fast
-See how FME enables live, contextual interactions with enterprise data across systems
-See demos, including ones you can try yourself
-Get tutorials and downloadable resources to help you start right away
Whether you’re exploring spatial computing for the first time or looking to scale AR across your organization, this session will give you the tools and insights to get started with confidence.
AI Emotional Actors: “When Machines Learn to Feel and Perform"AkashKumar809858
Welcome to the era of AI Emotional Actors.
The entertainment landscape is undergoing a seismic transformation. What started as motion capture and CGI enhancements has evolved into a full-blown revolution: synthetic beings not only perform but express, emote, and adapt in real time.
For reading further follow this link -
https://akash97.gumroad.com/l/meioex
Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generati...Aaryan Kansari
Agentic AI Explained: The Next Frontier of Autonomous Intelligence & Generative AI
Discover Agentic AI, the revolutionary step beyond reactive generative AI. Learn how these autonomous systems can reason, plan, execute, and adapt to achieve human-defined goals, acting as digital co-workers. Explore its promise, key frameworks like LangChain and AutoGen, and the challenges in designing reliable and safe AI agents for future workflows.
Sticky Note Bullets:
Definition: Next stage beyond ChatGPT-like systems, offering true autonomy.
Core Function: Can "reason, plan, execute and adapt" independently.
Distinction: Proactive (sets own actions for goals) vs. Reactive (responds to prompts).
Promise: Acts as "digital co-workers," handling grunt work like research, drafting, bug fixing.
Industry Outlook: Seen as a game-changer; Deloitte predicts 50% of companies using GenAI will have agentic AI pilots by 2027.
Key Frameworks: LangChain, Microsoft's AutoGen, LangGraph, CrewAI.
Development Focus: Learning to think in workflows and goals, not just model outputs.
Challenges: Ensuring reliability, safety; agents can still hallucinate or go astray.
Best Practices: Start small, iterate, add memory, keep humans in the loop for final decisions.
Use Cases: Limited only by imagination (e.g., drafting business plans, complex simulations).
13. CLA
Example
1 #To display CLA
2
3 import sys
4
5 #Get the no. of CLA
6 n = len(sys.argv)
7
8 #Get the arguments
9 args = sys.argv
10
11 #Print the 'n'
12 print("No. Of CLA: ", n)
13
14 #print the arguments in one shot
15 print(args)
16
17 #Print the arguments one by one
18 for i in args:
19 print(i)
14. CLA
Parsing CLA
●
argparse module is useful to develop user-friendly programs
●
This module automatically generates help and usage messages
●
May also display appropriate error messages
15. CLA
Parsing CLA: Steps
● Step-1: Import argparse module
import argparse
● Step-2: Create an Object of ArgumentParser
parser = argparse.ArgumentParser(description="This program displays square of two numbers")
● Step-2a: If programmer does not want to display description, then above step can
be skipped
parser = argparse.ArgumentParser()
● Step-3: Add the arguments to the parser
parser.add_argument("num", type=int, help="Enter only int number.")
● Step-4: Retrieve the arguments
args = parser.parse_args()
● Step-4: Retrieve the arguments
● Step-5: Access the arguments
args.num