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.
This document provides 7 habits for writing more functional Swift code. It discusses avoiding mutability and for-loops in favor of map, filter and reduce functions. It also covers being lazy, currying functions, writing domain-specific languages, and thinking about code in terms of data and functions rather than objects.
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.
Moore's Law may be dead, but CPUs acquire more cores every year. If you want to minimize response latency in your application, you need to use every last core - without wasting resources that would destroy performance and throughput. Traditional locks grind threads to a halt and are prone to deadlocks, and although actors provide a saner alternative, they compose poorly and are at best weakly-typed.
In this presentation, created exclusively for Scalar Conf, John reveals a new alternative to the horrors of traditional concurrency, directly comparing it to other leading approaches in Scala. Witness the beauty, simplicity, and power of declarative concurrency, as you discover how functional programming is the most practical solution to solving the tough challenges of concurrency.
This document discusses functional programming concepts like map, reduce, and filter and provides Swift code examples for applying these concepts. It begins with an introduction to functional programming and key concepts. It then covers Swift basics like function types and passing functions. The bulk of the document explains and demonstrates map, reduce, filter and their uses on arrays and optionals in Swift. It concludes with suggestions for further functional programming topics and resources.
The Ring programming language version 1.7 book - Part 26 of 196Mahmoud Samir Fayed
Ring provides several functions for manipulating strings. Strings can be accessed like lists, with individual characters retrieved using indexes. Functions like len(), lower(), upper(), left(), right(), and trim() allow manipulating case and extracting substrings. Strings can be compared using strcmp() and converted to and from lists with str2list() and list2str(). Substr() supports finding, extracting, and replacing substrings.
The document contains a list of 16 programs related to arrays in Swift. The programs cover topics like printing arrays, searching arrays, sorting arrays, inserting/deleting elements from arrays, and performing operations on elements in arrays. Functions are defined to implement each program with arrays and size passed as parameters.
This document summarizes an event being organized by the Department of Computer Science Engineering and Department of Electronics and Instrumentation Engineering at Kamaraj College of Engineering and Technology. The event is called "TECHSHOW '19" and is aimed at +2 school students. It will take place on November 30th, 2019 and will include notes on Python programming, including topics like sequence containers, indexing, base types, and functions.
Python programming -Tuple and Set Data typeMegha V
This document discusses tuples, sets, and frozensets in Python. It provides examples and explanations of:
- The basic properties and usage of tuples, including indexing, slicing, and built-in functions like len() and tuple().
- How to create sets using braces {}, that sets contain unique elements only, and built-in functions for sets like len(), max(), min(), union(), intersection(), etc.
- Frozensets are immutable sets that can be used as dictionary keys, and support set operations but not mutable methods like add() or remove().
The document discusses Kotlin collections and aggregate operations on collections. It explains that Kotlin collections can be mutable or immutable, and by default collections are immutable unless specified as mutable. It then covers various aggregate operations that can be performed on collections like any, all, count, fold, foldRight, forEach, max, min, none etc and provides code examples for each operation.
Помните легендарные Java Puzzlers? Да-да, те самые, с Джошом Блохом и Нилом Гафтером? Ну, по которым ещё книжку написали? Так вот, в Groovy всё ещё веселее.
В смысле — задачки ещё более странные, и ответы ещё более поразительные. Этот доклад для вас, Groovy-разработчики, мы покажем вам настоящие, большие и красивые подводные камни! И для вас, Java-разработчики, потому что таких вещей на Java-подобном синтакисе вы точно никогда не видели! И для вас, PHP-разработчики… хотя, нет, не для вас :)
Всем точно будет весело — ваши ведущие Женя и Барух будут зажигать, шутить, спорить, бросаться футболками в публику, и самое главное — заставят вас офигевать от Groovy.
This document provides an introduction to the Scala programming language. It discusses that Scala is a hybrid language that is both object-oriented and functional, runs on the JVM, and provides seamless interoperability with Java. It highlights features of Scala such as pattern matching, traits, case classes, immutable data structures, lazy evaluation, and actors for concurrency.
Python легко и просто. Красиво решаем повседневные задачиMaxim Kulsha
The document discusses various techniques for iteration in Python. It covers iterating over lists, dictionaries, files and more. It provides examples of iterating properly to avoid errors like modifying a list during iteration. Context managers are also discussed as a clean way to handle resources like file objects. Overall the document shares best practices for writing efficient and robust iteration code in Python.
The document describes the iterative method for finding roots of equations. It discusses:
1) How to set up an iterative process to find a root by taking an initial approximation x0 and repeatedly applying the function pi(x) to get closer approximations x1, x2, etc. until the change between successive values is very small.
2) An example of using the method to find the root of the quadratic equation 2x^2 - 4x + 1 = 0. It shows calculating successive approximations that converge to the root.
3) How the iterative method can be implemented in C++ using a function that takes the initial value and returns the updated value at each iteration until the change is negligible.
This document describes ScalaMeter, a performance regression testing framework. It discusses several problems that can occur when benchmarking code performance, including warmup effects from JIT compilation, interference from other processes, garbage collection triggering, and variability from other runtime events. It provides examples demonstrating these issues and discusses solutions like running benchmarks in a separate JVM, ignoring measurements impacted by garbage collection, and performing many repetitions to obtain a stable mean.
Scala collections provide a uniform approach to working with data structures. They are generic, immutable, and support higher-order functions like map and filter. The core abstractions are Traversable and Iterable, with subclasses including lists, sets, and maps. Collections aim to be object-oriented, persistent, and follow principles like the uniform return type. They allow fluent, expressive ways to transform, query, and manipulate data in a functional style.
This slide contains short introduction to different elements of functional programming along with some specific techniques with which we use functional programming in Swift.
The document summarizes various functions in the Kotlin standard library including let, with, apply, run, also, takeIf, takeUnless, use, and repeat. It provides code examples to demonstrate how each function works and what it is commonly used for. let, with, apply, and also allow passing a receiver object to a lambda and accessing it as "it". takeIf and takeUnless return the receiver if a predicate is true or false, respectively. use automatically closes resources. repeat runs an action a specified number of times.
The Key Difference between a List and a Tuple. The main difference between lists and a tuples is the fact that lists are mutable whereas tuples are immutable. A mutable data type means that a python object of this type can be modified. Let's create a list and assign it to a variable.
This document discusses input and print functions in Python. It explains that the print function displays information to the user and includes examples of printing different data types. It also explains that the input function accepts information from the user, stores it in a variable, and can include a prompt. Examples are provided of getting both string and numeric input and converting the input to other data types like integers. The document also covers simple formatting options for print like printing on new lines, adding separators, or printing on the same line.
This document discusses three functional patterns: continuations, format combinators, and nested data types. It provides examples of each pattern in Scala. Continuations are represented using explicit continuation-passing style and delimited control operators. Format combinators represent formatted output as typed combinators rather than untyped strings. Nested data types generalize recursive data types to allow the type parameter of recursive invocations to differ, as in square matrices represented as a nested data type. The document concludes by drawing an analogy between fast exponentiation and representing square matrices as a nested data type.
This presentation takes you on a functional programming journey, it starts from basic Scala programming language design concepts and leads to a concept of Monads, how some of them designed in Scala and what is the purpose of them
The Ring programming language version 1.2 book - Part 12 of 84Mahmoud Samir Fayed
This document describes string manipulation functions in Ring including:
- Getting the length of a string with len()
- Converting case with upper() and lower()
- Accessing characters with indexing and for loops
- Extracting substrings with left() and right()
- Trimming spaces with trim()
- Copying strings with copy()
- Counting lines with lines()
Beginners python cheat sheet - Basic knowledge O T
The document provides an overview of common Python data structures and programming concepts including variables, strings, lists, tuples, dictionaries, conditionals, functions, files, classes, and more. It includes examples of how to define, access, modify, loop through, and perform operations on each type of data structure. Key points covered include using lists to store ordered sets of items, dictionaries to store connections between pieces of information as key-value pairs, and classes to define custom object types with attributes and methods.
Why we are submitting this talk? Because Go is cool and we would like to hear more about this language ;-). In this talk we would like to tell you about our experience with development of microservices with Go. Go enables devs to create readable, fast and concise code, this - beyond any doubt is important. Apart from this we would like to leverage our test driven habbits to create bulletproof software. We will also explore other aspects important for adoption of a new language.
R is a free and open-source programming language for statistical analysis and graphics. It allows users to import, clean, transform, visualize and model data. Key features of R include its large collection of statistical and graphical techniques, ability to easily extend its functionality through user-contributed packages, and open-source nature which allows for free use and development. The document provides instructions on installing R, getting started with the R interface and commands, and an overview of common functions and operations for data analysis, visualization and statistics.
This document discusses monads and continuations in functional programming. It provides examples of using monads like Option and List to handle failure in sequences of operations. It also discusses delimited continuations as a low-level control flow primitive that can implement exceptions, concurrency, and suspensions. The document proposes using monads to pass implicit state through programs by wrapping computations in a state transformer (ST) monad.
Python programming -Tuple and Set Data typeMegha V
This document discusses tuples, sets, and frozensets in Python. It provides examples and explanations of:
- The basic properties and usage of tuples, including indexing, slicing, and built-in functions like len() and tuple().
- How to create sets using braces {}, that sets contain unique elements only, and built-in functions for sets like len(), max(), min(), union(), intersection(), etc.
- Frozensets are immutable sets that can be used as dictionary keys, and support set operations but not mutable methods like add() or remove().
The document discusses Kotlin collections and aggregate operations on collections. It explains that Kotlin collections can be mutable or immutable, and by default collections are immutable unless specified as mutable. It then covers various aggregate operations that can be performed on collections like any, all, count, fold, foldRight, forEach, max, min, none etc and provides code examples for each operation.
Помните легендарные Java Puzzlers? Да-да, те самые, с Джошом Блохом и Нилом Гафтером? Ну, по которым ещё книжку написали? Так вот, в Groovy всё ещё веселее.
В смысле — задачки ещё более странные, и ответы ещё более поразительные. Этот доклад для вас, Groovy-разработчики, мы покажем вам настоящие, большие и красивые подводные камни! И для вас, Java-разработчики, потому что таких вещей на Java-подобном синтакисе вы точно никогда не видели! И для вас, PHP-разработчики… хотя, нет, не для вас :)
Всем точно будет весело — ваши ведущие Женя и Барух будут зажигать, шутить, спорить, бросаться футболками в публику, и самое главное — заставят вас офигевать от Groovy.
This document provides an introduction to the Scala programming language. It discusses that Scala is a hybrid language that is both object-oriented and functional, runs on the JVM, and provides seamless interoperability with Java. It highlights features of Scala such as pattern matching, traits, case classes, immutable data structures, lazy evaluation, and actors for concurrency.
Python легко и просто. Красиво решаем повседневные задачиMaxim Kulsha
The document discusses various techniques for iteration in Python. It covers iterating over lists, dictionaries, files and more. It provides examples of iterating properly to avoid errors like modifying a list during iteration. Context managers are also discussed as a clean way to handle resources like file objects. Overall the document shares best practices for writing efficient and robust iteration code in Python.
The document describes the iterative method for finding roots of equations. It discusses:
1) How to set up an iterative process to find a root by taking an initial approximation x0 and repeatedly applying the function pi(x) to get closer approximations x1, x2, etc. until the change between successive values is very small.
2) An example of using the method to find the root of the quadratic equation 2x^2 - 4x + 1 = 0. It shows calculating successive approximations that converge to the root.
3) How the iterative method can be implemented in C++ using a function that takes the initial value and returns the updated value at each iteration until the change is negligible.
This document describes ScalaMeter, a performance regression testing framework. It discusses several problems that can occur when benchmarking code performance, including warmup effects from JIT compilation, interference from other processes, garbage collection triggering, and variability from other runtime events. It provides examples demonstrating these issues and discusses solutions like running benchmarks in a separate JVM, ignoring measurements impacted by garbage collection, and performing many repetitions to obtain a stable mean.
Scala collections provide a uniform approach to working with data structures. They are generic, immutable, and support higher-order functions like map and filter. The core abstractions are Traversable and Iterable, with subclasses including lists, sets, and maps. Collections aim to be object-oriented, persistent, and follow principles like the uniform return type. They allow fluent, expressive ways to transform, query, and manipulate data in a functional style.
This slide contains short introduction to different elements of functional programming along with some specific techniques with which we use functional programming in Swift.
The document summarizes various functions in the Kotlin standard library including let, with, apply, run, also, takeIf, takeUnless, use, and repeat. It provides code examples to demonstrate how each function works and what it is commonly used for. let, with, apply, and also allow passing a receiver object to a lambda and accessing it as "it". takeIf and takeUnless return the receiver if a predicate is true or false, respectively. use automatically closes resources. repeat runs an action a specified number of times.
The Key Difference between a List and a Tuple. The main difference between lists and a tuples is the fact that lists are mutable whereas tuples are immutable. A mutable data type means that a python object of this type can be modified. Let's create a list and assign it to a variable.
This document discusses input and print functions in Python. It explains that the print function displays information to the user and includes examples of printing different data types. It also explains that the input function accepts information from the user, stores it in a variable, and can include a prompt. Examples are provided of getting both string and numeric input and converting the input to other data types like integers. The document also covers simple formatting options for print like printing on new lines, adding separators, or printing on the same line.
This document discusses three functional patterns: continuations, format combinators, and nested data types. It provides examples of each pattern in Scala. Continuations are represented using explicit continuation-passing style and delimited control operators. Format combinators represent formatted output as typed combinators rather than untyped strings. Nested data types generalize recursive data types to allow the type parameter of recursive invocations to differ, as in square matrices represented as a nested data type. The document concludes by drawing an analogy between fast exponentiation and representing square matrices as a nested data type.
This presentation takes you on a functional programming journey, it starts from basic Scala programming language design concepts and leads to a concept of Monads, how some of them designed in Scala and what is the purpose of them
The Ring programming language version 1.2 book - Part 12 of 84Mahmoud Samir Fayed
This document describes string manipulation functions in Ring including:
- Getting the length of a string with len()
- Converting case with upper() and lower()
- Accessing characters with indexing and for loops
- Extracting substrings with left() and right()
- Trimming spaces with trim()
- Copying strings with copy()
- Counting lines with lines()
Beginners python cheat sheet - Basic knowledge O T
The document provides an overview of common Python data structures and programming concepts including variables, strings, lists, tuples, dictionaries, conditionals, functions, files, classes, and more. It includes examples of how to define, access, modify, loop through, and perform operations on each type of data structure. Key points covered include using lists to store ordered sets of items, dictionaries to store connections between pieces of information as key-value pairs, and classes to define custom object types with attributes and methods.
Why we are submitting this talk? Because Go is cool and we would like to hear more about this language ;-). In this talk we would like to tell you about our experience with development of microservices with Go. Go enables devs to create readable, fast and concise code, this - beyond any doubt is important. Apart from this we would like to leverage our test driven habbits to create bulletproof software. We will also explore other aspects important for adoption of a new language.
R is a free and open-source programming language for statistical analysis and graphics. It allows users to import, clean, transform, visualize and model data. Key features of R include its large collection of statistical and graphical techniques, ability to easily extend its functionality through user-contributed packages, and open-source nature which allows for free use and development. The document provides instructions on installing R, getting started with the R interface and commands, and an overview of common functions and operations for data analysis, visualization and statistics.
This document discusses monads and continuations in functional programming. It provides examples of using monads like Option and List to handle failure in sequences of operations. It also discusses delimited continuations as a low-level control flow primitive that can implement exceptions, concurrency, and suspensions. The document proposes using monads to pass implicit state through programs by wrapping computations in a state transformer (ST) monad.
This document provides a cheat sheet for common commands and functions in R for data manipulation, statistical analysis, and graphics. It summarizes key topics such as accessing and manipulating data, conducting statistical tests, fitting linear and generalized linear models, performing clustering and multivariate analyses, and creating basic plots and graphics. The cheat sheet is organized into sections covering basics, vectors and data types, data frames, input/output, indexing, missing values, numerical and tabulation functions, programming, operators, graphics, and statistical models and distributions.
The apply() function in R can apply functions over margins of arrays or matrices. It avoids explicit loops and applies the given function to each row or column or both. Some key advantages of apply() include avoiding explicit loops, ability to apply various functions like mean, median etc, and ability to apply user-defined functions. Similarly, lapply() and sapply() apply a function over the lists or vectors but lapply() returns a list while sapply() simplifies the output if possible. Functions like tapply() and by() are useful when dealing with categorical variables to apply functions across categories. mapply() applies a function to multiple arguments and is useful for multivariate functions.
1. The document provides examples of various functions in R including string functions, mathematical functions, statistical probability functions and other statistical functions. Examples are given for functions like substr, grep, sub, paste etc. to manipulate strings and functions like mean, sd, median etc. for statistical calculations.
2. Examples are shown for commonly used probability distribution functions like dnorm, pnorm, qnorm, rnorm etc. Other examples include functions for binomial, Poisson and uniform distributions.
3. The document also lists various other useful statistical functions like range, sum, diff, min, max etc. with examples. Examples are provided to illustrate the use of these functions through loops and to create a matrix.
A short list of the most useful R commands
reference: http://www.personality-project.org/r/r.commands.html
R programı ile ilgilenen veya yeni öğrenmeye başlayan herkes için hazırlanmıştır.
This document discusses functions and methods in Python. It defines functions and methods, and explains the differences between them. It provides examples of defining and calling functions, returning values from functions, and passing arguments to functions. It also covers topics like local and global variables, function decorators, generators, modules, and lambda functions.
Implement the following sorting algorithms Bubble Sort Insertion S.pdfkesav24
Implement the following sorting algorithms: Bubble Sort Insertion Sort. Selection Sort.
Merge Sort. Heap Sort. Quick Sort. For each of the above algorithms, measure the execution
time based on input sizes n, n + 10(i), n + 20(i), n + 30(i), .. ., n + 100(i) for n = 50000 and i =
100. Let the array to be sorted be randomly initialized. Use the same machine to measure all the
algorithms. Plot a graph to compare the execution times you collected in part(2).
Solution
This code wil create a graph for each plots comparing time for different sorting methods and also
save those plots in the current directory.
from random import shuffle
from time import time
import numpy as np
import matplotlib.pyplot as plt
def bubblesort(arr):
for i in range(len(arr)):
for k in range(len(arr)-1, i, -1):
if (arr[k] < arr[k-1]):
tmp = arr[k]
arr[k] = arr[k-1]
arr[k-1] = tmp
return arr
def selectionsort(arr):
for fillslot in range(len(arr)-1,0,-1):
positionOfMax=0
for location in range(1,fillslot+1):
if arr[location]>arr[positionOfMax]:
positionOfMax = location
temp = arr[fillslot]
arr[fillslot] = arr[positionOfMax]
arr[positionOfMax] = temp
return arr
def insertionsort(arr):
for i in range( 1, len( arr ) ):
tmp = arr[i]
k = i
while k > 0 and tmp < arr[k - 1]:
arr[k] = arr[k-1]
k -= 1
arr[k] = tmp
return arr
# def mergesort(arr):
#
# if len(arr)>1:
# mid = len(arr)//2
# lefthalf = arr[:mid]
# righthalf = arr[mid:]
#
# mergesort(lefthalf)
# mergesort(righthalf)
#
# i=0
# j=0
# k=0
# while i < len(lefthalf) and j < len(righthalf):
# if lefthalf[i] < righthalf[j]:
# arr[k]=lefthalf[i]
# i=i+1
# else:
# arr[k]=righthalf[j]
# j=j+1
# k=k+1
#
# while i < len(lefthalf):
# arr[k]=lefthalf[i]
# i=i+1
# k=k+1
#
# while j < len(righthalf):
# arr[k]=righthalf[j]
# j=j+1
# k=k+1
#
# return arr
def mergesort(x):
result = []
if len(x) < 2:
return x
mid = int(len(x)/2)
y = mergesort(x[:mid])
z = mergesort(x[mid:])
i = 0
j = 0
while i < len(y) and j < len(z):
if y[i] > z[j]:
result.append(z[j])
j += 1
else:
result.append(y[i])
i += 1
result += y[i:]
result += z[j:]
return result
def quicksort(arr):
less = []
equal = []
greater = []
if len(arr) > 1:
pivot = arr[0]
for x in arr:
if x < pivot:
less.append(x)
if x == pivot:
equal.append(x)
if x > pivot:
greater.append(x)
return quicksort(less)+equal+quicksort(greater) # Just use the + operator to join lists
else:
return arr
#### Heap sort
def heapsort(arr): #convert arr to heap
length = len(arr) - 1
leastParent = length / 2
for i in range(leastParent, -1, -1):
moveDown(arr, i, length)
# flatten heap into sorted array
for i in range(length, 0, -1):
if arr[0] > arr[i]:
swap(arr, 0, i)
moveDown(arr, 0, i - 1)
def moveDown(arr, first, last):
largest = 2 * first + 1
while largest <= last: #right child exists and is larger than left child
if (largest < last) and(arr[largest] < arr[largest + 1]):
largest += 1
# right child is larger than parent
if arr[largest] > arr[first]:
swap(arr, largest, first)# move down to largest child
first = largest
lar.
This document provides a cheat sheet of common MATLAB commands organized into the following categories: basic commands, plotting commands, creating matrices/special matrices, matrix operations, data analysis commands, and conditionals/loops. It lists key commands used for things like commenting code, saving/loading variables, plotting graphs, performing mathematical operations on matrices, generating random numbers, and using conditional statements and loops.
This document discusses tuples and sets in Python. It defines tuples as immutable sequences that can contain heterogeneous data types. Tuples can be indexed and sliced but not modified. Sets are unordered collections of unique and immutable elements. Common set operations include union, intersection, difference, and symmetric difference.
3 kotlin vs. java- what kotlin has that java does notSergey Bandysik
This document discusses Kotlin functions and lambda expressions. It explains that lambda expressions can be passed immediately as expressions or defined separately as functions. It also discusses that using higher-order functions can introduce runtime overhead which can be eliminated by inlining lambda expressions. Finally, it provides an example of an inline fun that demonstrates inlining and non-inlining of lambda expressions.
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
The Ring programming language version 1.9 book - Part 31 of 210Mahmoud Samir Fayed
This document provides documentation on mathematical functions available in the Ring programming language. It lists common trigonometric, logarithmic, exponential and other mathematical functions along with examples of their usage syntax and output. Key functions covered include sin(), cos(), tan(), log(), exp(), sqrt(), random(), ceil(), floor(), and others. Examples are provided to demonstrate calculating trigonometric functions with radians and degrees as well as functions returning random numbers, absolute values, powers and more.
The document contains 14 code snippets demonstrating various Python programming concepts:
1) Arithmetic and relational operators on integers
2) List methods like insert, remove, append etc.
3) Temperature conversion between Celsius and Fahrenheit
4) Calculating student marks percentage and grade
5) Printing Fibonacci series
6) Matrix addition and multiplication
7) Function to check if character is a vowel
8) Reading last 5 lines of a file
9) Importing and using math and random modules
10) Multithreading concept
11) Creating a 3D object plot
12) Creating and displaying a histogram
13) Plotting sine, cosine and polynomial curves
14) Creating a pulse vs height graph
The document discusses several R functions including ifelse(), diff(), sign(), lapply(), seq(), length(), rep(), testing vector equality with ==, all(), typeof(), and names(). The ifelse() function provides an alternative to if/else statements in R and takes vectors as arguments. The diff() function calculates the differences between vector elements. Sign() returns the signs of the values in a vector. Lapply() applies a function over the elements of a list or vector. Seq() generates sequences of numbers. Length() returns the number of elements in an object. Rep() replicates elements in a vector.
This document discusses condition statements (if/else), functions that return values, user input using the input() function, logical operators, for loops used with strings, and string formatting. It provides examples of calculating total hours worked by employees, accessing characters in strings using indexes, common string methods like upper()/lower(), and basic cryptography techniques like the Caesar cipher. An exercise is given to write a program that encrypts a message using the Caesar cipher technique of rotating each letter by 3 positions.
The document contains code for performing various operations on arrays and matrices such as sorting, finding maximum/minimum values, averages, intersections and unions of sets. Functions are defined to accept input arrays/matrices, perform sorting algorithms like selection sort, bubble sort, quicksort, perform basic matrix operations like addition, subtraction, multiplication and find highest/lowest scores in arrays. The main menu drives the program by calling different functions based on user input.
Cosmetics Shop Management System is a complete solution for managing a Shop, in other words, an enhanced tool that assists in organizing the day-to-day activities of a Shop. There is the need of an application for efficient management and handling customer orders. This Cosmetics Shop Management System keeps every record Shop and reducing paperwork
This document contains source code for a computer shop management system project. It includes functions for adding, modifying, deleting, and searching computer product records in a database. It also contains functions for generating sales invoices and reports. The main menu allows selecting between product management, sales/purchases, and reports generation. Overall, the source code provides a way to manage the entire operations of a computer shop using a database to store product and sales information.
Development of an interactive car sale system which lets a user to find a car and its details is the main objective of this project. The administrators can access, enter, modify and delete the details of every car. Administrators are responsible of maintaining the details of vehicles like the Manufacturer information,
This document contains the source code for a book shop management system project. It includes functions for adding, modifying, deleting book records from the database, and searching books by various criteria. It also includes functions for generating reports on book sales and purchases and printing invoices. The source code uses Python and connects to a MySQL database to manage the book data.
1) The document discusses various Python flow control statements including if, if-else, nested if-else, and elif statements with examples of using these to check conditions and execute code blocks accordingly.
2) Examples include programs to check number comparisons, even/odd numbers, positive/negative numbers, and using nested if-else for multi-level checks like checking triangle validity.
3) The last few sections discuss using if-else statements to classify triangles as equilateral, isosceles, or scalene and to check if a number is positive, negative, or zero.
The document discusses Python's if-else conditional statements. It provides examples of using if-else to check 1) if a user's age is greater than or equal to 18, 2) if a number is positive or negative, 3) if a number is even or odd, 4) if a number is divisible by 3 or 7, and 5) if a year is a leap year. The last example shows how to find the maximum between two numbers using if-else. The syntax and logic of if-else statements are explained through these examples.
This document discusses different types of flow control in Python programs. It explains that a program's control flow defines the order of execution and can be altered using control flow statements. There are three main types of control flow: sequential, conditional/selection, and iterative/looping.
Sequential flow executes code lines in order. Conditional/selection statements like if/else allow decisions based on conditions. Iterative/looping statements like for and while loops repeat code for a set number of iterations or as long as a condition is true. Specific conditional statements, loops, and examples are described in more detail.
This document discusses different types of operators in Python including arithmetic, comparison, assignment, logical, membership, and identity operators. It provides examples of using arithmetic operators like addition, subtraction, multiplication, division, floor division, exponentiation, and modulus on variables. It also covers operator precedence and use of operators with strings.
The document discusses various operators in Python including assignment, comparison, logical, identity, and membership operators. It provides examples of how each operator works and the output. Specifically, it explains that assignment operators are used to assign values to variables using shortcuts like +=, -=, etc. Comparison operators compare values and return True or False. Logical operators combine conditional statements using and, or, and not. Identity operators compare the memory location of objects using is and is not. Membership operators test if a value is present in a sequence using in and not in.
The print() function in Python allows users to customize output. The sep and end parameters can be used to control the separator between values and the ending text. Sep allows specifying the character or string inserted between values, like a comma, while end controls the string appended after the last value, like a new line. Examples demonstrate using sep and end to print values on separate lines, with different separators like commas and tabs, or append text to the end of a print statement.
This document discusses data types and variables in Python. It explains that a variable is a name that refers to a memory location used to store values. The main data types in Python are numbers, strings, lists, tuples, and dictionaries. It provides examples of declaring and initializing different types of variables, including integers, floats, characters, and strings. Methods for assigning values, displaying values, and accepting user input are also demonstrated. The document also discusses type conversion using functions like int(), float(), and eval() when accepting user input.
The document discusses user-defined functions in Python. It provides examples of different types of functions: default functions without parameters, parameterized functions that accept arguments, and functions that return values. It demonstrates how to define functions using the def keyword and call functions. The examples show functions to print messages, calculate mathematical operations based on user input, check if a number is even or odd, and display sequences of numbers in different patterns using loops. Finally, it provides an example of a program that uses multiple functions and user input to perform mathematical operations.
This document discusses random functions in Python. It explains how to import the random module and describes several functions:
- random() generates random float numbers between 0 and 1
- randrange() returns random integers within a given range
- randint() returns random integers within a range similar to randrange()
Examples are provided to demonstrate how to use these functions to generate random numbers between certain values or in lists.
Functions allow programmers to organize code into reusable blocks to perform related actions. There are three types of functions: built-in functions, modules, and user-defined functions. Built-in functions like int(), float(), str(), and abs() are predefined to perform common tasks. Modules like the math module provide additional mathematical functions like ceil(), floor(), pow(), sqrt(), and trigonometric functions. User-defined functions are created by programmers to customize functionality.
NATURAL ENVIRONMENT,CATEGORIES OF RESOURCES,NATURAL RESOURCES,RENEWABLE AND NON-RENEWABLE,EXHAUSTIBLE , NON-EXHAUSTIBLE RESOURCES,HOW ENVIRONMENT IS CRUCIAL FOR US
WHAT IS DICTIONARY IN PYTHON?
HOW TO CREATE A DICTIONARY
INITIALIZE THE DICTIONARY
ACCESSING KEYS AND VALUES FROM A DICTIONARY
LOOPS TO DISPLAY KEYS AND VALUES IN A DICTIONARY
METHODS IN A DICTIONARY
TO WATCH VIDEO OR PDF:
https://computerassignmentsforu.blogspot.com/p/dictinpyxii.html
Bonk coin airdrop_ Everything You Need to Know.pdfHerond Labs
The Bonk airdrop, one of the largest in Solana’s history, distributed 50% of its total supply to community members, significantly boosting its popularity and Solana’s network activity. Below is everything you need to know about the Bonk coin airdrop, including its history, eligibility, how to claim tokens, risks, and current status.
https://blog.herond.org/bonk-coin-airdrop/
Plooma is a writing platform to plan, write, and shape books your wayPlooma
Plooma is your all in one writing companion, designed to support authors at every twist and turn of the book creation journey. Whether you're sketching out your story's blueprint, breathing life into characters, or crafting chapters, Plooma provides a seamless space to organize all your ideas and materials without the overwhelm. Its intuitive interface makes building rich narratives and immersive worlds feel effortless.
Packed with powerful story and character organization tools, Plooma lets you track character development and manage world building details with ease. When it’s time to write, the distraction-free mode offers a clean, minimal environment to help you dive deep and write consistently. Plus, built-in editing tools catch grammar slips and style quirks in real-time, polishing your story so you don’t have to juggle multiple apps.
What really sets Plooma apart is its smart AI assistant - analyzing chapters for continuity, helping you generate character portraits, and flagging inconsistencies to keep your story tight and cohesive. This clever support saves you time and builds confidence, especially during those complex, detail packed projects.
Getting started is simple: outline your story’s structure and key characters with Plooma’s user-friendly planning tools, then write your chapters in the focused editor, using analytics to shape your words. Throughout your journey, Plooma’s AI offers helpful feedback and suggestions, guiding you toward a polished, well-crafted book ready to share with the world.
With Plooma by your side, you get a powerful toolkit that simplifies the creative process, boosts your productivity, and elevates your writing - making the path from idea to finished book smoother, more fun, and totally doable.
Get Started here: https://www.plooma.ink/
Revolutionize Your Insurance Workflow with Claims Management SoftwareInsurance Tech Services
Claims management software enhances efficiency, accuracy, and satisfaction by automating processes, reducing errors, and speeding up transparent claims handling—building trust and cutting costs. Explore More - https://www.damcogroup.com/insurance/claims-management-software
Wondershare PDFelement Pro 11.4.20.3548 Crack Free DownloadPuppy jhon
➡ 🌍📱👉COPY & PASTE LINK👉👉👉 ➤ ➤➤ https://drfiles.net/
Wondershare PDFelement Professional is professional software that can edit PDF files. This digital tool can manipulate elements in PDF documents.
Marketo & Dynamics can be Most Excellent to Each Other – The SequelBradBedford3
So you’ve built trust in your Marketo Engage-Dynamics integration—excellent. But now what?
This sequel picks up where our last adventure left off, offering a step-by-step guide to move from stable sync to strategic power moves. We’ll share real-world project examples that empower sales and marketing to work smarter and stay aligned.
If you’re ready to go beyond the basics and do truly most excellent stuff, this session is your guide.
How Insurance Policy Administration Streamlines Policy Lifecycle for Agile Op...Insurance Tech Services
A modern Policy Administration System streamlines workflows and integrates with core systems to boost speed, accuracy, and customer satisfaction across the policy lifecycle. Visit https://www.damcogroup.com/insurance/policy-administration-systems for more details!
From Chaos to Clarity - Designing (AI-Ready) APIs with APIOps CyclesMarjukka Niinioja
Teams delivering API are challenges with:
- Connecting APIs to business strategy
- Measuring API success (audit & lifecycle metrics)
- Partner/Ecosystem onboarding
- Consistent documentation, security, and publishing
🧠 The big takeaway?
Many teams can build APIs. But few connect them to value, visibility, and long-term improvement.
That’s why the APIOps Cycles method helps teams:
📍 Start where the pain is (one “metro station” at a time)
📈 Scale success across strategy, platform, and operations
🛠 Use collaborative canvases to get buy-in and visibility
Want to try it and learn more?
- Follow APIOps Cycles in LinkedIn
- Visit the www.apiopscycles.com site
- Subscribe to email list
-
Agentic Techniques in Retrieval-Augmented Generation with Azure AI SearchMaxim Salnikov
Discover how Agentic Retrieval in Azure AI Search takes Retrieval-Augmented Generation (RAG) to the next level by intelligently breaking down complex queries, leveraging full conversation history, and executing parallel searches through a new LLM-powered query planner. This session introduces a cutting-edge approach that delivers significantly more accurate, relevant, and grounded answers—unlocking new capabilities for building smarter, more responsive generative AI applications.
Traditional Retrieval-Augmented Generation (RAG) pipelines work well for simple queries—but when users ask complex, multi-part questions or refer to previous conversation history, they often fall short. That’s where Agentic Retrieval comes in: a game-changing advancement in Azure AI Search that brings LLM-powered reasoning directly into the retrieval layer.
This session unveils how agentic techniques elevate your RAG-based applications by introducing intelligent query planning, subquery decomposition, parallel execution, and result merging—all orchestrated by a new Knowledge Agent. You’ll learn how this approach significantly boosts relevance, groundedness, and answer quality, especially for sophisticated enterprise use cases.
Key takeaways:
- Understand the evolution from keyword and vector search to agentic query orchestration
- See how full conversation context improves retrieval accuracy
- Explore measurable improvements in answer relevance and completeness (up to 40% gains!)
- Get hands-on guidance on integrating Agentic Retrieval with Azure AI Foundry and SDKs
- Discover how to build scalable, AI-first applications powered by this new paradigm
Whether you're building intelligent copilots, enterprise Q&A bots, or AI-driven search solutions, this session will equip you with the tools and patterns to push beyond traditional RAG.
Who will create the languages of the future?Jordi Cabot
Will future languages be created by language engineers?
Can you "vibe" a DSL?
In this talk, we will explore the changing landscape of language engineering and discuss how Artificial Intelligence and low-code/no-code techniques can play a role in this future by helping in the definition, use, execution, and testing of new languages. Even empowering non-tech users to create their own language infrastructure. Maybe without them even realizing.
In a tight labor market and tighter economy, PMOs and resource managers must ensure that every team member is focused on the highest-value work. This session explores how AI reshapes resource planning and empowers organizations to forecast capacity, prevent burnout, and balance workloads more effectively, even with shrinking teams.
Maximizing Business Value with AWS Consulting Services.pdfElena Mia
An overview of how AWS consulting services empower organizations to optimize cloud adoption, enhance security, and drive innovation. Read More: https://www.damcogroup.com/aws-cloud-services/aws-consulting.
NTRODUCTION TO SOFTWARE TESTING
• Definition:
• Software testing is the process of evaluating and
verifying that a software application or system meets
specified requirements and functions correctly.
• Purpose:
• Identify defects and bugs in the software.
• Ensure the software meets quality standards.
• Validate that the software performs as intended in
various scenarios.
• Importance:
• Reduces risks associated with software failures.
• Improves user satisfaction and trust in the product.
• Enhances the overall reliability and performance of
the software
Invited Talk at RAISE 2025: Requirements engineering for AI-powered SoftwarE Workshop co-located with ICSE, the IEEE/ACM International Conference on Software Engineering.
Abstract: Foundation Models (FMs) have shown remarkable capabilities in various natural language tasks. However, their ability to accurately capture stakeholder requirements remains a significant challenge for using FMs for software development. This paper introduces a novel approach that leverages an FM-powered multi-agent system called AlignMind to address this issue. By having a cognitive architecture that enhances FMs with Theory-of-Mind capabilities, our approach considers the mental states and perspectives of software makers. This allows our solution to iteratively clarify the beliefs, desires, and intentions of stakeholders, translating these into a set of refined requirements and a corresponding actionable natural language workflow in the often-overlooked requirements refinement phase of software engineering, which is crucial after initial elicitation. Through a multifaceted evaluation covering 150 diverse use cases, we demonstrate that our approach can accurately capture the intents and requirements of stakeholders, articulating them as both specifications and a step-by-step plan of action. Our findings suggest that the potential for significant improvements in the software development process justifies these investments. Our work lays the groundwork for future innovation in building intent-first development environments, where software makers can seamlessly collaborate with AIs to create software that truly meets their needs.
Async-ronizing Success at Wix - Patterns for Seamless Microservices - Devoxx ...Natan Silnitsky
In a world where speed, resilience, and fault tolerance define success, Wix leverages Kafka to power asynchronous programming across 4,000 microservices. This talk explores four key patterns that boost developer velocity while solving common challenges with scalable, efficient, and reliable solutions:
1. Integration Events: Shift from synchronous calls to pre-fetching to reduce query latency and improve user experience.
2. Task Queue: Offload non-critical tasks like notifications to streamline request flows.
3. Task Scheduler: Enable precise, fault-tolerant delayed or recurring workflows with robust scheduling.
4. Iterator for Long-running Jobs: Process extensive workloads via chunked execution, optimizing scalability and resilience.
For each pattern, we’ll discuss benefits, challenges, and how we mitigate drawbacks to create practical solutions
This session offers actionable insights for developers and architects tackling distributed systems, helping refine microservices and adopting Kafka-driven async excellence.
2. INDEX
Program to accept the string and count number of vowels in it.
Program to accept the string and count number words whose first letter is vowel.
Program to accept the string and check it’s a palindrome or not
Program to accept the number from user and search it in an array of 10 numbers using linear search.
Program to accept the numbers in an array unsorted order and display it in selection sort.
Program to swap first element with last, second to second last and so on (reversing elements)
Program to create a function name swap2best() with array as a parameter and swap all the even
index value.
Program to print the left diagonal from a 2D array
Program to swap the first row of the array with the last row of the array
Program to print the lower half of the 2d array.
3. Program to accept the string and count number of vowels in it.
function vowelcount(sent)
k=string.len(sent)
x=1
count=0
while(x<=k)
do
ch=string.sub(sent,x,x)
if(ch=='a' or ch=='e' or ch=='i' or ch=='u' or ch=='o')
then
count=count+1
end
x=x+1
end
print("no of vowels are = "..count)
end
vowelcount("my first program of vowel")
------------------output---------------------------
no of vowels are = 6
4. Program to accept the string and count number words whose first letter is vowel.
function vowelcount(sent)
k=string.len(sent)
x=1
count=0
flag=1
while(x<=k)
do
ch=string.sub(sent,x,x)
if(flag==1)
then
ch=string.sub(sent,x,x)
if(ch=='a' or ch=='e' or ch=='i' or ch=='u' or ch=='o')
then
count=count+1
flag=0
end
end
if(ch==' ')
then
x=x+1
ch=string.sub(sent,x,x)
if(ch=='a' or ch=='e' or ch=='i' or ch=='u' or ch=='o')
then
count=count+1
end
end
x=x+1
end
print("no of vowels are = "..count)
end
vowelcount("its my irst program of vowel word in our")
-------------------output-------------------
no of vowels are = 5
5. Program to accept the string and check it’s a palindrome or not
function checkpal(nm)
k=string.len(nm)
for x=1,math.floor(string.len(nm)/2),1
do
if(string.sub(nm,x,x) == string.sub(nm,k,k))
then
flag=1
else
flag=-1
break
end
k=k-1
end
return flag
end
nm=io.read()
t=checkpal(nm)
if(t==1)
then
print("Its a pal")
else
print("Its not a pal")
end
--------------output----------------
madam
Its a pal
6. Program to accept the number from user and search it in an array of 10 numbers using linear search.
no={5,10,25,8,6,53,4,9,12,14}
x=1
pos=-1
print("enter the number to search")
search=tonumber(io.read())
while(x<=10)
do
if(no[x]==search)
then
pos=x
break
end
x=x+1
end
if(pos<0)
then
print("no not found")
else
print("no found at "..pos)
end
----------------------output----------------------
enter the number to search
6
no found at 5
7. Program to accept the numbers in an array unsorted order and display it in selection sort.
no={5,10,25,8,6,53,4,9,12,14}
x=1
tmp=0
print("before sorting")
while(x<=10)
do
io.write(no[x].." ")
x=x+1
end
print()
x=1
while(x<=10)
do
y=x+1
while(y<=10)
do
if(no[x]>no[y])
then
tmp=no[x]
no[x]=no[y]
no[y]=tmp
end
y=y+1
end
x=x+1
end
print("After sorting")
x=1
while(x<=10)
do
io.write(no[x].." ")
x=x+1
end
------------------output----------------------
before sorting
5 10 25 8 6 53 4 9 12 14
After sorting
4 5 6 8 9 10 12 14 25 53
8. program to swap first element with last, second to second last and so on (reversing elements)
no={5,10,25,8,6,53,4,9,12,14}
x=1
tmp=0
print("Before........")
while(x<=10)
do
io.write(no[x].." ")
x=x+1
end
x=1
print()
y=10
for x=1,math.floor(10/2)
do
tmp=no[x]
no[x]=no[y]
no[y]=tmp
y=y-1
end
print("nAfter........")
x=1
while(x<=10)
do
io.write(no[x].." ")
x=x+1
end
------------------output-------------------------
Before........
5 10 25 8 6 53 4 9 12 14
After........
14 12 9 4 53 6 8 25 10 5
9. Program to create a function name swap2best() with array as a parameter and swap all the even index value.
function swap2best(no)
x=1
tmp=0
print("Before........")
while(x<=10)
do
io.write(no[x].." ")
x=x+1
end
x=1
print()
for x=1,10,2
do
tmp=no[x]
no[x]=no[x+1]
no[x+1]=tmp
end
print("nAfter........")
x=1
while(x<=10)
do
io.write(no[x].." ")
x=x+1
end
end
no={10,20,30,40,50,60,70,80,90,110}
swap2best(no)
-------------------output--------------------
Before........
10 20 30 40 50 60 70 80 90 110
After........
20 10 40 30 60 50 80 70 110 90
10. Program to print the left diagonal from a 2D array
function display(no,N)
x=1
tmp=0
print("Before........")
while(x<=N)
do
y=1
while(y<=N)
do
io.write(no[x][y].."t")
y=y+1
end
print()
x=x+1
end
end
function displayleftdiagonal(no,N)
for x=1,N
do
print(no[x][x])
end
end
no={{1,2,3,4},{5,6,7,8},{9,10,11,12},{13,14,15,16}}
display(no,4)
displayleftdiagonal(no,4)
------------------output------------------
1 2 3 4
5 6 7 8
9 10 11 12
13 14 15 16
1
6
11
16
11. Program to swap the first row of the array with the last row of the array
function display(no,R,C)
x=1
tmp=0
print(".....................")
while(x<=R)
do
y=1
while(y<=C)
do
io.write(no[x][y].."t")
y=y+1
end
print()
x=x+1
end
end
function swapfirstwithlastR(no,R,C)
last=R
for x=1,C
do
tmp=no[1][x]
no[1][x]=no[last][x]
no[last][x]=tmp
end
end
no={{1,2,3,4,1,5,7},{5,6,7,8,8,9,4},{9,10,11,12,11,23,5},{13,14,15,16,1,2,8}}
display(no,4,7)
swapfirstwithlastR(no,4,7)
display(no,4,7)
------------------output--------------------
.....................
1 2 3 4 1 5 7
5 6 7 8 8 9 4
9 10 11 12 11 23 5
13 14 15 16 1 2 8
.....................
13 14 15 16 1 2 8
5 6 7 8 8 9 4
9 10 11 12 11 23 5
1 2 3 4 1 5 7
12. Program to print the lower half of the 2d array.
function display(no,R,C)
x=1
tmp=0
print(".....................")
while(x<=R)
do
y=1
while(y<=C)
do
io.write(no[x][y].."t")
y=y+1
end
print()
x=x+1
end
end
function lowerhalf(no,R,C)
for x=1,R
do
for y=1,C
do
if(x>=y)
then
io.write(no[x][y].." ")
end
end
print()
end
end
no={{1,2,3,4,1,5,7},{5,6,7,8,8,9,4},{9,10,11,12,11,23,5},{13,14,15,16,1,2,8}}
display(no,4,7)
lowerhalf(no,4,7)
-----------------output-------------------------
1 2 3 4 1 5 7
5 6 7 8 8 9 4
9 10 11 12 11 23 5
13 14 15 16 1 2 8
1
5 6
9 10 11
13 14 15 16