The document provides information on various list methods in Python like list creation, accessing items from lists, slicing lists, and common list methods like append(), count(), extend(), index(), insert(), pop(), copy(), remove(), reverse(), and sort(). It includes the syntax and examples to demonstrate how each method works on lists. Various programs are given to showcase inserting, removing, sorting, copying and reversing elements in lists using the different list methods.
The document discusses files and file operations in C/C++. It defines a file as a collection of bytes stored on a secondary storage device. There are different types of files like text files, data files, program files, and directory files. It describes opening, reading, writing, appending, and closing files using functions like fopen(), fread(), fwrite(), fclose(), etc. It also discusses random and sequential file access and modifying file contents using functions like fseek(), fread(), fwrite().
The document describes external sorting techniques used when data is too large to fit in main memory. It discusses two-way sorting which uses two tape drive pairs to alternately write sorted runs. It also covers multi-way merging which merges multiple runs simultaneously using a heap. The techniques can improve performance over standard internal sorting.
The document discusses various indexing techniques used to improve data access performance in databases, including ordered indices like B-trees and B+-trees, as well as hashing techniques. It covers the basic concepts, data structures, operations, advantages and disadvantages of each approach. B-trees and B+-trees store index entries in sorted order to support range queries efficiently, while hashing distributes entries uniformly across buckets using a hash function but does not support ranges.
This presentation includes all the details regarding the Backus Naur Form and the Extended Backus Naur Form.For more information visit : https://www.youtube.com/watch?v=hl2NLbIaU7U&t=255s
What is Link list? explained with animationsPratikNaik41
This Presentation expalins what are data structures and linked lists with animations.
It also contains a sample implementation of Linked List using C++language.
It uses C++ Pointers to define linked lists.
DATA STRUCUTRES AND LINKED LIST
WHAT ARE DATA STRUCTURES?
Classification Of Data Structures ?
WHAT IS LINKED LIST?
WHY USE LINKED LIST?
INSERTION AND DELETION IN LINKED LIST
LINKED LIST CODE
The document discusses different types of queues, including simple, circular, priority, and double-ended queues. It describes the basic queue operations of enqueue and dequeue, where new elements are added to the rear of the queue and existing elements are removed from the front. Circular queues are more memory efficient than linear queues by connecting the last queue element back to the first, forming a circle. Priority queues remove elements based on priority rather than order of insertion. Double-ended queues allow insertion and removal from both ends. Common applications of queues include CPU and disk scheduling, synchronization between asynchronous processes, and call center phone systems.
This document provides an overview of object-oriented programming concepts using C++. It discusses key OOP concepts like objects, classes, encapsulation, inheritance, polymorphism, and dynamic binding. It also covers C++ specific topics like functions, arrays, strings, modular programming, and classes and objects in C++. The document is intended to introduce the reader to the fundamentals of OOP using C++.
This document summarizes arrays and structures in C including:
1) Arrays are sets of index-value pairs that use consecutive memory locations. Structures group related data and can reference themselves.
2) C implements 1D arrays using consecutive memory locations accessed via indexes. Structures allow defining custom data types.
3) Operations on ordered lists include retrieving, inserting, deleting elements. Polynomials are represented as ordered pairs of exponents and coefficients. Addition involves comparing exponents and combining coefficients.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document discusses hashing and different techniques for implementing dictionaries using hashing. It begins by explaining that dictionaries store elements using keys to allow for quick lookups. It then discusses different data structures that can be used, focusing on hash tables. The document explains that hashing allows for constant-time lookups on average by using a hash function to map keys to table positions. It discusses collision resolution techniques like chaining, linear probing, and double hashing to handle collisions when the hash function maps multiple keys to the same position.
The C programming language was created in 1972 at Bell Labs by Dennis Ritchie. It is a high-level, structured programming language that incorporates features of low-level languages like assembly. C programs use header files, variables, operators, input/output functions, and control statements like if/else and loops. Keywords, data types, and functions make C a flexible yet efficient language used widely in software development.
The document discusses lists in Python, including how to create, access, modify, loop through, slice, sort, and perform other operations on list elements. Lists can contain elements of different data types, are indexed starting at 0, and support methods like append(), insert(), pop(), and more to manipulate the list. Examples are provided to demonstrate common list operations and functions.
The document discusses stacks, which are a fundamental data structure used in programs. It defines a stack as a linear list of items where additions and deletions are restricted to one end, called the top. Common stack operations include push, which adds an element to the top, and pop, which removes an element from the top. Stacks have applications in parsing expressions, reversing strings, implementing depth-first search algorithms, and calculating arithmetic expressions in prefix and postfix notation. Stacks can be implemented using static arrays or dynamic arrays/linked lists.
Functions allow programmers to structure C++ programs into modular segments of code to perform individual tasks. There are two types of functions: library functions and user-defined functions. User-defined functions are defined using a return type, function name, and parameters. Functions can be called by value or by reference and can also be inline, recursive, or friend functions.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
The document provides an overview of file handling in C++. It discusses key concepts such as streams, file types (text and binary), opening and closing files, file modes, input/output operations, and file pointers. Functions for reading and writing to text files include put(), get(), and getline(). Binary files use write() and read() functions. File pointers can be manipulated using seekg(), seekp(), tellg(), and tellp() to move through files.
This presentation introduces built-in functions in C programming. It defines built-in functions as functions provided by the C library that perform common tasks like file access, math operations, and graphics without needing to be defined by the programmer. It provides examples of commonly used built-in functions from header files like stdio.h for input/output, string.h for string manipulation, and math.h for math functions. The presentation concludes by noting advantages like code reusability but also potential disadvantages like increased complexity from functions.
The document discusses input/output files in Java. It covers the key classes used for reading and writing files in Java, including FileInputStream, FileOutputStream, FileReader, and FileWriter. It also discusses byte streams versus character streams, and provides examples of reading and writing to files in Java using these classes. Standard input/output streams like System.in and System.out are also covered.
Types of Statements in Python Programming LanguageExplore Skilled
The slide describes the types of flow control statements in Python including conditional statement , loop statement , control statement such as break and continue .
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Union in C allows defining a data type that contains multiple members of different data types that share the same memory location. The size of the memory allocated for a union is equal to the size of its largest member. Only one member can be accessed at a time since they share the same memory location. Accessing different members can corrupt the values stored as the memory is shared.
This document discusses insertion sort, including its mechanism, algorithm, runtime analysis, advantages, and disadvantages. Insertion sort works by iterating through an unsorted array and inserting each element into its sorted position by shifting other elements over. Its worst case runtime is O(n^2) when the array is reverse sorted, but it performs well on small, nearly sorted lists. While simple to implement, insertion sort is inefficient for large datasets compared to other algorithms.
Call by value or call by reference in C++Sachin Yadav
Call by value means passing the value directly to a function. The called function uses the value in a local variable; any changes to it DO NOT affect the source variable. In call by value method, the called function creates its own copies of original values sent to it. Any changes, that are made, occur on the function’s copy of values and are not reflected back to the calling function.
This document summarizes arrays and structures in C including:
1) Arrays are sets of index-value pairs that use consecutive memory locations. Structures group related data and can reference themselves.
2) C implements 1D arrays using consecutive memory locations accessed via indexes. Structures allow defining custom data types.
3) Operations on ordered lists include retrieving, inserting, deleting elements. Polynomials are represented as ordered pairs of exponents and coefficients. Addition involves comparing exponents and combining coefficients.
NumPy is a Python library that provides multidimensional array and matrix objects to perform scientific computing. It contains efficient functions for operations on arrays like arithmetic, aggregation, copying, indexing, slicing, and reshaping. NumPy arrays have advantages over native Python sequences like fixed size and efficient mathematical operations. Common NumPy operations include elementwise arithmetic, aggregation functions, copying and transposing arrays, changing array shapes, and indexing/slicing arrays.
This document discusses hashing and different techniques for implementing dictionaries using hashing. It begins by explaining that dictionaries store elements using keys to allow for quick lookups. It then discusses different data structures that can be used, focusing on hash tables. The document explains that hashing allows for constant-time lookups on average by using a hash function to map keys to table positions. It discusses collision resolution techniques like chaining, linear probing, and double hashing to handle collisions when the hash function maps multiple keys to the same position.
The C programming language was created in 1972 at Bell Labs by Dennis Ritchie. It is a high-level, structured programming language that incorporates features of low-level languages like assembly. C programs use header files, variables, operators, input/output functions, and control statements like if/else and loops. Keywords, data types, and functions make C a flexible yet efficient language used widely in software development.
The document discusses lists in Python, including how to create, access, modify, loop through, slice, sort, and perform other operations on list elements. Lists can contain elements of different data types, are indexed starting at 0, and support methods like append(), insert(), pop(), and more to manipulate the list. Examples are provided to demonstrate common list operations and functions.
The document discusses stacks, which are a fundamental data structure used in programs. It defines a stack as a linear list of items where additions and deletions are restricted to one end, called the top. Common stack operations include push, which adds an element to the top, and pop, which removes an element from the top. Stacks have applications in parsing expressions, reversing strings, implementing depth-first search algorithms, and calculating arithmetic expressions in prefix and postfix notation. Stacks can be implemented using static arrays or dynamic arrays/linked lists.
Functions allow programmers to structure C++ programs into modular segments of code to perform individual tasks. There are two types of functions: library functions and user-defined functions. User-defined functions are defined using a return type, function name, and parameters. Functions can be called by value or by reference and can also be inline, recursive, or friend functions.
In DBMS (DataBase Management System), the relation algebra is important term to further understand the queries in SQL (Structured Query Language) database system. In it just give up the overview of operators in DBMS two of one method relational algebra used and another name is relational calculus.
The document discusses various searching and sorting algorithms. It describes linear search, binary search, and interpolation search for searching unsorted and sorted lists. It also explains different sorting algorithms like bubble sort, selection sort, insertion sort, quicksort, shellsort, heap sort, and merge sort. Linear search searches sequentially while binary search uses divide and conquer. Sorting algorithms like bubble sort, selection sort, and insertion sort are in-place and have quadratic time complexity in the worst case. Quicksort, mergesort, and heapsort generally have better performance.
The document provides an overview of file handling in C++. It discusses key concepts such as streams, file types (text and binary), opening and closing files, file modes, input/output operations, and file pointers. Functions for reading and writing to text files include put(), get(), and getline(). Binary files use write() and read() functions. File pointers can be manipulated using seekg(), seekp(), tellg(), and tellp() to move through files.
This presentation introduces built-in functions in C programming. It defines built-in functions as functions provided by the C library that perform common tasks like file access, math operations, and graphics without needing to be defined by the programmer. It provides examples of commonly used built-in functions from header files like stdio.h for input/output, string.h for string manipulation, and math.h for math functions. The presentation concludes by noting advantages like code reusability but also potential disadvantages like increased complexity from functions.
The document discusses input/output files in Java. It covers the key classes used for reading and writing files in Java, including FileInputStream, FileOutputStream, FileReader, and FileWriter. It also discusses byte streams versus character streams, and provides examples of reading and writing to files in Java using these classes. Standard input/output streams like System.in and System.out are also covered.
Types of Statements in Python Programming LanguageExplore Skilled
The slide describes the types of flow control statements in Python including conditional statement , loop statement , control statement such as break and continue .
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Union in C allows defining a data type that contains multiple members of different data types that share the same memory location. The size of the memory allocated for a union is equal to the size of its largest member. Only one member can be accessed at a time since they share the same memory location. Accessing different members can corrupt the values stored as the memory is shared.
This document discusses insertion sort, including its mechanism, algorithm, runtime analysis, advantages, and disadvantages. Insertion sort works by iterating through an unsorted array and inserting each element into its sorted position by shifting other elements over. Its worst case runtime is O(n^2) when the array is reverse sorted, but it performs well on small, nearly sorted lists. While simple to implement, insertion sort is inefficient for large datasets compared to other algorithms.
Call by value or call by reference in C++Sachin Yadav
Call by value means passing the value directly to a function. The called function uses the value in a local variable; any changes to it DO NOT affect the source variable. In call by value method, the called function creates its own copies of original values sent to it. Any changes, that are made, occur on the function’s copy of values and are not reflected back to the calling function.
This document provides an overview of Python data types including sequences like lists, tuples, and strings. It discusses that lists are mutable sequences that allow items to be modified, while tuples and strings are immutable sequences that do not allow items to be changed. It also summarizes some common operations for sequences like accessing items, slicing to extract subsets, using the 'in' operator to check for membership, and concatenation using the + operator.
This document provides an overview of Python data types including sequences like lists, tuples, and strings. It discusses that lists are mutable sequences that allow items to be modified, while tuples and strings are immutable sequences that do not allow items to be changed. It also summarizes some common operations for sequences like accessing items, slicing to extract subsets, using the 'in' operator to check for membership, and concatenation using the + operator.
This document provides an overview of Python data types including tuples, lists, strings, and dictionaries. It describes how to define, access, modify, and perform common operations on each type. Tuples are immutable ordered sequences defined with parentheses while lists are mutable ordered sequences defined with square brackets. Strings are immutable sequences defined with quotes. Dictionaries store mappings between keys and values.
Processing data with Python, using standard library modules you (probably) ne...gjcross
Tutorial #2 from PyCon AU 2012
You have data.
You have Python.
You also have a lot of choices about the best way to work with that data...
Ever wondered when you would use a tuple, list, dictionary, set, ordered dictionary, bucket, queue, counter or named tuple? Phew!
Do you know when to use a loop, iterator or generator to work through a data container?
Why are there so many different "containers" to hold data?
What are the best ways to work with these data containers?
This tutorial will give you all the basics to effectively working with data containers and iterators in Python. Along the way we will cover some very useful modules from the standard library that you may not have used before and will end up wondering how you ever did without them.
This tutorial is aimed at Python beginners. Bring along your laptop so you can interactively work through some of the examples in the tutorial. If you can, install ipython (http://ipython.org/) as we will use it for the demonstrations.
This document contains notes from several coding sessions that cover various Python concepts:
1. Strings are immutable objects, so string functions return new strings rather than modifying the original. Common string functions include lower(), capitalize(), strip(), replace(), split(), and join().
2. Tuples are immutable like strings and are used to store multi-dimensional data like coordinates. They are created with parentheses and can be nested.
3. Lists, strings, and tuples are all examples of sequences that support operations like indexing, slicing, length, and iteration with for loops.
4. Dictionaries are mutable objects that store key-value pairs and are accessed using square bracket notation. They are considered unordered
UNIT-3 python and data structure alo.pptxharikahhy
Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc).
Python has a simple syntax similar to the English language.
Python has syntax that allows developers to write programs with fewer lines than some other programming languages.
Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick.
Python can be treated in a procedural way, an object-oriented way or a functional way.
Good to know
The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular.
In this tutorial Python will be written in a text editor. It is possible to write Python in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans or Eclipse which are particularly useful when managing larger collections of Python files.
Python Syntax compared to other programming languages
Python was designed for readability, and has some similarities to the English language with influence from mathematics.
Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses.
Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose.
This document discusses various data structures in Python including lists, dictionaries, tuples, sets, and strings. Lists are mutable sequences that can contain heterogeneous data types. Dictionaries are mutable and store key-value pairs with unique keys. Tuples are immutable sequences that can be nested. Sets store unordered and unique elements. Strings are immutable sequences of characters that support slicing, formatting, and other common string operations.
This document discusses strings, lists, tuples, and dictionaries in Python. It shows how to initialize and access elements in each of these data types. Strings can be initialized using single, double, or triple quotes. Lists store elements enclosed in square brackets and support indexing, slicing, concatenation, and other operations. Tuples are like lists but use parentheses; they are immutable. Dictionaries map keys to values using curly braces and colons. Elements in strings, lists, and tuples are accessed via integer indices, while dictionary items are accessed via their keys.
PRESENTATION ON STRING, LISTS AND TUPLES IN PYTHON.pptxkirtisharma7537
The presentation, titled "String Lists and Tuples (MCA - 2nd Semester Batch 2021-23)," was meticulously prepared and delivered by Kirti Sharma. The presentation was given to Dr. Vaishali Joshi, an Associate Professor at BVICAM. The primary goal of this presentation was to provide an in-depth understanding of three fundamental data types in Python: strings, lists, and tuples. These data types are crucial for anyone aspiring to excel in Python programming, and the presentation was designed to impart a solid foundation in these concepts.
The presentation begins with an introduction to strings, which are one of the most essential data types in Python. A string in Python is defined as a sequence of characters enclosed within quotes. These characters can be letters, numbers, or symbols. Python allows strings to be created in three different ways: using single quotes, double quotes, or triple quotes. For example, a string can be written as ‘flower’, “flower”, or ‘’’flower’’’. This flexibility allows for easier handling of strings, especially when the string itself contains quotes. Understanding the different ways to create strings is important because it allows programmers to write more readable and maintainable code, particularly when dealing with text that includes quotes or spans multiple lines.
Once the basic definition is established, the presentation delves into the concept of string slicing. String slicing is a powerful feature in Python that allows a programmer to extract a portion of a string. In Python, strings are indexed arrays of bytes representing characters in the string. This means that each character in a string has a corresponding index number, starting from zero for the first character and going up to the length of the string minus one for the last character. For example, in the string "amazing," the character ‘a’ is at index 0, ‘m’ is at index 1, and so on.
To slice a string, one can use the syntax string[start
], where "start" is the index where the slice begins, and "end" is the index where the slice ends. Importantly, the character at the "end" index is not included in the slice. For instance, if one slices the string "amazing" from index 1 to 4 using the command word[1:4], the result would be "maz," as it includes characters from index 1 to 3. This ability to extract specific parts of a string is invaluable in various programming scenarios, such as when parsing data or manipulating text.
The presentation also covers more advanced slicing techniques, including the use of negative indices and skip values. Negative indexing allows one to count characters from the end of the string, with -1 representing the last character, -2 the second-to-last, and so on. For example, word[-1] would return the last character of the string. This feature is particularly useful when one needs to work with the end portion of a string without knowing its exact length.
This document provides an overview of common compound data types in Python, including strings, tuples, lists, and dictionaries. It describes how to define, access elements, traverse, slice, mutate, and clone each type. Strings and tuples are immutable sequence types that can be indexed but not changed. Lists are mutable sequences that support element assignment. Dictionaries store key-value pairs and allow lookup by key. Slicing can be used to copy sequences to avoid aliasing references to the same data.
In computer science, a data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.
Vibrant Technologies is headquarted in Mumbai,India.We are the best Python training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Python classes in Mumbai according to our students and corporators
This document discusses Python strings and string methods. It defines strings as immutable ordered sequences of characters that can be indexed, sliced, modified, concatenated, and formatted. The document provides examples of creating, accessing, modifying, and formatting string values in Python using single quotes, double quotes, triple quotes, indexing, slicing, upper/lower case conversion, stripping, replacement, splitting, concatenation, and string formatting.
Fundamentals of Information Technology study MaterialBobby Murugesan
This is the study material of Fundamentals Information Technology of B.Sc Computer Science Students of Mother Teresa Women's University Kodaikanal. It covers the complete syllabus unit wise.
Computer skills for web designing and video editing_Lab Manual.pdfBobby Murugesan
The document provides code for creating various web elements using HTML tags. It includes code samples for creating definition lists, ordered and unordered lists, tables, images, hyperlinks, forms, and more. Instructions are provided for each code sample explaining how to create each element step-by-step using the appropriate HTML tags. The document serves as a manual for learning basic HTML tags and how to use them to design web pages and forms.
This document contains a table of contents and code snippets for various Python programming exercises, including finding the largest of three numbers, calculating mean, median, and mode of a list of numbers, swapping two variables, checking if a number is Armstrong, finding the square root of a number, checking if a number is odd or even, reversing a number, checking for palindromes, checking for leap years, finding the area of a triangle, counting characters in a string and storing in a dictionary, finding prime numbers, calculating length of a string, and performing basic math operations. The document provides sample input/output for each code snippet.
It shows the top most impressive google apps and its usages. Students can know about the apps developed by Google and the usage and its functionality too.
Rose Cultivation Practices by Kushal Lamichhane.pdfkushallamichhame
This includes the overall cultivation practices of Rose prepared by:
Kushal Lamichhane (AKL)
Instructor
Shree Gandhi Adarsha Secondary School
Kageshowri Manohara-09, Kathmandu, Nepal
Unit- 4 Biostatistics & Research Methodology.pdfKRUTIKA CHANNE
Blocking and confounding (when a third variable, or confounder, influences both the exposure and the outcome) system for Two-level factorials (a type of experimental design where each factor (independent variable) is investigated at only two levels, typically denoted as "high" and "low" or "+1" and "-1")
Regression modeling (statistical model that estimates the relationship between one dependent variable and one or more independent variables using a line): Hypothesis testing in Simple and Multiple regression models
Introduction to Practical components of Industrial and Clinical Trials Problems: Statistical Analysis Using Excel, SPSS, MINITAB®️, DESIGN OF EXPERIMENTS, R - Online Statistical Software to Industrial and Clinical trial approach
Slides from a Capitol Technology University presentation covering doctoral programs offered by the university. All programs are online, and regionally accredited. The presentation covers degree program details, tuition, financial aid and the application process.
Diptera: The Two-Winged Wonders, The Fly Squad: Order Diptera.pptxArshad Shaikh
Diptera, commonly known as flies, is a large and diverse order of insects that includes mosquitoes, midges, gnats, and horseflies. Characterized by a single pair of wings (hindwings are modified into balancing organs called halteres), Diptera are found in almost every environment and play important roles in ecosystems as pollinators, decomposers, and food sources. Some species, however, are significant pests and disease vectors, transmitting diseases like malaria, dengue, and Zika virus.
Analysis of Quantitative Data Parametric and non-parametric tests.pptxShrutidhara2
This presentation covers the following points--
Parametric Tests
• Testing the Significance of the Difference between Means
• Analysis of Variance (ANOVA) - One way and Two way
• Analysis of Co-variance (One-way)
Non-Parametric Tests:
• Chi-Square test
• Sign test
• Median test
• Sum of Rank test
• Mann-Whitney U-test
Moreover, it includes a comparison of parametric and non-parametric tests, a comparison of one-way ANOVA, two-way ANOVA, and one-way ANCOVA.
How to Manage & Create a New Department in Odoo 18 EmployeeCeline George
In Odoo 18's Employee module, organizing your workforce into departments enhances management and reporting efficiency. Departments are a crucial organizational unit within the Employee module.
Energy Balances Of Oecd Countries 2011 Iea Statistics 1st Edition Oecdrazelitouali
Energy Balances Of Oecd Countries 2011 Iea Statistics 1st Edition Oecd
Energy Balances Of Oecd Countries 2011 Iea Statistics 1st Edition Oecd
Energy Balances Of Oecd Countries 2011 Iea Statistics 1st Edition Oecd
How to Create Quotation Templates Sequence in Odoo 18 SalesCeline George
In this slide, we’ll discuss on how to create quotation templates sequence in Odoo 18 Sales. Odoo 18 Sales offers a variety of quotation templates that can be used to create different types of sales documents.
THE QUIZ CLUB OF PSGCAS BRINGS T0 YOU A FUN-FILLED, SEAT EDGE BUSINESS QUIZ
DIVE INTO THE PRELIMS OF BIZCOM 2024
QM: GOWTHAM S
BCom (2022-25)
THE QUIZ CLUB OF PSGCAS
How to Create an Event in Odoo 18 - Odoo 18 SlidesCeline George
Creating an event in Odoo 18 is a straightforward process that allows you to manage various aspects of your event efficiently.
Odoo 18 Events Module is a powerful tool for organizing and managing events of all sizes, from conferences and workshops to webinars and meetups.
Human Anatomy and Physiology II Unit 3 B pharm Sem 2
Respiratory system
Anatomy of respiratory system with special reference to anatomy
of lungs, mechanism of respiration, regulation of respiration
Lung Volumes and capacities transport of respiratory gases,
artificial respiration, and resuscitation methods
Urinary system
Anatomy of urinary tract with special reference to anatomy of
kidney and nephrons, functions of kidney and urinary tract,
physiology of urine formation, micturition reflex and role of
kidneys in acid base balance, role of RAS in kidney and
disorders of kidney
How to Configure Vendor Management in Lunch App of Odoo 18Celine George
The Vendor management in the Lunch app of Odoo 18 is the central hub for managing all aspects of the restaurants or caterers that provide food for your employees.
RE-LIVE THE EUPHORIA!!!!
The Quiz club of PSGCAS brings to you a fun-filled breezy general quiz set from numismatics to sports to pop culture.
Re-live the Euphoria!!!
QM: Eiraiezhil R K,
BA Economics (2022-25),
The Quiz club of PSGCAS
1. Sequence types: Tuples,
Lists, and Strings
M.BOBBY
ASSISTANT PROFESSOR & HEAD,
DEPARTMENT OF COMPUTER SCIENCE
SRI ADI CHUNCHANAGIRI WOMEN’S COLLEGE, CUMBUM
2. Sequence Types
1. Tuple: (‘john’, 32, [CMSC])
A simple immutable ordered sequence of items
Items can be of mixed types, including collection types
2. Strings: “John Smith”
◦ Immutable
◦ Conceptually very much like a tuple
3. List: [1, 2, ‘john’, (‘up’, ‘down’)]
Mutable ordered sequence of items of mixed types
3. Similar Syntax
All three sequence types (tuples, strings, and lists) share much
of the same syntax and functionality.
Key difference:
◦Tuples and strings are immutable
◦ Lists are mutable
The operations shown in this section can be applied to all
sequence types
◦most examples will just show the operation performed on one
4. Sequence Types 1
Define tuples using parentheses and commas
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
Define lists are using square brackets and commas
>>> li = [“abc”, 34, 4.34, 23]
Define strings using quotes (“, ‘, or “““).
>>> st = “Hello World”
>>> st = ‘Hello World’
>>> st = “““This is a multi-line
string that uses triple quotes.”””
5. Sequence Types 2
Access individual members of a tuple, list, or string using square bracket “array” notation
Note that all are 0 based…
>>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’)
>>> tu[1] # Second item in the tuple.
‘abc’
>>> li = [“abc”, 34, 4.34, 23]
>>> li[1] # Second item in the list.
34
>>> st = “Hello World”
>>> st[1] # Second character in string.
‘e’
6. Positive and negative indices
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Positive index: count from the left, starting with 0
>>> t[1]
‘abc’
Negative index: count from right, starting with –1
>>> t[-3]
4.56
7. Slicing: return copy of a subset
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Return a copy of the container with a subset of the original members. Start copying at
the first index, and stop copying before second.
>>> t[1:4]
(‘abc’, 4.56, (2,3))
Negative indices count from end
>>> t[1:-1]
(‘abc’, 4.56, (2,3))
8. Slicing: return copy of a =subset
>>> t = (23, ‘abc’, 4.56, (2,3), ‘def’)
Omit first index to make copy starting from beginning of the container
>>> t[:2]
(23, ‘abc’)
Omit second index to make copy starting at first index and going to end
>>> t[2:]
(4.56, (2,3), ‘def’)
9. Copying the Whole Sequence
[ : ] makes a copy of an entire sequence
>>> t[:]
(23, ‘abc’, 4.56, (2,3), ‘def’)
Note the difference between these two lines for mutable sequences
>>> l2 = l1 # Both refer to 1 ref,
# changing one affects both
>>> l2 = l1[:] # Independent copies, two refs
10. The ‘in’ Operator
Boolean test whether a value is inside a container:
>>> t = [1, 2, 4, 5]
>>> 3 in t
False
>>> 4 in t
True
>>> 4 not in t
False
For strings, tests for substrings
>>> a = 'abcde'
>>> 'c' in a
True
>>> 'cd' in a
True
>>> 'ac' in a
False
Be careful: the in keyword is also used in the syntax of for loops and list comprehensions
11. The + Operator
The + operator produces a new tuple, list, or string whose value is the concatenation of its arguments.
>>> (1, 2, 3) + (4, 5, 6)
(1, 2, 3, 4, 5, 6)
>>> [1, 2, 3] + [4, 5, 6]
[1, 2, 3, 4, 5, 6]
>>> “Hello” + “ ” + “World”
‘Hello World’
12. The * Operator
The * operator produces a new tuple, list, or string that “repeats” the original content.
>>> (1, 2, 3) * 3
(1, 2, 3, 1, 2, 3, 1, 2, 3)
>>> [1, 2, 3] * 3
[1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> “Hello” * 3
‘HelloHelloHello’