This document discusses tuples and dictionaries in Python. Tuples are immutable sequences that are defined using parentheses, while dictionaries are mutable mappings that associate keys with values. The document provides examples of creating, accessing, iterating over, and modifying tuples and dictionaries using various built-in functions and methods. It also compares the differences between tuples, lists, and dictionaries.
The document discusses Python lists, which are the most basic data structure in Python. Lists allow storing multiple elements of different data types. Elements within lists can be accessed using indexes and slices, and lists support operations like concatenation, repetition, membership testing, and iteration. The document covers how to create, access, update, delete elements in lists, as well as built-in list functions and methods.
This document provides information about data structures in Python. It discusses lists, tuples, sets, and dictionaries. For lists and tuples, it covers defining, indexing, slicing, unpacking, methods, built-in functions, and list comprehensions. Lists can contain heterogeneous elements and support methods like append(), pop(), sort(), and reverse(). Tuples are similar to lists but are immutable ordered sequences. They also support indexing, slicing, and unpacking. This document serves as a guide to working with common Python data structures.
This document summarizes tuples and dictionaries in Python. Tuples are immutable sequences that are defined using round brackets. They can contain heterogeneous elements and support operations like indexing, slicing, and iteration. Dictionaries allow storing elements with non-integer keys and accessing them via indexing. They are mutable and support operations like adding/deleting elements and various functions. The next lecture will involve practicing with tuples and dictionaries.
The document discusses container data types in Python, including lists, tuples, sets, and dictionaries.
Lists allow indexing, slicing, and various methods like append(), insert(), pop(), and sort(). Tuples are like lists but immutable, and have methods like count(), index(), and tuple comprehension. Sets store unique elements and support operations like union and intersection. Dictionaries map keys to values and allow accessing elements via keys along with functions like get() and update().
The document discusses different Python data types including lists, tuples, and dictionaries. It provides information on how to create, access, modify, and delete items from each data type. For lists, it covers indexing, slicing, and common list methods. For tuples, it discusses creation, concatenation, slicing, and built-in methods. For dictionaries, it explains how they are created as a collection of unique keys and values, and how to access, add, remove, and delete key-value pairs.
Tuple assignment allows multiple variables to be assigned values from an iterable like a list or tuple in a single statement. This is more concise than separate assignments and avoids using a temporary variable. For example, to swap the values of variables a and b, tuple assignment can be used: a, b = b, a. The left side must contain the same number of variables as there are elements on the right, and each value is assigned to the corresponding variable from left to right. Tuple assignment is useful for unpacking elements like splitting a string into parts.
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 summarizes common Python data types including numbers, strings, lists, tuples, dictionaries, and sets. It describes that Python data types can be sequential (allow indexes) or nonsequential (no indexes) and changeable or unchangeable. For numbers, basic math operations like addition and multiplication are covered. Strings are described as sequences of characters that can be concatenated or multiplied. Lists are described as sequences that can hold elements of any type and support operators like addition and multiplication. Example list operations like append, remove, and insert are demonstrated.
This document provides an overview of different collection data types in Python including tuples, dictionaries, and sets. It discusses the key properties and uses of each type. Tuples are immutable sequences, dictionaries store key-value pairs and allow fast lookup by key, and sets only allow unique elements and support mathematical set operations. The document also covers performance considerations and recommends sets for fast membership checking of hashable elements.
The document provides information on tuples in Python. It defines a tuple as an ordered sequence of elements of different data types. Elements are enclosed in parentheses and separated by commas. Tuples are immutable, whereas lists are mutable. The document discusses accessing and slicing tuples, concatenation, repetition, and other tuple operations like count, index, min, max, and sum. It also covers nested tuples, tuple assignment, and examples of tuple methods and built-in functions.
This document provides an introduction to using lists in Python. It defines what lists are in Python, how to create, access, update, and delete list elements, and some common list operations. It also provides examples of creating lists, accessing values at different indices, updating and deleting elements, and using basic operators like addition and multiplication. Finally, it proposes three exercises involving lists to practice these concepts.
Tuples in Python are a type of data sequence, just like lists, with the difference being that they are immutable (you cannot add or delete elements). If you know how to use lists, working with tuples in Python should be a piece of cake. However, if you feel that you have some Python knowledge gaps
The document discusses Python lists, tuples, and dictionaries. It provides examples of how to create, access, modify, and loop through each of these data types. Lists are ordered and changeable collections that allow duplicate elements. Tuples are ordered and unchangeable collections that allow duplicate elements. Dictionaries are unordered collections of key-value pairs that do not allow duplicate keys. The document demonstrates various methods and operations available for each data type, such as appending and removing elements from lists, accessing elements by index in lists and tuples, and adding or modifying elements in dictionaries.
Anton Kasyanov, Introduction to Python, Lecture4Anton Kasyanov
This document provides an introduction and overview of Python lists, tuples, dictionaries, and files. It discusses how lists can contain a grouping of similar items indexed from 0, how tuples are immutable lists, how dictionaries contain key-value pairs, and how to read files line by line in Python. It also assigns as homework to create a basic translation program that uses lists or dictionaries to translate words between two languages using a file-based dictionary.
Python is a widely used high-level programming language for general-purpose programming. Python is a simple, powerful and easy to learn the programming language. It is commonly used for Web and Internet development, Scientific and Numeric computing, Business application and Desktop GUI development etc. The basic data structures in python are lists, dictionaries, tuples, strings and sets
This document discusses various Python data structures including lists, tuples, dictionaries, and sets. It provides examples of how to create, access, update, and delete elements in each data structure. For lists, examples demonstrate how to create and modify lists, access elements, and use common list methods. Tuples are explained as immutable lists that cannot be modified. Dictionaries are described as storing elements through key-value pairs. Sets are defined as unordered collections of unique elements.
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.
The document discusses Python lists, tuples, and dictionaries. It covers accessing and updating values in lists and tuples, deleting lists and tuples, and various built-in functions for lists, tuples, and dictionaries such as len(), max(), min(), etc. Methods for lists, tuples, and dictionaries are also explained, such as list.append(), tuple.count(), dict.clear(), etc. The key differences between lists, tuples, and dictionaries are that lists are mutable, tuples are immutable but can be concatenated, and dictionaries have unique keys.
The document discusses various concepts related to lists in Python including:
- What lists are and their main properties like being ordered, containing arbitrary objects that can be accessed by index, and being nestable and mutable.
- Common list methods like insert(), remove(), sort(), etc.
- How to define and assign lists, access list elements, and modify lists.
- List slicing and how it allows accessing a subset of list elements.
- Passing lists to functions and how lists are mutable.
- Algorithms for generating prime numbers and sorting lists like selection sort.
- Basic searching algorithms like linear search and binary search and how they work.
- The concept of list
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.
Tuple assignment allows multiple variables to be assigned values from an iterable like a list or tuple in a single statement. This is more concise than separate assignments and avoids using a temporary variable. For example, to swap the values of variables a and b, tuple assignment can be used: a, b = b, a. The left side must contain the same number of variables as there are elements on the right, and each value is assigned to the corresponding variable from left to right. Tuple assignment is useful for unpacking elements like splitting a string into parts.
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 summarizes common Python data types including numbers, strings, lists, tuples, dictionaries, and sets. It describes that Python data types can be sequential (allow indexes) or nonsequential (no indexes) and changeable or unchangeable. For numbers, basic math operations like addition and multiplication are covered. Strings are described as sequences of characters that can be concatenated or multiplied. Lists are described as sequences that can hold elements of any type and support operators like addition and multiplication. Example list operations like append, remove, and insert are demonstrated.
This document provides an overview of different collection data types in Python including tuples, dictionaries, and sets. It discusses the key properties and uses of each type. Tuples are immutable sequences, dictionaries store key-value pairs and allow fast lookup by key, and sets only allow unique elements and support mathematical set operations. The document also covers performance considerations and recommends sets for fast membership checking of hashable elements.
The document provides information on tuples in Python. It defines a tuple as an ordered sequence of elements of different data types. Elements are enclosed in parentheses and separated by commas. Tuples are immutable, whereas lists are mutable. The document discusses accessing and slicing tuples, concatenation, repetition, and other tuple operations like count, index, min, max, and sum. It also covers nested tuples, tuple assignment, and examples of tuple methods and built-in functions.
This document provides an introduction to using lists in Python. It defines what lists are in Python, how to create, access, update, and delete list elements, and some common list operations. It also provides examples of creating lists, accessing values at different indices, updating and deleting elements, and using basic operators like addition and multiplication. Finally, it proposes three exercises involving lists to practice these concepts.
Tuples in Python are a type of data sequence, just like lists, with the difference being that they are immutable (you cannot add or delete elements). If you know how to use lists, working with tuples in Python should be a piece of cake. However, if you feel that you have some Python knowledge gaps
The document discusses Python lists, tuples, and dictionaries. It provides examples of how to create, access, modify, and loop through each of these data types. Lists are ordered and changeable collections that allow duplicate elements. Tuples are ordered and unchangeable collections that allow duplicate elements. Dictionaries are unordered collections of key-value pairs that do not allow duplicate keys. The document demonstrates various methods and operations available for each data type, such as appending and removing elements from lists, accessing elements by index in lists and tuples, and adding or modifying elements in dictionaries.
Anton Kasyanov, Introduction to Python, Lecture4Anton Kasyanov
This document provides an introduction and overview of Python lists, tuples, dictionaries, and files. It discusses how lists can contain a grouping of similar items indexed from 0, how tuples are immutable lists, how dictionaries contain key-value pairs, and how to read files line by line in Python. It also assigns as homework to create a basic translation program that uses lists or dictionaries to translate words between two languages using a file-based dictionary.
Python is a widely used high-level programming language for general-purpose programming. Python is a simple, powerful and easy to learn the programming language. It is commonly used for Web and Internet development, Scientific and Numeric computing, Business application and Desktop GUI development etc. The basic data structures in python are lists, dictionaries, tuples, strings and sets
This document discusses various Python data structures including lists, tuples, dictionaries, and sets. It provides examples of how to create, access, update, and delete elements in each data structure. For lists, examples demonstrate how to create and modify lists, access elements, and use common list methods. Tuples are explained as immutable lists that cannot be modified. Dictionaries are described as storing elements through key-value pairs. Sets are defined as unordered collections of unique elements.
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.
The document discusses Python lists, tuples, and dictionaries. It covers accessing and updating values in lists and tuples, deleting lists and tuples, and various built-in functions for lists, tuples, and dictionaries such as len(), max(), min(), etc. Methods for lists, tuples, and dictionaries are also explained, such as list.append(), tuple.count(), dict.clear(), etc. The key differences between lists, tuples, and dictionaries are that lists are mutable, tuples are immutable but can be concatenated, and dictionaries have unique keys.
The document discusses various concepts related to lists in Python including:
- What lists are and their main properties like being ordered, containing arbitrary objects that can be accessed by index, and being nestable and mutable.
- Common list methods like insert(), remove(), sort(), etc.
- How to define and assign lists, access list elements, and modify lists.
- List slicing and how it allows accessing a subset of list elements.
- Passing lists to functions and how lists are mutable.
- Algorithms for generating prime numbers and sorting lists like selection sort.
- Basic searching algorithms like linear search and binary search and how they work.
- The concept of list
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 key concepts in data warehouse architecture including:
1) The functions of data warehouse tools which extract, clean, transform, load, and refresh data from source systems.
2) Key terminologies like metadata, which provides information about the data warehouse contents, and dimensional modeling using facts, dimensions, and data cubes.
3) Common multidimensional data models like star schemas with a central fact table linked to dimension tables and snowflake schemas which further normalize dimension tables.
The document discusses the Data Mining Query Language (DMQL), which was proposed for the DBMiner data mining system. DMQL is based on SQL and allows users to define data mining tasks by specifying data warehouses, data marts, and types of knowledge to mine, such as characterization, discrimination, association, classification, and prediction. It also provides syntax for concept hierarchy specification to organize data attributes into different levels.
This document discusses stacks and queues as data structures. It begins with an overview of stacks, including their definition as a last-in, first-out abstract data type and common stack operations. Array implementation of stacks is described through examples of push and pop operations. The document also covers applications of stacks and different notation styles for arithmetic expressions. Next, queues are introduced as first-in, first-out data structures, with details on their array representation and operations like enqueue and dequeue. Implementation of queues using arrays and handling overflow/underflow conditions are explained.
Quicksort is a widely used sorting algorithm that follows the divide and conquer paradigm. It works by recursively choosing a pivot element in an array, partitioning the array such that all elements less than the pivot come before all elements greater than the pivot, and then applying the same approach recursively to the sub-arrays. This has the effect of sorting the array in place with each iteration reducing the problem size until the entire array is sorted. The document provides pseudocode to implement quicksort and explains the algorithm at a high level.
This document discusses the architecture of knowledge-based systems (KBS). It explains that a KBS contains a knowledge module called the knowledge base (KB) and a control module called the inference engine. The KB explicitly represents knowledge that can be easily updated by domain experts without programming expertise. A knowledge engineer acts as a liaison between domain experts and the computer implementation. Propositional logic is then introduced as a basic technique for representing knowledge in KBS. It represents statements as atomic or compound propositions connected by logical operators like negation, conjunction, disjunction, implication, and biconditional.
Knowledge representation techniques are used to store knowledge in artificial intelligence systems so they can understand the world and solve complex problems. There are several common techniques, including logic, rules, semantic networks, frames, and scripts. Ontological engineering is used to develop large, modular ontologies that represent complex domains and allow knowledge to be integrated and combined. For knowledge representation systems to be effective, they must adequately and efficiently represent, store, manipulate, and acquire new knowledge.
The document discusses various file allocation methods and disk scheduling algorithms. There are three main file allocation methods - contiguous allocation, linked allocation, and indexed allocation. Contiguous allocation suffers from fragmentation but allows fast sequential access. Linked allocation does not have external fragmentation but is slower. Indexed allocation supports direct access but has higher overhead. For disk scheduling, algorithms like FCFS, SSTF, SCAN, CSCAN, and LOOK are described. SSTF provides lowest seek time while SCAN and CSCAN have higher throughput but longer wait times.
This document discusses segmentation in operating systems. Segmentation divides memory into variable-sized segments rather than fixed pages. Each process is divided into segments like the main program, functions, variables, etc. There are two types of segmentation: virtual memory segmentation which loads segments non-contiguously and simple segmentation which loads all segments together at once but non-contiguously in memory. Segmentation uses a segment table to map the two-part logical address to the single physical address through looking up the segment base address.
This document discusses virtual memory and demand paging. It explains that virtual memory separates logical memory from physical memory, allowing for larger address spaces than physical memory. Demand paging brings pages into memory only when needed, reducing I/O and memory usage. When a page is accessed that is not in memory, a page fault occurs and the operating system handles bringing that page in from disk while selecting a page to replace using an algorithm like FIFO, LRU, or optimal.
A decision tree classifier is explained. Key points include:
- Nodes test attribute values, edges correspond to test outcomes, and leaves predict the class.
- Information gain measures how much a variable contributes to the classification.
- It is used to select the variable that best splits the data at each node, with the highest information gain splitting the root node.
- An example calculates information gain for road type, obstruction, and speed limit variables to classify car speed. Speed limit has the highest information gain of 1 and is used to build the decision tree.
The document discusses the Apriori algorithm for frequent itemset mining. It explains that the Apriori algorithm uses an iterative approach consisting of join and prune steps to discover frequent itemsets that occur together above a minimum support threshold. The algorithm first finds all frequent 1-itemsets, then generates and prunes longer candidate itemsets in each iteration until no further frequent itemsets are found.
The document discusses the key components of a big data architecture. It describes how a big data architecture is needed to handle large volumes of data from multiple sources that is too large for traditional databases. The architecture ingests data from various sources, stores it, enables both batch and real-time analysis, and delivers business insights to users. It also provides examples of Flipkart's data platform which includes components like an ingestion system, batch/streaming processing, and a messaging queue.
Transform tomorrow: Master benefits analysis with Gen AI today webinar
Wednesday 30 April 2025
Joint webinar from APM AI and Data Analytics Interest Network and APM Benefits and Value Interest Network
Presenter:
Rami Deen
Content description:
We stepped into the future of benefits modelling and benefits analysis with this webinar on Generative AI (Gen AI), presented on Wednesday 30 April. Designed for all roles responsible in value creation be they benefits managers, business analysts and transformation consultants. This session revealed how Gen AI can revolutionise the way you identify, quantify, model, and realised benefits from investments.
We started by discussing the key challenges in benefits analysis, such as inaccurate identification, ineffective quantification, poor modelling, and difficulties in realisation. Learnt how Gen AI can help mitigate these challenges, ensuring more robust and effective benefits analysis.
We explored current applications and future possibilities, providing attendees with practical insights and actionable recommendations from industry experts.
This webinar provided valuable insights and practical knowledge on leveraging Gen AI to enhance benefits analysis and modelling, staying ahead in the rapidly evolving field of business transformation.
*"The Segmented Blueprint: Unlocking Insect Body Architecture"*.pptxArshad Shaikh
Insects have a segmented body plan, typically divided into three main parts: the head, thorax, and abdomen. The head contains sensory organs and mouthparts, the thorax bears wings and legs, and the abdomen houses digestive and reproductive organs. This segmentation allows for specialized functions and efficient body organization.
*"Sensing the World: Insect Sensory Systems"*Arshad Shaikh
Insects' major sensory organs include compound eyes for vision, antennae for smell, taste, and touch, and ocelli for light detection, enabling navigation, food detection, and communication.
How to Manage Amounts in Local Currency in Odoo 18 PurchaseCeline George
In this slide, we’ll discuss on how to manage amounts in local currency in Odoo 18 Purchase. Odoo 18 allows us to manage purchase orders and invoices in our local currency.
Form View Attributes in Odoo 18 - Odoo SlidesCeline George
Odoo is a versatile and powerful open-source business management software, allows users to customize their interfaces for an enhanced user experience. A key element of this customization is the utilization of Form View attributes.
Ajanta Paintings: Study as a Source of HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
What is the Philosophy of Statistics? (and how I was drawn to it)jemille6
What is the Philosophy of Statistics? (and how I was drawn to it)
Deborah G Mayo
At Dept of Philosophy, Virginia Tech
April 30, 2025
ABSTRACT: I give an introductory discussion of two key philosophical controversies in statistics in relation to today’s "replication crisis" in science: the role of probability, and the nature of evidence, in error-prone inference. I begin with a simple principle: We don’t have evidence for a claim C if little, if anything, has been done that would have found C false (or specifically flawed), even if it is. Along the way, I’ll sprinkle in some autobiographical reflections.
All About the 990 Unlocking Its Mysteries and Its Power.pdfTechSoup
In this webinar, nonprofit CPA Gregg S. Bossen shares some of the mysteries of the 990, IRS requirements — which form to file (990N, 990EZ, 990PF, or 990), and what it says about your organization, and how to leverage it to make your organization shine.
The role of wall art in interior designingmeghaark2110
Wall patterns are designs or motifs applied directly to the wall using paint, wallpaper, or decals. These patterns can be geometric, floral, abstract, or textured, and they add depth, rhythm, and visual interest to a space.
Wall art and wall patterns are not merely decorative elements, but powerful tools in shaping the identity, mood, and functionality of interior spaces. They serve as visual expressions of personality, culture, and creativity, transforming blank and lifeless walls into vibrant storytelling surfaces. Wall art, whether abstract, realistic, or symbolic, adds emotional depth and aesthetic richness to a room, while wall patterns contribute to structure, rhythm, and continuity in design. Together, they enhance the visual experience, making spaces feel more complete, welcoming, and engaging. In modern interior design, the thoughtful integration of wall art and patterns plays a crucial role in creating environments that are not only beautiful but also meaningful and memorable. As lifestyles evolve, so too does the art of wall decor—encouraging innovation, sustainability, and personalized expression within our living and working spaces.
Rock Art As a Source of Ancient Indian HistoryVirag Sontakke
This Presentation is prepared for Graduate Students. A presentation that provides basic information about the topic. Students should seek further information from the recommended books and articles. This presentation is only for students and purely for academic purposes. I took/copied the pictures/maps included in the presentation are from the internet. The presenter is thankful to them and herewith courtesy is given to all. This presentation is only for academic purposes.
Happy May and Happy Weekend, My Guest Students.
Weekends seem more popular for Workshop Class Days lol.
These Presentations are timeless. Tune in anytime, any weekend.
<<I am Adult EDU Vocational, Ordained, Certified and Experienced. Course genres are personal development for holistic health, healing, and self care. I am also skilled in Health Sciences. However; I am not coaching at this time.>>
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Hopefully Before Summer, We can add our courses to the teacher/creator section. It's all within project management and preps right now. So wish us luck.
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Understanding Vibrations
If not experienced, it may seem weird understanding vibes? We start small and by accident. Usually, we learn about vibrations within social. Examples are: That bad vibe you felt. Also, that good feeling you had. These are common situations we often have naturally. We chit chat about it then let it go. However; those are called vibes using your instincts. Then, your senses are called your intuition. We all can develop the gift of intuition and using energy awareness.
Energy Healing
First, Energy healing is universal. This is also true for Reiki as an art and rehab resource. Within the Health Sciences, Rehab has changed dramatically. The term is now very flexible.
Reiki alone, expanded tremendously during the past 3 years. Distant healing is almost more popular than one-on-one sessions? It’s not a replacement by all means. However, its now easier access online vs local sessions. This does break limit barriers providing instant comfort.
Practice Poses
You can stand within mountain pose Tadasana to get started.
Also, you can start within a lotus Sitting Position to begin a session.
There’s no wrong or right way. Maybe if you are rushing, that’s incorrect lol. The key is being comfortable, calm, at peace. This begins any session.
Also using props like candles, incenses, even going outdoors for fresh air.
(See Presentation for all sections, THX)
Clearing Karma, Letting go.
Now, that you understand more about energies, vibrations, the practice fusions, let’s go deeper. I wanted to make sure you all were comfortable. These sessions are for all levels from beginner to review.
Again See the presentation slides, Thx.
1. 10. Python - Lists
• The list is a most versatile datatype available in Python, which
can be written as a list of comma-separated values (items)
between square brackets. Good thing about a list that items in
a list need not all have the same type:
• Creating a list is as simple as putting different comma-
separated values between squere brackets. For example:
list1 = ['physics', 'chemistry', 1997, 2000];
list2 = [1, 2, 3, 4, 5 ];
list3 = ["a", "b", "c", "d"];
Like string indices, list indices start at 0, and lists can be sliced,
concatenated and so on.
2. Accessing Values in Lists:
• To access values in lists, use the square brackets for slicing
along with the index or indices to obtain value available at that
index:
• Example:
list1 = ['physics', 'chemistry', 1997, 2000];
list2 = [1, 2, 3, 4, 5, 6, 7 ];
print "list1[0]: ", list1[0]
print "list2[1:5]: ", list2[1:5]
• This will produce following result:
list1[0]: physics
list2[1:5]: [2, 3, 4, 5]
3. Updating Lists:
• You can update single or multiple elements of lists by giving the
slice on the left-hand side of the assignment operator, and you
can add to elements in a list with the append() method:
• Example:
list1 = ['physics', 'chemistry', 1997, 2000];
print "Value available at index 2 : "
print list1[2];
list1[2] = 2001;
print "New value available at index 2 : "
print list1[2];
• This will produce following result:
Value available at index 2 :
1997
New value available at index 2 :
2001
4. Delete List Elements:
• To remove a list element, you can use either the del statement
if you know exactly which element(s) you are deleting or the
remove() method if you do not know.
• Example:
list1 = ['physics', 'chemistry', 1997, 2000];
print list1;
del list1[2];
print "After deleting value at index 2 : "
print list1;
• This will produce following result:
['physics', 'chemistry', 1997, 2000]
After deleting value at index 2 :
['physics', 'chemistry', 2000]
5. Basic List Operations:
• Lists respond to the + and * operators much like strings; they mean
concatenation and repetition here too, except that the result is a new
list, not a string.
• In fact, lists respond to all of the general sequence operations we used
on strings in the prior chapter :
Python Expression Results Description
len([1, 2, 3]) 3 Length
[1, 2, 3] + [4, 5, 6] [1, 2, 3, 4, 5, 6] Concatenation
['Hi!'] * 4 ['Hi!', 'Hi!', 'Hi!', 'Hi!'] Repetition
3 in [1, 2, 3] TRUE Membership
for x in [1, 2, 3]: print x, 1 2 3 Iteration
6. Indexing, Slicing, and Matrixes:
• Because lists are sequences, indexing and slicing work the same way for
lists as they do for strings.
• Assuming following input:
L = ['spam', 'Spam', 'SPAM!']
Python Expression Results Description
L[2] 'SPAM!' Offsets start at zero
L[-2] 'Spam' Negative: count from
the right
L[1:] ['Spam', 'SPAM!'] Slicing fetches sections
7. Built-in List Functions & Methods:
SN Function with Description
1 cmp(list1, list2)
Compares elements of both lists.
2 len(list)
Gives the total length of the list.
3 max(list)
Returns item from the list with max value.
4 min(list)
Returns item from the list with min value.
5 list(seq)
Converts a tuple into list.
8. SN Methods with Description
1 list.append(obj)
Appends object obj to list
2 list.count(obj)
Returns count of how many times obj occurs in list
3 list.extend(seq)
Appends the contents of seq to list
4 list.index(obj)
Returns the lowest index in list that obj appears
5 list.insert(index, obj)
Inserts object obj into list at offset index
6 list.pop(obj=list[-1])
Removes and returns last object or obj from list
7 list.remove(obj)
Removes object obj from list
8 list.reverse()
Reverses objects of list in place
9 list.sort([func])
Sorts objects of list, use compare func if given
9. 11. Python - Tuples
• A tuple is a sequence of immutable Python objects. Tuples are sequences,
just like lists. The only difference is that tuples can't be changed ie. tuples
are immutable and tuples use parentheses and lists use square brackets.
• Creating a tuple is as simple as putting different comma-separated values
and optionally you can put these comma-separated values between
parentheses also. For example:
tup1 = ('physics', 'chemistry', 1997, 2000);
tup2 = (1, 2, 3, 4, 5 );
tup3 = "a", "b", "c", "d";
The empty tuple is written as two parentheses containing nothing:
tup1 = ();
To write a tuple containing a single value you have to include a comma, even
though there is only one value:
tup1 = (50,);
• Like string indices, tuple indices start at 0, and tuples can be sliced,
concatenated and so on.
10. Accessing Values in Tuples:
• To access values in tuple, use the square brackets for slicing
along with the index or indices to obtain value available at that
index:
• Example:
tup1 = ('physics', 'chemistry', 1997, 2000);
tup2 = (1, 2, 3, 4, 5, 6, 7 );
print "tup1[0]: ", tup1[0]
print "tup2[1:5]: “, tup2[1:5]
• This will produce following result:
tup1[0]: physics
tup2[1:5]: [2, 3, 4, 5]
11. Updating Tuples:
• Tuples are immutable which means you cannot update them or
change values of tuple elements. But we able able to take
portions of an existing tuples to create a new tuples as follows:
• Example:
tup1 = (12, 34.56);
tup2 = ('abc', 'xyz');
tup3 = tup1 + tup2;
print tup3;
This will produce following result:
(12, 34.56, 'abc', 'xyz')
12. Delete Tuple Elements:
• Removing individual tuple elements is not possible. There is, of
course, nothing wrong with putting together another tuple with
the undesired elements discarded.
• To explicitly remove an entire tuple, just use the del statement:
• Example:
tup = ('physics', 'chemistry', 1997, 2000);
print tup;
del tup;
print "After deleting tup : " print tup;
• This will produce following result.
('physics', 'chemistry', 1997, 2000)
After deleting tup : Traceback (most recent call
last): File "test.py", line 9, in <module> print
tup; NameError: name 'tup' is not defined
13. Basic Tuples Operations:
• Tuples respond to the + and * operators much like strings; they
mean concatenation and repetition here too, except that the
result is a new tuple, not a string.
• In fact, tuples respond to all of the general sequence
operations we used on strings in the prior chapter :
Python Expression Results Description
len((1, 2, 3)) 3 Length
(1, 2, 3) + (4, 5, 6) (1, 2, 3, 4, 5, 6) Concatenation
['Hi!'] * 4 ('Hi!', 'Hi!', 'Hi!', 'Hi!')Repetition
3 in (1, 2, 3) TRUE Membership
for x in (1, 2, 3):
print x,
1 2 3 Iteration
14. Indexing, Slicing, and Matrixes:
• Because tuples are sequences, indexing and slicing work the
same way for tuples as they do for strings.
• Assuming following input:
L = ('spam', 'Spam', 'SPAM!')
Python
Expression
Results Description
L[2] 'SPAM!' Offsets start at zero
L[-2] 'Spam' Negative: count from the right
L[1:] ['Spam', 'SPAM!'] Slicing fetches sections
15. No Enclosing Delimiters:
• Any set of multiple objects, comma-separated, written without
identifying symbols, i.e., brackets for lists, parentheses for
tuples, etc., default to tuples, as indicated in these short
examples:
print 'abc', -4.24e93, 18+6.6j, 'xyz';
u, v = 1, 2;
print "Value of u , v : ", u,v;
print var;
• This will reduce following result:
abc -4.24e+93 (18+6.6j) xyz
Value of u , v : 1 2
16. Built-in Tuple Functions:
SN Function with Description
1 cmp(tuple1, tuple2)
Compares elements of both tuples.
2 len(tuple)
Gives the total length of the tuple.
3 max(tuple)
Returns item from the tuple with max value.
4 min(tuple)
Returns item from the tuple with min value.
5 tuple(seq)
Converts a list into tuple.