This document discusses stacks and their implementation and use. It begins by defining a stack as a list of elements where insertion and deletion can only occur at one end, called the top. The two basic stack operations are described as push, which inserts an element, and pop, which deletes an element. A stack follows the LIFO (last in, first out) principle. Stacks can be represented using arrays or linked lists. The push and pop operations for each representation are then explained in steps. The document also covers infix, postfix, and prefix notation for arithmetic expressions and provides an algorithm to convert infix to postfix notation using a stack.
The document discusses stacks and queues. It begins by defining a stack as a collection of homogeneous data elements where insertion and deletion occurs at one end, known as the top. The stack follows the LIFO (last in, first out) principle. Common stack operations like push and pop are introduced. Implementing stacks using both arrays and linked lists is covered. Applications of stacks like balancing symbols, recursion, undo operations are mentioned. The document then moves to discussing queues, their applications and implementations. Priority queues and their applications are also briefly covered.
The document describes insertion sort, a sorting algorithm. It lists the group members who researched insertion sort and provides an introduction. It then explains how insertion sort works by example, showing how it iterates through an array and inserts elements into the sorted portion. Pseudocode and analysis of insertion sort's runtime is provided. Comparisons are made between insertion sort and other algorithms like bubble sort, selection sort, and merge sort, analyzing their time complexities in best, average, and worst cases.
The document discusses different searching algorithms like linear search, binary search, and Fibonacci search. It explains the algorithms through pseudocode and examples. Linear search has worst case complexity of O(n) as it may need to search through the entire list. Binary search has worst case complexity of O(log n) as it divides the search space in half in each step. Fibonacci search generalizes binary search by dividing the search space in Fibonacci numbers ratios in each step.
The document discusses stacks and their implementation and use in evaluating arithmetic expressions. It defines a stack as a collection of elements that can only be inserted or removed from one end, called the top. Stacks follow a last-in, first-out (LIFO) principle. Arrays can be used to implement stacks, with a pointer tracking the current top element. Common stack operations like push, pop, peek, etc. are described. Stacks are useful for converting infix expressions to postfix and then evaluating them, as operators are processed after their operands in postfix notation.
The document discusses stacks and their implementation as linked lists and arrays. It defines a stack as a last-in, first-out data structure where insertion and deletion can only occur at one end, called the top. The key stack operations are push, which inserts at the top, pop, which removes from the top, and top, which retrieves the top element. Stacks have applications in postfix notation and converting infix to postfix expressions.
The document discusses stacks and queues. Stacks are linear lists where additions and removals occur at one end, called the top. Stacks follow LIFO order. Queues are linear lists where additions occur at one end, called the rear, and removals occur at the other end, called the front, following FIFO order. Common stack and queue operations and implementations using arrays are described. Examples like the Towers of Hanoi problem and rat in a maze demonstrate stack and queue applications.
This document discusses stacks and their implementation and use. It begins by defining a stack as a list of elements where insertion and deletion can only occur at one end, called the top. The two basic stack operations are described as push, which inserts an element, and pop, which deletes an element. A stack follows the LIFO (last in, first out) principle. Stacks can be represented using arrays or linked lists. The push and pop operations for each representation are then explained in steps. The document also covers infix, postfix, and prefix notation for arithmetic expressions and provides an algorithm to convert infix to postfix notation using a stack.
The document discusses stacks and queues. It begins by defining a stack as a collection of homogeneous data elements where insertion and deletion occurs at one end, known as the top. The stack follows the LIFO (last in, first out) principle. Common stack operations like push and pop are introduced. Implementing stacks using both arrays and linked lists is covered. Applications of stacks like balancing symbols, recursion, undo operations are mentioned. The document then moves to discussing queues, their applications and implementations. Priority queues and their applications are also briefly covered.
The document describes insertion sort, a sorting algorithm. It lists the group members who researched insertion sort and provides an introduction. It then explains how insertion sort works by example, showing how it iterates through an array and inserts elements into the sorted portion. Pseudocode and analysis of insertion sort's runtime is provided. Comparisons are made between insertion sort and other algorithms like bubble sort, selection sort, and merge sort, analyzing their time complexities in best, average, and worst cases.
The document discusses different searching algorithms like linear search, binary search, and Fibonacci search. It explains the algorithms through pseudocode and examples. Linear search has worst case complexity of O(n) as it may need to search through the entire list. Binary search has worst case complexity of O(log n) as it divides the search space in half in each step. Fibonacci search generalizes binary search by dividing the search space in Fibonacci numbers ratios in each step.
The document discusses stacks and their implementation and use in evaluating arithmetic expressions. It defines a stack as a collection of elements that can only be inserted or removed from one end, called the top. Stacks follow a last-in, first-out (LIFO) principle. Arrays can be used to implement stacks, with a pointer tracking the current top element. Common stack operations like push, pop, peek, etc. are described. Stacks are useful for converting infix expressions to postfix and then evaluating them, as operators are processed after their operands in postfix notation.
The document discusses stacks and their implementation as linked lists and arrays. It defines a stack as a last-in, first-out data structure where insertion and deletion can only occur at one end, called the top. The key stack operations are push, which inserts at the top, pop, which removes from the top, and top, which retrieves the top element. Stacks have applications in postfix notation and converting infix to postfix expressions.
The document discusses stacks and queues. Stacks are linear lists where additions and removals occur at one end, called the top. Stacks follow LIFO order. Queues are linear lists where additions occur at one end, called the rear, and removals occur at the other end, called the front, following FIFO order. Common stack and queue operations and implementations using arrays are described. Examples like the Towers of Hanoi problem and rat in a maze demonstrate stack and queue applications.
https://github.com/ashim888/dataStructureAndAlgorithm
Stack
Concept and Definition
• Primitive Operations
• Stack as an ADT
• Implementing PUSH and POP operation
• Testing for overflow and underflow conditions
Recursion
• Concept and Definition
• Implementation of:
¬ Multiplication of Natural Numbers
¬ Factorial
¬ Fibonacci Sequences
The Tower of Hanoi
The document summarizes sorting and searching algorithms. It describes linear and binary search algorithms and analyzes their performance. It also describes selection sort, bubble sort, and heapsort algorithms. Selection sort has O(n^2) performance while bubble sort and heapsort have comparable performance to selection sort. Heapsort improves on these and has O(nlogn) performance by using a heap data structure implemented as an array.
This document provides an overview and comparison of insertion sort and shellsort sorting algorithms. It describes how insertion sort works by repeatedly inserting elements into a sorted left portion of the array. Shellsort improves on insertion sort by making passes with larger increments to shift values into approximate positions before final sorting. The document discusses the time complexities of both algorithms and provides examples to illustrate how they work.
Stacks, Queues, and Deques describes different data structures and their properties. Stacks follow LIFO order with insertion and removal at the same end. Queues follow FIFO order with insertion at one end and removal at the other. Deques allow insertion and removal at both ends. These structures can be implemented using arrays or linked lists.
This document discusses various sorting algorithms including merge sort. It begins with an introduction to sorting and searching. It then provides pseudocode for the merge sort algorithm which works by dividing the array into halves, recursively sorting the halves, and then merging the sorted halves back together. An example is provided to illustrate the merge sort process. Key steps include dividing, conquering by recursively sorting subarrays, and combining through merging. The overall time complexity of merge sort is O(n log n).
Lecture 02: Preliminaries of Data structureNurjahan Nipa
This document provides an overview of algorithms and data structures. It begins with mathematical notation and complexity analysis. It then discusses control structures like sequence, selection, and iteration. Specific algorithms are described, including finding the largest element in an array, linear search, and the sieve method for finding prime numbers. Floor, ceiling, integer, absolute value, exponent, and logarithm functions are also covered. Overall, the document serves as a lecture outline on fundamental algorithmic concepts.
The document discusses circular linked lists and stacks. It begins by explaining circular linked lists, how they differ from regular singly and doubly linked lists, and how to implement operations like insertion and deletion in a circular linked list. It then covers stacks, including common stack operations like push and pop. Stacks can be implemented using either arrays or linked lists. The document provides examples of how to represent a stack using each data structure and perform operations on it. It also discusses applications of stacks, like evaluating arithmetic expressions in postfix notation using a stack.
This document summarizes a lecture on stacks from a data structures course. It defines stacks as ordered groups where elements are added and removed from the top in LIFO order. It describes basic stack operations like push, pop, and status checks. Stacks can be represented using arrays or linked lists. Array representations require tracking the top index, while linked lists use nodes connected by pointers. Pushing adds an element to the top, while popping removes from the top.
The document discusses stacks and queues, which are fundamental data structures that can insert and remove items. Stacks follow the LIFO (last in, first out) principle, while queues follow the FIFO (first in, first out) principle. The document provides implementations of stacks and queues using linked lists and arrays and examples of their applications in parsing, compilers, undo functions, and more.
The document discusses functions and non-functions through several examples of relations between x and y values. It provides tables and graphs of relations that illustrate functions, such as a y value being mapped to only one x value, as well as non-functions where a single x value is mapped to multiple y values or a y value is not mapped to any x values at all.
This document discusses data structures and algorithms for stacks and queues. It describes the common operations for stacks and queues like push, pop, enqueue, dequeue. It provides array-based and linked list-based implementations for both stacks and queues. For queues, it discusses two implementations using arrays: one using front and rear indexes, and another using a circular queue approach. The time complexities of common operations on stacks and queues are also analyzed.
This document discusses stacks as a linear data structure that follows the LIFO (Last-In First-Out) principle. Key points include:
- Stacks allow insertion and deletion from one end only (the top) through PUSH and POP operations.
- Common applications of stacks include reversing strings, checking validity of expressions with nested parentheses, and converting infix notation to postfix notation for arithmetic expressions.
- The document provides examples and algorithms for common stack operations like insertion, deletion, and display.
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
The document discusses sorting and searching algorithms. It introduces sorting as a way to make searching faster. Linear search and binary search are described as ways to search through unsorted and sorted lists, respectively. Common sorting algorithms like selection sort, insertion sort, and Shellsort are explained. Selection sort and insertion sort have quadratic runtimes while Shellsort is subquadratic. Maintaining stability is also discussed as a property of some sorting algorithms.
a presentation covering all the topics on Stacks and Queues in Data Structures using C++. Includes self explanatory diagrams, algorithms and program pieces.
The document discusses two methods for searching elements in an array: linear search and binary search.
Linear search sequentially checks each element of the unsorted array to find a match. It has a worst case time complexity of O(n) where n is the number of elements.
Binary search works on a sorted array. It repeatedly divides the search space in half by checking the middle element. This reduces the number of elements to search by half in each step. Binary search has a time complexity of O(log n).
The document then provides details on how linear search and binary search algorithms work including pseudocode examples. It also discusses sorting algorithms and uses selection sort as an example to sort an array in ascending order.
The document summarizes lecture 2 of the CIS-122 Data Structures course. It covers applications of stacks like arithmetic expressions, recursion, quicksort, and towers of Hanoi. It also discusses stack implementations using arrays and linked lists and provides examples of infix to postfix conversion and evaluating arithmetic expressions using a stack.
Describes basic understanding of priority queues, their applications, methods, implementation with sorted/unsorted list, sorting applications with insertion sort and selection sort with their running times.
This document provides an overview of insertion sort, including:
- How insertion sort works by dividing an array into sorted and unsorted parts, inserting unsorted elements into the sorted part in each pass.
- The algorithm scans the array from left to right, inserting each element into the correct position in the previously sorted subarray.
- Runtime is O(n^2) in the worst and average cases due to multiple comparisons and shifts that may be required.
- Pseudocode and Java code examples are provided to demonstrate implementation.
- Advantages are simplicity and efficiency for small datasets, while disadvantages include poor performance for large arrays compared to other algorithms.
A stack is a basic data structure that can be logically thought as linear structure represented by a real physical stack or pile, a structure where insertion and deletion of items take place at one end called the top of the stack.
SQL is a standard language for accessing and manipulating databases. It allows users to query, insert, update, and delete data. The document discusses SQL statements like SELECT, INSERT, UPDATE, DELETE and functions like JOIN, aggregate functions, and scalar functions. It provides examples of creating tables and manipulating data using DDL, DML, and DCL statements. Joins allow combining data from multiple tables based on common fields.
This document provides an introduction to SQL server functions, grouping, and joins. It discusses aggregate functions that return single values from columns like average, sum, min, and count. It also discusses scalar functions that return a single value for each row like upper, lower, and round. The document explains how to use the GROUP BY clause to group result sets and then run aggregate functions on the groups. It also covers the HAVING clause, which filters grouped data. Finally, it describes different types of joins like inner joins, left outer joins, and right outer joins.
https://github.com/ashim888/dataStructureAndAlgorithm
Stack
Concept and Definition
• Primitive Operations
• Stack as an ADT
• Implementing PUSH and POP operation
• Testing for overflow and underflow conditions
Recursion
• Concept and Definition
• Implementation of:
¬ Multiplication of Natural Numbers
¬ Factorial
¬ Fibonacci Sequences
The Tower of Hanoi
The document summarizes sorting and searching algorithms. It describes linear and binary search algorithms and analyzes their performance. It also describes selection sort, bubble sort, and heapsort algorithms. Selection sort has O(n^2) performance while bubble sort and heapsort have comparable performance to selection sort. Heapsort improves on these and has O(nlogn) performance by using a heap data structure implemented as an array.
This document provides an overview and comparison of insertion sort and shellsort sorting algorithms. It describes how insertion sort works by repeatedly inserting elements into a sorted left portion of the array. Shellsort improves on insertion sort by making passes with larger increments to shift values into approximate positions before final sorting. The document discusses the time complexities of both algorithms and provides examples to illustrate how they work.
Stacks, Queues, and Deques describes different data structures and their properties. Stacks follow LIFO order with insertion and removal at the same end. Queues follow FIFO order with insertion at one end and removal at the other. Deques allow insertion and removal at both ends. These structures can be implemented using arrays or linked lists.
This document discusses various sorting algorithms including merge sort. It begins with an introduction to sorting and searching. It then provides pseudocode for the merge sort algorithm which works by dividing the array into halves, recursively sorting the halves, and then merging the sorted halves back together. An example is provided to illustrate the merge sort process. Key steps include dividing, conquering by recursively sorting subarrays, and combining through merging. The overall time complexity of merge sort is O(n log n).
Lecture 02: Preliminaries of Data structureNurjahan Nipa
This document provides an overview of algorithms and data structures. It begins with mathematical notation and complexity analysis. It then discusses control structures like sequence, selection, and iteration. Specific algorithms are described, including finding the largest element in an array, linear search, and the sieve method for finding prime numbers. Floor, ceiling, integer, absolute value, exponent, and logarithm functions are also covered. Overall, the document serves as a lecture outline on fundamental algorithmic concepts.
The document discusses circular linked lists and stacks. It begins by explaining circular linked lists, how they differ from regular singly and doubly linked lists, and how to implement operations like insertion and deletion in a circular linked list. It then covers stacks, including common stack operations like push and pop. Stacks can be implemented using either arrays or linked lists. The document provides examples of how to represent a stack using each data structure and perform operations on it. It also discusses applications of stacks, like evaluating arithmetic expressions in postfix notation using a stack.
This document summarizes a lecture on stacks from a data structures course. It defines stacks as ordered groups where elements are added and removed from the top in LIFO order. It describes basic stack operations like push, pop, and status checks. Stacks can be represented using arrays or linked lists. Array representations require tracking the top index, while linked lists use nodes connected by pointers. Pushing adds an element to the top, while popping removes from the top.
The document discusses stacks and queues, which are fundamental data structures that can insert and remove items. Stacks follow the LIFO (last in, first out) principle, while queues follow the FIFO (first in, first out) principle. The document provides implementations of stacks and queues using linked lists and arrays and examples of their applications in parsing, compilers, undo functions, and more.
The document discusses functions and non-functions through several examples of relations between x and y values. It provides tables and graphs of relations that illustrate functions, such as a y value being mapped to only one x value, as well as non-functions where a single x value is mapped to multiple y values or a y value is not mapped to any x values at all.
This document discusses data structures and algorithms for stacks and queues. It describes the common operations for stacks and queues like push, pop, enqueue, dequeue. It provides array-based and linked list-based implementations for both stacks and queues. For queues, it discusses two implementations using arrays: one using front and rear indexes, and another using a circular queue approach. The time complexities of common operations on stacks and queues are also analyzed.
This document discusses stacks as a linear data structure that follows the LIFO (Last-In First-Out) principle. Key points include:
- Stacks allow insertion and deletion from one end only (the top) through PUSH and POP operations.
- Common applications of stacks include reversing strings, checking validity of expressions with nested parentheses, and converting infix notation to postfix notation for arithmetic expressions.
- The document provides examples and algorithms for common stack operations like insertion, deletion, and display.
The document discusses stacks in C++. It defines a stack as a data structure that follows LIFO (Last In First Out) principle where the last element added is the first to be removed. Stacks can be implemented using arrays or linked lists. The key operations on a stack are push which adds an element and pop which removes an element. Example applications of stacks include function call stacks, converting infix to postfix notation, and reversing arrays.
The document discusses sorting and searching algorithms. It introduces sorting as a way to make searching faster. Linear search and binary search are described as ways to search through unsorted and sorted lists, respectively. Common sorting algorithms like selection sort, insertion sort, and Shellsort are explained. Selection sort and insertion sort have quadratic runtimes while Shellsort is subquadratic. Maintaining stability is also discussed as a property of some sorting algorithms.
a presentation covering all the topics on Stacks and Queues in Data Structures using C++. Includes self explanatory diagrams, algorithms and program pieces.
The document discusses two methods for searching elements in an array: linear search and binary search.
Linear search sequentially checks each element of the unsorted array to find a match. It has a worst case time complexity of O(n) where n is the number of elements.
Binary search works on a sorted array. It repeatedly divides the search space in half by checking the middle element. This reduces the number of elements to search by half in each step. Binary search has a time complexity of O(log n).
The document then provides details on how linear search and binary search algorithms work including pseudocode examples. It also discusses sorting algorithms and uses selection sort as an example to sort an array in ascending order.
The document summarizes lecture 2 of the CIS-122 Data Structures course. It covers applications of stacks like arithmetic expressions, recursion, quicksort, and towers of Hanoi. It also discusses stack implementations using arrays and linked lists and provides examples of infix to postfix conversion and evaluating arithmetic expressions using a stack.
Describes basic understanding of priority queues, their applications, methods, implementation with sorted/unsorted list, sorting applications with insertion sort and selection sort with their running times.
This document provides an overview of insertion sort, including:
- How insertion sort works by dividing an array into sorted and unsorted parts, inserting unsorted elements into the sorted part in each pass.
- The algorithm scans the array from left to right, inserting each element into the correct position in the previously sorted subarray.
- Runtime is O(n^2) in the worst and average cases due to multiple comparisons and shifts that may be required.
- Pseudocode and Java code examples are provided to demonstrate implementation.
- Advantages are simplicity and efficiency for small datasets, while disadvantages include poor performance for large arrays compared to other algorithms.
A stack is a basic data structure that can be logically thought as linear structure represented by a real physical stack or pile, a structure where insertion and deletion of items take place at one end called the top of the stack.
SQL is a standard language for accessing and manipulating databases. It allows users to query, insert, update, and delete data. The document discusses SQL statements like SELECT, INSERT, UPDATE, DELETE and functions like JOIN, aggregate functions, and scalar functions. It provides examples of creating tables and manipulating data using DDL, DML, and DCL statements. Joins allow combining data from multiple tables based on common fields.
This document provides an introduction to SQL server functions, grouping, and joins. It discusses aggregate functions that return single values from columns like average, sum, min, and count. It also discusses scalar functions that return a single value for each row like upper, lower, and round. The document explains how to use the GROUP BY clause to group result sets and then run aggregate functions on the groups. It also covers the HAVING clause, which filters grouped data. Finally, it describes different types of joins like inner joins, left outer joins, and right outer joins.
The document discusses algorithms and their analysis. It begins by defining an algorithm and key aspects like correctness, input, and output. It then discusses two aspects of algorithm performance - time and space. Examples are provided to illustrate how to analyze the time complexity of different structures like if/else statements, simple loops, and nested loops. Big O notation is introduced to describe an algorithm's growth rate. Common time complexities like constant, linear, quadratic, and cubic functions are defined. Specific sorting algorithms like insertion sort, selection sort, bubble sort, merge sort, and quicksort are then covered in detail with examples of how they work and their time complexities.
The document discusses different types of parsing including:
1) Top-down parsing which starts at the root node and builds the parse tree recursively, requiring backtracking for ambiguous grammars.
2) Bottom-up parsing which starts at the leaf nodes and applies grammar rules in reverse to reach the start symbol using shift-reduce parsing.
3) LL(1) and LR parsing which are predictive parsing techniques using parsing tables constructed from FIRST and FOLLOW sets to avoid backtracking.
The document summarizes topics that will be covered in an advanced SQL training seminar, including SQL statement types, data types, aggregate functions, NULL handling, comparison operators, mathematical functions, joins, subqueries, views, materialized views, inline views, and optimizing SQL queries. Techniques for data sharing between databases are also mentioned. The seminar aims to provide in-depth knowledge of SQL concepts through explanations, examples, exercises and discussion.
This document outlines the objectives and units of study for the course GE3151 Problem Solving and Python Programming. The objectives include understanding algorithmic problem solving, learning to solve problems using Python conditionals and loops, defining functions, and using data structures like lists, tuples and dictionaries. The 5 units of study are: Computational Thinking and Problem Solving, Data Types Expressions and Statements, Control Flow Functions and Strings, Lists Tuples and Dictionaries, and Files Modules and Packages. Some example problems and programs are provided for each unit to illustrate the concepts covered.
Photon Technical Deep Dive: How to Think VectorizedDatabricks
Photon is a new vectorized execution engine powering Databricks written from scratch in C++. In this deep dive, I will introduce you to the basic building blocks of a vectorized engine by walking you through the evaluation of an example query with code snippets. You will learn about expression evaluation, compute kernels, runtime adaptivity, filter evaluation, and vectorized operations against hash tables.
This document contains a list of SQL interview questions and answers. Some of the questions covered include:
- Explain NVL, NVL2 and NULLIF functions
- How to create a third index on a table that already has two indexes
- The difference between a view and materialized view
Top-down parsing constructs the parse tree from the top-down and left-to-right. Recursive descent parsing uses backtracking to find the left-most derivation, while predictive parsing does not require backtracking by using a special form of grammars called LL(1) grammars. Non-recursive predictive parsing is also known as LL(1) parsing and uses a table-driven approach without recursion or backtracking.
MariaDB Server 10.3 - Temporale Daten und neues zur DB-KompatibilitätMariaDB plc
MariaDB Server 10.3 (RC) introduces enhancements for temporal data support, database compatibility, performance, flexibility, and scalability. Key features include system versioned tables for querying historical data, PL/SQL compatibility for stored functions, sequences, intersect and except operators, and user-defined aggregate functions. The Spider storage engine is also updated.
This document provides an overview of Oracle database and relational database management systems (RDBMS). It defines key terms like data, database, DBMS, RDBMS and describes table structure with rows and columns. Popular RDBMS like Oracle, SQL Server, DB2, Teradata and open-source options like PostgreSQL and MySQL are listed. Core SQL commands, joins, constraints, transactions and other relational database concepts are described at a high level.
Direct solution of sparse network equations by optimally ordered triangular f...Dimas Ruliandi
Triangular factorization method of a power network problem (in form of matrix). Direct solution can be found without calculating inverse matrix which usually considered an exhaustive method, especially in large scale network.
This tutorial will give you a quick start to SQL including examples. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works.
This document provides an overview of using VBA in Excel. It discusses where to place code in Excel objects like sheets and modules. It also covers declaring variables, loops, and manipulating pivot tables, ranges, and worksheets using VBA. Examples shown include filtering pivot tables, formatting cells, adding and removing hyperlinks, protecting and hiding sheets, and beautifying workbooks. The document emphasizes best practices for writing clear and well-structured VBA code.
MariaDB Server 10.3 provides enhancements for temporal data support, database compatibility, and performance. Key features include:
- System versioned tables to store and query historical data at different points in time.
- Improved Oracle compatibility with features like PL/SQL parsing, packages for stored functions, sequences, and additional data types.
- Performance improvements such as adding instant columns for InnoDB and statement-based lock wait timeouts.
- Other new features include user-defined aggregate functions, compressed columns, and proxy protocol support.
Structuring and packaging your python projectEyal Trabelsi
This document discusses best practices for structuring Python projects. It recommends keeping the root directory clean and organized with files like README, LICENSE, requirements.txt, tests, and documentation. Python projects should be logically broken up into modules and packages to separate concerns and avoid issues like circular dependencies. Modules are individual .py files while packages are folders containing an __init__.py file. The document also covers importing, sharing code as installable packages, and using setup.py to define packages for distribution.
Today as more and more companies become data-driven, Exploratory data analysis is taking major part of our work t allows one to understand the gist of what your data look like and what kinds of questions might be answered by them.
In this talk, we will discuss about few libraries that will make your EDA work much easier with few lines of python code.
Lecture about making you a happier developer.
Many of us spend a lot of time in the vanilla black and white terminal, and this talk is about making terminal fun.
It includes the following topics:
- ASCII Art History and tools
- Arcade game from terminal
- Animations
- Cool themes for terminal
- Pranks for the terminal
This session hold data about SQL in general and specifically on :
- string gotchas
- string functions
- string aggregations
- basic NLP
- spaghetti queries
Advance sql - window functions patterns and tricksEyal Trabelsi
This document discusses various ways that window functions can be used to analyze event data. It provides examples and templates for calculating cumulative sums, growth rates, identifying first events, sessionizing events, finding sequence lengths, joining on time intervals, and deduplicating records. Common use cases include analyzing trends over time, identifying changes or transitions, joining related events, and cleaning duplicate data. Templates are provided that can be adapted for different analyses involving partitions, orders, lags, leads and rankings.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
9. Null Definition
● Null signifies an unknown value or that a
value does not exist.
● The predicate is null can be used to check
for Null values.
10. Null Definition
● Null signifies an unknown value or that a
value does not exist.
● The predicate is null can be used to check
for Null values.
SELECT <column_list>
FROM [tablenames]
WHERE <column_a> IS NULL
12. Null Weird Behavior
● 5 + Null returns ?
● 5 < Null returns ?
● Null = Null ?
● Null in (Null) returns ?
● Null and true?
● Null and false?
● Null or true ?
● Null or false?
13. Null Weird Behavior
● 5 + Null returns ? Null
● 5 < Null returns ? Null
● Null = Null ? Null != Null
● Null in (Null) returns ? Null because in use = operator
● Null and true? Null
● Null and false? False
● Null or true ? True
● Null or false? Null
14. Null Weird Behavior
● The result of any arithmetic expression involving null is null.
15. Null Weird Behavior
● The result of any arithmetic expression involving null is null.
● Any comparison with null returns unknown.
16. Null Weird Behavior
● The result of any arithmetic expression involving null is null.
● Any comparison with null returns unknown.
● All aggregate operations except count(*) ignore tuples with null
values on the aggregated attributes.
17. Null Weird Behavior
● The result of any arithmetic expression involving null is null.
● Any comparison with null returns unknown.
● All aggregate operations except count(*) ignore tuples with null
values on the aggregated attributes.
● logic inference
21. Null Weird Behavior- Problem Example
a c
c a
d b
a b
NULL a
c b
T2T1
Retrieve rows of table table T1 which doesn’t exists in T2 ? (a is
the fk)
22. Null Weird Behavior- Solution 1
a c
c a
d b
a b
NULL a
c b
T2T1
Retrieve rows of table table T1 which doesn’t exists in T2 ? (a is
the fk)SELECT *
FROM T1
WHERE a NOT IN (SELECT a FROM T2)
23. Null Weird Behavior- Solution 1
a c
c a
d b
a b
NULL a
c b
T2T1
Retrieve rows of table table T1 which doesn’t exists in T2 ? (a is
the fk)SELECT *
FROM T1
WHERE a NOT IN (SELECT a FROM T2)
Is it correct ?
24. Null Weird Behavior- Solution 1
a c
c a
d b
a b
NULL a
c b
T2T1
Retrieve rows of table table T1 which doesn’t exists in T2 ? (a is
the fk)SELECT *
FROM T1
WHERE a NOT IN (SELECT a FROM T2)
No it will retrieve no rows!!
25. Null Weird Behavior- Solution 2
a c
c a
d b
a b
NULL a
c b
T2T1
Retrieve rows of table table T1 which doesn’t exists in T2 ? (a is
the fk)SELECT T1.*
FROM T1
LEFT JOIN T2 ON T1.a = T2.a
WHERE T2.a IS NULL
26. Null Weird Behavior- Solution 2
a c
c a
d b
a b
NULL a
c b
T2T1
Retrieve rows of table table T1 which doesn’t exists in T2 ? (a is
the fk)SELECT T1.*
FROM T1
LEFT JOIN T2 ON T1.a = T2.a
WHERE T2.a IS NULL
Is it correct ?
33. SQL Sanity- Joinability
When you want to check joinability ratios of T1 and T2
SELECT SUM(CAST(T1.pk IS NOT NULL AS INT) AS t1_size,
SUM(CAST(T2.pk IS NOT NULL AS INT) AS t2_size,
SUM(CAST(T2.pk IS NOT NULL AND AS INT) AS t2_size,
FROM T1
FULL OUTER JOIN T2
ON T2.pk = T2.pk
34. SQL Sanity- Joinability
When you want to check joinability ratios of T1 and T2
WITH T3 AS (SELECT T1.pk IS NOT NULL AS t1_row,
CAST(T2.pk IS NOT NULL AS INT) AS t2_row
FROM T1
FULL OUTER JOIN T2 ON T2.pk = T2.fk)
SELECT SUM(CAST(t1_row AS INT)) AS t1_size,
SUM(CAST(t2_row AS INT)) AS t2_size,
SUM(CAST(t1_row AND NOT t2_row AS INT)) AS only_t1_size,
SUM(CAST(NOT t1_row AND t2_row AS INT)) AS only_t2_size,
SUM(CAST(t1_row AND t2_row AS INT)) AS t1_t2_size,
FROM T3
42. Avoiding Spaghetti SQL
● Construct Rules and Conventions[example 1] [example 2]
● Automatically validate conventions in CI[tool 1]
43. Avoiding Spaghetti SQL
● Construct Rules and Conventions[example 1] [example 2]
● Automatically validate conventions in CI
● Break queries into multiple queries
[tool 1]
44. Avoiding Spaghetti SQL
● Construct Rules and Conventions[example 1] [example 2]
● Automatically validate conventions in CI
● Break queries into multiple queries
[tool 1]
WITH T2 AS
(subquery_statement_on_T1),
T3
AS(subquery_statement_on_T2)
SELECT *
FROM T3
CREATE TEMP TABLE T2 AS
(subquery_statement_on_T1);
CREATE TEMP TABLE T3 AS
(subquery_statement_on_T2);
SELECT *
FROM T3
With Statement: Create temp table Statement:
45.
46. Good Resources
● MUST SEE VIDEO
● Basic SQL reference
● Good SQL blog
● Another Good SQL blog
● http://www.silota.com/docs/recipes/