The document discusses different collection types in C#, including arrays, ArrayList, List, LinkedList, Dictionary, Queue and Stack. It provides code examples to demonstrate how to create and use each collection type, and describes their key properties and methods. Generic collections like List provide stronger typing and are preferred over non-generic collections like ArrayList. Collections like Dictionary provide fast retrieval based on keys, while Queue and Stack access elements based on FIFO and LIFO principles respectively.
The document discusses generic collections in .NET. Generic collections allow type-safe, strongly typed collections by separating the collection logic (add, search, remove, clear) from the data type. This is done through generics, which were applied to existing .NET collections like lists, dictionaries, stacks and queues. The key benefits are that generic collections provide both the strong typing of arrays and the flexibility of resizing of collections like arraylists. Examples are provided of how to declare and use generic lists, dictionaries, stacks and queues with different data types in C#.
Scala is a multi-paradigm programming language that runs on the Java Virtual Machine. It integrates features of object-oriented and functional programming languages. Some key features of Scala include: supporting both object-oriented and functional programming, providing improvements over Java in areas like syntax, generics, and collections, and introducing new features like pattern matching, traits, and implicit conversions.
This document provides an agenda and overview for a Spark workshop covering Spark basics and streaming. The agenda includes sections on Scala, Spark, Spark SQL, and Spark Streaming. It discusses Scala concepts like vals, vars, defs, classes, objects, and pattern matching. It also covers Spark RDDs, transformations, actions, sources, and the spark-shell. Finally, it briefly introduces Spark concepts like broadcast variables, accumulators, and spark-submit.
The document discusses Java collections framework. It describes that the framework includes interfaces like List, Set, and Map that define different types of collections. It also discusses some implementations of these interfaces like ArrayList, LinkedList, Vector. ArrayList is like an array but resizable, while LinkedList stores elements in memory locations linked by addresses, making insertion/deletion faster than ArrayList. The document also covers methods of collections like add, remove, contains.
This document provides an overview of Java collections APIs, including:
- A history of collections interfaces added in different JDK versions from 1.0 to 1.7.
- Descriptions of common collection interfaces like List, Set, Map and their implementations.
- Comparisons of performance and characteristics of different collection implementations.
- Explanations of common collection algorithms and concurrency utilities.
- References for further reading on collections and concurrency.
An Introduction to Higher Order Functions in Spark SQL with Herman van HovellDatabricks
Nested data types offer Apache Spark users powerful ways to manipulate structured data. In particular, they allow you to put complex objects like arrays, maps and structures inside of columns. This can help you model your data in a more natural way.
While this feature is certainly useful, it can quite bit cumbersome to manipulate data inside of complex objects because SQL (and Spark) do not have primitives for working with such data. In addition, it is time-consuming, non-performant, and non-trivial. During this talk we will discuss some of the commonly used techniques for working with complex objects, and we will introduce new ones based on Higher-order functions. Higher-order functions will be part of Spark 2.4 and are a simple and performant extension to SQL that allow a user to manipulate complex data such as arrays.
Spark schema for free with David SzakallasDatabricks
DataFrames are essential for high-performance code, but sadly lag behind in development experience in Scala. When we started migrating our existing Spark application from RDDs to DataFrames at Whitepages, we had to scratch our heads real hard to come up with a good solution. DataFrames come at a loss of compile-time type safety and there is limited support for encoding JVM types.
We wanted more descriptive types without the overhead of Dataset operations. The data binding API should be extendable. Schema for input files should be generated from classes when we don’t want inference. UDFs should be more type-safe. Spark does not provide these natively, but with the help of shapeless and type-level programming we found a solution to nearly all of our wishes. We migrated the RDD code without any of the following: changing our domain entities, writing schema description or breaking binary compatibility with our existing formats. Instead we derived schema, data binding and UDFs, and tried to sacrifice the least amount of type safety while still enjoying the performance of DataFrames.
This document provides information about functions in Apache Hive, including a cheat sheet covering user defined functions (UDFs) and built-in functions. It describes how to create UDFs, UDAFs, and UDTFs in Hive along with examples. The document also lists many common mathematical, string, date and other function types available in Hive with descriptions.
The document discusses generic collections in .NET. Generic collections allow type-safe, strongly typed collections by separating the collection logic (add, search, remove, clear) from the data type. This is done through generics, which were applied to existing .NET collections like lists, dictionaries, stacks and queues. The key benefits are that generic collections provide both the strong typing of arrays and the flexibility of resizing of collections like arraylists. Examples are provided of how to declare and use generic lists, dictionaries, stacks and queues with different data types in C#.
Scala is a multi-paradigm programming language that runs on the Java Virtual Machine. It integrates features of object-oriented and functional programming languages. Some key features of Scala include: supporting both object-oriented and functional programming, providing improvements over Java in areas like syntax, generics, and collections, and introducing new features like pattern matching, traits, and implicit conversions.
This document provides an agenda and overview for a Spark workshop covering Spark basics and streaming. The agenda includes sections on Scala, Spark, Spark SQL, and Spark Streaming. It discusses Scala concepts like vals, vars, defs, classes, objects, and pattern matching. It also covers Spark RDDs, transformations, actions, sources, and the spark-shell. Finally, it briefly introduces Spark concepts like broadcast variables, accumulators, and spark-submit.
The document discusses Java collections framework. It describes that the framework includes interfaces like List, Set, and Map that define different types of collections. It also discusses some implementations of these interfaces like ArrayList, LinkedList, Vector. ArrayList is like an array but resizable, while LinkedList stores elements in memory locations linked by addresses, making insertion/deletion faster than ArrayList. The document also covers methods of collections like add, remove, contains.
This document provides an overview of Java collections APIs, including:
- A history of collections interfaces added in different JDK versions from 1.0 to 1.7.
- Descriptions of common collection interfaces like List, Set, Map and their implementations.
- Comparisons of performance and characteristics of different collection implementations.
- Explanations of common collection algorithms and concurrency utilities.
- References for further reading on collections and concurrency.
An Introduction to Higher Order Functions in Spark SQL with Herman van HovellDatabricks
Nested data types offer Apache Spark users powerful ways to manipulate structured data. In particular, they allow you to put complex objects like arrays, maps and structures inside of columns. This can help you model your data in a more natural way.
While this feature is certainly useful, it can quite bit cumbersome to manipulate data inside of complex objects because SQL (and Spark) do not have primitives for working with such data. In addition, it is time-consuming, non-performant, and non-trivial. During this talk we will discuss some of the commonly used techniques for working with complex objects, and we will introduce new ones based on Higher-order functions. Higher-order functions will be part of Spark 2.4 and are a simple and performant extension to SQL that allow a user to manipulate complex data such as arrays.
Spark schema for free with David SzakallasDatabricks
DataFrames are essential for high-performance code, but sadly lag behind in development experience in Scala. When we started migrating our existing Spark application from RDDs to DataFrames at Whitepages, we had to scratch our heads real hard to come up with a good solution. DataFrames come at a loss of compile-time type safety and there is limited support for encoding JVM types.
We wanted more descriptive types without the overhead of Dataset operations. The data binding API should be extendable. Schema for input files should be generated from classes when we don’t want inference. UDFs should be more type-safe. Spark does not provide these natively, but with the help of shapeless and type-level programming we found a solution to nearly all of our wishes. We migrated the RDD code without any of the following: changing our domain entities, writing schema description or breaking binary compatibility with our existing formats. Instead we derived schema, data binding and UDFs, and tried to sacrifice the least amount of type safety while still enjoying the performance of DataFrames.
This document provides information about functions in Apache Hive, including a cheat sheet covering user defined functions (UDFs) and built-in functions. It describes how to create UDFs, UDAFs, and UDTFs in Hive along with examples. The document also lists many common mathematical, string, date and other function types available in Hive with descriptions.
This document provides an overview of functional programming concepts in Java 8 including lambdas and streams. It introduces lambda functions as anonymous functions without a name. Lambdas allow internal iteration over collections using forEach instead of external iteration with for loops. Method references provide a shorthand for lambda functions by "routing" function parameters. Streams in Java 8 enhance the library and allow processing data pipelines in a functional way.
Spark Schema For Free with David SzakallasDatabricks
DataFrames are essential for high-performance code, but sadly lag behind in development experience in Scala. When we started migrating our existing Spark application from RDDs to DataFrames at Whitepages, we had to scratch our heads real hard to come up with a good solution. DataFrames come at a loss of compile-time type safety and there is limited support for encoding JVM types.
We wanted more descriptive types without the overhead of Dataset operations. The data binding API should be extendable. Schema for input files should be generated from classes when we don’t want inference. UDFs should be more type-safe. Spark does not provide these natively, but with the help of shapeless and type-level programming we found a solution to nearly all of our wishes. We migrated the RDD code without any of the following: changing our domain entities, writing schema description or breaking binary compatibility with our existing formats. Instead we derived schema, data binding and UDFs, and tried to sacrifice the least amount of type safety while still enjoying the performance of DataFrames.
The document discusses Java's Collections framework. It provides an overview of collections and their benefits. The core collections framework forms a hierarchy with interfaces like Collection, Set, List, Queue, Map, SortedSet and SortedMap. The document describes the operations supported by these interfaces and common usage patterns including iteration, bulk operations and views. It also covers implementations of each interface and thread safety considerations.
This document provides information about Java collections framework. It discusses various collection interfaces like Collection, List, Set, Queue, Map and their implementations like ArrayList, LinkedList, HashSet, TreeSet, HashMap, TreeMap. It also covers topics like sorting collections using Comparable and Comparator interfaces, overriding equals() and hashCode() methods.
The document discusses Java's Collections framework, which provides a unified approach to store, retrieve, and manipulate groups of data. It describes the core interfaces like Collection, Set, List, Queue, and Map. It explains the benefits of the framework and common operations supported. It also covers iteration, implementations of interfaces, usage examples, and thread safety considerations.
Watch video (in Hebrew): http://parleys.com/play/53f7a9cce4b06208c7b7ca1e
Type classes are a fundamental feature of Scala, which allows you to layer new functionality on top of existing types externally, i.e. without modifying or recompiling existing code. When combined with implicits, this is a truly remarkable tool that enables many of the advanced features offered by the Scala library ecosystem. In this talk we'll go back to basics: how type classes are defined and encoded, and cover several prominent use cases.
A talk given at the Underscore meetup on 19 August, 2014.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
The document discusses ArrayLists in Java. Key points include:
- ArrayLists allow dynamic resizing as elements are added, with O(1) access time. They support methods for insertion, deletion, and updating elements.
- ArrayLists can be iterated over using a for-each loop or indexed access.
- Common ArrayList methods include add(), get(), contains(), clear(), remove(), and size().
- Iterators can be used to iterate over ArrayLists in a single direction. ListIterators allow bidirectional traversal and modification of elements.
This document provides an overview of JavaScript arrays, including:
- Declaring and initializing different types of arrays such as associative arrays and indexed arrays
- Common array methods like push(), pop(), splice(), and slice()
- Array attributes including length, indexOf, and typeOf
- Techniques for adding, removing, and modifying array elements
To learn important concept of Collection and its handling plus its advantages and different class & child class of Collection and their implementations. Important interview questions of the collection.
Collections in .net technology (2160711)Janki Shah
Collections in .NET Framework.
- What is collections?
- Needs of Collections/ importance of collection
- various most useful classes of collection such as
ArrayList, Hashtable, Stack, Queue, BitArray, SortedList
This is an quick introduction to Scalding and Monoids. Scalding is a Scala library that makes writing MapReduce jobs very easy. Monoids on the other hand promise parallelism and quality and they make some more challenging algorithms look very easy.
The talk was held at the Helsinki Data Science meetup on January 9th 2014.
Scalding - Hadoop Word Count in LESS than 70 lines of codeKonrad Malawski
Twitter Scalding is built on top of Cascading, which is built on top of Hadoop. It's basically a very nice to read and extend DSL for writing map reduce jobs.
Scalding: Twitter's Scala DSL for Hadoop/Cascadingjohnynek
Talk given at the 2012 Hadoop Summit in San Jose, CA.
Scalding is a Scala DSL for Cascading which brings natural functional programming to Hadoop. It is open-source, developed by Twitter and others.
Follow: twitter.com/scalding
github.com/twitter/scalding
ComputeFest 2012: Intro To R for Physical Sciencesalexstorer
This document provides an introduction to the R programming language presented by Alex Storer at ComputeFest 2012. It discusses why R should be used over other languages like MATLAB and Python, provides examples of basic R syntax and functions, and walks through an example of loading climate data and creating plots to visualize rainfall anomalies over time. The goal is to provide attendees with a foundation of R basics while working through a real data analysis problem.
Serialization is the process of converting data structures into a binary or textual format for transmission or storage. Avro is an open-source data serialization framework that uses JSON schemas and remote procedure calls (RPCs) to serialize data. It allows for efficient encoding of complex data structures and schema evolution. Avro provides APIs for Java, C, C++, C#, Python and Ruby to serialize and deserialize data according to Avro schemas.
The document discusses different options for performing data analysis on Hadoop clusters, including Scalding, Scoobi, and Scrunch. It provides a brief overview of each option and code examples. While the options are similar, the author notes they are working to develop a common API. The key takeaways are that functional programming is well-suited for mapreduce problems and using Scalding, Scoobi, or Scrunch can increase productivity over traditional mapreduce.
This document provides an introduction to Scala. It discusses:
- Who the author is and their background with Scala and Spark
- Why Scala is a scalable language that runs on the JVM and supports object oriented and functional programming
- How to install Scala and use the Scala interpreter
- Basic Scala syntax like defining values and variables, type inference, strings, tuples, objects, importing classes
- Common functions and operations like map, reduce, anonymous functions, pattern matching
- Code samples for RDD relations and SparkPi
- Tips for using Scala in practice including SBT and good IDEs like IntelliJ
The document discusses various aspects of arrays in C# including declaration, initialization, storing values, accessing values, passing arrays to methods, array properties and functions. It provides examples of one-dimensional and multi-dimensional arrays and covers topics like declaration, initialization, accessing values, passing arrays to methods and common array properties and functions.
This document provides an overview of functional programming concepts in Java 8 including lambdas and streams. It introduces lambda functions as anonymous functions without a name. Lambdas allow internal iteration over collections using forEach instead of external iteration with for loops. Method references provide a shorthand for lambda functions by "routing" function parameters. Streams in Java 8 enhance the library and allow processing data pipelines in a functional way.
Spark Schema For Free with David SzakallasDatabricks
DataFrames are essential for high-performance code, but sadly lag behind in development experience in Scala. When we started migrating our existing Spark application from RDDs to DataFrames at Whitepages, we had to scratch our heads real hard to come up with a good solution. DataFrames come at a loss of compile-time type safety and there is limited support for encoding JVM types.
We wanted more descriptive types without the overhead of Dataset operations. The data binding API should be extendable. Schema for input files should be generated from classes when we don’t want inference. UDFs should be more type-safe. Spark does not provide these natively, but with the help of shapeless and type-level programming we found a solution to nearly all of our wishes. We migrated the RDD code without any of the following: changing our domain entities, writing schema description or breaking binary compatibility with our existing formats. Instead we derived schema, data binding and UDFs, and tried to sacrifice the least amount of type safety while still enjoying the performance of DataFrames.
The document discusses Java's Collections framework. It provides an overview of collections and their benefits. The core collections framework forms a hierarchy with interfaces like Collection, Set, List, Queue, Map, SortedSet and SortedMap. The document describes the operations supported by these interfaces and common usage patterns including iteration, bulk operations and views. It also covers implementations of each interface and thread safety considerations.
This document provides information about Java collections framework. It discusses various collection interfaces like Collection, List, Set, Queue, Map and their implementations like ArrayList, LinkedList, HashSet, TreeSet, HashMap, TreeMap. It also covers topics like sorting collections using Comparable and Comparator interfaces, overriding equals() and hashCode() methods.
The document discusses Java's Collections framework, which provides a unified approach to store, retrieve, and manipulate groups of data. It describes the core interfaces like Collection, Set, List, Queue, and Map. It explains the benefits of the framework and common operations supported. It also covers iteration, implementations of interfaces, usage examples, and thread safety considerations.
Watch video (in Hebrew): http://parleys.com/play/53f7a9cce4b06208c7b7ca1e
Type classes are a fundamental feature of Scala, which allows you to layer new functionality on top of existing types externally, i.e. without modifying or recompiling existing code. When combined with implicits, this is a truly remarkable tool that enables many of the advanced features offered by the Scala library ecosystem. In this talk we'll go back to basics: how type classes are defined and encoded, and cover several prominent use cases.
A talk given at the Underscore meetup on 19 August, 2014.
Short (45 min) version of my 'Pragmatic Real-World Scala' talk. Discussing patterns and idioms discovered during 1.5 years of building a production system for finance; portfolio management and simulation.
The document discusses ArrayLists in Java. Key points include:
- ArrayLists allow dynamic resizing as elements are added, with O(1) access time. They support methods for insertion, deletion, and updating elements.
- ArrayLists can be iterated over using a for-each loop or indexed access.
- Common ArrayList methods include add(), get(), contains(), clear(), remove(), and size().
- Iterators can be used to iterate over ArrayLists in a single direction. ListIterators allow bidirectional traversal and modification of elements.
This document provides an overview of JavaScript arrays, including:
- Declaring and initializing different types of arrays such as associative arrays and indexed arrays
- Common array methods like push(), pop(), splice(), and slice()
- Array attributes including length, indexOf, and typeOf
- Techniques for adding, removing, and modifying array elements
To learn important concept of Collection and its handling plus its advantages and different class & child class of Collection and their implementations. Important interview questions of the collection.
Collections in .net technology (2160711)Janki Shah
Collections in .NET Framework.
- What is collections?
- Needs of Collections/ importance of collection
- various most useful classes of collection such as
ArrayList, Hashtable, Stack, Queue, BitArray, SortedList
This is an quick introduction to Scalding and Monoids. Scalding is a Scala library that makes writing MapReduce jobs very easy. Monoids on the other hand promise parallelism and quality and they make some more challenging algorithms look very easy.
The talk was held at the Helsinki Data Science meetup on January 9th 2014.
Scalding - Hadoop Word Count in LESS than 70 lines of codeKonrad Malawski
Twitter Scalding is built on top of Cascading, which is built on top of Hadoop. It's basically a very nice to read and extend DSL for writing map reduce jobs.
Scalding: Twitter's Scala DSL for Hadoop/Cascadingjohnynek
Talk given at the 2012 Hadoop Summit in San Jose, CA.
Scalding is a Scala DSL for Cascading which brings natural functional programming to Hadoop. It is open-source, developed by Twitter and others.
Follow: twitter.com/scalding
github.com/twitter/scalding
ComputeFest 2012: Intro To R for Physical Sciencesalexstorer
This document provides an introduction to the R programming language presented by Alex Storer at ComputeFest 2012. It discusses why R should be used over other languages like MATLAB and Python, provides examples of basic R syntax and functions, and walks through an example of loading climate data and creating plots to visualize rainfall anomalies over time. The goal is to provide attendees with a foundation of R basics while working through a real data analysis problem.
Serialization is the process of converting data structures into a binary or textual format for transmission or storage. Avro is an open-source data serialization framework that uses JSON schemas and remote procedure calls (RPCs) to serialize data. It allows for efficient encoding of complex data structures and schema evolution. Avro provides APIs for Java, C, C++, C#, Python and Ruby to serialize and deserialize data according to Avro schemas.
The document discusses different options for performing data analysis on Hadoop clusters, including Scalding, Scoobi, and Scrunch. It provides a brief overview of each option and code examples. While the options are similar, the author notes they are working to develop a common API. The key takeaways are that functional programming is well-suited for mapreduce problems and using Scalding, Scoobi, or Scrunch can increase productivity over traditional mapreduce.
This document provides an introduction to Scala. It discusses:
- Who the author is and their background with Scala and Spark
- Why Scala is a scalable language that runs on the JVM and supports object oriented and functional programming
- How to install Scala and use the Scala interpreter
- Basic Scala syntax like defining values and variables, type inference, strings, tuples, objects, importing classes
- Common functions and operations like map, reduce, anonymous functions, pattern matching
- Code samples for RDD relations and SparkPi
- Tips for using Scala in practice including SBT and good IDEs like IntelliJ
The document discusses various aspects of arrays in C# including declaration, initialization, storing values, accessing values, passing arrays to methods, array properties and functions. It provides examples of one-dimensional and multi-dimensional arrays and covers topics like declaration, initialization, accessing values, passing arrays to methods and common array properties and functions.
This document provides an overview of basic Java syntax including:
- How to create, compile, and execute simple Java programs
- Using arrays, loops, if/else statements, and comparing strings
- Building one-dimensional and multi-dimensional arrays
- Common data structures like Vector and Hashtable
- Errors handling and the Collections Framework
The document discusses various methods for reading data into R from different sources:
- CSV files can be read using read.csv()
- Excel files can be read using the readxl package
- SAS, Stata, and SPSS files can be imported using the haven package functions read_sas(), read_dta(), and read_sav() respectively
- SAS files with the .sas7bdat extension can also be read using the sas7bdat package
This document provides an overview of Java collections including common implementations like lists, maps, and queues. It discusses how collections allow storing and accessing multiple objects, the benefits of generics for type safety, and useful methods in the Collections class for sorting, shuffling, and copying collections. Code examples are provided for creating parameterized lists and maps, sorting lists using Comparator, and exercises for working with collections in practice.
This document discusses generics in Java and the benefits they provide. It explains that before generics, collections like ArrayList could hold multiple different types of objects, risking ClassCastExceptions. With generics, the type is specified within angle brackets, allowing the compiler to catch type errors and ensuring a collection only holds the specified type. An example shows how a non-generic list can hold integers and strings, while a generic list specified to hold integers no longer allows strings. Generics eliminate casting and type safety issues.
This document provides an overview of stacks and queues as data structures. It discusses stacks and their LIFO (last-in, first-out) nature, as well as queues and their FIFO (first-in, first-out) nature. It covers the basic operations of each like push, pop, peek for stacks and enqueue, dequeue for queues. It provides examples of how to implement stacks and queues in code as well as examples of their uses.
Core java by a introduction sandesh sharmaSandesh Sharma
This document provides an overview of core Java concepts including primitive types, wrappers, static methods and blocks, strings, abstract classes and interfaces, collections, equals and hashcode methods, and threads. It defines each concept, provides examples of usage, and notes key behaviors and properties. The document serves as a reference for fundamental Java programming concepts.
This document provides a taxonomy of Scala concepts including object-oriented features, pattern matching, functional programming, actors, futures, implicits, type theory, macros, and category theory. It aims to serve as a reference for many of the terms used in the Scala community. The document covers topics such as case classes, lazy definitions, imports, objects, tuples, pattern matching examples, immutable collections, higher order functions, parallel collections, partial functions, currying, actors, futures, implicit conversions, implicit parameters, implicit classes, type inference, type classes, higher kinded types, algebraic data types, macros, concepts and arrows in category theory, morphisms, and functors.
This is an intermediate conversion course for C++, suitable for second year computing students who may have learned Java or another language in first year.
Some key features of Scala include:
1. It allows blending of functional programming and object-oriented programming for more concise and powerful code.
2. The static type system allows for type safety while maintaining expressiveness through type inference, implicits, and other features.
3. Scala code interoperates seamlessly with existing Java code and libraries due to its compatibility with the JVM.
This document provides an introduction to Java data structures and the Java Collections Framework. It begins with an overview of the instructor and course topics, which include arrays, the Collections Framework, and collection algorithms. It then covers arrays in more detail, including how to declare, initialize, and access array elements. Next, it discusses the Collection interface and some common methods. It introduces the main collection interfaces in Java - List, Set, and Map - and some common implementations. It provides examples of using arrays, Lists, Sets and Maps. Finally, it summarizes the benefits of the Collections Framework and lists some key methods for the main collection interfaces.
The document discusses Java's Collections framework. It provides an overview of Collections and their benefits, describes the core Collections interfaces like Collection, Set, List, Queue, Map, SortedSet and SortedMap. It also discusses common operations, implementations, iteration, algorithms and thread safety considerations for Collections.
The document discusses real-time big data management and Apache Flink. It provides an overview of Apache Flink, including its architecture, components, and APIs for batch and streaming data processing. It also provides examples of word count programs in Java, Scala, and Java 8 that demonstrate how to write Flink programs for batch and streaming data.
The document discusses data structures and provides examples of using common Java collection classes like ArrayList and LinkedList. It also covers implementing stack, queue, and tree data structures. Specific examples include counting internet addresses, number base conversion using a stack, and building queue and binary tree classes.
This document discusses different implementations of stacks and queues using linked lists and arrays. It describes how to implement a stack using a linked list, with push and pop operations adding and removing nodes from the front of the list. Queues are described as first-in first-out data structures, with enqueue adding to the back and dequeue removing from the front. Examples are given of using stacks and queues for applications like balancing parentheses in expressions and evaluating postfix notation.
This chapter discusses different data structures including lists, stacks, queues, and trees. It covers the ArrayList and LinkedList classes in Java and how to implement stack, queue, and tree structures. Examples are provided to demonstrate counting IP addresses from a file using an ArrayList and LinkedList, converting a number to a different base using a stack, and building a queue class with a LinkedList. The chapter introduces binary trees and their applications.
Arrays are collections of similar type of elements stored in contiguous memory locations. Java arrays are fixed in size and indexed starting from 0. Arrays allow for random access of elements and code optimization. Common array types include single dimensional and multidimensional arrays. Single dimensional arrays store elements in a linear list while multidimensional arrays can be thought of as tables with rows and columns. Strings in Java are objects that are immutable, meaning their values cannot be modified after creation.
This document provides an overview of Java data structures including arrays, collections framework, and common collection interfaces like List, Set, and Map. It discusses how to use arrays to store and access data, the key methods of common collection interfaces, and hands-on exercises to work with arrays and collections in Java.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
1. collection types in C#.
• The .NET framework provides specialized classes for data storage
and retrieval. In one of the previous chapters, we have described
arrays. Collections are enhancement to the arrays.
• There are two distinct collection types in C#.
The standard collections, which are found under the
System.Collections namespace and
the generic collections, under System.Collections.Generic.
• The generic collections are more flexible and are the preferred way
to work with data. The generic collections or generics were
introduced in .NET framework 2.0. Generics enhance code reuse,
type safety, and performance.
• Generic programming is a style of computer programming in which
algorithms are written in terms of to-be-specified-later types that
are then instantiated when needed for specific types provided as
parameters.
2. ArrayList
• ArrayList is a collection from a standard System.Collections
namespace.
• It is a dynamic array. It provides random access to its elements.
• An ArrayList automatically expands as data is added. Unlike arrays,
an ArrayList can hold data of multiple data types.
• Elements in the ArrayList are accessed via an integer index. Indexes
are zero based. Indexing of elements and insertion and deletion at
the end of the ArrayList takes constant time. Inserting or deleting
an element in the middle of the dynamic array is more costly. It
takes linear time.
using System;
using System.Collections;
public class CSharpApp
{
class Empty {}
3. ArrayList
static void Main()
{
ArrayList da = new ArrayList();
da.Add("Visual Basic");
da.Add(344);
da.Add(55);
da.Add(new Empty());
da.Remove(55);
foreach(object el in da)
{
Console.WriteLine(el);
}}}
4. ArrayList
In the above example, we have created an ArrayList collection. We
have added some elements to it. They are of various data
type, string, int and a class object.
using System.Collections;
In order to work with ArrayList collection, we need to import
System.Collections namespace.
ArrayList da = new ArrayList();
An ArrayList collection is created.
da.Add("Visual Basic");
da.Add(344);
da.Add(55);
da.Add(new Empty()); da.Remove(55); We add five elements to the
array with the Add() method.
da.Remove(55); We remove one element.
5. List
• A List is a strongly typed list of objects that can be accessed by
index. It can be found under System.Collections.Generic
namespace.
using System;
using System.Collections.Generic;
public class CSharpApp
{
static void Main()
{
List<string> langs = new List<string>();
langs.Add("Java");
langs.Add("C#");
langs.Add("C");
langs.Add("C++");
7. List
In the preceding example, we work with the List collection.
using System.Collections.Generic;
In order to work with the List collection, we need to import
the System.Collections.Generic namespace.
List<string> langs = new List<string>();
A generic dynamic array is created. We specify that we will
work with strings with the type specified inside <>
characters.
• langs.Add("Java");
• langs.Add("C#");
• langs.Add("C"); ... We add elements to the List using the
Add() method.
8. List
• Console.WriteLine(langs.Contains("C#")); We check if
the List contains a specific string using the Contains()
method.
• Console.WriteLine(langs[1]);
Console.WriteLine(langs[2]); We access the second and
the third element of the List using the index notation.
• langs.Remove("C#"); langs.Remove("C"); We remove
two strings from the List.
• langs.Insert(4, "Haskell"); We insert a string at a
specific location.
• langs.Sort(); We sort the elements using the Sort()
method.
9. LinkedList
• LinkedList is a generic doubly linked list in C#.
• LinkedList only allows sequential access.
• LinkedList allows for constant-time insertions or removals, but only sequential
access of elements. Because linked lists need extra storage for references, they are
impractical for lists of small data items such as characters.
• Unlike dynamic arrays, arbitrary number of items can be added to the linked list
(limited by the memory of course) without the need to realocate, which is an
expensive operation.
using System;
using System.Collections.Generic;
public class CSharpApp
{
static void Main()
{
LinkedList<int> nums = new LinkedList<int>(); nums.AddLast(23);
11. LinkedList
• LinkedList<int> nums = new LinkedList<int>(); This is an
integer LinkedList.
• nums.AddLast(23); ... nums.AddFirst(7); We populate
the linked list using the AddLast() and AddFirst()
methods.
• LinkedListNode<int> node = nums.Find(6);
nums.AddBefore(node, 5); A LinkedList consists of
nodes. We find a specific node and add an element
before it.
• foreach(int num in nums) { Console.WriteLine(num); }
Printing all elements to the console.
12. Dictionary
• A dictionary, also called an associative array, is a collection
of unique keys and a collection of values, where each key is
associated with one value.
• Retrieving and adding values is very fast. Dictionaries take
more memory, because for each value there is also a key.
using System;
using System.Collections.Generic;
public class CSharpApp
{
static void Main()
{
13. Dictionary
Dictionary<string, string> domains = new Dictionary<string,
string>(); domains.Add("de", "Germany");
domains.Add("sk", "Slovakia");
domains.Add("us", "United States");
domains.Add("ru", "Russia");
domains.Add("hu", "Hungary");
domains.Add("pl", "Poland");
Console.WriteLine(domains["sk"]);
Console.WriteLine(domains["de"]);
Console.WriteLine("Dictionary has {0} items", domains.Count);
Console.WriteLine("Keys of the dictionary:");
List<string> keys = new List<string>(domains.Keys);
14. Dictionary
foreach(string key in keys)
{
Console.WriteLine("{0}", key);
}
Console.WriteLine("Values of the dictionary:");
List<string> vals = new List<string>(domains.Values);
foreach(string val in vals)
{
Console.WriteLine("{0}", val);
}
Console.WriteLine("Keys and values of the dictionary:");
foreach(KeyValuePair<string, string> kvp in domains)
{
Console.WriteLine("Key = {0}, Value = {1}", kvp.Key, kvp.Value);
}}}
15. Dictionary
We have a dictionary, where we map domain names to their country names.
Dictionary<string, string> domains = new Dictionary<string, string>();
We create a dictionary with string keys and values.
• domains.Add("de", "Germany");
• domains.Add("sk", "Slovakia");
• domains.Add("us", "United States"); ... We add some data to the
dictionary. The first string is the key. The second is the value.
• Console.WriteLine("Dictionary has {0} items", domains.Count); We print
the number of items by referring to the Count property.
• List<string> keys = new List<string>(domains.Keys);
• List<string> vals = new List<string>(domains.Values); foreach(string val in
vals) { Console.WriteLine("{0}", val); } These lines retrieve all values from
the dictionary.
• foreach(KeyValuePair<string, string> kvp in domains) {
Console.WriteLine("Key = {0}, Value = {1}", kvp.Key, kvp.Value); } Finally, we
print both keys and values of the dictionary.
16. Queues
• A queue is a First-In-First-Out (FIFO) data structure. The first
element added to the queue will be the first one to be removed.
Queues may be used to process messages as they appear or serve
customers as they come. The first customer which comes should be
served first.
• using System;
• using System.Collections.Generic;
• public class CSharpApp
• {
• static void Main()
• {
• Queue<string> msgs = new Queue<string>();
18. Queues
• In our example, we have a queue with messages.
• Queue<string> msgs = new Queue<string>(); A queue
of strings is created.
• msgs.Enqueue("Message 1"); msgs.Enqueue("Message
2"); ... The Enqueue() adds a message to the end of the
queue.
• Console.WriteLine(msgs.Dequeue()); The Dequeue()
method removes and returns the item at the beginning
of the queue.
• Console.WriteLine(msgs.Peek()); The Peek() method
returns the next item from the queue, but does not
remove it from the collection.
19. Stacks
• A stack is a Last-In-First-Out (LIFO) data structure.
• The last element added to the queue will be the first one to
be removed.
• The C language uses a stack to store local data in a function.
The stack is also used when implementing calculators.
• using System;
• using System.Collections.Generic;
• public class CSharpApp
• {
• static void Main()
• {
• Stack<int> stc = new Stack<int>();
20. Stacks
• stc.Push(1);
• stc.Push(4);
• stc.Push(3);
• stc.Push(6);
• stc.Push(4);
• Console.WriteLine(stc.Pop());
• Console.WriteLine(stc.Peek());
• Console.WriteLine(stc.Peek());
• Console.WriteLine();
• foreach(int item in stc)
• {
• Console.WriteLine(item);
• } } } We have a simple stack
21. Stacks
• example above.
• Stack<int> stc = new Stack<int>(); A Stack data
structure is created.
• stc.Push(1); stc.Push(4); ... The Push() method adds an
item at the top of the stack.
• Console.WriteLine(stc.Pop()); The Pop() method
removes and returns the item from the top of the
stack.
• Console.WriteLine(stc.Peek()); The Peek() method
returns the item from the top of the stack. It does not
remove it.
• 4 6 6 6 3 4 1 Output.