Key FeaturesBook DescriptionWhat you will learnRead data from a variety of data formatsTransform data to make it more useful and easier to analyzeProcess data concurrently and in parallel for faster performanceHarness multiple computers to analyze big dataUse powerful data analysis libraries such as Incanter, Hadoop, and Weka to get things done quicklyApply powerful clustering and data mining techniques to better understand your dataWho this book is forThis book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis.

zlib/Children's Books/Literature & Fiction/Bill Myers [Myers, Bill]/The Ghost of KRZY_16691640.epub

Clojure Data Analysis Cookbook : Dive Into Data Analysis with Clojure Through Over 100 Practical Recipes for Every Stage of the Analysis and Collection Process

Clojure Data Analysis Cookbook - Second Edition

Eric Rochester; Safari, an O’Reilly Media Company

Eric Richard Rochester

Bill Myers, Bill Myers

Rochester, Eric

Amaris Media International

Packt Publishing Limited

U. S. ISBN Agency

EBL-Schweitzer, Second edition (Online-ausg.), Birmingham, England, 2015

2nd revised edition, Place of publication not identified, 2015

Second edition., Birmingham, UK, England, 2015

United Kingdom and Ireland, United Kingdom

Bloodhounds, Inc, Post Hueneme, CA, 2015

United States, United States of America

Packt Publishing, Birmingham, UK, 2015

Feb 19, 2015

"Quick answers to common problems."
Includes index.

Source title: The Ghost of KRZY (Bloodhounds, Inc.)

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Importing Data for Analysis; Introduction; Creating a new project; Reading CSV data into Incanter datasets; Reading JSON data into Incanter datasets; Reading data from Excel with Incanter; Reading data from JDBC databases; Reading XML data into Incanter datasets; Scraping data from tables in web pages; Scraping textual data from web pages; Reading RDF data; Querying RDF data with SPARQL; Aggregating data from different formats; Chapter 2: Cleaning and Validating Data.
IntroductionCleaning data with regular expressions; Maintaining consistency with synonym maps; Identifying and removing duplicate data; Regularizing numbers; Calculating relative values; Parsing dates and times; Lazily processing very large data sets; Sampling from very large data sets; Fixing spelling errors; Parsing custom data formats; Validating data with Valip; Chapter 3: Managing Complexity with Concurrent Programming; Introduction; Managing program complexity with STM; Managing program complexity with agents; Getting better performance with commute; Combining agents and STM.
Maintaining consistency with ensureIntroducing safe side effects into the STM; Maintaining data consistency with validators; Monitoring processing with watchers; Debugging concurrent programs with watchers; Recovering from errors in agents; Managing large inputs with sized queues; Chapter 4: Improving Performance with Parallel Programming; Introduction; Parallelizing processing with pmap; Parallelizing processing with Incanter; Partitioning Monte Carlo simulations for better pmap performance; Finding the optimal partition size with simulated annealing; Combining function calls with reducers.
Parallelizing with reducersGenerating online summary statistics for data streams with reducers; Using type hints; Benchmarking with Criterium; Chapter 5: Distributed Data Processing with Cascalog; Introduction; Initializing Cascalog and Hadoop for distributed processing; Querying data with Cascalog; Distributing data with Apache HDFS; Parsing CSV files with Cascalog; Executing complex queries with Cascalog; Aggregating data with Cascalog; Defining new Cascalog operators; Composing Cascalog queries; Transforming data with Cascalog; Chapter 6: Working with Incanter Datasets; Introduction.
Loading Incanter's sample datasetsLoading Clojure data structures into datasets; Viewing datasets interactively with view; Converting datasets to matrices; Using infix formulas in Incanter; Selecting columns with ; Selecting rows with ; Filtering datasets with where; Grouping data with group-by; Saving datasets to CSV and JSON; Projecting from multiple datasets with join; Chapter 7: Statistical Data Analysis with Incanter; Introduction; Generating summary statistics with rollup; Working with changes in values; Scaling variables to simplify variable relationships.

Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process In Detail As data invades more and more of life and business, the need to analyze it effectively has never been greater. With Clojure and this book, you'll soon be getting to grips with every aspect of data analysis. You'll start with practical recipes that show you how to load and clean your data, then get concise instructions to perform all the essential analysis tasks from basic statistics to sophisticated machine learning and data clustering algorithms. Get a more intuitive handle on your data through hands-on visualization techniques that allow you to provide interesting, informative, and compelling reports, and use Clojure to publish your findings to the Web. What You Will Learn Read data from a variety of data formats Transform data to make it more useful and easier to analyze Process data concurrently and in parallel for faster performance Harness multiple computers to analyze big data Use powerful data analysis libraries such as Incanter, Hadoop, and Weka to get things done quickly Apply powerful clustering and data mining techniques to better understand your data Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you

<p>About This Book<br></p><ul> <li>Customize, configure, and handle events, and interact with figures using matplotlib </li> <li>Create highly intricate and complicated graphs using matplotlib </li> <li>Explore matplotlib's depths through examples and explanations in IPython notebooks </li></ul><p>Who This Book Is For<br></p><p>If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.<br></p>

"Valuable equipment is stolen late every night at Radio KRZY, but there's no way anyone could slip by the security system -- at least no one human!"--Back cover

2021-07-12