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[null,"1.","Record Nr.",null,"UNINA9911006788503321"]
[null,null,"Autore",null,"Rochester Eric"]
[null,null,"Titolo",null,"Clojure data analysis cookbook / / Eric Rochester"]
[null,null,"Pubbl/distr/stampa",null,"Birmingham, UK, : Packt Pub., c2013"]
[null,null,"ISBN",null,"1-68015-416-8"]
[{"$nulls":4},"1-299-44085-1"]
[{"$nulls":4},"1-78216-265-8"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (342 p.)"]
[null,null,"Disciplina",null,"005.133"]
[null,null,"Soggetti",null,"Database searching"]
[{"$nulls":4},"Clojure (Computer program language)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Importing Data for Analysis; Introduction; Creating a new project; Reading CSV data into Incanter datasets; Reading JSON data into Incanter datasets; Reading data from Excel with Incanter; Reading data from JDBC databases; Reading XML data into Incanter datasets; Scraping data from tables in web pages; Scraping textual data from web pages; Reading RDF data; Reading RDF data with SPARQL; Aggregating data from different formats; Chapter 2: Cleaning and Validating Data"]
[{"$nulls":4},"IntroductionCleaning data with regular expressions; Maintaining consistency with synonym maps; Identifying and removing duplicate data; Normalizing numbers; Rescaling values; Normalizing 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"]
[{"$nulls":4},"Maintaining consistency with ensureIntroducing safe side effects into the STM; Maintaining data consistency with validators; Tracking processing with watchers; Debugging concurrent programs with watchers; Recovering from errors in agents; Managing input with sized queues; Chapter 4: Improving Performance with Parallel Programming; Introduction; Parallelizing processing with pmap; Parallelizing "]
[{"$nulls":4},"processing with Incanter; Partitioning Monte Carlo simulations for better pmap performance; Finding the optimal partition size with simulated annealing; Parallelizing with reducers"]
[{"$nulls":4},"Generating online summary statistics with reducersHarnessing your GPU with OpenCL and Calx; Using type hints; Benchmarking with Criterium; Chapter 5: Distributed Data Processing with Cascalog; Introduction; Distributed processing with Cascalog and Hadoop; Querying data with Cascalog; Distributing data with Apache HDFS; Parsing CSV files with Cascalog; Complex queries with Cascalog; Aggregating data with Cascalog; Defining new Cascalog operators; Composing Cascalog queries; Handling errors in Cascalog workflows; Transforming data with Cascalog"]
[{"$nulls":4},"Executing Cascalog queries in the Cloud with PalletChapter 6: Working with Incanter Datasets; Introduction; Loading Incanter's sample datasets; Loading 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: Preparing for and Performing Statistical Data Analysis with Incanter; Introduction"]
[{"$nulls":4},"Generating summary statistics with rollup"]
[null,null,null,"Sommario/riassunto","Full of practical tips, the \"\"Clojure Data Analysis Cookbook\"\" will help you fully utilize your data through a series of step-by-step, real world recipes covering every aspect of data analysis.Prior experience with Clojure and data analysis techniques and workflows will be beneficial, but not essential."]