Clojure data analysis cookbook : dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

This book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis.

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Bibliographic Details
Main Author: Rochester, Eric, (Author)
Format: eBook
Language: English
Published: Birmingham, UK : Packt Publishing, 2015.
Edition: Second edition.
Subjects:
ISBN: 9781784399955
1784399957
1784390291
9781784390297
Physical Description: 1 online resource (1 volume) : illustrations

Cover

Table of contents

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008 150216s2015 enka o 001 0 eng d
040 |a UMI  |b eng  |e rda  |e pn  |c UMI  |d DEBBG  |d OCLCF  |d EBLCP  |d HEBIS  |d E7B  |d COO  |d YDXCP  |d OCLCQ  |d N$T  |d IDB  |d CNNOR  |d MERUC  |d VT2  |d D6H  |d OCLCQ  |d CEF  |d NLE  |d STF  |d OCLCQ  |d OCLCO  |d G3B  |d DKC  |d AU@  |d OCLCQ  |d UKAHL  |d OCLCQ  |d INARC  |d OCLCO  |d OCLCQ  |d OCLCO  |d OCLCL  |d OCLCQ 
020 |a 9781784399955  |q (electronic bk.) 
020 |a 1784399957  |q (electronic bk.) 
020 |z 1784399957 
020 |z 1784390291 
020 |z 9781784390297 
035 |a (OCoLC)903511543  |z (OCoLC)902957606 
100 1 |a Rochester, Eric,  |e author. 
245 1 0 |a Clojure data analysis cookbook :  |b dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process /  |c Eric Rochester. 
250 |a Second edition. 
264 1 |a Birmingham, UK :  |b Packt Publishing,  |c 2015. 
300 |a 1 online resource (1 volume) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a "Quick answers to common problems." 
500 |a Includes index. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book is for those with a basic knowledge of Clojure, who are looking to push the language to excel with data analysis. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Clojure (Computer program language) 
650 0 |a JavaScript (Computer program language) 
650 0 |a Computer programming. 
650 0 |a Application software  |x Development. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Rochester, Eric.  |t Clojure data analysis cookbook.  |b Second edition.  |d Birmingham, UK : Packt Publishing, 2015  |z 9781784399955 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpCDACDC02/clojure-data-analysis?kpromoter=marc  |y Full text