Clojure data analysis cookbook : over 110 recipes to help you dive into the world of practical data analysis using Clojure
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 benef...
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| Main Author | |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Birmingham, UK :
Packt Publishing,
©2013.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781782162650 1782162658 9781680154160 1680154168 178216264X 9781782162643 |
| Physical Description | 1 online resource (iv, 329 pages) : illustrations |
Cover
Table of Contents:
- 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.
- 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.
- 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 processing with Incanter; Partitioning Monte Carlo simulations for better pmap performance; Finding the optimal partition size with simulated annealing; Parallelizing with reducers.
- 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.
- 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.