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Clojure Data Analysis Cookbook - Second Edition
Eric Rochester
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eBook - ePub
Clojure Data Analysis Cookbook - Second Edition
Eric Rochester
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Sujet
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Visualizzazione di datiClojure Data Analysis Cookbook Second Edition
Table of Contents
Clojure Data Analysis Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files, eBooks, discount offers, and more
Why subscribe?
Free access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Importing Data for Analysis
Introduction
Creating a new project
Getting ready
How to do it...
How it works...
Reading CSV data into Incanter datasets
Getting ready
How to do itâŠ
How it worksâŠ
There's moreâŠ
Reading JSON data into Incanter datasets
Getting ready
How to do itâŠ
How it worksâŠ
Reading data from Excel with Incanter
Getting ready
How to do itâŠ
How it worksâŠ
Reading data from JDBC databases
Getting ready
How to do itâŠ
How it worksâŠ
See also
Reading XML data into Incanter datasets
Getting ready
How to do itâŠ
How it worksâŠ
There's moreâŠ
Navigating structures with zippers
Processing in a pipeline
Comparing XML and JSON
Scraping data from tables in web pages
Getting ready
How to do itâŠ
How it worksâŠ
See also
Scraping textual data from web pages
Getting ready
How to do itâŠ
How it worksâŠ
Reading RDF data
Getting ready
How to do itâŠ
How it worksâŠ
See also
Querying RDF data with SPARQL
Getting ready
How to do itâŠ
How it worksâŠ
There's moreâŠ
Aggregating data from different formats
Getting ready
How to do itâŠ
Creating the triple store
Scraping exchange rates
Loading currency data and tying it all together
How it worksâŠ
See also
2. Cleaning and Validating Data
Introduction
Cleaning data with regular expressions
Getting ready
How to do itâŠ
How it worksâŠ
There's more...
See also
Maintaining consistency with synonym maps
Getting ready
How to do itâŠ
How it worksâŠ
See also
Identifying and removing duplicate data
Getting ready
How to do itâŠ
How it worksâŠ
There's moreâŠ
Regularizing numbers
Getting ready
How to do itâŠ
How it worksâŠ
Calculating relative values
Getting ready
How to do itâŠ
How it worksâŠ
Parsing dates and times
Getting ready
How to do itâŠ
There's moreâŠ
Lazily processing very large data sets
Getting ready
How to do itâŠ
How it worksâŠ
Sampling from very large data sets
Getting ready
How to do itâŠ
Sampling by percentage
Sampling exactly
How it worksâŠ
Fixing spelling errors
Getting ready
How to do itâŠ
How it worksâŠ
There's moreâŠ
Parsing custom data formats
Getting ready
How to do itâŠ
How it worksâŠ
Validating data with Valip
Getting ready
How to do itâŠ
How it worksâŠ
3. Managing Complexity with Concurrent Programming
Introduction
Managing program complexity with STM
Getting ready
How to do itâŠ
How it worksâŠ
See also
Managing program complexity with agents
Getting ready
How to do itâŠ
How it worksâŠ
See also
Getting better performance with commute
Getting ready
How to do itâŠ
How it worksâŠ
Combining agents and STM
Getting ready
How to do itâŠ
How it worksâŠ
Maintaining consistency with ensure
Getting ready
How to do itâŠ
How it worksâŠ
Introducing safe side effects into the STM
Getting ready
How to do itâŠ
Maintaining data consistency with validators
Getting ready
How to do itâŠ
How it worksâŠ
See also
Monitoring processing with watchers
Getting ready
How to do itâŠ
How it worksâŠ
Debugging concurrent programs with watchers
Getting ready
How to do itâŠ
There's more...
Recovering from errors in agents
How to do itâŠ
Failing on errors
Continuing on errors
Using a custom error handler
There's more...
Managing large inputs with sized queues
How to do itâŠ
How it works...
4. Improving Performance with Parallel Programming
Introduction
Parallelizing processing with pmap
How to do itâŠ
How it worksâŠ
There's moreâŠ
See also
Parallelizing processing with Incanter
Getting ready
How to do itâŠ
How it worksâŠ
Partitioning Monte Carlo simulations for better pmap performance
Getting ready
How to do itâŠ
How it worksâŠ
Estimating with Monte Carlo simulations
Chunking data for pmap
Finding the optimal partition size with simulated annealing
Getting ready
How to do itâŠ
How it worksâŠ
There's moreâŠ
Combining function calls with reducers
Getting ready
How to do itâŠ
What happened here?
There's more...
See also
Parallelizing with reducers
Getting ready
How to do itâŠ
How it worksâŠ
See also
Generating online summary statistics for data streams with reducers
Getting ready
How to do itâŠ
Using type hints
Getting ready
How to do itâŠ
How it worksâŠ
See also
Benchmarking with Criterium
Getting ready
How to do itâŠ
How it worksâŠ
See also
5. Distributed Data Processing with Cascalog
Introduction
Initializing Cascalog and Hadoop for distributed processing
Getting ready
How to do itâŠ
How it worksâŠ
See also
Querying data with Cascalog
Getting ready
How to do itâŠ
How it worksâŠ
There's more
Distributing data with Apache HDFS
Getting ready
How to do itâŠ
How it worksâŠ
Parsing CSV files with Cascalog
Getting ready
How to do itâŠ
How it worksâŠ
There's more
Executing complex queries with Cascalog
Getting ready
How to do itâŠ
Aggregating data with Cascalog
Getting ready
How to do itâŠ
There's more
Defining new Cascalog operators
Getting ready
How to do itâŠ
Creating map operators
Creating map concatenation operators
Creating filter operators
C...