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Java Data Science Cookbook

Java Data Science Cookbook

By : Shams
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Java Data Science Cookbook

Java Data Science Cookbook

By: Shams

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (10 chapters)
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Parsing Tab Separated Value (TSV) file using Univocity

Unlike CSV files, Tab Separated Value (TSV) files contain data that is separated by tab delimiters. This recipe shows you how to retrieve data points from TSV files.

Getting ready

In order to perform this recipe, we will require the following:

  1. Download the Univocity JAR file from http://oss.sonatype.org/content/repositories/releases/com/univocity/univocity-parsers/2.2.1/univocity-parsers-2.2.1.jar. Include the JAR file in your project in Eclipse an external library.
  2. Create a TSV file from the following data using Notepad. The extension of the file should be .tsv. You save the file as C:/testTSV.tsv:
Year    Make    Model   Description Price 
1997    Ford    E350    ac, abs, moon   3000.00 
1999    Chevy   Venture "Extended Edition"      4900.00 
1996    Jeep    Grand Cherokee  MUST SELL!nair, moon roof, loaded  4799.00 
1999    Chevy   Venture "Extended Edition, Very Large"      5000.00 
        Venture "Extended Edition"      4900.00 

How to do it...

  1. Create a method named parseTsv(String) that takes the name of the file as a String argument:
            public void parseTsv(String fileName){ 
    
  2. The line separator for the TSV file in this recipe is a newline character or n. To set this character as the line separator, modify the settings:
            settings.getFormat().setLineSeparator("n"); 
    
  3. Using these settings, create a TSV parser:
            TsvParser parser = new TsvParser(settings); 
    
  4. Parse all rows of the TSV file at once as follows:
            List<String[]> allRows = parser.parseAll(new File(fileName)); 
    
  5. Iterate over the list object to print/process the rows as follows:
            for (int i = 0; i < allRows.size(); i++){ 
                     System.out.println(Arrays.asList(allRows.get(i))); 
                   } 
    
  6. Finally, close the method:
        } 

The full method with the driver method in a class will look like the following:

import java.io.File; 
import java.util.Arrays; 
import java.util.List; 
 
import com.univocity.parsers.tsv.TsvParser; 
import com.univocity.parsers.tsv.TsvParserSettings; 
 
public class TestTsv { 
   public void parseTsv(String fileName){ 
       TsvParserSettings settings = new TsvParserSettings(); 
       settings.getFormat().setLineSeparator("n"); 
       TsvParser parser = new TsvParser(settings); 
       List<String[]> allRows = parser.parseAll(new File(fileName)); 
       for (int i = 0; i < allRows.size(); i++){ 
         System.out.println(Arrays.asList(allRows.get(i))); 
       } 
   } 
} 
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Java Data Science Cookbook
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