Merging nested data

The HR team stores employee data across two separate JSON files: one containing basic information and another containing additional attributes. To build a complete employee profile, you need to join these tables using a common identifier.

An inner join combines rows from two tables where the join column matches - like merging two guest lists by name. The JsonReader, JsonReadOptions, and Table classes have been imported for you.

This exercise is part of the course

Importing Data in Java

Exercise instructions

Set the table name for the first JSON file.
Complete the builder chain for the second file.
Join the tables on the "id" column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

public class NestedEmployees {
    public static void main(String[] args) {

        // Set table name for first JSON
        JsonReadOptions opts1 = JsonReadOptions
                .builder("employees.json")
                .____("Main")
                .build();
        Table main = new JsonReader().read(opts1);

        // Complete builder chain for second JSON
        JsonReadOptions opts2 = JsonReadOptions
                .builder("employees_extra.json")
                .tableName("Extra")
                .____();
        Table extra = new JsonReader().read(opts2);

        System.out.println("Main columns: " + main.columnNames());
        System.out.println("Extra columns: " + extra.columnNames());

        // Join tables on id column
        Table joined = main.____("id").____(extra);

        System.out.println("\nJoined table (inner join on id):");
        System.out.println(joined.print());
    }
}

Edit and Run Code

This exercise is part of the course

Importing Data in Java

IntermediateSkill Level

5.0+

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Kickstart your journey into data handling with Java! In this first chapter, you'll learn how to read, validate, and manage data using Java I/O Streams and then dive into the powerful Tablesaw library to easily create, explore, and manipulate data tables, the perfect foundation for modern data analysis in Java.

Exercise 1: Understanding Data Import Fundamentals Exercise 2: Creating basic file readers Exercise 3: Implementing data validation Exercise 4: Java I/O Streams overview Exercise 5: Reading files with BufferedReader Exercise 6: Writing files with BufferedWriter Exercise 7: Stream chaining Exercise 8: Introduction to Tablesaw Exercise 9: Creating a table Exercise 10: Exploring table metadata Exercise 11: Modifying table structure

In this second chapter, we will move on to covering CSV processing in Tablesaw. Initially, you will learn to read and write CSV files with varying formats, and then we move on to modifying tables, adding and removing columns, filtering tables and manipulating the data within tables for data cleaning and processing.

Exercise 1: CSV processing with Tablesaw Exercise 2: Reading CSV files Exercise 3: Handling CSV parsing options Exercise 4: Writing formatted CSV files Exercise 5: Data structures in Tablesaw Exercise 6: Working with column types Exercise 7: Table filtering Exercise 8: Table queries Exercise 9: Data manipulation and analysis Exercise 10: Initial data exploration Exercise 11: Department analysis Exercise 12: Advanced Data Manipulation Techniques Exercise 13: Data cleaning and enhancement Exercise 14: Advanced filtering and analysis

Complete your Java data journey by mastering transformation patterns that allow you to modify columns, process rows, and summarize datasets into meaningful insights. You’ll also work with JSON data, from simple files to complex, nested structures - preparing you to handle modern APIs and configuration files.

Exercise 1: Data Transformation Patterns Exercise 2: Employee salary analysis Exercise 3: Total compensation calculator Exercise 4: Department summary statistics Exercise 5: JSON Data Basics with Tablesaw Exercise 6: Loading JSON files Exercise 7: JSON data analysis Exercise 8: JSON validation Exercise 9: Complex JSON Processing Exercise 10: Importing nested data Exercise 11: Merging nested data

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