Simplify Java Stream with JDFrame: A DataFrame‑Style API for Cleaner Code

Introduction

When frequently forgetting Stream APIs and copying long code, the author wanted a more semantic API similar to DataFrame models in Spark or Pandas, and discovered JVM‑level DataFrame tools such as tablesaw and joinery.

However those tools require hard‑coded field names, which is painful for developers who prefer cleaner code. The author therefore created a JVM‑level DataFrame‑like utility that simplifies Java 8 Stream processing with a more expressive syntax.

Quick Start

1.1 Add Dependency

<dependency>
  <groupId>io.github.burukeyou</groupId>
  <artifactId>jdframe</artifactId>
  <version>0.0.2</version>
</dependency>

1.2 Example

Calculate the total score of students whose age is between 9 and 16, then return the top two schools.

static List<Student> studentList = new ArrayList<>();
// …populate list…

SDFrame<FI2<String, BigDecimal>> sdf2 = SDFrame.read(studentList)
        .whereNotNull(Student::getAge)
        .whereBetween(Student::getAge, 9, 16)
        .groupBySum(Student::getSchool, Student::getScore)
        .sortDesc(FI2::getC2)
        .cutFirst(2);
sdf2.show();

Output:

c1  c2
三中 10
二中 7

API Overview

2.1 Matrix Information

void show(int n);               // print matrix info
List<String> columns();          // get column names
List<R> col(Function<T,R> fn);   // get a column
T head();                        // first element
List<T> head(int n);              // first n elements
T tail();                        // last element
List<T> tail(int n);              // last n elements

2.2 Filtering

.whereBetween(Student::getAge, 3, 6)          // [3,6]
.whereBetweenR(Student::getAge, 3, 6)         // (3,6]
.whereBetweenL(Student::getAge, 3, 6)         // [3,6)
.whereNotNull(Student::getName)               // not null or empty
.whereGt(Student::getAge, 3)                 // >3
.whereGe(Student::getAge, 3)                 // >=3
.whereLt(Student::getAge, 3)                 // <3
.whereIn(Student::getAge, Arrays.asList(3,7,8))
.whereNotIn(Student::getAge, Arrays.asList(3,7,8))
.whereEq(Student::getAge, 3)
.whereNotEq(Student::getAge, 3)
.whereLike(Student::getName, "jay")          // %jay%
.whereLikeLeft(Student::getName, "jay")      // jay%
.whereLikeRight(Student::getName, "jay")     // %jay

2.3 Aggregation

Student s1 = frame.max(Student::getAge);
Integer s2 = frame.maxValue(Student::getAge);
Student s3 = frame.min(Student::getAge);
Integer s4 = frame.minValue(Student::getAge);
BigDecimal s5 = frame.avg(Student::getAge);
BigDecimal s6 = frame.sum(Student::getAge);
MaxMin<Student> s7 = frame.maxMin(Student::getAge);
MaxMin<Integer> s8 = frame.maxMinValue(Student::getAge);

2.4 Distinct

List<Student> std = SDFrame.read(studentList).distinct().toLists();               // by object hash
std = SDFrame.read(studentList).distinct(Student::getSchool).toLists();          // by school
std = SDFrame.read(studentList).distinct(e -> e.getSchool() + e.getLevel()).toLists();
std = SDFrame.read(studentList).distinct(Student::getSchool).distinct(Student::getLevel).toLists();

2.5 Simple Group‑by Aggregation

List<FI2<String, BigDecimal>> a = frame.groupBySum(Student::getSchool, Student::getAge).toLists();
List<FI2<String, Integer>> a2 = frame.groupByMaxValue(Student::getSchool, Student::getAge).toLists();
List<FI2<String, Student>> a3 = frame.groupByMax(Student::getSchool, Student::getAge).toLists();
List<FI2<String, Integer>> a4 = frame.groupByMinValue(Student::getSchool, Student::getAge).toLists();
List<FI2<String, Long>> a5 = frame.groupByCount(Student::getSchool).toLists();
List<FI2<String, BigDecimal>> a6 = frame.groupByAvg(Student::getSchool, Student::getAge).toLists();
List<FI3<String, BigDecimal, Long>> a7 = frame.groupBySumCount(Student::getSchool, Student::getAge).toLists();
List<FI3<String, String, BigDecimal>> a8 = frame.groupBySum(Student::getSchool, Student::getLevel, Student::getAge).toLists();
List<FI4<String, String, String, BigDecimal>> a9 = frame.groupBySum(Student::getSchool, Student::getLevel, Student::getName, Student::getAge).toLists();

2.6 Sorting

SDFrame.read(studentList).sortDesc(Student::getAge);
SDFrame.read(studentList).sortDesc(Student::getAge).sortAsc(Student::getLevel);
SDFrame.read(studentList).sortAsc(Student::getAge);
SDFrame.read(studentList).sortAsc(Comparator.comparing(e -> e.getLevel() + e.getId()));

2.7 Join

append(T t);                     // add element
union(IFrame<T> other);           // addAll
join(IFrame<K> other, JoinOn<T,K> on, Join<T,K,R> join);          // inner join
leftJoin(...);                    // left join
rightJoin(...);                   // right join

Example of inner join between two matrices and printing the result is shown in the source.

2.8 Miscellaneous

Percent conversion :

SDFrame.read(list).mapPercent(Student::getScore, Student::setScore, 2)

Partition : SDFrame.read(list).partition(5).toLists() Generate order number :

SDFrame.read(list).sortDesc(Student::getAge).addSortNoCol(Student::setRank).show(30)

Generate ranking (same values share rank) :

SDFrame.read(list).addRankingSameColDesc(Student::getAge, Student::setRank).show(20)

Replenish missing entries :

SDFrame.read(list).replenish(Student::getSchool, allDim, school -> new Student(school)).show()

Group‑wise replenish :

SDFrame.read(list).replenish(Student::getSchool, Student::getLevel, (s,l) -> new Student(s,l)).show(30)

Frames Comparison

Two frames are provided: SDFrame behaves like a Java Stream – operations are lazy and only materialised on a terminal call, requiring a new read for further processing. JDFrame applies each operation immediately, so the intermediate state can be reused without re‑reading. Choose SDFrame for pure stream pipelines and JDFrame when you need “mid‑point” data.

Where to Get It

https://github.com/burukeYou/JDFrame

https://central.sonatype.com/artifact/io.github.burukeyou/jdframe

The library aims to bring DataFrame‑style, declarative data manipulation to the JVM, reducing boilerplate and improving readability of Java 8 Stream code.