由于经常记不住stream的一些API每次要复制来复制去并且又长又臭，想要更加语意化的api，于是想到了以前写大数据Spark pandnas 等DataFrame模型时的API， 然后发现其实也存在java的JVM层的DataFrame模型比如 tablesaw，joinery
但是他们得硬编码去指定字段名，这对于有代码洁癖的人实在难以忍受，而且我只是简单统计下数据，我想在一些场景下能不能使用匿名函数去指定的字段处理去处理，于是便有了这个
一个jvm层级的仿DataFrame工具，语意化和简化java8的stream流式处理工具
1.1、引入依赖
<dependency>
<groupId>io.github.burukeyou</groupId>
<artifactId>jdframe</artifactId>
<version>0.0.4</version>
</dependency>1.2、案例
统计每个学校的里学生年龄不为空并且年龄在9到16岁间的合计分数，并且获取合计分前2名的学校
static List<Student> studentList = new ArrayList<>();
static {
studentList.add(new Student(1,"a","一中","一年级",11, new BigDecimal(1)));
studentList.add(new Student(2,"a","一中","一年级",11, new BigDecimal(1)));
studentList.add(new Student(3,"b","一中","三年级",12, new BigDecimal(2)));
studentList.add(new Student(4,"c","二中","一年级",13, new BigDecimal(3)));
studentList.add(new Student(5,"d","二中","一年级",14, new BigDecimal(4)));
studentList.add(new Student(6,"e","三中","二年级",14, new BigDecimal(5)));
studentList.add(new Student(7,"e","三中","二年级",15, new BigDecimal(5)));
}
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();输出信息;
c1 c2
三中 10
二中 7 @Data
@AllArgsConstructor
@NoArgsConstructor
public class Student {
private int id;
private String name;
private String school;
private String level;
private Integer age;
private BigDecimal score;
private Integer rank;
public Student(String level, BigDecimal score) {
this.level = level;
this.score = score;
}
public Student(int id, String name, String school, String level, Integer age, BigDecimal score) {
this.id = id;
this.name = name;
this.school = school;
this.level = level;
this.age = age;
this.score = score;
}
}2.1、矩阵查看相关
void show(int n);
List<String> columns();
List<R> col(Function<T, R> function);
T head();
List<T> head(int n);
T tail();
List<T> tail(int n);
List<T> page(int page,int pageSize) 2.2、筛选相关
SDFrame.read(studentList)
.whereBetween(Student::getAge,3,6)
.whereBetweenR(Student::getAge,3,6)
.whereBetweenL(Student::getAge,3,6)
.whereNotNull(Student::getName)
.whereGt(Student::getAge,3)
.whereGe(Student::getAge,3)
.whereLt(Student::getAge,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")
.whereLikeLeft(Student::getName,"jay")
.whereLikeRight(Student::getName,"jay"); 2.3、汇总相关
JDFrame<Student> frame = JDFrame.read(studentList);
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、去重相关
原生steam只支持对象去重，不支持按特定字段去重
List<Student> std = null;
std = SDFrame.read(studentList).distinct().toLists();
std = SDFrame.read(studentList).distinct(Student::getSchool).toLists();
std = SDFrame.read(studentList).distinct(e -> e.getSchool() + e.getLevel()).toLists();
std =SDFrame.read(studentList).distinct(Student::getSchool).distinct(Student::getLevel).toLists(); 2.5、分组聚合相关
类似sql的 group by语义 简化处理分组和聚合的逻辑， 如果用原生stream需要写可能一大串逻辑.
JDFrame<Student> frame = JDFrame.from(studentList);
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、排序相关
简化原生stream的排序方式，直接指定字段即可，不用使用Comparator还要去关注升序还是降序
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、连接矩阵相关
API列表
append(T t);
union(IFrame<T> other);
join(IFrame<K> other, JoinOn<T,K> on, Join<T,K,R> join);
leftJoin(IFrame<K> other, JoinOn<T,K> on, Join<T,K,R> join);
rightJoin(IFrame<K> other, JoinOn<T,K> on, Join<T,K,R> join); 内连接例子：
System.out.println("======== 矩阵1 =======");
SDFrame<Student> sdf = SDFrame.read(studentList);
sdf.show(20);
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(10);
System.out.println("======== 矩阵2 =======");
sdf2.show();
SDFrame<UserInfo> frame = sdf.join(sdf2, (a, b) -> a.getSchool().equals(b.getC1()), (a, b) -> {
UserInfo userInfo = new UserInfo();
userInfo.setKey1(a.getSchool());
userInfo.setKey2(b.getC2().intValue());
userInfo.setKey3(String.valueOf(a.getId()));
return userInfo;
});
System.out.println("======== 连接后结果 =======");
frame.show(5);打印信息：
======== 矩阵1 =======
id name school level age score rank
1 a 一中 一年级 11 1
2 a 一中 一年级 11 1
3 b 一中 一年级 12 2
4 c 二中 一年级 13 3
5 d 二中 一年级 14 4
6 e 三中 二年级 14 5
7 e 三中 二年级 15 5
======== 矩阵2 =======
c1 c2
三中 10
二中 7
一中 4
======== 连接后结果 =======
key1 key2 key3 key4
一中 4 1
一中 4 2
一中 4 3
二中 7 4
二中 7 5 类似于
select a.*,b.* from sdf a inner join sdf2 b on a.school = b.c12.8、截取相关
cutFirst(int n);
cutLast(int n);
cut(Integer startIndex,Integer endIndex)
cutPage(int page,int pageSize)
cutFirstRank(Sorter<T> sorter, int n); 2.9、Frame参数设置相关
defaultScale(int scale, RoundingMode roundingMode); 2.10、其他
百分数转换
SDFrame<Student> map2 = SDFrame.read(studentList).mapPercent(Student::getScore, Student::setScore,2);分区
将每个5个元素分成一个小集合，用于将大任务拆成小任务
List<List<Student>> t = SDFrame.read(studentList).partition(5).toLists();生成序号列
按照age排序，然后根据当前顺序生成排序号到rank字段 （序号从1开始）
SDFrame.read(studentList)
.sortDesc(Student::getAge)
.addRowNumberCol(Student::setRank)
.show(30);输出信息:
id name school level age score rank
7 e 三中 二年级 15 5 1
5 d 二中 一年级 14 4 2
6 e 三中 二年级 14 5 3
4 c 二中 一年级 13 3 4
3 b 一中 三年级 12 2 5
1 a 一中 一年级 11 1 6
2 a 一中 一年级 11 1 7 补充条目
1、补充缺失的学校条目
List<String> allDim = Arrays.asList("一中","二中","三中","四中");
SDFrame.read(studentList).replenish(Student::getSchool,allDim,(school) -> new Student(school)).show();输出
id name school level age score rank
1 a 一中 一年级 11 1
2 a 一中 一年级 11 1
3 b 一中 一年级 12 2
4 c 二中 一年级 13 3
5 d 二中 一年级 14 4
6 e 三中 二年级 14 5
7 e 三中 二年级 15 5
0 四中 2、分组补充组内缺失的条目
按照学校进行分组， 汇总所有年级allDim. 然后与allDim比较补充每个分组内缺失的年级，缺失的年级按照ReplenishFunction生成补充条目
SDFrame.read(studentList).replenish(Student::getSchool,Student::getLevel,(school,level) -> new Student(school,level)).show(30);输出
id name school level age score rank
1 a 一中 一年级 11 1
2 a 一中 一年级 11 1
3 b 一中 三年级 12 2
0 一中 二年级
4 c 二中 一年级 13 3
5 d 二中 一年级 14 4
0 二中 三年级
0 二中 二年级
6 e 三中 二年级 14 5
7 e 三中 二年级 15 5
0 三中 一年级
0 三中 三年级 
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