Java8快速实现List转map 、分组、过滤等操作
定义1个Apple对象：
public class Apple {
private Integer id;
private String name;
private BigDecimal money;
private Integer num;
public Apple(Integer id, String name, BigDecimal money, Integer num) {
this.id = id;
this.name = name;
this.money = money;
this.num = num;
}
}
添加一些测试数据：
List<Apple> appleList = new ArrayList<>();//存放apple对象集合
Apple apple1 = new Apple(1,"苹果1",new BigDecimal("3.25"),10);
Apple apple12 = new Apple(1,"苹果2",new BigDecimal("1.35"),20);
Apple apple2 = new Apple(2,"香蕉",new BigDecimal("2.89"),30);
Apple apple3 = new Apple(3,"荔枝",new BigDecimal("9.99"),40);
appleList.add(apple1);
appleList.add(apple12);
appleList.add(apple2);
appleList.add(apple3);
1、分组
List里面的对象元素，以某个属性来分组，例如，以id分组，将id相同的放在一起：
//List 以ID分组 Map<Integer,List<Apple>>
Map<Integer, List<Apple>> groupBy = appleList.stream()
.collect(Collectors.groupingBy(Apple::getId));
System.err.println("groupBy:"+groupBy);
{1=[Apple{id=1, name='苹果1', money=3.25, num=10}, Apple{id=1, name='苹果2', money=1.35, num=20}], 2=[Apple{id=2, name='香蕉', money=2.89, num=30}], 3=[Apple{id=3, name='荔枝', money=9.99, num=40}]}
2、List转Map
id为key，apple对象为value，可以这么做：
/**
* List -> Map
* 需要注意的是：
* toMap 如果集合对象有重复的key，会报错Duplicate key ....
* apple1,apple12的id都为1。
* 可以用 (k1,k2)->k1 来设置，如果有重复的key,则保留key1,舍弃key2
*/
Map<Integer, Apple> appleMap = appleList.stream().collect(Collectors.toMap(Apple::getId, a -> a,(k1,k2)->k1));
打印appleMap
{1=Apple{id=1, name='苹果1', money=3.25, num=10}, 2=Apple{id=2, name='香蕉', money=2.89, num=30}, 3=Apple{id=3, name='荔枝', money=9.99, num=40}}
Map<Integer,String> userMap = userList.stream().collect(Collectors.toMap(User::getId,User::getName));
3、过滤Filter
从集合中过滤出来符合条件的元素：
//过滤出符合条件的数据
List<Apple> filterList = appleList.stream().filter(a -> a.getName().equals("香蕉")).collect(Collectors.toList());
System.err.println("filterList:"+filterList);
[Apple{id=2, name='香蕉', money=2.89, num=30}]
4.求和
将集合中的数据按照某个属性求和:
//计算 总金额
BigDecimal totalMoney = appleList.stream().map(Apple::getMoney).reduce(BigDecimal.ZERO, BigDecimal::add);
System.err.println("totalMoney:"+totalMoney); //totalMoney:17.48
5.查找流中最大 最小值
Collectors.maxBy 和 Collectors.minBy 来计算流中的最大或最小值。
Optional<Dish> maxDish = Dish.menu.stream().
collect(Collectors.maxBy(Comparator.comparing(Dish::getCalories)));
maxDish.ifPresent(System.out::println);
Optional<Dish> minDish = Dish.menu.stream().
collect(Collectors.minBy(Comparator.comparing(Dish::getCalories)));
minDish.ifPresent(System.out::println);
6.去重
import static java.util.Comparator.comparingLong;
import static java.util.stream.Collectors.collectingAndThen;
import static java.util.stream.Collectors.toCollection;
// 根据id去重
List<Person> unique = appleList.stream().collect(
collectingAndThen(
toCollection(() -> new TreeSet<>(comparingLong(Apple::getId))), ArrayList::new)
);
集中：
//获取所有年龄20岁以下的学生
ArrayList<Student> students = new ArrayList<>();
students.add(new Student(1, 19, "张三", "M", true));
students.add(new Student(1, 18, "李四", "M", false));
students.add(new Student(1, 21, "王五", "F", true));
students.add(new Student(1, 20, "赵六", "F", false));
//filter：过滤小于20岁
List<Student> list = students.stream().filter(student -> student.getStudentAge() < 20).collect(Collectors.toList());
//获取所有学生的姓名，并形成一个新的集合
List<String> collect = students.stream().map(Student::getStudentName).collect(Collectors.toList());
//判断流中是否至少存在一个符合条件的元素
if (students.stream().anyMatch(student -> student.getStudentAge() < 20)) {
System.out.println("集合中有年龄小于20的学生");
} else {
System.out.println("集合中没有年龄小于20的学生");
}
if (students.stream().allMatch(student -> student.getStudentAge() < 20)) {
System.out.println("集合所有学生的年龄都小于20");
} else {
System.out.println("集合中有年龄大于20的学生");
}
//findAny用于获取流中随机的某一个元素，并且利用短路在找到结果时，立即结束
Optional<Student> student1 = students.stream().filter(student -> student.getStudentSix().equals("F")).findAny();
System.out.println(student1.toString());
//通过counting()统计集合总数 方法一
Long colle = students.stream().collect(Collectors.counting());
System.out.println(colle);
//结果 4
//通过count()统计集合总数 方法二
long count = students.stream().count();
System.out.println(count);
//结果 4
//通过maxBy求最大值
Optional<Student> collect1 = students.stream().collect(Collectors.maxBy(Comparator.comparing(Student::getStudentAge)));
if (collect1.isPresent()) {
System.out.println(collect1);
}
//结果 Optional[Student{id=1, age=21, name='王五', sex='F', isPass=true}]
//通过max求最大值
Optional<Student> max = students.stream().max(Comparator.comparing(Student::getStudentAge));
if (max.isPresent()) {
System.out.println(max);
}
//结果 Optional[Student{id=1, age=21, name='王五', sex='F', isPass=true}]
//通过minBy求最小值
Optional<Student> collect2 = students.stream().collect(Collectors.minBy(Comparator.comparing(Student::getStudentAge)));
if (collect2.isPresent()) {
System.out.println(collect2);
}
//结果 Optional[Student{id=1, age=18, name='李四', sex='M', isPass=false}]
//通过min求最小值
Optional<Student> min = students.stream().min(Comparator.comparing(Student::getStudentAge));
if (min.isPresent()) {
System.out.println(min);
}
//结果 Optional[Student{id=1, age=18, name='李四', sex='M', isPass=false}]
//通过summingInt()进行数据汇总
Integer collect3 = students.stream().collect(Collectors.summingInt(Student::getStudentAge));
System.out.println(collect3);
//结果 78
//通过averagingInt()进行平均值获取
Double collect4 = students.stream().collect(Collectors.averagingInt(Student::getStudentAge));
System.out.println(collect4);
//结果 19.5
//通过joining()进行数据拼接
String collect5 = students.stream().map(Student::getStudentName).collect(Collectors.joining("-"));
System.out.println(collect5);
//结果 张三-李四-王五-赵六
//复杂结果的返回
IntSummaryStatistics collect6 = students.stream().collect(Collectors.summarizingInt(Student::getStudentAge));
double average = collect6.getAverage();
long sum = collect6.getSum();
long count1 = collect6.getCount();
int max1 = collect6.getMax();
int min1 = collect6.getMin();
//通过性别对学生进行分组
Map<String, List<Student>> groupCollect = students.stream().collect(Collectors.groupingBy(Student::getStudentSix));
//现根据是否新学生对学生分组，再根据性别分组
Map<String, Map<Boolean, List<Student>>> col = students.stream().collect(Collectors.groupingBy(Student::getStudentSix, Collectors.groupingBy(Student::getNewStudent)));
//{F={false=[Student(studentId=1, studentAge=20, studentName=赵六, studentSix=F, newStudent=false)], true=[Student(studentId=1, studentAge=21, studentName=王五, studentSix=F, newStudent=true)]}, M={false=[Student(studentId=1, studentAge=18, studentName=李四, studentSix=M, newStudent=false)], true=[Student(studentId=1, studentAge=19, studentName=张三, studentSix=M, newStudent=true)]}}
System.out.println(col.toString());
//根据年龄进行分组，获取并汇总人数
Map<Integer, Long> coll2e= students.stream().collect(Collectors.groupingBy(Student::getStudentAge, Collectors.counting()));
System.out.println(coll2e);
//要根据年龄与是否新生进行分组，并获取每组中年龄的学生
Map<Integer, Map<Boolean, Student>> collect33 = students.stream().collect(Collectors.groupingBy(Student::getStudentAge, Collectors.groupingBy(Student::getNewStudent,
Collectors.collectingAndThen(Collectors.maxBy(Comparator.comparing(Student::getStudentAge)), Optional::get))));
//{18={false=Student(studentId=1, studentAge=18, studentName=李四, studentSix=M, newStudent=false)}, 19={true=Student(studentId=1, studentAge=19, studentName=张三, studentSix=M, newStudent=true)}, 20={false=Student(studentId=1, studentAge=20, studentName=赵六, studentSix=F, newStudent=false)}, 21={true=Student(studentId=1, studentAge=21, studentName=王五, studentSix=F, newStudent=true)}}
System.out.println(collect33.toString());
List<Integer> integers = Arrays.asList(1, 2, 3, 4, 4, 5, 5, 6, 7, 8, 2, 2, 2, 2);
//distinct实现数据去重
List<Integer> distinct = integers.stream().distinct().collect(Collectors.toList());
//基于limit()实现数据截取 :获取数组的前五位
List<Integer> limit = integers.stream().limit(5).collect(Collectors.toList());
//从集合第三个开始截取5个数据
List<Integer> skip = integers.stream().skip(3).limit(5).collect(Collectors.toList());
skip.forEach(integer -> System.out.print(integer + " "));
System.out.println("");
//先从集合中截取5个元素，然后取后3个
List<Integer> coll = integers.stream().limit(5).skip(2).collect(Collectors.toList());
coll.forEach(integer -> System.out.print(integer + " "));
System.out.println("");
//对集合中的元素求和
Integer reduce = integers.stream().reduce(0, Integer::sum);
System.out.println(reduce);
/**
* 获取集合中的最大值
*/
//方法一
Optional<Integer> max11 = integers.stream().reduce(Integer::max);
if (max11.isPresent()) {
System.out.println(max11);
}
//方法二
Optional<Integer> max22 = integers.stream().max(Integer::compareTo);
if (max22.isPresent()) {
System.out.println(max22);
}
/**
* 获取集合中的最小值
*/
//方法一
Optional<Integer> min111 = integers.stream().reduce(Integer::min);
if (min111.isPresent()) {
System.out.println(min111);
}
//方法二
Optional<Integer> min222 = integers.stream().min(Integer::compareTo);
if (min222.isPresent()) {
System.out.println(min222.get());
}
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