JDK 1.8 使用Stream实现List操作小结

publicstaticvoidmain(Stringarg[]) {
List<Employee>list=newArrayList<Employee>();
list.add(newEmployee("宋江","男",1500));
list.add(newEmployee("武松","男",1200));
list.add(newEmployee("林冲","男",1000));
list.add(newEmployee("扈三娘","女",800));
list.add(newEmployee("顾大嫂","女",500));
// 筛选符合某条件的元素 -> 求性别为男的员工集合List<Employee>l1=list.stream().filter(e->e.getGender().equals("男")).collect(Collectors.toList());
// 结果 -> [Employee [name=宋江, gender=男, salary=1500.0], Employee [name=武松, gender=男, salary=1200.0], Employee [name=林冲, gender=男, salary=1000.0]]// 获取集合中的某一元素的集合 ->获取所有员工的姓名集合List<String>l2=list.stream().map(Employee::getName).collect(Collectors.toList());
// 结果 -> [宋江, 武松, 林冲, 扈三娘, 顾大嫂]// 分组函数groupingBy 按性别进行分组统计人数Map<String, Integer>map=list.stream().collect(Collectors.groupingBy(Employee::getGender, Collectors.summingInt(p->1)));
// 结果 -> {女=2, 男=3}// 集合中某元素的求和 -> 求性别为男的员工总岁数Doublesum=list.stream().filter(e->e.getGender().equals("男")).mapToDouble(Employee::getSalary).sum();
// 结果 -> 3700.0//归约是将集合中的所有元素经过指定运算，折叠成一个元素输出，如：求最值、平均数等，这些操作都是将一个集合的元素折叠成一个元素输出//求薪资最高的员工Employeeem=list.stream().reduce((e1, e2) ->e1.getSalary() >e2.getSalary() ?e1:e2).get();
// 或者直接用Collectors.maxBy 方法Employeee2=list.stream().collect(Collectors.maxBy(Comparator.comparing(Employee::getSalary))).get();
// 结果 -> Employee [name=宋江, gender=男, salary=1500.0]// 匹配元素// anyMatch 是否匹配任一元素,查看元素中是否有工资小于600的booleancheck1=list.stream().anyMatch(e->e.getSalary() <600); // 结果 -> true// allMatch 是否匹配所有元素 查看是否所有员工的工资都小于600booleancheck2=list.stream().allMatch(e->e.getSalary() <600); // 结果 -> false// noneMatch 是否未匹配所有元素 查看是否所有员工工资都大于400 booleancheck3=list.stream().noneMatch(e->e.getSalary() <400);// 结果 -> true// 求元素的平均值 -> 员工的平均工资// 求所有人的平均工资doubleavg=list.stream().collect(Collectors.averagingDouble(Employee::getSalary));
// 结果 -> 1000.0//按薪资从小到大排序List<Employee>l3=list.stream().sorted((s1, s2) ->s1.getSalary().compareTo(s2.getSalary())).collect(Collectors.toList());
// 结果 -> [Employee [name=顾大嫂, gender=女, salary=500.0], Employee [name=扈三娘, gender=女, salary=800.0], Employee [name=林冲, gender=男, salary=1000.0], //        Employee [name=武松, gender=男, salary=1200.0], Employee [name=宋江, gender=男, salary=1500.0]]// 截取 -> 求薪资最少的两个员工,在上述 l3的结果上截取List<Employee>l4=l3.stream().limit(2).collect(Collectors.toList());
// 结果-> [Employee [name=顾大嫂, gender=女, salary=500.0], Employee [name=扈三娘, gender=女, salary=800.0]]// 获取所有的名字，组成一条语句 参数分别为(CharSequence delimiter,CharSequence prefix,CharSequence suffix)Stringstr=list.stream().map(Employee::getName).collect(Collectors.joining(",", "[", "]"));
// 结果->[宋江,武松,林冲,扈三娘,顾大嫂]//用于收集统计数据（如计数，最小值，最大值，总和和平均值）的状态对象// 获取工资的最大值、最小值、平均值、求和等等DoubleSummaryStatisticsdoubleSummaryStatistics=list.stream().mapToDouble(Employee::getSalary).summaryStatistics();
doubleaverage=doubleSummaryStatistics.getAverage(); // 结果 -> 1000.00doublemax=doubleSummaryStatistics.getMax();         // 结果 -> 1500.0doublemin=doubleSummaryStatistics.getMin();         // 结果 -> 500.00longcount=doubleSummaryStatistics.getCount();       // 结果 -> 5doublesum2=doubleSummaryStatistics.getSum();        // 结果 -> 5000.00// list转换成map 某两个元素作为键值对// 员工的姓名最为key, 薪资作为value 转化成mapMap<String, Double>map1=list.stream().collect(Collectors.toMap(Employee::getName, Employee::getSalary));
// 结果 -> {武松=1200.0, 林冲=1000.0, 扈三娘=800.0, 顾大嫂=500.0, 宋江=1500.0}// 返回对象本身Map<String, Employee>map2=list.stream().collect(Collectors.toMap(Employee::getName, employee->employee));
// 结果 -> {武松=Employee [name=武松, gender=男, salary=1200.0], 林冲=Employee [name=林冲, gender=男, salary=1000.0], 扈三娘=Employee [name=扈三娘, gender=女, salary=800.0],//        顾大嫂=Employee [name=顾大嫂, gender=女, salary=500.0], 宋江=Employee [name=宋江, gender=男, salary=1500.0]}//在list转为map时，作为key的值有可能重复，这时候流的处理会抛出个异常：Java.lang.IllegalStateException:Duplicate key。//这时候就要在toMap方法中指定当key冲突时key的选择。(这里是选择第二个key覆盖第一个key)Map<String, Employee>map3=list.stream().collect(Collectors.toMap(Employee::getName,Function.identity(),(k1,k2)->k2));
// 返回的value 是集合// 员工的性别为key,对象集合为valueMap<String, List<Employee>>map4=list.stream().collect(Collectors.groupingBy(Employee::getGender));
// 结果 -> {女=[Employee [name=扈三娘, gender=女, salary=800.0], Employee [name=顾大嫂, gender=女, salary=500.0]], //          男=[Employee [name=宋江, gender=男, salary=1500.0], Employee [name=武松, gender=男, salary=1200.0], Employee [name=林冲, gender=男, salary=1000.0]]}// 利用skip 和limit 实现分页需求  list.stream().skip(pageSize * (pageNum - 1)).limit(pageSize).collect(Collectors.toList());// 查询第一页的信息, pageSize =10 pageNum =1List<Employee>l5=list.stream().skip(0).limit(10).collect(Collectors.toList());
// 结果-> [Employee [name=宋江, gender=男, salary=1500.0], Employee [name=武松, gender=男, salary=1200.0], Employee [name=林冲, gender=男, salary=1000.0], //         Employee [name=扈三娘, gender=女, salary=800.0], Employee [name=顾大嫂, gender=女, salary=500.0]]}