collectors 求和_Collectors.groupingBy() 分组、求和、统计、平均、最大（小）值

public classgroupingBy {public static voidmain(String[]args) {ArrayListlist = newArrayList<>();list.add(newStudent("王一",21,"11",1, newBigDecimal("193.0"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusMinutes(15L))));list.add(newStudent("郑二",22,"22",0, newBigDecimal("171.0"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusHours(16L))));list.add(newStudent("张三",23,"33",1, newBigDecimal("180.1"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusSeconds(500L))));list.add(newStudent("李四",24,"44",0, newBigDecimal("183.2"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusMinutes(18L))));list.add(newStudent("赵五",25,"55",1, newBigDecimal("178.3"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusMinutes(13L))));list.add(newStudent("韩六",26,"88",0, newBigDecimal("169.4"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusMinutes(1L))));list.add(newStudent("田七",27,"77",1, newBigDecimal("173.5"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusMinutes(17L))));list.add(newStudent("卢八",28,"88",0, newBigDecimal("159.6"),Timestamp.valueOf(LocalDateTime.now()),Timestamp.valueOf(LocalDateTime.now().plusMinutes(1L))));//        list.add(new Student("钱九", 29, null, 1, new BigDecimal("180.1")));//        list.add(new Student("魏十", null, "99", null, null));//        list.add(new Student("肖十一", null, "", null, null));//        list.add(new Student("柘十二", null, null, null, null));//1根据性别进行分组//1.1为null的数据不需要，直接过滤Map>collect = list.stream().filter(a -> a.getGender()!= null).collect(Collectors.groupingBy(Student::getGender));//1.2需要保留为null的数据Map extendsSerializable,List>collect1 = list.stream().collect(Collectors.groupingBy(a -> {if(a.getGender()== null) {return"null";}returna.getGender();}));//2根据性别分组后，再根据分数分组，>=60:A <60:B为null的数据保留Map extendsSerializable,Map>>collect2 = list.stream().collect(Collectors.groupingBy(a -> {if(a.getGender()== null) {return"null";}returna.getGender();},Collectors.groupingBy(b -> {String score = b.getScore();if(score == null) {return"B";}else{returnscore.compareTo("60")>= 0? "A": "B";}})));//3按照gender和height分组，TreeMap默认按key升序TreeMap key不能为nullTreeMap>collect3 = list.stream().collect(Collectors.groupingBy(a -> {if(a.getGender()== null|| a.getHeight()== null) {return"null";}returna.getGender()+ "->"+ a.getHeight();},TreeMap::new,Collectors.toList()));//统计gender为1和0的人数Map extendsSerializable,Long>collect4 = list.stream().collect(Collectors.groupingBy(a -> {if(a.getGender()== null) {return"mix";}returna.getGender();},counting()));//按gender分组后，统计60为分界的人数Map>collect5 = list.stream().collect(Collectors.groupingBy(//gender分组a -> {if(a.getGender()== null) {return"null";//gender为null}returna.getGender()== 1? "M": "F";},Collectors.groupingBy(//60为分界b -> {String score = b.getScore();if(score == null) {return"null";}else{returnscore.compareTo("60")>= 0? "A": "B";}},counting()//统计)));//统计60分界的男女各多少Map>collect6 = list.stream().filter(a -> StringUtils.isNoneBlank(a.getScore()))//过滤score为""和null的数据.filter(b -> b.getGender()!= null)//过滤gender为null的数据.collect(Collectors.groupingBy(c -> c.getScore().compareTo("60")>= 0? "G": "B",//以60分组Collectors.groupingBy(Student::getGender,//以gender分组counting()//统计)));//统计男女各年龄之和Mapcollect7 = list.stream().filter(a -> a.getAge()!= null|| a.getGender()!= null).collect(Collectors.groupingBy(Student::getGender,Collectors.summingInt(Student::getAge)));//分组元素存储在自定义集合Map>collect8 = list.stream().filter(a -> a.getGender()!= null).collect(Collectors.groupingBy(Student::getGender,Collectors.mapping(Student::getAge,toCollection(TreeSet::new))));//计算男女中最高的分数Mapcollect9 = list.stream().filter(a -> a.getGender()!= null).collect(Collectors.groupingBy(Student::getGender,Collectors.collectingAndThen(Collectors.maxBy(Comparator.comparing(Student::getScore)),Optional::get)));//计算男女中最低的分数Mapcollect10 = list.stream().filter(a -> a.getGender()!= null).collect(Collectors.toMap(Student::getGender,Function.identity(),BinaryOperator.minBy(Comparator.comparing(Student::getScore))));// //计算男女中最高的分数 分数相同去用时最短Mapcollect11 = list.stream().filter(a -> a.getGender()!= null).collect(Collectors.toMap(Student::getGender,//性别Function.identity(),//t->tBinaryOperator.maxBy(Comparator.comparing(Student::getScore)//分数比较.thenComparing(b -> b.getEndTime().getTime()- b.getStartTime().getTime()//分数相同，按时间比较,Comparator.reverseOrder()))//外层用的max，故此处排序取反));//相同的存放在list中TreeMapcollect12 = list.stream().filter(a -> a.getGender()!= null).collect(Collectors.toMap(Student::getGender,Function.identity(),BinaryOperator.maxBy(Comparator.comparing(Student::getScore).thenComparing(b -> b.getEndTime().getTime()- b.getStartTime().getTime(),Comparator.reverseOrder())),TreeMap::new));HashMap>hashMap = newHashMap<>();collect12.forEach((k,v)-> {Map>listMap = list.stream().filter(a ->

k.equals(a.getGender())&& v.getScore().equals(a.getScore())&& (a.getEndTime().getTime()-a.getStartTime().getTime())==(v.getEndTime().getTime()- v.getStartTime().getTime())).collect(Collectors.toMap(Student::getGender,t -> {Liststudents = Collections.singletonList(t);return newArrayList<>(students);},(v1,v2)-> {v2.addAll(v1);returnv2;}));hashMap.putAll(listMap);});//分数最高用时最短Optionalmax = list.stream().max(Comparator

.comparing(Student::getScore).thenComparing(a -> a.getEndTime().getTime()- a.getStartTime().getTime(),Comparator.reverseOrder()));Student student = max.isPresent()? max.get(): null;//最大值 分数 用时Student student1 = list.stream().max(Comparator

.comparing(Student::getScore).thenComparing(a -> a.getEndTime().getTime()- a.getStartTime().getTime(),Comparator.reverseOrder())).orElse(null);//男女各组的平均分数Mapcollect13 = list.stream().filter(a -> a.getGender()!= null).filter(b -> StringUtils.isNoneBlank(b.getScore())).collect(Collectors.groupingBy(Student::getGender,Collectors.averagingDouble(a -> Double.parseDouble(a.getScore()))));System.out.println();}}