1、首先准备两个类（苹果类，可以一个苹果颜色的枚举）
1. public class Apple { 2. // 名字 3. private String name; 4. // 重量 5. private double weight; 6. // 颜色（枚举类） 7. private Color color; 8. // 城市 9. private String nation; 10. 11. public Apple() { 12. } 13. 14. @Override 15. public String toString() { 16. return "Apple{" + 17. "name='" + name + '\'' + 18. ", weight=" + weight + 19. ", color=" + color + 20. ", nation='" + nation + '\'' + 21. '}'; 22. } 23. 24. @Override 25. public int hashCode() { 26. return Objects.hash(name, weight, color, nation); 27. } 28. 29. public String getName() { 30. return name; 31. } 32. 33. public void setName(String name) { 34. this.name = name; 35. } 36. 37. public double getWeight() { 38. return weight; 39. } 40. 41. public void setWeight(double weight) { 42. this.weight = weight; 43. } 44. 45. public Color getColor() { 46. return color; 47. } 48. 49. public void setColor(Color color) { 50. this.color = color; 51. } 52. 53. public String getNation() { 54. return nation; 55. } 56. 57. public void setNation(String nation) { 58. this.nation = nation; 59. } 60. 61. public Apple(String name, double weight, Color color, String nation) { 62. this.name = name; 63. this.weight = weight; 64. this.color = color; 65. this.nation = nation; 66. } 67. }
1. public enum Color { 2. /** 3. * 红色，绿色，蓝色 4. */ 5. RED("红色"),GREEN("绿色"),Blue("蓝色"); 6. private final String co; 7. 8. Color(String co) { 9. this.co = co; 10. } 11. public String getCo() { 12. return co; 13. } 14. }
2、向集合中加入数据，开始处理list集合
1. Apple apple1 = new Apple("Apple1", 32.23, Color.RED, "china"); 2. Apple apple2 = new Apple("Apple2", 23, Color.RED, "japan"); 3. Apple apple3 = new Apple("Apple3", 12.09, Color.GREEN, "america"); 4. Apple apple4 = new Apple("Apple4", 43.12, Color.Blue, "english"); 5. Apple apple5 = new Apple("Apple5", 12.09, Color.GREEN, "oss"); 6. Apple apple6 = new Apple("Apple6", 12.09, Color.GREEN, "fsd"); 7. Apple apple7 = new Apple("Apple7", 12.09, Color.Blue, "fa"); 8. Apple apple8 = new Apple("Apple8", 12.09, Color.Blue, "as"); 9. Apple apple9 = new Apple("Apple9", 10.232, Color.GREEN, "sfs"); 10. Apple apple10 = new Apple("Apple10", 43.22, Color.RED, "zz"); 11. List<Apple> appleList = Arrays.asList(apple1, apple2, apple3, apple4, apple5, apple6, apple7, apple8, apple9, apple10);
案例1：按照重量排序，重量一样时候按照城市排序
list.sort(Comparator.comparing(Apple::getWeight).thenComparing(Apple::getNation));
案例2：筛选要么是重量大于23的红苹果，要么是绿苹果的集合
1. // 方式一 2. Predicate<Apple> predicate = apple -> Color.RED.equals(apple.getColor()); 3. list.stream().filter(predicate.and(a -> a.getWeight() > 23).or(b -> b.getColor().equals(Color.GREEN))).forEach(System.out::println); 4. // 方式二 5. list.stream().filter(apple -> Color.RED.equals(apple.getColor()) && apple.getWeight() > 23 || Color.GREEN.equals(apple.getColor())).forEach(System.out::println);
案例3：挑选重量小于30的苹果，然后按照城市名字排序,并把名字打印出来
list.stream().filter(apple -> apple.getWeight() < 30).map(Apple::getNation).sorted().forEach(System.out::println);
案例4：筛选前二个红苹果
appleList.stream().filter(apple -> Color.RED.equals(apple.getColor())).limit(2).forEach(System.out::println);
案例5：筛选除了前两个的其他所有红苹果
appleList.stream().filter(apple -> Color.RED.equals(apple.getColor())).skip(2).forEach(System.out::println);
案例6：遍历所有每个苹果所在城市名字的长度
appleList.stream().map(apple -> apple.getNation().length()).forEach(System.out::println);
案例7：找到任意一个红苹果(第一个、是否全是红，是否有一个红色,是否没有红)
1. // 任意一个红苹果 2. appleList.stream().filter(apple -> Color.RED.equals(apple.getColor())).findAny().ifPresent(System.out::println); 3. // 第一个红苹果 4. Optional<Apple> first = appleList.stream().filter(apple -> Color.RED.equals(apple.getColor())).findFirst(); 5. System.out.println(first.orElse(new Apple("11", 23, Color.RED, "lalalla"))); 6. // 是否全是红 7. System.out.println(appleList.stream().allMatch(apple -> Color.RED.equals(apple.getColor()))); 8. // 是否有红 9. System.out.println(appleList.stream().anyMatch(apple -> Color.RED.equals(apple.getColor()))); 10. // 是否没有红 11. System.out.println(appleList.stream().noneMatch(apple -> Color.RED.equals(apple.getColor())));
案例8：计算所有苹果的重量和
1. // 方式一 2. System.out.println(appleList.stream().mapToDouble(Apple::getWeight).reduce(0.00, (a, b) -> 3. // 为了防止小数位过多 4. new BigDecimal(Double.toString(a)).add(new BigDecimal(Double.toString(b))).doubleValue() 5. )); 6. // 方式二 7. System.out.println(appleList.stream().mapToDouble(Apple::getWeight).sum());
案例9：找出最重的苹果
1. // 方式一 2. appleList.stream().map(Apple::getWeight).reduce(Double::max).ifPresent(System.out::println); 3. // 方式二 4. appleList.stream().max(Comparator.comparingDouble(Apple::getWeight)).ifPresent(apple -> System.out.println(apple.getWeight())); 5. // 方式三 6. appleList.stream().mapToDouble(Apple::getWeight).max().ifPresent(System.out::println);
案例10：返回所有的城市名字字符串，按照城市名字排序
1. System.out.println(appleList.stream().map(Apple::getNation) 2. .distinct() 3. .sorted() 4. // 效率不高 5. //.reduce("", (a, b) -> a + b)); 6. .collect(Collectors.joining(",")));
案例11：求出平均重量
1. // 方式一 2. appleList.stream().mapToDouble(Apple::getWeight).average().ifPresent(System.out::println); 3. // 方式二 4. System.out.println(appleList.stream().collect(Collectors.averagingDouble(Apple::getWeight)));
案例12：按照苹果颜色分组，颜色一样按照重量分组
Map<Color, Map<Double, List<Apple>>> collect = appleList.stream().collect(Collectors.groupingBy(Apple::getColor, Collectors.groupingBy(Apple::getWeight)));
案例13：按照颜色分组，并且显示每种颜色分组后的数量
1. Map<Color, Long> collect = appleList.stream().collect(Collectors.groupingBy(Apple::getColor, Collectors.counting())); 2. collect.entrySet().forEach(System.out::println); 3. // 补充，假如挑选所要结果后大于3的 4. collect.entrySet().stream().filter(colorLongEntry -> colorLongEntry.getValue() > 3).forEach(System.out::println);
案例14：按颜色分组，挑选出每个分组中重量最大的,重量一样的话，根据城市名字
1. Map<Color, Optional<Apple>> collect = appleList.stream().collect(Collectors.groupingBy(Apple::getColor, Collectors.maxBy(Comparator.comparingDouble(Apple::getWeight).thenComparing(Apple::getNation)))); 2. // 去掉optional 3. Map<Color, Apple> collect1 = collect.entrySet().stream().collect(Collectors.toMap(Map.Entry::getKey, colorOptionalEntry -> colorOptionalEntry.getValue().orElse(new Apple()), (a, b) -> a)); 4. System.out.println(collect1); 5. // 方式二 6. //Map<Color, Apple> collect3 = appleList.stream().collect(Collectors.groupingBy(Apple::getColor, Collectors.collectingAndThen(Collectors.maxBy(Comparator.comparingDouble(Apple::getWeight).thenComparing(Apple::getNation)), Optional::get))); 7. Map<Color, Apple> collect2 = appleList.stream().collect(Collectors.toMap(Apple::getColor, Function.identity(), BinaryOperator.maxBy(Comparator.comparingDouble(Apple::getWeight).thenComparing((Apple::getNation))))); 8. System.out.println("-----------------------------"); 9. System.out.println(collect2);
案例15：同时找出苹果的总数量，最大值，最小值，平均值
1. DoubleSummaryStatistics collect = appleList.stream().collect(Collectors.summarizingDouble(Apple::getWeight)); 2. System.out.println(collect);
案例16：按照颜色分组，找出每个分组下的最大值，总数。最小值，平均值
1. Map<Color, DoubleSummaryStatistics> collect1 = appleList.stream().collect(Collectors.groupingBy(Apple::getColor, Collectors.summarizingDouble(Apple::getWeight))); 2. collect1.entrySet().forEach(System.out::println);
案例17：重量大于20的分为一组，重量小于20的分为一组
1. // 方式一 2. Map<Boolean, List<Apple>> collect = appleList.stream().collect(Collectors.groupingBy(a -> a.getWeight() > 20)); 3. collect.forEach((aBoolean, apples) -> System.out.println(aBoolean + apples.toString())); 4. // 方式二 5. Map<Boolean, List<Apple>> collect1 = appleList.stream().collect(Collectors.partitioningBy(apple -> apple.getWeight() > 20)); 6. collect1.forEach((aBoolean, apples) -> System.out.println(aBoolean + apples.toString()));
案例18：基于案例17，每种分组再按照颜色分组
1. Map<Boolean, Map<Color, List<Apple>>> collect = appleList.stream().collect(Collectors.partitioningBy(apple -> apple.getWeight() > 20, Collectors.groupingBy(Apple::getColor))); 2. System.out.println(collect);
案例19：重量大于20的分为一组，重量小于20的分为一组,只要分组的重量最大的
Map<Boolean, Apple> collect = appleList.stream().collect(Collectors.partitioningBy(apple -> apple.getWeight() > 20, Collectors.collectingAndThen(Collectors.maxBy(Comparator.comparingDouble(Apple::getWeight)), Optional::get)));
案例20：按照重量大于20的一组，小于20的一组，找出每组个数
Map<Boolean, Long> collect = appleList.stream().collect(Collectors.partitioningBy(apple -> apple.getWeight() > 20, Collectors.counting()));
案例21：先根据重量是否大于20分组，再根据颜色是否为红色分组
Map<Boolean, Map<Boolean, List<Apple>>> collect = appleList.stream().collect(Collectors.partitioningBy(apple -> apple.getWeight() > 20, Collectors.partitioningBy(apple -> apple.getColor().equals(Color.RED))));
案例22：根据名字降序排序，为null的在最后
appleList.sort(Comparator.comparing(Apple::getName,Comparator.nullsLast(Comparator.reverseOrder())));
扩充知识：
1. public static void main(String[] args) { 2. // 生成1-100的偶数流 3. //test1(); 4. // 生成1-100内的勾股数 5. //test2(); 6. // iterate打印10个偶数 7. //test3(); 8. // 生成10个斐波那契数列 9. //test4(); 10. //生成随机数或者全是1的无限流 11. //test5(); 12. } 13. public static void test1(){ 14. // 打印个数(注意，rangeClosed包括两端，range不包括右端点) 15. IntStream.rangeClosed(1, 100).filter(value -> value % 2 == 0).forEach(System.out::println); 16. } 17. public static void test2(){ 18. IntStream.rangeClosed(1,100).boxed().flatMap( 19. a->IntStream.rangeClosed(a,100).mapToObj(b->new double[]{a,b,Math.sqrt(a*a+b*b)}) 20. ).filter(t->t[2]%1==0).forEach(result->System.out.println(result[0]+""+result[1]+result[2])); 21. } 22. public static void test3(){ 23. Stream.iterate(0,a->a+2).limit(10).forEach(System.out::println); 24. } 25. public static void test4(){ 26. Stream.iterate(new int[]{0,1},a->new int[]{a[1],a[0]+a[1]}).limit(10).forEach(result-> System.out.println(result[0])); 27. } 28. public static void test5(){ 29. // 生成随机数 30. Stream.generate(Math::random).limit(20).forEach(System.out::println); 31. // 生成全是1的无限流 32. Stream.generate(()->1).limit(20).forEach(System.out::println); 33. } 34. }
