【Java 动态数据统计图】动态数据统计思路Demo（动态，排序，containsKey）三（115）

上代码：

import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;public class day10 {public static void main(String[] args) {List<Map<String,Object>> list = new ArrayList<>();Map<String,Object> map1 = new HashMap<>();map1.put("region","西安");map1.put("num",3.3);Map<String,Object> map2 = new HashMap<>();map2.put("region","上海");map2.put("num",4.0);Map<String,Object> map3 = new HashMap<>();map3.put("region","北京");map3.put("num",5.4);Map<String,Object> map4 = new HashMap<>();map4.put("region","西安");map4.put("num",6.4);Map<String,Object> map5 = new HashMap<>();map5.put("region","西安");map5.put("num",6.4);Map<String,Object> map6 = new HashMap<>();map6.put("region","北京");map6.put("num",6.4);Map<String,Object> map7 = new HashMap<>();map7.put("region","成都");map7.put("num",6.4);Map<String,Object> map8 = new HashMap<>();map8.put("region","上海");map8.put("num",2.0);Map<String,Object> map9 = new HashMap<>();map9.put("region","上海");map9.put("num",2.0);Map<String,Object> map10 = new HashMap<>();map10.put("region","上海");map10.put("num",2.0);list.add(map1);list.add(map2);list.add(map3);list.add(map4);list.add(map5);list.add(map6);list.add(map7);list.add(map8);list.add(map9);list.add(map10);System.out.println("入参list:"+list);/** 思路：* 创建两个Map：* Map1（地域，相同地域num值加和）；* Map2（地域，相同地域统计次数累计）；* * 循环数据，通过containsKey判断是否包含key，* 包含：Map1 相同地域num值加和存进去；Map2 相同地域统计次数累计存进去；* 不包含：Map1和Map2直接存；* * */// Map存储：(region,num）,num为相同region的加和；Map<String,Object> regionMap = new HashMap<>();// Map存储：(region,统计次数）,统计次数为相同region的次数；Map<String,Object> countMap = new HashMap<>();for (int i = 0; i < list.size(); i++) {String region = String.valueOf(list.get(i).get("region"));String num = String.valueOf(list.get(i).get("num"));// regionMap包含region：将region当作key；if(regionMap.containsKey(region)) {String key = region;String value = String.valueOf(regionMap.get(key));  // 从regionMap获取value值；即地域对应的num值；regionMap.put(region, Double.parseDouble(value) + Double.parseDouble(num));}else {  // 不包含，直接存进去regionMap.put(region, num);}// countMap包含region：将region当作key；if(countMap.containsKey(region)) {String key2 = region;String value2 = String.valueOf(countMap.get(key2));  // 从countMap获取value值；即地域对应的统计次数值；countMap.put(region, Integer.valueOf(value2) + 1);}else {  // 不包含，直接存进去countMap.put(region, 1);}}System.out.println("regionMap:"+regionMap);System.out.println("countMap:"+countMap);List<String> regionList = new LinkedList<String>();  // 地域List<String> numberList = new LinkedList<String>();  // 数值List<String> countList = new LinkedList<String>();  // 统计次数List<String> avgList = new LinkedList<String>();  // 平均值// 封装地域集合：（北京，5.9）Iterator<Map.Entry<String, Object>> it = regionMap.entrySet().iterator();while(it.hasNext()){Map.Entry<String, Object> entry = it.next();String key = entry.getKey();String value = String.valueOf(entry.getValue());regionList.add(key);numberList.add(value);}// 封装统计次数集合：（北京，1）Iterator<Map.Entry<String, Object>> it2 = countMap.entrySet().iterator();while(it2.hasNext()){Map.Entry<String, Object> entry = it2.next();String key = entry.getKey();String value = String.valueOf(entry.getValue());countList.add(value);}// 计算平均值：封装集合avgListif(numberList.size() == countList.size()) {for (int i = 0; i < numberList.size(); i++) {double fenzi = Double.parseDouble(numberList.get(i));int fenmu = Integer.valueOf(countList.get(i));if(fenmu > 0) {Double x =  fenzi/fenmu;// double类型的数据当分母的数值趋近0的时候，返回来的数值就是一个NAN:if(Double.isNaN(x)){x = 0.0;}avgList.add(String.valueOf(x));}}}System.out.println("regionList:"+regionList);System.out.println("numberList:"+numberList);System.out.println("countList:"+countList);System.out.println("avgList:"+avgList);// 最终输出集合：List<Map<String,Object>> resultList = new ArrayList<>();// 根据地域集合与平均值集合封装新集合resultList：if(regionList.size() == avgList.size()) {for (int i = 0; i < regionList.size(); i++) {Map<String,Object> map = new HashMap<>();map.put("title",regionList.get(i));map.put("value",avgList.get(i).substring(0,avgList.get(i).indexOf(".")+2));resultList.add(map);}  	}System.out.println("最终输出集合resultList:"+resultList);//排序：value值大的Map往前排，斌且插入新字段：sortValue，代表序列;Double[] valueSort = new Double[resultList.size()];String[] titleSort = new String[resultList.size()];// 排序后最终输出集合：List<Map<String,Object>> listFinal= new ArrayList<>();// 获取排序的数组：for (int i = 0; i < resultList.size(); i++) {valueSort[i] = Double.parseDouble(String.valueOf(resultList.get(i).get("value")));titleSort[i] = String.valueOf(resultList.get(i).get("title"));}// 数组排序：BubblSortUtils.bubbleSortDescMultipleDouble(valueSort,titleSort);// 数组封装listFinal：for (int i = 0; i < valueSort.length; i++) {String ch = String.valueOf(titleSort[i]);for (int j = 0; j < resultList.size(); j++) {String value = String.valueOf(resultList.get(j).get("title"));if(ch.equals(value)){listFinal.add(resultList.get(j));}}}// listFinal中添加排序字段：sortValuefor (int i = 0; i < listFinal.size(); i++) {listFinal.get(i).put("sortValue",i+1);}   System.out.println("最终排序后输出集合listFinal:"+listFinal);}
}

测试输出：

入参list:[{num=3.3, region=西安}, {num=4.0, region=上海}, {num=5.4, region=北京}, {num=6.4, region=西安}, {num=6.4, region=西安}, {num=6.4, region=北京}, {num=6.4, region=成都}, {num=2.0, region=上海}, {num=2.0, region=上海}, {num=2.0, region=上海}]
regionMap:{成都=6.4, 上海=10.0, 西安=16.1, 北京=11.8}
countMap:{成都=1, 上海=4, 西安=3, 北京=2}
regionList:[成都, 上海, 西安, 北京]
numberList:[6.4, 10.0, 16.1, 11.8]
countList:[1, 4, 3, 2]
avgList:[6.4, 2.5, 5.366666666666667, 5.9]
最终输出集合resultList:[{title=成都, value=6.4}, {title=上海, value=2.5}, {title=西安, value=5.3}, {title=北京, value=5.9}]
最终排序后输出集合listFinal:[{sortValue=1, title=成都, value=6.4}, {sortValue=2, title=北京, value=5.9}, {sortValue=3, title=西安, value=5.3}, {sortValue=4, title=上海, value=2.5}]