需求
业务需要导出的Excel的数字内容保留两位小数,并且四舍五入
代码实现
百度一圈所抄袭的代码
DecimalFormat dfScale2 = new DecimalFormat("###.##");
dfScale2.format(1.125D);
发现问题
导出数据很诡异.不是所有数据都是如所想的四舍五入.
经过排查最终发现是RoundingMode的问题,应该使用HALF_UP,
DecimalFormat 默认使用的是HALF_EVEN
DecimalFormat dfScale2 = new DecimalFormat("###.##");
System.out.println("dfScale2.getRoundingMode()=" + dfScale2.getRoundingMode());
//输出结果
dfScale2.getRoundingMode()=HALF_EVEN
//
RoundingMode.HALF_EVEN
想了解HALF_EVEN，去官网API看了下
HALF_EVEN 被舍位是5(如保留两位小数的2.115),后面还有非0值进1(如保留两位小数的2.11500001 格式化为2.12),5后面没有数字或者都是0时,前面是偶数则舍,是奇数则进1,目标是让被舍前一位变为偶数.
- CEILING 向更大的值靠近
- Rounding mode to round towards positive infinity.
- DOWN向下取整
- Rounding mode to round towards zero.
- FLOOR 向更小的值靠近
- Rounding mode to round towards negative infinity.
- HALF_DOWN 五舍六入
- Rounding mode to round towards “nearest neighbor” unless both neighbors are equidistant, in which case round down.
- HALF_EVEN
- Rounding mode to round towards the “nearest neighbor” unless both neighbors are equidistant, in which case, round towards the even neighbor.
- HALF_UP 四舍五入
- Rounding mode to round towards “nearest neighbor” unless both neighbors are equidistant, in which case round up.
- UNNECESSARY 设置这个模式,对于精确值格式化会抛出异常
- Rounding mode to assert that the requested operation has an exact result, hence no rounding is necessary.
- UP 向远离数字0进行进位.
- Rounding mode to round away from zero.
错误的代码测试RoundingMode.HALF_EVEN
为了更好的理解HALF_EVEN,写了些测试代码但是发现自己更迷惘了…搞不清楚到底HALF_EVEN是什么机制进舍…输出结果的尾数很不规律.
import java.math.BigDecimal;
import java.math.RoundingMode;
import java.text.DecimalFormat;
import java.util.*;
public class LocalTest {
//定义一个保留两位小数格式的 DecimalFormat 的变量 dfScale2
@Test
public void testDecimalFormat() {
DecimalFormat dfScale2 = new DecimalFormat("###.##");
System.out.println("dfScale2.getRoundingMode()=" + dfScale2.getRoundingMode());
System.out.println("dfScale2.format(1.125D)=" + dfScale2.format(1.125D));
System.out.println("dfScale2.format(1.135D)=" + dfScale2.format(1.135D));
System.out.println("dfScale2.format(1.145D)=" + dfScale2.format(1.145D));
System.out.println("dfScale2.format(1.225D)=" + dfScale2.format(1.225D));
System.out.println("dfScale2.format(1.235D)=" + dfScale2.format(1.235D));
System.out.println("dfScale2.format(1.245D)=" + dfScale2.format(1.245D));
System.out.println();
System.out.println("dfScale2.format(2.125D)=" + dfScale2.format(2.125D));
System.out.println("dfScale2.format(2.135D)=" + dfScale2.format(2.135D));
System.out.println("dfScale2.format(2.145D)=" + dfScale2.format(2.145D));
System.out.println("dfScale2.format(2.225D)=" + dfScale2.format(2.225D));
System.out.println("dfScale2.format(2.235D)=" + dfScale2.format(2.235D));
System.out.println("dfScale2.format(2.245D)=" + dfScale2.format(2.245D));
System.out.println();
System.out.println("dfScale2.format(3.125D)=" + dfScale2.format(3.125D));
System.out.println("dfScale2.format(3.135D)=" + dfScale2.format(3.135D));
System.out.println("dfScale2.format(3.145D)=" + dfScale2.format(3.145D));
System.out.println("dfScale2.format(3.225D)=" + dfScale2.format(3.225D));
System.out.println("dfScale2.format(3.235D)=" + dfScale2.format(3.235D));
System.out.println("dfScale2.format(3.245D)=" + dfScale2.format(3.245D));
System.out.println();
System.out.println("dfScale2.format(4.125D)=" + dfScale2.format(4.125D));
System.out.println("dfScale2.format(4.135D)=" + dfScale2.format(4.135D));
System.out.println("dfScale2.format(4.145D)=" + dfScale2.format(4.145D));
System.out.println("dfScale2.format(4.225D)=" + dfScale2.format(4.225D));
System.out.println("dfScale2.format(4.235D)=" + dfScale2.format(4.235D));
System.out.println("dfScale2.format(4.245D)=" + dfScale2.format(4.245D));
}
}
dfScale2.getRoundingMode()=HALF_EVEN dfScale2.format(1.125D)=1.12 dfScale2.format(1.135D)=1.14 dfScale2.format(1.145D)=1.15 dfScale2.format(1.225D)=1.23 dfScale2.format(1.235D)=1.24 dfScale2.format(1.245D)=1.25 dfScale2.format(2.125D)=2.12 dfScale2.format(2.135D)=2.13 dfScale2.format(2.145D)=2.15 dfScale2.format(2.225D)=2.23 dfScale2.format(2.235D)=2.23 dfScale2.format(2.245D)=2.25 dfScale2.format(3.125D)=3.12 dfScale2.format(3.135D)=3.13 dfScale2.format(3.145D)=3.15 dfScale2.format(3.225D)=3.23 dfScale2.format(3.235D)=3.23 dfScale2.format(3.245D)=3.25 dfScale2.format(4.125D)=4.12 dfScale2.format(4.135D)=4.13 dfScale2.format(4.145D)=4.14 dfScale2.format(4.225D)=4.22 dfScale2.format(4.235D)=4.24 dfScale2.format(4.245D)=4.25
正确的代码测试RoundingMode.HALF_EVEN
突然发现自己忽略了一个事情,测试的参数都是用的double类型.想起来double类型不精准.但是侥幸心理以及知识不牢靠以为 3位小数应该影响不大吧.改了下代码,把参数改为BigDecimal类型
使用BigDecimal时,参数尽量传入字符串,要比传入double精准.
new BigDecimal("1.125")
@Test
public void testDecimalFormat() {
DecimalFormat dfScale2 = new DecimalFormat("###.##");
dfScale2.setRoundingMode(RoundingMode.HALF_EVEN);
System.out.println("dfScale2.getRoundingMode()=" + dfScale2.getRoundingMode());
System.out.println("dfScale2.format(new BigDecimal(\"1.1251\"))=" + dfScale2.format(new BigDecimal("1.1251")));
System.out.println("dfScale2.format(new BigDecimal(\"1.1351\"))=" + dfScale2.format(new BigDecimal("1.1351")));
System.out.println("dfScale2.format(new BigDecimal(\"1.1451\"))=" + dfScale2.format(new BigDecimal("1.1451")));
System.out.println("dfScale2.format(new BigDecimal(\"1.2250\"))=" + dfScale2.format(new BigDecimal("1.2250")));
System.out.println("dfScale2.format(new BigDecimal(\"1.2350\"))=" + dfScale2.format(new BigDecimal("1.2350")));
System.out.println("dfScale2.format(new BigDecimal(\"1.2450\"))=" + dfScale2.format(new BigDecimal("1.2450")));
System.out.println("dfScale2.format(new BigDecimal(\"1.22501\"))=" + dfScale2.format(new BigDecimal("1.22501")));
System.out.println("dfScale2.format(new BigDecimal(\"1.23505\"))=" + dfScale2.format(new BigDecimal("1.23505")));
System.out.println("dfScale2.format(new BigDecimal(\"1.24508\"))=" + dfScale2.format(new BigDecimal("1.24508")));
dfScale2.getRoundingMode()=HALF_EVEN
dfScale2.format(new BigDecimal("1.1251"))=1.13
dfScale2.format(new BigDecimal("1.1351"))=1.14
dfScale2.format(new BigDecimal("1.1451"))=1.15
dfScale2.format(new BigDecimal("1.2250"))=1.22
dfScale2.format(new BigDecimal("1.2350"))=1.24
dfScale2.format(new BigDecimal("1.2450"))=1.24
dfScale2.format(new BigDecimal("1.22501"))=1.23
dfScale2.format(new BigDecimal("1.23505"))=1.24
dfScale2.format(new BigDecimal("1.24508"))=1.25
结论
1、警觉doulbe的不精确所引起RoundingMode结果不稳定的问题,即使是四舍五入的模式,对double类型参数使用也会有不满足预期的情况.
2、使用数字格式化时,要注意默认RoundingMode模式是否是自己需要的.如果不是记得手动设置下.
以上为个人经验，希望能给大家一个参考，也希望大家多多支持脚本之家。