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
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dfScale2.format( 1 .125D);
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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
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DecimalFormat dfScale2 = new DecimalFormat( "###.##" );
System.out.println( "dfScale2.getRoundingMode()=" + dfScale2.getRoundingMode());
//输出结果
dfScale2.getRoundingMode()=HALF_EVEN
//
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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.
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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));
}
}
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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
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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.
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new BigDecimal( "1.125" )
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@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" )));
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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
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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服务器之家。
原文链接:https://blog.csdn.net/baixf/article/details/88792219