import math def round_towards_zero(num): if num >= 0: return math.floor(num) else: return math.ceil(num) num = 3.45 result = round_towards_zero(num) # 向零舍入 print(result) # 输出: 3 num = -3.45 result = round_towards_zero(num) # 向零舍入 print(result) # 输出: -3 ...
importmathdefround_towards_zero(number):""" 对给定的数字进行最靠近零的取整 """ifnumber>0:returnmath.floor(number)# 正数使用向下取整else:returnmath.ceil(number)# 负数使用向上取整# 测试函数print(round_towards_zero(3.7))# 输出: 3print(round_towards_zero(-3.7))# 输出: -3print(round_towards_...
Round away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise round towards zero. 如果rounding之后,最后的数字是0或5,就向UP方向(远离0的方向)舍入;否则,就像靠近0的方向舍入。 >>> Decimal('1.204').quantize(Decimal('.00'), rounding=ROUND_05UP) Decimal('...
如果x是标量,则这是标量。 See Also --- ceil, trunc, rint Notes --- Some spreadsheet programs calculate the "floor-towards-zero", in other words ``floor(-2.5) == -2``. NumPy instead uses the defi np.ceil()同理 示例 代码语言:javascript 代码运行次数:0 运行 AI代码解释 #-*-coding:ut...
ROUND_05UP (awayfromzeroiflast digit after rounding towards zero would have been 0or5; otherwise towards zero) 直接阅读上面的解释十分抽象,下面我结合例子来解释一下在正负数不同的情况下 他们究竟有着什么样的行为 首先给出一组负数的后一位超过5的数据: ...
ROUND_05UP (awayfromzeroiflast digit after rounding towards zero would have been 0or5; otherwise towards zero) 直接阅读上面的解释十分抽象,下面我结合例子来解释一下在正负数不同的情况下 他们究竟有着什么样的行为 首先给出一组负数的后一位超过5的数据: ...
pythondecimal.quantize（）参数rounding的各参数解释与⾏为我最开始其实是由于疑惑ROUND_FLOOR和 ROUND_DOWN的表现区别才看了⼀波⽂档,但是感觉拉出⼀票以前没有留意过的东西。贴⼀个decimal⽂档⾥⾯的解释：ROUND_CEILING (towards Infinity),ROUND_DOWN (towards zero),ROUND_FLOOR (towards -Infinity)...
df=pd.DataFrame({'date':pd.date_range(start='2020-01-10',periods=100,freq='D'),'cat':pd.Series(['A','B','C']).sample(n=100,replace=True),'val':(np.random.randn(100)+10).round(2),'val2':(np.random.random(100)*10).round(2),'val3':np.random.randint(20,50,size=100...
Round toward 0 – directed rounding towards zero (also known astruncation).Round toward +∞ – ...
ROUND_UP (away from zero). ROUND_05UP (away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise towards zero) x = Decimal('-3.1415926535') + Decimal('-2.7182818285') print x print x.quantize(Decimal('1.0000'), ROUND_HALF_EVEN) ...