print(“Rounded down to next integer:\n”, valuesRoundDown) 复制代码 首先,我们导入math模块。这样就可以使用math.ceil()和math.floor()舍入功能。然后,我们创建一个名为的列表values,其中包含多个浮点值。 为了将这些值四舍五入为整数,我们进行了三个列表推导。第一个round()针对每个列表值执行。其他两个在...
int函数虽然使用ROUND DOWN方式,但是是取整,得到的结果是integer。如果你的需求是要保留小数点后几位,就要使用decimal模块的接口: >>> from decimal import * >>> Decimal('1.2222').quantize(Decimal('.00'), rounding=ROUND_DOWN) Decimal('1.22') >>> Decimal('1.9999').quantize(Decimal('.00'), roundi...
import math def truncate_float(num, decimal_places): integer_part = int(num) # 获取整数部分 decimal_part = num - integer_part # 获取小数部分 power = 10 ** decimal_places # 计算保留的位数 truncated_decimal = math.floor(decimal_part * power) / power # 截断小数部分 result = integer_...
To round every value down to the nearest integer, use np.floor(): Python >>> np.floor(data) array([[-1., -3., -1., 0.], [ 0., 0., -1., 0.], [-1., -1., 0., -1.]]) You can also truncate each value to its integer component with np.trunc(): Python >>>...
Round numbers down to the nearest integer: # Import math libraryimport math# Round numbers down to the nearest integerprint(math.floor(0.6))print(math.floor(1.4))print(math.floor(5.3)) print(math.floor(-5.3))print(math.floor(22.6))print(math.floor(10.0)) Try it Yourself » Definition...
The // operator first divides the number on the left by the number on the right and then rounds down to an integer. This might not give the value you expect when one of the numbers is negative.For example, -3 // 2 returns -2. First, -3 is divided by 2 to get -1.5. Then -...
box_img_int = tuple(map(int, box_image)) # convert to integer or it will not work properly # Get scroll region box box_scroll = [min(box_img_int[0], box_canvas[0]), min(box_img_int[1], box_canvas[1]), max(box_img_int[2], box_canvas[2]), max(box_img_int[3], box...
box = wx.BoxSizer(integer orient) 参数orient 代表方向: wx.VERTICAL – 竖直 wx.HORIZONTAL – 水平 box.Add(wx.Window window, integer proportion=0, integer flag=0, integer border=0) 参数proportion表示在给定的方向中,控件按照什么比例来调整大小: ...
upload_to='book_cover', null=True, blank=True) tags = models.ManyToManyField(Tags, verbose_name='书籍标签', blank=True) prizes = models.ManyToManyField(Prizes, verbose_name='获奖详情', blank=True) sump = models.IntegerField(verbose_name="收藏人数", default=0) rate_num = models.Integer...
topic)[0] # create integer class values X = docs.body X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, stratify=y) 我们继续从训练集中学习词汇,并使用默认设置的CountVectorizer转换两个数据集,以获得近 26,000 个特征: vectorizer = CountVectorizer() X_train_dtm ...