data['result'] = data.apply(lambdax: function(x), axis=1) data
case when online_time < 12 then '(3,12]' when online_time >= 12 and online_time < 24 then '[12,24)' when online_time >= 24 and online_time < 36 then '[24,36)' when online_time >= 36 and online_time < 48 then '[36,48)' when online_time >= 48 and online_time < 60...
df["省"] = df["地址"].apply(lambda x: x[0:x.find("省")+1]) df["市"] = df["地址"].apply(lambda x: x[x.find("省")+1:x.find("市")+1]) df["县"] = df["地址"].apply(lambda x: x[x.find("市")+1:]) 这里直接用了python里面的切片(slicing)语法:sequence[start:stop...
df["省"] = df["地址"].apply(lambda x: x[0:x.find("省")+1]) df["市"] = df["地址"].apply(lambda x: x[x.find("省")+1:x.find("市")+1]) df["县"] = df["地址"].apply(lambda x: x[x.find("市")+1:]) 这里直接用了python里面的切片(slicing)语法:sequence[start:stop...
表语句： DROP TABLE IF EXISTS `yj_item_plan`; CREATE TABLE `yj_item_plan` ( `id` big...
(lambda x: x > 2)(3) # => True (lambda x, y: x ** 2 + y ** 2)(2, 1) # => 5 我们还可以将函数作为参数使用map和filter,实现元素的批量处理和过滤。关于Python中map、reduce和filter的使用,具体可以查看之前的文章: # There are built-in higher order functions ...
再进一步判断这项技能的分数是否满足写进简历的标准。 这种在外层条件满足的前提下, 再增加额外的判断的过程, 就是条件判断嵌套。 如果我们输入"Python", 再输入60,程序最后会打印出来什么? 11.1 条件判断嵌套的执行流程 首先,我们可以把原代码进行概括,来看下图: ...
delattr(Person, 'feed')) print(hasattr(Person, 'feed')) setattr(Person, 'feed', lambda x: ...
For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates (((1+2)+3)+4)+5). If initial is present, it is placed before the items of the sequence in the calculation, and serves as a default when the sequence is empty. 1...
c = fn(*args, **kwargs)next(c)returncreturnwrapperdefcat(f, case_insensitive, child):ifcase_insensitive: line_processor =lambdal: l.lower()else: line_processor =lambdal: lforlineinf: child.send(line_processor(line))@coroutinedefgrep(substring, case_insensitive, child):ifcase_insensitive:...