将一个数字的位数相加

Sum the digits of a number(将一个数字的位数相加)
本文介绍了将一个数字的位数相加的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

问题描述

如果我想找到一个数字的数字之和,即:

If I want to find the sum of the digits of a number, i.e.:

  • 输入:932
  • 输出:14,即(9 + 3 + 2)

最快的方法是什么?

我本能地这样做了:

sum(int(digit) for digit in str(number))

我在网上找到了这个:

sum(map(int, str(number)))

哪种方法最适合速度,还有其他更快的方法吗?

Which is best to use for speed, and are there any other methods which are even faster?

推荐答案

你发的两行都很好,但是你可以纯整数做,效率最高:

Both lines you posted are fine, but you can do it purely in integers, and it will be the most efficient:

def sum_digits(n):
    s = 0
    while n:
        s += n % 10
        n //= 10
    return s

或使用 divmod:

def sum_digits2(n):
    s = 0
    while n:
        n, remainder = divmod(n, 10)
        s += remainder
    return s

<小时>

没有增加分配的版本更快:


Even faster is the version without augmented assignments:

def sum_digits3(n):
   r = 0
   while n:
       r, n = r + n % 10, n // 10
   return r

<小时>

> %timeit sum_digits(n)
1000000 loops, best of 3: 574 ns per loop

> %timeit sum_digits2(n)
1000000 loops, best of 3: 716 ns per loop

> %timeit sum_digits3(n)
1000000 loops, best of 3: 479 ns per loop

> %timeit sum(map(int, str(n)))
1000000 loops, best of 3: 1.42 us per loop

> %timeit sum([int(digit) for digit in str(n)])
100000 loops, best of 3: 1.52 us per loop

> %timeit sum(int(digit) for digit in str(n))
100000 loops, best of 3: 2.04 us per loop

这篇关于将一个数字的位数相加的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持跟版网!

本站部分内容来源互联网,如果有图片或者内容侵犯了您的权益,请联系我们,我们会在确认后第一时间进行删除!

相关文档推荐

groupby multiple coords along a single dimension in xarray(在xarray中按单个维度的多个坐标分组)
Group by and Sum in Pandas without losing columns(Pandas中的GROUP BY AND SUM不丢失列)
Group by + New Column + Grab value former row based on conditionals(GROUP BY+新列+基于条件的前一行抓取值)
Groupby and interpolate in Pandas(PANDA中的Groupby算法和插值算法)
Pandas - Group Rows based on a column and replace NaN with non-null values(PANAS-基于列对行进行分组,并将NaN替换为非空值)
Grouping pandas DataFrame by 10 minute intervals(按10分钟间隔对 pandas 数据帧进行分组)