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如何在Julia中重复字符串中的单个字符

julia

如何在Julia中重复字符串中的单个字符,julia,Julia,这显示了如何在Python中重复字符串中的单个字符 >>> s = '123abc' >>> n = 3 >>> ''.join([c*n for c in s]) '111222333aaabbbccc' 你在朱莉娅身上会怎么做 编辑 作为朱莉娅的新人，我对这门语言所能提供的东西感到惊讶 例如，我认为上面的Python代码与任何语言中的代码一样简单。然而，正如我下面的答案所示，Julia等价代码join（[c^n表示s中的c]）可以说是更

这显示了如何在Python中重复字符串中的单个字符

>>> s = '123abc'
>>> n = 3
>>> ''.join([c*n for c in s])
'111222333aaabbbccc'
你在朱莉娅身上会怎么做

编辑

作为朱莉娅的新人，我对这门语言所能提供的东西感到惊讶

例如，我认为上面的Python代码与任何语言中的代码一样简单。然而，正如我下面的答案所示，Julia等价代码
join（[c^n表示s中的c]）
可以说是更简单的，对于任何语言来说都可能达到最简单的程度

另一方面，@niczky12表明，在
字符串
函数中添加省略号运算符后，速度可以比稍微简单的
连接
函数大幅度提高

在一个例子中，朱莉娅因简洁而闪耀。在另一种情况下，茱莉亚因速度而闪耀

对于Python程序员来说，当他们注意到
c^n
在Python中只是
c*n
时，第一种情况应该可以立即阅读。当他们看到使用省略号操作符提高速度时，额外的复杂性可能不会阻止他们学习Julia。读者可能开始认为我希望许多Python程序员会认真对待Julia。他们不会错的

感谢@rickhg12hs对基准点的建议。我学到了很多。

你可以用Julia comprehension或生成器来完成

julia> VERSION
v"1.0.0"

julia> s = "123abc"
"123abc"

# n is number of times to repeat each character.
julia> n = 3
3

# Using a Julia comprehension with [...]
julia> join([c^n for c in s])
"111222333aaabbbccc"

# Using a Julia generator without the [...]
julia> join(c^n for c in s)
"111222333aaabbbccc"
对于小弦，在速度上应该没有什么实际差异

编辑

TL；DR：一般来说，生成器的速度比理解速度快一些。但是，请参见案例3了解相反的情况。记忆评估结果非常相似。

@rickhg12hs建议最好有基准

使用great BenchmarkTools包，结果如下

n=重复每个字符的次数

s=“abcdefghijklmnopqrstuvxyz”在每种情况下

在每种情况下，首先列出理解时间中位数C，然后列出生成器时间中位数G。时间四舍五入似乎是适当的，原始数字在编号摘要的下面。当然，越小越好

对记忆的估计没有太大的不同

1。n=26，C=3.8 vs.G=2.8μs，G快


julia> using BenchmarkTools

julia> n = 26;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  3.55 KiB
  allocs estimate:  39
  --------------
  minimum time:     3.688 μs (0.00% GC)
  median time:      3.849 μs (0.00% GC)
  mean time:        4.956 μs (16.27% GC)
  maximum time:     5.211 ms (99.85% GC)
  --------------
  samples:          10000
  evals/sample:     8

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  3.19 KiB
  allocs estimate:  36
  --------------
  minimum time:     2.661 μs (0.00% GC)
  median time:      2.756 μs (0.00% GC)
  mean time:        3.622 μs (19.94% GC)
  maximum time:     4.638 ms (99.89% GC)
  --------------
  samples:          10000
  evals/sample:     9



julia> n = 260;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  19.23 KiB
  allocs estimate:  39
  --------------
  minimum time:     8.125 μs (0.00% GC)
  median time:      10.691 μs (0.00% GC)
  mean time:        18.559 μs (35.36% GC)
  maximum time:     43.930 ms (99.92% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  18.88 KiB
  allocs estimate:  36
  --------------
  minimum time:     7.270 μs (0.00% GC)
  median time:      8.126 μs (0.00% GC)
  mean time:        10.872 μs (18.04% GC)
  maximum time:     10.592 ms (99.87% GC)
  --------------
  samples:          10000
  evals/sample:     4



julia> n = 2600; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  150.16 KiB
  allocs estimate:  39
  --------------
  minimum time:     51.746 μs (0.00% GC)
  median time:      63.293 μs (0.00% GC)
  mean time:        77.315 μs (2.79% GC)
  maximum time:     3.721 ms (96.85% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  149.80 KiB
  allocs estimate:  36
  --------------
  minimum time:     47.897 μs (0.00% GC)
  median time:      63.720 μs (0.00% GC)
  mean time:        88.716 μs (17.58% GC)
  maximum time:     42.457 ms (99.83% GC)
  --------------
  samples:          10000
  evals/sample:     1



julia> n = 26000; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  39
  --------------
  minimum time:     457.589 μs (0.00% GC)
  median time:      666.710 μs (0.00% GC)
  mean time:        729.592 μs (10.91% GC)
  maximum time:     42.673 ms (98.76% GC)
  --------------
  samples:          6659
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     475.977 μs (0.00% GC)
  median time:      516.176 μs (0.00% GC)
  mean time:        659.001 μs (10.36% GC)
  maximum time:     42.268 ms (98.41% GC)
  --------------
  samples:          7548
  evals/sample:     1


2。n=260，C=10.7 vs.G=8.1μs，G快


julia> using BenchmarkTools

julia> n = 26;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  3.55 KiB
  allocs estimate:  39
  --------------
  minimum time:     3.688 μs (0.00% GC)
  median time:      3.849 μs (0.00% GC)
  mean time:        4.956 μs (16.27% GC)
  maximum time:     5.211 ms (99.85% GC)
  --------------
  samples:          10000
  evals/sample:     8

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  3.19 KiB
  allocs estimate:  36
  --------------
  minimum time:     2.661 μs (0.00% GC)
  median time:      2.756 μs (0.00% GC)
  mean time:        3.622 μs (19.94% GC)
  maximum time:     4.638 ms (99.89% GC)
  --------------
  samples:          10000
  evals/sample:     9



julia> n = 260;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  19.23 KiB
  allocs estimate:  39
  --------------
  minimum time:     8.125 μs (0.00% GC)
  median time:      10.691 μs (0.00% GC)
  mean time:        18.559 μs (35.36% GC)
  maximum time:     43.930 ms (99.92% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  18.88 KiB
  allocs estimate:  36
  --------------
  minimum time:     7.270 μs (0.00% GC)
  median time:      8.126 μs (0.00% GC)
  mean time:        10.872 μs (18.04% GC)
  maximum time:     10.592 ms (99.87% GC)
  --------------
  samples:          10000
  evals/sample:     4



julia> n = 2600; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  150.16 KiB
  allocs estimate:  39
  --------------
  minimum time:     51.746 μs (0.00% GC)
  median time:      63.293 μs (0.00% GC)
  mean time:        77.315 μs (2.79% GC)
  maximum time:     3.721 ms (96.85% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  149.80 KiB
  allocs estimate:  36
  --------------
  minimum time:     47.897 μs (0.00% GC)
  median time:      63.720 μs (0.00% GC)
  mean time:        88.716 μs (17.58% GC)
  maximum time:     42.457 ms (99.83% GC)
  --------------
  samples:          10000
  evals/sample:     1



julia> n = 26000; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  39
  --------------
  minimum time:     457.589 μs (0.00% GC)
  median time:      666.710 μs (0.00% GC)
  mean time:        729.592 μs (10.91% GC)
  maximum time:     42.673 ms (98.76% GC)
  --------------
  samples:          6659
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     475.977 μs (0.00% GC)
  median time:      516.176 μs (0.00% GC)
  mean time:        659.001 μs (10.36% GC)
  maximum time:     42.268 ms (98.41% GC)
  --------------
  samples:          7548
  evals/sample:     1


3。n=2600，C=62.3与G=63.7μs相比，C更快


julia> using BenchmarkTools

julia> n = 26;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  3.55 KiB
  allocs estimate:  39
  --------------
  minimum time:     3.688 μs (0.00% GC)
  median time:      3.849 μs (0.00% GC)
  mean time:        4.956 μs (16.27% GC)
  maximum time:     5.211 ms (99.85% GC)
  --------------
  samples:          10000
  evals/sample:     8

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  3.19 KiB
  allocs estimate:  36
  --------------
  minimum time:     2.661 μs (0.00% GC)
  median time:      2.756 μs (0.00% GC)
  mean time:        3.622 μs (19.94% GC)
  maximum time:     4.638 ms (99.89% GC)
  --------------
  samples:          10000
  evals/sample:     9



julia> n = 260;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  19.23 KiB
  allocs estimate:  39
  --------------
  minimum time:     8.125 μs (0.00% GC)
  median time:      10.691 μs (0.00% GC)
  mean time:        18.559 μs (35.36% GC)
  maximum time:     43.930 ms (99.92% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  18.88 KiB
  allocs estimate:  36
  --------------
  minimum time:     7.270 μs (0.00% GC)
  median time:      8.126 μs (0.00% GC)
  mean time:        10.872 μs (18.04% GC)
  maximum time:     10.592 ms (99.87% GC)
  --------------
  samples:          10000
  evals/sample:     4



julia> n = 2600; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  150.16 KiB
  allocs estimate:  39
  --------------
  minimum time:     51.746 μs (0.00% GC)
  median time:      63.293 μs (0.00% GC)
  mean time:        77.315 μs (2.79% GC)
  maximum time:     3.721 ms (96.85% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  149.80 KiB
  allocs estimate:  36
  --------------
  minimum time:     47.897 μs (0.00% GC)
  median time:      63.720 μs (0.00% GC)
  mean time:        88.716 μs (17.58% GC)
  maximum time:     42.457 ms (99.83% GC)
  --------------
  samples:          10000
  evals/sample:     1



julia> n = 26000; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  39
  --------------
  minimum time:     457.589 μs (0.00% GC)
  median time:      666.710 μs (0.00% GC)
  mean time:        729.592 μs (10.91% GC)
  maximum time:     42.673 ms (98.76% GC)
  --------------
  samples:          6659
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     475.977 μs (0.00% GC)
  median time:      516.176 μs (0.00% GC)
  mean time:        659.001 μs (10.36% GC)
  maximum time:     42.268 ms (98.41% GC)
  --------------
  samples:          7548
  evals/sample:     1


4。n=26000，C=667与G=516μs相比，G更快


julia> using BenchmarkTools

julia> n = 26;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  3.55 KiB
  allocs estimate:  39
  --------------
  minimum time:     3.688 μs (0.00% GC)
  median time:      3.849 μs (0.00% GC)
  mean time:        4.956 μs (16.27% GC)
  maximum time:     5.211 ms (99.85% GC)
  --------------
  samples:          10000
  evals/sample:     8

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  3.19 KiB
  allocs estimate:  36
  --------------
  minimum time:     2.661 μs (0.00% GC)
  median time:      2.756 μs (0.00% GC)
  mean time:        3.622 μs (19.94% GC)
  maximum time:     4.638 ms (99.89% GC)
  --------------
  samples:          10000
  evals/sample:     9



julia> n = 260;

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  19.23 KiB
  allocs estimate:  39
  --------------
  minimum time:     8.125 μs (0.00% GC)
  median time:      10.691 μs (0.00% GC)
  mean time:        18.559 μs (35.36% GC)
  maximum time:     43.930 ms (99.92% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  18.88 KiB
  allocs estimate:  36
  --------------
  minimum time:     7.270 μs (0.00% GC)
  median time:      8.126 μs (0.00% GC)
  mean time:        10.872 μs (18.04% GC)
  maximum time:     10.592 ms (99.87% GC)
  --------------
  samples:          10000
  evals/sample:     4



julia> n = 2600; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  150.16 KiB
  allocs estimate:  39
  --------------
  minimum time:     51.746 μs (0.00% GC)
  median time:      63.293 μs (0.00% GC)
  mean time:        77.315 μs (2.79% GC)
  maximum time:     3.721 ms (96.85% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  149.80 KiB
  allocs estimate:  36
  --------------
  minimum time:     47.897 μs (0.00% GC)
  median time:      63.720 μs (0.00% GC)
  mean time:        88.716 μs (17.58% GC)
  maximum time:     42.457 ms (99.83% GC)
  --------------
  samples:          10000
  evals/sample:     1



julia> n = 26000; 

julia> @benchmark join([c^n for c in s])
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  39
  --------------
  minimum time:     457.589 μs (0.00% GC)
  median time:      666.710 μs (0.00% GC)
  mean time:        729.592 μs (10.91% GC)
  maximum time:     42.673 ms (98.76% GC)
  --------------
  samples:          6659
  evals/sample:     1

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     475.977 μs (0.00% GC)
  median time:      516.176 μs (0.00% GC)
  mean time:        659.001 μs (10.36% GC)
  maximum time:     42.268 ms (98.41% GC)
  --------------
  samples:          7548
  evals/sample:     1



在版本1.0.0（2018-08-08）中测试代码

当我试图编写
map（x->x^3，“123abc”）
时，我遇到了一个错误


julia> map(x -> x^3, "123abc")
ERROR: ArgumentError: map(f, s::AbstractString) requires f to return AbstractChar; try map(f, collect(s)) or a comprehension instead


所以，还有另一种方法


julia> map(x -> x^3, collect("123abc"))
6-element Array{String,1}:
 "111"
 "222"
 "333"
 "aaa"
 "bbb"
 "ccc"

julia> join(map(x -> x^3, collect("123abc")))
"111222333aaabbbccc"


而且，
重复
可能更方便


julia> repeat(collect("123abc"), inner=3)
18-element Array{Char,1}:
 '1'
 '1'
 '1'
 '2'
 '2'
 '2'
 '3'
 '3'
 '3'
 'a'
 'a'
 'a'
 'b'
 'b'
 'b'
 'c'
 'c'
 'c'
julia> join(repeat(collect("123abc"), inner=3))
"111222333aaabbbccc"



除了上面的答案，我还发现
string
函数运行得更快。以下是我的基准：


julia> n = 2;

julia> s = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";

julia> string((c^n for c in s)...) # proof that it works
"AABBCCDDEEFFGGHHIIJJKKLLMMNNOOPPQQRRSSTTUUVVWWXXYYZZ"

julia> n = 26000;

julia> @benchmark join(c^n for c in s)
BenchmarkTools.Trial:
  memory estimate:  1.44 MiB
  allocs estimate:  36
  --------------
  minimum time:     390.616 μs (0.00% GC)
  median time:      425.861 μs (0.00% GC)
  mean time:        484.638 μs (6.54% GC)
  maximum time:     45.006 ms (98.99% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark string((c^n for c in s)...)
BenchmarkTools.Trial:
  memory estimate:  1.29 MiB
  allocs estimate:  31
  --------------
  minimum time:     77.480 μs (0.00% GC)
  median time:      101.667 μs (0.00% GC)
  mean time:        126.455 μs (0.00% GC)
  maximum time:     832.524 μs (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1


如您所见，它比@Julia Learner提出的
join
解决方案快3倍左右。

我在0.7上测试了上述内容，但没有任何弃用警告，所以我假设它在1.0上也可以正常工作。甚至当硬编码
n=3
时，您也可以使用：
join（c^3表示“123abc”中的c）`生成
“111222333aaabbbccc”`。可能一个用户只有小字符串，但是有很多小字符串。好建议。
@benchmark
表达式可以在变量之前使用一些
$
，是吗？你也应该比较最小值而不是中间值。我发现
字符串（（c^n代表s中的c）…）
比我的机器上的
加入
解决方案快4倍。如何在你的机器上运行
@benchmark foldl（（x，y）->x*string（y）^$n，“，$s）
它以2.242毫秒（分钟）的速度运行，中值为4.1ms。我还收到了一个弃用警告。啊，我仍然在使用Julia v0.6.4（没有弃用警告），这似乎是目前为止这里介绍的方法中速度最快的。当我安装v0.7/v1.0时，我会玩更多。@niczky12喜欢它！你的解决方案很酷。作为Julia的新手，我甚至没有想过使用带有
…
省略号操作符的字符串。实际上，我想我先尝试了字符串，但是使用
string（[c^n表示s中的c]）
的结果非常糟糕。我不习惯省略号运算符。你如何从概念上看待它？为什么它的输出效果比
string（[c^n代表s中的c]）
好得多？一般来说，当运行时元素的数量（这里是字符）发生变化时，最好避免使用省略号（splatting），因为函数必须为每个特定数量的元素重新编译。所以它很快，但只有在不考虑编译时间的情况下。对于像这样的非常短的操作来说尤其如此，对于长时间运行的函数来说更是如此。Python代码不应该是
''。join（…）
？@phg是的，我一定在想Julia！我还将
c^n
错误，即Julia，编辑成
c*n
即Python。多谢。