<pre><code>In [79]: arr = np.array(['Cat','Dog','Mouse'])
In [80]: cnt = np.array([2,3,1])
</code></pre>
<p>各种备选方案的时间安排。相对位置可能会随数组的大小而变化(以及是从列表还是数组开始)。所以你自己做测试:</p>
<pre><code>In [93]: timeit ''.join(np.repeat(arr,cnt))
7.98 µs ± 57.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [94]: timeit ''.join([str(wd)*i for wd,i in zip(arr,cnt)])
5.96 µs ± 167 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [95]: timeit ''.join(arr.astype(object)*cnt)
13.3 µs ± 50.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [96]: timeit ''.join(np.char.multiply(arr,cnt))
27.4 µs ± 307 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
In [100]: timeit ''.join(np.frompyfunc(lambda w,i: w*i,2,1)(arr,cnt))
10.4 µs ± 164 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [101]: %%timeit f = np.frompyfunc(lambda w,i: w*i,2,1)
...: ''.join(f(arr,cnt))
7.95 µs ± 93.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
In [102]: %%timeit x=arr.tolist(); y=cnt.tolist()
...: ''.join([str(wd)*i for wd,i in zip(x,y)])
1.36 µs ± 39.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
</code></pre>
<p><code>np.repeat</code>适用于所有类型的数组。你知道吗</p>
<p>列表理解使用字符串乘法,不应该被立即忽略。通常是最快的,尤其是从列表开始的时候。你知道吗</p>
<p>Object dtype将string dtype转换为Python字符串,然后将操作委托给string multiply。你知道吗</p>
<p><code>np.char</code>将字符串方法应用于数组的元素。虽然方便,但很少快。你知道吗</p>
<h2>编辑</h2>
<pre><code>In [104]: timeit ''.join(np.repeat(arr,cnt).tolist())
4.04 µs ± 197 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
</code></pre>