擅长:python、mysql、java
<p>一点%timeit比较以供参考:</p>
<pre><code>%timeit [f'User{i}' for i in range(10)]
2.39 µs ± 106 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit np.core.defchararray.add('User', np.arange(10).astype(str))
23.9 µs ± 1.85 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
</code></pre>
<p>对于更大的阵列</p>
<pre><code>%timeit [f'User{i}' for i in range(1000)]
214 µs ± 8.73 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
%timeit np.core.defchararray.add('User', np.arange(1000).astype(str))
1.18 ms ± 15.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
</code></pre>
<p>现在无法检查Python2,但是本地Python3(3.7.5,64位)在这里很难被打败!对于较大的数组大小,本机Python的性能优势大致收敛到x6。你知道吗</p>