擅长:python、mysql、java
<p>只想对两种解决方案(对于30K行DF)进行时间比较:</p>
<pre><code>In [1]: df = DataFrame({'foo':['a','b','c'], 'bar':[1, 2, 3], 'new':['apple', 'banana', 'pear']})
In [2]: big = pd.concat([df] * 10**4, ignore_index=True)
In [3]: big.shape
Out[3]: (30000, 3)
In [4]: %timeit big.apply(lambda x:'%s_%s_%s' % (x['bar'],x['foo'],x['new']),axis=1)
1 loop, best of 3: 881 ms per loop
In [5]: %timeit big['bar'].astype(str)+'_'+big['foo']+'_'+big['new']
10 loops, best of 3: 44.2 ms per loop
</code></pre>
<p>还有几个选择:</p>
<pre><code>In [6]: %timeit big.ix[:, :-1].astype(str).add('_').sum(axis=1).str.cat(big.new)
10 loops, best of 3: 72.2 ms per loop
In [11]: %timeit big.astype(str).add('_').sum(axis=1).str[:-1]
10 loops, best of 3: 82.3 ms per loop
</code></pre>