<p>如果需要循环(如果数据很大,则速度较慢):</p>
<pre><code>for i, x in population.iterrows():
print (train * x.values)
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 0.0 -0.0 -0.0 -18.265003
1 17.899545 -15.503942 -0.0 -0.0 -0.0 4.398419
4 16.432750 -22.490190 -0.0 -0.0 -0.0 -2.433374
5 15.905368 -4.812785 0.0 0.0 0.0 -1.074326
6 16.991823 -15.946251 0.0 0.0 0.0 -1.482333
feature0 feature1 feature2 feature3 feature4 feature5
0 0.0 -3.921346 0.0 -7.250185 -0.0 -0.0
1 0.0 -15.503942 -0.0 -0.053619 -0.0 0.0
4 0.0 -22.490190 -0.0 -15.247781 -0.0 -0.0
5 0.0 -4.812785 0.0 3.742221 0.0 -0.0
6 0.0 -15.946251 0.0 8.057511 0.0 -0.0
feature0 feature1 feature2 feature3 feature4 feature5
0 0.0 -0.0 0.0 -0.0 -0.0 -18.265003
1 0.0 -0.0 -0.0 -0.0 -0.0 4.398419
4 0.0 -0.0 -0.0 -0.0 -0.0 -2.433374
5 0.0 -0.0 0.0 0.0 0.0 -1.074326
6 0.0 -0.0 0.0 0.0 0.0 -1.482333
feature0 feature1 feature2 feature3 feature4 feature5
0 0.0 -0.0 13.611829 -0.0 -11.773605 -18.265003
1 0.0 -0.0 -0.741729 -0.0 -6.734652 4.398419
4 0.0 -0.0 -4.611659 -0.0 -13.941488 -2.433374
5 0.0 -0.0 18.291712 0.0 3.631887 -1.074326
6 0.0 -0.0 8.299577 0.0 8.057510 -1.482333
</code></pre>
<hr/>
<p>或每行分开:</p>
^{pr2}$
<hr/>
<p>或对于多索引数据帧:</p>
<pre><code>d = pd.concat([train * population.values[i] for i in range(population.shape[0])],
keys=population.index.tolist())
print (d)
feature0 feature1 feature2 feature3 feature4 feature5
0 0 18.279579 -3.921346 0.000000 -0.000000 -0.000000 -18.265003
1 17.899545 -15.503942 -0.000000 -0.000000 -0.000000 4.398419
4 16.432750 -22.490190 -0.000000 -0.000000 -0.000000 -2.433374
5 15.905368 -4.812785 0.000000 0.000000 0.000000 -1.074326
6 16.991823 -15.946251 0.000000 0.000000 0.000000 -1.482333
1 0 0.000000 -3.921346 0.000000 -7.250185 -0.000000 -0.000000
1 0.000000 -15.503942 -0.000000 -0.053619 -0.000000 0.000000
4 0.000000 -22.490190 -0.000000 -15.247781 -0.000000 -0.000000
5 0.000000 -4.812785 0.000000 3.742221 0.000000 -0.000000
6 0.000000 -15.946251 0.000000 8.057511 0.000000 -0.000000
2 0 0.000000 -0.000000 0.000000 -0.000000 -0.000000 -18.265003
1 0.000000 -0.000000 -0.000000 -0.000000 -0.000000 4.398419
4 0.000000 -0.000000 -0.000000 -0.000000 -0.000000 -2.433374
5 0.000000 -0.000000 0.000000 0.000000 0.000000 -1.074326
6 0.000000 -0.000000 0.000000 0.000000 0.000000 -1.482333
3 0 0.000000 -0.000000 13.611829 -0.000000 -11.773605 -18.265003
1 0.000000 -0.000000 -0.741729 -0.000000 -6.734652 4.398419
4 0.000000 -0.000000 -4.611659 -0.000000 -13.941488 -2.433374
5 0.000000 -0.000000 18.291712 0.000000 3.631887 -1.074326
6 0.000000 -0.000000 8.299577 0.000000 8.057510 -1.482333
</code></pre>
<p>然后按<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.xs.html" rel="nofollow noreferrer">^{<cd1>}</a>选择:</p>
<pre><code>print (d.xs(0))
feature0 feature1 feature2 feature3 feature4 feature5
0 18.279579 -3.921346 0.0 -0.0 -0.0 -18.265003
1 17.899545 -15.503942 -0.0 -0.0 -0.0 4.398419
4 16.432750 -22.490190 -0.0 -0.0 -0.0 -2.433374
5 15.905368 -4.812785 0.0 0.0 0.0 -1.074326
6 16.991823 -15.946251 0.0 0.0 0.0 -1.482333
</code></pre>