<p>我认为您正在寻找某种<a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.stack.html#pandas.DataFrame.stack" rel="nofollow noreferrer">^{<cd1>}</a>方法,而不是迭代(通常,在处理数据帧时,迭代是最后的手段,因为通常有矢量化方法来实现大多数数据重组任务)。你知道吗</p>
<p>以数据帧为例:</p>
<pre><code>>>> df
0 1 2 \
0 Consumer Products Consumer Products Financial Services
1 Collaboration Future of Supply Chains Financial Services
2 Financial Services Consumer Products Collaboration
3 Collaboration Financial Services Future of Supply Chains
4 Consumer Products Future of Supply Chains Financial Services
Pageviews
0 1210
1 1528
2 1716
3 1403
4 1090
</code></pre>
<p>您可以执行以下操作:</p>
<pre><code>new_df = (df.set_index('Pageviews')
.stack()
.reset_index(0))
>>> new_df
Pageviews 0
0 1210 Consumer Products
1 1210 Consumer Products
2 1210 Financial Services
3 1528 Collaboration
4 1528 Future of Supply Chains
5 1528 Financial Services
6 1716 Financial Services
7 1716 Consumer Products
8 1716 Collaboration
9 1403 Collaboration
10 1403 Financial Services
11 1403 Future of Supply Chains
12 1090 Consumer Products
13 1090 Future of Supply Chains
14 1090 Financial Services
</code></pre>