<p>使用<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.explode.html" rel="nofollow noreferrer">^{<cd1>}</a>怎么样</p>
<pre><code>import pandas as pd
df = pd.DataFrame({
'OvIdx' : 3 * [range(4)],
'Average' : average,
'Max' : max, # should be renamed/assigned as max_ instead
'Largest(5th)': largest
}).explode('OvIdx').set_index('OvIdx').astype(int)
print(df)
</code></pre>
<p>显示</p>
<pre><code> Average Max Largest(5th)
OvIdx
0 24 45 14
1 24 45 14
2 24 45 14
3 24 45 14
0 36 76 23
1 36 76 23
2 36 76 23
3 36 76 23
0 34 54 22
1 34 54 22
2 34 54 22
3 34 54 22
</code></pre>
<p>从这里开始,您仍然可以执行所有您想要的计算和/或获得一个NumPy数组,执行<code>df.values</code></p>
<p/><hr/>
在您的评论之后,您还可以将您的列作为单独的实体,例如。
<pre><code>>>> df.Average.tolist()
[24, 24, 24, 24, 36, 36, 36, 36, 34, 34, 34, 34]
>>> df.Max.tolist()
[45, 45, 45, 45, 76, 76, 76, 76, 54, 54, 54, 54]
>>> df['Largest(5th)'].tolist() # as string key since the name is a little bit exotic
[14, 14, 14, 14, 23, 23, 23, 23, 22, 22, 22, 22]
</code></pre>
<p>不过,哪种方法开始有点过火了</p>