<p>虽然面板允许添加维度,但层次索引是一种更常见的替代方法。E、 g.,来自<a href="https://jakevdp.github.io/PythonDataScienceHandbook/03.05-hierarchical-indexing.html" rel="nofollow noreferrer">Python Data Science Handbook</a>:</p>
<blockquote>
<p>While Pandas does provide Panel and Panel4D objects that natively handle three-dimensional and four-dimensional data (see Aside: Panel Data), a far more common pattern in practice is to make use of hierarchical indexing (also known as multi-indexing) to incorporate multiple index levels within a single index. In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional Series and two-dimensional DataFrame objects.</p>
</blockquote>
<p>对你来说</p>
<blockquote>
<p>I have 12 dataframes of the same shape for 12 years of data collection. I need to use this as a panel to to plot the various column values across the time series axis (years).</p>
</blockquote>
<p>假设您的数据帧位于<code>df_2015</code>、<code>df_2016</code>和{<cd3>}。您可以执行以下操作:</p>
<pre><code>df_2015['year'] = 2015
df_2016['year'] = 2016
df_2017['year'] = 2017
df = pd.concat([df_2015, df_2016, df_2017]).set_index(['Grave Crimes', 'year'])
</code></pre>
<p>现在要获得<code>'Abduction'</code>所有年份的数据,例如,使用</p>
^{pr2}$