<p>从</p>
<pre><code>>>> df
val1 val2 val3
city_id
houston,tx 1 2 0
houston,tx 0 0 1
houston,tx 2 1 1
somewhere,ew 4 3 7
</code></pre>
<p>我可能会的</p>
<pre><code>>>> df.groupby(df.index).sum()
val1 val2 val3
city_id
houston,tx 3 3 2
somewhere,ew 4 3 7
</code></pre>
<p>或者</p>
<pre><code>>>> df.reset_index().groupby("city_id").sum()
val1 val2 val3
city_id
houston,tx 3 3 2
somewhere,ew 4 3 7
</code></pre>
<p>第一种方法将索引值(在本例中是<code>city_id</code>值)传递给<code>groupby</code>,并告诉它使用这些值作为组键,第二种方法重置索引,然后选择<code>city_id</code>列。有关更多示例,请参见文档的<a href="http://pandas.pydata.org/pandas-docs/dev/groupby.html#aggregation">this section</a>。注意,<code>DataFrameGroupBy</code>对象中还有很多其他方法:</p>
<pre><code>>>> df.groupby(df.index)
<pandas.core.groupby.DataFrameGroupBy object at 0x1045a1790>
>>> df.groupby(df.index).max()
val1 val2 val3
city_id
houston,tx 2 2 1
somewhere,ew 4 3 7
>>> df.groupby(df.index).mean()
val1 val2 val3
city_id
houston,tx 1 1 0.666667
somewhere,ew 4 3 7.000000
</code></pre>