擅长:python、mysql、java
<p>使用<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.idxmax.html" rel="nofollow noreferrer">^{<cd1>}</a>获取最大值的索引,只过滤列<code>id</code>和<code>type</code>和<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer">^{<cd4>}</a>:</p>
<pre><code>df = df.merge(df.loc[df.groupby('id')['time'].idxmax(), ['id','type']])
print (df)
time id type
0 2013-11-02 1 xF1yz
1 2013-11-02 1 xF1yz
2 2006-07-07 5 F5spo
3 2006-07-06 5 F5spo
</code></pre>
<p>或将<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html" rel="nofollow noreferrer">^{<cd5>}</a>与<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html" rel="nofollow noreferrer">^{<cd6>}</a>一起使用:</p>
<pre><code>df = df.merge(df.sort_values('time').drop_duplicates('id', keep='last')[["id", "type"]])
</code></pre>