<p><strong>方法1:布尔索引与<code>groupby.transform</code></strong></p>
<pre><code>df[df.groupby('Buyer_ID')['Order_ID'].transform('nunique').eq(1)]
</code></pre>
<p><strong>方法2:<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.filter.html" rel="nofollow noreferrer">^{<cd2>}</a></strong></p>
<pre><code>df.groupby('Buyer_ID').filter(lambda x: x['Order_ID'].nunique()==1)
</code></pre>
<p><strong>方法3:<code>boolean indexing</code>与<code>Series.map</code></strong></p>
<pre><code>df[df['Buyer_ID'].map(df.groupby('Buyer_ID')['Order_ID'].nunique().eq(1))]
</code></pre>
<p><strong>输出</strong></p>
<pre><code> Order_ID Order_Date Buyer_ID
4 567868 01/05/19 a346556
5 567868 01/05/19 a346556
6 234534 01/10/19 a678909
</code></pre>
<hr/>
<p>如果要删除重复项,请在末尾使用<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop_duplicates.html" rel="nofollow noreferrer">^{<cd5>}</a>:</p>
<pre><code>df[df.groupby('Buyer_ID')['Order_ID'].transform('nunique').eq(1)].drop_duplicates()
Order_ID Order_Date Buyer_ID
4 567868 01/05/19 a346556
6 234534 01/10/19 a678909
</code></pre>