<pre><code>df1=pd.DataFrame(data={'store_name':['store1','store2','store3','store4','store5'],
'district_id':[[1,2,3,4,5], [1,2], 3, [4,7,10], [8,10]]})
df2=pd.DataFrame(data={'district_id':[1,2,3,4,5,6,7,8,9,10],
'district_name':['District1', 'District2', 'District3', 'District4', 'District5', 'District6', 'District7', 'District8', 'District9', 'District10']})
</code></pre>
<p>步骤1:使用<code>explode()</code>将值拆分为行</p>
<pre><code>df3=df1.explode('district_id').reset_index(drop=True)
</code></pre>
<p>步骤2:将<code>merge()</code>与<code>on='district_id'</code>一起使用</p>
<pre><code>df4=pd.merge(df3,df2, on='district_id' )
</code></pre>
<p>步骤3:使用<code>groupby()</code>&<code>agg()</code>以获取包含列表的列</p>
<pre><code>df5=df4.groupby('district_name').agg(list).reset_index()
store_name district_id district_name
0 store1 [1, 2, 3, 4, 5] [District1,District2,District3,District4,District5]
1 store2 [1, 2] [District1,District2]
2 store3 [3] [District3]
3 store4 [4, 7, 10] [District4,District7,District10]
4 store5 [10, 8] [District10,District8]
</code></pre>
<p>然后,它可以根据需要进行拆分</p>