<p>更好的解决方案(我认为):</p>
<pre><code>df['sum_smaller_amount'] = (df_sort.groupby('group_id')['amount']
.transform(lambda x: x.mask(x.duplicated(),0).cumsum()) -
df['amount'])
</code></pre>
<p>输出:</p>
<pre><code> amount sum_smaller_amount
group_id id
100 0 30 10.0
1 40 40.0
2 10 0.0
101 3 20 0.0
4 25 20.0
5 80 85.0
6 40 45.0
</code></pre>
<hr/>
<p>另一种方法是使用笛卡尔积和过滤器:</p>
<pre><code>df.merge(df.reset_index(), on='group_id', suffixes=('_sum_smaller',''))\
.query('amount_sum_smaller < amount')\
.groupby(['group_id','id'])[['amount_sum_smaller']].sum()\
.join(df, how='right').fillna(0)
</code></pre>
<p>输出:</p>
<pre><code> amount_sum_smaller amount
group_id id
100 0 10.0 30
1 40.0 40
2 0.0 10
101 3 0.0 20
4 20.0 25
5 85.0 80
6 45.0 40
</code></pre>