<p>您可以使用<code>groupby()</code>和<code>cumsum()</code>,正如我之前在评论中建议的那样:</p>
<pre><code>df = pd.DataFrame({'Date':['2001-10-01','2001-10-01','2001-10-02','2001-10-02','2001-10-03','2001-10-03','2001-10-04','2001-10-04'],
'Hospital':['Hospital A','Hospital B','Hospital A','Hospital B','Hospital A','Hospital B','Hospital A','Hospital B'],
'Total Operations':[1101,32,1184,74,1350,72,1364,232],
'Errors':[0,0,0,0,0,0,0,0]})
df['Aggreated Operations'] = df.groupby(['Hospital'])['Total Operations'].cumsum()
df['Aggreated Erros'] = df.groupby(['Hospital'])['Errors'].cumsum()
print(df)
</code></pre>
<p>这将输出:</p>
<pre><code> Date Hospital ... Aggreated Operations Aggreated Erros
0 2001-10-01 Hospital A ... 1101 0
1 2001-10-01 Hospital B ... 32 0
2 2001-10-02 Hospital A ... 2285 0
3 2001-10-02 Hospital B ... 106 0
4 2001-10-03 Hospital A ... 3635 0
5 2001-10-03 Hospital B ... 178 0
6 2001-10-04 Hospital A ... 4999 0
7 2001-10-04 Hospital B ... 410 0
</code></pre>