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<p>包含以下数据帧:</p>
<pre><code>import pandas as pd
df=pd.DataFrame(data=[[1,5179530,'rs10799170',8.1548,'E001'], [1,5179530,'rs10799170',8.1548,'E002'], [1,5179530,'rs10799170',8.1548,'E003'], [1,455521,'rs235884',2.584,'E003'], [1,455521,'rs235884',2.584,'E007']], col umns=['CHR','BP','SNP','CM','ANNOT'])
CHR BP SNP CM ANNOT
0 1 5179530 rs10799170 8.1548 E001
1 1 5179530 rs10799170 8.1548 E002
2 1 5179530 rs10799170 8.1548 E003
3 1 455521 rs235884 2.5840 E003
4 1 455521 rs235884 2.5840 E007
</code></pre>
<p>我想得到</p>
^{pr2}$
<p>我分别尝试了<code>groupby()</code>和{<cd2>}</p>
<pre><code>df.groupby(['CHR','BP','SNP','CM']).sum()
CHR BP SNP CM ANNOT
1 455521 rs235884 2.5840 E003E007
5179530 rs10799170 8.1548 E001E002E003
pd.get_dummies(df['ANNOT'])
E001 E002 E003 E007
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 1 0
4 0 0 0 1
</code></pre>
<p>但我不知道如何将两者结合起来,或者是否有其他方法。在</p>