您可以使用以下groupby-agg操作：
In [38]: result = df.groupby(['a'], as_index=False).agg(
{'c':['mean','std'],'b':'first', 'd':'first'})
然后重命名各列并对其重新排序：
In [39]: result.columns = ['a','c','e','b','d']
In [40]: result.reindex(columns=sorted(result.columns))
Out[40]:
a b c d e
0 Apple 3 4.5 7 0.707107
1 Banana 4 4.0 8 NaN
2 Cherry 7 1.0 3 NaN
熊猫默认情况下会计算样本std。要计算总体标准：
def pop_std(x):
return x.std(ddof=0)
result = df.groupby(['a'], as_index=False).agg({'c':['mean',pop_std],'b':'first', 'd':'first'})
result.columns = ['a','c','e','b','d']
result.reindex(columns=sorted(result.columns))
产量
a b c d e
0 Apple 3 4.5 7 0.5
1 Banana 4 4.0 8 0.0
2 Cherry 7 1.0 3 0.0
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