Python（pandaps）使用数字数据，但添加非数字数据b

Question

我有一个CSV文件，如下所示：

Build,Avg,Min,Max
BuildA,56.190,39.123,60.1039
BuildX,57.11,40.102,60.200
BuildZER,55.1134,35.129404123,60.20121

我想得到每列的平均值、最小值、最大值，并将这些统计值作为一个新行。我排除非数字列（build列），然后运行统计信息。我通过以下方式实现：

^{pr2}$

如果我将这些数据写入CSV，它看起来像：

,Avg,Min,Max
0,56.19,39.123,60.1039
1,57.11,40.102,60.2
2,55.1134,35.129404123,60.20121
Min,55.1134,35.129404123,60.1039
Average,55.8817,37.3709520615,60.1522525
Max,57.11,40.102,60.20121

这与我想要的很接近，但是我希望Build列再次成为column one，并且在Min、Average、Max上有构建名。基本上是这样：

Builds,Avg,Min,Max
BuildA,56.19,39.123,60.1039
BuildX,57.11,40.102,60.2
BuildZER,55.1134,35.129404123,60.20121
Min,55.1134,35.129404123,60.1039
Average,55.8817,37.3709520615,60.1522525
Max,57.11,40.102,60.20121

我试图通过以下方式实现这一点：

df.insert(0,'builds', save)
with open('fakedata.csv', 'w') as f:
    df.to_csv(f)

但这给了我一个CSV：

,builds,Avg,Min,Max
0,Build1,56.19,39.123,60.1039
1,Build2,57.11,40.102,60.2
2,Build3,55.1134,35.129404123,60.20121
Min,,55.1134,35.129404123,60.1039
Average,,55.8817,37.3709520615,60.1522525
Max,,57.11,40.102,60.20121

我该怎么解决这个问题？在

Answer 1

IIUC：

df_out = pd.concat([df.set_index('Build'),df.set_index('Build').agg(['max','min','mean'])]).rename(index={'max':'Max','min':'Min','mean':'Average'}).reset_index()

输出：

^{pr2}$