Pandas / Python如何在保持df结构的同时切换数据框中的索引/列？

Question

I have a Pandas dataframe that looks like this:

      X1     X1     X1     X2     X2    X2
ABC   12.4   34.3   25.4   29.3   53.2  38.9
DEF   22.3   28.6   32.8   24.6   29.4  25.3

The left column is the index, and the top values are column labels. I am trying to swap the column names and index so that it looks like this:

      ABC    ABC    ABC    DEF    DEF   DEF
X1   12.4   34.3   25.4    22.3   28.6  32.8
X2   29.3   53.2   38.9    24.6   29.4  25.3

I can get the axes switched using stack and unstack if I add a numbered index, but the replicates are listed vertically instead of horizontally. I can't figure out how to do it so that the individual replicates stay side-by-side, which is necessary for what I am trying to do with the table. The replicates need to stay separate, I do not want the Average/Sum/etc.

Any help/suggestions would be greatly appreciated.

Thanks!

edit:

This code gives a dataframe that is similar in structure to my actual data but with fewer columns:

names = ["G1","G2","G3","G4", "G5", "G6", "G7", "G8"]
df = pd.DataFrame([(7.345,"NaN","NaN",239.947,295.893,349.834),(13.872,"NaN","NaN",20.485,14.852,29.598),(764.298,"NaN","NaN",492.854,432.943,539.950),(0.00385,"NaN","NaN",0.184,0.384,0.285),(285.836,"NaN","NaN",495.284,395.486,368.952),(7.385,"NaN","NaN",5.293,4.295,4.692),(21.693,"NaN","NaN",25.843,15.843,15.386),(8.583,"NaN","NaN",4.397,6.295,6.39)], names, ["S1", "S1", "S1", "482.1", "482.1", "482.1"])

Giving this dataframe:

           S1   S1   S1    482.1    482.1    482.1
G1    7.34500  NaN  NaN  239.947  295.893  349.834
G2   13.87200  NaN  NaN   20.485   14.852   29.598
G3  764.29800  NaN  NaN  492.854  432.943  539.950
G4    0.00385  NaN  NaN    0.184    0.384    0.285
G5  285.83600  NaN  NaN  495.284  395.486  368.952
G6    7.38500  NaN  NaN    5.293    4.295    4.692
G7   21.69300  NaN  NaN   25.843   15.843   15.386
G8    8.58300  NaN  NaN    4.397    6.295    6.390

Running:

df2 = df.copy()
m = dict(zip(df2.index.unique(), df2.columns.unique()))
df2.index = df2.index.map(m.get)
df2.columns = df2.columns.map({v : k for k, v in m.items()}.get)

gives:

              G1   G1   G1       G2       G2       G2
S1       7.34500  NaN  NaN  239.947  295.893  349.834
482.1   13.87200  NaN  NaN   20.485   14.852   29.598
NaN    764.29800  NaN  NaN  492.854  432.943  539.950
NaN      0.00385  NaN  NaN    0.184    0.384    0.285
NaN    285.83600  NaN  NaN  495.284  395.486  368.952
NaN      7.38500  NaN  NaN    5.293    4.295    4.692
NaN     21.69300  NaN  NaN   25.843   15.843   15.386
NaN      8.58300  NaN  NaN    4.397    6.295    6.390

The column and index labels have moved, but the data associated with them have not, and several columns are missing. Running:

df2 = df.copy()
m = dict(zip(df2.index.unique(), df2.columns.unique()))
df2 = df2.rename(index=m, columns={v : k for k, v in m.items()})

gives:

              G1   G1   G1       G2       G2       G2
S1       7.34500  NaN  NaN  239.947  295.893  349.834
482.1   13.87200  NaN  NaN   20.485   14.852   29.598
G3     764.29800  NaN  NaN  492.854  432.943  539.950
G4       0.00385  NaN  NaN    0.184    0.384    0.285
G5     285.83600  NaN  NaN  495.284  395.486  368.952
G6       7.38500  NaN  NaN    5.293    4.295    4.692
G7      21.69300  NaN  NaN   25.843   15.843   15.386
G8       8.58300  NaN  NaN    4.397    6.295    6.390

Which is also wrong for similar reasons.

Answer 1

New_df=df.T.groupby(level=0).agg(lambda x : x.values.tolist()).stack().apply(pd.Series).unstack().sort_index(level=1,axis=1)
New_df.columns=New_df.columns.droplevel(level=0)
New_df
Out[229]: 
     ABC   ABC   ABC   DEF   DEF   DEF
X1  12.4  34.3  25.4  22.3  28.6  32.8
X2  29.3  53.2  38.9  24.6  29.4  25.3