Order columns of a pandas dataframe according to the values in a row

Question

[updated to simplify]

tl;dr:

In [29]: new_columns = df.columns[df.ix[df.last_valid_index()].argsort()]

In [30]: df[new_columns]
Out[30]: 
        aaa       ppp       fff       ddd
0  0.328281  0.375458  1.188905  0.503059
1  0.305457  0.186163  0.077681 -0.543215
2  0.684265  0.681724  0.210636 -0.532685
3 -1.134292  1.832272  0.067946  0.250131
4 -0.834393  0.010211  0.649963 -0.551448
5 -1.032405 -0.749949  0.442398  1.274599

---

Some explanation follows. First, build the DataFrame:

In [24]: df = pd.DataFrame(np.random.randn(6, 4), columns=['ddd', 'fff', 'aaa', 'ppp'])

In [25]: df
Out[25]: 
        ddd       fff       aaa       ppp
0  0.503059  1.188905  0.328281  0.375458
1 -0.543215  0.077681  0.305457  0.186163
2 -0.532685  0.210636  0.684265  0.681724
3  0.250131  0.067946 -1.134292  1.832272
4 -0.551448  0.649963 -0.834393  0.010211
5  1.274599  0.442398 -1.032405 -0.749949

Get the last row:

In [26]: last_row = df.ix[df.last_valid_index()]

Get the indices that would sort it:

In [27]: last_row.argsort()
Out[27]: 
ddd    2
fff    3
aaa    1
ppp    0
Name: 5, Dtype: int32

Use this to index df:

In [28]: df[last_row.argsort()]
Out[28]: 
        aaa       ppp       fff       ddd
0  0.328281  0.375458  1.188905  0.503059
1  0.305457  0.186163  0.077681 -0.543215
2  0.684265  0.681724  0.210636 -0.532685
3 -1.134292  1.832272  0.067946  0.250131
4 -0.834393  0.010211  0.649963 -0.551448
5 -1.032405 -0.749949  0.442398  1.274599

Profit!

The sort_values method does this directly when given axis=1 argument.

sorted_df = df.sort_values(df.last_valid_index(), axis=1)

---

So, it is no longer necessary to transpose the dataframe to sort by a row. Also, the sort method is now deprecated.

I would use transpose and the sort method (which works on columns):

df = pd.DataFrame(np.random.randn(10, 4), columns=['ddd', 'fff', 'aaa', 'ppp'])
last_row_name = df.index[-1]
sorted_df = df.T.sort(columns=last_row_name).T

You might suffer a performance hit but it is quick and easy.