[Python] python pandas remove duplicate columns

What is the easiest way to remove duplicate columns from a dataframe?

I am reading a text file that has duplicate columns via:

import pandas as pd

df=pd.read_table(fname)


The column names are:

Time, Time Relative, N2, Time, Time Relative, H2, etc...


All the Time and Time Relative columns contain the same data. I want:

Time, Time Relative, N2, H2


All my attempts at dropping, deleting, etc such as:

df=df.T.drop_duplicates().T


Result in uniquely valued index errors:

Reindexing only valid with uniquely valued index objects


Sorry for being a Pandas noob. Any Suggestions would be appreciated.


Additional Details

Pandas version: 0.9.0
Python Version: 2.7.3
Windows 7
(installed via Pythonxy 2.7.3.0)

data file (note: in the real file, columns are separated by tabs, here they are separated by 4 spaces):

Time Time Relative N2[%] Time Time Relative H2[ppm]
2/12/2013 9:20:55 AM 6.177 9.99268e+001 2/12/2013 9:20:55 AM 6.177 3.216293e-005
2/12/2013 9:21:06 AM 17.689 9.99296e+001 2/12/2013 9:21:06 AM 17.689 3.841667e-005
2/12/2013 9:21:18 AM 29.186 9.992954e+001 2/12/2013 9:21:18 AM 29.186 3.880365e-005
... etc ...
2/12/2013 2:12:44 PM 17515.269 9.991756+001 2/12/2013 2:12:44 PM 17515.269 2.800279e-005
2/12/2013 2:12:55 PM 17526.769 9.991754e+001 2/12/2013 2:12:55 PM 17526.769 2.880386e-005
2/12/2013 2:13:07 PM 17538.273 9.991797e+001 2/12/2013 2:13:07 PM 17538.273 3.131447e-005

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