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 [s] N2[%] Time Time Relative [s] 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