python - Filter pandas DataFrame columns -

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i have dataframe this:

                    b        c        d       2000-01-03 -0.59885  0.18141 -0.68828  0.77572 2000-01-04  0.83935  0.15993  0.95911 -1.12959 2000-01-05  2.80215 -0.10858 -1.62114 -0.20170 2000-01-06  0.71670 -0.26707  1.36029  1.74254

i filter columns based on value of first row. e.g. want take columns first value >0. , result expect this:

             b        d       2000-01-03  0.18141  0.77572 2000-01-04  0.15993 -1.12959 2000-01-05 -0.10858 -0.20170 2000-01-06 -0.26707  1.74254

update jeff suggestion wrote code:

cols = [] firstrow = df.ix[0,:] in range(len(firstrow)):     if firstrow[i]>0:     cols.append(i)  return df.ix[:, list(cols)].values.copy()

is there more elegant way this?

this using data generated below, can apply example. iloc[-2] selects 2nd last row, , creates boolean array loc takes boolean array , select applicable columns

in [2]: df = dataframe(np.random.randn(4,4),columns=list('abcd'),              index=date_range('20000103',periods=4))  in [3]: df out[3]:                             b         c         d 2000-01-03 -0.132896 -0.151352  0.960943 -0.007701 2000-01-04 -1.653279 -1.101331 -2.083493 -1.920517 2000-01-05 -1.190868  0.983487  0.804209  0.962575 2000-01-06  0.232290  2.152097  0.414457  1.023253  in [6]: df.loc[:,df.iloc[-2]<0] out[6]:                     2000-01-03 -0.132896 2000-01-04 -1.653279 2000-01-05 -1.190868 2000-01-06  0.232290

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