atn   file
datetime
2012-10-08 14:00:00  23.007462      1
2012-10-08 14:30:00  27.045666      1
2012-10-08 15:00:00  31.483825      1
2012-10-08 15:30:00  37.540651      2
2012-10-08 16:00:00  43.564573      2
2012-10-08 16:00:00  48.589852      2
2012-10-08 16:00:00  55.289452      2

datetime             atn
file
1      2012-10-08 14:00:00  23.007462
2      2012-10-08 15:30:00  37.540651

dt.groupby(by="file").aggregate("first")

dt2 = dt.reset_index()
dt2.groupby(by="file").aggregate("first")

datetime        atn
file
1     1.349705e+18  23.007462
2     1.349710e+18  37.540651

2012-10-08 14:00:00,  23.007462,     1
2012-10-08 14:30:00,  27.045666,     1
2012-10-08 15:00:00,  31.483825,     1
2012-10-08 15:30:00,  37.540651,     2
2012-10-08 16:00:00,  43.564573,     2
2012-10-08 16:00:00,  48.589852,     2
2012-10-08 16:00:00,  55.289452,     2

dt = pandas.read_clipboard(sep=",", parse_dates=True, index_col=0,
                           names=["datetime", "atn", "file"])

dt3 = dt2.groupby(by="file").aggregate("first")
dt3.dtypes

datetime    float64
atn         float64

dt3['datetime'] = pd.Series(dt3['datetime'], dtype='datetime64[ns]')

In [29]: dt2 = pd.read_clipboard(sep=",", index_col=0,
                           names=["datetime", "atn", "file"])

In [30]: dt2
Out[30]:
                           atn  file
datetime
2012-10-08 14:00:00  23.007462     1
2012-10-08 14:30:00  27.045666     1
2012-10-08 15:00:00  31.483825     1
2012-10-08 15:30:00  37.540651     2
2012-10-08 16:00:00  43.564573     2
2012-10-08 16:00:00  48.589852     2
2012-10-08 16:00:00  55.289452     2

In [31]: dt2.reset_index().groupby(by="file").aggregate("first")
Out[31]:
                 datetime        atn
file
1     2012-10-08 14:00:00  23.007462
2     2012-10-08 15:30:00  37.540651

In [32]:

In [33]: dt = pd.read_clipboard(sep=",", parse_dates=True, index_col=0,
                           names=["datetime", "atn", "file"])
KeyboardInterrupt

In [33]: dt = pd.read_clipboard(sep=",", parse_dates=True, index_col=0,
                           names=["datetime", "atn", "file"])

In [34]: dt.reset_index().groupby(by="file").aggregate("first")
Out[34]:
          datetime        atn
file
1     1.349705e+18  23.007462
2     1.349710e+18  37.540651

In [40]: new_dt = dt.reset_index().groupby(by="file").aggregate("first")

In [41]: new_dt
Out[41]:
          datetime        atn
file
1     1.349705e+18  23.007462
2     1.349710e+18  37.540651

In [42]: new_dt.dtypes
Out[42]:
datetime    float64
atn         float64

In [43]: new_dt2 = dt2.reset_index().groupby(by="file").aggregate("first")

In [44]: new_dt2.dtypes
Out[44]:
datetime     object
atn         float64