29 04 13 18 15 00    7.667
        29 04 13 18 30 00    7.000
        29 04 13 18 45 00    7.000
        29 04 13 19 00 00    7.333
        29 04 13 19 15 00    7.000
           
    import pandas as pd
        from cStringIO import StringIO
        def parse_all_fields(day_col, month_col, year_col, hour_col, minute_col,second_col):
        day_col = _maybe_cast(day_col)
        month_col = _maybe_cast(month_col)
        year_col = _maybe_cast(year_col)
        hour_col = _maybe_cast(hour_col)
        minute_col = _maybe_cast(minute_col)
        second_col = _maybe_cast(second_col)
        return lib.try_parse_datetime_components(day_col, month_col, year_col, hour_col, minute_col, second_col)
        ##Read the .txt file
        data1 = pd.read_table('0132_3.TXT', sep='s+', names=['Day','Month','Year','Hour','Min','Sec','Value'])
        data1[:10]
     
        Out[21]:
     
        Day,Month,Year,Hour, Min, Sec, Value
        29 04 13 18 15 00    7.667
        29 04 13 18 30 00    7.000
        29 04 13 18 45 00    7.000
        29 04 13 19 00 00    7.333
        29 04 13 19 15 00    7.000
     
        data2 = pd.read_table(StringIO(data1), parse_dates={'datetime':['Day','Month','Year','Hour''Min','Sec']}, date_parser=parse_all_fields, dayfirst=True)
           
    TypeError                                 Traceback (most recent call last)
        <ipython-input-22-8ee408dc19c3> in <module>()
        ----> 1 data2 = pd.read_table(StringIO(data1), parse_dates={'datetime':   ['Day','Month','Year','Hour''Min','Sec']}, date_parser=parse_all_fields, dayfirst=True)
     
        TypeError: expected read buffer, DataFrame found
           
    In [1]: df = pd.read_csv('0132_3.TXT', header=None, sep='s+s', parse_dates=[[0]])
     
    In [2]: df
    Out[2]:
                        0      1
    0 2013-04-29 00:00:00  7.667
    1 2013-04-29 00:00:00  7.000
    2 2013-04-29 00:00:00  7.000
    3 2013-04-29 00:00:00  7.333
    4 2013-04-29 00:00:00  7.000
           
    In [11]: def date_parser(ss):
                 day, month, year, hour, min, sec = ss.split()
                 return pd.Timestamp('20%s-%s-%s %s:%s:%s' % (year, month, day, hour, min, sec))
     
    In [12]: df = pd.read_csv('0132_3.TXT', header=None, sep='s+s', parse_dates=[[0]], date_parser=date_parser)
     
    In [13]: df
    Out[13]:
                        0      1
    0 2013-04-29 18:15:00  7.667
    1 2013-04-29 18:30:00  7.000
    2 2013-04-29 18:45:00  7.000
    3 2013-04-29 19:00:00  7.333
    4 2013-04-29 19:15:00  7.000