那是你要的吗？
In [67]: dfOut[67]: Open High Low Close Volume Adj Close WeekDate2015-09-14 116.580002 116.889999 114.860001 115.309998 58363400 112.896168 2015-09-182015-09-15 115.930000 116.529999 114.419998 116.279999 43341200 113.845864 2015-09-182015-09-16 116.250000 116.540001 115.440002 116.410004 37173500 113.973148 2015-09-182015-09-17 115.660004 116.489998 113.720001 113.919998 64112600 111.535266 2015-09-182015-09-18 112.209999 114.300003 111.870003 113.449997 74285300 111.075104 2015-09-182015-09-21 113.669998 115.370003 113.660004 115.209999 50222000 112.798263 2015-09-252015-09-22 113.379997 114.180000 112.519997 113.400002 50346200 111.026155 2015-09-252015-09-23 113.629997 114.720001 113.300003 114.320000 35756700 111.926895 2015-09-252015-09-24 113.250000 115.500000 112.370003 115.000000 50219500 112.592660 2015-09-252015-09-25 116.440002 116.690002 114.019997 114.709999 56151900 112.308730 2015-09-25In [68]: df.groupby('Week').apply(lambda x: x.High.max() - x.Low.min())Out[68]:Week2015-09-18 5.0199962015-09-25 4.319999dtype: float64设置DF：
In [75]: from pandas_datareader import data as webIn [76]: df = web.DataReader('aapl', 'yahoo', '2015-09-14', '2015-09-25')In [77]: df.ix[:5, 'Week'] = df.index[df.index.weekday == 4][0]In [78]: df.ix[5:, 'Week'] = df.index[df.index.weekday == 4][-1]In [79]: dfOut[79]: Open High Low Close Volume Adj Close WeekDate2015-09-14 116.580002 116.889999 114.860001 115.309998 58363400 112.896168 2015-09-182015-09-15 115.930000 116.529999 114.419998 116.279999 43341200 113.845864 2015-09-182015-09-16 116.250000 116.540001 115.440002 116.410004 37173500 113.973148 2015-09-182015-09-17 115.660004 116.489998 113.720001 113.919998 64112600 111.535266 2015-09-182015-09-18 112.209999 114.300003 111.870003 113.449997 74285300 111.075104 2015-09-182015-09-21 113.669998 115.370003 113.660004 115.209999 50222000 112.798263 2015-09-252015-09-22 113.379997 114.180000 112.519997 113.400002 50346200 111.026155 2015-09-252015-09-23 113.629997 114.720001 113.300003 114.320000 35756700 111.926895 2015-09-252015-09-24 113.250000 115.500000 112.370003 115.000000 50219500 112.592660 2015-09-252015-09-25 116.440002 116.690002 114.019997 114.709999 56151900 112.308730 2015-09-25
