python - Convert DataFrame with index-OHLC data to index-O index-L index-H index-O (flatten OHLC data) -


i have dataframe looks that

                 open       high     low      close   volume (btc)  volume (currency)  weighted price date                                                                                                  2013-05-07  112.25000  114.00000   97.52  109.60013  139626.724860    14898971.673747      106.705731 2013-05-08  109.60013  116.77700  109.50  113.20000   61680.324704     6990518.957611      113.334665 2013-05-09  113.20000  113.71852  108.80  112.79900   26894.458204     3003068.410660      111.661235 2013-05-10  112.79900  122.50000  111.54  117.70000   77443.672681     9140709.083964      118.030418 2013-05-11  117.70000  118.74000  113.00  113.47000   25532.277740     2952016.798507      115.619015

i'm looking way transform kind of data to

index       open index+1     low index+2     high index+3     open index+4     low index+5     high

so in sample should looks like

date 2013-05-07 00:00     112.25000 2013-05-07 08:00     97.52 2013-05-07 16:00     114.00000 2013-05-08 00:00     109.60013 2013-05-08 08:00     109.50 2013-05-08 16:00     116.77700     ...

my first idea resample dataframe

but first problem when i'm doing

df2 = df.resample('8h', how='mean')

i 

                          open       high        low      close   volume (btc)  volume (currency)  weighted price  2013-05-07 00:00:00  112.25000  114.00000   97.52000  109.60013  139626.724860    14898971.673747      106.705731 2013-05-07 08:00:00        nan        nan        nan        nan            nan                nan             nan 2013-05-07 16:00:00        nan        nan        nan        nan            nan                nan             nan 2013-05-08 00:00:00  109.60013  116.77700  109.50000  113.20000   61680.324704     6990518.957611      113.334665 2013-05-08 08:00:00        nan        nan        nan        nan            nan                nan             nan 2013-05-08 16:00:00        nan        nan        nan        nan            nan                nan             nan 2013-05-09 00:00:00  113.20000  113.71852  108.80000  112.79900   26894.458204     3003068.410660      111.661235 ...

i need build column modulo 3 values

like this

                        modcol   2013-05-07 00:00:00          0  2013-05-07 08:00:00          1  2013-05-07 16:00:00          2  2013-05-08 00:00:00          0  2013-05-08 08:00:00          1  2013-05-08 16:00:00          2  2013-05-09 00:00:00          3  ...

so use np.where make price column (open if mod==0, low if mod==1 , high if mod==2)

my problem if don't know how build modcol column

heres how create mod columns

in [1]: series(range(10)) out[1]:  0    0 1    1 2    2 3    3 4    4 5    5 6    6 7    7 8    8 9    9 dtype: int64  in [2]: series(range(10)) % 3 out[2]:  0    0 1    1 2    2 3    0 4    1 5    2 6    0 7    1 8    2 9    0 dtype: int64

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