python - minimize rows with common values, add columns for additional values -

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i have array, of following format:

564387.29   7371625.14  0.00    33030.00    -132.96 -1031.50  564387.29   7371625.14  0.00    1530.00     -133.85 -1039.27  564387.29   7371625.14  0.00    47970.00    -138.35 -1044.40  564387.32   7371625.14  0.00    47970.00    -166.41 -999.27  564387.32   7371625.14  0.00    33030.00    -241.74 -1889.71  564387.32   7371625.14  0.00    1530.00     -135.42 -857.31  564387.35   7371625.14  0.00    33030.00    -174.06 -990.66  564387.35   7371625.14  0.00    1530.00     -178.17 -927.11  564387.35   7371625.14  0.00    47970.00    -116.65 -1810.97

i make array pandas dataframe, , sort them based on columns 1, 2 , 4:

564387.29   7371625.14  0.00    1530.00     -133.85 -1039.27  564387.29   7371625.14  0.00    33030.00    -132.96 -1031.50  564387.29   7371625.14  0.00    47970.00    -138.35 -1044.40  564387.32   7371625.14  0.00    1530.00     -135.42 -857.31  564387.32   7371625.14  0.00    33030.00    -241.74 -1889.71  564387.32   7371625.14  0.00    47970.00    -166.41 -999.27  564387.35   7371625.14  0.00    1530.00     -178.17 -927.11  564387.35   7371625.14  0.00    33030.00    -174.06 -990.66  564387.35   7371625.14  0.00    47970.00    -116.65 -1810.97

the final step reduce number of rows adding variable on 4 additional columns following:

564387.29   7371625.14  0.00    1530.00 -133.85 -1039.27    -132.96 -1031.50    -138.35 -1044.40  564387.32   7371625.14  0.00    1530.00 -135.42 -857.31     -241.74 -1889.71    -166.41 -999.27  564387.35   7371625.14  0.00    1530.00 -178.17 -927.11     -174.06 -990.66     -116.65 -1810.97

but cannot seem find way - looked on many numpy , pandas discussion , couldn't ideas

  1. sort according specific colomns
  2. group them , apply our customized function

followings example:

in [121]: def func(df):    .....:     df = df.reset_index(drop=true)    .....:     s = [df.ix[0][3]]    .....:     index, rw in df.iterrows():    .....:         s.append(rw[4])    .....:         s.append(rw[5])    .....:     return pd.series(s)    .....:   in [122]: df.sort([0, 1, 3]).reset_index(drop=true).groupby([0, 1, 2]).apply(func) out[122]:                             0       1        2       3        4       5        6    0         1          2                                                          564387.29 7371625.14 0  1530 -133.85 -1039.27 -132.96 -1031.50 -138.35 -1044.40 564387.32 7371625.14 0  1530 -135.42  -857.31 -241.74 -1889.71 -166.41  -999.27 564387.35 7371625.14 0  1530 -178.17  -927.11 -174.06  -990.66 -116.65 -1810.97

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