Sort column for each index for multi-index data frame

Question

I have a dataframe which has multi-level indexing. Here is a snippet of it:

import pandas as pd

data = {'EVENT_ID': [112335580,112335580,112335580,112335580,112335580,112335580,112335580,112335580, 112335582,
                     112335582,112335582,112335582,112335582,112335582,112335582,112335582,112335582,112335582,
                     112335582,112335582,112335582],

 'SELECTION_ID': [6356576,2554439,2503211,6297034,4233251,2522967,5284417,7660920,8112876,7546023,8175276,8145908,
                  8175274,7300754,8065540,8175275,8106158,8086265,2291406,8065533,8125015],

 'BSP': [5.080818565,6.651493872,6.374683435,24.69510797,7.776082305,11.73219964,270.0383021,4,8.294425408,335.3223613,
         14.06040142,2.423340019,126.7205863,70.53780982,21.3328554,225.2711962,92.25113066,193.0151362,3.775394142,
         95.3786641,17.86333041],

  'WIN_LOSE':[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]}

df = pd.DataFrame(data, columns=['EVENT_ID', 'SELECTION_ID', 'BSP','WIN_LOSE'])
df

df.set_index(['EVENT_ID', 'SELECTION_ID'], inplace=True)
df.sortlevel(level=0, ascending=True, sort_remaining=True)

I want to sort the BSP column for each EVENT_ID index separately.

I have tried the following:

data.assign(BSP=data.groupby(level=0).rank(ascending=False))

This does not work as it messes up the indexing and doesn't seem to sort the column anyway.

I have also tried just sorting in terms of the column but this also clearly just messes up the indexing.

Christian Sloper · Accepted Answer · 2019-02-27 13:56:50Z

4

This sorts by BSP ascending for each event ID:

df = pd.DataFrame(data, columns=['EVENT_ID', 'SELECTION_ID', 'BSP','WIN_LOSE'])
df = df.sort_values(["EVENT_ID","BSP"])
df.set_index(['EVENT_ID', 'SELECTION_ID'], inplace=True)

answered Feb 27, 2019 at 13:56

Christian Sloper

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