Python Pandas max value in a group as a new column

Question

I am trying to calculate a new column which contains maximum values for each of several groups. I'm coming from a Stata background so I know the Stata code would be something like this:

by group, sort: egen max = max(odds) 

For example:

data = {'group' : ['A', 'A', 'B','B'],
    'odds' : [85, 75, 60, 65]}

Then I would like it to look like:

    group    odds    max
     A        85      85
     A        75      85
     B        60      65
     B        65      65

Eventually I am trying to form a column that takes 1/(max-min) * odds where max and min are for each group.

Answer 1

Use groupby + transform:

df['max'] = df.groupby('group')['odds'].transform('max')

This is equivalent to the verbose:

maxima = df.groupby('group')['odds'].max()
df['max'] = df['group'].map(maxima)

The transform method aligns the groupby result to the groupby indexer, so no explicit mapping is required.

Answer 2

Using the approach from jpp above works, but it also gives a "SettingWithCopyWarning". While this may not be an issue, I believe the code below would remove that warning:

df = df.assign(max = df.groupby('group')['odds'].transform('max')).values

Answer 3

df['max'] = df.group_col.map(lambda x: df.groupby('group_col').odds.max()[x])