Assigning results of groupby() to dataframe column

Question

This groupby() script:

df.groupby(['period', 'tier']).type.value_counts()/df.groupby(['period']).period_days.unique()

produces this object:

    period              tier      type        
first_period_1of2   10-99_cr      tup    [35]
                    100-199_cr    tup    [34]
                    200-299_cr    tup    [18]
                    300-500_cr    tup    [17]
first_period_2of2   10-99_cr      tup    [38]
                    100-199_cr    tup    [45]
                    200-299_cr    tup    [17]
                    300-500_cr    tup    [14]
second_period_1of2  30-99_cr      tup    [35]
                    100-199_cr    tup    [46]
                    200-299_cr    tup    [18]
                    300-500_cr    tup    [25]
second_period_2of2  30-99_cr      tup    [32]
                    100-199_cr    tup    [43]
                    200-299_cr    tup    [7]
                    300-500_cr    tup    [56]
dtype: object

I would like to apply the results of the groupby() script ([35],[34],[18]...) back to the original dataframe, in a new column, assigned to the rows where the groupby() conditions are met.

df.shape
(22588, 17)

jezrael · Accepted Answer · 2020-04-28 08:57:10Z

1

I think you need:

s = df.groupby(['period', 'tier']).type.value_counts()/df.groupby(['period']).period_days.unique()
df = df.join(s.rename('new'), on=['period','tier'])

answered Apr 28, 2020 at 8:57

jezrael

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1 Comment

LeoGER Over a year ago

Hi @jezrael, thanks for your comment! However, I received this error message: ValueError: len(left_on) must equal the number of levels in the index of "right"

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