'assign to grouped dataframe in Pandas

I want to calculate lags of multiple columns. I am able to do that for each column separately as shown below. How can I avoid the duplicate groupby and sorting.

### Pandas previous week values

search  = search.assign(asp_lstwk2 = search.sort_values(by = 'firstdayofweek').groupby('asin_bk')['asp'].shift(1))\
                 .assign(lbb_lstwk2 = search.sort_values(by = 'firstdayofweek').groupby('asin_bk')['lbb'].shift(1))\
                .assign(repoos_lstwk2 = search.sort_values(by = 'firstdayofweek').groupby('asin_bk')['repoos'].shift(1))\
                .assign(ordered_units_lstwk2 = search.sort_values(by = 'firstdayofweek').groupby('asin_bk')['ordered_units'].shift(1))

Solution 1:^[1]

Try:

search = search.join(search.sort_values(by = 'firstdayofweek')
                     .groupby('asin_bk')[['asp','lbb','repoos','ordered_units']]
                     .shift().add_suffix('_lstwk2'))

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'assign to grouped dataframe in Pandas

Solution 1:[1]

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Solution 1:^[1]