df.groupby('l_customer_id_i').agg(lambda x: ','.join(x))
does already return a dataframe, so you cannot loop over the groups anymore.
In general:
-
df.groupby(...)
returns aGroupBy
object (a DataFrameGroupBy or SeriesGroupBy), and with this, you can iterate through the groups (as explained in the docs here). You can do something like:grouped = df.groupby('A') for name, group in grouped: ...
-
When you apply a function on the groupby, in your example
df.groupby(...).agg(...)
(but this can also betransform
,apply
,mean
, …), you combine the result of applying the function to the different groups together in one dataframe (the apply and combine step of the ‘split-apply-combine’ paradigm of groupby). So the result of this will always be again a DataFrame (or a Series depending on the applied function).