I have a dataframe that I need to group, then subgroup. From the subgroups I need to return what the subgroup is as well as the unique values for a column.
df = pandas.DataFrame({'country': pandas.Series(['US',
'Canada', 'US', 'US']),
'gender': pandas.Series(['male','female','male','female']),
'industry': pandas.Series(['real estate','shipping',
'telecom','real estate']),
'income': pandas.Series([1, 2, 3, 4])})
def subgroup(g):
return g.groupby(['gender'])
s = df.groupby(['country']).apply(subgroup)
From s, how can I compute the uniques of "industry" as well as which "gender" it's grouped for?
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| US | male | [real estate, telecom] |
| |----------------------------------
| | female | [real estate] |
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| Canada | female | [shipping] |
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