I have the below dataframe created using groupby and sum :-
year_month Country
2008-01 Afghanistan 2
Albania 3
Argentina 4
2008-02 Afghanistan 3
Albania 4
Argentina 5
I need to unstack and want name to be renamed as der_value_Afghanistan, der_value_Albania etc as column names rather than Afghanistan etc. Since it could be 100 or more, is there any way to rename it all together rather than manually?
year_month der_value_Afghanistan der_value_Albania der_value_Argentina
I think need Series.unstack
with DataFrame.add_prefix
:
df = s.unstack().add_prefix('der_value_')
print (df)
Country der_value_Afghanistan der_value_Albania der_value_Argentina
year_month
2008-01 2 3 4
2008-02 3 4 5
For index
to column add DataFrame.rename_axis
with DataFrame.reset_index
:
df = s.unstack().add_prefix('der_value_').rename_axis(None, axis=1).reset_index()
print (df)
year_month der_value_Afghanistan der_value_Albania der_value_Argentina
0 2008-01 2 3 4
1 2008-02 3 4 5
Modify MultiInex
before unstack
is also possible by MultiIndex.from_arrays
:
a = s.index.get_level_values(0)
b = 'der_value_' + s.index.get_level_values(1)
s.index = pd.MultiIndex.from_arrays([a, b], names=s.index.names)
print (s)
year_month Country
2008-01 der_value_Afghanistan 2
der_value_Albania 3
der_value_Argentina 4
2008-02 der_value_Afghanistan 3
der_value_Albania 4
der_value_Argentina 5
Name: a, dtype: int64
df = s.unstack()
print (df)
Country der_value_Afghanistan der_value_Albania der_value_Argentina
year_month
2008-01 2 3 4
2008-02 3 4 5
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