I have the below Pandas dataframe. It has two levels of indexing in the columns Segment Name and Variables.
mean
seg1 daily_time_spend_on_sight 25
age 36
area_income 1250
clicked_on_ad 250
seg2 daily_time_spend_on_sight 10
age 26
area_income 950
clicked_on_ad 125
I need to change the level 0 index to appear in all the records pertaining to it:
mean
seg1 daily_time_spend_on_sight 25
seg1 age 36
seg1 area_income 1250
seg1 clicked_on_ad 250
seg2 daily_time_spend_on_sight 10
seg2 age 26
seg2 area_income 950
seg2 clicked_on_ad 125
If you have a dataset like below with multi index you can use .reset_index()
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
s = pd.Series(np.random.randn(8), index=index)
print(s)
first second
bar one -0.632949
two -1.418744
baz one -1.318791
two 0.194042
foo one -0.139960
two 0.971686
qux one -0.257964
two 1.911748
dtype: float64
s.reset_index() will give
first second 0 0 bar one -0.632949 1 bar two -1.418744 2 baz one -1.318791 3 baz two 0.194042 4 foo one -0.139960 5 foo two 0.971686 6 qux one -0.257964 7 qux two 1.911748
You can also use pd.option_context('display.multi_sparse', False) i.e
with pd.option_context('display.multi_sparse', False):
print(s)
Output :
first second bar one 1.157404 bar two -0.000333 baz one -0.774613 baz two -1.962658 foo one 1.337555 foo two 0.856814 qux one 0.506146 qux two 0.755346 dtype: float64
For more info about multi indexing you can visit here
Hope it helps