Reputation: 395
I've been looking for a way to sort each row-level 0, column-level 1 pair within a MultiIndex DataFrame by the values they contain, but so far I haven't had any luck. For example, if my DataFrame looks like
import numpy as np
import pandas as pd
np.random.seed(7)
tup = (('A', 'B'), np.arange(2))
index = pd.MultiIndex.from_product(tup, names=('row-lvl 0', 'row-lvl 1'))
tup = (('X', 'Y'), ('q', 'p'))
columns = pd.MultiIndex.from_product(tup, names=('col-lvl 0', 'col-lvl 1'))
data = np.random.rand(4, 4)
df = pd.DataFrame(data, index=index, columns=columns)
print(df)
col-lvl 0 X Y
col-lvl 1 q p q p
row-lvl 0 row-lvl 1
A 0 0.076308 0.779919 0.438409 0.723465
1 0.977990 0.538496 0.501120 0.072051
B 0 0.268439 0.499883 0.679230 0.803739
1 0.380941 0.065936 0.288146 0.909594
I would like it to be sorted in ascending order to look like
col-lvl 0 X Y
col-lvl 1 q p q p
row-lvl 0 row-lvl 1
A 0 0.076308 0.538496 0.438409 0.072051
1 0.977990 0.779919 0.501120 0.723465
B 0 0.268439 0.065936 0.288146 0.803739
1 0.380941 0.499883 0.679230 0.909594
I've read the pandas documentation for sort_values and sort_index but they didn't seem to be what I'm looking for. Any help with this would be greatly appreciated.
Upvotes: 0
Views: 55
Reputation: 323316
This is not sort_values
, since you ignore the index
impact only check the value
for x , y in df.groupby(level=0):
... df.loc[x]=np.sort(y.values,0)
...
df
col-lvl 0 X Y
col-lvl 1 q p q p
row-lvl 0 row-lvl 1
A 0 0.076308 0.538496 0.438409 0.072051
1 0.977990 0.779919 0.501120 0.723465
B 0 0.268439 0.065936 0.288146 0.803739
1 0.380941 0.499883 0.679230 0.909594
Upvotes: 1