python pandas: using slice to build a multiindex slicing in pandas-CodePudding

I have a double Multiindex dataframe as follows. I slice the rows with idx = pd.IndexSlice but I dont know how to do the same with the columns so provided this data:

df = pd.DataFrame(data=pd.DataFrame(data=np.random.randint(0, 10, size=(9, 5))))
# rows
list1 = ['2021-01-01','2022-02-01','2022-03-01']
list2 = ['PHOTO', 'QUE','TXR']
combinations = [(x, y) for x in list1 for y in list2]     
df.index = pd.MultiIndex.from_tuples(combinations, names = ["DATE","DB"])
df.index.set_names(["DATE","DB"], inplace=True)
#columns
list1c = [('AB30','ACTIVE','A2'),('CD55','ACTIVE','A1'),('ZT52','UNACTIVE','A2'),('MIKE','PENSIONER','A2'),('ZZ00001','ACTIVE','A1')]
df.columns = pd.MultiIndex.from_tuples(list1c, names = ["UserID","KIND","DEPARTMENT"])

I dont understand why the following does not work:

idx_cols = (slice(None, None, None), slice(None, ['ACTIVE', 'UNACTIVE'], None), slice(None, ['A1'], None))
df.loc[:, idx_cols]

gives the error:

UnsortedIndexError: 'MultiIndex slicing requires the index to be lexsorted: slicing on levels [1, 2], lexsort depth 0'

If I try:

df.columns.levels

I get:

FrozenList([['AB30', 'CD55', 'MIKE', 'ZT52', 'ZZ00001'], ['ACTIVE', 'PENSIONER', 'UNACTIVE'], ['A1', 'A2']])

so level 0 are the names, level 1 ['ACTIVE', 'PENSIONER', 'UNACTIVE'] and level 2 ['A1', 'A2']

How can I solve this problem?

CodePudding user response：

Try using:

idx_cols = pd.IndexSlice[:, ['ACTIVE', 'UNACTIVE'], ["A1"]]
# or 
idx_cols = pd.IndexSlice[slice(None), ['ACTIVE', 'UNACTIVE'], ["A1"]]
df.loc[:, idx_cols]

Outputs:

UserID             AB30   CD55     ZT52      MIKE ZZ00001
KIND             ACTIVE ACTIVE UNACTIVE PENSIONER  ACTIVE
DEPARTMENT           A2     A1       A2        A2      A1
DATE       DB                                            
2021-01-01 PHOTO      2      0        0         2       0
           QUE        8      8        8         5       4
           TXR        1      9        2         5       3
2022-02-01 PHOTO      9      5        1         6       8
           QUE        1      4        3         1       0
           TXR        9      5        1         9       9
2022-03-01 PHOTO      0      9        8         5       9
           QUE        9      0        8         6       6
           TXR        8      4        8         0       0