from sklearn import preprocessing

    lb = preprocessing.LabelBinarizer()
    #fit all the possible label codes
    lb.fit(icd10['ICD10'])
    temp = lb.transform(infoDF['Target'])
    for i,x in enumerate(lb.classes_):
        infoDF[x] = temp[ : , i]

File "<ipython-input-42-2b1db450b16e>", line 3, in <module>
    lb.fit(icd10['ICD10'])

  File "C:Usersana.stanAppDataLocalContinuumanaconda3libsite-packagessklearnpreprocessinglabel.py", line 413, in fit
    self.classes_ = unique_labels(y)

  File "C:Usersana.stanAppDataLocalContinuumanaconda3libsite-packagessklearnutilsmulticlass.py", line 96, in unique_labels
    raise ValueError("Unknown label type: %s" % repr(ys))

ValueError: Unknown label type: (0              1
1              2
2              3
3              4
4              5
5              6
6              7
7              8
8              9
9             10
10            11
11            12
12            13
13            14
14            15
15            16
16            17
17            18
18            19
19            20
20            21
21            22
22           A00
23          A000
24          A001
25          A009
26       A00–A09
27           A01
28          A010
29          A011

19433       Z960
19434       Z961
19435       Z962
19436       Z963
19437       Z964
19438       Z965
19439       Z966
19440       Z967
19441       Z968
19442       Z969
19443        Z97
19444       Z970
19445       Z971
19446       Z972
19447       Z973
19448       Z974
19449       Z975
19450       Z978
19451        Z98
19452       Z980
19453       Z981
19454       Z982
19455       Z988
19456        Z99
19457       Z990
19458       Z991
19459       Z992
19460       Z993
19461       Z998
19462       Z999
Name: ICD10, Length: 19463, dtype: object,)