[python] LabelEncoder: TypeError: '>' not supported between instances of 'float' and 'str'

I'm facing this error for multiple variables even treating missing values. For example:

le = preprocessing.LabelEncoder()
categorical = list(df.select_dtypes(include=['object']).columns.values)
for cat in categorical:
    print(cat)
    df[cat].fillna('UNK', inplace=True)
    df[cat] = le.fit_transform(df[cat])
#     print(le.classes_)
#     print(le.transform(le.classes_))


---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-24-424a0952f9d0> in <module>()
      4     print(cat)
      5     df[cat].fillna('UNK', inplace=True)
----> 6     df[cat] = le.fit_transform(df[cat].fillna('UNK'))
      7 #     print(le.classes_)
      8 #     print(le.transform(le.classes_))

C:\Users\paula.ceccon.ribeiro\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py in fit_transform(self, y)
    129         y = column_or_1d(y, warn=True)
    130         _check_numpy_unicode_bug(y)
--> 131         self.classes_, y = np.unique(y, return_inverse=True)
    132         return y
    133 

C:\Users\paula.ceccon.ribeiro\AppData\Local\Continuum\Anaconda3\lib\site-packages\numpy\lib\arraysetops.py in unique(ar, return_index, return_inverse, return_counts)
    209 
    210     if optional_indices:
--> 211         perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
    212         aux = ar[perm]
    213     else:

TypeError: '>' not supported between instances of 'float' and 'str'

Checking the variable that lead to the error results ins:

df['CRM do Médico'].isnull().sum()
0

Besides nan values, what could be causing this error?

Or use a cast with split to uniform type of str

unique, counts = numpy.unique(str(a).split(), return_counts=True)

As string data types have variable length, it is by default stored as object type. I faced this problem after treating missing values too. Converting all those columns to type 'category' before label encoding worked in my case.

df[cat]=df[cat].astype('category')

And then check df.dtypes and perform label encoding.