LabelEncoder error using Sklearn in a for loop

Question

I am running Python 3 using spyder 2, and when I attempt to run this code:

from sklearn.preprocessing import LabelEncoder
cv=train.dtypes.loc[train.dtypes=='object'].index
print (cv)

le=LabelEncoder()
for i in cv:
    train[i]=le.fit_transform(train[i])
    test[i]=le.fit_transform(test[i])

I get this error:

le=LabelEncoder()
for i in cv:
    train[i]=le.fit_transform(train[i])
    test[i]=le.fit_transform(test[i])


Traceback (most recent call last):

  File "<ipython-input-5-8739984f61b2>", line 3, in <module>
    train[i]=le.fit_transform(train[i])

  File "C:\Users\myname\Anaconda3\lib\site-packages\sklearn\preprocessing\label.py", line 127, in fit_transform
    self.classes_, y = np.unique(y, return_inverse=True)

  File "C:\Users\myname\Anaconda3\lib\site-packages\numpy\lib\arraysetops.py", line 195, in unique
    perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')

TypeError: unorderable types: str() > float()

Oddly enough, if I call the encoder on a specified column in my data, the output is successful. For instance:

le.fit_transform(test['Race'])

Results in:

le.fit_transform(test['Race'])
Out[7]: array([2, 4, 4, ..., 4, 1, 4], dtype=int64)

I've tried:

float(le.fit_transform(train[i]))
str(le.fit_transform(train[i]))

Both have not worked.

Could someone please help me out?

score 3 · Accepted Answer · edited Dec 10 '16 at 15:03

Had the same problem too. Turns out that I missed checking for missing values. Check if you have any left (in your case):

print(train.apply(lambda x : sum(x.isnull())))
print(test.apply(lambda x : sum(x.isnull())))

If you have some either replace them with a parameter (mean, med, mod...) or simply encode them as a String, i.e. for an arbitrary variable VAR :

parameter = train[VAR].mean() # parameter = "Nan"
train[VAR].fillna(parameter, inplace = True )

LabelEncoder error using Sklearn in a for loop

1 Answers1