Q:

Passing categorical data to Sklearn Decision Tree

There are several posts about how to encode categorical data to Sklearn Decision trees, but from Sklearn documentation, we got these

Some advantages of decision trees are:
(...)
Able to handle both numerical and categorical data.  Other techniques are usually specialized in analyzing datasets that have only one type of variable.  See the algorithms for more information. 

But running the following script
import pandas as pd 
from sklearn.tree import DecisionTreeClassifier

data = pd. DataFrame()
data['A'] = ['a','a','b','a']
data['B'] = ['b','b','a','b']
data['C'] = [0, 0, 1, 0]
data['Class'] = ['n','n','y','n']

tree = DecisionTreeClassifier()
tree.fit(data[['A','B','C']], data['Class'])

outputs the following error:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/site-packages/sklearn/tree/tree.py", line 154, in fit
    X = check_array(X, dtype=DTYPE, accept_sparse="csc")
  File "/usr/local/lib/python2.7/site-packages/sklearn/utils/validation.py", line 377, in check_array
    array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: b

I know that in R it is possible to pass categorical data, with Sklearn, is it possible? 

A:

(This is just a reformat of my comment above from 2016...it still holds true.) 
The accepted answer for this question is misleading. 
As it stands, sklearn decision trees do not handle categorical data - see issue #5442. 
The recommended approach of using Label Encoding converts to integers which the DecisionTreeClassifier() will treat as numeric.  If your categorical data is not ordinal, this is not good - you'll end up with splits that do not make sense.  
Using a OneHotEncoder is the only current valid way, allowing arbitrary splits not dependent on the label ordering, but is computationally expensive. 

A:

(..)
Able to handle both numerical and categorical data. 

This only means that you can use

the DecisionTreeClassifier class for classification problems
the DecisionTreeRegressor class for regression. 

In any case you need to one-hot encode categorical variables before you fit a tree with sklearn, like so:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier

data = pd. DataFrame()
data['A'] = ['a','a','b','a']
data['B'] = ['b','b','a','b']
data['C'] = [0, 0, 1, 0]
data['Class'] = ['n','n','y','n']

tree = DecisionTreeClassifier()

one_hot_data = pd.get_dummies(data[['A','B','C']],drop_first=True)
tree.fit(one_hot_data, data['Class'])

A:

For nominal categorical variables, I would not use LabelEncoderbut  sklearn.preprocessing. OneHotEncoder or pandas.get_dummies instead because there is usually no order in these type of variables. 