One hot encoding of string categorical features

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If you are on sklearn>0.20.dev0 

In [11]: from sklearn.preprocessing import OneHotEncoder
    ...: cat = OneHotEncoder()
    ...: X = np.array([['a', 'b', 'a', 'c'], [0, 1, 0, 1]], dtype=object).T
    ...: cat.fit_transform(X).toarray()
    ...: 
Out[11]: array([[1., 0., 0., 1., 0.],
           [0., 1., 0., 0., 1.],
           [1., 0., 0., 1., 0.],
           [0., 0., 1., 0., 1.]])

If you are on sklearn==0.20.dev0 

In [30]: cat = CategoricalEncoder()

In [31]: X = np.array([['a', 'b', 'a', 'c'], [0, 1, 0, 1]], dtype=object).T

In [32]: cat.fit_transform(X).toarray()
Out[32]:
array([[ 1.,  0., 0.,  1.,  0.],
       [ 0.,  1.,  0.,  0.,  1.],
       [ 1.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  1.,  0.,  1.]])

Another way to do it is to use category_encoders.

Here is an example:

% pip install category_encoders
import category_encoders as ce
le =  ce.OneHotEncoder(return_df=False, impute_missing=False, handle_unknown="ignore")
X = np.array([['a', 'dog', 'red'], ['b', 'cat', 'green']])
le.fit_transform(X)
array([[1, 0, 1, 0, 1, 0],
       [0, 1, 0, 1, 0, 1]])

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