You need to pass in a bytes object, rather than a str. The typical way to go from str (a unicode string in Python 3) to bytes is to use the .encode() method on the string and specify the encoding you wish to use.

my_bytes = my_string.encode('utf-8')

Your categorical variable has two levels, so there is no actual difference between dummy-coding vs. simply entering the variable into the analysis. That is, to dummy code you would create one new variable with two values but your original variable is already one variable with two values. Dummy-coding is important for variables with more than two possible values. So, in this case the computer won't consider Pave > Grvl.

But if you have more than two variables then you should use dummy variables.

For your data, you can use pandas.get_dummies() or sklearn's one hot encoder to achieve your result.

You just do the transform() on the test data (and do not fit the encoder again). The values that don't occur in "training" datasets would be encoded as 0 in all of the categories (as long as you won't change the handle_unknown parameter). For example:

import category_encoders as ce

train = pd.DataFrame({"var1": ["A", "B", "A", "B", "C"], "var2":["A", "A", "A", "A", "B"]})

encoder = ce.BinaryEncoder(cols = ['var1', 'var2'] , return_df = True)
x_train_data = encoder.fit_transform(train)

#   var1_0  var1_1  var2_0  var2_1
#0  0       1       0       1
#1  1       0       0       1
#2  0       1       0       1
#3  1       0       0       1
#4  1       1       1       0

test = pd.DataFrame({"var1": ["C", "D", "B"], "var2":["A", "C", "F"]})
x_test_data = encoder.transform(test)

#   var1_0  var1_1  var2_0  var2_1
#0  1       1       0       1
#1  0       0       0       0
#2  1       0       0       0

'D' doesn't occur in var1 in training data, so it was encoded as 0 0. 'C' and 'F'don't occur in var2 in training data, so they were both encoded as 0 0.