from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
X = [['Male', 1], ['Female', 3], ['Female', 2]]
enc.fit(X)

print(enc.categories_)
#Output: [array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]

encoding = enc.categories_
encoding_sex = dict(zip((encoding[0]), range(len(encoding[0]))))
print(encoding_sex)
# Output: {'Female': 0, 'Male': 1}

encoding = enc.categories_
encoding_feature = lambda x: dict(zip(x, range(len(x))))
encoding_full = [encoding_feature(feature_elem) for feature_elem in encoding]
print(encoding_full)
# Output: [{'Female': 0, 'Male': 1}, {1: 0, 2: 1, 3: 2}]