在fit_transform之后获取sklearn.LabelEncoder（）映射

Question

I'm trying to get the mappings of label encoder to figure out which code got each string for a column in my df.

Here is the encoding code:

y[:]=LabelEncoder().fit_transform(y[:])

I would like to get something like this as output:

A:1
B:2
C:3

Thanks for the help!

Answer 1

You should refrain from in-line initialisation if you want to be able to make use of the mappings or inverse-transformation later.

data = ['A', 'A', 'B', 'C', 'B', 'B'] # `y`

le = LabelEncoder()
mapped = le.fit_transform(data)

mapping = dict(zip(le.classes_, range(1, len(le.classes_)+1)))
print(mapping)
# {'A': 1, 'B': 2, 'C': 3}

Better still, if you want to reverse the encoding, use inverse_transform :

print(le.inverse_transform(mapped))
# ['A' 'A' 'B' 'C' 'B' 'B']