Reverse Label Encoding giving error

Question

I label encoded my categorical data into numerical data using label encoder

data['Resi'] = LabelEncoder().fit_transform(data['Resi'])

But I when I try to find how they are mapped internally using

list(LabelEncoder.inverse_transform(data['Resi']))

I am getting below error

---

TypeError                                 Traceback (most recent call last)
<ipython-input-67-419ab6db89e2> in <module>()
----> 1 list(LabelEncoder.inverse_transform(data['Resi']))

TypeError: inverse_transform() missing 1 required positional argument: 'y'

How to fix this

Sample data

Resi
IP
IP
IP
IP
IP
IE
IP
IP
IP
IP
IP
IPD
IE
IE
IP
IE
IP
IP
IP

Answer 1

You can check label encoding:

>>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6])
array([0, 0, 1, 2])
>>> le.inverse_transform([0, 0, 1, 2])
array([1, 1, 2, 6])

And for your solution:

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder().fit(data['Resi'])
data['Resi'] = le.transform(data['Resi'])
print (data.tail())
    Resi
14     1
15     0
16     1
17     1
18     1

L = list(le.inverse_transform(data['Resi']))
print (L)
['IP', 'IP', 'IP', 'IP', 'IP', 'IE', 'IP', 'IP', 'IP', 
 'IP', 'IP', 'IPD', 'IE', 'IE', 'IP', 'IE', 'IP', 'IP', 'IP']

EDIT:

d = dict(zip(le.classes_, le.transform(le.classes_)))
print (d)
{'IE': 0, 'IPD': 2, 'IP': 1}