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

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