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

Can you please share some data? What does the `LabelEncoder.classes_` shows? — Vivek Kumar, Nov 02 '17 at 05:54

jezrael · Accepted Answer · 2017-11-02T06:27:31.117

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}

@jezral, How do I find which label corresponds which category. I.e IP =0, IPD=1, so on — NgBrandon, Nov 02 '17 at 06:14

score 1 · Answer 2 · edited Feb 16 '19 at 07:11

You are not storing the LabelEncoder() object anywhere. You need to save it like this:

le = LabelEncoder()

And then call fit(), or transform().

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder

ls = ['IP', 'IP', 'IP', 'IP', 'IP', 'IE', 'IP', 'IP', 'IP', 'IP', 'IP', 'IPD', 'IE', 'IE', 'IP', 'IE', 'IP', 'IP', 'IP']

data = pd.DataFrame(np.array(ls).reshape(-1,1), columns=['Resi'])

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

df['resi'] = LabelEncoder().fit_transform(df['resi'])
list(le.inverse_transform(data['Resi']))

Out: 
['IP',
 'IP',
 'IP',
 'IP',
 'IP',
 'IE',
 'IP',
 'IP',
 'IP',
 'IP',
 'IP',
 'IPD',
 'IE',
 'IE',
 'IP',
 'IE',
 'IP',
 'IP',
 'IP']

How can I find which label correspond to which category – NgBrandon Nov 02 '17 at 06:16 — NgBrandon, Nov 02 '17 at 06:16

score 0 · Answer 3 · edited Jun 05 '20 at 00:05

0

encoder = LabelEncoder()  
encoder.inverse_transform(data['Resi'])

edited Jun 05 '20 at 00:05

David Buck

3,752
35
31
35

answered Jun 04 '20 at 22:02

Sadabrata Konar

1

Please put your answer always in context instead of just pasting code. See [here](https://stackoverflow.com/help/how-to-answer) for more details. – gehbiszumeis Jun 05 '20 at 07:46

Reverse Label Encoding giving error

3 Answers3

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