Encoding Multiple Categorical Data with Python using sklearn.preprocessing.LabelEncoder() takes too much processing time on 2D array inputs

Consider for some reason I am trying to encode a feature. Let's say my feature name is title. For the title feature, for one record I might have different words: title = 'Apple', 'Jobs'. Let me illustrate:

ID      title  
0  ['Apple', 'Jobs']  
1  ['Wozniak']
2  ['Apple', 'Wozniak']
3  ['Jobs', 'Wozniak']  

As you could see my unique values are :

unique = ['Apple','Jobs','Wozniak']

And previously I was using label encoder as:

from sklearn.preprocessing import LabelEncoder

le.fit(unique)
for i in df['title'].index:
    df['title'][i] = le.transform(df['title'][i])

And I used to get something like:

ID      title  
0  [782, 256]  
1  [331]
2  [782, 331]
3  [256, 331] 

which was exactly what I wanted; yet, this takes too much time because I have too many values to iterate and encode. Thus, I am looking for an algorithm that is smarter and preferably with a lower time complexity or smaller running time.

source https://stackoverflow.com/questions/74843758/encoding-multiple-categorical-data-with-python-using-sklearn-preprocessing-label