Python Pandas: LabelEncoding fitting unknown variables - pandas

Question

Hi I have a dataframe full of strings and I want to encode these strings and store their corresponding codes.
I want to produce these codes on one column and fit onto another column.
When I fit these codes on some other column that has a string that I haven't seen on my training column I want to create another unique value for that.
I have tried LabelEncoding function but it gives error on the previously unseen strings.
For example a have dataframe:
col1 col2
a a
b b
c e
d f
After training LabelEncoding on first column I get something like this:
col1 col2
1 a
2 b
3 e
4 f
After fitting on the created codes onthe second column I want to have something like this:
col1 col2
1 1
2 2
3 5
4 6
What is the easiest way to do this. Thank you.

Answer 1

Created df dataframe by copying sample from OP's post as follows.
df=pd.read_clipboard()
Its value will be as follows when we print it:
col1 col2
0 a a
1 b b
2 c e
3 d f
Could you please try following. I have given here only 1st 6 alphabets you could mention all in case you have them in your actual Input_file.
dict1 = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6}
df.applymap(lambda s: dict1.get(s) if s in dict1 else s)
Output will be as follows.
col1 col2
0 1 1
1 2 2
2 3 5
3 4 6

Answer 2

You could encoding yourself using pd.factorize:
v, k = pd.factorize(sorted(df.stack().unique()))
m = dict(zip(k.tolist(), (v+1).tolist()))
df.replace(m)
Output:
col1 col2
0 1 1
1 2 2
2 3 5
3 4 6
I think the real trick is to stack col1 and col2 then encoding the values of both list as one.
le = LabelEncoder()
le.fit(df.stack())