Label encoding across multiple columns in scikit-learn

Question

I\'m trying to use scikit-learn\'s LabelEncoder to encode a pandas DataFrame of string labels. As the dataframe has many (50+) columns, I want to a

Answer 1

It is possible to do this all in pandas directly and is well-suited for a unique ability of the replace method.

First, let's make a dictionary of dictionaries mapping the columns and their values to their new replacement values.

transform_dict = {}
for col in df.columns:
    cats = pd.Categorical(df[col]).categories
    d = {}
    for i, cat in enumerate(cats):
        d[cat] = i
    transform_dict[col] = d

transform_dict
{'location': {'New_York': 0, 'San_Diego': 1},
 'owner': {'Brick': 0, 'Champ': 1, 'Ron': 2, 'Veronica': 3},
 'pets': {'cat': 0, 'dog': 1, 'monkey': 2}}

Since this will always be a one to one mapping, we can invert the inner dictionary to get a mapping of the new values back to the original.

inverse_transform_dict = {}
for col, d in transform_dict.items():
    inverse_transform_dict[col] = {v:k for k, v in d.items()}

inverse_transform_dict
{'location': {0: 'New_York', 1: 'San_Diego'},
 'owner': {0: 'Brick', 1: 'Champ', 2: 'Ron', 3: 'Veronica'},
 'pets': {0: 'cat', 1: 'dog', 2: 'monkey'}}

Now, we can use the unique ability of the replace method to take a nested list of dictionaries and use the outer keys as the columns, and the inner keys as the values we would like to replace.

df.replace(transform_dict)
   location  owner  pets
0         1      1     0
1         0      2     1
2         0      0     0
3         1      1     2
4         1      3     1
5         0      2     1

We can easily go back to the original by again chaining the replace method

df.replace(transform_dict).replace(inverse_transform_dict)
    location     owner    pets
0  San_Diego     Champ     cat
1   New_York       Ron     dog
2   New_York     Brick     cat
3  San_Diego     Champ  monkey
4  San_Diego  Veronica     dog
5   New_York       Ron     dog