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 avoid creating a LabelEncoder object for each column; I'd rather just have one big LabelEncoder objects that works across all my columns of data.

Throwing the entire DataFrame into LabelEncoder creates the below error. Please bear in mind that I'm using dummy data here; in actuality I'm dealing with about 50 columns of string labeled data, so need a solution that doesn't reference any columns by name.

import pandas
from sklearn import preprocessing 

df = pandas.DataFrame({
    'pets': ['cat', 'dog', 'cat', 'monkey', 'dog', 'dog'], 
    'owner': ['Champ', 'Ron', 'Brick', 'Champ', 'Veronica', 'Ron'], 
    'location': ['San_Diego', 'New_York', 'New_York', 'San_Diego', 'San_Diego', 
                 'New_York']
})

le = preprocessing.LabelEncoder()

le.fit(df)

Traceback (most recent call last): File "", line 1, in File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/label.py", line 103, in fit y = column_or_1d(y, warn=True) File "/Users/bbalin/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py", line 306, in column_or_1d raise ValueError("bad input shape {0}".format(shape)) ValueError: bad input shape (6, 3)

Any thoughts on how to get around this problem?

Answer 1

You can easily do this though,

df.apply(LabelEncoder().fit_transform)

EDIT2:

In scikit-learn 0.20, the recommended way is

OneHotEncoder().fit_transform(df)

as the OneHotEncoder now supports string input. Applying OneHotEncoder only to certain columns is possible with the ColumnTransformer.

EDIT3:

Since this answer is over a year ago, and generated many upvotes (including a bounty), I should probably extend this further.

For inverse_transform and transform, you have to do a little bit of hack.

from collections import defaultdict
d = defaultdict(LabelEncoder)

With this, you now retain all columns LabelEncoder as dictionary.

# Encoding the variable
fit = df.apply(lambda x: d[x.name].fit_transform(x))

# Inverse the encoded
fit.apply(lambda x: d[x.name].inverse_transform(x))

# Using the dictionary to label future data
df.apply(lambda x: d[x.name].transform(x))

MOAR EDIT:

Using Neuraxle's FlattenForEach step, it's possible to do this as well to use the same LabelEncoder on all the flattened data at once:

FlattenForEach(LabelEncoder(), then_unflatten=True).fit_transform(df)

For using separate LabelEncoders depending for your columns of data, or if only some of your columns of data needs to be label-encoded and not others, then using a ColumnTransformer is a solution that allows for more control on your column selection and your LabelEncoder instances.