Label encoding across multiple columns in scikit-learn

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?