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.

EDIT:

Since this original 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 Label Encoder 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))