Much easier to use Pandas for basic one-hot encoding. If you're looking for more options you can use scikit-learn.

For basic one-hot encoding with Pandas you pass your data frame into the get_dummies function.

For example, if I have a dataframe called imdb_movies:

...and I want to one-hot encode the Rated column, I do this:

pd.get_dummies(imdb_movies.Rated)

This returns a new dataframe with a column for every "level" of rating that exists, along with either a 1 or 0 specifying the presence of that rating for a given observation.

Usually, we want this to be part of the original dataframe. In this case, we attach our new dummy coded frame onto the original frame using "column-binding.

We can column-bind by using Pandas concat function:

rated_dummies = pd.get_dummies(imdb_movies.Rated)
pd.concat([imdb_movies, rated_dummies], axis=1)

We can now run an analysis on our full dataframe.

SIMPLE UTILITY FUNCTION

I would recommend making yourself a utility function to do this quickly:

def encode_and_bind(original_dataframe, feature_to_encode):
    dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])
    res = pd.concat([original_dataframe, dummies], axis=1)
    return(res)

Usage:

encode_and_bind(imdb_movies, 'Rated')

Result:

Also, as per @pmalbu comment, if you would like the function to remove the original feature_to_encode then use this version:

def encode_and_bind(original_dataframe, feature_to_encode):
    dummies = pd.get_dummies(original_dataframe[[feature_to_encode]])
    res = pd.concat([original_dataframe, dummies], axis=1)
    res = res.drop([feature_to_encode], axis=1)
    return(res) 

You can encode multiple features at the same time as follows:

features_to_encode = ['feature_1', 'feature_2', 'feature_3',
                      'feature_4']
for feature in features_to_encode:
    res = encode_and_bind(train_set, feature)