How to handle nominal data in scikit learn, python?
Depends what type of model you're using, make_pipeline(LabelEncoder, OneHotEncoder) or pd.get_dummies) is the usual choice, and can work well with classifiers from either linear_model or tree. LabelEncoder on its own would another choice, although this won't work well unless there's a natural ordering on your labels (like level of education or something) or unless you're using very deep trees, which are able to separate individual labels.
I am new to data mining. I have a data set which includes directors' names. What is the right way to convert them to something that Scikit learn estimators can use without problem? 
From what I found on the internet I thought that sklearn.preprocessing.LabelEncoder is the right choice.