How can I fit categorical data types for random forest classification?
You need to convert the categorical features into numeric attributes. A common approach is to use one-hot encoding, but that's definitely not the only option. If you have a variable with a high number of categorical levels, you should consider combining levels or using the hashing trick. Sklearn comes equipped with several approaches (check the "see also" section): One Hot Encoder and Hashing Trick

If you're not committed to sklearn, the h2o random forest implementation handles categorical features directly.