Giving higher priority to certain inputs in SKLearn Random Forest
You've kind of answered your own question there!

One of the strengths of machine learning with algorithms like Random Forests is that they are able to determine the feature (input) importance themselves. My advice would not be to weight them for now (not sure you actually can with SKLearn's RF) and let the model determine it itself.

What you can do is give additional weight to the rows (observations) of your data set, or give additional weight to different classes of the label (for example, if there aren't many 'P's in your label but they're important, you can bump up their weight relative to the other classes).