how to represent feature importance in xgboost in percentage?

I am looking for a way to represent the feature importance numbers in percentage. I read through articles and API documentation for XGboost in python and it gives me the feature importance score, which represents how significant a feature is in predicting the dependent variable. for example, these are the feature scores, that I get for my model.

{'f1': 63,
 'f2': 32,
 'f3': 79,
 'f4': 297,
 'f5': 371,
 'f6': 171,
 'f7': 308,
 'f8': 416,
 'f9': 90,
 'f10': 197,
 'f11': 141,
 'f12': 167}

Now, is there a way I can represent this in percentages, for example, for feature f1, 63 divided by something could give me these values in percentages?