Random Forest - Explanation Parameter
You need to be very careful with your assumptions about "important" parameters. All the parameters have their function, that is precisely why they are implemented on the first place. If with importance you mean potential to affect your outputs/efficiency, perhaps there are some more relevant than others but most likely this relevance will also depend on the nature of your task and you data.

Having said that, I recommend you navigate through the scikit-learn documentation which is usually helpful and it contains a description including all these parameters: 


http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html


Moreover, they usually include nice and simple usage examples that can be helpful: here and here.

It is important that you practice and practice and you will start to get a feeling about the importance of each parameter and its relevance to the task at hand.

Finally, there are hundreds of blogs with nice tutorials that will give you a little bit more of insights about parameter tuning and "importance" ;) 

You can try this once for example. 

Good luck!