Should I use pandas get_dummies and create additional columns or use my own encoding code that keeps 1 column? I am running the Kaggle Video Games sales dataset through an XGboost algo.

I want to encode the categorical column of "Game Rating" from E, M, etc. to 0-5

when I use:
    data = pd.get_dummies(data=data, columns=['Game_Rating]) 

pandas adds a column to my dataframe for each unique observation in Game_Rating

Rating_E  Rating_E10+  Rating_EC  Rating_K-A  Rating_M  Rating_RP  Rating_T
1            0          0           0         0          0         0
0            0          0           0         0          0         0
1            0          0           0         0          0         0
1            0          0           0         0          0         0
0            0          0           0         0          0         0


My question is two-fold:
Does Pandas have the ability to replace categorical values of a dataframe inline, preserving the original column structure

if not:

I have a (slower) method that does the encoding inline and preserves the column structure of the dataframe.  I am trying to keep the dataframe as similiar to the original dataset as possible for future processing. What are the risks I may not be seeing of using my own (inline) encoding method?
credit for this answer goes to @timleathart in the comments above:

You can get the same effect very efficiently in pandas by using df['your column name'].astype('category').cat.codes. Be careful when doing this, because it implies an ordering on the columns. Your learning algorithm might overfit to spurious relationships that appear in the training data