Python Pandas divide row by groupby mean

I have a large data set that i need to be able to get the averages $ values of two columns grouped by unique ship names in column A. I will provide a scaled down data set and the ipynb file i have created so far. in the data set, i have two tabs. 1. "casino_test_sample" This is how the report comes to me from the system. 2. "final look" This is how i need to to look after the code is run. As you can see, i was able to remove the columns i dont need and add the columns i do. Its ugly, you may be able to do it cleaner. I have color coded specific rows and columns in "final look" Red = i dont want this first column to show in the .csv file Blue = all used column headings are renamed to match the requirements of the receiver. Yellow = i need these for the calculation but i dont want them in the "final look". green = This is the two calculated columns i cannot do myself. The "COIN_IN_IDX_VS_HS_AVG" is the result of dividing the "ELEC COIN IN AMT" mean value by vessel, into the individual ELEC COIN IN AMT rows to give me an index value. The "NET_WIN_IDX_VS_HS_AVG" column is the same principle, just using the "TWPD" values. I have entered an excel formula in columns "Q" and "R" so you can understand how i got these values. In the end, i want to be able to drop the original "casino_test_sample" into my folder, run my code and spit out the "final look" minus the red and yellow columns.