Python Pandas divide row by groupby mean
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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.
ID Proiect: #31575041
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