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I'm want to normalize sales data of multiple point of sales (POS), Products and weeks. The dataframe looks like this:

    pos product sales   week
0   1   car     250      1
1   2   tank    400      2
2   2   car     300      1
3   1   tank    500      2

The goal is to normalize the data between 0,1 for each point of sale and product, e.g the minimum and maximum relate to the minimum and maximum of sales within a specific product and a specific point of sale. I guess I can just create a column for each combination (example below) and then normalize each column but I'm looking for a more elegant solution.

    pos     1         2
    product car tank  car   tank
week                
1           250  0    300     0
2           0    500    0   400

Thanks, Al

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As you require it to be per week vs point of sale, you have to group them by those columns. Once grouped, you could run do the standard split-apply-combine, where based on the grouping, you get splits or groups, you can apply a function that normalizes data in this group and then you combine all the groups back.

You can read about it more here : https://pandas.pydata.org/pandas-docs/stable/groupby.html

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  • $\begingroup$ Hi Nischal, Thanks for your help. I'm rather new in the field and find it hard to understand. Can you focus me please? $\endgroup$ – Almog Nov 5 '18 at 17:10
  • $\begingroup$ He provided a link that is helpful - maybe start there and "get back" to us with questions? $\endgroup$ – javadba Dec 5 '18 at 20:20

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