Assigning a desired proportion of Total
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Please take a look at the below table (DataFrame):
Product Channel Germany France India Italy USA Canada Russia China Poland Spain Portugal
0 Rings Train 1893.0
1 Rings Airplane 4608.0 13057.0
2 Rings Truck 4528.0
3 Shoes Train 6423.0 3212.0 6669.0
4 Shoes Airplane 3803.0 3549.0 78588.0 24367.0 3169.0
5 Shoes Truck 20185.0 8624.0 9358.0 6606.0 1284.0 16698.0 11560.0 24589.0 13028.0 8819.0 18350.0
6 Watch Train 2035.0 1018.0 2113.0
7 Watch Airplane 653.0 586.0 14997.0 4464.0 688.0
8 Watch Truck 2574.0 1100.0 1193.0 842.0 164.0 2129.0 1474.0 3135.0 1661.0 1124.0 2340.0
PROPORTION 0.09256 0.04204 0.05837 0.03434 0.33865 0.05582 0.03864 0.08220 0.04355 0.14100 0.07278
The proportion indicates the share of each country of the Total.
What would be the right approach to modify the individual numbers, so that e.g. Germany and France make up exactly 10%?
I can only change the rows which are not empty and the total of each row has to remain the same after this is done.
The change done to the other countries should ideally be proportional to their size, e.g. USA should give/take most of the numbers as they make up 1/3 of the whole.
Is this a form of linear programming or are other methods used for a task like this? I'd appreciate some thoughts what field/library I should look into to make this work, if the specific solution to this task itself is too complex.
python pandas optimization
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
add a comment |
Please take a look at the below table (DataFrame):
Product Channel Germany France India Italy USA Canada Russia China Poland Spain Portugal
0 Rings Train 1893.0
1 Rings Airplane 4608.0 13057.0
2 Rings Truck 4528.0
3 Shoes Train 6423.0 3212.0 6669.0
4 Shoes Airplane 3803.0 3549.0 78588.0 24367.0 3169.0
5 Shoes Truck 20185.0 8624.0 9358.0 6606.0 1284.0 16698.0 11560.0 24589.0 13028.0 8819.0 18350.0
6 Watch Train 2035.0 1018.0 2113.0
7 Watch Airplane 653.0 586.0 14997.0 4464.0 688.0
8 Watch Truck 2574.0 1100.0 1193.0 842.0 164.0 2129.0 1474.0 3135.0 1661.0 1124.0 2340.0
PROPORTION 0.09256 0.04204 0.05837 0.03434 0.33865 0.05582 0.03864 0.08220 0.04355 0.14100 0.07278
The proportion indicates the share of each country of the Total.
What would be the right approach to modify the individual numbers, so that e.g. Germany and France make up exactly 10%?
I can only change the rows which are not empty and the total of each row has to remain the same after this is done.
The change done to the other countries should ideally be proportional to their size, e.g. USA should give/take most of the numbers as they make up 1/3 of the whole.
Is this a form of linear programming or are other methods used for a task like this? I'd appreciate some thoughts what field/library I should look into to make this work, if the specific solution to this task itself is too complex.
python pandas optimization
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
add a comment |
Please take a look at the below table (DataFrame):
Product Channel Germany France India Italy USA Canada Russia China Poland Spain Portugal
0 Rings Train 1893.0
1 Rings Airplane 4608.0 13057.0
2 Rings Truck 4528.0
3 Shoes Train 6423.0 3212.0 6669.0
4 Shoes Airplane 3803.0 3549.0 78588.0 24367.0 3169.0
5 Shoes Truck 20185.0 8624.0 9358.0 6606.0 1284.0 16698.0 11560.0 24589.0 13028.0 8819.0 18350.0
6 Watch Train 2035.0 1018.0 2113.0
7 Watch Airplane 653.0 586.0 14997.0 4464.0 688.0
8 Watch Truck 2574.0 1100.0 1193.0 842.0 164.0 2129.0 1474.0 3135.0 1661.0 1124.0 2340.0
PROPORTION 0.09256 0.04204 0.05837 0.03434 0.33865 0.05582 0.03864 0.08220 0.04355 0.14100 0.07278
The proportion indicates the share of each country of the Total.
What would be the right approach to modify the individual numbers, so that e.g. Germany and France make up exactly 10%?
I can only change the rows which are not empty and the total of each row has to remain the same after this is done.
The change done to the other countries should ideally be proportional to their size, e.g. USA should give/take most of the numbers as they make up 1/3 of the whole.
Is this a form of linear programming or are other methods used for a task like this? I'd appreciate some thoughts what field/library I should look into to make this work, if the specific solution to this task itself is too complex.
python pandas optimization
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
Please take a look at the below table (DataFrame):
Product Channel Germany France India Italy USA Canada Russia China Poland Spain Portugal
0 Rings Train 1893.0
1 Rings Airplane 4608.0 13057.0
2 Rings Truck 4528.0
3 Shoes Train 6423.0 3212.0 6669.0
4 Shoes Airplane 3803.0 3549.0 78588.0 24367.0 3169.0
5 Shoes Truck 20185.0 8624.0 9358.0 6606.0 1284.0 16698.0 11560.0 24589.0 13028.0 8819.0 18350.0
6 Watch Train 2035.0 1018.0 2113.0
7 Watch Airplane 653.0 586.0 14997.0 4464.0 688.0
8 Watch Truck 2574.0 1100.0 1193.0 842.0 164.0 2129.0 1474.0 3135.0 1661.0 1124.0 2340.0
PROPORTION 0.09256 0.04204 0.05837 0.03434 0.33865 0.05582 0.03864 0.08220 0.04355 0.14100 0.07278
The proportion indicates the share of each country of the Total.
What would be the right approach to modify the individual numbers, so that e.g. Germany and France make up exactly 10%?
I can only change the rows which are not empty and the total of each row has to remain the same after this is done.
The change done to the other countries should ideally be proportional to their size, e.g. USA should give/take most of the numbers as they make up 1/3 of the whole.
Is this a form of linear programming or are other methods used for a task like this? I'd appreciate some thoughts what field/library I should look into to make this work, if the specific solution to this task itself is too complex.
python pandas optimization
python pandas optimization
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
edited Nov 24 '18 at 12:25
Bishonen_PL
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
asked Nov 24 '18 at 12:07
Bishonen_PLBishonen_PL
13910
13910
add a comment |
add a comment |
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