Extract row with maximum value in DataFrameGroupBy - pandas

Question

Newbie trying to break my addiction to excel. I have a data set of paid invoices with the vendor and country where it was paid along with the amount. I want know for each vendor, which country they have the greatest invoice amount and what percentage of their total business is in that country. Using this data set I want the result to be:
Desired output
import pandas as pd
import numpy as np
df = pd.DataFrame({'Company' : ['bar','foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo', 'bar'],
'Country' : ['two','one', 'one', 'two', 'three', 'two', 'two', 'one', 'three', 'one'],
'Amount' : [4, 2, 2, 6, 4, 5, 6, 7, 8, 9],
'Pct' : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]})
CoCntry = df.groupby(['Company', 'Country'])
CoCntry.aggregate(np.sum)
After looking at multiple examples including: Extract row with max value and Getting max value using groupby
2: Python : Getting the Row which has the max value in groups using groupby I've gotten as far as creating a DataFrameGroupBy summarizing the invoice data by country. I’m struggling with how to find the max row. After which I must figure out how to calculate the percent. Advice welcome.

Answer 1

You can use transform for return Series Pct of summed values per groups by first level Company. Then filter Dataframe by max value per groups with idxmax and last divide Amount column with Series Pct:
g = CoCntry.groupby(level='Company')['Amount']
Pct = g.transform('sum')
print (Pct)
Company Country
bar one 25
three 25
two 25
foo one 28
three 28
two 28
Name: Amount, dtype: int64
CoCntry = CoCntry.loc[g.idxmax()]
print (CoCntry)
Amount Pct
Company Country
bar one 11 0
foo two 11 0
CoCntry.Pct = CoCntry.Amount.div(Pct)
print (CoCntry.reset_index())
Company Country Amount Pct
0 bar one 11 0.440000
1 foo two 11 0.392857
Similar another solution:
CoCntry = df.groupby(['Company', 'Country']).Amount.sum()
print (CoCntry)
Company Country
bar one 11
three 4
two 10
foo one 9
three 8
two 11
Name: Amount, dtype: int64
g = CoCntry.groupby(level='Company')
Pct = g.sum()
print (Pct)
Company
bar 25
foo 28
Name: Amount, dtype: int64
maxCoCntry = CoCntry.loc[g.idxmax()].to_frame()
maxCoCntry['Pct'] = maxCoCntry.Amount.div(Pct, level=0)
print (maxCoCntry.reset_index())
Company Country Amount Pct
0 bar one 11 0.440000
1 foo two 11 0.392857

Answer 2

setup
df = pd.DataFrame({'Company' : ['bar','foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo', 'bar'],
'Country' : ['two','one', 'one', 'two', 'three', 'two', 'two', 'one', 'three', 'one'],
'Amount' : [4, 2, 2, 6, 4, 5, 6, 7, 8, 9],
})
solution
# sum total invoice per country per company
comp_by_country = df.groupby(['Company', 'Country']).Amount.sum()
# sum total invoice per company
comp_totals = df.groupby('Company').Amount.sum()
# percent of per company per country invoice relative to company
comp_by_country_pct = comp_by_country.div(comp_totals).rename('Pct')
answer to OP question
Which 'Country' has greatest total invoice for 'Company' and what percentage of that companies total business.
comp_by_country_pct.loc[
comp_by_country_pct.groupby(level=0).idxmax()
].reset_index()
Company Country Pct
0 bar one 0.440000
1 foo two 0.392857