Pandas - Dynamic column aggregation based on another column

[theroadbacktonature]

An algorithm runs daily and generates a file. The file can have dynamic columns in each run.

First run : country,date,exchange_rate,sale_amt,profit_amt,1st_purch,2nd_purch
Second run: country,date,exchange_rate,sale_amt,profit_amt,1st_purch,2nd_purch,3rd_purch
Third run : country,date,exchange_rate,sale_amt,profit_amt,1st_purch,2nd_purch,3rd_purch,margin_amt

Only the 'amt' columns should be divided by exchange_rate and agregated (sum). Remaining columns can be aggregated (sum) as it is.

Below is input (not output):

Output:
country   date        sale_amt    profit_amt  1st_purch   2nd_purch   exchange_rate
US        2019-12-01  100           10          11          21             2
US        2019-12-02  200           20          12          22             2
US        2019-12-03  300           30          13          23             2
US        2019-12-04  400           40          14          34             2
US        2019-12-05  500           50          15          25             2

Desired Output:

Output:
country  sale_amt    profit_amt  1st_purch   2nd_purch
  US       750          75          65         125    

Logic:
sale_amt=sum(sale_amt/exchange_rate)
profit_amt=sum(profit_amt/exchange_rate)
1st_purch=sum(1st_purch)
2nd_purch=sum(2nd_purch)

I wrote below code snippet, but unable to generate the correct expression to include division by exchange_rate for currency columns.

columns = df.columns
groupbyColumns = ["country"]
columnNotRequiredAgg=["date","exchange_rate,"] #
aggCols = list(set(columns) - set(columnNotRequiredAgg))

expr = {x:'sum' if 'amt' in x else 'sum' for x in aggCols } <<-- how to write correct logic
df.groupby(groupbyColumns).agg(expr)