Using Python: How to divide two different indicators (amount and exchange rate) in case two attributes match (Country AND date)?

I downloaded and imported a csv file from an economics research database which contains three columns (screenshot of the table): “Country-Code”, “Time”, “Indicator”. There are basically two types of indicators (1. amount in local currency and 2. EUR exchange rate). How can I create a new column “EUR_amount” in Python that divdes the amount with the rate in case the countrycode and the month is the same for both items, e.g. EUR = amount/rate where country and time matches?

Any help highly appreciated! (Please keep in mind that I am quite a noob with python and this is my first question on stackoverflow ever.) Thanks a lot in advance.

Edit: Adding this code after receiving feedback from mozway (thanks for that):

import pandas as pd
df = pd.DataFrame({'country_code':['EU','UK','US','EU','UK','US','EU','UK','US','EU','UK','US','EU','UK','US','EU','UK','US'],
               'date':['2019-03','2019-03','2019-03','2019-04','2019-04','2019-04','2019-05','2019-05','2019-05','2019-03','2019-03','2019-03','2019-04','2019-04','2019-04','2019-05','2019-05','2019-05'],
              'item':['exposure','exposure','exposure','exposure','exposure','exposure','exposure','exposure','exposure','FX-rate','FX-rate','FX-rate','FX-rate','FX-rate','FX-rate','FX-rate','FX-rate','FX-rate'],
              'value':[15000,9000,13000,16500,8750,17000,17000,7999,25000,1.00,1.25,0.90,1,1.23,0.93,1.00,1.24,0.95]})
print(df)

So, to restate my question: How can I divide the item exposure with the item FX-rate under the condition of country_code AND date are matching?

Answer

You can first split the data frames into two parts – exposure and FX-rate

fx = df[df["item"]=="FX-rate"]
exp = df[df["item"]!="FX-rate"]

After that, you can use

merged_df = pd.merge(fx,exp,on=["country_code","date"],how='outer')

See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html for other arguments and examples.

The above will result in

country_code date item_x value_x item_y value_y
EU 2019-03 FX-rate 1.00 exposure 15000.0
UK 2019-03 FX-rate 1.25 exposure 9000.0
US 2019-03 FX-rate 0.90 exposure 13000.0
EU 2019-04 FX-rate 1.00 exposure 16500.0
UK 2019-04 FX-rate 1.23 exposure 8750.0
US 2019-04 FX-rate 0.93 exposure 17000.0
EU 2019-05 FX-rate 1.00 exposure 17000.0
UK 2019-05 FX-rate 1.24 exposure 7999.0
US 2019-05 FX-rate 0.95 exposure 25000.0

Next is just a matter of division

merged_df["Convert"] = merged_df["value_y"]/merged_df["value_x"]