MultiIndexing based on row values - python-3.x

Question

Trying to create a simple program that finds negative values in a pandas dataframe and combines them with their matching row. Basically I have data that looks like this:
LastName DrugName RxNumber Amount ClientTotalCost
ADAMS Drug 100001 30 10.69
ADAMS Drug 100001 -25 -8.95
...
The idea is that I need to match up fills and refunds, then combine them into a single row. So, in the example above we'd have one row that looks like this:
LastName DrugName RxNumber Amount ClientTotalCost
ADAMS Drug 100001 5 1.74
Also, if a refund doesn't match to a fill row, I'm supposed to just delete it, which I've been accomplishing with .drop()
I'm imagining I can build a multiindex for this somehow, where each row that's a negative is marked as a refund and each fill row is marked as a fill. Then I just have some kind of for loop that goes through the list and attempts to match a certain number of times based on name/number of refunds.
Here's what I was trying:
pbm_negative_index = raw_pbm_data.loc['LastName','DrugName','RXNumber','ClientTotalCost']
names = pbm_negative_index = raw_pbm_data.loc[: , 'LastName']
unique_names = unique(pbm_negative_index)
for n in unique_names:
edf["Refund"] = edf["ClientTotalCost"].shift(1, fill_value=edf["ClientTotalCost"].head(1)) < 0
This obviously doesn't work and I'd like to use the indexing tools in Pandas to achieve a similar result.

Answer 1

Your specification reduces to two simple steps:
aggregate +ve & -ve matching rows
drop remaining -ve rows after aggregation
df = pd.read_csv(io.StringIO("""LastName DrugName RxNumber Amount ClientTotalCost
ADAMS Drug 100001 30 10.69
ADAMS Drug 100001 -25 -8.95
ADAMS2 Drug. 100001 -5 -1.95
"""), sep="\s+")
# aggregate
dfa = df.groupby(["LastName","DrugName","RxNumber"],as_index=False).agg({"Amount":"sum","ClientTotalCost":"sum"})
# drop remaining -ve amounts
dfa = dfa.drop(dfa.loc[dfa.Amount.lt(0)].index)
LastName
DrugName
RxNumber
Amount
ClientTotalCost
0
ADAMS
Drug
100001
5
1.74