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I have a DF loaded with orders. Some of them contains negative quantities, and the reason for that is that they are actually cancellations of prior orders.

Problem, there is no unique key that can help me find back which order corresponds to which cancellation.

So I've built the following code ('cancelations' is a subset of the original data containing only the rows that correspond to... well... cancelations):

for i, item in cancelations.iterrows(): 
 #find a row similar to the cancelation we are currently studying: 
 #We use item[1] to access second value of the tuple given back by iterrows()
 mask1 = (copy['CustomerID'] == item['CustomerID']) 
 mask2 = (copy['Quantity'] == item['Quantity'])
 mask3 = (copy['Description'] == item['Description']) 
 subset = copy[ mask1 & mask2 & mask3]
 if subset.shape[0] >0: #if we find one or several corresponding orders :
 print('possible corresponding orders:', subset.index.tolist())
 copy = copy.drop(subset.index.tolist()[0]) #retrieve only the first ot them from the copy of the data

So, this works, but :
first, it takes forever to run; and second, I read somewhere that whenever you find yourself writing complex code to manipulate dataframes, there's already a method for it.
So perhaps one of you know something that could help me ?

thank you for your time !

edit : note that sometimes, we can have several orders that could correspond to the cancelation at hand. This is why I didn't use drop_duplicates with only some columns specified... because it eliminates all duplicates (or all but one) : I need to drop only one of them.