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Filter pandas dataframe by multiple columns, using tuple from list of tuples

So I have been referencing this previous question posted here Filter pandas dataframe from tuples.

But the problem I am trying to solve is slightly different.

I have a list of tuples. Each tuple represents a different set of filters I would like to apply to a dataframe accross multiple columns, so I can isolate the records to perform additional tasks.

Whenever I try and filter by a single tuple within the list of tuples, the dataframe I get back has no records.. If I break the tuple values out in a very long form way it works fine. Not sure what I am missing or not thinking about here..

Using the same example from the post I have been referencing....

AB_col = [(0,230), (10,215), (15, 200), (20, 185), (40, 177), 
                (0,237), (10,222), (15, 207), (20, 192), (40, 184)]

sales = [{'account': 'Jones LLC', 'A': 0, 'B': 230, 'C': 140},
         {'account': 'Alpha Co',  'A': 20, 'B': 192, 'C': 215},
         {'account': 'Blue Inc',  'A': 50,  'B': 90,  'C': 95 }]
df = pd.DataFrame(sales)

example dataframe

The answer from the other question

df = df[df[["A","B"]].apply(tuple, 1).isin(AB_col)]

Which returns

example results

However, I want to only get one record back, that matches the first tuple in the list of tuples. So I tried this

df[df[["A"]].apply(tuple,1).isin(AB_col[0])]

But get no records returned

my modification results

However, I can do this which gets me the results I want, but when I have essentially a list of tuples that is every combination of column values to use a filters for different levels of calculations, this seems like way too much code to have to use to product the desired results

df[(df['A']==AB_col[0][0]) & (df['B']==AB_col[0][1])]

Which gets me results I want

long form results but what i need

Is there a way to get to this same result more efficiently?

Thanks!



source https://stackoverflow.com/questions/73129334/filter-pandas-dataframe-by-multiple-columns-using-tuple-from-list-of-tuples
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