Is there a way in which it is possible to read a CSV file in a dictionary such that custom rows together in a tuple are the key and the rest (in a tuple) is the value?
Here is an example table:
A |B |C |D |E
--------------
0 |0 |0 |5 |1
0 |1 |0 |2 |8
1 |0 |0 |5 |7
1 |1 |0 |3 |7
0 |0 |1 |9 |0
The result:
dictionary[(A,B,C)] = (D, E)
print(dictionary[(0,0,0)]) # (5,1)
Is there an efficient way to achieve that?
Considering the pandas dataframe df:
A B C D E
0 0 0 5 1
0 1 0 2 8
1 0 0 5 7
1 1 0 3 7
0 0 1 9 0
Let's zip the columns A, B, C and D, E together and make the key value pair of tuples inside dict comprehension:
k, v = ['A', 'B', 'C'], ['D', 'E']
dct = {tuple(x): tuple(y) for x, y in zip(df[k].to_numpy(), df[v].to_numpy())}
Result:
{(0, 0, 0): (5, 1),
(0, 1, 0): (2, 8),
(1, 0, 0): (5, 7),
(1, 1, 0): (3, 7),
(0, 0, 1): (9, 0)}
I am not sure if this is the best solution, but it works:
pd.concat([df[['A', 'B', 'C']].apply(tuple, 1),
df[['D', 'E']].apply(tuple, 1)], axis=1)\
.set_index(0)[1].to_dict()
#{(0, 0, 0): (5, 1), (0, 1, 0): (2, 8), (1, 0, 0): (5, 7),
# (1, 1, 0): (3, 7), (0, 0, 1): (9, 0)}