Krishna Pacifici wrote:
Thanks for the help.
Actually this is part of a much larger project, but I have unfortunately
pigeon-holed myself into needing to do these things without a whole lot
of flexibility.
To give a specific example I have the following dictionary where I need
to remove values that are duplicated with other values and remove values
that are duplicates of the keys, but still retain it as a dictionary.
Each value is itself a class with many attributes that I need to call
later on in the program, but I cannot have duplicates because it would
mess up some estimation part of my model.
d =
{36: [35, 37, 26, 46], 75: [74, 76, 65, 85], 21: [20, 22, 11, 31], 22:
[21, 23, 12, 32], 26: [25, 27, 16, 36], 30: [20, 31, 40]}
So I want a new dictionary that would get rid of the duplicate values of
21, 22, 36 and 20 and give me back a dictionary that looked like this:
new_d=
{36: [35, 37, 26, 46], 75: [74, 76, 65, 85], 21: [20, 11, 31], 22: [23,
12, 32], 26: [25, 27, 16], 30: [40]}
I understand that a dictionary may not be the best approach, but like I
said I have sort of pigeon-holed myself by the way that I am simulating
my data and the estimation model that I am using. Any suggestions or
comments about the above problem would be greatly appreciated.
>>> d = {36: [35, 37, 26, 46], 75: [74, 76, 65, 85], 21: [20, 22, 11,
31], 22: [21, 23, 12, 32], 26: [25, 27, 16, 36], 30: [20, 31, 40]}
>>> new_d = {}
>>> seen = set(d.keys())
>>> for k, v in d.items():
... new_d[k] = [x for x in v if x not in seen]
... seen |= set(new_d[k])
...
>>> new_d
{36: [35, 37, 46], 75: [74, 76, 65, 85], 21: [20, 11, 31], 22: [23, 12,
32], 26: [25, 27, 16], 30: [40]}
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