[FIXED] Can I reduce a tuple list by key using Python?

Issue

I am currently working on showing some visuals about how my NER model has performed. The data I currently have looks like this:

counter_list = [
    ('Name', {'p':0.56,'r':0.56,'f':0.56}),
    ('Designation', {'p':0.10,'r':0.20,'f':0.14}),
    ('Location', {'p':0.56,'r':0.56,'f':0.56}),
    ('Name', {'p':0.14,'r':0.14,'f':0.14}),
    ('Designation', {'p':0.10,'r':0.20,'f':0.14}),
    ('Location', {'p':0.56,'r':0.56,'f':0.56})
]

I would like to eliminate the duplicates and add their respective values to only one of each kind. So the output to look like this:

[
    ('Name', {'p':0.7,'r':0.7,'f':0.7}),
    ('Designation', {'p':0.2,'r':0.4,'f':0.28}),
    ('Location', {'p':1.12,'r':1.12,'f':1.12})
]

I have tried to use the reduce function but it gives me only the output for 'Name' entry only.

result = functools.reduce(lambda x, y: (x[0], Counter(x[1])+Counter(y[1])) if x[0]==y[0] else (x[0],x[1]), counter_list)

What would be the right approach? I am trying to create some visuals with the final results, to determine which item has the higher 'f','p' or 'r' component.

Solution

What not use pandas and its ~.groupby method?

>>> import pandas as pd
>>> keys, data = zip(*counter_list)
>>> df = pd.DataFrame(data=data, index=keys).groupby(level=0).sum()
>>> df
                p     r     f
Designation  0.20  0.40  0.28
Location     1.12  1.12  1.12
Name         0.70  0.70  0.70

and then do

>>> list(df.T.to_dict().items())
[
    ('Designation', {'p': 0.2, 'r': 0.4, 'f': 0.28}), 
    ('Location', {'p': 1.12, 'r': 1.12, 'f': 1.12}), 
    ('Name', {'p': 0.7, 'r': 0.7, 'f': 0.7})
]

Answered By - keepAlive