Ndtyjky

I need to find mean of an array which is like: [('a', 5), ('b', 2), ('a', 4), ('b', 6)]

Result should be like; [('a', 4.5), ('b', 4)]

Raw solution without additional libraries could look like this:

def mean(l):

    result = {}

    for key, value in l:

        if key not in result:

            result[key] = 

        result[key].append(value)



    return [(k, sum(v)/len(v)) for k, v in result.items()]



lst = [('a', 5), ('b', 2), ('a', 4), ('b', 6)]

m = mean(lst)



print(m)

# [('a', 4.5), ('b', 4.0)]

You can put all your tuples in a defaultdict, using the first value to group them into a list and then calculate the mean:

from collections import defaultdict



d = defaultdict(list)



for key,value in [('a', 5), ('b', 2), ('a', 4), ('b', 6)]:

    d[key].append(value)



mean = 



for k,values in d.items():

    # mean.append((k,sum(values)/float(len(values)))) #python 2

    mean.append((k,sum(values)/len(values)))



print(mean) # [('a', 4.5), ('b', 4.0)]

We can use pandas for this:

import pandas as pd



pd.DataFrame(data).groupby(0)[1].mean().to_dict()

this will give us:

>>> pd.DataFrame(data).groupby(0)[1].mean().to_dict()

{'a': 4.5, 'b': 4.0}

or we can convert this to a list of 2-tuples with:

list(pd.DataFrame(data).groupby(0)[1].mean().to_dict().items())

which gives:

>>> list(pd.DataFrame(data).groupby(0)[1].mean().to_dict().items())

[('a', 4.5), ('b', 4.0)]

The above is thus more a "declarative" approach: we specify what we want, not much how we want to do this.

You can collect the numbers with a collections.defaultdict(), then apply statistics.mean() on each group of numbers:

from statistics import mean

from collections import defaultdict



lst = [('a', 5), ('b', 2), ('a', 4), ('b', 6)]



d = defaultdict(list)

for k, v in lst:

    d[k].append(v)



means = [(k, mean(v)) for k, v in d.items()]



print(means)

# [('a', 4.5), ('b', 4)]

You can also use itertools.groupby() to group the tuples:

from statistics import mean

from itertools import groupby

from operator import itemgetter



lst = [("a", 5), ("b", 2), ("a", 4), ("b", 6)]



means = [

    (k, mean(map(itemgetter(1), g)))

    for k, g in groupby(sorted(lst, key=itemgetter(0)), key=itemgetter(0))

]



print(means)

[('a', 4.5), ('b', 4)]

If you wish, you can also try the below reusable code (without using any external libraries).

>>> def get_mean(l):

...     d = {}

...     for k, v in l:

...         if k in d:

...             d[k].append(v)

...         else:

...             d[k] = [v]

...     result = [(k, sum(d[k])/len(d[k])) for k in d]

...     return result

...

>>> l = [('a', 5), ('b', 2), ('a', 4), ('b', 6)]

>>> new_l = get_mean(l)

>>> new_l

[('a', 4.5), ('b', 4.0)]

>>>