CODEWITHSUNDEEP
python
Rachel
Rachel

Reputation: 383

Finding mean of a values in a dictionary without using .values() etc

I have a dictionary that looks like:

G={'E': 18.0, 'D': 17.0, 'C': 19.0, 'B': 15.0, 'A': 0}

I have to find the mean of the values e.g. mean(18,17,19,15,0) using a simple for loop without using built in functions like .values(), .items() and so on. I tried the following but am getting an error:

d=[float(sum(values)) / len(values) for key, values in G]
    return (d)   
ValueError: need more than 1 value to unpack

Can someone help me fix this????

Upvotes: 11

Views: 63923

Answers (10)

inverted_index
inverted_index

Reputation: 2437

Suppose you have a dictionary with multiple keys, each having a list of values:

your_averaged_dictionary = {key: np.mean(values) for key, values in your_dictionary}

Upvotes: 0

Aaron Hall
Aaron Hall

Reputation: 395543

To do this with a "simple for loop", using your constraints against using the dict methods:

G = {'E': 18.0, 'D': 17.0, 'C': 19.0, 'B': 15.0, 'A': 0}


count = 0
_sum = 0
for key in G:
    count += 1
    _sum += G[key]

print('this is the mean: ', _sum/count)

If you're supposed to avoid dict methods, clearly this is an academic exercise.

Without that constraint:

The statistics module in the standard library has a mean method, which would be my first thought (as the standard library does not require third party packages.):

>>> G={'E': 18.0, 'D': 17.0, 'C': 19.0, 'B': 15.0, 'A': 0}
>>> from statistics import mean
>>> mean(G[k] for k in G)
13.8

Third party packages like numpy and pandas have objects with a mean method:

>>> from numpy import array
>>> array([G[k] for k in G]).mean()
13.8

>>> from pandas import Series
>>> Series([G[k] for k in G]).mean()
13.8

If we allow ourselves to use the values() method, this gets a little simpler with iterable unpacking. For some reason the other answers violate that condition, so I figure I should show the more efficient way of doing it:

>>> Series([*G.values()]).mean()
13.8

Upvotes: 12

insanely_sin
insanely_sin

Reputation: 1026

import numpy as np
np.mean(list(dict.values()))

Upvotes: 12

Sergio R
Sergio R

Reputation: 161

If you use numpy:

import numpy as np

np.array(list(dict.values())).mean()

Upvotes: 16

Adam Erickson
Adam Erickson

Reputation: 6363

What I suggest instead of the current answers is adopting a functional programming paradigm that is reusable and flexible. For example, creating a function to calculate any statistic on list values contained within a simple dict:

def summarize_dict(dictionary, function):
        dict_new = {}
    for k,v in dictionary.items():
        dict_new[k] = function(v)
    return dict_new

Testing:

import numpy as np

keys = ["a","b","c","d","e"]
values = [range(2),range(4),range(6),range(8),range(10)]
dictionary = dict(zip(keys, values))

summarize_dict(dictionary, np.mean)

Yields:

{'a': 0.5, 'b': 1.5, 'c': 2.5, 'd': 3.5, 'e': 4.5}

Upvotes: 1

WGS
WGS

Reputation: 14179

Another for-loop, this one not needing a counter for items.

G = {'E': 18.0, 'D': 17.0, 'C': 19.0, 'B': 15.0, 'A': 0}

sum = 0
for k in G:
    sum += float(G[k])

print "Mean: " + str(sum/len(G))

Results to:

Mean: 13.8
[Finished in 0.3s]

Actually, given your data, there's no need to use float. On my end, removing float and just leaving sum += G[k] still outputs the same since the values are not strings anyway.

Upvotes: 0

barrios
barrios

Reputation: 1124

In Python 3.4 upwards there is a very clear way:

import statistics
numbers = [G[key] for key in G]
mean_ = statistics.mean(numbers)

Upvotes: 4

ebarr
ebarr

Reputation: 7842

You want:

mean = sum([G[key] for key in G])/float(len(G))

Your original code will also produce a:

TypeError: 'int' object is not iterable

when you try to sum the values.

Upvotes: 1

Gassa
Gassa

Reputation: 8844

Use G.values() to get all the values from a dictionary.

G = {'E': 18.0, 'D': 17.0, 'C': 19.0, 'B': 15.0, 'A': 0}
d = float(sum(G.values())) / len(G)
print (d)

This prints 13.8.

Note that there is a difference between Python 2 and Python 3 here. In Python 2, G.values() is a newly constructed list of values. In Python 3, it is a generator, which can be thought of as a “lazy list”. The same thing is called G.itervalues() in Python 2.

Upvotes: 4

Gian
Gian

Reputation: 13955

Iteration over a dictionary iterates over its keys. Try just using for key in G, and then using G[key] appropriately instead of values.

Alternatively, use the iteritems() method of the dictionary to get key, value pairs from G, i.e.:

d=[float(sum(values)) / len(values) for key, values in G.iteritems()]

(For the record, your actual method of computing a mean doesn't look right to me, but you may as well fix the iteration problem first).

Upvotes: 4

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