Finding the Average of a List

Finding the average of a list

For Python 3.8+, use statistics.fmean for numerical stability with floats. (Fast.)

For Python 3.4+, use statistics.mean for numerical stability with floats. (Slower.)

xs = [15, 18, 2, 36, 12, 78, 5, 6, 9]

import statistics
statistics.mean(xs)  # = 20.11111111111111

For older versions of Python 3, use

sum(xs) / len(xs)

For Python 2, convert len to a float to get float division:

sum(xs) / float(len(xs))

Calculating average of list from certain point in list?

Suppose you want to round off the list from an index (Here 3)

mylist = [2, 8, 6, 7, 3, 2, 1]

def round_list(lst, index):
    avg = [sum(lst[int(index):])/(len(lst)-index)]
    new_lst = (lst[:int(index)]) + avg
    return new_lst

new_list = round_list(mylist, 3)
print(new_list)

output : [2, 8, 6, 3.25]

Calculate average of a list of list of list of numbers

Use numpy's mean method. Numpy is very efficient and speed optimized library.

import numpy as np

AllProcessTimes = np.array([ [[1,2,3], [1,2,3], [1,2,3]],
                    [[1,2,3], [1,2,3], [1,2,3]],
                    [[1,2,3], [1,2,3], [1,2,3]],
                    [[1,2,3], [1,2,3], [1,2,3]],
                    [[2,4,6], [3,6,9], [4,8,12]] ])

AverageProcessTimes = np.mean(AllProcessTimes, axis=0)

How to find the average of the list elements within a dictionary?

You can use statistics.mean to compute the mean, and max with a custom key to get the key with a max mean:

sweets = {'cadbury': [180,90], 'candy': [190], 'milk chocolate': [150, 160], 'dark chocolate': [100], 'white chocolate': [180],
 'ice cream': [122]}

from statistics import mean
max(sweets, key=lambda x: mean(sweets[x]))

Output: 'candy'

Finding the average of a list in python

Problem & Solution

The main problem with your code is that after reading your file and parsing the output, newvar is a list of strings:

['0', '1', '2', '3', '4', '5']

Since sum will not handle strings - and even if it could, you wouldn't get your desired output - you need to convert the strings to integers. This can be done using a simple list comprehension:

newvar = [int(s) for s in newvar]

You can then use sum as normal:

sum(newvar) / len(newvar)

Which has the output:

2

Improvements

Here are some general improvements to your original code:

• Use the context manager statement with to open your files. That way, you can be sure your files are always closed.

• Rather than using a slice to filter out alphanumeric lines, you can use a 'filter' in the list comprehension above to skip over them.

Here is the modified code:

with open('stats.txt', 'r') as file:
    nums = [int(n) for n in file.read().splitlines() if n.isdigit()] 
avg = sum(nums) / len(nums)
print(avg)

List of tuples and mean calculation

A pandas approach:

import pandas as pd

tuples = [(120, 'x'), (1120, 'y'), (1330, 'x'), (0, 't'), (1, 'x'), (0, 'd'), (2435, 'x')]
df = pd.DataFrame(tuples)
df[0][df[0]!=0].mean() #1001.2

Careful timing would be needed to see if this is any better than what you are currently doing. The actual mean calculation should be faster, but the gain could well be negated by the cost of conversion.

Finding average value in list of dictionaries based on another unique value

You need to group weights by index. defaultdict from the built-in collections module is useful here.

from collections import defaultdict
total = defaultdict(int)
cnts = defaultdict(int)
for d in values:
    # add weights
    total[d['index']] += d['weight']
    # count indexes
    cnts[d['index']] += 1
# find the mean
[{'index': k, 'mean weight': total[k]/cnts[k]} for k in total]
# [{'index': 0, 'mean weight': 0.5}, {'index': 1, 'mean weight': 0.5}]

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