Finding min, max, and avg of every N-values in a list

Question

I have just started learning Python for data Analytics and I found a simple but unsolvable for me problem. I want to find min, max and average of every N-numbers in a list. And I am stuck on the best and fastest way to do it. Do I have to split the list on sublists and then join the output or is there a faster way?

For example: I have a list of

lst = [6, 6, 5, 6, 7, 11, 10, 9, 9, 8, 13, 13, 13, 13, 14]

and I want to find the min, max and avg of every 5 numbers and append them on seperatily lists like:

avg = [6.0, 9.4, 13.2]
min = [5.0, 8.0, 13.0]
max = [7.0, 11.0, 14.0]

Answer 1

If i understand you correctly:

Here is an example

lst = [6, 6, 5, 6, 7, 11, 10, 9, 9, 8, 13, 13, 13, 13, 14]

def Average(lst):
    return sum(lst) / len(lst)

new = [Average(lst[i:i+5]) for i in range(0, len(lst), 5)]

UPDATE: for all:

[[Average(lst[i:i+5]) for i in range(0, len(lst), 5)], [max(lst[i:i+5]) for i in range(0, len(lst), 5)], [min(lst[i:i+5]) for i in range(0, len(lst), 5)]]

however write them seperately;)

Answer 2

Here is a simple solution for the example you gave:

lst = [6, 6, 5, 6, 7, 11, 10, 9, 9, 8, 13, 13, 13, 13, 14]

lavg = [sum(lst[i:i+5])/len((lst[i:i+5])) for i in range(0, len(lst), 5)]
lmin = [min(lst[i:i+5]) for i in range(0, len(lst), 5)]
lmax = [max(lst[i:i+5]) for i in range(0, len(lst), 5)]

print(lavg)
print(lmin)
print(lmax)

output is:

[6.0, 9.4, 13.2]
[5, 8, 13]
[7, 11, 14]

Answer 3

I'd do it this way: find out the amount of "slices" your list contains, round it up to the bigger number, and then run a loop in range of this value, using incremented variable to assess the indexes of each slice. And within each iteration of the loop, apply the functions you need - max() , min() or whatever.

If needed, you can append them to the pre-created lists, or simply print them out, as you wish

lst = [6, 6, 5, 6, 7, 11, 10, 9, 9, 8, 13, 13, 13, 13, 14]
slices = round(len(lst) / 5)
slicer = 0
for i in range(slices):
    max_val = max(lst[slicer:slicer + 5])
    slicer +=5
    print(max_val)

Answer 4

Note: I do not generally believe in offering answers for questions that show little effort, but other answers have been offered, and this one only builds on those existing answers.

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The other answers seem oriented toward iterating over groups of 5 elements for each stat you wish to accumulate.

For large datasets this will be inefficient. What if you want to generate them all on a single pass?

stats = [[], [], []]
for sublist in (lst[i:i+5] for i in range(0, len(lst), 5)):
    stats[0].append(sum(sublist) / len(sublist))
    stats[1].append(min(sublist))
    stats[2].append(max(sublist))

stats = [tuple(stat) for stat in stats]

If we further wanted to generalize this, let's call the function applyn because it applies n functions to an iterable item and returns the results as a list of tuples.

def applyn(iter, *funs):
    results = [[] for _ in funs]
    for x in iter:
        for i, f in enumerate(funs):
            results[i].append(f(x))
    return [tuple(x) for x in results]

Now, we can write:

stats = applyn((lst[i:i+5] for i in range(0, len(lst), 5)), 
               lambda x: sum(x) / len(x), 
               min, max)

And we get:

[(6.0, 9.4, 13.2), (5, 8, 13), (7, 11, 14)]