How to efficiently average same entries of lists in a list

[xquad]

Hello,

I am working on data from which I calculate (using peakutilis) 4 peaks.
These positions of the 4 peaks are stored in a list like:
[3141, 4989, 5499, 6714]

I have over a hundred nearly similar data sets, and I want to efficiently calculate the average of each entry position (entry in the list)

For now I am doing it like this:

indexlist = []
for t, scope_y in enumerate(scope_ys):
    scope_y = scope_y[0]
    indexes = peakutils.indexes(scope_y, thres=0.07, min_dist=500)
    indexlist.append(indexes)

v = np.asarray(indexlist)
a_lst = []
b_lst = []
c_lst = []
d_lst = []

for i in range(len(v)):
    a = v[i,0]
    a_lst.append(a)
    b = v[i,1]
    b_lst.append(b)
    c = v[i,2]
    c_lst.append(c)
    d = v[i,3]
    d_lst.append(d)
for i in range(4):
    a_avg = int(np.average(a_lst))
    b_avg = int(np.average(b_lst))
    c_avg = int(np.average(c_lst))
    d_avg = int(np.average(d_lst))
indexes = [a_avg,b_avg,c_avg,d_avg]
print(indexes) 

Do you know how to do this in a more efficient way?

Regards,
xquad

[Jeff900]

Do you mean like the average from the every item on the same position from the lists? Or in other words, the average from all first items for example?

[xquad]

The first case: The average from every item on the same position from the lists.
And with efficiently I mean fewer lines of codes and if its possible also in a way that is takes less computation into account.

[Jeff900]

The first case: The average from every item on the same position from the lists.

Have you tried something with pandas or something already? Maybe you can convert al lists to a pandas dataframe. From then it should be quite efficient (at least in code), to get the average from one column.

Maybe this post can help you out: https://stackoverflow.com/questions/3103...erage-mean

[xquad]

Havent worked with pandas yet. Its pretty handy! Thanks for the recom.

[xquad]

The code shrinks down to:

indexlist = []
for t, scope_y in enumerate(scope_ys):
    scope_y = scope_y[0]
    indexes = peakutils.indexes(scope_y, thres=0.07, min_dist=500)
    indexlist.append(indexes)

df = pd.DataFrame.from_records(indexlist)
average = np.ones(4)
for i in range(len(indexes)):
    average[i] = int(np.average(df.iloc[:,i]))

Te data type is a np array and looks like this: [3141. 4989. 5499. 6714.] such that in a subsequent for loop

for i, laser in enumerate(average):

the error occurs:
"slice indices must be integers or None or have an __index__ method"