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Calculation on list of numpy array

I'm trying to do some calculation (mean, sum, etc.) on a list containing numpy arrays.
For example:
list = [array([2, 3, 4]),array([4, 4, 4]),array([6, 5, 4])]
How can retrieve the mean (for example) ?
In a list like [4,4,4] or a numpy array like array([4,4,4]) ?
Thanks in advance for your help!
EDIT : Sorry, I didn't explain properly what I was aiming to do : I would like to get the mean of i-th index of the arrays. For example, for index 0 :
(2+4+6)/3 = 4
I don't want this :
(2+3+4)/3 = 3
Therefore the end result will be
[4,4,4] / and not [3,4,5]

If L were a list of scalars then calculating the mean could be done using the straight forward expression:
sum(L) / len(L)
Luckily, this works unchanged on lists of arrays:
L = [np.array([2, 3, 4]), np.array([4, 4, 4]), np.array([6, 5, 4])]
sum(L) / len(L)
# array([4., 4., 4.])
For this example this happens to be quitea bit faster than the numpy function
np.mean
timeit(lambda: np.mean(L, axis=0))
# 13.708808058872819
timeit(lambda: sum(L) / len(L))
# 3.4780975924804807

You can use a for loop and iterate through the elements of your array, if your list is not too big:
mean = []
for i in range(len(list)):
mean.append(np.mean(list[i]))

Given a 1d array a, np.mean(a) should do the trick.
If you have a 2d array and want the means for each one separately, specify np.mean(a, axis=1).
There are equivalent functions for np.sum, etc.
https://docs.scipy.org/doc/numpy/reference/generated/numpy.mean.html
https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html

You can use map
import numpy as np
my_list = [np.array([2, 3, 4]),np.array([4, 4, 4]),np.array([6, 5, 4])]
np.mean(my_list,axis=0) #[4,4,4]
Note: Do not name your variable as list as it will shadow the built-ins

Python sum values from multiple lists (more than two)

Looking for a pythonic way to sum values from multiple lists:
I have got the following list of lists:
a = [0,5,2]
b = [2,1,1]
c = [1,1,1]
d = [5,3,4]
my_list = [a,b,c,d]
I am looking for the output:
[8,10,8]
I`ve used:
print ([sum(x) for x in zip(*my_list )])
but zip only works when I have 2 elements in my_list.
Any idea?

zip works for an arbitrary number of iterables:
>>> list(map(sum, zip(*my_list)))
[8, 10, 8]
which is, of course, roughly equivalent to your comprehension which also works:
>>> [sum(x) for x in zip(*my_list)]
[8, 10, 8]

Numpy has a nice way of doing this, it is also able to handle very large arrays. First we create the my_list as a numpy array as such:
import numpy as np
a = [0,5,2]
b = [2,1,1]
c = [1,1,1]
d = [5,3,4]
my_list = np.array([a,b,c,d])
To get the sum over the columns, you can do the following
np.sum(my_list, axis=0)
Alternatively, the sum over the rows can be retrieved by
np.sum(my_list, axis=1)

I'd make it a numpy array and then sum along axis 0:
my_list = numpy.array([a,b,c,d])
my_list.sum(axis=0)
Output:
[ 8 10 8]

How to sort a list based on the output of numpy's argsort function

I have a list like this:
myList = [10,30,40,20,50]
Now I use numpy's argsort function to get the indices for the sorted list:
import numpy as np
so = np.argsort(myList)
which gives me the output:
array([0, 3, 1, 2, 4])
When I want to sort an array using so it works fine:
myArray = np.array([1,2,3,4,5])
myArray[so]
array([1, 4, 2, 3, 5])
But when I apply it to another list, it does not work but throws an error
myList2 = [1,2,3,4,5]
myList2[so]
TypeError: only integer arrays with one element can be converted to an
index
How can I now use so to sort another list without using a for-loop and without converting this list to an array first?

myList2 is a normal python list, and it does not support that kind of indexing.
You would either need to convert that to a numpy.array , Example -
In [8]: np.array(myList2)[so]
Out[8]: array([1, 4, 2, 3, 5])
Or you can use list comprehension -
In [7]: [myList2[i] for i in so]
Out[7]: [1, 4, 2, 3, 5]

You can't. You have to convert it to an array then back.
myListSorted = list(np.array(myList)[so])
Edit: I ran some benchmarks comparing the NumPy way to the list comprehension. NumPy is ~27x faster
>>> from timeit import timeit
>>> import numpy as np
>>> myList = list(np.random.rand(100))
>>> so = np.argsort(myList) #converts list to NumPy internally
>>> timeit(lambda: [myList[i] for i in so])
12.29590070003178
>>> myArray = np.random.rand(100)
>>> so = np.argsort(myArray)
>>> timeit(lambda: myArray[so])
0.42915570305194706

Using the reduce function on a multidimensional array

So i have a particular array, that has 2 seperate arrays withing itself. What I am looking to do is to average together those 2 seperate arrays, so for instance, if i have my original array such as [(2,3,4),(4,5,6)] and I want an output array like [3,5], how would i do this? My attempt to do this is as follows:
averages = reduce(sum(array)/len(array), [array])

>>> map(lambda x: sum(x)/len(x), [(2,3,4),(4,5,6)])
[3, 5]

reduce is not a good choice here. Just use a list comprehension:
>>> a = [(2,3,4),(4,5,6)]
>>> [sum(t)/len(t) for t in a]
[3, 5]
Note that / is integer division by default in python2.
If you have numpy available, you have a nicer option:
>>> import numpy as np
>>> a = np.array(a)
>>> a.mean(axis=1)
array([ 3., 5.])

You can do this with a list comphrehesion:
data = [(2,3,4),(4,5,6)]
averages = [ sum(tup)/len(tup) for tup in data ]

Vectorised code for selecting elements of a 2D array

Well the following code obviously returns the element in position ind in matrix:
def select_coord(a,ind):
return a[ind]
However I don't know how to vectorise this. In other words:
b=np.asarray([[2,3,4,5],[7,6,8,10]])
indices=np.asarray([2,3])
select_coord(b,indices)
Should return [4,10].
Which can be written with a for loop:
def new_select_record(a,indices):
ret=[]
for i in range a.shape[0]:
ret.append(a[indices[i]])
return np.asarray(ret)
Is there a way to write this in a vectorised manner?

To get b[0, 2], b[1, 3]:
>>> import numpy as np
>>> b = np.array([[2,3,4,5], [7,6,8,10]])
>>> indices = np.array([2, 3])
>>> b[np.arange(len(indices)), indices]
array([ 4, 10])

how about: np.diag(b[:,[2,3]])?