Calculate mean across dimension in a 2D array
PythonArraysMultidimensional ArrayNumpyMeanPython Problem Overview
I have an array a like this:
a = [[40, 10], [50, 11]]
I need to calculate the mean for each dimension separately, the result should be this:
[45, 10.5]
45 being the mean of a[*][0] and 10.5 the mean of a[*][1].
What is the most elegant way of solving this without using a loop?
Python Solutions
Solution 1 - Python
a.mean() takes an axis argument:
In [1]: import numpy as np
In [2]: a = np.array([[40, 10], [50, 11]])
In [3]: a.mean(axis=1) # to take the mean of each row
Out[3]: array([ 25. , 30.5])
In [4]: a.mean(axis=0) # to take the mean of each col
Out[4]: array([ 45. , 10.5])
Or, as a standalone function:
In [5]: np.mean(a, axis=1)
Out[5]: array([ 25. , 30.5])
The reason your slicing wasn't working is because this is the syntax for slicing:
In [6]: a[:,0].mean() # first column
Out[6]: 45.0
In [7]: a[:,1].mean() # second column
Out[7]: 10.5
Solution 2 - Python
Here is a non-numpy solution:
>>> a = [[40, 10], [50, 11]]
>>> [float(sum(l))/len(l) for l in zip(*a)]
[45.0, 10.5]
Solution 3 - Python
If you do this a lot, NumPy is the way to go.
If for some reason you can't use NumPy:
>>> map(lambda x:sum(x)/float(len(x)), zip(*a))
[45.0, 10.5]