Calculate mean across dimension in a 2D array

python arrays multidimensional-array numpy mean
otmezger picture otmezger · Apr 4, 2013 · Viewed 133.2k times · Source

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?

Answer

askewchan picture askewchan · Apr 4, 2013

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

Related questions

Convert a 1D array to a 2D array in numpy

I want to convert a 1-dimensional array into a 2-dimensional array by specifying the number of columns in the 2D array. Something that would work like this: > import numpy as np > A = np.array([1,2,3,4,5,6]) > B = vec2matrix(…

Read More
Selecting specific rows and columns from NumPy array

I've been going crazy trying to figure out what stupid thing I'm doing wrong here. I'm using NumPy, and I have specific row indices and specific column indices that I want to select from. Here's the gist of my problem: …

Read More
Using numpy to build an array of all combinations of two arrays

I'm trying to run over the parameters space of a 6 parameter function to study it's numerical behavior before trying to do anything complex with it so I'm searching for a efficient way to do this. My function takes float values …

Read More