Given three lists:
A = [1, 2, 3]
B = [4, 5, 6]
C = [7, 8, 9]
How do I calculate the mean of the following (the columns)?
Not the following (the rows),
import numpy as np
A = np.array([1,2,3])
B = np.array([4,5,6])
C = np.array([7,8,9])
What should I do?
Using numpy.ndarray.transpose:
>>> np.array([
... [1,2,3],
... [4,5,6],
... [7,8,9]
... ]).transpose().mean(axis=1)
array([ 4., 5., 6.])
or using numpy.transpose:
>>> np.transpose([
... [1,2,3],
... [4,5,6],
... [7,8,9]
... ]).mean(axis=1)
array([ 4., 5., 6.])
UPDATE
As Dave Hirschfeld commented, mean over axis=0 is alot better:
>>> np.array([
... [1,2,3],
... [4,5,6],
... [7,8,9]
... ]).mean(axis=0)
array([ 4., 5., 6.])
axis=0
import numpy as np
A = np.array([1,2,3])
B = np.array([4,5,6])
C = np.array([7,8,9])
Z = zip(A,B,C)
for z in Z :
print np.mean(np.asarray(z))
