I have a numpy matrix A
where the data is organised column-vector-vise i.e A[:,0]
is the first data vector, A[:,1]
is the second and so on
You can also use matrix
instead of array
. Then you won't need to reshape:
>>> A = np.matrix([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
>>> m = A.mean(axis=1)
>>> A - m
matrix([[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.]])
As is typical, you can do this a number of ways. Each of the approaches below works by adding a dimension to the mean
vector, making it a 4 x 1 array, and then NumPy's broadcasting takes care of the rest. Each approach creates a view of mean
, rather than a deep copy. The first approach (i.e., using newaxis
) is likely preferred by most, but the other methods are included for the record.
In addition to the approaches below, see also ovgolovin's answer, which uses a NumPy matrix to avoid the need to reshape mean
altogether.
For the methods below, we start with the following code and example array A
.
import numpy as np
A = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
mean = A.mean(axis=1)
>>> A - mean[:, np.newaxis]
array([[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.]])
None
The documentation states that None
can be used instead of newaxis
. This is because
>>> np.newaxis is None
True
Therefore, the following accomplishes the task.
>>> A - mean[:, None]
array([[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.]])
That said, newaxis
is clearer and should be preferred. Also, a case can be made that newaxis
is more future proof. See also: Numpy: Should I use newaxis or None?
>>> A - mean.reshape((mean.shape[0]), 1)
array([[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.]])
You can alternatively change the shape of mean
directly.
>>> mean.shape = (mean.shape[0], 1)
>>> A - mean
array([[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.],
[-1., 0., 1.]])
Looks like some of these answers are pretty old, I just tested this on numpy 1.13.3:
>>> import numpy as np
>>> a = np.array([[1,1,3],[1,0,4],[1,2,2]])
>>> a
array([[1, 1, 3],
[1, 0, 4],
[1, 2, 2]])
>>> a = a - a.mean(axis=0)
>>> a
array([[ 0., 0., 0.],
[ 0., -1., 1.],
[ 0., 1., -1.]])
I think this is much cleaner and simpler. Have a try and let me know if this is somehow inferior than the other answers.
Yes. pylab.demean
:
In [1]: X = scipy.rand(2,3)
In [2]: X.mean(axis=1)
Out[2]: array([ 0.42654669, 0.65216704])
In [3]: Y = pylab.demean(X, axis=1)
In [4]: Y.mean(axis=1)
Out[4]: array([ 1.85037171e-17, 0.00000000e+00])
Source:
In [5]: pylab.demean??
Type: function
Base Class: <type 'function'>
String Form: <function demean at 0x38492a8>
Namespace: Interactive
File: /usr/lib/pymodules/python2.7/matplotlib/mlab.py
Definition: pylab.demean(x, axis=0)
Source:
def demean(x, axis=0):
"Return x minus its mean along the specified axis"
x = np.asarray(x)
if axis == 0 or axis is None or x.ndim <= 1:
return x - x.mean(axis)
ind = [slice(None)] * x.ndim
ind[axis] = np.newaxis
return x - x.mean(axis)[ind]