Right now I am doing this by iterating, but there has to be a way to accomplish this task using numpy functions. My goal is to take a 2D array and average J columns at a time, producing a new array with the same number of rows as the original, but with columns/J columns.
So I want to take this:
J = 2 // two columns averaged at a time
[[1 2 3 4]
[4 3 7 1]
[6 2 3 4]
[3 4 4 1]]
and produce this:
[[1.5 3.5]
[3.5 4.0]
[4.0 3.5]
[3.5 2.5]]
Is there a simple way to accomplish this task? I also need a way such that if I never end up with an unaveraged remainder column. So if, for example, I have an input array with 5 columns and J=2, I would average the first two columns, then the last three columns.
Any help you can provide would be great.
data.reshape(-1,j).mean(axis=1).reshape(data.shape[0],-1)
If your j
divides data.shape[1]
, that is.
Example:
In [40]: data
Out[40]:
array([[7, 9, 7, 2],
[7, 6, 1, 5],
[8, 1, 0, 7],
[8, 3, 3, 2]])
In [41]: data.reshape(-1,j).mean(axis=1).reshape(data.shape[0],-1)
Out[41]:
array([[ 8. , 4.5],
[ 6.5, 3. ],
[ 4.5, 3.5],
[ 5.5, 2.5]])
First of all, it looks to me like you're not averaging the columns at all, you're just averaging two data points at a time. Seems to me like your best off reshaping the array, so your that you have a Nx2 data structure that you can feed directly to mean
. You may have to pad it first if the number of columns isn't quite compatible. Then at the end, just do a weighted average of the padded remainder column and the one before it. Finally reshape back to the shape you want.
To play off of the example provided by TheodrosZelleke:
In [1]: data = np.concatenate((data, np.array([[5, 6, 7, 8]]).T), 1)
In [2]: data
Out[2]:
array([[7, 9, 7, 2, 5],
[7, 6, 1, 5, 6],
[8, 1, 0, 7, 7],
[8, 3, 3, 2, 8]])
In [3]: cols = data.shape[1]
In [4]: j = 2
In [5]: dataPadded = np.concatenate((data, np.zeros((data.shape[0], j - cols % j))), 1)
In [6]: dataPadded
Out[6]:
array([[ 7., 9., 7., 2., 5., 0.],
[ 7., 6., 1., 5., 6., 0.],
[ 8., 1., 0., 7., 7., 0.],
[ 8., 3., 3., 2., 8., 0.]])
In [7]: dataAvg = dataPadded.reshape((-1,j)).mean(axis=1).reshape((data.shape[0], -1))
In [8]: dataAvg
Out[8]:
array([[ 8. , 4.5, 2.5],
[ 6.5, 3. , 3. ],
[ 4.5, 3.5, 3.5],
[ 5.5, 2.5, 4. ]])
In [9]: if cols % j:
dataAvg[:, -2] = (dataAvg[:, -2] * j + dataAvg[:, -1] * (cols % j)) / (j + cols % j)
dataAvg = dataAvg[:, :-1]
....:
In [10]: dataAvg
Out[10]:
array([[ 8. , 3.83333333],
[ 6.5 , 3. ],
[ 4.5 , 3.5 ],
[ 5.5 , 3. ]])
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