Average consective nth row in numpy

February 20, 2023

I have a 3D numpy array.

x=np.random.randint(low=0,high=10,size=(100,64,1000))

I want to average every 4th row, for example first 4, then 4-8, 8-12 and so on.
I tried the following way

x =np.split(x,len(x)/4)
np.mean(np.stack(x),1)

I am bit confused if its the correct way? Or if there is a better way. Also how to do if first dimension is not completely divisible by 4.
For example, I can do this way

x =np.array_split(x,len(x)/4)
np.stack([np.mean(i,0) for i in x],0)

Thanks

EDIT:
Here is my use case.
This is sensor data, where 100 is the number of time data been collected (trials), 64 is the number of channels of sensor and 1000 is sensor signal (length). I want sensor signal to be average for first 4 trials, then next 4 trials and so on.

>Solution :

Using reshape and mean

Try this with a reshape and then mean over the specific axis –

x = np.random.randint(low=0,high=10,size=(100,64,1000))

#Reshape to (4, 25, 65, 1000) and then reduce 0th axis with a mean
x = x.reshape(4,-1,64,1000).mean(0)
x.shape