在numpy数组的仅一列中计数非零

Question

I have a Numpy array that is created as follows

data=np.zeros(500,dtype='float32, (50000,2)float32')

This array is filled with values that I acquire from some measurements, and is supposed to reflect that during each time point (room for 500 time points) we can acquire 50.000 x- and y- coords.

Later in my code is use a bisect -like search for which I need to know howmany X-coords (measurement points) are actually in my array which I originally did with np.count_nonzero(data) , this yielded the following problem:

Fake data:

1 1
2 2
3 0
4 4
5 0
6 6
7 7
8 8
9 9
10 10

the non zero count returns 18 values here, the code then goes into the bisect -like search using data[time][1][0][0] as min X-coord and data[time][1][(np.count_nonzero(data)][0] as max x-coord which results in the array stopping at 9 instead of 10.

I could use a while loop to manually count non-zero values (in the X-coord column) in the array but that would be silly, I assume that there is some builtin numpy functionality for this. My question is then what builtin functionality or modification of my np.count_nonzero(data) I need since the documentation doesn't offer much information in that regards ( link to numpy doc).

-- Simplified question --

Can I use Numpy functionality to count the non-zero values for a singular column only? (ie between data[time][1][0][0] and data[time][1][max][0] )

Answer 1

Maybe a better approach would be to filter the array using nonzero and iterate over the result:

nonZeroData = data[np.nonzero(data[time][1])]

To count zeros only from the second column:

nonZeroYCount = np.count_nonzero(data[time][1][:, 1])

Answer 2

If I understand you correctly, to select elements from data[time][1][0][0] to data[time][1][max][0] :

data[time][1][:max+1,0]

EDIT :

To count all non-zero for every time:

(data["f1"][:,:,0] != 0).sum(1)

Answer 3

Why not consider using data != 0 to get the bool matrix?

You can use:

stat = sum(data != 0) to count the non-zero entries.

I am not sure what shape your data array has but hope you can see what I mean. :)