I was wondering if it was possible to transform these next process into a loop so that I can use one word for this (not as a vector):
Data0 = np.zeros(dem0.shape, dtype=np.int32)
Data0[zipp[0] >= 0 ] = 1
Data1 = np.zeros(dem1.shape, dtype=np.int32)
Data1[zipp[1] >= 0 ] = 1
Data2 = np.zeros(dem2.shape, dtype=np.int32)
Data2[zipp[2] >= 0 ] = 1
Data3 = np.zeros(dem3.shape, dtype=np.int32)
Data3[zipp[3] >= 0 ] = 1
As you can see, there are 4 shapes for each layer (four layers total). I am trying to put a specific/corresponding "zipp" vector position to each dem.shape for each layer I have (in vector zipp[i] each i is an array of each dem).
What I want it to do is to replace with the number 1 those values greater than or equal to zero in the array contained in zipp[i] for each layer/shape/dem.
However, as a result, I must deliver this as a word not a vector or array, so I've been thinking of a loop but haven't been illuminated enough just yet.
Thank you :)
I'm not quite sure what you mean by delivering the result "as a word not a vector or array", but assuming all of these arrays have the same shape you can reduce this to a couple of lines (maybe someone else knows how to do it in 1):
data = np.zeros_like(zipp, dtype=np.int32)
data[zipp >= 0] = 1
If just you want to return a boolean array of where zipp is greater than or equal to 0 you can do that in 1 line like this:
bool = np.greater_equal(zipp, 0)