I am using numpy.
I have one array Y and one matrix X. This is for a regression. They arrays has labels, e.g. 0,1,2,3,4,5. I need to create a new array that has label 0 removed for all rows and the corresponding row in X removed as well. What is the most efficient means to do this?
e.g.
for i in xrange(y.shape):
if y==0:
pop y pop X
Numpy arrays are not good at appending/removing rows. If you know which rows are to be deleted, just extract the other rows (you need) and create a new array.
I don't understand your question very well, so please correct me if I am wrong:
x = x[y != 0]
y = y[y != 0]
Example:
import numpy as np
x = np.array([[11, 12, 13], [21, 22, 23], [31, 32, 33]])
y = np.array([1, 0, 3])
x = x[y != 0]
y = y[y != 0]
now:
x == array([[11, 12, 13],
[31, 32, 33]])
y == array([1, 3])
If you know that you will always have that empty row no matter what, I don't see why you even need NUMPY to do this...
Z = Z[:][1:]
If it's just the first row this will actually work for the matrix, and of course the array
Z = Z[1:]
I like @eumiro's solution if you don't care about the placement of the items in the matrix, but their solution will remove all zeros and shift elements I believe.
