Numpy: Filtering rows by multiple conditions?
you can use multiple filters in a slice, something like this:
x = np.arange(90.).reshape(30, 3)#set the first 10 rows of cols 1,2 to be zerox[0:10, 0:2] = 0.0x[(x[:,0] == 0.) & (x[:,1] == 0.) & (x[:,2] > 10)]#should give only a few rowsarray([[ 0., 0., 11.], [ 0., 0., 14.], [ 0., 0., 17.], [ 0., 0., 20.], [ 0., 0., 23.], [ 0., 0., 26.], [ 0., 0., 29.]])
How about this -
meta[meta[:,2]<X * np.all(meta[:,0:2]==0,1),:]Sample run -
In [89]: metaOut[89]: array([[ 1, 2, 3, 4], [ 0, 0, 2, 0], [ 9, 0, 11, 12]])In [90]: XOut[90]: 4In [91]: meta[meta[:,2]<X * np.all(meta[:,0:2]==0,1),:]Out[91]: array([[0, 0, 2, 0]])