NumPy 2D数组普通切片与基于布尔的切片

Question

I am new to Numpy, and I was experimenting with 2D Arrays Numpy, I made the following observations when an array is sliced in 2 different ways

a = numpy.array([[1,2,3,4,5],[1,2,3,4,5]])
slice1 = a[:,:3]
slice1[0,0] = 100
print(a) 
-- gives, 100 2 3 4 5
           1  2 3 4 5

This behaviour is expected as per numpy docs But Consider this slicing

a = numpy.array([[1,2,3,4,5],[1,2,3,4,5]])
slice2 = a[[True,True],:3]
slice2[0,0] = 100
print(a) 
-- gives us
1 2 3 4 5
1 2 3 4 5

Could anyone explain the difference between these 2 approaches

Answer 1

From the NumPy indexing documentation :

Advanced indexing always returns a copy of the data (contrast with basic slicing that returns a view ).

After all, there's no way to make the strides work if you want to make some_arr[[True, False, False, True, True, False]] return a view.