Numpy 4d Array Slicing

Why does slicing a 4d array give me a 3d array? I expected a 4d array with extent 1 in one of the dimensions. Example: print X.shape (1783, 1, 96, 96) Slice array: print X[11,:,:,

Solution 1:

Per the docs:

An integer, i, returns the same values as i:i+1except the dimensionality of the returned object is reduced by 1. In particular, a selection tuple with the p-th element an integer (and all other entries :) returns the corresponding sub-array with dimension N - 1. If N = 1 then the returned object is an array scalar.

---

Thus, when your index is an integer, the value(s) at that index is(are) returned and the corresponding axis is removed. In one dimension the behavior is as you would expect:

In [6]: a = np.arange(5); a
Out[6]: array([0, 1, 2, 3, 4])

In [7]: a[2]
Out[7]: 2

In [8]: a[2].shape
Out[8]: ()

a is 1-dimensional, a[2] is 0-dimensional.

In higher dimensions, if X is 4-dimensional and of shape (1783,1,96,96), then X[11,:,:,:] returns all the values where the first axis index equals 11 and then that axis is removed. So X[11,:,:,:].shape is (1,96,96).

When the slice specifies a range, such as a[2:3] then all the values within that range are returned and the axis is not removed:

In[9]: a[2:3]Out[9]: array([2])

In[10]: a[2:3].shapeOut[10]: (1,)

Similarly, X[11:12, :, :, :] has shape (1,1,96,96).