Anatomy of NumPy arrays

If one looks into what an ndarray is actually made of, one can see that it consists of the following:

• one dimensional contiguous block of memory: raw data
• indexing scheme: how to locate an element
• data type descriptor: how to interpret an element

NumPy indexing

There are many possible ways of arranging the elements of a N-dimensional array in a 1-dimensional block (i.e. memory). NumPy uses striding where a N-dimensional index (n[0], n[1], …, n[-1]) corresponds to the offset from the beginning of a 1-dimensional block.

If n[k] is the index in dimension k for an element and s[k] is the stride in that dimension, then the offset for an element is:

offset = sum(s[k] * n[k] for k in range(N))

When one assigns a variable to a slice of another array

b = a[1:8:2, 3:12:3]

the variable b has the same raw data as a, but only different
strides. Thus, changing the contents of b will also change the contents of
a, i.e. b is view to a as discussed earlier.

In addition to slicing NumPy allows indexing also with integer arrays or
Boolean masks:

a = np.arange(0.0, 1.0, 0.1)
ind = np.array([1, 1, 0, 4])
b = a[ind] # b = array([0.1, 0.1, 0. , 0.4])

m = a > 0.5
b = a[m] # b = array([0.6, 0.7, 0.8, 0.9])

In these cases b cannot be created just by modifying the strides, so b
will hold a copy of the data in a. Now, modifications of b are not
affecting a.

Attributes of an ndarray

a = np.array(...)

• a.flags : various information about memory layout
• a.strides: bytes to step in each dimension when traversing
• a.itemsize: size of one array element in bytes
• a.data: Python buffer object pointing to start of arrays data
• a.__array_interface__: Python internal interface

Try to investigate attributes of different types of arrays!