# Indexing and slicing arrays

There are two basic methods to access the data in a NumPy array; let's call that array for `A`

. Both methods use the same syntax, `A[obj]`

, where `obj`

is a Python object that performs the selection. We are already familiar with the first method of accessing a single element. The second method is the subject of this section, namely **slicing**. This concept is exactly what makes NumPy and SciPy so incredibly easy to manage.

The basic slice method is a Python object of the form `slice(start,stop,step)`

, or in a more compact notation, `start:stop:step`

. Initially, the three variables, `start`

, `stop`

, and `step`

are non-negative integer values, with `start`

less than or equal to `stop`

.

This represents the sequence of indices *k = start + (i * step)*, where *k* runs from `start`

to the largest integer *k_max = start + step*int((stop-start)/step)*, or *i *from `0`

to the largest integer equal to *int((stop - start) / step)*. When a slice method is invoked on any of the dimensions of `ndarray`

, it...