Array views and copies

In order to control precisely how memory is used, NumPy offers the concept of view of an array. Views are smaller arrays that share the same data as a larger array. This works just like a reference to one single object (refer to section Basic Types in Chapter 1, Getting Started).

Array views

The simplest example of a view is given by a slice of an array:

M = array([[1.,2.],[3.,4.]])
v = M[0,:] # first row of M

The preceding slice is a view of M. It shares the same data as M. Modifying v will modify M as well:

v[-1] = 0.
v # array([[1.,0.]])
M # array([[1.,0.],[3.,4.]]) # M is modified as well

It is possible to access the object that owns the data using the array attribute base:

v.base # array([[1.,0.],[3.,4.]])
v.base is M # True

If an array owns its data, the attribute base is none :

M.base # None

Slices as views

There are precise rules on which slices will return views and which ones will return copies. Only basic slices (mainly index expressions with :) return views...