Numpy – NDARRAY OBJECT

The most important object defined in Numpy is the N-dimensional array type also ndarray. It describes the collection of items of the same type. Items in the collection can access using a zero-based index.

Every item in a ndarray Takes the same size as the block in the memory. Each element in ndarray is an object of data-type object (called dtype).

Any item extracted from the ndarray object ( by slicing ) represents the python object of the array scalar types. The following diagram shows a relationship between ndarray and data-type object (dtype) and array scalar type:

An instance of ndarray class can be constructed in different array creation routines described Later in the tutorial. The basic ndarray created using an array function in Numpy is as follows:

numpy.array

It Creates a ndarray from any object exposing array interface, or from any method that returns an array.

numpy. array (object, dtype = None, copy = true, order = none, subok = false, ndmin = 0)

The above constructor Takes the following Parameters for NDARRAY:

The following examples on Numpy – NDARRAY OBJECT

Example 1

Import numpy as np 

a=np. Array ((1,2,3 ))

Print a

Output:

(1, 2, 3)

Example 2

# minimum dimensions 

Import numpy as np 

a = np. array ( ( 1 , 2, 3, 4 , 5 ) , ndmin = 2 ) 

Print a

Output:

( ( 1,  2,  3,  4, 5 ) )

The ndarray object consists of a contiguous – dimensional segment of computer memory, combined with an indexing scheme that maps each item to a location in the memory block. The memory block holds the elements in row-major order (c style) or a column-major order ( Fortran or Matlab style ).