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The most important object and defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index.

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

Any item extracted from the ndarray object (by slicing) is represented by a Python object of one of the array scalar types. The following diagram shows a relationship between ndarray, data-type object (type), and array scalar type −

An instance of ndarray class can be constructed by different array creation routines described later in the site. The basic ndarray is created using an array function in NumPy 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 −

Sr.No.	Parameter & Description
1	object any object exposing the array interface method returns an array or any (nested) sequence.
2	dtypeDesired data type of array, optional
3	copyOptional. By default (true), the object is copied
4	orderC (row-major) or F (column-major) or A (any) (default)
5	subokBy default returned array is forced to be a base class array. If true, sub-classes passed through
6	ndminSpecifies minimum dimensions of the resultant array

NumPy Ndarray Object are take a look at the following examples to understand better.

Example 1

import numpy as np 
a = np.array([1,2,3]) 
print a

The output is as follows −

[1, 2, 3]

Example 2

# more than one dimensions 
import numpy as np 
a = np.array([[1, 2], [3, 4]]) 
print a

The output is as follows −

[[1, 2] 
 [3, 4]]

Example 3

# minimum dimensions 
import numpy as np 
a = np.array([1, 2, 3,4,5], ndmin = 2) 
print a

The output is as follows −

[[1, 2, 3, 4, 5]]

Example 4

# dtype parameter 
import numpy as np 
a = np.array([1, 2, 3], dtype = complex) 
print a

The output is as follows −

[ 1.+0.j,  2.+0.j,  3.+0.j]

The ndarray object consists of a contiguous one-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).