The most important object 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 an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype).

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

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

numpy.array

It creates an 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 −

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 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 a row-major order (C style) or a column-major order (FORTRAN or MatLab style).