ndarray

An ndarray is a multi-dimensional array of items of the same type and size. The number of dimensions and items contained in the array is defined with a tuple of N non-negative integers that specify each dimension’s size. An ndarray has an associated data-type object which specifies the dtype stored in the ndarray.

Like other container objects in Python, ndarray items can be accessed by indexing and slicing the array. There are also a large number of methods and attributes of the ndarray that can be used to access and manipulate its contents.

Separate instances of an ndarray can share contents so that changes in one ndarray can be reflected in another. This happens when an ndarray is created as a “view” of another ndarray known as the “base”.

Creating a ndarray

There are several routines for creating ndarray objects. These are preferred to using the ndarray constructor, which operates at a very low level. Here are a few examples:

Method Syntax Description
.empty() numpy.empty(shape,dtype) Creates a ndarray of the given shape tuple, and the optional dtype (default is numpy.float64) with uninitialized values.
.empty_like() numpy.empty_like(model,dtype) Creates a ndarray based on the shape of the model, with the optional dtype (default is data type of model) with uninitialized values.
.ones() numpy.ones(shape,dtype) Operates the same as .empty(), but initializes all the array elements with a value of one.
.ones_like() numpy.ones_like(model,dtype) Operates the same as .empty_like(), but initializes all the array elements with a value of one.
.zeros() numpy.zeros(shape,dtype) Operates the same as .empty(), but initializes all the array elements with a value of zero.
.zeros_like() numpy.zeros_like(model,dtype) Operates the same as .empty_like(), but initializes all the array elements with a value of zero.
.full() numpy.full(shape,value,dtype) Operates the same as .empty(), but initializes all the array elements with the specified value.
.full_like() numpy.full_like(model,value,dtype) Operates the same as .empty_like(), but initializes all the array elements with the specified value.
.array() numpy.array(object,dtype) Creates an ndarray based on the given object (such as a list of lists) with the optional dtype. If not specified, the data type will be based on the contents of object.

Example

The following shows various methods of creating an ndarray.

import numpy as np
nd1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
nd2 = np.ones_like(nd1)
nd3 = np.full((2,2),5)
print(nd1)
print(nd2)
print(nd3)

This produces the following output:

[[1 2 3]
[4 5 6]
[7 8 9]]
[[1 1 1]
[1 1 1]
[1 1 1]]
[[5 5]
[5 5]]

Operations on an ndarray

The standard mathematical operations, when applied to ndarrays are evaluated internally as equivalent universal functions (“ufuncs”) which are functions that operate on ndarrays on an element-by-element basis. There are over 60 of these universal functions and there are ufuncs for each mathematical operation. Some of the most popular operations are summarized below:

Operator Ufunc Description
+ numpy.add(X,Y) Adds arguments, element-wise.
- numpy.subtract(X,Y) Subtracts arguments, element-wise.
* numpy.multiply(X,Y) Multiplies arguments, element-wise.
/ numpy.divide(X,Y) Division of arguments, element-wise.
** numpy.power(X,Y) First array raised to powers of second array, element-wise.
% numpy.mod(X,Y) Integer remainder of division, element-wise.
// numpy.floor_divide(X,Y) Integer result of division, rounded down, element-wise.
@ numpy.matmul(X,Y) Matrix multiplication of arguments. (The @ operator was introduced in Python 3.5)

Example

The following example creates an ndarray and performs several operations on it.

import numpy as np
nd1 = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(nd1)
print(nd1 + 5)
print(nd1 % 2)
print(np.matmul(nd1, nd1 % 2))

This produces the following output:

[[1 2 3]
[4 5 6]
[7 8 9]]
[[ 6 7 8]
[ 9 10 11]
[12 13 14]]
[[1 0 1]
[0 1 0]
[1 0 1]]
[[ 4 2 4]
[10 5 10]
[16 8 16]]

ndarray

.reshape()
Rearranges the data of an ndarray into a new shape.
.transpose()
Reverses or permutes the axes of an ndarray.

Contributors

Interested in helping build Docs? Read the Contribution Guide or share your thoughts in this feedback form.

Learn Python:NumPy on Codecademy