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Python Arrays Explained With Examples

Arrays in python are helpful for organizing and working with data of the same type efficiently. learning about arrays is valuable because it allows you to perform calculations on large datasets and understand how to store and manipulate data effectively..

Python arrays are homogenous data structures. They are used to store multiple items but allow only the same type of data. They are available in Python by importing the array module.

Python Arrays – A Beginners Guide

List, a built-in type in Python, is also capable of storing multiple values. But they are different from arrays because they are not bound to any specific type.

So, to summarize, arrays are not fundamental types, but lists are internal to Python. An array accepts values of one kind while lists are independent of the data type. In order to know more about lists, check out our tutorial on Python List .

However, in this tutorial, you’ll get to know how to create an array, add/update, index, remove, and slice.

What is Array in Python?

An array is a container used to contain a fixed number of items. But, there is an exception that values should be of the same type. The following are two terms often used with arrays.

Array element – Every value in an array represents an element.
Array index – Every element has some position in the array known as the index.

Let’s now see how Python represents an array.

Array Illustration

The array is made up of multiple parts. And each section of the array is an element. We can access all the values by specifying the corresponding integer index.

The first element starts at index 0 and so on. At the 9th index, the 10th item would appear. Check the below graphical illustration.

Declare Array in Python

You have first to import the array module in your Python script. After that, declare the array variable as per the below syntax.

In the above statements, “array_var” is the name of the array variable. And we’ve used the array() function which takes two parameters. “TypeCode” is the type of array whereas “Initializers” are the values to set in the array.

The argument “TypeCode” can be any value from the below chart.

In the above diagram, we’ve listed down all possible type codes for Python and C Types. But we’ll only be using the Python Types “i” for integers and “d” for floats here in our examples.

Also, note that there is one Unicode type shown in the chart. Its support ended with Python version 3.3. So, it is best not to use it in your programs.

Let’s consider a simple case to create an array of 10 integers.

We first imported the array module and then used the Python range() function to produce ten integers. We’ve also printed the numbers that our array variable would hold. Here is the outcome of the above program.

In the next sections, we’ll cover all actions that can be performed using arrays.

Array Operations

Indexing an array.

We can use indices to retrieve elements of an array. See the below example:

Arrays have their first element stored at the zeroth index. Also, you can see that if we use the -ve index, then it gives us elements from the tail end.

The output is:

Slicing arrays

The slice operator “:” is commonly used to slice strings and lists . However, it does work for the arrays also. Let’s see with the help of examples.

When you execute the above script, it produces the following output:

The following two points, you should note down:

When you pass both the left and right operands to the slice operator, then they act as the indexes.
If you take one of them whether the left or right one, then it represents the no. of elements.

Add/Update an array

We can make changes to an array in different ways. Some of these are as follows:

Assignment operator to change or update an array
Append() method to add one element
Extend() method to add multiple items

We’ll now understand each of these approaches with the help of examples.

Let’s begin by using the assignment operator to update an existing array variable.

Now, we’ll apply the append() and extend() methods on a given array. These work the same for lists in Python. See the tutorial below.

Difference Between List Append() and Extend()

This program yields the following:

The point to note is that both append() and extend() add elements to the end. The next tip is an interesting one. We can join two or more arrays using the “+” operator. If interested, learn about the different operators available in Python .

The above script shows the following result after execution:

Remove array elements

There are multiple ways that we can follow to remove elements from an array. Here are these:

Python del operator
Remove() method
Pop() method

Let’s first check how Python del works to delete array members.

The output is as follows:

Now, let’s try to utilize the remove() and pop() methods. The former removes the given value from the array whereas the latter deletes the item at a specified index.

After running this code, we get the below result:

Reverse array

Last but not least is how we can reverse the elements of an array in Python. There can be many approaches to this. However, we’ll take the following two:

Slice operator in Python
Python List comprehension

Check out the below sample code to invert the element in a given array.

The above code produces the following output after running:

Now, we are mentioning a bonus method to reverse the array using the reversed() call. This function inverts the elements and returns a “list_reverseiterator” type object. However, if you want more info, read our in-depth tutorial on 7 ways to reverse a list in Python .

Here is the output of the above example.

We hope that after wrapping up this tutorial, you should feel comfortable using Python arrays. However, you may practice more with examples to gain confidence.

Also, to learn Python from scratch to depth, do read our step-by-step Python tutorial .

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Python Array Tutorial – Define, Index, Methods

In this article, you'll learn how to use Python arrays. You'll see how to define them and the different methods commonly used for performing operations on them.

The article covers arrays that you create by importing the array module . We won't cover NumPy arrays here.

The differences between Lists and Arrays
When to use arrays
Define arrays
Find the length of arrays
Array indexing
Search through arrays
Loop through arrays
Slice an array
Change an existing value
Add a new value
Remove a value

Let's get started!

What are Python Arrays?

Arrays are a fundamental data structure, and an important part of most programming languages. In Python, they are containers which are able to store more than one item at the same time.

Specifically, they are an ordered collection of elements with every value being of the same data type. That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type.

What's the Difference between Python Lists and Python Arrays?

Lists are one of the most common data structures in Python, and a core part of the language.

Lists and arrays behave similarly.

Just like arrays, lists are an ordered sequence of elements.

They are also mutable and not fixed in size, which means they can grow and shrink throughout the life of the program. Items can be added and removed, making them very flexible to work with.

However, lists and arrays are not the same thing.

Lists store items that are of various data types . This means that a list can contain integers, floating point numbers, strings, or any other Python data type, at the same time. That is not the case with arrays.

As mentioned in the section above, arrays store only items that are of the same single data type . There are arrays that contain only integers, or only floating point numbers, or only any other Python data type you want to use.

When to Use Python Arrays

Lists are built into the Python programming language, whereas arrays aren't. Arrays are not a built-in data structure, and therefore need to be imported via the array module in order to be used.

Arrays of the array module are a thin wrapper over C arrays, and are useful when you want to work with homogeneous data.

They are also more compact and take up less memory and space which makes them more size efficient compared to lists.

If you want to perform mathematical calculations, then you should use NumPy arrays by importing the NumPy package. Besides that, you should just use Python arrays when you really need to, as lists work in a similar way and are more flexible to work with.

How to Use Arrays in Python

In order to create Python arrays, you'll first have to import the array module which contains all the necessary functions.

There are three ways you can import the array module :

By using import array at the top of the file. This includes the module array . You would then go on to create an array using array.array() .
Instead of having to type array.array() all the time, you could use import array as arr at the top of the file, instead of import array alone. You would then create an array by typing arr.array() . The arr acts as an alias name, with the array constructor then immediately following it.
Lastly, you could also use from array import * , with * importing all the functionalities available. You would then create an array by writing the array() constructor alone.

How to Define Arrays in Python

Once you've imported the array module , you can then go on to define a Python array.

The general syntax for creating an array looks like this:

Let's break it down:

variable_name would be the name of the array.
The typecode specifies what kind of elements would be stored in the array. Whether it would be an array of integers, an array of floats or an array of any other Python data type. Remember that all elements should be of the same data type.
Inside square brackets you mention the elements that would be stored in the array, with each element being separated by a comma. You can also create an empty array by just writing variable_name = array(typecode) alone, without any elements.

Below is a typecode table, with the different typecodes that can be used with the different data types when defining Python arrays:

Tying everything together, here is an example of how you would define an array in Python:

First we included the array module, in this case with import array as arr .
Then, we created a numbers array.
We used arr.array() because of import array as arr .
Inside the array() constructor, we first included i , for signed integer. Signed integer means that the array can include positive and negative values. Unsigned integer, with H for example, would mean that no negative values are allowed.
Lastly, we included the values to be stored in the array in square brackets.

Keep in mind that if you tried to include values that were not of i typecode, meaning they were not integer values, you would get an error:

In the example above, I tried to include a floating point number in the array. I got an error because this is meant to be an integer array only.

Another way to create an array is the following:

The example above imported the array module via from array import * and created an array numbers of float data type. This means that it holds only floating point numbers, which is specified with the 'd' typecode.

How to Find the Length of an Array in Python

To find out the exact number of elements contained in an array, use the built-in len() method.

It will return the integer number that is equal to the total number of elements in the array you specify.

In the example above, the array contained three elements – 10, 20, 30 – so the length of numbers is 3 .

Array Indexing and How to Access Individual Items in an Array in Python

Each item in an array has a specific address. Individual items are accessed by referencing their index number .

Indexing in Python, and in all programming languages and computing in general, starts at 0 . It is important to remember that counting starts at 0 and not at 1 .

To access an element, you first write the name of the array followed by square brackets. Inside the square brackets you include the item's index number.

The general syntax would look something like this:

Here is how you would access each individual element in an array:

Remember that the index value of the last element of an array is always one less than the length of the array. Where n is the length of the array, n - 1 will be the index value of the last item.

Note that you can also access each individual element using negative indexing.

With negative indexing, the last element would have an index of -1 , the second to last element would have an index of -2 , and so on.

Here is how you would get each item in an array using that method:

How to Search Through an Array in Python

You can find out an element's index number by using the index() method.

You pass the value of the element being searched as the argument to the method, and the element's index number is returned.

If there is more than one element with the same value, the index of the first instance of the value will be returned:

How to Loop through an Array in Python

You've seen how to access each individual element in an array and print it out on its own.

You've also seen how to print the array, using the print() method. That method gives the following result:

What if you want to print each value one by one?

This is where a loop comes in handy. You can loop through the array and print out each value, one-by-one, with each loop iteration.

For this you can use a simple for loop:

You could also use the range() function, and pass the len() method as its parameter. This would give the same result as above:

How to Slice an Array in Python

To access a specific range of values inside the array, use the slicing operator, which is a colon : .

When using the slicing operator and you only include one value, the counting starts from 0 by default. It gets the first item, and goes up to but not including the index number you specify.

When you pass two numbers as arguments, you specify a range of numbers. In this case, the counting starts at the position of the first number in the range, and up to but not including the second one:

Methods For Performing Operations on Arrays in Python

Arrays are mutable, which means they are changeable. You can change the value of the different items, add new ones, or remove any you don't want in your program anymore.

Let's see some of the most commonly used methods which are used for performing operations on arrays.

How to Change the Value of an Item in an Array

You can change the value of a specific element by speficying its position and assigning it a new value:

How to Add a New Value to an Array

To add one single value at the end of an array, use the append() method:

Be aware that the new item you add needs to be the same data type as the rest of the items in the array.

Look what happens when I try to add a float to an array of integers:

But what if you want to add more than one value to the end an array?

Use the extend() method, which takes an iterable (such as a list of items) as an argument. Again, make sure that the new items are all the same data type.

And what if you don't want to add an item to the end of an array? Use the insert() method, to add an item at a specific position.

The insert() function takes two arguments: the index number of the position the new element will be inserted, and the value of the new element.

How to Remove a Value from an Array

To remove an element from an array, use the remove() method and include the value as an argument to the method.

With remove() , only the first instance of the value you pass as an argument will be removed.

See what happens when there are more than one identical values:

Only the first occurence of 10 is removed.

You can also use the pop() method, and specify the position of the element to be removed:

And there you have it - you now know the basics of how to create arrays in Python using the array module . Hopefully you found this guide helpful.

To learn more about Python, check out freeCodeCamp's Scientific Computing with Python Certification .

You'll start from the basics and learn in an interactive and beginner-friendly way. You'll also build five projects at the end to put into practice and help reinforce what you learned.

Thanks for reading and happy coding!

References: Python documentation

Python Numerical Methods

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists , the content is also available at Berkeley Python Numerical Methods .

The copyright of the book belongs to Elsevier. We also have this interactive book online for a better learning experience. The code is released under the MIT license . If you find this content useful, please consider supporting the work on Elsevier or Amazon !

< 2.6 Data Structure - Dictionaries | Contents | 2.8 Summary and Problems >

Introducing Numpy Arrays ¶

In the 2nd part of this book, we will study the numerical methods by using Python. We will use array/matrix a lot later in the book. Therefore, here we are going to introduce the most common way to handle arrays in Python using the Numpy module . Numpy is probably the most fundamental numerical computing module in Python.

NumPy is important in scientific computing, it is coded both in Python and C (for speed). On its website, a few important features for Numpy is listed:

a powerful N-dimensional array object

sophisticated (broadcasting) functions

tools for integrating C/C++ and Fortran code

useful linear algebra, Fourier transform, and random number capabilities

Here we will only introduce you the Numpy array which is related to the data structure, but we will gradually touch on other aspects of Numpy in the following chapters.

In order to use Numpy module, we need to import it first. A conventional way to import it is to use “np” as a shortened name.

WARNING! Of course, you could call it any name, but conventionally, “np” is accepted by the whole community and it is a good practice to use it for obvious purposes.

To define an array in Python, you could use the np.array function to convert a list.

TRY IT! Create the following arrays:

\(x = \begin{pmatrix} 1 & 4 & 3 \\ \end{pmatrix}\)

\(y = \begin{pmatrix} 1 & 4 & 3 \\ 9 & 2 & 7 \\ \end{pmatrix}\)

NOTE! A 2-D array could use a nested lists to represent, with the inner list represent each row.

Many times we would like to know the size or length of an array. The array shape attribute is called on an array M and returns a 2 × 3 array where the first element is the number of rows in the matrix M and the second element is the number of columns in M. Note that the output of the shape attribute is a tuple. The size attribute is called on an array M and returns the total number of elements in matrix M.

TRY IT! Find the rows, columns and the total size for array y.

NOTE! You may notice the difference that we only use y.shape instead of y.shape() , this is because shape is an attribute rather than a method in this array object. We will introduce more of the object-oriented programming in a later chapter. For now, you need to remember that when we call a method in an object, we need to use the parentheses, while the attribute don’t.

Very often we would like to generate arrays that have a structure or pattern. For instance, we may wish to create the array z = [1 2 3 … 2000]. It would be very cumbersome to type the entire description of z into Python. For generating arrays that are in order and evenly spaced, it is useful to use the arange function in Numpy.

TRY IT! Create an array z from 1 to 2000 with an increment 1.

Using the np.arange , we could create z easily. The first two numbers are the start and end of the sequence, and the last one is the increment. Since it is very common to have an increment of 1, if an increment is not specified, Python will use a default value of 1. Therefore np.arange(1, 2000) will have the same result as np.arange(1, 2000, 1) . Negative or noninteger increments can also be used. If the increment “misses” the last value, it will only extend until the value just before the ending value. For example, x = np.arange(1,8,2) would be [1, 3, 5, 7].

TRY IT! Generate an array with [0.5, 1, 1.5, 2, 2.5].

Sometimes we want to guarantee a start and end point for an array but still have evenly spaced elements. For instance, we may want an array that starts at 1, ends at 8, and has exactly 10 elements. For this purpose you can use the function np.linspace . linspace takes three input values separated by commas. So A = linspace(a,b,n) generates an array of n equally spaced elements starting from a and ending at b.

TRY IT! Use linspace to generate an array starting at 3, ending at 9, and containing 10 elements.

Getting access to the 1D numpy array is similar to what we described for lists or tuples, it has an index to indicate the location. For example:

For 2D arrays, it is slightly different, since we have rows and columns. To get access to the data in a 2D array M, we need to use M[r, c], that the row r and column c are separated by comma. This is referred to as array indexing. The r and c could be single number, a list and so on. If you only think about the row index or the column index, than it is similar to the 1D array. Let’s use the \(y = \begin{pmatrix} 1 & 4 & 3 \\ 9 & 2 & 7 \\ \end{pmatrix}\) as an example.

TRY IT! Get the element at first row and 2nd column of array y .

TRY IT! Get the first row of array y .

TRY IT! Get the last column of array y .

TRY IT! Get the first and third column of array y .

There are some predefined arrays that are really useful. For example, the np.zeros , np.ones , and np.empty are 3 useful functions. Let’s see the examples.

TRY IT! Generate a 3 by 5 array with all the as 0.

TRY IT! Generate a 5 by 3 array with all the element as 1.

NOTE! The shape of the array is defined in a tuple with row as the first item, and column as the second. If you only need a 1D array, then it could be only one number as the input: np.ones(5) .

TRY IT! Generate a 1D empty array with 3 elements.

NOTE! The empty array is not really empty, it is filled with random very small numbers.

You can reassign a value of an array by using array indexing and the assignment operator. You can reassign multiple elements to a single number using array indexing on the left side. You can also reassign multiple elements of an array as long as both the number of elements being assigned and the number of elements assigned is the same. You can create an array using array indexing.

TRY IT! Let a = [1, 2, 3, 4, 5, 6]. Reassign the fourth element of A to 7. Reassign the first, second, and thrid elements to 1. Reassign the second, third, and fourth elements to 9, 8, and 7.

TRY IT! Create a zero array b with shape 2 by 2, and set \(b = \begin{pmatrix} 1 & 2 \\ 3 & 4 \\ \end{pmatrix}\) using array indexing.

WARNING! Although you can create an array from scratch using indexing, we do not advise it. It can confuse you and errors will be harder to find in your code later. For example, b[1, 1] = 1 will give the result \(b = \begin{pmatrix} 0 & 0 \\ 0 & 1 \\ \end{pmatrix}\) , which is strange because b[0, 0], b[0, 1], and b[1, 0] were never specified.

Basic arithmetic is defined for arrays. However, there are operations between a scalar (a single number) and an array and operations between two arrays. We will start with operations between a scalar and an array. To illustrate, let c be a scalar, and b be a matrix.

b + c , b − c , b * c and b / c adds a to every element of b, subtracts c from every element of b, multiplies every element of b by c, and divides every element of b by c, respectively.

TRY IT! Let \(b = \begin{pmatrix} 1 & 2 \\ 3 & 4 \\ \end{pmatrix}\) . Add and substract 2 from b. Multiply and divide b by 2. Square every element of b. Let c be a scalar. On your own, verify the reflexivity of scalar addition and multiplication: b + c = c + b and cb = bc.

Describing operations between two matrices is more complicated. Let b and d be two matrices of the same size. b − d takes every element of b and subtracts the corresponding element of d. Similarly, b + d adds every element of d to the corresponding element of b.

TRY IT! Let \(b = \begin{pmatrix} 1 & 2 \\ 3 & 4 \\ \end{pmatrix}\) and \(d = \begin{pmatrix} 3 & 4 \\ 5 & 6 \\ \end{pmatrix}\) . Compute b + d and b - d.

There are two different kinds of matrix multiplication (and division). There is element-by-element matrix multiplication and standard matrix multiplication. For this section, we will only show how element-by-element matrix multiplication and division work. Standard matrix multiplication will be described in later chapter on Linear Algebra. Python takes the * symbol to mean element-by-element multiplication. For matrices b and d of the same size, b * d takes every element of b and multiplies it by the corresponding element of d. The same is true for / and **.

TRY IT! Compute b * d, b / d, and b**d.

The transpose of an array, b, is an array, d, where b[i, j] = d[j, i]. In other words, the transpose switches the rows and the columns of b. You can transpose an array in Python using the array method T .

TRY IT! Compute the transpose of array b.

Numpy has many arithmetic functions, such as sin, cos, etc., can take arrays as input arguments. The output is the function evaluated for every element of the input array. A function that takes an array as input and performs the function on it is said to be vectorized .

TRY IT! Compute np.sqrt for x = [1, 4, 9, 16].

Logical operations are only defined between a scalar and an array and between two arrays of the same size. Between a scalar and an array, the logical operation is conducted between the scalar and each element of the array. Between two arrays, the logical operation is conducted element-by-element.

TRY IT! Check which elements of the array x = [1, 2, 4, 5, 9, 3] are larger than 3. Check which elements in x are larger than the corresponding element in y = [0, 2, 3, 1, 2, 3].

Python can index elements of an array that satisfy a logical expression.

TRY IT! Let x be the same array as in the previous example. Create a variable y that contains all the elements of x that are strictly bigger than 3. Assign all the values of x that are bigger than 3, the value 0.

Guide to Arrays in Python

An array is a structured way to store multiple items (like numbers, characters, or even other arrays) in a specific order, and you can quickly access, modify, or remove any item if you know its position (index).

In this guide, we'll give you a comprehensive overview of the array data structure. First of all, we'll take a look at what arrays are and what are their main characteristics. We'll then transition into the world of Python, exploring how arrays are implemented, manipulated, and applied in real-world scenarios.

Understanding the Array Data Structure

Arrays are among the oldest and most fundamental data structures used in computer science and programming. Their simplicity, combined with their efficiency in certain operations, makes them a staple topic for anyone delving into the realm of data management and manipulation.

An array is a collection of items, typically of the same type , stored in contiguous memory locations .

This contiguous storage allows arrays to provide constant-time access to any element, given its index. Each item in an array is called an element , and the position of an element in the array is defined by its index , which usually starts from zero .

For instance, consider an array of integers: [10, 20, 30, 40, 50] . Here, the element 20 has an index of 1 :

There are multiple advantages of using arrays to store our data. For example, due to their memory layout, arrays allow for O(1) (constant) time complexity when accessing an element by its index. This is particularly beneficial when we need random access to elements. Additionally, arrays are stored in contiguous memory locations , which can lead to better cache locality and overall performance improvements in certain operations. Another notable advantage of using arrays is that, since arrays have a fixed size once declared, it's easier to manage memory and avoid unexpected overflows or out-of-memory errors.

Note : Arrays are especially useful in scenarios where the size of the collection is known in advance and remains constant , or where random access is more frequent than insertions and deletions.

On the other side, arrays come with their own set of limitations . One of the primary limitations of traditional arrays is their fixed size . Once an array is created, its size cannot be changed. This can lead to issues like wasted memory (if the array is too large) or the need for resizing (if the array is too small). Besides that, inserting or deleting an element in the middle of an array requires shifting of elements, leading to O(n) time complexity for these operations.

To sum this all up, let's illustrate the main characteristics of arrays using the song playlist example from the beginning of this guide. An array is a data structure that:

Is Indexed: Just like each song on your playlist has a number (1, 2, 3, ...), each element in an array has an index. But, in most programming languages, the index starts at 0. So, the first item is at index 0, the second at index 1, and so on.

Has Fixed Size : When you create a playlist for, say, 10 songs, you can't add an 11th song without removing one first. Similarly, arrays have a fixed size. Once you create an array of a certain size, you can't add more items than its capacity.

Is Homogeneous : All songs in your playlist are music tracks. Similarly, all elements in an array are of the same type. If you have an array of integers, you can't suddenly store a text string in it.

Has Direct Access : If you want to listen to the 7th song in your playlist, you can jump directly to it. Similarly, with arrays, you can instantly access any element if you know its index.

Contiguous Memory : This is a bit more technical. When an array is created in a computer's memory, it occupies a continuous block of memory. Think of it like a row of adjacent lockers in school. Each locker is next to the other, with no gaps in between.

Python and Arrays

Python, known for its flexibility and ease of use, offers multiple ways to work with arrays. While Python does not have a native array data structure like some other languages, it provides powerful alternatives that can function similarly and even offer extended capabilities.

At first glance, Python's list might seem synonymous with an array, but there are subtle differences and nuances to consider:

Looking at this table, it comes naturally to ask - "When to use which?" . Well, if you need a collection that can grow or shrink dynamically and can hold mixed data types, Python's list is the way to go. However, for scenarios requiring a more memory-efficient collection with elements of the same type, you might consider using Python's array module or external libraries like NumPy.

The array Module in Python

When most developers think of arrays in Python, they often default to thinking about lists. However, Python offers a more specialized array structure through its built-in array module. This module provides a space-efficient storage of basic C-style data types in Python.

While Python lists are incredibly versatile and can store any type of object, they can sometimes be overkill, especially when you only need to store a collection of basic data types, like integers or floats. The array module provides a way to create arrays that are more memory efficient than lists for specific data types.

Creating an Array

To use the array module, you first need to import it:

Once imported, you can create an array using the array() constructor:

Here, the 'i' argument indicates that the array will store signed integers . There are several other type codes available, such as 'f' for floats and 'd' for doubles.

Accessing and Modifying Elements

You can access and modify elements in an array just like you would with a list:

And now, let's modify the element by changing it's value to 6 :

Array Methods

The array module provides several methods to manipulate arrays:

append() - Adds an element to the end of the array:

extend() - Appends iterable elements to the end:

pop() - Removes and returns the element at the given position:

remove() : Removes the first occurrence of the specified value:

reverse() : Reverses the order of the array:

Note: There are more methods than we listed here. Refer to the official Python documentation to see a list of all available methods in the array module.

While the array module offers a more memory-efficient way to store basic data types, it's essential to remember its limitations . Unlike lists, arrays are homogeneous . This means all elements in the array must be of the same type. Also, you can only store basic C-style data types in arrays. If you need to store custom objects or other Python types, you'll need to use a list or another data structure.

NumPy Arrays

NumPy, short for Numerical Python, is a foundational package for numerical computations in Python. One of its primary features is its powerful N-dimensional array object , which offers fast operations on arrays, including mathematical, logical, shape manipulation, and more.

NumPy arrays are more versatile than Python's built-in array module and are a staple in data science and machine learning projects.

Why Use NumPy Arrays?

The first thing that comes to mind is performance . NumPy arrays are implemented in C and allow for efficient memory storage and faster operations due to optimized algorithms and the benefits of contiguous memory storage.

While Python's built-in arrays are one-dimensional, NumPy arrays can be multi-dimensional , making them ideal for representing matrices or tensors.

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Finally, NumPy provides a vast array of functions to operate on these arrays, from basic arithmetic to advanced mathematical operations, reshaping, splitting, and more.

Note: When you know the size of the data in advance, pre-allocating memory for arrays (especially in NumPy) can lead to performance improvements.

Creating a NumPy Array

To use NumPy, you first need to install it ( pip install numpy ) and then import it:

Once imported, you can create a NumPy array using the array() function:

You can also create multi-dimensional arrays:

This will give us:

Besides these basic ways we can create arrays, NumPy provides us with other clever ways we can create arrays. One of which is the arange() method. It creates arrays with regularly incrementing values:

Another one is the linspace() method, which creates arrays with a specified number of elements, spaced equally between specified beginning and end values:

Accessing and modifying elements in a NumPy array is intuitive:

Doing pretty much the same for multi-dimensional arrays:

Will change the value of the element in the second row (index 1 ) and the third column (index 2 ):

Changing the Shape of an Array

NumPy offers many functions and methods to manipulate and operate on arrays. For example, you can use the reshape() method to change the shape of an array . Say we have a simple array:

And we want to reshape it to a 3x4 matrix. All you need to do is use the reshape() method with desired dimensions passed as arguments:

This will result in:

Matrix Multiplication

The numpy.dot() method is used for matrix multiplication . It returns the dot product of two arrays. For one-dimensional arrays, it is the inner product of the arrays. For 2-dimensional arrays, it is equivalent to matrix multiplication , and for N-D, it is a sum product over the last axis of the first array and the second-to-last of the second array.

Let's see how it works. First, let's compute the dot product of two 1-D arrays (the inner product of the vectors):

32 is, in fact, the inner product of the two arrays - (1 4 + 2 5 + 3*6) . Next, we can perform matrix multiplication of two 2-D arrays:

Which will give us:

NumPy arrays are a significant step up from Python's built-in lists and the array module, especially for scientific and mathematical computations. Their efficiency, combined with the rich functionality provided by the NumPy library, makes them an indispensable tool for anyone looking to do numerical operations in Python.

Advice: This is just a quick overview of what you can do with the NumPy library. For more information about the library, you can read our "NumPy Tutorial: A Simple Example-Based Guide"

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NumPy: Get and set values in an array using various indexing

This article explains how to get and set values, such as individual elements or subarrays (e.g., rows or columns), in a NumPy array ( ndarray ) using various indexing.

Indexing on ndarrays — NumPy v1.26 Manual

Basics of selecting values in an ndarray

Specify with integers, specify with slices, specify with a list of boolean values: boolean indexing, specify with a list of integers: fancy indexing, combine different specification formats, assign new values to selected ranges, views and copies.

See the following articles for information on deleting, concatenating, and adding to ndarray .

NumPy: Delete rows/columns from an array with np.delete()
NumPy: Concatenate arrays with np.concatenate, np.stack, etc.
NumPy: Insert elements, rows, and columns into an array with np.insert()

The NumPy version used in this article is as follows. Note that functionality may vary between versions.

Individual elements or subarrays (such as rows or columns) in an ndarray can be selected by specifying their positions or ranges in each dimension with commas, as in [○, ○, ○, ...] . The trailing : can be omitted, making [○, ○, :, :] equivalent to [○, ○] .

For a 2D array, [i] selects the ith row, and [:, i] selects the ith column (indexes start from 0 ). More details will be provided later.

Positions in each dimension can be specified not only as integers but also in other formats such as lists or slices, allowing for the selection of any subarray.

Positions (indexes) are specified as integers ( int ).

Indexes start from 0 , and negative values can be used to specify positions from the end ( -1 represents the last). Specifying a non-existent position results in an error.

The same applies to multi-dimensional arrays. Positions are specified for each dimension.

You can omit the specification of later dimensions.

In a 2D array, [i] , equivalent to [i, :] , selects the ith row as a 1D array, and [:, i] selects the ith column.

Ranges can be selected with slices ( start:end:step ).

NumPy: Slicing ndarray

Example with a 1D array:

Example with a 2D array:

The trailing : can be omitted.

Using a slice i:i+1 selects a single row or column, preserving the array's dimensions, unlike selection with an integer ( int ), which reduces the dimensions.

NumPy: Get the number of dimensions, shape, and size of ndarray

Slices preserve the original array's dimensions, while integers reduce them. This difference can affect outcomes or cause errors in operations like concatenation, even with the same range selected.

Specifying a list or ndarray of Boolean values ( True or False ) matching the dimensions' sizes selects True positions, similar to masking.

An error occurs if the sizes do not match.

Rows or columns can be extracted using a slice : .

Note that specifying a list of Boolean values for multiple dimensions simultaneously does not yield the expected result. Using np.ix_() is necessary.

numpy.ix_ — NumPy v1.26 Manual

As with slices, selecting a range of width 1 (a single row or column) preserves the original array's number of dimensions.

A comparison on an ndarray yields a Boolean ndarray . Using this for indexing with [] selects True values, producing a flattened 1D array.

NumPy: Compare two arrays element-wise

Specify multiple conditions using & (AND), | (OR), and ~ (NOT) with parentheses () . Using and , or , not , or omitting parentheses results in an error.

How to fix "ValueError: The truth value ... is ambiguous" in NumPy, pandas

For methods of extracting rows or columns that meet certain conditions using Boolean indexing, refer to the following article.

NumPy: Extract or delete elements, rows, and columns that satisfy the conditions

It is also possible to select ranges with a list or ndarray of integers.

Order can be inverted or repeated, and using negative values is allowed. Essentially, it involves creating a new array by selecting specific positions from the original array.

As with Boolean indexing, specifying lists for multiple dimensions simultaneously does not yield the expected result. Using np.ix_() is necessary.

As in the 1D example, the order can be inverted or repeated, and negative values are also permissible.

When selecting with a list of one element, the original array's number of dimensions is preserved, in contrast to specifying with an integer.

Different formats can be used to specify each dimension.

A combination of a list of Boolean values and a list of integers requires the use of np.ix_() .

Note that np.ix_() can only accept 1D lists or arrays.

For example, when specifying multiple lists for arrays of three dimensions or more, np.ix_() is required. However, it cannot be combined with integers or slices in the same selection operation.

Integers can be specified as lists containing a single element. In this case, the resulting array retains the same number of dimensions as the original ndarray .

You can use range() to achieve similar functionality as slices. For example, to simulate slicing, retrieve the size of the target dimension using the shape attribute and pass it to range() as in range(a.shape[n])[::] .

How to use range() in Python

You can assign new values to selected ranges in an ndarray .

Specifying a scalar value on the right side assigns that value to all elements in the selected range on the left side.

Arrays can also be specified on the right side.

If the shape of the selected range on the left side matches that of the array on the right side, it is directly assigned. Non-contiguous locations pose no problem.

If the shape of the selected range on the left side does not match that of the array on the right side, it is assigned through broadcasting.

An error occurs if the shapes cannot be broadcast.

For more information on broadcasting, refer to the following article.

NumPy: Broadcasting rules and examples

The specification format used for each dimension when selecting subarrays determines whether a view or a copy of the original array is returned.

For example, using slices returns a view.

Whether two arrays refer to the same memory can be checked using np.shares_memory() .

NumPy: Views and copies of arrays

In the case of a view, changing the value in the selected subarray also changes the value in the original array, and vice versa.

Boolean indexing or fancy indexing returns a copy.

In this case, changing the value in the selected subarray does not affect the original array, and vice versa.

To create a copy of a subarray selected with a slice and process it separately from the original ndarray , use the copy() method.

When combining different specification formats, using Boolean or fancy indexing returns a copy.

Using integers and slices returns a view.

Related Categories

Array creation #

Array creation routines

Introduction #

There are 6 general mechanisms for creating arrays:

Conversion from other Python structures (i.e. lists and tuples)

Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.)

Replicating, joining, or mutating existing arrays

Reading arrays from disk, either from standard or custom formats

Creating arrays from raw bytes through the use of strings or buffers

Use of special library functions (e.g., random)

You can use these methods to create ndarrays or Structured arrays . This document will cover general methods for ndarray creation.

1) Converting Python sequences to NumPy Arrays #

NumPy arrays can be defined using Python sequences such as lists and tuples. Lists and tuples are defined using [...] and (...) , respectively. Lists and tuples can define ndarray creation:

a list of numbers will create a 1D array,

a list of lists will create a 2D array,

further nested lists will create higher-dimensional arrays. In general, any array object is called an ndarray in NumPy.

When you use numpy.array to define a new array, you should consider the dtype of the elements in the array, which can be specified explicitly. This feature gives you more control over the underlying data structures and how the elements are handled in C/C++ functions. If you are not careful with dtype assignments, you can get unwanted overflow, as such

An 8-bit signed integer represents integers from -128 to 127. Assigning the int8 array to integers outside of this range results in overflow. This feature can often be misunderstood. If you perform calculations with mismatching dtypes , you can get unwanted results, for example:

Notice when you perform operations with two arrays of the same dtype : uint32 , the resulting array is the same type. When you perform operations with different dtype , NumPy will assign a new type that satisfies all of the array elements involved in the computation, here uint32 and int32 can both be represented in as int64 .

The default NumPy behavior is to create arrays in either 32 or 64-bit signed integers (platform dependent and matches C long size) or double precision floating point numbers. If you expect your integer arrays to be a specific type, then you need to specify the dtype while you create the array.

2) Intrinsic NumPy array creation functions #

NumPy has over 40 built-in functions for creating arrays as laid out in the Array creation routines . These functions can be split into roughly three categories, based on the dimension of the array they create:

1 - 1D array creation functions #

The 1D array creation functions e.g. numpy.linspace and numpy.arange generally need at least two inputs, start and stop .

numpy.arange creates arrays with regularly incrementing values. Check the documentation for complete information and examples. A few examples are shown:

Note: best practice for numpy.arange is to use integer start, end, and step values. There are some subtleties regarding dtype . In the second example, the dtype is defined. In the third example, the array is dtype=float to accommodate the step size of 0.1 . Due to roundoff error, the stop value is sometimes included.

numpy.linspace will create arrays with a specified number of elements, and spaced equally between the specified beginning and end values. For example:

The advantage of this creation function is that you guarantee the number of elements and the starting and end point. The previous arange(start, stop, step) will not include the value stop .

2 - 2D array creation functions #

The 2D array creation functions e.g. numpy.eye , numpy.diag , and numpy.vander define properties of special matrices represented as 2D arrays.

np.eye(n, m) defines a 2D identity matrix. The elements where i=j (row index and column index are equal) are 1 and the rest are 0, as such:

numpy.diag can define either a square 2D array with given values along the diagonal or if given a 2D array returns a 1D array that is only the diagonal elements. The two array creation functions can be helpful while doing linear algebra, as such:

vander(x, n) defines a Vandermonde matrix as a 2D NumPy array. Each column of the Vandermonde matrix is a decreasing power of the input 1D array or list or tuple, x where the highest polynomial order is n-1 . This array creation routine is helpful in generating linear least squares models, as such:

3 - general ndarray creation functions #

The ndarray creation functions e.g. numpy.ones , numpy.zeros , and random define arrays based upon the desired shape. The ndarray creation functions can create arrays with any dimension by specifying how many dimensions and length along that dimension in a tuple or list.

numpy.zeros will create an array filled with 0 values with the specified shape. The default dtype is float64 :

numpy.ones will create an array filled with 1 values. It is identical to zeros in all other respects as such:

The random method of the result of default_rng will create an array filled with random values between 0 and 1. It is included with the numpy.random library. Below, two arrays are created with shapes (2,3) and (2,3,2), respectively. The seed is set to 42 so you can reproduce these pseudorandom numbers:

numpy.indices will create a set of arrays (stacked as a one-higher dimensioned array), one per dimension with each representing variation in that dimension:

This is particularly useful for evaluating functions of multiple dimensions on a regular grid.

3) Replicating, joining, or mutating existing arrays #

Once you have created arrays, you can replicate, join, or mutate those existing arrays to create new arrays. When you assign an array or its elements to a new variable, you have to explicitly numpy.copy the array, otherwise the variable is a view into the original array. Consider the following example:

In this example, you did not create a new array. You created a variable, b that viewed the first 2 elements of a . When you added 1 to b you would get the same result by adding 1 to a[:2] . If you want to create a new array, use the numpy.copy array creation routine as such:

For more information and examples look at Copies and Views .

There are a number of routines to join existing arrays e.g. numpy.vstack , numpy.hstack , and numpy.block . Here is an example of joining four 2-by-2 arrays into a 4-by-4 array using block :

Other routines use similar syntax to join ndarrays. Check the routine’s documentation for further examples and syntax.

4) Reading arrays from disk, either from standard or custom formats #

This is the most common case of large array creation. The details depend greatly on the format of data on disk. This section gives general pointers on how to handle various formats. For more detailed examples of IO look at How to Read and Write files .

Standard Binary Formats #

Various fields have standard formats for array data. The following lists the ones with known Python libraries to read them and return NumPy arrays (there may be others for which it is possible to read and convert to NumPy arrays so check the last section as well)

Examples of formats that cannot be read directly but for which it is not hard to convert are those formats supported by libraries like PIL (able to read and write many image formats such as jpg, png, etc).

Common ASCII Formats #

Delimited files such as comma separated value (csv) and tab separated value (tsv) files are used for programs like Excel and LabView. Python functions can read and parse these files line-by-line. NumPy has two standard routines for importing a file with delimited data numpy.loadtxt and numpy.genfromtxt . These functions have more involved use cases in Reading and writing files . A simple example given a simple.csv :

Importing simple.csv is accomplished using numpy.loadtxt :

More generic ASCII files can be read using scipy.io and Pandas .

5) Creating arrays from raw bytes through the use of strings or buffers #

There are a variety of approaches one can use. If the file has a relatively simple format then one can write a simple I/O library and use the NumPy fromfile() function and .tofile() method to read and write NumPy arrays directly (mind your byteorder though!) If a good C or C++ library exists that read the data, one can wrap that library with a variety of techniques though that certainly is much more work and requires significantly more advanced knowledge to interface with C or C++.

6) Use of special library functions (e.g., SciPy, Pandas, and OpenCV) #

NumPy is the fundamental library for array containers in the Python Scientific Computing stack. Many Python libraries, including SciPy, Pandas, and OpenCV, use NumPy ndarrays as the common format for data exchange, These libraries can create, operate on, and work with NumPy arrays.

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Python Array

In this tutorial, we will focus on a module named array . The array module allows us to store a collection of numeric values.

Note: When people say arrays in Python, more often than not, they are talking about Python lists . If that's the case, visit the Python list tutorial.

Creating Python Arrays

To create an array of numeric values, we need to import the array module. For example:

Here, we created an array of float type. The letter d is a type code. This determines the type of the array during creation.

Commonly used type codes are listed as follows:

We will not discuss different C types in this article. We will use two type codes in this entire article: i for integers and d for floats.

Note : The u type code for Unicode characters is deprecated since version 3.3. Avoid using as much as possible.

Accessing Python Array Elements

We use indices to access elements of an array:

Note : The index starts from 0 (not 1) similar to lists.

Slicing Python Arrays

We can access a range of items in an array by using the slicing operator : .

Changing and Adding Elements

Arrays are mutable; their elements can be changed in a similar way as lists.

We can add one item to the array using the append() method, or add several items using the extend() method.

We can also concatenate two arrays using + operator.

Removing Python Array Elements

We can delete one or more items from an array using Python's del statement.

We can use the remove() method to remove the given item, and pop() method to remove an item at the given index.

Check this page to learn more about Python array and array methods .

Python Lists Vs Arrays

In Python, we can treat lists as arrays. However, we cannot constrain the type of elements stored in a list. For example:

If you create arrays using the array module, all elements of the array must be of the same numeric type.

When to use arrays?

Lists are much more flexible than arrays. They can store elements of different data types including strings. And, if you need to do mathematical computation on arrays and matrices, you are much better off using something like NumPy .

So, what are the uses of arrays created from the Python array module?

The array.array type is just a thin wrapper on C arrays which provides space-efficient storage of basic C-style data types. If you need to allocate an array that you know will not change, then arrays can be faster and use less memory than lists.

Unless you don't really need arrays (array module may be needed to interface with C code), the use of the array module is not recommended.

Introduction

Sorry about that.

Destructuring in Python

Destructuring (also called unpacking) is where we take a collection, like a list or a tuple, and we break it up into individual values. This allows us to do things like destructuring assignments, where we assign values to several variables at once from a single collection.

Today let's talk about:

Standard destructuring assignments, with tuples or lists

Destructuring dictionaries in Python

Using destructuring in for loops
How to ignore certain values when destructuring
How to collect values left over when destructuring, using *

Standard destructuring assignments

Python, like many programming languages, allows us to assign more than one variable at a time on a single line. We just have to provide the same number of values on both sides of the assignment. For example:

Here we assign the value 5 to x , and 11 to y . The values are assigned entirely based on order, so if we switch the variable order, or the order of the values we intend to assign, we will end up with different results.

How this works is fairly straightforward, but what's less obvious is that this is an example of destructuring. What if I wrote it like this?

One thing that a lot of newer Python programmers in particular don't realise is that parentheses have nothing at all to do with tuples. In fact, it's the commas which tell Python something is a tuple: we just add brackets for readability in a lot of cases. In some instances the brackets are actually necessary, in order to isolate the tuple from the syntax around it, such as when we put a tuple inside a list. However, in this case the brackets are still not part of the tuple syntax. Adding brackets around a single number doesn't turn it into a tuple.

With all that in mind, we actually end up destructuring a tuple in the example above, splitting the tuple (5, 11) into its component values, so that we can bind those values to the two variable names.

We're not limited to just tuples here. We can also destructure a list, for example, or even a set. You're unlikely to want to perform a destructuring assignment with a set, however, since the order is not guaranteed, and we therefore end up with variables we don't really know the values of.

If we try to destructure a collection with more or fewer values than we provide variables, we end up with a ValueError .

A dictionary is a collection of key-value pairs, so when destructuring a dictionary, things can get a bit confusing!

Now, before you run that code, think about what the values of x and y should be!

The answer is that x = "name" and y = "age" . That's because by default when we use a dictionary as an unidimensional collection (such as a list or tuple), what you get back are the keys. It's the same if you call list(my_dict) , you get back ["name", "age"] .

Often when we talk about "dictionary destructuring", what most people refer to is the **kwargs part of functions. If you want to learn more about that, check out Day 17 of our 30 Days of Python free course, Flexible Functions with *args and **kwargs** .

If you wanted to destructure the dictionary values only, you can do:

Note that destructuring dictionaries only works well in modern Python versions (3.7+), because dictionaries are ordered collections now. In older Python versions, dictionaries were unordered (so items could move around). It didn't make sense to do dictionary destructuring then.

Destructuring in for loops

A couple of months back, we wrote a snippet post on enumerate , which is a vital component in writing good, Pythonic loops. In case you're not familiar, the syntax for enumerate looks like this:

enumerate takes an iterable collection as an argument and returns an enumerate object containing a tuple for each item in the collection. Each tuple contains a counter value, which increments with each iteration, along with a value from the provided collection.

As you can see in the example above, we provide two variable names when creating our for loop: counter and letter . This is actually a very common example of destructuring. For each tuple in the enumerate object, the first value gets assigned to counter , and the second value is assigned to letter .

Once again, there isn't any magic going on here with the variable names, and the assignment is entirely based on the order of the values. If we were to switch the position of counter and letter , we'd end up with some confusing variable names.

It's possible to do this type of destructuring with as many values as we like, and this is not limited to the enumerate function. For example, we could do something like this:

Here we break apart each tuple in the people list, assigning the values to name , age , and profession respectively.

This is a lot better than something like the code below, where we rely on indices instead of good descriptive names:

Raymond Hettinger — one of the core Python developers — said something in one of his talks that really stuck with me. He said that in Python, you should basically never be referring to items by their index: there is nearly always a better way.

Destructuring is a good example of one of those better ways.

Ignoring Values

So, what do we do if we have a collection of values and we don't want to assign all of them? We can use an _ in place of a variable name.

For example, if we take one of the tuples from the the people list above, and we only care about the name and profession, we can do the following:

This would also work just as well inside a loop, and can also be used when we don't care about any of the values. An example might be when using range to ensure a set number of iterations.

Using * to Collect Values

In some situations, we might want to isolate one or two values in a collection, and then keep the other items together. We featured an example of a situation like this our post on list rotation .

In Python, we can use the * operator to collect leftover values when performing a destructuring assignment. For example, we might have a list of numbers, and we want to grab the first number, and then assign the remaining numbers to a second variable:

Here, the first value ( 1 ) is assigned to head , while the rest of the numbers end up in a new list called tail .

We can also do this the other way around, creating a new list with everything but the last value, and assigning the last value to its own variable.

This is interesting, but unless we need to preserve the original list, we already have the pop method for stuff like this. However, we can do something else with this syntax. We can can assign any number of variables, and then gather up the remainder. We might grab the first and last items, and then gather up the middle, for example:

Alternatively, we might want to grab the first three items and then bundle up the rest:

There are tonnes of possibilities.

Wrapping Up

I hope you learnt something new from this short post on destructuring in Python!

This isn't actually the end of the story, as there are also ways to pack and unpack collections using * and ** , so feel free to read this article to learn more.

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Python – Initialize empty array of given length

As we know Array is a collection of items stored at contiguous memory locations. In Python, a List ( Dynamic Array ) can be treated as an Array. In this article, we will learn how to initialize an empty array of some given size. Let’s see different Pythonic ways to create an empty list in Python with a certain size.

Python – Initialize an empty array of a given length

Below, are the methods to Initialize an empty array of a given length in Python.

Using * Operator
Using List Comprehension
Using For Loop
Using NumPy
Using repeat() Method

Method 1: Initialize empty array using * Operator

In this example, we are creating different types of empty using an asterisk (*) operator.

Method 2: Create an empty list in python with a certain size using list comprehension

In this example, we are using Python List comprehension for 1D and 2D empty arrays.

Method 3: Create an empty list of a specific size in Python using a loop

In this example, we are using a Python loop for 1D and 2D empty arrays.

Method 4: Initialize empty array using Numpy

In this method, we are using the Numpy module to generate an empty matrix of 1D and 2D sizes using np.empty() .

Method 5: Initialize empty array using repeat() Method

In this method, we can use the repeat() function from the itertools module to create a new iterator that returns the same value a certain number of times. This can be used to create an empty array of a specific size by providing a value of None and the desired length.

This method has a time complexity of O(n) and an auxiliary space of O(n) where n is the desired length of the array.

Reference : List in Python

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3 ways to initialize a Python Array

Hey, folks! In this article, we will be focusing on some Easy Ways to Initialize a Python Array .

What is a Python array?

Python Array is a data structure that holds similar data values at contiguous memory locations.

When compared to a List (dynamic Arrays), Python Arrays stores the similar type of elements in it. While a Python List can store elements belonging to different data types in it.

Now, let us look at the different ways to initialize an array in Python.

Method 1: Using for loop and Python range() function

Python for loop and range() function together can be used to initialize an array with a default value.

Python range() function accepts a number as argument and returns a sequence of numbers which starts from 0 and ends by the specified number, incrementing by 1 each time.

Python for loop would place 0(default-value) for every element in the array between the range specified in the range() function.

We have created an array — ‘arr’ and initalized it with 5 elements carrying a default value (0).

Method 2: Python NumPy module to create and initialize array

Python NumPy module can be used to create arrays and manipulate the data in it efficiently. The numpy.empty() function creates an array of a specified size with a default value = ‘None’.

Method 3: Direct method to initialize a Python array

While declaring the array, we can initialize the data values using the below command:

As seen in the above example, we have created two arrays with the default values as ‘0’ and ‘P’ along with the specified size with it.

By this, we have come to the end of this topic. Please feel free to comment below in case, you come across any doubt.

Python array initialization — Documentation

Python bytearray(): Manipulating Low-Level Data Efficiently

The Python bytearray() function converts strings or collections of integers into a mutable sequence of bytes. It provides developers the usual methods Python affords to both mutable and byte data types. Python’s bytearray() built-in allows for high-efficiency manipulation of data in several common situations.

1 Rationale
2 Basic Use & Syntax
3 Byte Strings vs. Byte Arrays
4 Mutable vs. Immutable
5.1 Escaped Character Sequences
5.2 Default Values
6 Final Thoughts

The Python bytearray() function’s powerful features do come with some responsibility. Developers must be mindful of encodings, be aware of source data format, and have a basic working knowledge of common character sets like ASCII .

In this article, you’ll learn the rationale, common use cases, advanced use cases, and potential pitfalls of Python’s bytearray() built-in function. There’s even a section with some notable quirks at the bottom.

TL;DR — Python’s bytearray() function converts strings and sequences of integers into bytes to provide developers the ability to efficiently update (mutate) data without additional memory allocation.

The bytearray object was introduced in Python 2 and has served as a ‘ mutable counterpart ‘ to the bytes object since. Unlike the bytes object, the bytearray object doesn’t come with a literal notational representation and must be instantiated directly.

For example, b'string' provides literal representation for a bytes object whereas developers must use the syntax bytearray('string', encoding='ascii') to create a new bytearray object. I regard this as only a minor inconvenience given the benefits Python’s bytearray data type provides developers.

Data like strings are regarded as immutable (more on that below) meaning they can’t be changed directly. To change (mutate) a string, developers must copy the string and assigned the new version to an object located in memory. Consider the following example:

Note that our string has been changed but now has a different id (memory location.) That means that a is now a different object in memory and our original object is garbage. That’s all well-and-good when one is changing around a few strings.

Such easy string manipulation is one of the language features that make Python such a popular programming language . Unfortunately, this approach isn’t efficient for changing large numbers of such data. That’s where the power of the bytearry() function shines.

The official Python documentation describes the intended usage of bytearry() as follows:

The bytearray class is a mutable sequence of integers in the range 0 <= x < 256. It has most of the usual methods of mutable sequences, described in Mutable Sequence Types, as well as most methods that the bytes type has, see Bytes and Bytearray Operations.

The reference to the Bytes and Bytearray Operations documentation should not be passed over either. That reference provides the following elaboration:

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data.

This will make more sense in just a bit when we take a look at cases where the bytearray() function displays escaped character sequences. For now, let’s consider some common cases and basic syntax!

Basic Use & Syntax

The bytearray() function returns a new array of bytes. This can result from an input string or a collection of integers in the range 0-255 (inclusive). If you are familiar with binary notation you may have already guessed that makes perfect accommodation for eight-bit representations .

Python’s built-in bytearray() takes three possible arguments:

source – the data to be converted to an array of bytes, either a string or a collection of integers.
encoding – required for strings, optional for collections of integers
errors – an optional parameter to specify error handling. See here for options .

Let’s take the bytearray() function for a quick spin to demonstrate the basic use of these arguments:

Well, that didn’t go well. Bytes are essential just numerical representations of information. Without knowing how to interpret that information, Python will make a fuss. Arguably, a default encoding could be used but that would cause another set of problems. Let’s see what happens when we let Python know how to interpret the data:

Now we’ve got a bytearray object in memory signifying as much by showing a bytes literal within parenthesis (more on that below.) Let’s take a look at how the bytearray() function handles a series of integer values. Remember, only values in the range of 0 < val < 255 are considered valid here.

Well, that didn’t go well either. When given a series of numbers, Python doesn’t need to know the encoding because it limits the values to 0-255. The character representation of any value in this range is assumed to be of the ISO8859-1 standard, more commonly known as the Latin-1 character set (a superset of the ASCII encoding.) Let’s try this again:

Now we’ve got a bytearray object from a list of integer values! But what can we do with this? This is where the bytearray() function starts demonstrating its utility as a mutable object. We’ll cover what mutable vs. immutable means in just a second. Before we get there, however, let’s stop for a second and consider what the difference between a byte string and a byte array might be.

Byte Strings vs. Byte Arrays

At this point, you may be wondering what the difference between a sequence of bytes and a collection of bytes might be. In a word— mutability . Both represent data in binary format, both can easily handle two-digit hexadecimal inputs, and both rely on developers to be mindful of encodings to ensure proper representation of numerical values and the resulting translations. So what gives?

Bytes are treated with more finality than bytearray objects. That is, they are suited for applications where one doesn’t need to change the data often . For example, a bytes object would be better suited for storing data received from a network transmission. For large transmissions, maybe even as an array of bytes (but not bytearray objects.)

The bytearray object is better suited for situations where frequent manipulation of data is expected . For example, the updating of data received from a network transmission before retransmission. bytearray objects also provide methods afforded to mutable objects such as sizing, slicing, member assignment, insertion, reversal, and many more.

To fully appreciate the difference between Python’s bytes and bytearay objects we’ll need to consider the difference between mutable vs. immutable data types. Don’t worry, it’s a fairly simple concept and with a basic example one that’s easy to grasp!

Mutable vs. Immutable

In Python, strings, numbers, and tuples are immutable—meaning their values can’t be changed. They can be copied or reassigned but not manipulated directly. Data structures like lists, dictionaries , and sets are mutable and allow much more efficient manipulation of their constituent parts.

Specifically, additional memory allocation is minimized when updating part of data contained in the middle of a list whereas updating the middle of a string would require a near-doubling. This describes how byte arrays can be applied in representing immutable data types to improve efficiency in certain contexts.

Let’s review our original TL;DR example from above, this time with a little more explanation:

Here we’ve shown that trying to directly change the text of our string throws a TypeError . Storing that string as a byte array allows direct manipulation of the individual character values. After manipulation, we can then convert the resulting values to a string via the decode() method—ensuring to specify the proper encoding.

Is this an example of efficient use of the bytearray() function? Probably not. Imagine having to update large sequences of textual data, such as in many database applications or large network data consumptions. The ability to manipulate data without allocating additional memory would be essential. That’s where byte arrays really shine.

TL;DR – representing a string as an array of bytes allows one to manipulate it more efficiently. There is an initial overhead cost in memory but a decreasing cost as manipulative operations amass. Always mind your encoding.

The bytearray() function deals with some pretty nuanced utility. This makes no surprise of the sometimes spooky behavior exhibited by this built-in. Below are a few cases where the bytearray() function might behave in ways that might need some explanation. These certainly aren’t bugs but should be kept in mind nonetheless.

Escaped Character Sequences

Given the bytearray() restriction of valid integer arguments to a range of 0-255 one might expect some quicky behavior to arise. A ValueError gets thrown anytime a non-compliant value is attempted—but that’s expected. What’s quirky is how Python will represent ordinal values for non-printable characters. Consider the following:

Note that bytearry() will display escaped characters regardless of how an array of integers is passed in ( \x prefix.) However, note that the characters with integer values matching ordinal values of printable characters are represented as that character. In other words: 1 is displayed as \x01 but 65 is displayed as A since it’s the ordinal value of a printable character. Check out the article on Python’s ord() function to get a feel for that interoperability.

Default Values

The bytearray() can be initialized as an empty array or one with default values by passing a non-iterable integer argument. Consider the following:

Note here that passing the integer value of 5 as an argument initializes the bytearray() object with five values of null —represented by the escaped character sequence \x00 . The documentation covering valid bytearray() arguments isn’t entirely clear on this point in my opinion and can lead to some confusion. Note the last example places the 5 in Python’s bracketed list notation. This results in a bytearray() object of length one with the single escaped character sequence of \x05 . Again, not a bug but certainly quirky. Note : null is not the same as 0 .

Final Thoughts

Python’s bytearray() function provides an easy-to-use utility for memory-efficient manipulation of data. Developers of network applications, large string-processing use cases, and programs that encode and decode large swaths of data can all stand in admiration of its high-level accessibility of low-lever data.

Python certainly isn’t the canonical language for developing memory-efficient applications—at least not directly. The bytearray() function offers the utility that makes a case that, while not the canonical choice, Python is certainly up to the task if needed. For more insight into the handling of binary data check out the article What’s a Byte Stream Anyway ?

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Functions Defined

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3

Compound Data Types

Lists (known as arrays in other languages) are one of the compound data types that Python understands. Lists can be indexed, sliced and manipulated with other built-in functions. More about lists in Python 3

Intuitive Interpretation

Calculations are simple with Python, and expression syntax is straightforward: the operators + , - , * and / work as expected; parentheses () can be used for grouping. More about simple math functions in Python 3 .

All the Flow You’d Expect

Python knows the usual control flow statements that other languages speak — if , for , while and range — with some of its own twists, of course. More control flow tools in Python 3

Quick & Easy to Learn

Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview.

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Indexing on ndarrays
ndarrays. #. ndarrays can be indexed using the standard Python x[obj] syntax, where x is the array and obj the selection. There are different kinds of indexing available depending on obj : basic indexing, advanced indexing and field access. Most of the following examples show the use of indexing when referencing data in an array.
How Arrays Work in Python
Python Array Methods. You can use various built-in Python methods when working with lists and arrays. Below are the methods you can use on arrays and lists in Python. The Append() method. If you want to add an item to the end of a list, you can utilize the append method. Example:
Python Arrays
What is Array in Python? An array is a container used to contain a fixed number of items. But, there is an exception that values should be of the same type. The following are two terms often used with arrays. Array element - Every value in an array represents an element. Array index - Every element has some position in the array known as ...
Assign value to an individual cell in a two dimensional python array
Let's say I have the following empty two dimensional array in Python: q = [[None]*5]*4 I want to assign a value of 5 to the first row in the first column of q. Instinctively, I do the following: q[0][0] = 5 However, this produces:
Python Array Tutorial
That is the most important thing to remember about Python arrays - the fact that they can only hold a sequence of multiple items that are of the same type. ... You can change the value of a specific element by speficying its position and assigning it a new value: import array as arr #original array numbers = arr.array('i',[10,20,30]) #change ...
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To define an array in Python, you could use the np.array function to convert a list. TRY IT! Create the following arrays: \(x = \begin{pmatrix} 1 & 4 & 3 \\ \end{pmatrix}\) ... You can reassign a value of an array by using array indexing and the assignment operator. You can reassign multiple elements to a single number using array indexing on ...
Guide to Arrays in Python
While the array module offers a more memory-efficient way to store basic data types, it's essential to remember its limitations.Unlike lists, arrays are homogeneous.This means all elements in the array must be of the same type. Also, you can only store basic C-style data types in arrays. If you need to store custom objects or other Python types, you'll need to use a list or another data structure.
Multidimensional Arrays in Python: A Complete Guide
It is a Python library that gives users access to a multidimensional array object, a variety of derived objects (such as masked arrays and matrices), and a selection of functions for quick operations on arrays and multi-dimensional matrices. The standard way of Python language creates lists which are very similar to arrays but remember, there ...
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Array creation
There are 6 general mechanisms for creating arrays: Conversion from other Python structures (i.e. lists and tuples) Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.) Replicating, joining, or mutating existing arrays. Reading arrays from disk, either from standard or custom formats. Creating arrays from raw bytes through ...
Python Array
The list contains a collection of items and it supports add/update/delete/search operations. That's why there is not much use of a separate data structure in Python to support arrays. An array contains items of the same type but Python list allows elements of different types. This is the only feature wise difference between an array and a list.
Array in Python
The array can be handled in Python by a module named " array ". They can be useful when we have to manipulate only specific data type values. Properties of Arrays. Each array element is of the same data type and size. For example: For an array of integers with the int data type, each element of the array will occupy 4 bytes.
Python Array of Numeric Values
In this tutorial, we will focus on a module named array.The array module allows us to store a collection of numeric values.. Note: When people say arrays in Python, more often than not, they are talking about Python lists.If that's the case, visit the Python list tutorial.
Two Dimensional Array in Python
So, Python does all the array related operations using the list object. The array is an ordered collection of elements in a sequential manner. Syntax to declare an array: array-name = [] Two-dimensional arrays are basically array within arrays. Here, the position of a data item is accessed by using two indices.
Destructuring in Python
Python, like many programming languages, allows us to assign more than one variable at a time on a single line. We just have to provide the same number of values on both sides of the assignment. For example: x, y = 5, 11 Here we assign the value 5 to x, and 11 to y. The values are assigned entirely based on order, so if we switch the variable ...
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Arrays in Python: What are Python Arrays & How to use them?
An array is basically a data structure which can hold more than one value at a time. It is a collection or ordered series of elements of the same type. Example: a=arr.array('d',[1.2,1.3,2.3]) We can loop through the array items easily and fetch the required values by just specifying the index number.
Python
As we know Array is a collection of items stored at contiguous memory locations. In Python, a List (Dynamic Array) can be treated as an Array.In this article, we will learn how to initialize an empty array of some given size. Let's see different Pythonic ways to create an empty list in Python with a certain size.. Python - Initialize an empty array of a given length
3 ways to initialize a Python Array
What is a Python array? Python Array is a data structure that holds similar data values at contiguous memory locations.. When compared to a List(dynamic Arrays), Python Arrays stores the similar type of elements in it. While a Python List can store elements belonging to different data types in it. Now, let us look at the different ways to initialize an array in Python.
How to Append an Item to a Python Array
To explicitly use arrays instead of lists in Python, you need to import the "array" module. Here's a series of commands designed to show you how to create an array, append an item to it, and print the output: import array. my_array = array.array('i', [1, 2, 3]) my_array.append(4) print(my_array) As you can see, first we import the ...
Python bytearray(): Manipulating Low-Level Data Efficiently
The Python bytearray() function converts strings or collections of integers into a mutable sequence of bytes. It provides developers the usual methods Python affords to both mutable and byte data types. Python's bytearray() built-in allows for high-efficiency manipulation of data in several common situations. Table of Contents show 1 Rationale 2 Basic Use & Syntax […]
Welcome to Python.org
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3. Python is a programming language that lets you work quickly and integrate systems more effectively. Learn More.