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Beginner Python exercises

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Python Programming Challenges

Practice your Python skills with these programming challenges. The tasks are meant to be challenging for beginners. If you find them too difficult, try completing our lessons for beginners first.

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Python Practice for Beginners: 15 Hands-On Problems

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Want to put your Python skills to the test? Challenge yourself with these 15 Python practice exercises taken directly from our Python courses!

There’s no denying that solving Python exercises is one of the best ways to practice and improve your Python skills . Hands-on engagement with the language is essential for effective learning. This is exactly what this article will help you with: we've curated a diverse set of Python practice exercises tailored specifically for beginners seeking to test their programming skills.

These Python practice exercises cover a spectrum of fundamental concepts, all of which are covered in our Python Data Structures in Practice and Built-in Algorithms in Python courses. Together, both courses add up to 39 hours of content. They contain over 180 exercises for you to hone your Python skills. In fact, the exercises in this article were taken directly from these courses!

In these Python practice exercises, we will use a variety of data structures, including lists, dictionaries, and sets. We’ll also practice basic programming features like functions, loops, and conditionals. Every exercise is followed by a solution and explanation. The proposed solution is not necessarily the only possible answer, so try to find your own alternative solutions. Let’s get right into it!

Python Practice Problem 1: Average Expenses for Each Semester

John has a list of his monthly expenses from last year:

He wants to know his average expenses for each semester. Using a for loop, calculate John’s average expenses for the first semester (January to June) and the second semester (July to December).

Explanation

We initialize two variables, first_semester_total and second_semester_total , to store the total expenses for each semester. Then, we iterate through the monthly_spending list using enumerate() , which provides both the index and the corresponding value in each iteration. If you have never heard of enumerate() before – or if you are unsure about how for loops in Python work – take a look at our article How to Write a for Loop in Python .

Within the loop, we check if the index is less than 6 (January to June); if so, we add the expense to first_semester_total . If the index is greater than 6, we add the expense to second_semester_total .

After iterating through all the months, we calculate the average expenses for each semester by dividing the total expenses by 6 (the number of months in each semester). Finally, we print out the average expenses for each semester.

Python Practice Problem 2: Who Spent More?

John has a friend, Sam, who also kept a list of his expenses from last year:

They want to find out how many months John spent more money than Sam. Use a for loop to compare their expenses for each month. Keep track of the number of months where John spent more money.

We initialize the variable months_john_spent_more with the value zero. Then we use a for loop with range(len()) to iterate over the indices of the john_monthly_spending list.

Within the loop, we compare John's expenses with Sam's expenses for the corresponding month using the index i . If John's expenses are greater than Sam's for a particular month, we increment the months_john_spent_more variable. Finally, we print out the total number of months where John spent more money than Sam.

Python Practice Problem 3: All of Our Friends

Paul and Tina each have a list of their respective friends:

Combine both lists into a single list that contains all of their friends. Don’t include duplicate entries in the resulting list.

There are a few different ways to solve this problem. One option is to use the + operator to concatenate Paul and Tina's friend lists ( paul_friends and tina_friends ). Afterwards, we convert the combined list to a set using set() , and then convert it back to a list using list() . Since sets cannot have duplicate entries, this process guarantees that the resulting list does not hold any duplicates. Finally, we print the resulting combined list of friends.

If you need a refresher on Python sets, check out our in-depth guide to working with sets in Python or find out the difference between Python sets, lists, and tuples .

Python Practice Problem 4: Find the Common Friends

Now, let’s try a different operation. We will start from the same lists of Paul’s and Tina’s friends:

In this exercise, we’ll use a for loop to get a list of their common friends.

For this problem, we use a for loop to iterate through each friend in Paul's list ( paul_friends ). Inside the loop, we check if the current friend is also present in Tina's list ( tina_friends ). If it is, it is added to the common_friends list. This approach guarantees that we test each one of Paul’s friends against each one of Tina’s friends. Finally, we print the resulting list of friends that are common to both Paul and Tina.

Python Practice Problem 5: Find the Basketball Players

You work at a sports club. The following sets contain the names of players registered to play different sports:

How can you obtain a set that includes the players that are only registered to play basketball (i.e. not registered for football or volleyball)?

This type of scenario is exactly where set operations shine. Don’t worry if you never heard about them: we have an article on Python set operations with examples to help get you up to speed.

First, we use the | (union) operator to combine the sets of football and volleyball players into a single set. In the same line, we use the - (difference) operator to subtract this combined set from the set of basketball players. The result is a set containing only the players registered for basketball and not for football or volleyball.

If you prefer, you can also reach the same answer using set methods instead of the operators:

It’s essentially the same operation, so use whichever you think is more readable.

Python Practice Problem 6: Count the Votes

Let’s try counting the number of occurrences in a list. The list below represent the results of a poll where students were asked for their favorite programming language:

Use a dictionary to tally up the votes in the poll.

In this exercise, we utilize a dictionary ( vote_tally ) to count the occurrences of each programming language in the poll results. We iterate through the poll_results list using a for loop; for each language, we check if it already is in the dictionary. If it is, we increment the count; otherwise, we add the language to the dictionary with a starting count of 1. This approach effectively tallies up the votes for each programming language.

If you want to learn more about other ways to work with dictionaries in Python, check out our article on 13 dictionary examples for beginners .

Python Practice Problem 7: Sum the Scores

Three friends are playing a game, where each player has three rounds to score. At the end, the player whose total score (i.e. the sum of each round) is the highest wins. Consider the scores below (formatted as a list of tuples):

Create a dictionary where each player is represented by the dictionary key and the corresponding total score is the dictionary value.

This solution is similar to the previous one. We use a dictionary ( total_scores ) to store the total scores for each player in the game. We iterate through the list of scores using a for loop, extracting the player's name and score from each tuple. For each player, we check if they already exist as a key in the dictionary. If they do, we add the current score to the existing total; otherwise, we create a new key in the dictionary with the initial score. At the end of the for loop, the total score of each player will be stored in the total_scores dictionary, which we at last print.

Python Practice Problem 8: Calculate the Statistics

Given any list of numbers in Python, such as …

 … write a function that returns a tuple containing the list’s maximum value, sum of values, and mean value.

We create a function called calculate_statistics to calculate the required statistics from a list of numbers. This function utilizes a combination of max() , sum() , and len() to obtain these statistics. The results are then returned as a tuple containing the maximum value, the sum of values, and the mean value.

The function is called with the provided list and the results are printed individually.

Python Practice Problem 9: Longest and Shortest Words

Given the list of words below ..

… find the longest and the shortest word in the list.

To find the longest and shortest word in the list, we initialize the variables longest_word and shortest_word as the first word in the list. Then we use a for loop to iterate through the word list. Within the loop, we compare the length of each word with the length of the current longest and shortest words. If a word is longer than the current longest word, it becomes the new longest word; on the other hand, if it's shorter than the current shortest word, it becomes the new shortest word. After iterating through the entire list, the variables longest_word and shortest_word will hold the corresponding words.

There’s a catch, though: what happens if two or more words are the shortest? In that case, since the logic used is to overwrite the shortest_word only if the current word is shorter – but not of equal length – then shortest_word is set to whichever shortest word appears first. The same logic applies to longest_word , too. If you want to set these variables to the shortest/longest word that appears last in the list, you only need to change the comparisons to <= (less or equal than) and >= (greater or equal than), respectively.

If you want to learn more about Python strings and what you can do with them, be sure to check out this overview on Python string methods .

Python Practice Problem 10: Filter a List by Frequency

Given a list of numbers …

… create a new list containing only the numbers that occur at least three times in the list.

Here, we use a for loop to iterate through the number_list . In the loop, we use the count() method to check if the current number occurs at least three times in the number_list . If the condition is met, the number is appended to the filtered_list .

After the loop, the filtered_list contains only numbers that appear three or more times in the original list.

Python Practice Problem 11: The Second-Best Score

You’re given a list of students’ scores in no particular order:

Find the second-highest score in the list.

This one is a breeze if we know about the sort() method for Python lists – we use it here to sort the list of exam results in ascending order. This way, the highest scores come last. Then we only need to access the second to last element in the list (using the index -2 ) to get the second-highest score.

Python Practice Problem 12: Check If a List Is Symmetrical

Given the lists of numbers below …

… create a function that returns whether a list is symmetrical. In this case, a symmetrical list is a list that remains the same after it is reversed – i.e. it’s the same backwards and forwards.

Reversing a list can be achieved by using the reverse() method. In this solution, this is done inside the is_symmetrical function.

To avoid modifying the original list, a copy is created using the copy() method before using reverse() . The reversed list is then compared with the original list to determine if it’s symmetrical.

The remaining code is responsible for passing each list to the is_symmetrical function and printing out the result.

Python Practice Problem 13: Sort By Number of Vowels

Given this list of strings …

… sort the list by the number of vowels in each word. Words with fewer vowels should come first.

Whenever we need to sort values in a custom order, the easiest approach is to create a helper function. In this approach, we pass the helper function to Python’s sorted() function using the key parameter. The sorting logic is defined in the helper function.

In the solution above, the custom function count_vowels uses a for loop to iterate through each character in the word, checking if it is a vowel in a case-insensitive manner. The loop increments the count variable for each vowel found and then returns it. We then simply pass the list of fruits to sorted() , along with the key=count_vowels argument.

Python Practice Problem 14: Sorting a Mixed List

Imagine you have a list with mixed data types: strings, integers, and floats:

Typically, you wouldn’t be able to sort this list, since Python cannot compare strings to numbers. However, writing a custom sorting function can help you sort this list.

Create a function that sorts the mixed list above using the following logic:

• If the element is a string, the length of the string is used for sorting.
• If the element is a number, the number itself is used.

As proposed in the exercise, a custom sorting function named custom_sort is defined to handle the sorting logic. The function checks whether each element is a string or a number using the isinstance() function. If the element is a string, it returns the length of the string for sorting; if it's a number (integer or float), it returns the number itself.

The sorted() function is then used to sort the mixed_list using the logic defined in the custom sorting function.

If you’re having a hard time wrapping your head around custom sort functions, check out this article that details how to write a custom sort function in Python .

Python Practice Problem 15: Filter and Reorder

Given another list of strings, such as the one below ..

.. create a function that does two things: filters out any words with three or fewer characters and sorts the resulting list alphabetically.

Here, we define filter_and_sort , a function that does both proposed tasks.

First, it uses a for loop to filter out words with three or fewer characters, creating a filtered_list . Then, it sorts the filtered list alphabetically using the sorted() function, producing the final sorted_list .

The function returns this sorted list, which we print out.

Want Even More Python Practice Problems?

We hope these exercises have given you a bit of a coding workout. If you’re after more Python practice content, head straight for our courses on Python Data Structures in Practice and Built-in Algorithms in Python , where you can work on exciting practice exercises similar to the ones in this article.

Additionally, you can check out our articles on Python loop practice exercises , Python list exercises , and Python dictionary exercises . Much like this article, they are all targeted towards beginners, so you should feel right at home!

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Beginner Python Exercises

Here are some enjoyable Python Exercises for you to solve! We strive to offer a huge selection of Python Exercises so you can internalize the Python Concepts through these exercises. 

Among these Python Exercises you can find the most basic Python Concepts about Python Syntax and built-in functions and methods .

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Choose the topics you'd like to practice from our extensive exercise list.

Python Beginner Exercises consist of some 125+ exercises that can be solved by beginner coders and newcomers to the Python world.

Majority of these exercises are online and interactive which offers an easier and convenient entry point for beginners.

First batch of the exercises starts with print function as a warm up but quickly, it becomes all about data. 

You can start exercising with making variable assignments and move on with the most fundamental data types in Python:

Exercise 1: print() function    |   (3) : Get your feet wet and have some fun with the universal first step of programming.

Exercise 2: Variables    |   (2) : Practice assigning data to variables with these Python exercises.

Exercise 3: Data Types    |   (4) : Integer (int), string (str) and float are the most basic and fundamental building blocks of data in Python.

Exercise 4: Type Conversion    |   (8) : Practice converting between basic data types of Python when applicable.

Exercise 5: Data Structures    |   (6) : Next stop is exercises of most commonly used Python Data Structures. Namely;

• Dictionaries and
• Strings are placed under the microscope.

Exercise 6: Lists    |   (14) : It’s hard to overdo Python list exercises. They are fun and very fundamental so we prepared lots of them. You will also have the opportunity to practice various Python list methods.

Exercise 7: Tuples    |   (8) : Python Tuples Exercises with basic applications as well as common tuples methods

Exercise 8: Dictionaries    |   (11) : Practice with Python Dictionaries and some common dictionary methods.

Exercise 9: Strings    |   (14) : Basic string operations as well as many string methods can be practiced through 10+ Python String Exercises we have prepared for you.

Next batch consist of some builtin Python functions and Python methods. The difference between function and method in Python is that functions are more like standalone blocks of codes that take arguments while methods apply directly on a class of objects.

That’s why we can talk about list methods, string methods, dictionary methods in Python but functions can be used alone as long as appropriate arguments are passed in them.

Exercise 10: len() function     |   (5) : Python’s len function tells the length of an object. It’s definitely a must know and very useful in endless scenarios. Whether it’s manipulating strings or counting elements in a list, len function is constantly used in computer programming.

Exercise 11: .sort() method     |   (7) : Practice sort method in Beginner Python Exercises and later on you’ll have the opportunity practice sorted function in Intermediate Python Exercises.

Exercise 12: .pop() method     |   (3) : A list method pop can be applied to Python list objects. It’s pretty straightforward but actually easy to confuse. These exercises will help you understand pop method better.

Exercise 13: input() function     |   (6) : Input function is a fun and useful Python function that can be used to acquire input values from the user.

Exercise 14: range() function     |   (5) : You might want to take a real close look at range function because it can be very useful in numerous scenarios.

These range exercises offer an opportunity to get a good practice and become proficient with the usage of Python’s range function.

Exercise 15: Error Handling    |   (7) : Error Handling is a must know coding skill and Python has some very explanatory Error Codes that will make a coder’s life easier if he/she knows them!

Exercise 16: Defining Functions    |   (9) : Practicing user defined Python functions will take your programming skills to the next step. Writing functions are super useful and fun. It makes code reusable too.

Exercise 17: Python Slicing Notation     |   (8) : Python’s slicing notations are very interesting but can be confusing for beginner coders. These exercises will help you master them.

Exercise 18: Python Operators    |   (6) : Operators are another fundamental concept. We have prepared multiple exercises so they can be confident in using Python Operators.

If you struggle with any of the exercises, you can always refer to the Beginner Python Lessons .

If you’d like to challenge yourself with the next level of Python exercises check out the Intermediate Python Exercises we have prepared for you.

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Basic Python exercises that are simple and straightforward.

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Slightly more complex Python exercises and more Python functions to practice.

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Project-like Python exercises to connect the dots and prepare for real world tasks.

Thank you for checking out our Python programming content. All of the Python lessons and exercises we provide are free of charge. Also, majority of the Python exercises have an online and interactive interface which can be helpful in the early stages of learning computer programming.

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Top 10 Python Programming Homework Help Sites

Every student who wants to achieve good results in programming has to deal with ongoing homework challenges if they want to be truly successful in their academic studies. Classes in programming are not an exception. The thing about programming is that at times you may cope with it without completely understanding the entire process that led to the solution.

Understanding the series of your actions is the toughest aspect of this contemporary and challenging topic of study. And frequently, if not always, the most difficult part of this educational road comes down to resisting the urge to handle everything “blindly” and taking your time to fully understand all the aspects on your own. And in case you don’t have a personal teacher who can explain everything to you, this article offers you 10 websites where professionals can play the role of this teacher by helping you out with homework in programming. And particularly in Python.

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FavTutor is another strictly narrow-specialized programming help-oriented website that deserves to be mentioned in this article. Its main difference from the previous websites is that it provides help in another format. It is not a task-handling service, but a discipline-explaining service.

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Besides the standard pack of homework help services LetsTacle also offers articles and Academy service, which is an option of buying services of a personal Online Python Tutor.

9. HOMEWORK HELP ONLINE

Website: HomeworkHelpOnline.net

HomeworkHelpOnline is a unique programming homework help website in the way that it offers you to order a complete assignment, but it also navigates you on how you can cope with your tasks yourself in their self-help section. HomeworkHelpOnline works in 4 directions:

• Programming

Each direction consists of a large number of branches. The programming includes the following:

• R / Statistics
• SQL Database
• Neural Networks

10. ALL ASSIGNMENT HELP

Website: AllAssignmentHelp.com

The academic assistance website AllassignmentHelp focuses on several math-related and IT-related disciplines. Along with providing assistance with completing tasks that include programming in Python, this site also provides many helpful tools and resources. The resources in the list consist of:

• Free samples
• Question bank
• Academy courses

Additionally, it provides resources like a Reference Generator, Word Counts, Grammar Checker, and Free Plagiarism Report. All of which are extremely helpful and beneficial for academic writing of any kind.

Python Online Compiler

Python Programming

Homework help & tutoring.

Our name 24HourAnswers means you can submit work 24 hours a day - it doesn't mean we can help you master what you need to know in 24 hours. If you make arrangements in advance, and if you are a very fast learner, then yes, we may be able to help you achieve your goals in 24 hours. Remember, high quality, customized help that's tailored around the needs of each individual student takes time to achieve. You deserve nothing less than the best, so give us the time we need to give you the best.

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Python is a popular programming language dating back to the early 90s. It is supported by a large number of frameworks, particularly in the web sphere, and designed to maximize code readability. The most recent version is Python 3, which differs from Python 2 due to its improvements that make code easier to write.  As a language, Python is largely used as dynamically typed, object oriented and procedural, but it is multi-paradigm and also supports functional programming and strong typing. It is also reflective, which means Python programs are able to modify themselves during execution. 

Python Online Tutors

If you're looking for extra Python homework help online, there are many platforms and services that you can turn to for help. Whether you need assistance with Python homework or you want to become a more proficient programmer, there are plenty of ways to improve your coding skill set when it comes to Python programming.

One of the most effective strategies for receiving Python help is working with a professional Python tutor. Tutors can provide individualized guidance to ensure you receive the support you need to become more confident in your programming skills and cultivate the ability to use Python in a variety of applications.

When searching for a Python tutor online, finding a qualified individual with a background in programming is an essential part of improving your education and developing the ability to use these skills in a real-life setting. The right tutor will be able to adapt their instruction for your learning, ensuring that you get the most out of your Python tutoring and gain more confidence in programming inside and outside the classroom.

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Whenever you're consulting the internet for additional information, it is essential to confirm the source you're using is trustworthy. With our service, you gain access to a diverse range of highly accredited and knowledgeable P ython private tutors online , all of whom are reliable and professionally qualified. Our Python tutors are also available around the clock to answer any questions you have about Python programming.

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Because we're available 24 hours a day, you can turn to us for help with Python homework at any time — even if it's the middle of the night and you need a professional Python programmer to walk you through an assignment or answer a question. As we're an online source of information, you won't have to search for a Python tutor in your local area, eliminating any obstacles that could prevent you from getting the Python homework help you need.

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Python Tutorial Help

Python programs are usually written in .py files. The language is split into a number of implementations, including CPython, Jython and IronPython written in C, Java and C# respectively. These different interpretations add extra pieces of functionality and quirks - such as IronPython is able to make use of the .NET Framework, and JPython integrates with Java classes. The core concepts remain the same.

Having set up your environment or installed an IDE, you are ready to start writing Python applications. You can follow the tutorial below to learn some core operations that can be performed in Python. Note that the lines beginning with # are comments, which means they do not get executed in your application. The best way to learn is to type out these programs yourself (rather than copy-paste) as you’ll get a better feel for what it means to write Python.

# First, let's do a basic print operation

print ("Hello, world!")

If you run this application you’ll see the text string in the brackets outputs to the console. This is because ‘print()’ is a method, and we are calling it with an argument (our text).

We can make this a bit more advanced by introducing variables.

# Create a variable and print it

name = "Dave"

print ("Hello " + name)

The + operator when used on text strings concatenates them. As a result, we will see an output based on the constant “Hello “ being joined with the name in the variable. If, however, we add numbers together we will instead perform a mathematical operation:

# Let's do some sums

print (1 + 2)

print (1 - 2)

print (9 / 3)

print (2 * 5)

# Two *s raises the number before to the power of the number after

print ((2 * 5) ** 2)

One thing you’ll notice is that the division operator converts the result to a floating point number (3.0) whereas the other operations remain as integers.  If, however, you use a floating point number in the calculation you’ll create a floating point result, as in this example:

# Output will be 10.0, not 10

print (2.0 * 5)

We briefly touched on variables before. It is important to assign values to a variable before you use it, or you will receive an error:

# This is OK

myVariable = 3

print (myVariable)

# This is not

print (aNewVariable)

We can also do conditional operations on our variables. Note that the indentation is used to separate code blocks, and until we de-indent our program, we continue to operate inside the ‘if’ block.

# The interior statements will execute as myVariable (3) is

# greater than or equal to 3

if myVariable >= 3:

    print ("Condition passed")

    print ("myVariable is more than 2")

# As a result, this won't execute

    print ("Condition failed")

    print ("myVariable is less than 3")

In this next example we’ll create a list by using square brackets and then iterate over it by using the ‘for’ keyword. This extracts each value in the list, and puts it in the variable ‘number’ before executing the block of code within the 'for' loop. Then the code moves on to the next item in the list:

# Let's count!

for number in [1,2,3,4,5]:

    print(number)

The Python code you encounter in the world will consist of these elements - methods, loops, conditions and variables. Understanding these fundamentals is core to being able to move on to more advanced concepts and frameworks, as well as being able to read and write your own Python applications.

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To learn why our Python help is so effective, schedule a session with our online Python tutors today. Unlike other online sources, our tutors can be trusted to fulfill multiple needs and enhance your education. With their professional backgrounds and advanced degrees in coding, they'll make sure you receive the accurate information and coding advice you're looking for. 

Whether you're just starting out or you're an advanced Python programmer, we have a tutor that can help you master your Python programming coursework. If you're stuck on a question, submit your homework and one of our Python tutors will help you solve it! If you just want some extra help, contact a Python programming tutor for an online tutoring session. We also have a homework library so that you can get some extra homework practice whenever you need it.  

To fulfill our tutoring mission of online education, our college homework help and online tutoring centers are standing by 24/7, ready to assist college students who need homework help with all aspects of Python programming. Our Python tutors can help with all your projects, large or small, and we challenge you to find better online Python programming tutoring anywhere.

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Python Lab Assignments

You can not learn a programming language by only reading the language construct. It also requires programming - writing your own code and studying those of others. Solve these assignments, then study the solutions presented here.

CS50's Introduction to Artificial Intelligence with Python

Learn to use machine learning in Python in this introductory course on artificial intelligence.

Associated Schools

Harvard School of Engineering and Applied Sciences

What you'll learn.

Graph search algorithms

Reinforcement learning

Machine learning

Artificial intelligence principles

How to design intelligent systems

How to use AI in Python programs

Course description

AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.

Instructors

David J. Malan

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An introduction to the intellectual enterprises of computer science and the art of programming.

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This is CS50’s introduction to technology for students who don’t (yet!) consider themselves computer persons.

• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
• Python Web Dev
• DSA with Python
• Python OOPs
• Dictionaries

Python Operators

Precedence and associativity of operators in python.

• Python Arithmetic Operators
• Difference between / vs. // operator in Python
• Python - Star or Asterisk operator ( * )
• What does the Double Star operator mean in Python?
• Division Operators in Python
• Modulo operator (%) in Python
• Python Logical Operators
• Python OR Operator
• Difference between 'and' and '&' in Python
• not Operator in Python | Boolean Logic

Ternary Operator in Python

• Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

• Walrus Operator in Python 3.8
• Increment += and Decrement -= Assignment Operators in Python
• Merging and Updating Dictionary Operators in Python 3.9
• New '=' Operator in Python3.8 f-string

Python Relational Operators

• Comparison Operators in Python
• Python NOT EQUAL operator
• Difference between == and is operator in Python
• Chaining comparison operators in Python
• Python Membership and Identity Operators
• Difference between != and is not operator in Python

In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators. 

• OPERATORS: These are the special symbols. Eg- + , * , /, etc.
• OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators and Membership Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient. 

There are two types of division operators: 

Float division

• Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

• P – Parentheses
• E – Exponentiation
• M – Multiplication (Multiplication and division have the same precedence)
• D – Division
• A – Addition (Addition and subtraction have the same precedence)
• S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

• x % 10 -> yields the last digit
• x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

• Logical not
• logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

• Bitwise NOT
• Bitwise Shift
• Bitwise AND
• Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical. 

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5. 

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax :   [on_true] if [expression] else [on_false] 

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python . 

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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Substance 3D Painter 10.0  brings support of Illustrator (.ai) files, integrates Substance 3D Assets, imports Fonts via the Text resources, adds layer stack functionalities in the Python API, and several quality of life improvements.

Release date:  16 May 2024

Major features

New text resource.

This new version introduces the  Text resource  which is a way to load font files to write text in different contexts (brush, fill projection, Substance image inputs, etc.) to embellish your textures.

Browse your fonts in the Assets window Fonts are now listed in the Assets window under their own filter. They are gathered from different locations on the operating system (and also from the Libraries).

Drag and drop fonts like any other resources Fonts can be used as text resources like any other kind of resource. Drag and drop them to automatically create fill projection. They can also be used in brushes or as input in Substance filters.

Text resource parameters When creating a text resource, you can tweak a few parameters to adjust the look of your text: vertical and horizontal alignment, automatic or manual size, line and character spacing, color, etc.

Wide range of characters and feature supported The Text resource supports right to left writing as well as ligatures . (To be able to write non latin characters a compatible font is required.)

Import custom fonts like regular resource You can import your own fonts files directly into your Library or project like any other resources. Some types of fonts are not supported however, for more information see this  documentation page .

For more information about the  Text resource , see the  dedicated documentation page .

New import of Illustrator files (.Ai)

Following the support for .svg files, this new version also adds the ability to import Illustrator files ( .ai ).

Illustrator (.Ai) file support In this new version .ai files can now be imported and rendered in Painter to be used as resource in brushes, fill projections or as Substance image inputs.

.svg and .ai files share common settings SVG and Illustrator documents share similar settings, notably the resolution, crop area and scope selection parameters. This means that vectorial resource can be managed in a similar way.

Artboard selection Illustrator documents support artboard , when using an .ai file you can also choose between different artboards available via the dedicated setting.

Improved scope selection The scope selection window has been improved with the support of thumbnails, making it easier to browse and select only specific elements. For performance reasons thumbnails are off by default and can be enabled with the Show thumbnails checkbox.

Importing Illustrator ( .ai ) files is currently only supported on Windows and MacOS.

New Substance 3D Assets integration

A new window is available which embeds the Substance 3D Assets website directly inside Painter. This integration makes it easier to browse and download resources directly in your own library.

New Substance 3D Assets window A new dock is available in the interface to browse Substance 3D Assets. If the dock is not visible and closed it can be found again in the dock toolbar on the right of the interface.

Download manager You can see the assets currently being downloaded via the dedicated manager using the bottom left button of the window. Assets that may fail to download can be started again from this list.

Find your downloaded assets easily The button in the bottom right of the window opens a menu with a few actions to help navigate the website but also to shows were assets have been donwloaded.

Upon the first launch a login into your account will be necessary to download assets. This login is then be cached for future uses.  

The Substance 3D Assets dock is not available in the Steam version.

New layer stack module in Python API

This release sees the addition of the new layer stack module in our Python API. This API allows to control the layer stack of a project, opening the door to the creation of advanced layer stack plugins and custom tools.

New Layer stack API The new  layerstack  module allows to control the layer stack of a project in many ways. You can:

• Query and set the selection of layers and effects.
• Create new layers, folders and effects (including filters, anchor points, etc.).
• Instantiate layers.
• Get and set parameters of layers and effects, load resources into them.
• Get and set Substance parameters.

Scoped modifications and pause of the engine Manipulating the layer stack could lead to long computations, this is why we also exposed the possibility to pause and unpause the engine from the API (like in the UI). We also made it possible to group modifications together, for both performance reasons but also to undo a single time multiple operations.

Basic color management With the exposition of the layer stack we needed to introduce the notion of color management in our API. A new  colormanagement  module has been added to create, tweak colors and choose the color space of bitmaps. (This part of the API isn't complete yet and will be expanded in future versions.)

Query export preset information Export presets are now exposed in our API, allowing to query the list of presets (both predefined and custom). Their content can also be retrieved in a similar format to our existing export textures API.

New possibilities ahead! This new part of the API allows to do a lot of new things, like saving and restoring a selection of layers or changing the random seed of all the resources in a project for example:

For more information on the API, see the documentation included with the application (via  Help > Scripting documentation > Python API ) which includes many code snippets to easily get started.

Examples of layer stack plugins can also be found in our  online documentation .

Improved normal map painting

In this release we reworked the normal map painting workflow. We notably changed the way we accumulate and blend normal brush stamps. This changes were made to address issues related to painting flow maps.

Fixed accumulation issue Painting over and over an area in the normal channel will no longer saturate or clamp and create holes or artifacts. Switching the normal channel to RGB32F is also no longer needed.

Fixed undo breaking painted strokes Undoing a brush stroke no longer breaks other already painted strokes.

Transparency on zero alpha Brush stamps made with a texture with an alpha at zero will now draw as transparent. The example below shows a brush stamp (left) versus a planar projection (right).

For more information on painting flow map, see the  documentation page .

Improved transform manipulators

Several improvements have been made to enhance the usage of the transform manipulators.

Precision mode with CTRL Pressing control while dragging on a manipulator will now enter into a new precision mode which allows more meticulous operations. This change applies to the translate, rotate and scale manipulators. Here is an example before and after pressing CTRL while dragging:

New scale behavior The scale intensity is now based on the current scale value itself and not on the scene size anymore. This makes relative changes easier to do, especially at small values. Combined with the precise mode it makes scaling a lot more pleasant. Another change is scaling down until 0 will no longer go into negative values. This avoid the issue of wanting to scale down a projection and flipping it by accident.

Improved surface manipulator rotation The surface decal manipulator is now a lot more stable when dragging around a surface. It doesn't increase its rotation when just doing back and forth translations. Here is the old behavior compared to the new one:

Camera aligned projection on drag and drop Drag and dropping a resource into the viewport allows to create a warp projection directly on the surface of the mesh. This projection was previously incorrectly rotated, it is now aligned to the camera.

A few other improvements have been added, notably:

Updated Tile Generator The  Tile Generator blending mode parameter can now be changed and will modify the result as expected. The resource has also been updated to the latest version available in  Substance 3D Designer .

Fixed banding/quality issues on some filters Several filters were stuck on 8 bit precision instead of 16 bit, leading to banding/artifacts when using them (like the histogram scan or directional blur). This is now fixed.

Color space in SBSAR output When the Legacy or OCIO color management workflow is enabled, the SBSAR export will now reference the color space names used in the project on the respective outputs.

Faster resource discovery With the introduction of the  Text resource  we added a new cache to make crawling of resources on the disk faster on next startup. This is quite notable when resources are installed on a HDD or when a library has gigabytes of resources. This new cache can be disabled with a command line, see the dedicated  documentation page  for more information.

Many thanks to the website is this arabic ? which was of great help during the development of this version.

Reference to artworks used in the medias above:

• Man wearing black shirt by Lucas Gouvêa
• Pink and green by Pawel Czerwinski
• unDraw illsutrations
• Claude Monet

Release notes

Release date: 2024/05/16 Summary: Major release, edition of the layer stack with Python API, read native Illustrator files, integration of 3D Assets and new text resource

• [Illustrator] Use Illustrator files with art boards in Painter
• [Illustrator][SVG] Add previews in scope selection
• [Substance 3D Assets] Browse, select and download 3D Assets directly in Painter
• [Substance 3D Assets][UI] New panel
• [Substance 3D Assets] Support environment maps and materials
• [Substance 3D Assets] Allow to reload and navigate and open location folder in new Substance 3D Assets panel
• [Substance 3D Assets] Addition of a download manager
• [Text Resource] Allow to use embeddable fonts
• [Text Resource] Allow to render a font/text on a mesh
• [Text Resource] Display fonts from user and other shared paths in Assets panel with a new category
• [Text Resource][Properties] Add support for advanced font properties
• [Text Resource] Allow to search/view fonts in mini-shelves
• [Text Resource] Add error message/dialog when importing an incompatible font
• Miscellaneous
• [Fill projection] Improve Scale manipulator behavior when using small values
• [Manipulators] Add new precise mode when pressing CTRL shortcut
• [Manipulators] Improve surface manipulator stability when translating
• [Export] Add colorspace name in SBSAR outputs
• [Performance] Improve library discovery time of assets on disk
• [Substance] Update to Substance engine version 9.1.2
• [Drag and Drop] Align decal rotation to camera when dropping in viewport
• [Python] Edition of the layer stack
• [Python] Allow to select layer, effect, mask, geo mask in UI
• [Python] Allow to get/set layer blending modes
• [Python] Allow to get/set fill layer projection settings
• [Python] Allow to query Substance material color from a fill layer
• [Python] Allow to query and set uniform colors and resources in layers and effects
• [Python] Allow to create and edit text resources in layer stack
• [Python] Allow to edit active channels on layers and effects
• [Python] Allow to batch actions to have a single undo/redo
• [Python] Allow to load/edit vectorial source parameters
• [Python] Allow to edit layer and effects color properties with color management
• [Python] Allow to query and create instanced layers
• [Python] Allow to add color selection effect
• [Python] Allow to control bitmap image color management
• [Python] Allow to pause/unpause engine
• [Python] Allow to navigate to siblings and parent nodes
• [Python] Allow to create filter/generator effect
• [Python] Allow to add level effect
• [Python] Allow to add smart mask on a layer
• [Python] Allow to create/edit anchor points
• [Python] Allow to get/Set mask on layers
• [Python] Allow to create compare mask effect
• [Python] Allow to query and use presets from Substance resources
• [Python] Allow to list presets and their values via internal_properties function for Substance resources
• [Python] Allow to list predefined export presets
• [Python] Allow to list export presets available in the library
• [Python] Allow to retrieve the content of export presets
• [Crash] Undoing "Remove shader instance" with Ctrl-Z
• [Crash] Create a layer on empty stack if last selection was an effect
• [SVG] Issue with custom cropped area value
• [Auto-Unwrap] Recomputing only the packing without any change to UV orientation results in crash
• [Drag and drop] Lag due to external resources are preloaded multiple times
• [UI] Drag and drop resource thumbnail can hide warning message in layer stack
• [Performance] Masked UV tiles are still computed
• [USD] Wrong highlight for scope selection
• [Resource] Bitmap image gets corrupted after painting in normal channel and saving project
• [USD] Support left-handed vertex mesh ordering
• [Substance] Reset to default always go back to zero for angle widget
• [Engine] Painting with an SVG in a stencil doesn't work
• [Engine] Normal map brush strokes break after an undo
• [Content] Graphic to Material filter has incorrect alpha blending and color space
• [Content] Blending modes on Tile Generator are not working
• [Content] Histogram scan filter produces banding in some cases
• [Content] Baked lighting stylized does not take painted height into account
• [Python] Unexpected error when retrieving instanced layer information after shader change

Known Issues :

• [Color Management] HDR color space conversions with ACE on Linux produce clamped colors
• [Crash][Linux][AMD] Dragging and dropping resources in layer stack on Wayland OS
• [Regression][UI] Right Click Menu is too small on HD screens
• [Crash][Python] USD export triggered by TextureStateEvent
• [Save] Spp project file is lost when "save as" fails
• [MacOS Intel] Crash when importing some presets
• [Illustrator] Cannot import Ai files after server crash without restarting Painter
• [Import] Assets with same name but different extensions are overridden

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