Python Practice for Beginners: 15 Hands-On Problems with Solutions

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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).


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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35 Python Programming Exercises and Solutions

To understand a programming language deeply, you need to practice what you’ve learned. If you’ve completed learning the syntax of Python programming language, it is the right time to do some practice programs.

In this article, I’ll list down some problems that I’ve done and the answer code for each exercise. Analyze each problem and try to solve it by yourself. If you have any doubts, you can check the code that I’ve provided below. I’ve also attached the corresponding outputs.

1. Python program to check whether the given number is even or not.

2. python program to convert the temperature in degree centigrade to fahrenheit, 3. python program to find the area of a triangle whose sides are given, 4. python program to find out the average of a set of integers, 5. python program to find the product of a set of real numbers, 6. python program to find the circumference and area of a circle with a given radius, 7. python program to check whether the given integer is a multiple of 5, 8. python program to check whether the given integer is a multiple of both 5 and 7, 9. python program to find the average of 10 numbers using while loop, 10. python program to display the given integer in a reverse manner, 11. python program to find the geometric mean of n numbers, 12. python program to find the sum of the digits of an integer using a while loop, 13. python program to display all the multiples of 3 within the range 10 to 50, 14. python program to display all integers within the range 100-200 whose sum of digits is an even number, 15. python program to check whether the given integer is a prime number or not, 16. python program to generate the prime numbers from 1 to n, 17. python program to find the roots of a quadratic equation, 18. python program to print the numbers from a given number n till 0 using recursion, 19. python program to find the factorial of a number using recursion, 20. python program to display the sum of n numbers using a list, 21. python program to implement linear search, 22. python program to implement binary search, 23. python program to find the odd numbers in an array, 24. python program to find the largest number in a list without using built-in functions, 25. python program to insert a number to any position in a list, 26. python program to delete an element from a list by index, 27. python program to check whether a string is palindrome or not, 28. python program to implement matrix addition, 29. python program to implement matrix multiplication, 30. python program to check leap year, 31. python program to find the nth term in a fibonacci series using recursion, 32. python program to print fibonacci series using iteration, 33. python program to print all the items in a dictionary, 34. python program to implement a calculator to do basic operations, 35. python program to draw a circle of squares using turtle.

real world problems solved using python

For practicing more such exercises, I suggest you go to  and sign up. You’ll be able to practice Python there very effectively.

Once you become comfortable solving coding challenges, it’s time to move on and build something cool with your skills. If you know Python but haven’t built an app before, I suggest you check out my  Create Desktop Apps Using Python & Tkinter  course. This interactive course will walk you through from scratch to building clickable apps and games using Python.

I hope these exercises were helpful to you. If you have any doubts, feel free to let me know in the comments.

Happy coding.

I'm the face behind Pythonista Planet. I learned my first programming language back in 2015. Ever since then, I've been learning programming and immersing myself in technology. On this site, I share everything that I've learned about computer programming.

11 thoughts on “ 35 Python Programming Exercises and Solutions ”

I don’t mean to nitpick and I don’t want this published but you might want to check code for #16. 4 is not a prime number.

Thanks man for pointing out the mistake. I’ve updated the code.

# 8. Python program to check whether the given integer is a multiple of both 5 and 7:

You can only check if integer is a multiple of 35. It always works the same – just multiply all the numbers you need to check for multiplicity.

For reverse the given integer n=int(input(“enter the no:”)) n=str(n) n=int(n[::-1]) print(n)

very good, tnks

Please who can help me with this question asap

A particular cell phone plan includes 50 minutes of air time and 50 text messages for $15.00 a month. Each additional minute of air time costs $0.25, while additional text messages cost $0.15 each. All cell phone bills include an additional charge of $0.44 to support 911 call centers, and the entire bill (including the 911 charge) is subject to 5 percent sales tax.

We are so to run the code in phyton

this is best app

Hello Ashwin, Thanks for sharing a Python practice

May be in a better way for reverse.

#”’ Reverse of a string

v_str = str ( input(‘ Enter a valid string or number :- ‘) ) v_rev_str=” for v_d in v_str: v_rev_str = v_d + v_rev_str

print( ‘reverse of th input string / number :- ‘, v_str ,’is :- ‘, v_rev_str.capitalize() )

#Reverse of a string ”’

Problem 15. When searching for prime numbers, the maximum search range only needs to be sqrt(n). You needlessly continue the search up to //n. Additionally, you check all even numbers. As long as you declare 2 to be prime, the rest of the search can start at 3 and check every other number. Another big efficiency improvement.

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Cracking the Coding Interview: Solve 5 Real World Problems

real world problems solved using python

Preparing for tech interviews is no easy task. You need the skills to break down the problem and to deploy the right tools. Educative has always been on the mission to make coding interview prep more accessible for engineers. We’ve learned firsthand that the best way to succeed is not to memorize 1500+ Leetcode problems . Understanding patterns are the key to cracking the coding interview for top tech companies.

That’s why we want to approach interview prep a bit differently today by tackling some real world problems faced by tech companies. Learning how to build real world features (like “how to merge recommendations on Amazon”) is more fun, and it’s much easier to remember what you learned. If you can understand a problem’s underlying pattern, you can apply it to just about any question.

We will dive into how to crack coding interviews, solutions for a few common real world coding problems from FAANG companies and build 5 features. We will offer our solutions in Java and Python.

Crack the coding interview

Finding the motivation to prepare for the coding interview should be simple. Programmers everywhere are practicing coding problems and learning patterns , and you should too, if you want to compete for your dream job.

While preparing for a coding interview with a top tech company, it is essential to have a strong foundation in data structures, algorithms, dynamic programming, and problem-solving fundamentals .

Review the fundamental data structures , including arrays, linked lists, stacks, queues, trees, and graphs. Practice implementing these data structures and familiarize yourself with their operations and time complexities. Once you are comfortable with the basics, progress to more advanced data structures like hash tables, heaps, and tries.

Practice standard sorting and searching algorithms like quicksort, merge sort, binary search, and linear search. Review how to implement these algorithms and analyze the time and space complexity. Then, depending on the programming language, migrate to more complex algorithms like dynamic programming, graph algorithms, and divide and conquer algorithms . You must show your recruiter that you understand when to use each type of algorithm and how to optimize them for different technical questions.

Dynamic programming solves complex problems by breaking them down into smaller sub-problems and solving them recursively . Practice solving dynamic programming problems such as the knapsack problem and the maximum subarray problem . Identifying and solving these dynamic programming problems is vital to crack the coding interview.

In order to crack the coding interview, problem-solving skills are just as necessary as technical skills. Identify the problem type, break it into smaller parts, and develop a plan for solving it. Creative problem-solving is at the core of every developer , and the interviewer wants to see you demonstrate it. Try coding your solutions on a whiteboard or paper to simulate the interview experience.

In addition to mastering these concepts, it is essential to keep up with the latest technologies and relevant trends in the industry. Read tech blogs, follow news outlets, attend meetups and hackathons, and network with other software engineers on sites like GitHub and LinkedIn. Keep your skills sharpened by working on individual projects and contributing to open-source projects.

With these tips and strategies, you can ace your coding interview with a top tech company and land your dream job as a software engineer.

For more on how to crack the coding interview with a top tech company and start collecting job offers, take a look at this article about the behavioral interview questions on the Educative blog.

Understanding how to take on coding problems

Below are 10 tips and tricks for handling coding problems in your programming interview. Take these steps into each question during your interview preparation and the interview process to guarantee you’re giving yourself the best chance at success.

The first step to solving any coding problem is to understand it completely. Ensure you read the problem statement carefully , ask clarifying questions, and understand the inputs and outputs.

Feel free to ask questions if you need more information. Technical interviewers want to see that you can communicate and ask insightful questions . Especially for the trickiest algorithm problems, don’t handcuff yourself by neglecting to ask clarifying questions.

Don’t shy away from thinking out loud. As you work through the problem, explain your thought process out loud. Thinking out loud helps the interviewer understand your approach and thought process . It also acts as a fail-safe to catch any mistakes or oversights you may make.

Break the problem down into more manageable pieces . Doing this will help you tackle the problem more effectively and stay calm. It is also why understanding the overarching patterns are so helpful when solving coding problems.

Try to devise multiple solutions , even if you only implement some. This practice shows your ability to think creatively with different approaches.

Write code that is the easiest to read and understand . This includes using descriptive variable names, proper formatting, and commenting on your code when necessary.

Make sure to test your code thoroughly before submitting it. Mainly, this refers to testing edge cases and ensuring your code handles errors.

Be aware of your time constraints. Time management is absolutely vital in any coding interview. Make sure to pace yourself and spend only a little time on any one part of a coding problem. Solving a coding problem yourself is much different than solving it in front of an audience and with a time restraint. Take yourself through mock interviews in order to better prepare yourself for the actual interview.

Practice more than you think you should. Although it may appear straightforward, this learning strategy is excellent for learning anything, not just coding problems. The more you practice coding problems, the more comfortable and confident you’ll become in a coding interview. While practicing, you must focus on practicing the coding patterns behind each question instead of trying to memorize over a thousand unique coding problems.

Try to stay calm and focused during the coding interview. Take deep breaths and stay hydrated. If you’re feeling especially nervous, remember that it’s more than okay to make mistakes or not know an answer. The interviewer is more interested in your problem-solving skills and critical thought process than in getting a perfectly optimized solution.

Now, let’s start with a tutorial on five programming interview questions you’ll likely see in interviews from big tech companies !

This tutorial at a glance:

Netflix Feature: Group Similar Titles (hash maps)

Facebook feature: friend circles (dfs), google calendar feature: find meeting rooms (heaps), amazon feature: products in price range (bst), twitter feature: add likes (strings), where to go from here.

Answer any interview problem by learning the patterns behind common questions. Grokking Coding Interview Patterns in Python Grokking Coding Interview Patterns in JavaScript Grokking Coding Interview Patterns in Java Grokking Coding Interview Patterns in Go Grokking Coding Interview Patterns in C++

Netflix is one of the biggest video streaming platforms out there. The Netflix engineering team is always looking for new ways to display content. For this first problem, imagine you’re a developer on these teams.

Task: Our task here is to improve search results by enabling users to see relevant search results without being hindered by typos, which we are calling the “Group Similar Titles” feature.

First, we need to determine how to individually group any character combination for a given title. Let’s imagine that our library has the following titles: "duel", "dule", "speed", "spede", "deul", "cars" . You are tasked to design a functionality so that if a user misspells a word (for example speed as spede ), they will still be shown the correct title.

First, we need to split our titles into sets of words so that the words in a set are anagrams. We have three sets: {"duel", "dule", "deul"} , {"speed", "spede"} , and {"cars"} . The search results should include all members of the set that the string is found in.

Note: It’s best to pre-compute our sets rather than forming them when a user searches.

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Lee Vaughan is a programmer, pop culture enthusiast, educator, and author of Impractical Python Projects (No Starch Press, 2018). As an executive-level scientist at ExxonMobil, he constructed and reviewed computer models, developed and tested software, and trained geoscientists and engineers. He wrote both Impractical Python Projects and Real-World Python to help self-learners hone their Python skills and have fun doing it!

Who Should Read This Book

You can think of this as a sophomore Python book. It isn’t a tutorial on programming basics but rather a way for you to continue training using a project-based approach. This way, you won’t have to waste your money or shelf space rehashing concepts you’ve already learned. Vaughan explains every step of the projects, and you’ll receive detailed instructions about using the libraries and modules, including how to install them.

These projects will appeal to anyone who wants to use programming to conduct experiments, test theories, simulate nature, or just have fun. As you work through them, you’ll increase your knowledge of Python libraries and modules and learn handy shortcuts, useful functions, and helpful techniques. Rather than focus on isolated modular code snippets, these projects teach you how to build complete, working programs involving real-world applications, datasets, and issues.

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No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.

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  • Publisher ‏ : ‎ No Starch Press (November 5, 2020)
  • Language ‏ : ‎ English
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Lee Vaughan is the author of the "Quick Success Data Science" series on and the programming books, "Impractical Python Projects: Playful Programming Activities to Make You Smarter," "Real-World Python: A Hacker's Guide to Solving Problems with Code," and "Python Tools for Scientists: An Introduction to Using Anaconda, JupyterLab, and Python's Scientific Libraries." He currently lives in The Woodlands, Texas.

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Lawrence "Larry" Weru, S.M.'23

Lawrence "Larry" Weru, S.M.'23

Real-World Python: A Hacker's Guide to Solving Problems with Code

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Top 12 Fascinating Python Applications in Real-World [2024]

Top 12 Fascinating Python Applications in Real-World [2024]

It is a well-established fact that Python is one of the most popular programming languages in both the coding and Data Science communities. But have you ever wondered why Python is so popular? What is the secret behind Python’s worldwide success and fame?

We’ll give you the answer in one line – Python is one of the top programming languages of all with a slew of applications of Python.

Whatever be your development and Data Science need, you name it – Python can take care of it as well as other related Python applications. Python is an open-source, high-level, general-purpose programming language that incorporates the features of object-oriented, structural, and functional programming.

Some believe that Java is a better language. However, it goes with saying that the former is much faster, but Python is easier to handle/read, versatile, and comes with a simple syntax. As per Stack Overflow, Python—general use and interpreted language rank fourth on the list of most popular languages for coding. It also finds immense use for Python applications.

Created way back in 1989 by Guido Van Rossum, Python stresses on the DRY (Don’t Repeat Yourself) principle, which enhances the readability of Python code. Python’s robust string manipulation, a massive collection of user-friendly libraries, and easy shell access make it a useful tool for quickly automating repetitive tasks.

While Python’s simple syntax allows for writing readable code, which can be further applied to complex software development processes to facilitate test-driven software application development, machine learning, and data analytics. Python can run on all the major operating systems, including Windows, Linux, and iOS.

Since it functions on cross-platform operating systems, Python can be used to develop a host of applications, including web apps, gaming apps, enterprise-level applications, ML apps, image processing, text processing, and so much more. 

But beyond its innate simplicity and versatility, what makes Python stand out are its vast assortments of libraries and packages that can cater to a wide range of development as well as Data Science requirements.

Understanding Python applications

Given that Python is now used in data science and related applications, its growing popularity among developers is natural. The common applications of Python are so relevant that it has now become a significant resource for those looking to begin a career in data science. Having a good grasp of Python allows you to strengthen your analytical skills. Today, a data scientist or even someone in the IT sector is expected to come with relevant and new-age skills.

Our learners also read –  Learn python online free !

A common query few might have in mind is, whether Python-run blockchain is tough to learn. It is a technology that is complex yet groundbreaking after all! However, this must never deter you from trying to get a grasp on the same. The core Blockchain concepts are mining, decentralization, and consensus mechanism. This is an immutable blockchain that is also secured cryptographically.

If you want to kick-start a career in this domain, begin by understanding the fundamentals of the same and upskill your Python programming skills to develop blockchain applications. Start your journey by studying these top four concepts of blockchain first-

  •     Smart Contracts
  •     Shared Ledger
  •     Consensus/Trust Mechanism
  •     Cryptography

What makes Python the “Best of the Best?”

The top application of Python ensures that the language remains popular at a professional level. Here are some of the most noteworthy features of Python that make it an excellent tool for Python application professionals of all skill levels:

Python = Simplicity

We cannot stress this point enough, but Python is not only easy to learn but also easy to use and implement across any application of python. With a syntax similar to English, you can master the nitty-gritty of python application and coding in a few days. Moreover, Python is dynamically-typed, which makes indentation mandatory, thereby enhancing its readability factor. 

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It is an open-source language

You don’t need to pay charges to install and use Python – it is open-source. What this means is that the source code of Python is freely available to the public. You can download it from Python’s official website . Not only that, Python supports the FLOSS (Free/Libre and Open Source Software) model, which means you can also change it and distribute it. This allows the Python community to tweak it and improve its features continuously.

Also read: Python Developer Salary in India

It is a high-level language

Since Python is a high-level language, you need not remember its system architecture, not do you need to perform memory management. This feature contributes to Python’s user-friendliness. 

It is interpreted

Unlike compiled languages like C++ and Jave wherein you must compile the code and then run it, Python is an interpreted language. What this means is that instead of executing the source code all at once, Python executes it line by line. This makes it easier to debug a Python code because you can do it while writing the code.

Also read : Free data structures and algorithm course !

It is both object-oriented and functional

An object-oriented programming language is one that can model real-world data, while a functional language focuses on functions (code that can be reused). Python supports both object-oriented and functional programming features. Also, unlike Java, Python supports multiple inheritances. Naturally, this opens up a lot of scope around the topic- what are the applications of python?

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It is portable.

Python is portable and highly flexible, meaning, a Python code written for a Windows machine or a Linux machine can also run on iOS, and vice versa – you don’t need to make any alterations in the code. So, with Python eliminates the need to write different code for different machines (just make sure there’s no system-dependent feature in your Python code).

It is extensible and embeddable

Python is an extensible language, as it allows you to write specific parts of your Python code in other programming languages such as C++. Similarly, you can also embed your Python code in the source code of other languages. This allows you to integrate Python’s scripting functionalities into a code written in another language. This also leads to a number of applications of Python programming being developed as per need. When you look up on what are the applications of Python programming , you can get an idea of myriad Python uses .

It comes with a vast collection of libraries

When you download Python, you will automatically download the extensive collection of Python libraries with it. These libraries are built-in, so you don’t have to write individual code for every single thing. Python has libraries and packages for web browsers, threading, databases, regular expressions, image manipulation, documentation-generation, unit-testing, CGI, email, and much more.

Now that we’ve talked at length about how great a tool Python is let’s check out twelve real-world applications of Python or popular python uses.

One of the great things about Python is it owns a huge collection of data science libraries like SciPy, NumPy, and Matplotlib. All these libraries help you to conduct visualization and data analysis easily.

Python is famous in the scientific community because of its large collection of libraries for physics, math, machine learning, and engineering. For instance, TensorFlow is a Google Brain library used in ML projects. So, there are plenty of application areas of Python .

It supports different paradigms

Python is a versatile programming language and supports multiple paradigms like functional, object-oriented, and procedural programming. It suggests that you can choose the paradigm that best outfits your requirements and preferences.

Python features a simple syntax for ease of learning and use. This characteristic helps you to explore various application areas of Python, including automation.

It should be your foremost choice to learn various programming paradigms.

Python owns a huge standard library that contains modules for string handling, data manipulation, Internet protocols, file I/O, and operating system interfaces. Python’s syntax makes it easy to learn and understand python programs that are typically used for automation tasks.

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It owns a strong community of developers

Python comes with a powerful community of developers who are always dedicated to assisting you. Many online resources like chat rooms and forums are available. It aids you in solving different Python programming problems.

Python provides support for debugging tools and unit testing. It is, therefore, an ideal language for even advanced software development. You get ample resources to get started with the application of Python due to its vast and friendly community.

It is popular in data science and machine learning

Python has become quite popular in machine learning and data science applications. Its flexibility and readability make it a wonderful choice for prototyping new ideas and algorithms. Many libraries are available to help you easily execute complex data analysis tasks.

For instance, the SciPy library contains scientific and mathematical computation tools. The Pandas library is widely used for manipulation and data analysis. These are only a few of the numerous libraries that Python offers.

One of the key reasons why Python is also a popular programming language in machine learning is that it offers various powerful tools for data handling. These tools help you to conduct tasks like data modelling, transformation, and cleaning. Moreover, various machine learning libraries like Theano and TensorFlow help you to build an advanced application of Python . So, Python is worth considering if you are fascinated by machine learning and/or data science.

Check out all trending Python tutorial concepts in 2024

12 Real-world Applications of Python

Python is a very stable programming language choice that is in use at the developers’ end as well as thought of as an apt choice for automation of deployment automation. Of course, it has a lot of uses in web-related development. Even the non-developer groups believe that once they have a hang of the framework in Python, it is a preferred language for conducting data-related work.

1. Web Development

When it comes to web development, Python should be your go-to tool. Why? 

That’s because Python offers numerous options for web development. For instance, you have Django, Pyramid, Flask, and Bottle for developing web frameworks and even advanced content management systems like Plone and Django CMS. These web frameworks are packed with standard libraries and modules which simplify tasks like content management, database interaction, and interfacing with internet protocols like HTTP, SMTP, XML, JSON, FTP, IMAP, and POP.

Python web frameworks are known for their security, scalability, and flexibility. To add to that, Python’s Package Index comes with useful libraries like Requests, BeautifulSoup, Paramiko, Feedparser, and Twisted Python. 

Web development is an amazing application of Python programming . The reason is it offers a broad range of frameworks like Flask, Django, Bottle, and more that streamline developers’ tasks. Python also contains inbuilt libraries and tools that make the web development process quite effortless. You can effectively build the best Python app using its characteristics like wonderful visualization, convenience in development, enhanced security, and quick development process.

2. Game Development

As we mentioned earlier, Python comes loaded with many useful extensions (libraries) that come in handy for the development of interactive games. For instance, libraries like PySoy (a 3D game engine that supports Python 3) and PyGame are two Python-based libraries used widely for game development . Python is the foundation for popular games like Battlefield 2, Frets on Fire, World of Tanks, Disney’s Toontown Online, Vega Strike, and Civilization-IV. 

Apart from game development, game designers can also use Python for developing tools to simplify specific actions such as level design or dialog tree creation, and even use those tools to export those tasks in formats that can be used by the primary game engine. Also, Python is used as a scripting language by many game engines.

Python is also used to develop many contemporary popular game titles like World of Tanks, Sims 4, Eve Online, and Civilization IV. A few other titles that use Python are Doki Doki Literature Club, Mount & Blade, Disney’s Toontown Online, and Frets on Fire. So, the application of Python programming is not only limited to the professional world but game development too.

3. Scientific and Numeric Applications

Thanks to its massive library base, Python has become a crucial tool in scientific and numeric computing. In fact, Python provides the skeleton for applications that deal with computation and scientific data processing. Apps like FreeCAD (3D modeling software) and Abaqus (finite element method software) are coded in Python.

Some of the most useful Python packages for scientific and numeric computation include:

  • SciPy (scientific numeric library)
  • Pandas (data analytics library)
  • IPython (command shell)
  • Numeric Python (fundamental numeric package)
  • Natural Language Toolkit (Mathematical And text analysis)

  4. Artificial Intelligence and Machine Learning 

AI and ML models and projects are inherently different from traditional software models. When we talk about AI/ML projects, the tools and technologies used and the skillset required is totally different from those used in the development of conventional software projects. AI/ML applications require a language that is stable, secure, flexible, and is equipped with tools that can handle the various unique requirements of such projects. Python has all these qualities, and hence, it has become one of the most favored languages of Data Science professionals and Python is a must tool in data science courses.

Python’s simplicity, consistency, platform independence, great collection of resourceful libraries, and an active community make it the perfect tool for developing AI and ML applications. Some of the best Python packages for AI and ML are:

  • SciPy for advanced computing
  • Pandas for general-purpose data analysis
  • Seaborn for data visualization
  • Keras, TensorFlow, and Scikit-learn for ML
  • NumPy for high-performance scientific computing and data analysis

  Apart from these libraries, there are also other Python-based libraries like NLTK, Caffee, PyTorch, and Accord.NET, that are useful for AI and ML projects.

Two of the trendiest subjects right now are Artificial Intelligence and Machine Learning. With the inbuilt tools and libraries, it facilitates the development of ML and AI algorithms. Moreover, it provides easy, concise, and readable code that makes it simpler for developers to write complicated algorithms.

Few of the built-in tools and libraries that enhance ML and AI processes through the application of Python programming are:

  • Keras for Machine learning
  • Numpy for complex data analysis
  • SciPy for technical computing

5.Desktop GUI

Python not only boasts of an English-like syntax, but it also features a modular architecture and the ability to work on multiple operating systems. These aspects, combined with its rich text processing tools, make Python an excellent choice for developing desktop-based GUI applications. 

Python offers many GUI toolkits and frameworks that make desktop application development a breeze. PyQt, PyGtk, Kivy, Tkinter, WxPython, PyGUI, and PySide are some of the best Python-based GUI frameworks that allow developers to create highly functional Graphical User Interfaces (GUIs).

Python is a dynamic programming language that assists developers in easily and efficiently creating GUIs. It features a long list of inbuilt tools like kivy, PyQT,wxWidgets, and several other libraries. These libraries help you to efficiently and securely build a functional GUI.

Python’s modular programming approach and easy-to-understand syntax are the basis for responsive and super-fast GUI. So, they streamline the whole development process. A few of the prominent tools available for GUI development using applications of Python are Tkinter, PyQt, wxWidgets, Python GTK+, and Kivy.

6. Software Development

Python packages and applications aim to simplify the process of software development. From developing complex applications that involve scientific and numeric computing to developing desktop and web applications, Python can do it all. This is the reason why Software Developers use Python as a support language for build control, testing, and management.

For instance, SCons is designed explicitly for build control, Buildbot and Apache Gump allow for automated continuous compilation and testing, and Roundup and Trac are great for bug tracking and project management.

Python also supports data analyzation and visualization, thereby further simplifying the process of creating custom solutions minus the extra effort and time investment.

Python is ideal for software development. Famous applications like Google, Reddit, and Netflix use Python. It offers the following great features for software development:

  • Platform independence 
  • High compatibility
  • Inbuilt frameworks and libraries to streamline development
  • Enhanced code reusability and readability

Python also provides enhanced features to work with swiftly growing technologies like AI and ML. These features make applications of Python the famous choice for software development.

7. Enterprise-level/Business Applications

Enterprise-level software or business applications are strikingly different from standard applications, as in the former demands features like readability, extensibility, and scalability. Essentially, business applications are designed to fit the requirements of an organization rather than the needs of individual customers.

Thus, these applications must be capable of integrating with legacy systems like existing databases and non-web apps. Since business applications are developed, keeping in mind the custom requirements to cater to the specific needs of an organization’s operating model, the entire development process becomes very complicated. 

This is where Python can make a significant difference. Python high performance, scalability, flexibility, and readability are just the features required for developing fully-functional and efficient business applications. Furthermore, Python has other tools for business application development, like:

  • Odoo, an all-in-one management software that forms a complete suite of enterprise management applications.
  • Tryton, a three-tier, high-level, general-purpose application platform, is another fantastic tool for building business applications.

Learn more about: Top Python tools

Business applications vastly vary from average consumer software. Firstly, provide a set of explicit features instead of plenty of features. Secondly, they target a small user group, commonly an organization. One of the best things about Python is that it perfectly delivers performance-efficient custom solutions. So, it can work on both business applications and consumer applications.

One of the most crucial facets of any application is security. Python’s security features are standout when it comes to business applications. This is because it is built considering information security.  One of the great applications of Python programming is scalability through which a business can expand its horizon.

 8. Education programs and training courses

If there’s any beginner-friendly programming language, it is Python. We’ve said it many times before, and we’re repeating it – Python has an extremely straightforward syntax that’s similar to the English language. It has a short learning curve and hence, is an excellent choice for beginners. Python’s easy learning curve and simplicity are the two main reasons why it is one of the most used programming languages in educational programs, both at beginner and advanced levels. 

However, Python is not just great as an introductory language – even professional developers and coders all around the world rely heavily on Python.

Python features a shorter learning curve compared to other programming languages. So, you can quickly learn the development of applications of Python programming. This facet makes it one of the best options for educational programs. Platforms like Coursera, Udemy, edX, Harvard, and Python Institute are among the leading online providers of Python educational courses.

9. Language Development

Over the years, Python’s design and module architecture has been the inspiration behind the development of many new programming languages such as Boo, Swift, CoffeeScript, Cobra, and OCaml. All of these languages share numerous similarities with Python on grounds like object model, syntax, and indentation.

10. Operating Systems

Yes, Python is the secret ingredient behind many operating systems as well, most popularly of Linux distributions. Linux-based Ubuntu’s Ubiquity Installer and Fedora and Red Hat Enterprise’s Anaconda Installer are coded in Python. Even Gentoo Linux leverages Python Portage (package management system). Usually, Python is combined with the C programming language to design and develop operating systems.

11. Web Scraping Applications

Python is a nifty tool for extracting voluminous amounts of data from websites and web pages. The pulled data is generally used in different real-world processes, including job listings, price comparison, R&D, etc. 

BeautifulSoup, MechanicalSoup, Scrapy, LXML , Python Requests, Selenium, and Urllib are some of the best Python-based web scraping tools.

In other words, web scraping is an automated process for easily and quickly extracting information from websites. Python presents various features that make it appropriate for web scraping and justify the applications of Python programming . Some of the features are:

  • Easy to understand and use
  • A concise syntax that improves the readability and saves your time
  • The web scraping process is made easy and efficient with various tools and libraries like matplotlib, Pandas, and Selenium

12. Image Processing and Graphic Design Applications:

Alongside all the uses mentioned above, Python also finds a unique use case in image processing and graphic design applications. The programming language is used globally to design and build 2D imaging software like Inkscape, GIMP, Paint Shop Pro, and Scribus. Also, Python is used in several 3D animation packages such as Blender, Houdini, 3ds Max, Maya, Cinema 4D, and Lightwave, to name a few.

With so many uses up its sleeve, Python ranks as a highly loved language for programming. It is a top pick of software engineers and hackers, too, since it is laced with flexibility, versatility, and object-oriented specifications.

Read our popular Data Science Articles

Other real time applications of python.

  • Python in IoT (Internet of Things)

Python has emerged as a prominent language in the realm of Internet of Things (IoT) development. Its versatility, simplicity, and a rich ecosystem of libraries make it an ideal choice for building robust IoT solutions. Python facilitates the development of IoT applications, from sensor data processing to communication with cloud platforms, ensuring seamless connectivity in the IoT ecosystem.

Python seamlessly integrates with a wide array of IoT devices and platforms. Its adaptability allows developers to connect and communicate with various sensors, actuators, and IoT hardware. Python’s compatibility with popular IoT platforms, such as Raspberry Pi and Arduino, empowers developers to create innovative IoT projects with ease.

Python serves as the backbone for numerous IoT projects, ranging from smart home automation to industrial IoT applications. Frameworks like MicroPython and Zerynth provide Python support for microcontrollers, enabling developers to deploy Python code directly on resource-constrained IoT devices. Additionally, popular IoT frameworks like Home Assistant leverage Python for building comprehensive home automation solutions.

  • Advancements in Python Web Frameworks

Modern Python web frameworks play a pivotal role in web development, offering efficient tools for building scalable and feature-rich applications. Frameworks such as Django, Flask, and FastAPI provide developers with the scaffolding needed to streamline development workflows. Django, known for its batteries-included approach, offers a comprehensive set of features, while Flask embraces simplicity and flexibility. FastAPI, a newcomer, stands out for its speed and automatic OpenAPI documentation.

Each Python web framework has its unique strengths and use cases. Django, a high-level framework, is renowned for rapid development and a built-in admin interface. Flask, a microframework, grants developers more flexibility by allowing them to choose components as needed. FastAPI, designed for building APIs quickly, stands out for its automatic validation and support for asynchronous programming. Comparing these frameworks helps developers choose the one that aligns with their project requirements.

Python web development continues to evolve with emerging trends. Serverless architecture, powered by platforms like AWS Lambda and Azure Functions, is gaining traction, allowing developers to build scalable applications without managing server infrastructure. Microservices architecture, supported by Python frameworks, is fostering the development of modular and maintainable web applications. Moreover, the rise of frontend technologies like Vue.js and React, coupled with Python backend services, contributes to a more dynamic and interactive user experience.

  • Python forNatural Language Processing (NLP)

Python has become a leading language for Natural Language Processing (NLP), enabling developers to work with and analyze human language data. Its simplicity and a wealth of NLP libraries make it accessible for tasks such as text parsing, sentiment analysis, and language translation. Python provides a conducive environment for handling the intricacies of natural language, making it a preferred choice for NLP practitioners.

Python boasts powerful libraries dedicated to NLP tasks. The Natural Language Toolkit (NLTK) offers a comprehensive set of tools for tasks like tokenization, stemming, and part-of-speech tagging. SpaCy, another popular library, stands out for its speed and efficiency in processing large amounts of text. These libraries empower developers to implement sophisticated NLP algorithms with ease, making Python a cornerstone in the field.

Python’s influence extends to the creation of advanced language models and chatbots. With frameworks like TensorFlow and PyTorch, developers can build and train complex language models for tasks like language generation and understanding. Python’s simplicity and extensive community support also contribute to the development of conversational agents and chatbots, enhancing user interactions across various platforms.

Some other real-world applications of Python:

  • Automation and robotics through inbuilt tools and libraries like Dart, PyDy, pyro, and PyRobot
  • Image processing through tools and libraries like OpenCV, Blender, PIL, and Houdini
  • Scientific applications and the best Python app are developed through libraries like Pandas, SciPy, Matplotlib

Latest Trends and Updates in Python

To stay ahead in the tech world, it’s crucial for developers and enthusiasts alike to be aware of the latest trends, updates, and developments in the Python ecosystem. Let’s delve into the exciting advancements that are shaping the future of Python with the help of python course for beginners.

  • Adoption of Python in Machine Learning and AI:

Python’s dominance in the realm of machine learning and artificial intelligence continues to grow. Libraries such as TensorFlow and PyTorch are witnessing widespread adoption, empowering developers to create sophisticated models and applications. The seamless integration of Python with machine learning frameworks reinforces its position as the go-to language for AI enthusiasts.

  • Web Development with FastAPI:

FastAPI, a modern, fast, and highly performant web framework for building APIs with Python 3.7 and above, has gained significant traction. Its simplicity, automatic validation, and support for asynchronous programming make it an attractive choice for developers working on web applications and APIs. FastAPI’s rising popularity showcases Python’s adaptability to evolving web development needs.

  • Serverless Computing and Python:

Serverless architecture has become a popular paradigm in cloud computing, and Python is playing a pivotal role in this space. Platforms like AWS Lambda, Azure Functions, and Google Cloud Functions seamlessly support Python, enabling developers to build scalable and cost-effective serverless applications of python.

  • Increased Focus on Data Science and Visualization:

With the rise of data-driven decision-making, Python’s role in data science and visualization is more prominent than ever. Libraries like Pandas, NumPy, and Plotly continue to be instrumental in handling, analyzing, and visualizing data. Python’s simplicity and extensive ecosystem contribute to its widespread adoption in data-centric applications.

  • Microservices Architecture

Python’s versatility extends to microservices architecture, with frameworks like Flask and Django providing robust support. Microservices enable developers to build scalable and maintainable applications by breaking them down into smaller, independent services. Python’s ease of use and diverse ecosystem contribute to the efficiency of microservices development.

  • Enhanced Type Hinting and Static Analysis

Type hinting, introduced in Python 3.5, has gained momentum, leading to improved static analysis tools and support in popular IDEs. The push towards more statically-typed Python code enhances code quality, readability, and developer collaboration. Tools like MyPy and Pyright contribute to the growing emphasis on static analysis in the Python community.

  • Python in DevOps and Automation

Python’s simplicity and readability make it a preferred choice for DevOps tasks and automation scripts. Infrastructure as Code (IaC) tools like Ansible leverage Python for defining and managing infrastructure. The language’s extensive standard library and third-party modules facilitate seamless automation across various domains.

  • Quantum Computing with Python

As quantum computing gains momentum, Python is becoming a language of choice for quantum programming. Libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python’s accessibility is lowering the barrier for entry into the fascinating field of quantum computing.

After reading about all these versatile and diverse real-world applications of Python , it is safe to conclude that Python is capable of handling almost any development requirement. In the last few years, Python applications have gained newfound traction in the field of Data Science as well, particularly in Machine Learning.

Python has brought in a lot of changes to the industry given it is easy to use as well as comes packed with powerful libraries. Additionally, it offers a wide range of applications that boost productivity. The jobs in Python pay hefty packages over time and Python developers are in high demand. The ease of learning Python makes it an option to bag a steady and well-paying job too.

If you are curious to learn about python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.


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Frequently Asked Questions (FAQs)

Python has a solution for every field. It is the most versatile language till now and has a bright future ahead. There is a long list of fields where Python is considered to be the most suitable programming language. Developers in this language are sought after because the language is gradually becoming the go to solution in a diverse set of different areas. The major fields include Machine Learning and AI, Web Development, Data Analytics, Game Development, IoT, Application Development, and Game Development. Many sectors including the healthcare sector, finance sector, aerospace sector, and banking sector rely heavily on Python. There are many big names that have either built their applications on Python or have completely shifted their tech stack to Python. Some of these include YouTube, Google, Instagram, and Pinterest.

Python is a very versatile language and if you have a good knowledge of Python, there can be various career opportunities in your hand. Some of these opportunities are as follows: You can be a Python developer right after acquiring the Python knowledge. Python developers are responsible for building websites, optimize data algorithms, or write clean and efficient Python codes. A data analyst has to deal with large sets of data, analyze them and create visualizations out of them. If you are a Python geek and love to play with data then this job is for you. Project management is in high demand as a project manager is highly responsible for the business and marketing of the companies. A machine learning engineer trains the machines or models for making predictions on the basis of the data provided to them.

Python has a wide range of rich libraries and modules but being a Python geek, you must be handy with the top and most used Python libraries. The following are some of the most popular Python libraries: TensorFlow is a boon to Machine Learning engineers. This library is developed by Google and can be considered a computational library. Numpy is again a machine learning library used by other Python libraries like TensorFlow to perform internal operations. Keras is another popular Python library that provides a convenient mechanism for neural networks.

Python has become a dominant language in Machine Learning and AI due to its simplicity, extensive libraries (such as TensorFlow and PyTorch), and readability. Real time applications of python provides a conducive environment for developing complex algorithms, making it a preferred choice for data scientists and machine learning engineers.

Python's popularity in software development can be attributed to its platform independence, high compatibility, extensive built-in frameworks and libraries, enhanced code reusability, and readability. These features make it an excellent tool for developing applications ranging from web development to enterprise-level solutions.

Python plays a crucial role in quantum computing, and libraries like Qiskit and Cirq enable developers to experiment with quantum algorithms and simulations. Python's accessibility and support for quantum programming are contributing to its adoption in this cutting-edge field.

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The 3-body problem is real, and it’s really unsolvable

Oh god don’t make me explain math

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Rosalind Chao as Ye Wenjie standing in the middle of three overlapping circles

Everybody seems to be talking about 3 Body Problem , the new Netflix series based on Cixin Liu’s Remembrance of Earth’s Past book trilogy . Fewer people are talking about the two series’ namesake: The unsolvable physics problem of the same name.

This makes sense, because it’s confusing . In physics, the three-body problem attempts to find a way to predict the movements of three objects whose gravity interacts with each of the others — like three stars that are close together in space. Sounds simple enough, right? Yet I myself recently pulled up the Wikipedia article on the three-body problem and closed the tab in the same manner that a person might stagger away from a bright light. Apparently the Earth, sun, and moon are a three-body system? Are you telling me we don’t know how the moon moves ? Scientists have published multiple solutions for the three-body problem? Are you telling me Cixin Liu’s books are out of date?

All I’d wanted to know was why the problem was considered unsolvable, and now memories of my one semester of high school physics were swimming before my eyes like so many glowing doom numbers. However, despite my pains, I have readied several ways that we non-physicists can be confident that the three-body problem is, in fact, unsolvable.

Reason 1: This is a special definition of ‘unsolvable’

Jin Cheng (Jess Hong) holds up an apple in a medieval hall in 3 Body Problem.

The three-body problem is extra confusing, because scientists are seemingly constantly finding new solutions to the three-body problem! They just don’t mean a one-solution-for-all solution. Such a formula does exist for a two-body system, and apparently Isaac Newton figured it out in 1687 . But systems with more than two bodies are, according to physicists, too chaotic (i.e., not in the sense of a child’s messy bedroom, but in the sense of “chaos theory”) to be corralled by a single solution.

When physicists say they have a new solution to the three-body problem, they mean that they’ve found a specific solution for three-body systems that have certain theoretical parameters. Don’t ask me to explain those parameters, because they’re all things like “the three masses are collinear at each instant” or “a zero angular momentum solution with three equal masses moving around a figure-eight shape.” But basically: By narrowing the focus of the problem to certain arrangements of three-body systems, physicists have been able to derive formulas that predict the movements of some of them, like in our solar system. The mass of the Earth and the sun create a “ restricted three-body problem ,” where a less-big body (in this case, the moon) moves under the influence of two massive ones (the Earth and the sun).

What physicists mean when they say the three-body problem has no solution is simply that there isn’t a one-formula-fits-all solution to every way that the gravity of three objects might cause those objects to move — which is exactly what Three-Body Problem bases its whole premise on.

Reason 2: 3 Body Problem picked an unsolved three-body system on purpose

A woman floating in front of three celestial bodies (ahem) in 3 Body Problem

Henri Poincaré’s research into a general solution to the three-body problem formed the basis of what would become known as chaos theory (you might know it from its co-starring role in Jurassic Park ). And 3 Body Problem itself isn’t about any old three-body system. It’s specifically about an extremely chaotic three-body system, the exact kind of arrangement of bodies that Poincaré was focused on when he showed that the problem is “unsolvable.”

[ Ed. note: The rest of this section includes some spoilers for 3 Body Problem .]

In both Liu’s books and Netflix’s 3 Body Problem , humanity faces an invasion by aliens (called Trisolarans in the English translation of the books, and San-Ti in the TV series) whose home solar system features three suns in a chaotic three-body relationship. It is a world where, unlike ours, the heavens are fundamentally unpredictable. Periods of icy cold give way to searing heat that give way to swings in gravity that turn into temporary reprieves that can never be trusted. The unpredictable nature of the San-Ti environment is the source of every detail of their physicality, their philosophy, and their desire to claim Earth for their own.

In other words, 3 Body Problem ’s three-body problem is unsolvable because Liu wanted to write a story with an unsolvable three-body system, so he chose one of the three-body systems for which we have not discovered a solution, and might never.

Reason 3: Scientists are still working on the three-body problem

Perhaps the best reason I can give you to believe that the three-body problem is real, and is really unsolvable, is that some scientists published a whole set of new solutions for specific three-body systems very recently .

If physicists are still working on the three-body problem, we can safely assume that it has not been solved. Scientists, after all, are the real experts. And I am definitely not.

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