python multiple assignments

Multiple assignment in Python: Assign multiple values or the same value to multiple variables

In Python, the = operator is used to assign values to variables.

You can assign values to multiple variables in one line.

Assign multiple values to multiple variables

Assign the same value to multiple variables.

You can assign multiple values to multiple variables by separating them with commas , .

You can assign values to more than three variables, and it is also possible to assign values of different data types to those variables.

When only one variable is on the left side, values on the right side are assigned as a tuple to that variable.

If the number of variables on the left does not match the number of values on the right, a ValueError occurs. You can assign the remaining values as a list by prefixing the variable name with * .

For more information on using * and assigning elements of a tuple and list to multiple variables, see the following article.

• Unpack a tuple and list in Python

You can also swap the values of multiple variables in the same way. See the following article for details:

• Swap values ​​in a list or values of variables in Python

You can assign the same value to multiple variables by using = consecutively.

For example, this is useful when initializing multiple variables with the same value.

After assigning the same value, you can assign a different value to one of these variables. As described later, be cautious when assigning mutable objects such as list and dict .

You can apply the same method when assigning the same value to three or more variables.

Be careful when assigning mutable objects such as list and dict .

If you use = consecutively, the same object is assigned to all variables. Therefore, if you change the value of an element or add a new element in one variable, the changes will be reflected in the others as well.

If you want to handle mutable objects separately, you need to assign them individually.

after c = []; d = [] , c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d .) 3. Data model — Python 3.11.3 documentation

You can also use copy() or deepcopy() from the copy module to make shallow and deep copies. See the following article.

• Shallow and deep copy in Python: copy(), deepcopy()

Related Categories

Related articles.

• NumPy: arange() and linspace() to generate evenly spaced values
• Chained comparison (a < x < b) in Python
• pandas: Get first/last n rows of DataFrame with head() and tail()
• pandas: Filter rows/columns by labels with filter()
• Get the filename, directory, extension from a path string in Python
• Sign function in Python (sign/signum/sgn, copysign)
• How to flatten a list of lists in Python
• None in Python
• Create calendar as text, HTML, list in Python
• NumPy: Insert elements, rows, and columns into an array with np.insert()
• Shuffle a list, string, tuple in Python (random.shuffle, sample)
• Add and update an item in a dictionary in Python
• Cartesian product of lists in Python (itertools.product)
• Remove a substring from a string in Python
• pandas: Extract rows that contain specific strings from a DataFrame

Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python variables - assign multiple values, many values to multiple variables.

Python allows you to assign values to multiple variables in one line:

Note: Make sure the number of variables matches the number of values, or else you will get an error.

One Value to Multiple Variables

And you can assign the same value to multiple variables in one line:

Unpack a Collection

If you have a collection of values in a list, tuple etc. Python allows you to extract the values into variables. This is called unpacking .

Unpack a list:

Learn more about unpacking in our Unpack Tuples Chapter.

COLOR PICKER

Contact Sales

If you want to use W3Schools services as an educational institution, team or enterprise, send us an e-mail: [email protected]

Report Error

If you want to report an error, or if you want to make a suggestion, send us an e-mail: [email protected]

Top Tutorials

Top references, top examples, get certified.

Multiple Assignment Syntax in Python

• python-tricks

The multiple assignment syntax, often referred to as tuple unpacking or extended unpacking, is a powerful feature in Python. There are several ways to assign multiple values to variables at once.

Let's start with a first example that uses extended unpacking . This syntax is used to assign values from an iterable (in this case, a string) to multiple variables:

a : This variable will be assigned the first element of the iterable, which is 'D' in the case of the string 'Devlabs'.

*b : The asterisk (*) before b is used to collect the remaining elements of the iterable (the middle characters in the string 'Devlabs') into a list: ['e', 'v', 'l', 'a', 'b']

c : This variable will be assigned the last element of the iterable: 's'.

The multiple assignment syntax can also be used for numerous other tasks:

Swapping Values

This swaps the values of variables a and b without needing a temporary variable.

Splitting a List

first will be 1, and rest will be a list containing [2, 3, 4, 5] .

Assigning Multiple Values from a Function

This assigns the values returned by get_values() to x, y, and z.

Ignoring Values

Here, you're ignoring the first value with an underscore _ and assigning "Hello" to the important_value . In Python, the underscore is commonly used as a convention to indicate that a variable is being intentionally ignored or is a placeholder for a value that you don't intend to use.

Unpacking Nested Structures

This unpacks a nested structure (Tuple in this example) into separate variables. We can use similar syntax also for Dictionaries:

In this case, we first extract the 'person' dictionary from data, and then we use multiple assignment to further extract values from the nested dictionaries, making the code more concise.

Extended Unpacking with Slicing

first will be 1, middle will be a list containing [2, 3, 4], and last will be 5.

Split a String into a List

*split, is used for iterable unpacking. The asterisk (*) collects the remaining elements into a list variable named split . In this case, it collects all the characters from the string.

The comma , after *split is used to indicate that it's a single-element tuple assignment. It's a syntax requirement to ensure that split becomes a list containing the characters.

Mastering Multiple Variable Assignment in Python

Python's ability to assign multiple variables in a single line is a feature that exemplifies the language's emphasis on readability and efficiency. In this detailed blog post, we'll explore the nuances of assigning multiple variables in Python, a technique that not only simplifies code but also enhances its readability and maintainability.

Introduction to Multiple Variable Assignment

Python allows the assignment of multiple variables simultaneously. This feature is not only a syntactic sugar but a powerful tool that can make your code more Pythonic.

What is Multiple Variable Assignment?

• Simultaneous Assignment : Python enables the initialization of several variables in a single line, thereby reducing the number of lines of code and making it more readable.
• Versatility : This feature can be used with various data types and is particularly useful for unpacking sequences.

Basic Multiple Variable Assignment

The simplest form of multiple variable assignment in Python involves assigning single values to multiple variables in one line.

Syntax and Examples

Parallel Assignment : Assign values to several variables in parallel.

• Clarity and Brevity : This form of assignment is clear and concise.
• Efficiency : Reduces the need for multiple lines when initializing several variables.

Unpacking Sequences into Variables

Python takes multiple variable assignment a step further with unpacking, allowing the assignment of sequences to individual variables.

Unpacking Lists and Tuples

Direct Unpacking : If you have a list or tuple, you can unpack its elements into individual variables.

Unpacking Strings

Character Assignment : You can also unpack strings into variables with each character assigned to one variable.

Using Underscore for Unwanted Values

When unpacking, you may not always need all the values. Python allows the use of the underscore ( _ ) as a placeholder for unwanted values.

Ignoring Unnecessary Values

Discarding Values : Use _ for values you don't intend to use.

Swapping Variables Efficiently

Multiple variable assignment can be used for an elegant and efficient way to swap the values of two variables.

Swapping Variables

No Temporary Variable Needed : Swap values without the need for an additional temporary variable.

Advanced Unpacking Techniques

Python provides even more advanced ways to handle multiple variable assignments, especially useful with longer sequences.

Extended Unpacking

Using Asterisk ( * ): Python 3 introduced a syntax for extended unpacking where you can use * to collect multiple values.

Best Practices and Common Pitfalls

While multiple variable assignment is a powerful feature, it should be used judiciously.

• Readability : Ensure that your use of multiple variable assignments enhances, rather than detracts from, readability.
• Matching Lengths : Be cautious of the sequence length. The number of elements must match the number of variables being assigned.

Multiple variable assignment in Python is a testament to the language’s design philosophy of simplicity and elegance. By understanding and effectively utilizing this feature, you can write more concise, readable, and Pythonic code. Whether unpacking sequences or swapping values, multiple variable assignment is a technique that can significantly improve the efficiency of your Python programming.

Trey Hunner

I help developers level-up their python skills, multiple assignment and tuple unpacking improve python code readability.

Mar 7 th , 2018 4:30 pm | Comments

Whether I’m teaching new Pythonistas or long-time Python programmers, I frequently find that Python programmers underutilize multiple assignment .

Multiple assignment (also known as tuple unpacking or iterable unpacking) allows you to assign multiple variables at the same time in one line of code. This feature often seems simple after you’ve learned about it, but it can be tricky to recall multiple assignment when you need it most .

In this article we’ll see what multiple assignment is, we’ll take a look at common uses of multiple assignment, and then we’ll look at a few uses for multiple assignment that are often overlooked.

Note that in this article I will be using f-strings which are a Python 3.6+ feature. If you’re on an older version of Python, you’ll need to mentally translate those to use the string format method.

How multiple assignment works

I’ll be using the words multiple assignment , tuple unpacking , and iterable unpacking interchangeably in this article. They’re all just different words for the same thing.

Python’s multiple assignment looks like this:

Here we’re setting x to 10 and y to 20 .

What’s happening at a lower level is that we’re creating a tuple of 10, 20 and then looping over that tuple and taking each of the two items we get from looping and assigning them to x and y in order.

This syntax might make that a bit more clear:

Parenthesis are optional around tuples in Python and they’re also optional in multiple assignment (which uses a tuple-like syntax). All of these are equivalent:

Multiple assignment is often called “tuple unpacking” because it’s frequently used with tuples. But we can use multiple assignment with any iterable, not just tuples. Here we’re using it with a list:

And with a string:

Anything that can be looped over can be “unpacked” with tuple unpacking / multiple assignment.

Here’s another example to demonstrate that multiple assignment works with any number of items and that it works with variables as well as objects we’ve just created:

Note that on that last line we’re actually swapping variable names, which is something multiple assignment allows us to do easily.

Alright, let’s talk about how multiple assignment can be used.

Unpacking in a for loop

You’ll commonly see multiple assignment used in for loops.

Let’s take a dictionary:

Instead of looping over our dictionary like this:

You’ll often see Python programmers use multiple assignment by writing this:

When you write the for X in Y line of a for loop, you’re telling Python that it should do an assignment to X for each iteration of your loop. Just like in an assignment using the = operator, we can use multiple assignment here.

Is essentially the same as this:

We’re just not doing an unnecessary extra assignment in the first example.

So multiple assignment is great for unpacking dictionary items into key-value pairs, but it’s helpful in many other places too.

It’s great when paired with the built-in enumerate function:

And the zip function:

If you’re unfamiliar with enumerate or zip , see my article on looping with indexes in Python .

Newer Pythonistas often see multiple assignment in the context of for loops and sometimes assume it’s tied to loops. Multiple assignment works for any assignment though, not just loop assignments.

An alternative to hard coded indexes

It’s not uncommon to see hard coded indexes (e.g. point[0] , items[1] , vals[-1] ) in code:

When you see Python code that uses hard coded indexes there’s often a way to use multiple assignment to make your code more readable .

Here’s some code that has three hard coded indexes:

We can make this code much more readable by using multiple assignment to assign separate month, day, and year variables:

Whenever you see hard coded indexes in your code, stop to consider whether you could use multiple assignment to make your code more readable.

Multiple assignment is very strict

Multiple assignment is actually fairly strict when it comes to unpacking the iterable we give to it.

If we try to unpack a larger iterable into a smaller number of variables, we’ll get an error:

If we try to unpack a smaller iterable into a larger number of variables, we’ll also get an error:

This strictness is pretty great. If we’re working with an item that has a different size than we expected, the multiple assignment will fail loudly and we’ll hopefully now know about a bug in our program that we weren’t yet aware of.

Let’s look at an example. Imagine that we have a short command line program that parses command-line arguments in a rudimentary way, like this:

Our program is supposed to accept 2 arguments, like this:

But if someone called our program with three arguments, they will not see an error:

There’s no error because we’re not validating that we’ve received exactly 2 arguments.

If we use multiple assignment instead of hard coded indexes, the assignment will verify that we receive exactly the expected number of arguments:

Note : we’re using the variable name _ to note that we don’t care about sys.argv[0] (the name of our program). Using _ for variables you don’t care about is just a convention.

An alternative to slicing

So multiple assignment can be used for avoiding hard coded indexes and it can be used to ensure we’re strict about the size of the tuples/iterables we’re working with.

Multiple assignment can be used to replace hard coded slices too!

Slicing is a handy way to grab a specific portion of the items in lists and other sequences.

Here are some slices that are “hard coded” in that they only use numeric indexes:

Whenever you see slices that don’t use any variables in their slice indexes, you can often use multiple assignment instead. To do this we have to talk about a feature that I haven’t mentioned yet: the * operator.

In Python 3.0, the * operator was added to the multiple assignment syntax, allowing us to capture remaining items after an unpacking into a list:

The * operator allows us to replace hard coded slices near the ends of sequences.

These two lines are equivalent:

These two lines are equivalent also:

With the * operator and multiple assignment you can replace things like this:

With more descriptive code, like this:

So if you see hard coded slice indexes in your code, consider whether you could use multiple assignment to clarify what those slices really represent.

Deep unpacking

This next feature is something that long-time Python programmers often overlook. It doesn’t come up quite as often as the other uses for multiple assignment that I’ve discussed, but it can be very handy to know about when you do need it.

We’ve seen multiple assignment for unpacking tuples and other iterables. We haven’t yet seen that this is can be done deeply .

I’d say that the following multiple assignment is shallow because it unpacks one level deep:

And I’d say that this multiple assignment is deep because it unpacks the previous point tuple further into x , y , and z variables:

If it seems confusing what’s going on above, maybe using parenthesis consistently on both sides of this assignment will help clarify things:

We’re unpacking one level deep to get two objects, but then we take the second object and unpack it also to get 3 more objects. Then we assign our first object and our thrice-unpacked second object to our new variables ( color , x , y , and z ).

Take these two lists:

Here’s an example of code that works with these lists by using shallow unpacking:

And here’s the same thing with deeper unpacking:

Note that in this second case, it’s much more clear what type of objects we’re working with. The deep unpacking makes it apparent that we’re receiving two 2-itemed tuples each time we loop.

Deep unpacking often comes up when nesting looping utilities that each provide multiple items. For example, you may see deep multiple assignments when using enumerate and zip together:

I said before that multiple assignment is strict about the size of our iterables as we unpack them. With deep unpacking we can also be strict about the shape of our iterables .

This works:

But this buggy code works too:

Whereas this works:

But this does not:

With multiple assignment we’re assigning variables while also making particular assertions about the size and shape of our iterables. Multiple assignment will help you clarify your code to both humans (for better code readability ) and to computers (for improved code correctness ).

Using a list-like syntax

I noted before that multiple assignment uses a tuple-like syntax, but it works on any iterable. That tuple-like syntax is the reason it’s commonly called “tuple unpacking” even though it might be more clear to say “iterable unpacking”.

I didn’t mention before that multiple assignment also works with a list-like syntax .

Here’s a multiple assignment with a list-like syntax:

This might seem really strange. What’s the point of allowing both list-like and tuple-like syntaxes?

I use this feature rarely, but I find it helpful for code clarity in specific circumstances.

Let’s say I have code that used to look like this:

And our well-intentioned coworker has decided to use deep multiple assignment to refactor our code to this:

See that trailing comma on the left-hand side of the assignment? It’s easy to miss and it makes this code look sort of weird. What is that comma even doing in this code?

That trailing comma is there to make a single item tuple. We’re doing deep unpacking here.

Here’s another way we could write the same code:

This might make that deep unpacking a little more obvious but I’d prefer to see this instead:

The list-syntax in our assignment makes it more clear that we’re unpacking a one-item iterable and then unpacking that single item into value and times_seen variables.

When I see this, I also think I bet we’re unpacking a single-item list . And that is in fact what we’re doing. We’re using a Counter object from the collections module here. The most_common method on Counter objects allows us to limit the length of the list returned to us. We’re limiting the list we’re getting back to just a single item.

When you’re unpacking structures that often hold lots of values (like lists) and structures that often hold a very specific number of values (like tuples) you may decide that your code appears more semantically accurate if you use a list-like syntax when unpacking those list-like structures.

If you’d like you might even decide to adopt a convention of always using a list-like syntax when unpacking list-like structures (frequently the case when using * in multiple assignment):

I don’t usually use this convention myself, mostly because I’m just not in the habit of using it. But if you find it helpful, you might consider using this convention in your own code.

When using multiple assignment in your code, consider when and where a list-like syntax might make your code more descriptive and more clear. This can sometimes improve readability.

Don’t forget about multiple assignment

Multiple assignment can improve both the readability of your code and the correctness of your code. It can make your code more descriptive while also making implicit assertions about the size and shape of the iterables you’re unpacking.

The use for multiple assignment that I often see forgotten is its ability to replace hard coded indexes , including replacing hard coded slices (using the * syntax). It’s also common to overlook the fact that multiple assignment works deeply and can be used with both a tuple-like syntax and a list-like syntax.

It’s tricky to recognize and remember all the cases that multiple assignment can come in handy. Please feel free to use this article as your personal reference guide to multiple assignment.

Get practice with multiple assignment

You don’t learn by reading articles like this one, you learn by writing code .

To get practice writing some readable code using tuple unpacking, sign up for Python Morsels using the form below. If you sign up to Python Morsels using this form, I’ll immediately send you an exercise that involves tuple unpacking.

Intro to Python courses often skip over some fundamental Python concepts .

Sign up below and I'll share ideas new Pythonistas often overlook .

You're nearly signed up. You just need to check your email and click the link there to set your password .

Right after you've set your password you'll receive your first Python Morsels exercise.

What is Multiple Assignment in Python and How to use it?

When working with Python , you’ll often come across scenarios where you need to assign values to multiple variables simultaneously.

Python provides an elegant solution for this through its support for multiple assignments. This feature allows you to assign values to multiple variables in a single line, making your code cleaner, more concise, and easier to read.

In this blog, we’ll explore the concept of multiple assignments in Python and delve into its various use cases.

Understanding Multiple Assignment

Multiple assignment in Python is the process of assigning values to multiple variables in a single statement. Instead of writing individual assignment statements for each variable, you can group them together using a single line of code.

In this example, the variables x , y , and z are assigned the values 10, 20, and 30, respectively. The values are separated by commas, and they correspond to the variables in the same order.

Simultaneous Assignment

Multiple assignment takes advantage of simultaneous assignment. This means that the values on the right side of the assignment are evaluated before any variables are assigned. This avoids potential issues when variables depend on each other.

In this snippet, the values of x and y are swapped using multiple assignments. The right-hand side y, x evaluates to (10, 5) before assigning to x and y, respectively.

Unpacking Sequences

One of the most powerful applications of multiple assignments is unpacking sequences like lists, tuples, and strings. You can assign the individual elements of a sequence to multiple variables in a single line.

In this example, the tuple (3, 4) is unpacked into the variables x and y . The value 3 is assigned to x , and the value 4 is assigned to y .

Multiple Return Values

Functions in Python can return multiple values, which are often returned as tuples. With multiple assignments, you can easily capture these return values.

Here, the function get_coordinates() returns a tuple (5, 10), which is then unpacked into the variables x and y .

Swapping Values

We’ve already seen how multiple assignments can be used to swap the values of two variables. This is a concise way to achieve value swapping without using a temporary variable.

Iterating through Sequences

Multiple assignment is particularly useful when iterating through sequences. It allows you to iterate over pairs of elements in a sequence effortlessly.

In this loop, each tuple (x, y) in the points list is unpacked and the values are assigned to the variables x and y for each iteration.

Discarding Values

Sometimes you might not be interested in all the values from an iterable. Python allows you to use an underscore (_) to discard unwanted values.

In this example, only the value 10 from the tuple is assigned to x , while the value 20 is discarded.

Multiple assignments is a powerful feature in Python that makes code more concise and readable. It allows you to assign values to multiple variables in a single line, swap values without a temporary variable, unpack sequences effortlessly, and work with functions that return multiple values. By mastering multiple assignments, you’ll enhance your ability to write clean, efficient, and elegant Python code.

Related: How input() function Work in Python?

Vilashkumar is a Python developer with expertise in Django, Flask, API development, and API Integration. He builds web applications and works as a freelance developer. He is also an automation script/bot developer building scripts in Python, VBA, and JavaScript.

Related Posts

How to Scrape Email and Phone Number from Any Website with Python

How to Build Multi-Threaded Web Scraper in Python

How to Search and Locate Elements in Selenium Python

CRUD Operation using AJAX in Django Application

Leave a comment cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Unpacking And Multiple Assignment in

About unpacking and multiple assignment.

Unpacking refers to the act of extracting the elements of a collection, such as a list , tuple , or dict , using iteration. Unpacked values can then be assigned to variables within the same statement. A very common example of this behavior is for item in list , where item takes on the value of each list element in turn throughout the iteration.

Multiple assignment is the ability to assign multiple variables to unpacked values within one statement. This allows for code to be more concise and readable, and is done by separating the variables to be assigned with a comma such as first, second, third = (1,2,3) or for index, item in enumerate(iterable) .

The special operators * and ** are often used in unpacking contexts. * can be used to combine multiple lists / tuples into one list / tuple by unpacking each into a new common list / tuple . ** can be used to combine multiple dictionaries into one dictionary by unpacking each into a new common dict .

When the * operator is used without a collection, it packs a number of values into a list . This is often used in multiple assignment to group all "leftover" elements that do not have individual assignments into a single variable.

It is common in Python to also exploit this unpacking/packing behavior when using or defining functions that take an arbitrary number of positional or keyword arguments. You will often see these "special" parameters defined as def some_function(*args, **kwargs) and the "special" arguments used as some_function(*some_tuple, **some_dict) .

*<variable_name> and **<variable_name> should not be confused with * and ** . While * and ** are used for multiplication and exponentiation respectively, *<variable_name> and **<variable_name> are used as packing and unpacking operators.

Multiple assignment

In multiple assignment, the number of variables on the left side of the assignment operator ( = ) must match the number of values on the right side. To separate the values, use a comma , :

If the multiple assignment gets an incorrect number of variables for the values given, a ValueError will be thrown:

Multiple assignment is not limited to one data type:

Multiple assignment can be used to swap elements in lists . This practice is pretty common in sorting algorithms . For example:

Since tuples are immutable, you can't swap elements in a tuple .

The examples below use lists but the same concepts apply to tuples .

In Python, it is possible to unpack the elements of list / tuple / dictionary into distinct variables. Since values appear within lists / tuples in a specific order, they are unpacked into variables in the same order:

If there are values that are not needed then you can use _ to flag them:

Deep unpacking

Unpacking and assigning values from a list / tuple inside of a list or tuple ( also known as nested lists/tuples ), works in the same way a shallow unpacking does, but often needs qualifiers to clarify the values context or position:

You can also deeply unpack just a portion of a nested list / tuple :

If the unpacking has variables with incorrect placement and/or an incorrect number of values, you will get a ValueError :

Unpacking a list/tuple with *

When unpacking a list / tuple you can use the * operator to capture the "leftover" values. This is clearer than slicing the list / tuple ( which in some situations is less readable ). For example, we can extract the first element and then assign the remaining values into a new list without the first element:

We can also extract the values at the beginning and end of the list while grouping all the values in the middle:

We can also use * in deep unpacking:

Unpacking a dictionary

Unpacking a dictionary is a bit different than unpacking a list / tuple . Iteration over dictionaries defaults to the keys . So when unpacking a dict , you can only unpack the keys and not the values :

If you want to unpack the values then you can use the values() method:

If both keys and values are needed, use the items() method. Using items() will generate tuples with key-value pairs. This is because of dict.items() generates an iterable with key-value tuples .

Packing is the ability to group multiple values into one list that is assigned to a variable. This is useful when you want to unpack values, make changes, and then pack the results back into a variable. It also makes it possible to perform merges on 2 or more lists / tuples / dicts .

Packing a list/tuple with *

Packing a list / tuple can be done using the * operator. This will pack all the values into a list / tuple .

Packing a dictionary with **

Packing a dictionary is done by using the ** operator. This will pack all key - value pairs from one dictionary into another dictionary, or combine two dictionaries together.

Usage of * and ** with functions

Packing with function parameters.

When you create a function that accepts an arbitrary number of arguments, you can use *args or **kwargs in the function definition. *args is used to pack an arbitrary number of positional (non-keyworded) arguments and **kwargs is used to pack an arbitrary number of keyword arguments.

Usage of *args :

Usage of **kwargs :

*args and **kwargs can also be used in combination with one another:

You can also write parameters before *args to allow for specific positional arguments. Individual keyword arguments then have to appear before **kwargs .

Arguments have to be structured like this:

def my_function(<positional_args>, *args, <key-word_args>, **kwargs)

If you don't follow this order then you will get an error.

Writing arguments in an incorrect order will result in an error:

Unpacking into function calls

You can use * to unpack a list / tuple of arguments into a function call. This is very useful for functions that don't accept an iterable :

Using * unpacking with the zip() function is another common use case. Since zip() takes multiple iterables and returns a list of tuples with the values from each iterable grouped:

Learn Unpacking And Multiple Assignment

Unlock 3 more exercises to practice unpacking and multiple assignment.

• 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

Assigning multiple variables in one line in Python

• How to input multiple values from user in one line in Python?
• Python | Assign multiple variables with list values
• Filtering a List of Dictionary on Multiple Values in Python
• Print Single and Multiple variable in Python
• Python - Solve the Linear Equation of Multiple Variable
• How to Catch Multiple Exceptions in One Line in Python?
• How to check multiple variables against a value in Python?
• Assign Function to a Variable in Python
• Accessing Python Function Variable Outside the Function
• Replace Multiple Lines From A File Using Python
• Break a long line into multiple lines in Python
• How To Combine Multiple Lists Into One List Python
• Python | Add similar value multiple times in list
• Convert List of Tuples To Multiple Lists in Python
• Read a file line by line in Python
• Assigning Values to Variables
• Get Index of Multiple List Elements in Python
• How To Print A Variable's Name In Python
• Exporting variable to CSV file in Python
• Python | Append multiple lists at once
• Global and Local Variables in Python
• Get Variable Name As String In Python
• Python program to find number of local variables in a function
• Returning Multiple Values in Python
• Convert String into Variable Name in Python
• How to Print String and Int in the Same Line in Python
• How to Assign Multiple Variables in One Line in PHP ?
• List As Input in Python in Single Line
• Assigning values to variables in R programming - assign() Function

A variable is a segment of memory with a unique name used to hold data that will later be processed. Although each programming language has a different mechanism for declaring variables, the name and the data that will be assigned to each variable are always the same. They are capable of storing values of data types.

The assignment operator(=) assigns the value provided to its right to the variable name given to its left. Given is the basic syntax of variable declaration:

 Assign Values to Multiple Variables in One Line

Given above is the mechanism for assigning just variables in Python but it is possible to assign multiple variables at the same time. Python assigns values from right to left. When assigning multiple variables in a single line, different variable names are provided to the left of the assignment operator separated by a comma. The same goes for their respective values except they should be to the right of the assignment operator.

While declaring variables in this fashion one must be careful with the order of the names and their corresponding value first variable name to the left of the assignment operator is assigned with the first value to its right and so on. 

Variable assignment in a single line can also be done for different data types.

Not just simple variable assignment, assignment after performing some operation can also be done in the same way.

Assigning different operation results to multiple variable.

Here, we are storing different characters in a different variables.

Please Login to comment...

Similar reads.

• python-basics
• Technical Scripter 2020
• Technical Scripter

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

If there’s just one variable but multiple values, it becomes a tuple:

If there’s a mismatched number of variables and values, there’s going to be a ValueError .

• Python »
• 3.12.3 Documentation »
• The Python Language Reference »
• 7. Simple statements
• Theme Auto Light Dark |

7. Simple statements ¶

A simple statement is comprised within a single logical line. Several simple statements may occur on a single line separated by semicolons. The syntax for simple statements is:

7.1. Expression statements ¶

Expression statements are used (mostly interactively) to compute and write a value, or (usually) to call a procedure (a function that returns no meaningful result; in Python, procedures return the value None ). Other uses of expression statements are allowed and occasionally useful. The syntax for an expression statement is:

An expression statement evaluates the expression list (which may be a single expression).

In interactive mode, if the value is not None , it is converted to a string using the built-in repr() function and the resulting string is written to standard output on a line by itself (except if the result is None , so that procedure calls do not cause any output.)

7.2. Assignment statements ¶

Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for attributeref , subscription , and slicing .)

An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right.

Assignment is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy ).

Assignment of an object to a target list, optionally enclosed in parentheses or square brackets, is recursively defined as follows.

If the target list is a single target with no trailing comma, optionally in parentheses, the object is assigned to that target.

If the target list contains one target prefixed with an asterisk, called a “starred” target: The object must be an iterable with at least as many items as there are targets in the target list, minus one. The first items of the iterable are assigned, from left to right, to the targets before the starred target. The final items of the iterable are assigned to the targets after the starred target. A list of the remaining items in the iterable is then assigned to the starred target (the list can be empty).

Else: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as follows.

If the target is an identifier (name):

If the name does not occur in a global or nonlocal statement in the current code block: the name is bound to the object in the current local namespace.

Otherwise: the name is bound to the object in the global namespace or the outer namespace determined by nonlocal , respectively.

The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called.

If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, TypeError is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily AttributeError ).

Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the right-hand side expression, a.x can access either an instance attribute or (if no instance attribute exists) a class attribute. The left-hand side target a.x is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of a.x do not necessarily refer to the same attribute: if the right-hand side expression refers to a class attribute, the left-hand side creates a new instance attribute as the target of the assignment:

This description does not necessarily apply to descriptor attributes, such as properties created with property() .

If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated.

If the primary is a mutable sequence object (such as a list), the subscript must yield an integer. If it is negative, the sequence’s length is added to it. The resulting value must be a nonnegative integer less than the sequence’s length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, IndexError is raised (assignment to a subscripted sequence cannot add new items to a list).

If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping’s key type, and the mapping is then asked to create a key/value pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed).

For user-defined objects, the __setitem__() method is called with appropriate arguments.

If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence’s length. The bounds should evaluate to integers. If either bound is negative, the sequence’s length is added to it. The resulting bounds are clipped to lie between zero and the sequence’s length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the target sequence allows it.

CPython implementation detail: In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages.

Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘simultaneous’ (for example a, b = b, a swaps two variables), overlaps within the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints [0, 2] :

The specification for the *target feature.

7.2.1. Augmented assignment statements ¶

Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement:

(See section Primaries for the syntax definitions of the last three symbols.)

An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once.

An augmented assignment expression like x += 1 can be rewritten as x = x + 1 to achieve a similar, but not exactly equal effect. In the augmented version, x is only evaluated once. Also, when possible, the actual operation is performed in-place , meaning that rather than creating a new object and assigning that to the target, the old object is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-hand side before evaluating the right-hand side. For example, a[i] += f(x) first looks-up a[i] , then it evaluates f(x) and performs the addition, and lastly, it writes the result back to a[i] .

With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place behavior, the binary operation performed by augmented assignment is the same as the normal binary operations.

For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments.

7.2.2. Annotated assignment statements ¶

Annotation assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement:

The difference from normal Assignment statements is that only a single target is allowed.

For simple names as assignment targets, if in class or module scope, the annotations are evaluated and stored in a special class or module attribute __annotations__ that is a dictionary mapping from variable names (mangled if private) to evaluated annotations. This attribute is writable and is automatically created at the start of class or module body execution, if annotations are found statically.

For expressions as assignment targets, the annotations are evaluated if in class or module scope, but not stored.

If a name is annotated in a function scope, then this name is local for that scope. Annotations are never evaluated and stored in function scopes.

If the right hand side is present, an annotated assignment performs the actual assignment before evaluating annotations (where applicable). If the right hand side is not present for an expression target, then the interpreter evaluates the target except for the last __setitem__() or __setattr__() call.

The proposal that added syntax for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments.

The proposal that added the typing module to provide a standard syntax for type annotations that can be used in static analysis tools and IDEs.

Changed in version 3.8: Now annotated assignments allow the same expressions in the right hand side as regular assignments. Previously, some expressions (like un-parenthesized tuple expressions) caused a syntax error.

7.3. The assert statement ¶

Assert statements are a convenient way to insert debugging assertions into a program:

The simple form, assert expression , is equivalent to

The extended form, assert expression1, expression2 , is equivalent to

These equivalences assume that __debug__ and AssertionError refer to the built-in variables with those names. In the current implementation, the built-in variable __debug__ is True under normal circumstances, False when optimization is requested (command line option -O ). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace.

Assignments to __debug__ are illegal. The value for the built-in variable is determined when the interpreter starts.

7.4. The pass statement ¶

pass is a null operation — when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example:

7.5. The del statement ¶

Deletion is recursively defined very similar to the way assignment is defined. Rather than spelling it out in full details, here are some hints.

Deletion of a target list recursively deletes each target, from left to right.

Deletion of a name removes the binding of that name from the local or global namespace, depending on whether the name occurs in a global statement in the same code block. If the name is unbound, a NameError exception will be raised.

Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a slicing is in general equivalent to assignment of an empty slice of the right type (but even this is determined by the sliced object).

Changed in version 3.2: Previously it was illegal to delete a name from the local namespace if it occurs as a free variable in a nested block.

7.6. The return statement ¶

return may only occur syntactically nested in a function definition, not within a nested class definition.

If an expression list is present, it is evaluated, else None is substituted.

return leaves the current function call with the expression list (or None ) as return value.

When return passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the function.

In a generator function, the return statement indicates that the generator is done and will cause StopIteration to be raised. The returned value (if any) is used as an argument to construct StopIteration and becomes the StopIteration.value attribute.

In an asynchronous generator function, an empty return statement indicates that the asynchronous generator is done and will cause StopAsyncIteration to be raised. A non-empty return statement is a syntax error in an asynchronous generator function.

7.7. The yield statement ¶

A yield statement is semantically equivalent to a yield expression . The yield statement can be used to omit the parentheses that would otherwise be required in the equivalent yield expression statement. For example, the yield statements

are equivalent to the yield expression statements

Yield expressions and statements are only used when defining a generator function, and are only used in the body of the generator function. Using yield in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

For full details of yield semantics, refer to the Yield expressions section.

7.8. The raise statement ¶

If no expressions are present, raise re-raises the exception that is currently being handled, which is also known as the active exception . If there isn’t currently an active exception, a RuntimeError exception is raised indicating that this is an error.

Otherwise, raise evaluates the first expression as the exception object. It must be either a subclass or an instance of BaseException . If it is a class, the exception instance will be obtained when needed by instantiating the class with no arguments.

The type of the exception is the exception instance’s class, the value is the instance itself.

A traceback object is normally created automatically when an exception is raised and attached to it as the __traceback__ attribute. You can create an exception and set your own traceback in one step using the with_traceback() exception method (which returns the same exception instance, with its traceback set to its argument), like so:

The from clause is used for exception chaining: if given, the second expression must be another exception class or instance. If the second expression is an exception instance, it will be attached to the raised exception as the __cause__ attribute (which is writable). If the expression is an exception class, the class will be instantiated and the resulting exception instance will be attached to the raised exception as the __cause__ attribute. If the raised exception is not handled, both exceptions will be printed:

A similar mechanism works implicitly if a new exception is raised when an exception is already being handled. An exception may be handled when an except or finally clause, or a with statement, is used. The previous exception is then attached as the new exception’s __context__ attribute:

Exception chaining can be explicitly suppressed by specifying None in the from clause:

Additional information on exceptions can be found in section Exceptions , and information about handling exceptions is in section The try statement .

Changed in version 3.3: None is now permitted as Y in raise X from Y .

Added the __suppress_context__ attribute to suppress automatic display of the exception context.

Changed in version 3.11: If the traceback of the active exception is modified in an except clause, a subsequent raise statement re-raises the exception with the modified traceback. Previously, the exception was re-raised with the traceback it had when it was caught.

7.9. The break statement ¶

break may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop.

It terminates the nearest enclosing loop, skipping the optional else clause if the loop has one.

If a for loop is terminated by break , the loop control target keeps its current value.

When break passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the loop.

7.10. The continue statement ¶

continue may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop. It continues with the next cycle of the nearest enclosing loop.

When continue passes control out of a try statement with a finally clause, that finally clause is executed before really starting the next loop cycle.

7.11. The import statement ¶

The basic import statement (no from clause) is executed in two steps:

find a module, loading and initializing it if necessary

define a name or names in the local namespace for the scope where the import statement occurs.

When the statement contains multiple clauses (separated by commas) the two steps are carried out separately for each clause, just as though the clauses had been separated out into individual import statements.

The details of the first step, finding and loading modules, are described in greater detail in the section on the import system , which also describes the various types of packages and modules that can be imported, as well as all the hooks that can be used to customize the import system. Note that failures in this step may indicate either that the module could not be located, or that an error occurred while initializing the module, which includes execution of the module’s code.

If the requested module is retrieved successfully, it will be made available in the local namespace in one of three ways:

If the module name is followed by as , then the name following as is bound directly to the imported module.

If no other name is specified, and the module being imported is a top level module, the module’s name is bound in the local namespace as a reference to the imported module

If the module being imported is not a top level module, then the name of the top level package that contains the module is bound in the local namespace as a reference to the top level package. The imported module must be accessed using its full qualified name rather than directly

The from form uses a slightly more complex process:

find the module specified in the from clause, loading and initializing it if necessary;

for each of the identifiers specified in the import clauses:

check if the imported module has an attribute by that name

if not, attempt to import a submodule with that name and then check the imported module again for that attribute

if the attribute is not found, ImportError is raised.

otherwise, a reference to that value is stored in the local namespace, using the name in the as clause if it is present, otherwise using the attribute name

If the list of identifiers is replaced by a star ( '*' ), all public names defined in the module are bound in the local namespace for the scope where the import statement occurs.

The public names defined by a module are determined by checking the module’s namespace for a variable named __all__ ; if defined, it must be a sequence of strings which are names defined or imported by that module. The names given in __all__ are all considered public and are required to exist. If __all__ is not defined, the set of public names includes all names found in the module’s namespace which do not begin with an underscore character ( '_' ). __all__ should contain the entire public API. It is intended to avoid accidentally exporting items that are not part of the API (such as library modules which were imported and used within the module).

The wild card form of import — from module import * — is only allowed at the module level. Attempting to use it in class or function definitions will raise a SyntaxError .

When specifying what module to import you do not have to specify the absolute name of the module. When a module or package is contained within another package it is possible to make a relative import within the same top package without having to mention the package name. By using leading dots in the specified module or package after from you can specify how high to traverse up the current package hierarchy without specifying exact names. One leading dot means the current package where the module making the import exists. Two dots means up one package level. Three dots is up two levels, etc. So if you execute from . import mod from a module in the pkg package then you will end up importing pkg.mod . If you execute from ..subpkg2 import mod from within pkg.subpkg1 you will import pkg.subpkg2.mod . The specification for relative imports is contained in the Package Relative Imports section.

importlib.import_module() is provided to support applications that determine dynamically the modules to be loaded.

Raises an auditing event import with arguments module , filename , sys.path , sys.meta_path , sys.path_hooks .

7.11.1. Future statements ¶

A future statement is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of Python where the feature becomes standard.

The future statement is intended to ease migration to future versions of Python that introduce incompatible changes to the language. It allows use of the new features on a per-module basis before the release in which the feature becomes standard.

A future statement must appear near the top of the module. The only lines that can appear before a future statement are:

the module docstring (if any),

blank lines, and

other future statements.

The only feature that requires using the future statement is annotations (see PEP 563 ).

All historical features enabled by the future statement are still recognized by Python 3. The list includes absolute_import , division , generators , generator_stop , unicode_literals , print_function , nested_scopes and with_statement . They are all redundant because they are always enabled, and only kept for backwards compatibility.

A future statement is recognized and treated specially at compile time: Changes to the semantics of core constructs are often implemented by generating different code. It may even be the case that a new feature introduces new incompatible syntax (such as a new reserved word), in which case the compiler may need to parse the module differently. Such decisions cannot be pushed off until runtime.

For any given release, the compiler knows which feature names have been defined, and raises a compile-time error if a future statement contains a feature not known to it.

The direct runtime semantics are the same as for any import statement: there is a standard module __future__ , described later, and it will be imported in the usual way at the time the future statement is executed.

The interesting runtime semantics depend on the specific feature enabled by the future statement.

Note that there is nothing special about the statement:

That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions.

Code compiled by calls to the built-in functions exec() and compile() that occur in a module M containing a future statement will, by default, use the new syntax or semantics associated with the future statement. This can be controlled by optional arguments to compile() — see the documentation of that function for details.

A future statement typed at an interactive interpreter prompt will take effect for the rest of the interpreter session. If an interpreter is started with the -i option, is passed a script name to execute, and the script includes a future statement, it will be in effect in the interactive session started after the script is executed.

The original proposal for the __future__ mechanism.

7.12. The global statement ¶

The global statement is a declaration which holds for the entire current code block. It means that the listed identifiers are to be interpreted as globals. It would be impossible to assign to a global variable without global , although free variables may refer to globals without being declared global.

Names listed in a global statement must not be used in the same code block textually preceding that global statement.

Names listed in a global statement must not be defined as formal parameters, or as targets in with statements or except clauses, or in a for target list, class definition, function definition, import statement, or variable annotation.

CPython implementation detail: The current implementation does not enforce some of these restrictions, but programs should not abuse this freedom, as future implementations may enforce them or silently change the meaning of the program.

Programmer’s note: global is a directive to the parser. It applies only to code parsed at the same time as the global statement. In particular, a global statement contained in a string or code object supplied to the built-in exec() function does not affect the code block containing the function call, and code contained in such a string is unaffected by global statements in the code containing the function call. The same applies to the eval() and compile() functions.

7.13. The nonlocal statement ¶

When the definition of a function or class is nested (enclosed) within the definitions of other functions, its nonlocal scopes are the local scopes of the enclosing functions. The nonlocal statement causes the listed identifiers to refer to names previously bound in nonlocal scopes. It allows encapsulated code to rebind such nonlocal identifiers. If a name is bound in more than one nonlocal scope, the nearest binding is used. If a name is not bound in any nonlocal scope, or if there is no nonlocal scope, a SyntaxError is raised.

The nonlocal statement applies to the entire scope of a function or class body. A SyntaxError is raised if a variable is used or assigned to prior to its nonlocal declaration in the scope.

The specification for the nonlocal statement.

Programmer’s note: nonlocal is a directive to the parser and applies only to code parsed along with it. See the note for the global statement.

7.14. The type statement ¶

The type statement declares a type alias, which is an instance of typing.TypeAliasType .

For example, the following statement creates a type alias:

This code is roughly equivalent to:

annotation-def indicates an annotation scope , which behaves mostly like a function, but with several small differences.

The value of the type alias is evaluated in the annotation scope. It is not evaluated when the type alias is created, but only when the value is accessed through the type alias’s __value__ attribute (see Lazy evaluation ). This allows the type alias to refer to names that are not yet defined.

Type aliases may be made generic by adding a type parameter list after the name. See Generic type aliases for more.

type is a soft keyword .

Added in version 3.12.

Introduced the type statement and syntax for generic classes and functions.

Table of Contents

• 7.1. Expression statements
• 7.2.1. Augmented assignment statements
• 7.2.2. Annotated assignment statements
• 7.3. The assert statement
• 7.4. The pass statement
• 7.5. The del statement
• 7.6. The return statement
• 7.7. The yield statement
• 7.8. The raise statement
• 7.9. The break statement
• 7.10. The continue statement
• 7.11.1. Future statements
• 7.12. The global statement
• 7.13. The nonlocal statement
• 7.14. The type statement

Previous topic

6. Expressions

8. Compound statements

• Report a Bug
• Show Source

Sign up on Python Hint

Join our community of friendly folks discovering and sharing the latest topics in tech.

We'll never post to any of your accounts without your permission.

Multiple assignment semantics

on 12 months ago

Unpacking tuples in Python

Using the star operator (*) in multiple assignments, swapping variables with multiple assignment, conclusion:, port matlab bounding ellipsoid code to python.

31460 views

11 months ago

How to pass variables from python script to bash script

16238 views

TFIDF for Large Dataset

55340 views

How to debug Django unit tests?

47362 views

10 months ago

Migrating to pip+virtualenv from setuptools

59243 views

Related Posts

Assigning list to one value in that list

Python: anything wrong with dynamically assigning instance methods as instance attributes, what is happening when i assign a list with self references to a list copy with the slice syntax `mylist[:] = [mylist, mylist, ...]`, why give a local variable an initial value immediately before assigning to it, automatically echo the result of an assignment statement in ipython, how to assign random values from a list to a column in a pandas dataframe, understanding python variable assignment, how to assign columns while ignoring index alignment, python del() built-in can't be used in assignment, how does python assign values after assignment operator, python assigning two variables on one line, numpy assigning to slice, when is the array copied, how to use a python list to assign a std::vector in c++ using swig, assigning (yield) to a variable, how to create groups and assign permission during project setup in django, installation.

Copyright 2023 - Python Hint

Term of Service

Privacy Policy

Cookie Policy

Python Multiple Assignments

Table of Contents

In Python, you can use multiple assignments to assign values to multiple variables in a single line. This can make your code more concise and readable.

Multiple Assignments

Python allows us to assign the same value to multiple variables.

For Example

Consider the following statement:

This statement will assign value 5 to all three variables in a single statement.

In the normal approach, we use different statements to assign values.

# Assigning values in different statements a = 1 b = 2 c = 3

We can also assign different values to multiple variables. Assigning multiple values in a single statement.

For example :

# Multiple assignment a, b, c = 1, 2, 3

This statement will assign value 1 to a variable, value 2 to b variable, and 3  to the c variable.

This feature is also used for unpacking lists and tuples.

# Unpacking a list numbers = [1, 2, 3] x, y, z = numbers

It’s a powerful feature that can enhance the readability of your code when used appropriately.

Python Tutorials

Python Tutorial on this website can be found at:

https://www.testingdocs.com/python-tutorials/

More information on Python is available at the official website:

https://www.python.org

Related Posts

Popular Python IDEs[ 2023 ]

Python Anonymous Functions

Python Libraries for Data Science

Install Python on Windows 11

Python Scatter Plots

Python Label Widget

Python program calculate simple interest.

Python Program Area of a Rectangle

Python Program Addition of Two Numbers

Python applications & uses.

DEV Community

Posted on Jan 13, 2021

multiple assignment in Python

hey people what's going on? hope you're doing well.

i'm going to explain multiple assignment in python , so sit back relax and enjoy the article.

multiple assignment allows us to assign multiple variables at the same time using one line of code .

here's an example of us using standard assignment. let's say we have a variable name and i will set this to a value of my name.

let's say edge equals 21 and how about a variable called attractive?

i think i'm gonna set this to true okay, so we have a bunch of variables and then we can print the value of these variables with some print statements.

so let's print name age and attractive. so we have name age attractive and as you would expect this prints tux 21 and true.

now another way in which we could write the same code is to use multiple assignment and this allows usto assign multiple variables at the same time using one line of code .

so i'm going to turn all of these lines into comments and this time we will only use one line of code.

But to do this we're going to list all of our variables separated with a comma so that would be name comma edge comma attractive.

We will set them equal to those values but in the same order separated by commas so that would be tux comma 21 comma true and this would do the same thing, only using one line of code.

You can print the variables on one line too

If you have a large program or use loops/if statements keep in mind the variable scope

Top comments (0)

Templates let you quickly answer FAQs or store snippets for re-use.

Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink .

Hide child comments as well

For further actions, you may consider blocking this person and/or reporting abuse

Implementing a RAG System on DuckDB Using JinaAI and SuperDuperDB

Fernando Guerra - Mar 12

Understanding Dataclasses in Python

Paulo GP - Mar 1

Create a simple dashboard of tokens on TON blockchain using the Stonfi API

Ivan Romanovich 🧐 - Mar 12

How to Convert Rows to Columns and Columns to Rows in Pandas DataFrame using Python?

Luca Liu - Mar 12

We're a place where coders share, stay up-to-date and grow their careers.