value assignment in python

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()

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Python Conditional Assignment

When you want to assign a value to a variable based on some condition, like if the condition is true then assign a value to the variable, else assign some other value to the variable, then you can use the conditional assignment operator.

In this tutorial, we will look at different ways to assign values to a variable based on some condition.

1. Using Ternary Operator

The ternary operator is very special operator in Python, it is used to assign a value to a variable based on some condition.

It goes like this:

Here, the value of variable will be value_if_true if the condition is true, else it will be value_if_false .

Let's see a code snippet to understand it better.

You can see we have conditionally assigned a value to variable c based on the condition a > b .

2. Using if-else statement

if-else statements are the core part of any programming language, they are used to execute a block of code based on some condition.

Using an if-else statement, we can assign a value to a variable based on the condition we provide.

Here is an example of replacing the above code snippet with the if-else statement.

3. Using Logical Short Circuit Evaluation

Logical short circuit evaluation is another way using which you can assign a value to a variable conditionally.

The format of logical short circuit evaluation is:

It looks similar to ternary operator, but it is not. Here the condition and value_if_true performs logical AND operation, if both are true then the value of variable will be value_if_true , or else it will be value_if_false .

Let's see an example:

But if we make condition True but value_if_true False (or 0 or None), then the value of variable will be value_if_false .

So, you can see that the value of c is 20 even though the condition a < b is True .

So, you should be careful while using logical short circuit evaluation.

While working with lists , we often need to check if a list is empty or not, and if it is empty then we need to assign some default value to it.

Let's see how we can do it using conditional assignment.

Here, we have assigned a default value to my_list if it is empty.

Assign a value to a variable conditionally based on the presence of an element in a list.

Now you know 3 different ways to assign a value to a variable conditionally. Any of these methods can be used to assign a value when there is a condition.

The cleanest and fastest way to conditional value assignment is the ternary operator .

if-else statement is recommended to use when you have to execute a block of code based on some condition.

Happy coding! 😊

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How To Use Assignment Expressions in Python

DavidMuller and Kathryn Hancox

How To Use Assignment Expressions in Python

The author selected the COVID-19 Relief Fund to receive a donation as part of the Write for DOnations program.

Introduction

Python 3.8 , released in October 2019, adds assignment expressions to Python via the := syntax. The assignment expression syntax is also sometimes called “the walrus operator” because := vaguely resembles a walrus with tusks.

Assignment expressions allow variable assignments to occur inside of larger expressions. While assignment expressions are never strictly necessary to write correct Python code, they can help make existing Python code more concise. For example, assignment expressions using the := syntax allow variables to be assigned inside of if statements , which can often produce shorter and more compact sections of Python code by eliminating variable assignments in lines preceding or following the if statement.

In this tutorial, you will use assignment expressions in several examples to produce concise sections of code.

Prerequisites

To get the most out of this tutorial, you will need:

Python 3.8 or above. Assignment expressions are a new feature added starting in Python 3.8. You can view the How To Install Python 3 and Set Up a Programming Environment on an Ubuntu 18.04 Server tutorial for help installing and upgrading Python.

The Python Interactive Console. If you would like to try out the example code in this tutorial you can use the How To Work with the Python Interactive Console tutorial.

Some familiarity with while loops, if statements, list comprehensions, and functions in Python 3 is useful, but not necessary. You can review our How To Code in Python 3 tutorial series for background knowledge.

Using Assignment Expressions in if Statements

Let’s start with an example of how you can use assignment expressions in an if statement.

Consider the following code that checks the length of a list and prints a statement:

If you run the previous code, you will receive the following output:

You initialize a list named some_list that contains three elements. Then, the if statement uses the assignment expression ((list_length := len(some_list)) to bind the variable named list_length to the length of some_list . The if statement evaluates to True because list_length is greater than 2 . You print a string using the list_length variable, which you bound initially with the assignment expression, indicating the the three-element list is too long.

Note: Assignment expressions are a new feature introduced in Python 3.8 . To run the examples in this tutorial, you will need to use Python 3.8 or higher.

Had we not used assignment expression, our code might have been slightly longer. For example:

This code sample is equivalent to the first example, but this code requires one extra standalone line to bind the value of list_length to len(some_list) .

Another equivalent code sample might just compute len(some_list) twice: once in the if statement and once in the print statement. This would avoid incurring the extra line required to bind a variable to the value of len(some_list) :

Assignment expressions help avoid the extra line or the double calculation.

Note: Assignment expressions are a helpful tool, but are not strictly necessary. Use your judgement and add assignment expressions to your code when it significantly improves the readability of a passage.

In the next section, we’ll explore using assignment expressions inside of while loops.

Using Assignment Expressions in while Loops

Assignment expressions often work well in while loops because they allow us to fold more context into the loop condition.

Consider the following example that embeds a user input function inside the while loop condition:

If you run this code, Python will continually prompt you for text input from your keyboard until you type the word stop . One example session might look like:

The assignment expression (directive := input("Enter text: ")) binds the value of directive to the value retrieved from the user via the input function. You bind the return value to the variable directive , which you print out in the body of the while loop. The while loop exits whenever the you type stop .

Had you not used an assignment expression, you might have written an equivalent input loop like:

This code is functionally identical to the one with assignment expressions, but requires four total lines (as opposed to two lines). It also duplicates the input("Enter text: ") call in two places. Certainly, there are many ways to write an equivalent while loop, but the assignment expression variant introduced earlier is compact and captures the program’s intention well.

So far, you’ve used assignment expression in if statements and while loops. In the next section, you’ll use an assignment expression inside of a list comprehension.

Using Assignment Expressions in List Comprehensions

We can also use assignment expressions in list comprehensions . List comprehensions allow you to build lists succinctly by iterating over a sequence and potentially adding elements to the list that satisfy some condition. Like list comprehensions, we can use assignment expressions to improve readability and make our code more concise.

Consider the following example that uses a list comprehension and an assignment expression to build a list of multiplied integers:

If you run the previous code, you will receive the following:

You define a function named slow_calculation that multiplies the given number x with itself. A list comprehension then iterates through 0 , 1 , and 2 returned by range(3) . An assignment expression binds the value result to the return of slow_calculation with i . You add the result to the newly built list as long as it is greater than 0. In this example, 0 , 1 , and 2 are all multiplied with themselves, but only the results 1 ( 1 * 1 ) and 4 ( 2 * 2 ) satisfy the greater than 0 condition and become part of the final list [1, 4] .

The slow_calculation function isn’t necessarily slow in absolute terms, but is meant to illustrate an important point about effeciency. Consider an alternate implementation of the previous example without assignment expressions:

Running this, you will receive the following output:

In this variant of the previous code, you use no assignment expressions. Instead, you call slow_calculation up to two times: once to ensure slow_calculation(i) is greater than 0 , and potentially a second time to add the result of the calculation to the final list. 0 is only multiplied with itself once because 0 * 0 is not greater than 0 . The other results, however, are doubly calculated because they satisfy the greater than 0 condition, and then have their results recalculated to become part of the final list [1, 4] .

You’ve now combined assignment expressions with list comprehensions to create blocks of code that are both efficient and concise.

In this tutorial, you used assignment expressions to make compact sections of Python code that assign values to variables inside of if statements, while loops, and list comprehensions.

For more information on other assignment expressions, you can view PEP 572 —the document that initially proposed adding assignment expressions to Python.

You may also want to check out our other Python content on our topic page .

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Variables and Assignment ¶

When programming, it is useful to be able to store information in variables. A variable is a string of characters and numbers associated with a piece of information. The assignment operator , denoted by the “=” symbol, is the operator that is used to assign values to variables in Python. The line x=1 takes the known value, 1, and assigns that value to the variable with name “x”. After executing this line, this number will be stored into this variable. Until the value is changed or the variable deleted, the character x behaves like the value 1.

TRY IT! Assign the value 2 to the variable y. Multiply y by 3 to show that it behaves like the value 2.

A variable is more like a container to store the data in the computer’s memory, the name of the variable tells the computer where to find this value in the memory. For now, it is sufficient to know that the notebook has its own memory space to store all the variables in the notebook. As a result of the previous example, you will see the variable “x” and “y” in the memory. You can view a list of all the variables in the notebook using the magic command %whos .

TRY IT! List all the variables in this notebook

Note that the equal sign in programming is not the same as a truth statement in mathematics. In math, the statement x = 2 declares the universal truth within the given framework, x is 2 . In programming, the statement x=2 means a known value is being associated with a variable name, store 2 in x. Although it is perfectly valid to say 1 = x in mathematics, assignments in Python always go left : meaning the value to the right of the equal sign is assigned to the variable on the left of the equal sign. Therefore, 1=x will generate an error in Python. The assignment operator is always last in the order of operations relative to mathematical, logical, and comparison operators.

TRY IT! The mathematical statement x=x+1 has no solution for any value of x . In programming, if we initialize the value of x to be 1, then the statement makes perfect sense. It means, “Add x and 1, which is 2, then assign that value to the variable x”. Note that this operation overwrites the previous value stored in x .

There are some restrictions on the names variables can take. Variables can only contain alphanumeric characters (letters and numbers) as well as underscores. However, the first character of a variable name must be a letter or underscores. Spaces within a variable name are not permitted, and the variable names are case-sensitive (e.g., x and X will be considered different variables).

TIP! Unlike in pure mathematics, variables in programming almost always represent something tangible. It may be the distance between two points in space or the number of rabbits in a population. Therefore, as your code becomes increasingly complicated, it is very important that your variables carry a name that can easily be associated with what they represent. For example, the distance between two points in space is better represented by the variable dist than x , and the number of rabbits in a population is better represented by nRabbits than y .

Note that when a variable is assigned, it has no memory of how it was assigned. That is, if the value of a variable, y , is constructed from other variables, like x , reassigning the value of x will not change the value of y .

EXAMPLE: What value will y have after the following lines of code are executed?

WARNING! You can overwrite variables or functions that have been stored in Python. For example, the command help = 2 will store the value 2 in the variable with name help . After this assignment help will behave like the value 2 instead of the function help . Therefore, you should always be careful not to give your variables the same name as built-in functions or values.

TIP! Now that you know how to assign variables, it is important that you learn to never leave unassigned commands. An unassigned command is an operation that has a result, but that result is not assigned to a variable. For example, you should never use 2+2 . You should instead assign it to some variable x=2+2 . This allows you to “hold on” to the results of previous commands and will make your interaction with Python must less confusing.

You can clear a variable from the notebook using the del function. Typing del x will clear the variable x from the workspace. If you want to remove all the variables in the notebook, you can use the magic command %reset .

In mathematics, variables are usually associated with unknown numbers; in programming, variables are associated with a value of a certain type. There are many data types that can be assigned to variables. A data type is a classification of the type of information that is being stored in a variable. The basic data types that you will utilize throughout this book are boolean, int, float, string, list, tuple, dictionary, set. A formal description of these data types is given in the following sections.

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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.

Video: Python Variable Names

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Python - Assignment Operators

Python - Logical Operators
Python - Bitwise Operators
Python - Membership Operators
Python - Identity Operators
Python - Operator Precedence
Python - Comments
Python - User Input
Python - Numbers
Python - Booleans
Python Control Statements
Python - Control Flow
Python - Decision Making
Python - If Statement
Python - If else
Python - Nested If
Python - Match-Case Statement
Python - Loops
Python - for Loops
Python - for-else Loops
Python - While Loops
Python - break Statement
Python - continue Statement
Python - pass Statement
Python - Nested Loops
Python Functions & Modules
Python - Functions
Python - Default Arguments
Python - Keyword Arguments
Python - Keyword-Only Arguments
Python - Positional Arguments
Python - Positional-Only Arguments
Python - Arbitrary Arguments
Python - Variables Scope
Python - Function Annotations
Python - Modules
Python - Built in Functions
Python Strings
Python - Strings
Python - Slicing Strings
Python - Modify Strings
Python - String Concatenation
Python - String Formatting
Python - Escape Characters
Python - String Methods
Python - String Exercises
Python Lists
Python - Lists
Python - Access List Items
Python - Change List Items
Python - Add List Items
Python - Remove List Items
Python - Loop Lists
Python - List Comprehension
Python - Sort Lists
Python - Copy Lists
Python - Join Lists
Python - List Methods
Python - List Exercises
Python Tuples
Python - Tuples
Python - Access Tuple Items
Python - Update Tuples
Python - Unpack Tuples
Python - Loop Tuples
Python - Join Tuples
Python - Tuple Methods
Python - Tuple Exercises
Python Sets
Python - Sets
Python - Access Set Items
Python - Add Set Items
Python - Remove Set Items
Python - Loop Sets
Python - Join Sets
Python - Copy Sets
Python - Set Operators
Python - Set Methods
Python - Set Exercises
Python Dictionaries
Python - Dictionaries
Python - Access Dictionary Items
Python - Change Dictionary Items
Python - Add Dictionary Items
Python - Remove Dictionary Items
Python - Dictionary View Objects
Python - Loop Dictionaries
Python - Copy Dictionaries
Python - Nested Dictionaries
Python - Dictionary Methods
Python - Dictionary Exercises
Python Arrays
Python - Arrays
Python - Access Array Items
Python - Add Array Items
Python - Remove Array Items
Python - Loop Arrays
Python - Copy Arrays
Python - Reverse Arrays
Python - Sort Arrays
Python - Join Arrays
Python - Array Methods
Python - Array Exercises
Python File Handling
Python - File Handling
Python - Write to File
Python - Read Files
Python - Renaming and Deleting Files
Python - Directories
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Object Oriented Programming
Python - OOPs Concepts
Python - Classes & Objects
Python - Class Attributes
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Python - Static Methods
Python - Constructors
Python - Access Modifiers
Python - Inheritance
Python - Polymorphism
Python - Method Overriding
Python - Method Overloading
Python - Dynamic Binding
Python - Dynamic Typing
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Python - Packages
Python - Inner Classes
Python - Anonymous Class and Objects
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Python Multithreading
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Computer Glossary

Python Assignment Operator

The = (equal to) symbol is defined as assignment operator in Python. The value of Python expression on its right is assigned to a single variable on its left. The = symbol as in programming in general (and Python in particular) should not be confused with its usage in Mathematics, where it states that the expressions on the either side of the symbol are equal.

Example of Assignment Operator in Python

Consider following Python statements −

At the first instance, at least for somebody new to programming but who knows maths, the statement "a=a+b" looks strange. How could a be equal to "a+b"? However, it needs to be reemphasized that the = symbol is an assignment operator here and not used to show the equality of LHS and RHS.

Because it is an assignment, the expression on right evaluates to 15, the value is assigned to a.

In the statement "a+=b", the two operators "+" and "=" can be combined in a "+=" operator. It is called as add and assign operator. In a single statement, it performs addition of two operands "a" and "b", and result is assigned to operand on left, i.e., "a".

Augmented Assignment Operators in Python

In addition to the simple assignment operator, Python provides few more assignment operators for advanced use. They are called cumulative or augmented assignment operators. In this chapter, we shall learn to use augmented assignment operators defined in Python.

Python has the augmented assignment operators for all arithmetic and comparison operators.

Python augmented assignment operators combines addition and assignment in one statement. Since Python supports mixed arithmetic, the two operands may be of different types. However, the type of left operand changes to the operand of on right, if it is wider.

The += operator is an augmented operator. It is also called cumulative addition operator, as it adds "b" in "a" and assigns the result back to a variable.

The following are the augmented assignment operators in Python:

Augmented Addition Operator
Augmented Subtraction Operator
Augmented Multiplication Operator
Augmented Division Operator
Augmented Modulus Operator
Augmented Exponent Operator
Augmented Floor division Operator

Augmented Addition Operator (+=)

Following examples will help in understanding how the "+=" operator works −

It will produce the following output −

Augmented Subtraction Operator (-=)

Use -= symbol to perform subtract and assign operations in a single statement. The "a-=b" statement performs "a=a-b" assignment. Operands may be of any number type. Python performs implicit type casting on the object which is narrower in size.

Augmented Multiplication Operator (*=)

The "*=" operator works on similar principle. "a*=b" performs multiply and assign operations, and is equivalent to "a=a*b". In case of augmented multiplication of two complex numbers, the rule of multiplication as discussed in the previous chapter is applicable.

Augmented Division Operator (/=)

The combination symbol "/=" acts as divide and assignment operator, hence "a/=b" is equivalent to "a=a/b". The division operation of int or float operands is float. Division of two complex numbers returns a complex number. Given below are examples of augmented division operator.

Augmented Modulus Operator (%=)

To perform modulus and assignment operation in a single statement, use the %= operator. Like the mod operator, its augmented version also is not supported for complex number.

Augmented Exponent Operator (**=)

The "**=" operator results in computation of "a" raised to "b", and assigning the value back to "a". Given below are some examples −

Augmented Floor division Operator (//=)

For performing floor division and assignment in a single statement, use the "//=" operator. "a//=b" is equivalent to "a=a//b". This operator cannot be used with complex numbers.

Variables in Python

Table of Contents

Variable Assignment

Variable types in python, object references, object identity, variable names, reserved words (keywords).

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Variables in Python

In the previous tutorial on Basic Data Types in Python , you saw how values of various Python data types can be created. But so far, all the values shown have been literal or constant values:

If you’re writing more complex code, your program will need data that can change as program execution proceeds.

Here’s what you’ll learn in this tutorial: You will learn how every item of data in a Python program can be described by the abstract term object , and you’ll learn how to manipulate objects using symbolic names called variables .

Free PDF Download: Python 3 Cheat Sheet

Take the Quiz: Test your knowledge with our interactive “Python Variables” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Test your understanding of Python variables and object references.

Think of a variable as a name attached to a particular object. In Python, variables need not be declared or defined in advance, as is the case in many other programming languages. To create a variable, you just assign it a value and then start using it. Assignment is done with a single equals sign ( = ):

This is read or interpreted as “ n is assigned the value 300 .” Once this is done, n can be used in a statement or expression, and its value will be substituted:

Just as a literal value can be displayed directly from the interpreter prompt in a REPL session without the need for print() , so can a variable:

Later, if you change the value of n and use it again, the new value will be substituted instead:

Python also allows chained assignment, which makes it possible to assign the same value to several variables simultaneously:

The chained assignment above assigns 300 to the variables a , b , and c simultaneously.

In many programming languages, variables are statically typed. That means a variable is initially declared to have a specific data type, and any value assigned to it during its lifetime must always have that type.

Variables in Python are not subject to this restriction. In Python, a variable may be assigned a value of one type and then later re-assigned a value of a different type:

What is actually happening when you make a variable assignment? This is an important question in Python, because the answer differs somewhat from what you’d find in many other programming languages.

Python is a highly object-oriented language . In fact, virtually every item of data in a Python program is an object of a specific type or class. (This point will be reiterated many times over the course of these tutorials.)

Consider this code:

When presented with the statement print(300) , the interpreter does the following:

Creates an integer object
Gives it the value 300
Displays it to the console

You can see that an integer object is created using the built-in type() function:

A Python variable is a symbolic name that is a reference or pointer to an object. Once an object is assigned to a variable, you can refer to the object by that name. But the data itself is still contained within the object.

For example:

This assignment creates an integer object with the value 300 and assigns the variable n to point to that object.

The following code verifies that n points to an integer object:

Now consider the following statement:

What happens when it is executed? Python does not create another object. It simply creates a new symbolic name or reference, m , which points to the same object that n points to.

Python variable references to the same object (illustration)

Next, suppose you do this:

Now Python creates a new integer object with the value 400 , and m becomes a reference to it.

References to separate objects in Python (diagram)

Lastly, suppose this statement is executed next:

Now Python creates a string object with the value "foo" and makes n reference that.

There is no longer any reference to the integer object 300 . It is orphaned, and there is no way to access it.

Tutorials in this series will occasionally refer to the lifetime of an object. An object’s life begins when it is created, at which time at least one reference to it is created. During an object’s lifetime, additional references to it may be created, as you saw above, and references to it may be deleted as well. An object stays alive, as it were, so long as there is at least one reference to it.

When the number of references to an object drops to zero, it is no longer accessible. At that point, its lifetime is over. Python will eventually notice that it is inaccessible and reclaim the allocated memory so it can be used for something else. In computer lingo, this process is referred to as garbage collection .

In Python, every object that is created is given a number that uniquely identifies it. It is guaranteed that no two objects will have the same identifier during any period in which their lifetimes overlap. Once an object’s reference count drops to zero and it is garbage collected, as happened to the 300 object above, then its identifying number becomes available and may be used again.

The built-in Python function id() returns an object’s integer identifier. Using the id() function, you can verify that two variables indeed point to the same object:

After the assignment m = n , m and n both point to the same object, confirmed by the fact that id(m) and id(n) return the same number. Once m is reassigned to 400 , m and n point to different objects with different identities.

Deep Dive: Caching Small Integer Values From what you now know about variable assignment and object references in Python, the following probably won’t surprise you: Python >>> m = 300 >>> n = 300 >>> id ( m ) 60062304 >>> id ( n ) 60062896 Copied! With the statement m = 300 , Python creates an integer object with the value 300 and sets m as a reference to it. n is then similarly assigned to an integer object with value 300 —but not the same object. Thus, they have different identities, which you can verify from the values returned by id() . But consider this: Python >>> m = 30 >>> n = 30 >>> id ( m ) 1405569120 >>> id ( n ) 1405569120 Copied! Here, m and n are separately assigned to integer objects having value 30 . But in this case, id(m) and id(n) are identical! For purposes of optimization, the interpreter creates objects for the integers in the range [-5, 256] at startup, and then reuses them during program execution. Thus, when you assign separate variables to an integer value in this range, they will actually reference the same object.

The examples you have seen so far have used short, terse variable names like m and n . But variable names can be more verbose. In fact, it is usually beneficial if they are because it makes the purpose of the variable more evident at first glance.

Officially, variable names in Python can be any length and can consist of uppercase and lowercase letters ( A-Z , a-z ), digits ( 0-9 ), and the underscore character ( _ ). An additional restriction is that, although a variable name can contain digits, the first character of a variable name cannot be a digit.

Note: One of the additions to Python 3 was full Unicode support , which allows for Unicode characters in a variable name as well. You will learn about Unicode in greater depth in a future tutorial.

For example, all of the following are valid variable names:

But this one is not, because a variable name can’t begin with a digit:

Note that case is significant. Lowercase and uppercase letters are not the same. Use of the underscore character is significant as well. Each of the following defines a different variable:

There is nothing stopping you from creating two different variables in the same program called age and Age , or for that matter agE . But it is probably ill-advised. It would certainly be likely to confuse anyone trying to read your code, and even you yourself, after you’d been away from it awhile.

It is worthwhile to give a variable a name that is descriptive enough to make clear what it is being used for. For example, suppose you are tallying the number of people who have graduated college. You could conceivably choose any of the following:

All of them are probably better choices than n , or ncg , or the like. At least you can tell from the name what the value of the variable is supposed to represent.

On the other hand, they aren’t all necessarily equally legible. As with many things, it is a matter of personal preference, but most people would find the first two examples, where the letters are all shoved together, to be harder to read, particularly the one in all capital letters. The most commonly used methods of constructing a multi-word variable name are the last three examples:

Example: numberOfCollegeGraduates
Example: NumberOfCollegeGraduates
Example: number_of_college_graduates

Programmers debate hotly, with surprising fervor, which of these is preferable. Decent arguments can be made for all of them. Use whichever of the three is most visually appealing to you. Pick one and use it consistently.

You will see later that variables aren’t the only things that can be given names. You can also name functions, classes, modules, and so on. The rules that apply to variable names also apply to identifiers, the more general term for names given to program objects.

The Style Guide for Python Code , also known as PEP 8 , contains Naming Conventions that list suggested standards for names of different object types. PEP 8 includes the following recommendations:

Snake Case should be used for functions and variable names.
Pascal Case should be used for class names. (PEP 8 refers to this as the “CapWords” convention.)

There is one more restriction on identifier names. The Python language reserves a small set of keywords that designate special language functionality. No object can have the same name as a reserved word.

In Python 3.6, there are 33 reserved keywords:

Python Keywords

You can see this list any time by typing help("keywords") to the Python interpreter. Reserved words are case-sensitive and must be used exactly as shown. They are all entirely lowercase, except for False , None , and True .

Trying to create a variable with the same name as any reserved word results in an error:

This tutorial covered the basics of Python variables , including object references and identity, and naming of Python identifiers.

You now have a good understanding of some of Python’s data types and know how to create variables that reference objects of those types.

Next, you will see how to combine data objects into expressions involving various operations .

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Python Variable Assignment

Explaining one of the most fundamental concepts of python programming language.

Farhad Malik

Towards Data Science

This article aims to explain how Python variable assignment works.

The Basics: Variables — Object Types And Scope

Variables store information that can be used and/or changed in your program. This information can be an integer, text, collection, etc.
Variables are used to hold user inputs, local states of your program, etc.
Variables have a name so that they can be referenced in the code.
The fundamental concept to understand is that everything is an object in Python.

Python supports numbers, strings, sets, lists, tuples, and dictionaries. These are the standard data types. I will explain each of them in detail.

Declare And Assign Value To Variable

Assignment sets a value to a variable.

To assign variable a value, use the equals sign (=)

Assigning a value is known as binding in Python. In the example above, we have assigned the value of 2 to mySecondVariable.

Written by Farhad Malik

My personal blog, aiming to explain complex mathematical, financial and technological concepts in simple terms. Contact: [email protected]

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Different Forms of Assignment Statements in Python

We use Python assignment statements to assign objects to names. The target of an assignment statement is written on the left side of the equal sign (=), and the object on the right can be an arbitrary expression that computes an object.

There are some important properties of assignment in Python :-

Assignment creates object references instead of copying the objects.
Python creates a variable name the first time when they are assigned a value.
Names must be assigned before being referenced.
There are some operations that perform assignments implicitly.

Assignment statement forms :-

1. Basic form:

This form is the most common form.

2. Tuple assignment:

When we code a tuple on the left side of the =, Python pairs objects on the right side with targets on the left by position and assigns them from left to right. Therefore, the values of x and y are 50 and 100 respectively.

3. List assignment:

This works in the same way as the tuple assignment.

4. Sequence assignment:

In recent version of Python, tuple and list assignment have been generalized into instances of what we now call sequence assignment – any sequence of names can be assigned to any sequence of values, and Python assigns the items one at a time by position.

5. Extended Sequence unpacking:

It allows us to be more flexible in how we select portions of a sequence to assign.

Here, p is matched with the first character in the string on the right and q with the rest. The starred name (*q) is assigned a list, which collects all items in the sequence not assigned to other names.

This is especially handy for a common coding pattern such as splitting a sequence and accessing its front and rest part.

6. Multiple- target assignment:

In this form, Python assigns a reference to the same object (the object which is rightmost) to all the target on the left.

7. Augmented assignment :

The augmented assignment is a shorthand assignment that combines an expression and an assignment.

There are several other augmented assignment forms:

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Python for absolute beginners, next episode, variables and assignment.

Overview Teaching: 15 min Exercises: 15 min Questions How can I store data in programs? Objectives Write scripts that assign values to variables and perform calculations with those values. Correctly trace value changes in scripts that use assignment.

Use variables to store values

Variables are one of the fundamental building blocks of Python. A variable is like a tiny container where you store values and data, such as filenames, words, numbers, collections of words and numbers, and more.

The variable name will point to a value that you “assign” it. You might think about variable assignment like putting a value “into” the variable, as if the variable is a little box 🎁

(In fact, a variable is not a container as such but more like an adress label that points to a container with a given value. This difference will become relevant once we start talking about lists and mutable data types.)

You assign variables with an equals sign ( = ). In Python, a single equals sign = is the “assignment operator.” (A double equals sign == is the “real” equals sign.)

Variables are names for values.
In Python the = symbol assigns the value on the right to the name on the left.
The variable is created when a value is assigned to it.
Here, Python assigns an age to a variable age and a name in quotation marks to a variable first_name :

Variable names

Variable names can be as long or as short as you want, but there are certain rules you must follow.

Cannot start with a digit.
Cannot contain spaces, quotation marks, or other punctuation.
May contain an underscore (typically used to separate words in long variable names).
Having an underscore at the beginning of a variable name like _alistairs_real_age has a special meaning. So we won’t do that until we understand the convention.
The standard naming convention for variable names in Python is the so-called “snake case”, where each word is separated by an underscore. For example my_first_variable . You can read more about naming conventions in Python here .

Use meaningful variable names

Python doesn’t care what you call variables as long as they obey the rules (alphanumeric characters and the underscore). As you start to code, you will almost certainly be tempted to use extremely short variables names like f . Your fingers will get tired. Your coffee will wear off. You will see other people using variables like f . You’ll promise yourself that you’ll definitely remember what f means. But you probably won’t.

So, resist the temptation of bad variable names! Clear and precisely-named variables will:

Make your code more readable (both to yourself and others).
Reinforce your understanding of Python and what’s happening in the code.
Clarify and strengthen your thinking.

Use meaningful variable names to help other people understand what the program does. The most important “other person” is your future self!

Python is case-sensitive

Python thinks that upper- and lower-case letters are different, so Name and name are different variables. There are conventions for using upper-case letters at the start of variable names so we will use lower-case letters for now.

Off-Limits Names

The only variable names that are off-limits are names that are reserved by, or built into, the Python programming language itself — such as print , True , and list . Some of these you can overwrite into variable names (not ideal!), but Jupyter Lab (and many other environments and editors) will catch this by colour coding your variable. If your would-be variable is colour-coded green, rethink your name choice. This is not something to worry too much about. You can get the object back by resetting your kernel.

Use print() to display values

We can check to see what’s “inside” variables by running a cell with the variable’s name. This is one of the handiest features of a Jupyter notebook. Outside the Jupyter environment, you would need to use the print() function to display the variable.

You can run the print() function inside the Jupyter environment, too. This is sometimes useful because Jupyter will only display the last variable in a cell, while print() can display multiple variables. Additionally, Jupyter will display text with \n characters (which means “new line”), while print() will display the text appropriately formatted with new lines.

Python has a built-in function called print() that prints things as text.
Provide values to the function (i.e., the things to print) in parentheses.
To add a string to the printout, wrap the string in single or double quotations.
The values passed to the function are called ‘arguments’ and are separated by commas.
When using the print() function, we can also separate with a ‘+’ sign. However, when using ‘+’ we have to add spaces in between manually.
print() automatically puts a single space between items to separate them.
And wraps around to a new line at the end.

Variables must be created before they are used

If a variable doesn’t exist yet, or if the name has been misspelled, Python reports an error (unlike some languages, which “guess” a default value).

The last line of an error message is usually the most informative. This message lets us know that there is no variable called eye_color in the script.

Variables Persist Between Cells Variables defined in one cell exist in all other cells once executed, so the relative location of cells in the notebook do not matter (i.e., cells lower down can still affect those above). Notice the number in the square brackets [ ] to the left of the cell. These numbers indicate the order, in which the cells have been executed. Cells with lower numbers will affect cells with higher numbers as Python runs the cells chronologically. As a best practice, we recommend you keep your notebook in chronological order so that it is easier for the human eye to read and make sense of, as well as to avoid any errors if you close and reopen your project, and then rerun what you have done. Remember: Notebook cells are just a way to organize a program! As far as Python is concerned, all of the source code is one long set of instructions.

Variables can be used in calculations

We can use variables in calculations just as if they were values. Remember, we assigned 42 to age a few lines ago.

This code works in the following way. We are reassigning the value of the variable age by taking its previous value (42) and adding 3, thus getting our new value of 45.

Use an index to get a single character from a string

The characters (individual letters, numbers, and so on) in a string are ordered. For example, the string ‘AB’ is not the same as ‘BA’. Because of this ordering, we can treat the string as a list of characters.
Each position in the string (first, second, etc.) is given a number. This number is called an index or sometimes a subscript.
Indices are numbered from 0 rather than 1.
Use the position’s index in square brackets to get the character at that position.

Use a slice to get a substring

A part of a string is called a substring. A substring can be as short as a single character. A slice is a part of a string (or, more generally, any list-like thing). We take a slice by using [start:stop] , where start is replaced with the index of the first element we want and stop is replaced with the index of the element just after the last element we want. Mathematically, you might say that a slice selects [start:stop] . The difference between stop and start is the slice’s length. Taking a slice does not change the contents of the original string. Instead, the slice is a copy of part of the original string.

Use the built-in function len() to find the length of a string

The built-in function len() is used to find the length of a string (and later, of other data types, too).

Note that the result is 6 and not 7. This is because it is the length of the value of the variable (i.e. 'helium' ) that is being counted and not the name of the variable (i.e. element )

Also note that nested functions are evaluated from the inside out, just like in mathematics. Thus, Python first reads the len() function, then the print() function.

Choosing a Name Which is a better variable name, m , min , or minutes ? Why? Hint: think about which code you would rather inherit from someone who is leaving the library: ts = m * 60 + s tot_sec = min * 60 + sec total_seconds = minutes * 60 + seconds Solution minutes is better because min might mean something like “minimum” (and actually does in Python, but we haven’t seen that yet).

Swapping Values Draw a table showing the values of the variables in this program after each statement is executed. In simple terms, what do the last three lines of this program do? x = 1.0 y = 3.0 swap = x x = y y = swap Solution swap = x # x->1.0 y->3.0 swap->1.0 x = y # x->3.0 y->3.0 swap->1.0 y = swap # x->3.0 y->1.0 swap->1.0 These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom.

Predicting Values What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.) initial = "left" position = initial initial = "right" Solution initial = "left" # Initial is assigned the string "left" position = initial # Position is assigned the variable initial, currently "left" initial = "right" # Initial is assigned the string "right" print(position) left The last assignment to position was “left”

Can you slice integers? If you assign a = 123 , what happens if you try to get the second digit of a ? Solution Numbers are not stored in the written representation, so they can’t be treated like strings. a = 123 print(a[1]) TypeError: 'int' object is not subscriptable

Slicing What does the following program print? library_name = 'social sciences' print('library_name[1:3] is:', library_name[1:3]) If thing is a variable name, low is a low number, and high is a high number: What does thing[low:high] do? What does thing[low:] (without a value after the colon) do? What does thing[:high] (without a value before the colon) do? What does thing[:] (just a colon) do? What does thing[number:negative-number] do? Solution library_name[1:3] is: oc It will slice the string, starting at the low index and ending an element before the high index It will slice the string, starting at the low index and stopping at the end of the string It will slice the string, starting at the beginning on the string, and ending an element before the high index It will print the entire string It will slice the string, starting the number index, and ending a distance of the absolute value of negative-number elements from the end of the string

Key Points Use variables to store values. Use meaningful variable names. Python is case-sensitive. Use print() to display values. Variables must be created before they are used. Variables persist between cells. Variables can be used in calculations. Use an index to get a single character from a string. Use a slice to get a substring. Use the built-in function len to find the length of a string.

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Python variables and assignment.

Sep 26, 2018 • 14 Minute Read

Introduction

Python is a great language for many tasks. It is commonly used for system administration tasks, as well as building websites, processing data, and text. It is also shaping up to be the language of choice for Machine Learning (ML), leveraging powerful modules for performing math and for visualizations.

As with the basis of most programming languages, you may use variables in Python to hold and manipulate values. This guide shows you the basics of creation and use of variables in Python.

To best benefit from this guide, you may want to follow along and run the code examples throughout this guide. The code examples are entered into Python's REPL interpreter. If your system does not already have a python interpreter, you can download one from here . Just pick the version matching your operating system and follow the installation instructions. This guide targets Python version 3.6 and the sample code was tested against that version.

Variables hold values. In Python, variables do not require forward declaration - all you need to do is provide a variable name and assign it some value.

The Python interpreter shows you a prompt that looks like this: >>> . Each line you type into the interpreter is taken one at a time, parsed by the interpreter, and if the line is complete, executed as well.

If you enter one = 1 in the Python interpreter and hit "Enter", the interpreter will just show you a new line prompt.

The new line prompt >>> is empty. But Python actually did a few things:

A variable named one was created.
The value 1 was assigned to the variable one .

This is not apparent from the blank line output. But the interpreter can show you the value of any variable if you just type the variable name and hit enter:

The value 1 is shown because Python evaluates the line and reports the value returned. Previously, the line contained a statement. The variable one was assigned a value. That operation evaluated a statement, so nothing was printed as a result. A more explicit way to print the value of a variable is to use the print() function.

Let's create another variable named greeting and assign it the value 'hi!' :

Here we created a variable and assigned it a string value. Note the variable name greeting . It was chosen to contain, well, a greeting of some sort. Python, of course, has no way to tell that the string value 'hi' is indeed a greeting. A variable is, well, variable ! We can re-assign a variable later. The value stored in a variable is simply the last one assigned to it.

The initial value 'hi once' is lost once the second assignment to the value 'hi again!' was evaluated. The current value of the variable remains 'hi again! for the duration of the session unless otherwise assigned a new value later.

Both variable names x and greeting consist of characters only. Python allows you to name variables to your liking, as long as the names follow these rules:

Variable names may contain letters, digits (0-9) or the underscore character _ .
Variable names must begin with a letter from A-Z or the underscore _ character. Either lowercase or uppercase letters are acceptable.
Variable names may not be a reserved word in Python.

Following the rules above, all of these variable assignments are legal:

All the above variable names are acceptable. But just because they are acceptable does not mean you should use them. The Python community has further developed naming conventions which should be followed. For example, even though a single-character identifier is perfectly legal, you are strongly discouraged from using the characters l (lower case el) or O (uppercase oh) or I (uppercase eye). This is because in some fonts these are hard to distinguish from the digits 1 (one) and 0 (zero). For more on variable naming, see this reference .

The following variable names are not acceptable. If you attempt to use them, python will produce an error and no variable would be created.

An initial character which is not an underscore or a letter from A-Z or a-z will produce an error. The backtick (`) character for example:

An identifier starting with a digit is not legal.

An identifier containing a space isn't legal:

Also, we can't use reserved words as variable names. In python, the word and is a reserved word. The following assignment will therefore fail:

In all of the failed cases above, the Python interpreter raised an error and refused to carry out the assignment or creation of the variable. You may note that the caret ^ character points to different position in the erroneous identifier. This is due to the interpreter's attempt to match the identifier to an acceptable syntax. But either way, the outcome is the same: invalid variable names result in an error.

As a reference, Python's reserved words list includes:


and	as	assert	break
class	continue	def	del
elif	else	except	False
finally	for	from	global
if	import	in	is
lambda	None	nonlocal	not
or	pass	raise	return
True	try	while	with
yield

Variables and Type

Python does not require you to declare a variable. You do not need to tell Python ahead of time that you intend to reserve space for a variable. All you do is assign a variable a value. But this does not mean you can use a variable without having Python allocate it first. For example, the following line will fail in my session:

This error appears because there is no identifier named imaginary_thing as far as Python can tell. Python will joyfully accept a variable by that name, but it requires that any variable being used must already be assigned .

The act of assignment to a variable allocates the name and space for the variable to contain a value.

We saw that we can assign a variable a numeric value as well as a string (text) value. We also saw that we can re-assign a variable, providing it a new value which replaces any previous value it contained.

Python tracks the value of a variable by letting you access it via the variable name. Python also tracks the type of the value assigned to a variable. To tell what the value type is, you can use the built-in type() function. In the following examples, we use the type() function to display the value type :

In each of the examples above, Python infers the value type by parsing the right-hand part of the assignment and deciding the type accordingly. The existence of the decimal point in the value 3.14 clued Python to assign the type float whereas the bare number 42 produced an int .

Python also supports boolean data types. Booleans are assigned a value of True or False (both of which are keywords by the way).

An integer data type is also created when you use hexadecimal or octal or binary literals. To type a value as octal, prefix the number with 0o . To type a value is hexadecimal, prefix it with 0x . For a binary literal, prefix with 0b .

If you want to ensure the value of a variable is of int type, you may use the built-in int() class constructor:

The above statement assigned the variable type class int to x . In order to store the number 3.14 to an integer value, the int() function discarded the fraction part.

Similarly, you can use the float() class constructor function to ensure that a bare number - expressed in decimal, hex, or octal forms - would yield a float data type:

Mixing Types

We saw that values do indeed have a type and that Python tracks variable value as well as type. Lastly though - what does this type mean? Python will allow you to perform operations that fit the type.

For example, you may wish to divide the value of a variable by 3:

But the division operator does not work on a string. So you can't divide the string 'one two three' into 3:

The error Python raises is descriptive of the fact that the operator / (used for numeric division) is not defined for the types string and integer. Python is aware of the type assigned to the variable x which is how it made that determination. While you may be able to define your own operators on any types you wish, the point remains that Python does need the type system in order to map values, operators, and variables to the correct internal function. Python is a dynamically typed, but typed nonetheless.

The None Type

Many programming languages support the notion of null . Null is treated as a special value denoting "not-a-value", something which would let us denote an "empty" or undefined value. Python's version of that is the keyword None , which is backed by the class NoneType . Note that assigning a variable to None does not get rid of the variable. Space is still allocated for the variable - only the value is set to None . If you want to remove the variable altogether you may use the del statement:

In the example above, after deleting the variable, any attempt to use that variable produces an error stating it is not (or no longer) defined.

Checking for Type Equality

While the type() function lets us glean which type a variable contains. When comparing numbers, we may need to check that they are identical - that both their value and type match. The is operator provides for such identity checking. Numeric values may compare as equal to each other using the equality test == yet not match on their type. Consider this example:

In the above example, x is assigned the integer value 1 and y is assigned the float value 1.0 . When tested using the equality match == , the result is True . Yet when tested using the object identity operator is , the result is False since float and int are different types.

Python does let you define your own operators on your objects, so you could add support for both the equality and the identity operators on your classes. The default behavior of most non-numeric classes though is that two instances of an object would not evaluate as equal or identical to each other.

Strings are a bit different. Strings in Python are immutable reference types. To complicate thing more, two strings containing the same exact sequence of characters and compared for object identity may produce either True or False .This is due to internal implementation details and may vary across Python interpreters.

To summarize: Python lets you create variables simply by assigning a value to the variable, without the need to declare the variable upfront. The value assigned to a variable determines the variable type. Different types may support some operations which others don't. If you want to control the type of variable assigned, you may use the specific class constructor to assign the value, such as int() or float() . Bare numbers expressed without a decimal point - or as hex or octal literals - will produce an integer. You can get the class type of a variable by using the type() function, or test whether a type matches some specific type using the is operator.

Python variables provide a simple and dynamic way to create variables, yet maintains a powerful type system to ensure safe operations on your data.

Explore these Python courses from Pluralsight to continue learning:

Python Variables Course
Python Language Path
Core Python Course

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Simple assignment operator in Python

Different assignment operators in python.

Rajat Gupta

Software Developer

Published on Thu Jun 30 2022

Assignment operators in Python are in-fix which are used to perform operations on variables or operands and assign values to the operand on the left side of the operator. They perform arithmetic, logical, and bitwise computations.

Assignment Operators in Python

Add and equal operator, subtract and equal operator, multiply and equal operator, divide and equal operator, modulus and equal operator, double divide and equal operator, exponent assign operator.

Bitwise and operator

Bitwise OR operator

Bitwise XOR Assignment operator

Bitwise right shift assignment operator

Bitwise left shift assignment operator.

The Simple assignment operator in Python is denoted by = and is used to assign values from the right side of the operator to the value on the left side.

This operator adds the value on the right side to the value on the left side and stores the result in the operand on the left side.

This operator subtracts the value on the right side from the value on the left side and stores the result in the operand on the left side.

The Multiply and equal operator multiplies the right operand with the left operand and then stores the result in the left operand.

It divides the left operand with the right operand and then stores the quotient in the left operand.

The modulus and equal operator finds the modulus from the left and right operand and stores the final result in the left operand.

The double divide and equal or the divide floor and equal operator divides the left operand with the right operand and stores the floor result in the left operand.

It performs exponential or power calculation and assigns value to the left operand.

Bitwise And operator

Performs Bitwise And operation on both variables and stores the result in the left operand. The Bitwise And operation compares the corresponding bits of the left operand to the bits of the right operand and if both bits are 1, the corresponding result is also 1 otherwise 0.

The binary value of 3 is 0011 and the binary value of 5 is 0101, so when the Bitwise And operation is performed on both the values, we get 0001, which is 1 in decimal.

Performs Bitwise OR operator on both variables and stores the result in the left operand. The Bitwise OR operation compares the corresponding bits of the left operand to the bits of the right operand and if any one of the bits is 1, the corresponding result is also 1 otherwise 0.

The binary value of 5 is 0101 and the binary value of 10 is 1010, so when the Bitwise OR operation is performed on both the values, we get 1111, which is 15 in decimal .

Bitwise XOR operator

Performs Bitwise XOR operator on both variables and stores the result in the left operand. The Bitwise XOR operation compares the corresponding bits of the left operand to the bits of the right operand and if only one of the bits is 1, the corresponding result is also 1 otherwise 0.

The binary value of 5 is 0101 and the binary value of 9 is 1001, so when the Bitwise XOR operation is performed on both the values, we get 1100, which is 12 in decimal.

This operator performs a Bitwise right shift on the operands and stores the result in the left operand.

The binary value of 15 is 1111, so when the Bitwise right shift operation is performed on ‘a’, we get 0011, which is 3 in decimal.

This operator performs a Bitwise left shift on the operands and stores the result in the left operand.

The binary value of 15 is 1111, so when the Bitwise left shift operation is performed on ‘a’, we get 11110, which is 30 in decimal.

Closing Thoughts

In this tutorial, we read about different types of assignment operators in Python which are special symbols used to perform arithmetic, logical, and bitwise operations on the operands and store the result in the left side operand. One can read about other Python concepts here .

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PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
import foo as bar
except Exc as var
with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

NAME = EXPR
if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

Python Variables and Assignment

Python variables, variable assignment rules, every value has a type, memory and the garbage collector, variable swap, variable names are superficial labels, assignment = is shallow, decomp by var.

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How can I assign by value in python

I understand that, due to the way Python works x = []; y = x; x.append(1); y will print [1] . However, the reverse, say,

will print ([1,3],[1,3]) . If I understand correctly, both z and temp point to the same list, so changing one will change the other, seeing as lists are mutable. How can I prevent this from happening? Namely, I want to make a for loop that will copy z into temp , change it in different ways, and push it onto a queue. For that to work, z must always contain the base array, therefore I need that changing temp doesn't change z .

EDIT: I tried changing z into a tuple so that z=z, , then calling z[0] instead of z . Still this doesn't solve my problem.

Technically, Python is only pass-by-value - the difference you are seeing is more between mutable and immutable types. As for your question: You could create a copy of the list with e.g. newlist = oldlist[:] . – miku Commented Oct 10, 2013 at 15:45
possible duplicate of Creating Python variables that are independent of each other – Wooble Commented Oct 10, 2013 at 15:49
3 @miku No, mutability has nothing to do with assignment just creating another reference. After a = b , a is b is always true, no matter the types involved. The only thing mutability impacts is how easy it is to stumble over this fact. – user395760 Commented Oct 10, 2013 at 15:49

2 Answers 2

Copying a list is easy ... Just slice it:

This will create a shallow copy -- mutations to elements in the list will show up in the elements in z , but not changes to temp directly.

For more general purposes, python has a copy module that you can use:

Or, possibly:

In this specific case, I'm actually working with a POD class with 3 lists. The copy module works like a charm. Thanks. – andrepd Commented Oct 10, 2013 at 15:57

Why not make temp a copy of z :

[:] easily makes a shallow copy of a list.

However, you might also be interested in copy.copy and copy.deepcopy , both of which come from Python's copy module.

In this specific case, I'm actually working with a POD class with 3 lists. The copy module works like a charm. Thanks. – andrepd Commented Oct 10, 2013 at 18:21

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COMMENTS

Python's Assignment Operator: Write Robust Assignments
Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value. To execute an assignment statement like the above, Python runs the following steps: Evaluate the right-hand expression to produce a concrete value or object.
Assignment Operators in Python
Assignment Operator. Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand. Python. # Assigning values using # Assignment Operator a = 3 b = 5 c = a + b # Output print(c) Output. 8.
Multiple assignment in Python: Assign multiple values or the same value
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; Assign the same value to multiple variables. You can assign the same value to multiple variables by using = consecutively.
Python Conditional Assignment (in 3 Ways)
Let's see a code snippet to understand it better. a = 10. b = 20 # assigning value to variable c based on condition. c = a if a > b else b. print(c) # output: 20. You can see we have conditionally assigned a value to variable c based on the condition a > b. 2. Using if-else statement.
How To Use Assignment Expressions in Python
Python 3.8 or above. Assignment expressions are a new feature added starting in Python 3.8. ... An assignment expression binds the value result to the return of slow_calculation with i. You add the result to the newly built list as long as it is greater than 0.
Variables and Assignment
Variables and Assignment¶. When programming, it is useful to be able to store information in variables. A variable is a string of characters and numbers associated with a piece of information. The assignment operator, denoted by the "=" symbol, is the operator that is used to assign values to variables in Python.The line x=1 takes the known value, 1, and assigns that value to the variable ...
python
Using conditions in variable assignments in Python. 3. Sorting three numbers in ascending order without using functions. 3. One line if assignment in python. 1. ... One-line multiple variable value assignment with an "if" condition. 3. One line if assignment in python. 1. Python one-liner if else statement. 0. Assign, compare and use value in ...
Assignment Expressions: The Walrus Operator
In this lesson, you'll learn about the biggest change in Python 3.8: the introduction of assignment expressions.Assignment expression are written with a new notation (:=).This operator is often called the walrus operator as it resembles the eyes and tusks of a walrus on its side.. Assignment expressions allow you to assign and return a value in the same expression.
Python Variables
W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.
Python
Python Assignment Operator. The = (equal to) symbol is defined as assignment operator in Python. The value of Python expression on its right is assigned to a single variable on its left. The = symbol as in programming in general (and Python in particular) should not be confused with its usage in Mathematics, where it states that the expressions on the either side of the symbol are equal.
Variables in Python
To create a variable, you just assign it a value and then start using it. Assignment is done with a single equals sign ( = ): Python. >>> n = 300. This is read or interpreted as " n is assigned the value 300 .". Once this is done, n can be used in a statement or expression, and its value will be substituted: Python.
Python Variable Assignment. Explaining One Of The Most Fundamental
Declare And Assign Value To Variable. Assignment sets a value to a variable. To assign variable a value, use the equals sign (=) myFirstVariable = 1 mySecondVariable = 2 myFirstVariable = "Hello You" Assigning a value is known as binding in Python. In the example above, we have assigned the value of 2 to mySecondVariable.
Different Forms of Assignment Statements in Python
An assignment operator is an operator that is used to assign some value to a variable. Like normally in Python, we write "a = 5" to assign value 5 to variable 'a'. Augmented assignment operators have a special role to play in Python programming. It basically combines the functioning of the arithmetic or bitwise operator with the assignment operator
Variables and Assignment
In Python, a single equals sign = is the "assignment operator." (A double equals sign == is the "real" equals sign.) Variables are names for values. In Python the = symbol assigns the value on the right to the name on the left. The variable is created when a value is assigned to it. Here, Python assigns an age to a variable age and a ...
Python Variables and Assignment
In each of the examples above, Python infers the value type by parsing the right-hand part of the assignment and deciding the type accordingly. The existence of the decimal point in the value 3.14 clued Python to assign the type float whereas the bare number 42 produced an int.. Python also supports boolean data types. Booleans are assigned a value of True or False (both of which are keywords ...
Different Assignment operators in Python
Simple assignment operator in Python. The Simple assignment operator in Python is denoted by = and is used to assign values from the right side of the operator to the value on the left side. Input: a = b + c Add and equal operator. This operator adds the value on the right side to the value on the left side and stores the result in the operand ...
variables
There is conditional assignment in Python 2.5 and later - the syntax is not very obvious hence it's easy to miss. Here's how you do it: x = true_value if condition else false_value For further reference, check out the Python 2.5 docs.
PEP 572
Unparenthesized assignment expressions are prohibited for the value of a keyword argument in a call. Example: foo(x = y := f(x)) # INVALID foo(x=(y := f(x))) # Valid, though probably confusing. This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.
Python : When is a variable passed by reference and when by value
34. Everything in Python is passed and assigned by value, in the same way that everything is passed and assigned by value in Java. Every value in Python is a reference (pointer) to an object. Objects cannot be values. Assignment always copies the value (which is a pointer); two such pointers can thus point to the same object.
Python Variables and Assignment
A Python variable is a named bit of computer memory, keeping track of a value as the code runs. A variable is created with an "assignment" equal sign =, with the variable's name on the left and the value it should store on the right: x = 42. In the computer's memory, each variable is like a box, identified by the name of the variable.
arrays
temp = z[:] This will create a shallow copy -- mutations to elements in the list will show up in the elements in z, but not changes to temp directly. For more general purposes, python has a copy module that you can use: temp = copy.copy(z) Or, possibly: temp = copy.deepcopy(z) answered Oct 10, 2013 at 15:43. mgilson.

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

Assign multiple values to multiple variables

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How To Use Assignment Expressions in Python

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Prerequisites

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Using Assignment Expressions in while Loops

Using Assignment Expressions in List Comprehensions

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Introduction

Variables and Type

Mixing Types

The None Type

Checking for Type Equality

Simple assignment operator in Python

Assignment Operators in Python

Bitwise OR operator

Bitwise right shift assignment operator

Bitwise And operator

Bitwise XOR operator

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