assignment statement vs variable

1.4 — Variable assignment and initialization

In the previous lesson ( 1.3 -- Introduction to objects and variables ), we covered how to define a variable that we can use to store values. In this lesson, we’ll explore how to actually put values into variables and use those values.

As a reminder, here’s a short snippet that first allocates a single integer variable named x , then allocates two more integer variables named y and z :

Variable assignment

After a variable has been defined, you can give it a value (in a separate statement) using the = operator . This process is called assignment , and the = operator is called the assignment operator .

By default, assignment copies the value on the right-hand side of the = operator to the variable on the left-hand side of the operator. This is called copy assignment .

Here’s an example where we use assignment twice:

This prints:

When we assign value 7 to variable width , the value 5 that was there previously is overwritten. Normal variables can only hold one value at a time.

One of the most common mistakes that new programmers make is to confuse the assignment operator ( = ) with the equality operator ( == ). Assignment ( = ) is used to assign a value to a variable. Equality ( == ) is used to test whether two operands are equal in value.

Initialization

One downside of assignment is that it requires at least two statements: one to define the variable, and another to assign the value.

These two steps can be combined. When an object is defined, you can optionally give it an initial value. The process of specifying an initial value for an object is called initialization , and the syntax used to initialize an object is called an initializer .

In the above initialization of variable width , { 5 } is the initializer, and 5 is the initial value.

Different forms of initialization

Initialization in C++ is surprisingly complex, so we’ll present a simplified view here.

There are 6 basic ways to initialize variables in C++:

You may see the above forms written with different spacing (e.g. int d{7}; ). Whether you use extra spaces for readability or not is a matter of personal preference.

Default initialization

When no initializer is provided (such as for variable a above), this is called default initialization . In most cases, default initialization performs no initialization, and leaves a variable with an indeterminate value.

We’ll discuss this case further in lesson ( 1.6 -- Uninitialized variables and undefined behavior ).

Copy initialization

When an initial value is provided after an equals sign, this is called copy initialization . This form of initialization was inherited from C.

Much like copy assignment, this copies the value on the right-hand side of the equals into the variable being created on the left-hand side. In the above snippet, variable width will be initialized with value 5 .

Copy initialization had fallen out of favor in modern C++ due to being less efficient than other forms of initialization for some complex types. However, C++17 remedied the bulk of these issues, and copy initialization is now finding new advocates. You will also find it used in older code (especially code ported from C), or by developers who simply think it looks more natural and is easier to read.

For advanced readers

Copy initialization is also used whenever values are implicitly copied or converted, such as when passing arguments to a function by value, returning from a function by value, or catching exceptions by value.

Direct initialization

When an initial value is provided inside parenthesis, this is called direct initialization .

Direct initialization was initially introduced to allow for more efficient initialization of complex objects (those with class types, which we’ll cover in a future chapter). Just like copy initialization, direct initialization had fallen out of favor in modern C++, largely due to being superseded by list initialization. However, we now know that list initialization has a few quirks of its own, and so direct initialization is once again finding use in certain cases.

Direct initialization is also used when values are explicitly cast to another type.

One of the reasons direct initialization had fallen out of favor is because it makes it hard to differentiate variables from functions. For example:

List initialization

The modern way to initialize objects in C++ is to use a form of initialization that makes use of curly braces. This is called list initialization (or uniform initialization or brace initialization ).

List initialization comes in three forms:

As an aside…

Prior to the introduction of list initialization, some types of initialization required using copy initialization, and other types of initialization required using direct initialization. List initialization was introduced to provide a more consistent initialization syntax (which is why it is sometimes called “uniform initialization”) that works in most cases.

Additionally, list initialization provides a way to initialize objects with a list of values (which is why it is called “list initialization”). We show an example of this in lesson 16.2 -- Introduction to std::vector and list constructors .

List initialization has an added benefit: “narrowing conversions” in list initialization are ill-formed. This means that if you try to brace initialize a variable using a value that the variable can not safely hold, the compiler is required to produce a diagnostic (usually an error). For example:

In the above snippet, we’re trying to assign a number (4.5) that has a fractional part (the .5 part) to an integer variable (which can only hold numbers without fractional parts).

Copy and direct initialization would simply drop the fractional part, resulting in the initialization of value 4 into variable width . Your compiler may optionally warn you about this, since losing data is rarely desired. However, with list initialization, your compiler is required to generate a diagnostic in such cases.

Conversions that can be done without potential data loss are allowed.

To summarize, list initialization is generally preferred over the other initialization forms because it works in most cases (and is therefore most consistent), it disallows narrowing conversions, and it supports initialization with lists of values (something we’ll cover in a future lesson). While you are learning, we recommend sticking with list initialization (or value initialization).

Best practice

Prefer direct list initialization (or value initialization) for initializing your variables.

Author’s note

Bjarne Stroustrup (creator of C++) and Herb Sutter (C++ expert) also recommend using list initialization to initialize your variables.

In modern C++, there are some cases where list initialization does not work as expected. We cover one such case in lesson 16.2 -- Introduction to std::vector and list constructors .

Because of such quirks, some experienced developers now advocate for using a mix of copy, direct, and list initialization, depending on the circumstance. Once you are familiar enough with the language to understand the nuances of each initialization type and the reasoning behind such recommendations, you can evaluate on your own whether you find these arguments persuasive.

Value initialization and zero initialization

When a variable is initialized using empty braces, value initialization takes place. In most cases, value initialization will initialize the variable to zero (or empty, if that’s more appropriate for a given type). In such cases where zeroing occurs, this is called zero initialization .

Q: When should I initialize with { 0 } vs {}?

Use an explicit initialization value if you’re actually using that value.

Use value initialization if the value is temporary and will be replaced.

Initialize your variables

Initialize your variables upon creation. You may eventually find cases where you want to ignore this advice for a specific reason (e.g. a performance critical section of code that uses a lot of variables), and that’s okay, as long the choice is made deliberately.

Related content

For more discussion on this topic, Bjarne Stroustrup (creator of C++) and Herb Sutter (C++ expert) make this recommendation themselves here .

We explore what happens if you try to use a variable that doesn’t have a well-defined value in lesson 1.6 -- Uninitialized variables and undefined behavior .

Initialize your variables upon creation.

Initializing multiple variables

In the last section, we noted that it is possible to define multiple variables of the same type in a single statement by separating the names with a comma:

We also noted that best practice is to avoid this syntax altogether. However, since you may encounter other code that uses this style, it’s still useful to talk a little bit more about it, if for no other reason than to reinforce some of the reasons you should be avoiding it.

You can initialize multiple variables defined on the same line:

Unfortunately, there’s a common pitfall here that can occur when the programmer mistakenly tries to initialize both variables by using one initialization statement:

In the top statement, variable “a” will be left uninitialized, and the compiler may or may not complain. If it doesn’t, this is a great way to have your program intermittently crash or produce sporadic results. We’ll talk more about what happens if you use uninitialized variables shortly.

The best way to remember that this is wrong is to consider the case of direct initialization or brace initialization:

Because the parenthesis or braces are typically placed right next to the variable name, this makes it seem a little more clear that the value 5 is only being used to initialize variable b and d , not a or c .

Unused initialized variables warnings

Modern compilers will typically generate warnings if a variable is initialized but not used (since this is rarely desirable). And if “treat warnings as errors” is enabled, these warnings will be promoted to errors and cause the compilation to fail.

Consider the following innocent looking program:

When compiling this with the g++ compiler, the following error is generated:

and the program fails to compile.

There are a few easy ways to fix this.

• If the variable really is unused, then the easiest option is to remove the defintion of x (or comment it out). After all, if it’s not used, then removing it won’t affect anything.
• Another option is to simply use the variable somewhere:

But this requires some effort to write code that uses it, and has the downside of potentially changing your program’s behavior.

The [[maybe_unused]] attribute C++17

In some cases, neither of the above options are desirable. Consider the case where we have a bunch of math/physics values that we use in many different programs:

If we use these a lot, we probably have these saved somewhere and copy/paste/import them all together.

However, in any program where we don’t use all of these values, the compiler will complain about each variable that isn’t actually used. While we could go through and remove/comment out the unused ones for each program, this takes time and energy. And later if we need one that we’ve previously removed, we’ll have to go back and re-add it.

To address such cases, C++17 introduced the [[maybe_unused]] attribute, which allows us to tell the compiler that we’re okay with a variable being unused. The compiler will not generate unused variable warnings for such variables.

The following program should generate no warnings/errors:

Additionally, the compiler will likely optimize these variables out of the program, so they have no performance impact.

In future lessons, we’ll often define variables we don’t use again, in order to demonstrate certain concepts. Making use of [[maybe_unused]] allows us to do so without compilation warnings/errors.

Question #1

What is the difference between initialization and assignment?

Show Solution

Initialization gives a variable an initial value at the point when it is created. Assignment gives a variable a value at some point after the variable is created.

Question #2

What form of initialization should you prefer when you want to initialize a variable with a specific value?

Direct list initialization (aka. direct brace initialization).

Question #3

What are default initialization and value initialization? What is the behavior of each? Which should you prefer?

Default initialization is when a variable initialization has no initializer (e.g. int x; ). In most cases, the variable is left with an indeterminate value.

Value initialization is when a variable initialization has an empty brace (e.g. int x{}; ). In most cases this will perform zero-initialization.

You should prefer value initialization to default initialization.

CS101: Introduction to Computer Science I

Variables and Assignment Statements

Read this chapter, which covers variables and arithmetic operations and order precedence in Java.

9. Assignment Statements

No. The incorrect splittings are highlighted in red:

Assignment Statement

So far, the example programs have been using the value initially put into a variable. Programs can change the value in a variable. An  assignment statement  changes the value that is held in a variable. The program uses an assignment statement.

The assignment statement puts the value 123 into the variable. In other words, while the program is executing there will be a 64 bit section of memory that holds the value 123.

Remember that the word "execute" is often used to mean "run". You speak of "executing a program" or "executing" a line of the program.

Question 10:

Next: Execution Control Expressions , Previous: Arithmetic , Up: Top   [ Contents ][ Index ]

7 Assignment Expressions

As a general concept in programming, an assignment is a construct that stores a new value into a place where values can be stored—for instance, in a variable. Such places are called lvalues (see Lvalues ) because they are locations that hold a value.

An assignment in C is an expression because it has a value; we call it an assignment expression . A simple assignment looks like

We say it assigns the value of the expression value-to-store to the location lvalue , or that it stores value-to-store there. You can think of the “l” in “lvalue” as standing for “left,” since that’s what you put on the left side of the assignment operator.

However, that’s not the only way to use an lvalue, and not all lvalues can be assigned to. To use the lvalue in the left side of an assignment, it has to be modifiable . In C, that means it was not declared with the type qualifier const (see const ).

The value of the assignment expression is that of lvalue after the new value is stored in it. This means you can use an assignment inside other expressions. Assignment operators are right-associative so that

is equivalent to

This is the only useful way for them to associate; the other way,

would be invalid since an assignment expression such as x = y is not valid as an lvalue.

Warning: Write parentheses around an assignment if you nest it inside another expression, unless that is a conditional expression, or comma-separated series, or another assignment.

404 Not found

• Assignment Statement

An Assignment statement is a statement that is used to set a value to the variable name in a program .

Assignment statement allows a variable to hold different types of values during its program lifespan. Another way of understanding an assignment statement is, it stores a value in the memory location which is denoted by a variable name.

The symbol used in an assignment statement is called as an operator . The symbol is ‘=’ .

Note: The Assignment Operator should never be used for Equality purpose which is double equal sign ‘==’.

The Basic Syntax of Assignment Statement in a programming language is :

variable = expression ;

variable = variable name

expression = it could be either a direct value or a math expression/formula or a function call

Few programming languages such as Java, C, C++ require data type to be specified for the variable, so that it is easy to allocate memory space and store those values during program execution.

data_type variable_name = value ;

In the above-given examples, Variable ‘a’ is assigned a value in the same statement as per its defined data type. A data type is only declared for Variable ‘b’. In the 3 rd line of code, Variable ‘a’ is reassigned the value 25. The 4 th line of code assigns the value for Variable ‘b’.

Assignment Statement Forms

This is one of the most common forms of Assignment Statements. Here the Variable name is defined, initialized, and assigned a value in the same statement. This form is generally used when we want to use the Variable quite a few times and we do not want to change its value very frequently.

Tuple Assignment

Generally, we use this form when we want to define and assign values for more than 1 variable at the same time. This saves time and is an easy method. Note that here every individual variable has a different value assigned to it.

(Code In Python)

Sequence Assignment

(Code in Python)

Multiple-target Assignment or Chain Assignment

In this format, a single value is assigned to two or more variables.

Augmented Assignment

In this format, we use the combination of mathematical expressions and values for the Variable. Other augmented Assignment forms are: &=, -=, **=, etc.

Browse more Topics Under Data Types, Variables and Constants

• Concept of Data types
• Built-in Data Types
• Constants in Programing Language 
• Access Modifier
• Variables of Built-in-Datatypes
• Declaration/Initialization of Variables
• Type Modifier

Few Rules for Assignment Statement

Few Rules to be followed while writing the Assignment Statements are:

• Variable names must begin with a letter, underscore, non-number character. Each language has its own conventions.
• The Data type defined and the variable value must match.
• A variable name once defined can only be used once in the program. You cannot define it again to store other types of value.
• If you assign a new value to an existing variable, it will overwrite the previous value and assign the new value.

FAQs on Assignment Statement

Q1. Which of the following shows the syntax of an  assignment statement ?

• variablename = expression ;
• expression = variable ;
• datatype = variablename ;
• expression = datatype variable ;

Answer – Option A.

Q2. What is an expression ?

• Same as statement
• List of statements that make up a program
• Combination of literals, operators, variables, math formulas used to calculate a value
• Numbers expressed in digits

Answer – Option C.

Q3. What are the two steps that take place when an  assignment statement  is executed?

• Evaluate the expression, store the value in the variable
• Reserve memory, fill it with value
• Evaluate variable, store the result
• Store the value in the variable, evaluate the expression.

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1.6. Variables and Assignment ¶

Each set-off line in this section should be tried in the Shell.

Nothing is displayed by the interpreter after this entry, so it is not clear anything happened. Something has happened. This is an assignment statement , with a variable , width , on the left. A variable is a name for a value. An assignment statement associates a variable name on the left of the equal sign with the value of an expression calculated from the right of the equal sign. Enter

Once a variable is assigned a value, the variable can be used in place of that value. The response to the expression width is the same as if its value had been entered.

The interpreter does not print a value after an assignment statement because the value of the expression on the right is not lost. It can be recovered if you like, by entering the variable name and we did above.

Try each of the following lines:

The equal sign is an unfortunate choice of symbol for assignment, since Python’s usage is not the mathematical usage of the equal sign. If the symbol ↤ had appeared on keyboards in the early 1990’s, it would probably have been used for assignment instead of =, emphasizing the asymmetry of assignment. In mathematics an equation is an assertion that both sides of the equal sign are already, in fact, equal . A Python assignment statement forces the variable on the left hand side to become associated with the value of the expression on the right side. The difference from the mathematical usage can be illustrated. Try:

so this is not equivalent in Python to width = 10 . The left hand side must be a variable, to which the assignment is made. Reversed, we get a syntax error . Try

This is, of course, nonsensical as mathematics, but it makes perfectly good sense as an assignment, with the right-hand side calculated first. Can you figure out the value that is now associated with width? Check by entering

In the assignment statement, the expression on the right is evaluated first . At that point width was associated with its original value 10, so width + 5 had the value of 10 + 5 which is 15. That value was then assigned to the variable on the left ( width again) to give it a new value. We will modify the value of variables in a similar way routinely.

Assignment and variables work equally well with strings. Try:

Try entering:

Note the different form of the error message. The earlier errors in these tutorials were syntax errors: errors in translation of the instruction. In this last case the syntax was legal, so the interpreter went on to execute the instruction. Only then did it find the error described. There are no quotes around fred , so the interpreter assumed fred was an identifier, but the name fred was not defined at the time the line was executed.

It is both easy to forget quotes where you need them for a literal string and to mistakenly put them around a variable name that should not have them!

Try in the Shell :

There fred , without the quotes, makes sense.

There are more subtleties to assignment and the idea of a variable being a “name for” a value, but we will worry about them later, in Issues with Mutable Objects . They do not come up if our variables are just numbers and strings.

Autocompletion: A handy short cut. Idle remembers all the variables you have defined at any moment. This is handy when editing. Without pressing Enter, type into the Shell just

Assuming you are following on the earlier variable entries to the Shell, you should see f autocompleted to be

This is particularly useful if you have long identifiers! You can press Alt-/ several times if more than one identifier starts with the initial sequence of characters you typed. If you press Alt-/ again you should see fred . Backspace and edit so you have fi , and then and press Alt-/ again. You should not see fred this time, since it does not start with fi .

1.6.1. Literals and Identifiers ¶

Expressions like 27 or 'hello' are called literals , coming from the fact that they literally mean exactly what they say. They are distinguished from variables, whose value is not directly determined by their name.

The sequence of characters used to form a variable name (and names for other Python entities later) is called an identifier . It identifies a Python variable or other entity.

There are some restrictions on the character sequence that make up an identifier:

The characters must all be letters, digits, or underscores _ , and must start with a letter. In particular, punctuation and blanks are not allowed.

There are some words that are reserved for special use in Python. You may not use these words as your own identifiers. They are easy to recognize in Idle, because they are automatically colored orange. For the curious, you may read the full list:

There are also identifiers that are automatically defined in Python, and that you could redefine, but you probably should not unless you really know what you are doing! When you start the editor, we will see how Idle uses color to help you know what identifies are predefined.

Python is case sensitive: The identifiers last , LAST , and LaSt are all different. Be sure to be consistent. Using the Alt-/ auto-completion shortcut in Idle helps ensure you are consistent.

What is legal is distinct from what is conventional or good practice or recommended. Meaningful names for variables are important for the humans who are looking at programs, understanding them, and revising them. That sometimes means you would like to use a name that is more than one word long, like price at opening , but blanks are illegal! One poor option is just leaving out the blanks, like priceatopening . Then it may be hard to figure out where words split. Two practical options are

• underscore separated: putting underscores (which are legal) in place of the blanks, like price_at_opening .
• using camel-case : omitting spaces and using all lowercase, except capitalizing all words after the first, like priceAtOpening

Use the choice that fits your taste (or the taste or convention of the people you are working with).

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CS101: Introduction to Computer Science I

Variables and Assignment Statements

Read this chapter, which covers variables and arithmetic operations and order precedence in Java.

5. Syntax of Variable Declaration

Syntax of variable deceleration.

The word  syntax  means the grammar of a programming language. We can talk about the syntax of just a small part of a program, such as the syntax of variable declaration.

There are several ways to declare variables:

• This declares a variable, declares its data type, and reserves memory for it. It says nothing about what value is put in memory. (Later in these notes you will learn that in some circumstances the variable is automatically initialized, and that in other circumstances the variable is left uninitialized.)
• This declares a variable, declares its data type, reserves memory for it, and puts an initial value into that memory. The initial value must be of the correct data type.
• This declares  two  variables, both of the same data type, reserves memory for each, but puts nothing in any variable. You can do this with more than two variables, if you want.
• This declares  two  variables, both of the same data type, reserves memory, and puts an initial value in each variable. You can do this all on one line if there is room. Again, you can do this for more than two variables as long as you follow the pattern.

If you have several variables of different types, use several declaration statements. You can even use several declaration statements for several variables of the same type.

Question 4:

Is the following correct?

1.7 Java | Assignment Statements & Expressions

An assignment statement designates a value for a variable. An assignment statement can be used as an expression in Java.

After a variable is declared, you can assign a value to it by using an assignment statement . In Java, the equal sign = is used as the assignment operator . The syntax for assignment statements is as follows:

An expression represents a computation involving values, variables, and operators that, when taking them together, evaluates to a value. For example, consider the following code:

You can use a variable in an expression. A variable can also be used on both sides of the =  operator. For example:

In the above assignment statement, the result of x + 1  is assigned to the variable x . Let’s say that x is 1 before the statement is executed, and so becomes 2 after the statement execution.

To assign a value to a variable, you must place the variable name to the left of the assignment operator. Thus the following statement is wrong:

Note that the math equation  x = 2 * x + 1  ≠ the Java expression x = 2 * x + 1

Which is equivalent to:

And this statement

is equivalent to:

Note: The data type of a variable on the left must be compatible with the data type of a value on the right. For example, int x = 1.0 would be illegal, because the data type of x is int (integer) and does not accept the double value 1.0 without Type Casting .

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Python Enhancement Proposals

• Python »
• PEP Index »

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