multiple assignment if 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 If-Else Statements with Multiple Conditions

• November 11, 2022 November 11, 2022

Conditional statements, or if-else statements, allow you to control the flow of your code. Understanding the power of if-else statements allows you to become a stronger Python programmer. This is because they allow you to execute only certain parts of your code if a condition or multiple conditions are met. In this tutorial, you’ll learn how to use Python if-else statements with multiple conditions .

By the end of this tutorial, you’ll have learned:

• How to use Python if-else statements with multiple conditions
• How to check if all conditions are met in Python if-else statements
• How to check if only some conditions are met in Python if-else statemens
• How to write complex if-else statements with multiple conditions in Python

Table of Contents

Understanding Python if-else Statements

One of the benefits of Python is how clear the syntax is. This is also true for if-else statements, which follow simple English syntax in order to control flow of your program . Python if-else statements, allow you to run code if a condition is met. They follow the syntax shown below:

To better understand how Python handles multiple conditions, it’s important to understand how logical operators work. Let’s take a look at this next.

Understanding Logical Operators in Python

While Python provides many more comparison operators , in this tutorial we’ll focus on two: and and or . These comparison operators allow us to chain multiple conditions using logical statements. To break this down:

• and ensures that both conditions are met, otherwise returning False
• or ensures that at least one condition must be true, otherwise returning False

Now that we’ve covered some of the basics, let’s dive into how to use multiple conditions in Python if-else statements.

Using Multiple Conditions in Python if-else Statements

In Python if-else statements, we can use multiple conditions which can be used with logical and and or operators. Let’s take a look at how we can write multiple conditions into a Python if-else statement:

We can see that the first condition uses the logical and operator to check if both conditions are true. If this isn’t met, the else block is executed. Let’s dive into how these operators work in action.

Checking For Multiple Conditions to be True in Python if-else Statements

We can use Python if-else statements to check that all conditions are true by using one or more and statements. This allows us to check that every condition is true. If a single condition is not true, then the flow statement moves onto the next condition.

Let’s see how we can check if multiple conditions are true in Python:

Let’s break down what the code block above is doing:

• A variable, age , is defined with the value 32
• An if-else statement first checks if the age is 0 or older and less than 20. If either of these arent true, the next block is checked.
• The elif statement checks if the age is between 20 and 30. If this isn’t true, the else block is executed

With and conditions, Python will check each item sequentially. If one condition is not true, Python will not need to check the others and move onto the next block.

This is actually the same as running the all() function in a block, which will check if all items that are passed into the function are True. We could rewrite the if-else statement above using the all() function, as shown below:

Checking For Some Conditions to be True in Python if-else Statements

The Python or operator can be used to check if only one condition is true. This can allow you to write programs which can, for example, check if a weekday is a weekend day or not. This can allow us to check if a value passed meets either condition. Let’s see what this looks like:

In the code block above, we wrote an if-else block that checks whether the weekday we passed in is either Saturday or Sunday. If neither of these conditions evaluate to be true, the else block is executed.

This is actually the same as using the Python any() function, which will check if any of the items passed in are true. Let’s see how we can re-write our if-else block using the any() function:

In the next section, you’ll learn how to write complex if-else statements that combine and and or in creative ways.

Writing Complex if-else Statements with Multiple Conditions

We can write complex if-else statements with multiple conditions that combine using and and or operators. This can be very helpful when you’re writing complex conditional code that allows for some conditions to be true, while enforcing others.

Let’s take a look at an example of how this works:

In the code block above, we wrap the or statement in brackets. This means that this or condition only applies to the code in the brackets. Following that, Python will check if the user is active.

Let’s take a look at what this conditional flow looks like:

• Since age is 67, the code in the brackets will evaluate to True , since it meets the age >= 65 condition.
• Python then checks if the user is active, which evaluates to True as well

By doing this, Python allows you write complex conditions. By combining and and or operators, you can write if-else statements without nesting your conditions.

In this tutorial, you learned how to write complex if-else statements with multiple conditions. Python if-else statements allow you to control the flow of your code. By using multiple conditions, you can write more sophisticated code.

You first learned how to check if all conditions were true, using the and operator. Then, you learned how to check if any conditions were true, using the or operator. Finally, you learned how to write complex if-else statements by combining and and or .

Additional Resources

To learn more about related topics, check out the tutorials below:

• NumPy where: Process Array Elements Conditionally
• Python While Loop with Multiple Conditions
• Python if-else statements: Official Documentation

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

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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! 😊

Multiple Assignment Syntax in Python

• python-tricks

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

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

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

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

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

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

Swapping Values

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

Splitting a List

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

Assigning Multiple Values from a Function

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

Ignoring Values

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

Unpacking Nested Structures

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

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

Extended Unpacking with Slicing

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

Split a String into a List

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

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

How to Check Multiple Conditions in a Python if statement

• multiple conditions

Conditional statements are commands for handling decisions, which makes them a fundamental programming concept.  They help you selectively execute certain parts of your program if some condition is met.  In this article, we’ll tell you all you need to know about using multiple conditional statements in Python. And we’ll show you plenty of examples to demonstrate the mechanics of how it all works.

Python has a simple and clear syntax, meaning the code is easy to read and interpret.  This is especially true for conditional statements, which can almost be read like an English sentence.  This makes Python a great language to learn for beginners.  For those of you who are new to Python, consider taking our Python Basics course; it will kickstart your programming journey and give you solid foundational skills in Python.

Python if Statement

The starting point for handling conditions is a single if statement, which checks if a condition is true.  If so, the indented block of code directly under the if statement is executed.  The condition must evaluate  either True or False . If you’d like to learn the details of Python’s if statements, you’ll find more in this article on Python terms for beginners .  Part 2 of Python Terms for Beginners is also a worthwhile read when you’re just getting started with programming.

The if statement in Python takes the following form:

Before we go further, let’s take a look at the comparison operators.  In Python, there are six possibilities:

• Equals: a == b
• Not Equal: a != b
• Less than : a < b
• Less than or equal to: a <= b
• Greater than : a > b
• Greater than or equal to : a >= b

Note that the equals comparison operator ( == ) is different from the assignment operator ( = ).

Now let’s try evaluating an example condition:

Here, we set the variable temperature = 35 . In the next line, we test if this value is greater than 25, which returns the Boolean value True . Now let’s put this in an if statement:

The condition evaluates to true, which then executes the indented block ( print('Warm') ).  This example is equivalent to writing “If the temperature is greater than 25, print the word “Warm”. As you can see from the code, it’s quite like the written sentence!

Logical Operators

If we want to join two or more conditions in the same if statement, we need a logical operator. There are three possible logical operators in Python:

• and – Returns True if both statements are true.
• or – Returns True if at least one of the statements is true.
• not – Reverses the Boolean value; returns False if the statement is true, and True if the statement is false.

To implement these, we need a second condition to test.  So, let’s create another variable and test if it’s above a threshold:

The or operator requires only one condition to be True . To show this, we’ll reduce the temperature and use the or comparison operator:

Notice that or only requires one condition to evaluate to True .  If both conditions evaluate to True , the indented block of code directly below will still be executed.

The not operator can seem a little confusing at first, but it just reverses the truth value of a condition.  For example:

We can use it to test if the temperature is colder (i.e. not hotter) that a threshold:

Using these as building blocks, you can start to put together more complicated tests:

This if statement is equivalent to “If temperature is greater than 30 (i.e. evaluates false) OR humidity is less than 70 (evaluates to true) and it’s not raining (evaluates to true) , then write …”. In code, it might look like this:

Python’s if-elif-else Statements

So, what happens when the condition in the if statement evaluates to False?  Then we can check multiple conditions by simply adding an else-if statement, which is shortened to elif in Python.  Here’s an example using elif to define different temperature categories:

Notice the use of the comparison operator > in the if statement and of <= in the elif statement. The second operator means if the temperature is 30 exactly, it belongs to the ' Warm ' category. The final step is to add an else at the end, which captures everything else not defined in the if and elif conditions.

The final else statement handles anything else that does not fall within the other statements. In this case, temperature <= 20 will print ' Cool '.  Also note that the elif statement can be written more concisely in Python (in this example, 20 < temperature <= 30 ).

If you wanted to make more categories, you could add more elif statements. The elif and else statements are optional. But it’s always good form to finish with an else statement, to make sure anything unexpected is still captured.  This can be useful for debugging more complicated conditional statements.  For example, if we’re quantifying the amount of rain in millimeters per hour, we could do something like this:

Having the final else statement here will alert you if there is an unexpected error somewhere, e.g. a negative value.

Now That You Know Multiple Conditions in Python …

Now you should have all you need to know to start implementing multiple conditional statements in Python.  These examples were designed to show you the basics of how these statements work, so take the next step and extend what you’ve learnt here.  For example, try combining if-elif-else statements in a loop.  Define a list of values, loop through them, and test their values.  If you need some background material on for loops in Python, check out How to Write a For Loop in Python .

If you’re interested in learning more about data structures in Python, we’ve got you covered.  In Arrays vs. Lists in Python , we explain the difference between those two structures.  We also have an article that goes into detail on lists, tuples and sets  and another that explains the dictionary data structure in Python .  With a bit of practice, you’ll soon master Python’s conditions, loops, and data structures.

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In computer programming, the if statement is a conditional statement. It is used to execute a block of code only when a specific condition is met. For example,

Suppose we need to assign different grades to students based on their scores.

• If a student scores above 90 , assign grade A
• If a student scores above 75 , assign grade B
• If a student scores above 65 , assign grade C

These conditional tasks can be achieved using the if statement.

• Python if Statement

An if statement executes a block of code only if the specified condition is met.

Here, if the condition of the if statement is:

• True - the body of the if statement executes.
• False - the body of the if statement is skipped from execution.

Let's look at an example.

Note: Be mindful of the indentation while writing the if statements. Indentation is the whitespace at the beginning of the code.

Here, the spaces before the print() statement denote that it's the body of the if statement.

• Example: Python if Statement

Sample Output 1

In the above example, we have created a variable named number . Notice the test condition ,

As the number is greater than 0 , the condition evaluates True . Hence, the body of the if statement executes.

Sample Output 2

Now, let's change the value of the number to a negative integer, say -5 .

Now, when we run the program, the output will be:

This is because the value of the number is less than 0 . Hence, the condition evaluates to False . And, the body of the if statement is skipped.

An if statement can have an optional else clause. The else statement executes if the condition in the if statement evaluates to False .

Here, if the condition inside the if statement evaluates to

• True - the body of if executes, and the body of else is skipped.
• False - the body of else executes, and the body of if is skipped

• Example: Python if…else Statement

In the above example, we have created a variable named number .

Since the value of the number is 10 , the condition evaluates to True . Hence, code inside the body of if is executed.

If we change the value of the variable to a negative integer, let's say -5 , our output will be:

Here, the test condition evaluates to False . Hence code inside the body of else is executed.

• Python if…elif…else Statement

The if...else statement is used to execute a block of code among two alternatives.

However, if we need to make a choice between more than two alternatives, we use the if...elif...else statement.

• if condition1 - This checks if condition1 is True . If it is, the program executes code block 1 .
• elif condition2 - If condition1 is not True , the program checks condition2 . If condition2 is True , it executes code block 2 .
• else - If neither condition1 nor condition2 is True , the program defaults to executing code block 3 .

• Example: Python if…elif…else Statement

Since the value of the number is 0 , both the test conditions evaluate to False .

Hence, the statement inside the body of else is executed.

• Python Nested if Statements

It is possible to include an if statement inside another if statement. For example,

Here's how this program works.

More on Python if…else Statement

In certain situations, the if statement can be simplified into a single line. For example,

This code can be compactly written as

This one-liner approach retains the same functionality but in a more concise format.

Python doesn't have a ternary operator. However, we can use if...else to work like a ternary operator in other languages. For example,

can be written as

We can use logical operators such as and and or within an if statement.

Here, we used the logical operator and to add two conditions in the if statement.

We also used >= (comparison operator) to compare two values.

Logical and comparison operators are often used with if...else statements. Visit Python Operators to learn more.

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

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

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

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

Understanding Multiple Assignment

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

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

Simultaneous Assignment

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

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

Unpacking Sequences

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

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

Multiple Return Values

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

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

Swapping Values

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

Iterating through Sequences

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

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

Discarding Values

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

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

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

Related: How input() function Work in Python?

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

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

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

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

One Value to Multiple Variables

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

Unpack a Collection

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

Unpack a list:

Learn more about unpacking in our Unpack Tuples Chapter.

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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

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

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

 Assign Values to Multiple Variables in One Line

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

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

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

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

Assigning different operation results to multiple variable.

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

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