python conditional assignment operator

Python Conditional Assignment

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

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

1. Using Ternary Operator

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

It goes like this:

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

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

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

2. Using if-else statement

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

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

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

3. Using Logical Short Circuit Evaluation

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

The format of logical short circuit evaluation is:

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

Let's see an example:

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

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

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

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

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

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

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

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

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

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

Happy coding! 😊

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Python Ternary: How to Use It and Why It’s Useful (with Examples)

What is a python ternary operator, and when is it useful this tutorial will walk you through everything you need to know..

The Python ternary operator (or conditional operator ), tests if a condition is true or false and, depending on the outcome, returns the corresponding value — all in just one line of code. In other words, it's a compact alternative to the common multiline if-else control flow statements in situations when we only need to "switch" between two values. The ternary operator was introduced in Python 2.5.

The syntax consists of three operands, hence the name "ternary":

Here are these operands:

condition — a Boolean expression to test for true or false
a — the value that will be returned if the condition is evaluated to be true
b — the value that will be returned if the condition is evaluated to be false

The equivalent of a common if-else statement, in this case, would be the following:

Let's look at a simple example:

While the ternary operator is a way of re-writing a classic if-else block, in a certain sense, it behaves like a function since it returns a value. Indeed, we can assign the result of this operation to a variable:

my_var = a if condition else b

For example:

Before we had the ternary operator, instead of a if condition else b , we would use condition and a or b . For example, instead of running the following . . .

. . . we would run this:

However, if the value of a in the syntax condition and a or b evaluates to False (e.g., if a is equal to 0 , or None , or False ), we would receive inaccurate results. The example of a ternary operator below looks logically controversial (we want to return False if 2 > 1; otherwise, we want to return True ) but it is technically correct since it's up to us to decide which value to return if the condition evaluates to True — and which value to return if the condition evaluates to False . In this case, we expect False , and we got it:

Using the "old-style" syntax instead of the ternary operator for the same purpose, we would still expect False . However, we received an unexpected result:

To avoid such issues, it's always better to use the ternary operator in similar situations.

Limitations of Python Ternary Operator

Note that each operand of the Python ternary operator is an expression , not a statement , meaning that we can't use assignment statements inside any of them. Otherwise, the program throws an error:

If we need to use statements, we have to write a full if-else block rather than the ternary operator:

Another limitation of the Python ternary operator is that we shouldn't use it for testing multiple expressions (i.e., the if-else blocks with more than two cases). Technically, we still can do so. For example, take the following piece of code:

We can rewrite this code using nested ternary operators :

( Side note: In the above piece of code, we omitted the print() statement since the Python ternary operator always returns a value.)

While the second piece of code looks more compact than the first one, it's also much less readable. To avoid readability issues, we should opt to use the Python ternary operator only when we have simple if-else statements.

How to Use a Python Ternary Operator

Now, we'll discuss various ways of applying the Python ternary operator. Let's say we want to check if the water at a certain temperature is boiling or not. At standard atmospheric pressure, water boils at 100 degrees Celsius. Suppose that we want to know if the water in our kettle is boiling given that its temperature reaches 90 degrees Celsius. In this case, we can simply use the if-else block:

We can re-write this piece of code using a simple Python ternary operator:

( Side note: above, we omitted the print() statement since the Python ternary operator always returns a value.)

The syntax for both pieces of code above is already familiar. However, there are some other ways to implement the Python ternary operator that we haven't considered yet.

Using Tuples

The first way to re-organize the Python ternary operator is by writing its tupled form. If the standard syntax for the Python ternary operator is a if condition else b , here we would re-write it as (b, a)[condition] , like this:

In the syntax above, the first item of the tuple is the value that will be returned if the condition evaluates to False (since False==0 ), while the second is the value that will be returned if the condition evaluates to True (since True==1 ).

This way of using the Python ternary operator isn't popular compared to its common syntax because, in this case, both elements of the tuple are evaluated since the program first creates the tuple and only then checks the index. In addition, it can be counterintuitive to identify where to place the true value and where to place the false value.

Using Dictionaries

Instead of tuples, we can also use Python dictionaries, like this:

Now, we don't have the issue of differentiating between the true and false values. However, as with the previous case, both expressions are evaluated before returning the right one.

Using Lambdas

Finally, the last way of implementing the Python ternary operator is by applying Lambda functions. To do so, we should re-write the initial syntax a if condition else b in the following form: (lambda: b, lambda: a)[condition]()

Note that in this case, we can become confused about where to put the true and false values. However, the advantage of this approach over the previous two is that it performs more efficiently because only one expression is evaluated.

Let's sum up what we learned in this tutorial about the Python ternary operator:

How the Python ternary operator works
When its preferable to a common if-else block
The syntax of the Python ternary operator
The equivalent of the Python ternary operator written in a common if-else block
The old version of the Python ternary operator and its problems
The limitations of the Python ternary operator
Nested Python ternary operators and their effect on code readability
How to apply the Python ternary operator using tuples, dictionaries, and Lambda functions — including the pros and cons of each method

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Conditional Assignment Operator in Python

Python How-To's
Conditional Assignment Operator in …

Meaning of ||= Operator in Ruby

Implement ruby’s ||= conditional assignment operator in python using the try...except statement, implement ruby’s ||= conditional assignment operator in python using local and global variables.

There isn’t any exact equivalent of Ruby’s ||= operator in Python. However, we can use the try...except method and concepts of local and global variables to emulate Ruby’s conditional assignment operator ||= in Python.

The basic meaning of this operator is to assign the value of the variable y to variable x if variable x is undefined or is falsy value, otherwise no assignment operation is performed.

But this operator is much more complex and confusing than other simpler conditional operators like += , -= because whenever any variable is encountered as undefined, the console throws out NameError .

a+=b evaluates to a=a+b .

a||=b looks as a=a||b but actually behaves as a||a=b .

We use try...except to catch and handle errors. Whenever the try except block runs, at first, the code lying within the try block executes. If the block of code within the try block successfully executes, then the except block is ignored; otherwise, the except block code will be executed, and the error is handled. Ruby’s ||= operator can roughly be translated in Python’s try-catch method as :

Here, if the variable x is defined, the try block will execute smoothly with no NameError exception. Hence, no assignment operation is performed. If x is not defined, the try block will generate NameError , then the except block gets executed, and variable x is assigned to 10 .

The scope of local variables is confined within a specific code scope, whereas global variables have their scope defined in the entire code space.

All the local variables in a particular scope are available as keys of the locals dictionary in that particular scope. All the global variables are stored as keys of the globals dictionary. We can access those variables whenever necessary using the locals and the globals dictionary.

We can check if a variable exists in any of the dictionaries and set its value only if it does not exist to translate Ruby’s ||= conditional assignment operator in Python.

Here, if the variable x is present in either global or local scope, we don’t perform any assignment operation; otherwise, we assign the value of x to 10 . It is similar to x||=10 in Ruby.

Conditional expression (ternary operator) in Python

Python has a conditional expression (sometimes called a "ternary operator"). You can write operations like if statements in one line with conditional expressions.

6. Expressions - Conditional expressions — Python 3.11.3 documentation

Basics of the conditional expression (ternary operator)

If ... elif ... else ... by conditional expressions, list comprehensions and conditional expressions, lambda expressions and conditional expressions.

See the following article for if statements in Python.

Python if statements (if, elif, else)

In Python, the conditional expression is written as follows.

The condition is evaluated first. If condition is True , X is evaluated and its value is returned, and if condition is False , Y is evaluated and its value is returned.

If you want to switch the value based on a condition, simply use the desired values in the conditional expression.

If you want to switch between operations based on a condition, simply describe each corresponding expression in the conditional expression.

An expression that does not return a value (i.e., an expression that returns None ) is also acceptable in a conditional expression. Depending on the condition, either expression will be evaluated and executed.

The above example is equivalent to the following code written with an if statement.

You can also combine multiple conditions using logical operators such as and or or .

Boolean operators in Python (and, or, not)

By combining conditional expressions, you can write an operation like if ... elif ... else ... in one line.

However, it is difficult to understand, so it may be better not to use it often.

The following two interpretations are possible, but the expression is processed as the first one.

In the sample code below, which includes three expressions, the first expression is interpreted like the second, rather than the third:

By using conditional expressions in list comprehensions, you can apply operations to the elements of the list based on the condition.

See the following article for details on list comprehensions.

List comprehensions in Python

Conditional expressions are also useful when you want to apply an operation similar to an if statement within lambda expressions.

In the example above, the lambda expression is assigned to a variable for convenience, but this is not recommended by PEP8.

Refer to the following article for more details on lambda expressions.

Lambda expressions in Python

Related Categories

Conditional Statements 

There are reading-comprehension exercises included throughout the text. These are meant to help you put your reading to practice. Solutions for the exercises are included at the bottom of this page.

In this section, we will be introduced to the if , else , and elif statements. These allow you to specify that blocks of code are to be executed only if specified conditions are found to be true, or perhaps alternative code if the condition is found to be false. For example, the following code will square x if it is a negative number, and will cube x if it is a positive number:

Please refer to the “Basic Python Object Types” subsection to recall the basics of the “boolean” type, which represents True and False values. We will extend that discussion by introducing comparison operations and membership-checking, and then expanding on the utility of the built-in bool type.

Comparison Operations 

Comparison statements will evaluate explicitly to either of the boolean-objects: True or False . There are eight comparison operations in Python:

Operation	Meaning
	strictly less than
	less than or equal
	strictly greater than
	greater than or equal
	equal
	not equal
	object identity
not	negated object identity

The first six of these operators are familiar from mathematics:

Note that = and == have very different meanings. The former is the assignment operator, and the latter is the equality operator:

Python allows you to chain comparison operators to create “compound” comparisons:

Whereas == checks to see if two objects have the same value, the is operator checks to see if two objects are actually the same object. For example, creating two lists with the same contents produces two distinct lists, that have the same “value”:

Thus the is operator is most commonly used to check if a variable references the None object, or either of the boolean objects:

Use is not to check if two objects are distinct:

bool and Truth Values of Non-Boolean Objects 

Recall that the two boolean objects True and False formally belong to the int type in addition to bool , and are associated with the values 1 and 0 , respectively:

Likewise Python ascribes boolean values to non-boolean objects. For example,the number 0 is associated with False and non-zero numbers are associated with True . The boolean values of built-in objects can be evaluated with the built-in Python command bool :

and non-zero Python integers are associated with True :

The following built-in Python objects evaluate to False via bool :

Zero of any numeric type: 0 , 0.0 , 0j

Any empty sequence, such as an empty string or list: '' , tuple() , [] , numpy.array([])

Empty dictionaries and sets

Thus non-zero numbers and non-empty sequences/collections evaluate to True via bool .

The bool function allows you to evaluate the boolean values ascribed to various non-boolean objects. For instance, bool([]) returns False wherease bool([1, 2]) returns True .

if , else , and elif 

We now introduce the simple, but powerful if , else , and elif conditional statements. This will allow us to create simple branches in our code. For instance, suppose you are writing code for a video game, and you want to update a character’s status based on her/his number of health-points (an integer). The following code is representative of this:

Each if , elif , and else statement must end in a colon character, and the body of each of these statements is delimited by whitespace .

The following pseudo-code demonstrates the general template for conditional statements:

In practice this can look like:

In its simplest form, a conditional statement requires only an if clause. else and elif clauses can only follow an if clause.

Similarly, conditional statements can have an if and an else without an elif :

Conditional statements can also have an if and an elif without an else :

Note that only one code block within a single if-elif-else statement can be executed: either the “if-block” is executed, or an “elif-block” is executed, or the “else-block” is executed. Consecutive if-statements, however, are completely independent of one another, and thus their code blocks can be executed in sequence, if their respective conditional statements resolve to True .

Reading Comprehension: Conditional statements

Assume my_list is a list. Given the following code:

What will happen if my_list is [] ? Will IndexError be raised? What will first_item be?

Assume variable my_file is a string storing a filename, where a period denotes the end of the filename and the beginning of the file-type. Write code that extracts only the filename.

my_file will have at most one period in it. Accommodate cases where my_file does not include a file-type.

"code.py" $\rightarrow$ "code"

"doc2.pdf" $\rightarrow$ "doc2"

"hello_world" $\rightarrow$ "hello_world"

Inline if-else statements 

Python supports a syntax for writing a restricted version of if-else statements in a single line. The following code:

can be written in a single line as:

This is suggestive of the general underlying syntax for inline if-else statements:

The inline if-else statement :

The expression A if <condition> else B returns A if bool(<condition>) evaluates to True , otherwise this expression will return B .

This syntax is highly restricted compared to the full “if-elif-else” expressions - no “elif” statement is permitted by this inline syntax, nor are multi-line code blocks within the if/else clauses.

Inline if-else statements can be used anywhere, not just on the right side of an assignment statement, and can be quite convenient:

We will see this syntax shine when we learn about comprehension statements. That being said, this syntax should be used judiciously. For example, inline if-else statements ought not be used in arithmetic expressions, for therein lies madness:

Short-Circuiting Logical Expressions 

Armed with our newfound understanding of conditional statements, we briefly return to our discussion of Python’s logic expressions to discuss “short-circuiting”. In Python, a logical expression is evaluated from left to right and will return its boolean value as soon as it is unambiguously determined, leaving any remaining portions of the expression unevaluated . That is, the expression may be short-circuited .

For example, consider the fact that an and operation will only return True if both of its arguments evaluate to True . Thus the expression False and <anything> is guaranteed to return False ; furthermore, when executed, this expression will return False without having evaluated bool(<anything>) .

To demonstrate this behavior, consider the following example:

According to our discussion, the pattern False and short-circuits this expression without it ever evaluating bool(1/0) . Reversing the ordering of the arguments makes this clear.

In practice, short-circuiting can be leveraged in order to condense one’s code. Suppose a section of our code is processing a variable x , which may be either a number or a string . Suppose further that we want to process x in a special way if it is an all-uppercased string. The code

is problematic because isupper can only be called once we are sure that x is a string; this code will raise an error if x is a number. We could instead write

but the more elegant and concise way of handling the nestled checking is to leverage our ability to short-circuit logic expressions.

See, that if x is not a string, that isinstance(x, str) will return False ; thus isinstance(x, str) and x.isupper() will short-circuit and return False without ever evaluating bool(x.isupper()) . This is the preferable way to handle this sort of checking. This code is more concise and readable than the equivalent nested if-statements.

Reading Comprehension: short-circuited expressions

Consider the preceding example of short-circuiting, where we want to catch the case where x is an uppercased string. What is the “bug” in the following code? Why does this fail to utilize short-circuiting correctly?

Links to Official Documentation 

Truth testing

Boolean operations

Comparisons

‘if’ statements

Reading Comprehension Exercise Solutions: 

Conditional statements

If my_list is [] , then bool(my_list) will return False , and the code block will be skipped. Thus first_item will be None .

First, check to see if . is even contained in my_file . If it is, find its index-position, and slice the string up to that index. Otherwise, my_file is already the file name.

Short-circuited expressions

fails to account for the fact that expressions are always evaluated from left to right. That is, bool(x.isupper()) will always be evaluated first in this instance and will raise an error if x is not a string. Thus the following isinstance(x, str) statement is useless.

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Python conditional operator.

Last Updated on December 18, 2023 by Ankit Kochar

Python, a versatile and widely-used programming language, offers various tools and features to streamline code and improve readability. One such essential tool is the conditional operator, which allows for concise and efficient decision-making within Python code. The conditional operator, also known as the ternary operator, enables developers to write compact conditional statements, enhancing code efficiency and maintainability. In this article, we’ll delve into the usage, syntax, and applications of the Python conditional operator, exploring how it simplifies decision-making and contributes to more elegant code solutions.

What is Python Conditional Operator?

The Python conditional operator, also known as the ternary operator, provides a shorthand way of writing an if-else statement. It is a way to evaluate a condition and return one of two values based on the result of that condition. The syntax of the conditional operator is simpler and more concise than an if-else statement, making it a popular choice for many developers.

Syntax of Python Conditional Operator

The syntax of the Python conditional operator is as follows:

In this syntax, the condition is evaluated first. If the condition is true, then value_if_true is returned. Otherwise, value_if_false is returned. The condition can be any expression that returns a boolean value, such as a comparison or a logical operation. The value_if_true and value_if_false can be any expressions that return a value of any data type.

Examples of Python Conditional Operators

Here are some examples of Python conditional operators with explanations and code:

Example – 1 Check if a number is positive or negative Let’s check if a number is positive or negative using the python conditional operator. Below is the implementation and explanation of the code.

Explanation: In this example, we check whether the number num is positive or negative using the conditional operator. If num is greater than zero, the value of the result is set to "Positive". Otherwise, the value of the result is set to "Negative".

Example – 2 Find the maximum of two numbers Let’s try to find the maximum of two numbers using the python conditional operator. Below is the implementation and explanation of the code.

Explanation: In this example, we find the maximum of two numbers a and b using the conditional operator. If a is greater than b, the value of max_num is set to a. Otherwise, the value of max_num is set to b.

Example – 3 Convert a boolean value to a string Using the python conditional operator, let’s try to convert a boolean value to a string. Below is the implementation and explanation of the code.

Explanation: In this example, we convert a boolean value is_valid to a string using the conditional operator. If is_valid is True, the value of msg is set to "Valid". Otherwise, the value of msg is set to "Invalid".

Example – 4 Check if a string is empty Let’s check if a string is empty or not using the python conditional operator. Below is the implementation and explanation of the code.

Explanation: In this example, we check whether a string is empty or not using the conditional operator. If the string is not empty, the value of the result is set to "Not Empty". Otherwise, the value of the result is set to "Empty".

Example – 5 Convert a number to its absolute value Let’s try to convert a number to its absolute value by using the python conditional operator. Below is the implementation and explanation of the code.

Explanation: In this example, we convert a number num to its absolute value using the conditional operator. If num is greater than or equal to zero, the value of abs_num is set to num. Otherwise, the value of abs_num is set to -num.

Example – 6 Check if a list is empty Using the python conditional operator let’s check if a list is empty or not. Below is the implementation and explanation of the code.

Explanation: In this example, we check whether a list my_list is empty or not using the conditional operator. If my_list is not empty, the value of the result is set to "Not Empty". Otherwise, the value of the result is set to "Empty".

Nested Python Conditional Operator

A nested Python conditional operator is an expression that contains one or more conditional operators within another conditional operator. The nested conditional operator allows for more complex conditions to be evaluated in a concise and efficient way.

The syntax for a nested conditional operator is similar to that of a regular conditional operator:

In this example, if condition1 is true, then value_if_true is returned. If condition1 is false, then condition2 is evaluated. If condition2 is true, then value_if_false is returned. If condition2 is false, then value_if_false2 is returned.

Here is an example of a nested conditional operator that checks the sign of a number:

Explanation: In this example, the first condition num > 0 checks whether num is positive. If it is, then "Positive" is returned. If num is not positive, then the second condition num == 0 checks whether num is equal to zero. If it is, then "Zero" is returned. If num is not equal to zero, then "Negative" is returned.

Using a nested conditional operator can simplify code and make it more efficient by avoiding unnecessary computations. However, it is important to ensure that the conditions are evaluated in the correct order and that the resulting code is still readable and maintainable.

Examples of Nested Python Conditional Operator

Here are some more examples of Nested Python conditional operators with explanation and code:

Example 1: Check if a number is divisible by both 2 and 3 Below is the code implementation and explanation.

Explanation: In this example, the condition num % 2 == 0 and num % 3 == 0 checks if num is divisible by both 2 and 3. If it is, then "Divisible by both" is returned. Otherwise, "Not divisible by both" is returned.

Example 2: Determine the grade based on a student’s score Below is the implementation and explanation of the code.

Explanation: In this example, the nested conditional operator checks the score against multiple conditions to determine the grade. If the score is greater than or equal to 90, an "A" grade is returned. If the score is between 80 and 89, a "B" grade is returned, and so on, until a failing grade of "F" is returned if none of the other conditions are met.

Example 3: Calculate the maximum of three numbers Below is the implementation and explanation of the code.

Explanation: In this example, the nested conditional operator is used to determine the maximum of three numbers. Condition a > b is evaluated first. If it is true, then a is returned as the maximum. If it is false, then b > c is evaluated. If it is true, then b is returned as the maximum. If it is false, then c is returned as the maximum.

Conclusion The Python conditional operator stands as a powerful tool for streamlining decision-making within code. Its concise syntax and ability to write conditional statements in a single line offer a more elegant and readable alternative to traditional if-else structures. By mastering the ternary operator, developers can enhance the efficiency and clarity of their Python programs. Its applications range from simplifying assignments to streamlining conditional expressions, contributing to more maintainable and concise code. Understanding and leveraging the Python conditional operator empowers developers to write efficient, clean, and expressive code.

FAQs Related to Python Conditional Operator

Here are some frequently asked questions (FAQs) about the Python conditional operator:

1. What is the difference between an if-else statement and a conditional operator? An if-else statement is used to execute a block of code if a certain condition is met, whereas a conditional operator is used to return one of two values based on the outcome of a condition. The conditional operator is a shorthand notation for writing if-else statements and can be used to simplify code.

2. What is the syntax of the Python conditional operator? The syntax of the Python conditional operator is: value_if_true if condition else value_if_false.

3. How is the conditional operator different from the if-else statement? The conditional operator allows for writing concise conditional statements in a single line, while the if-else statement spans multiple lines and is more suitable for complex conditions or multiple branches of execution.

4. What are the advantages of using the Python conditional operator? Using the conditional operator can lead to more compact and readable code. It helps in reducing unnecessary lines, making the code more expressive and easier to understand.

5. When should I use the Python conditional operator? The conditional operator is best suited for simple conditional expressions where brevity and readability are crucial. However, for complex conditions or multiple lines of code, using if-else statements might be more appropriate for better code clarity.

6. Can the conditional operator be nested in Python? Yes, the Python conditional operator can be nested. However, nesting it excessively might reduce code readability, so it’s essential to maintain a balance between brevity and clarity.

Python Ternary Operator – Conditional Operators in Python

The ternary operator in Python provides a convenient way to evaluate one expression or another based on a boolean condition, essentially functioning as a compact if-else statement. While not used extensively in Python due to the language‘s significant whitespace and focus on readability, the ternary operator can be handy particularly for simple conditional assignments or function returns.

In this comprehensive guide, we will cover everything you need to know about using ternary operators in Python, including:

What is the ternary operator and what is it used for?
Syntax and terminology
Basic usage examples
Chaining ternary operators
Ternary operator vs if-else statements
Common mistakes to avoid
Advanced usage with dictionaries, loops, and more

So if you want to learn all about this useful but overlooked Python feature, read on!

What is the Ternary Operator in Python?

The ternary operator in Python performs a basic conditional test, evaluating a binary true/false condition and returning one of two results depending on the outcome of that test.

Here is the basic syntax:

As you can see, the ternary operator allows you to set up the conditional test (condition), a value to return if that test passes (value_if_true), and an alternative value to return if the test fails (value_if_false).

Based on the result of condition, either value_if_true or value_if_false will be evaluated and returned.

So in plain English, the ternary operator allows you to write compact if-else expressions in one line, with the conditional test first, then the “true” outcome, an else keyword, and finally the “false” outcome.

This can be extremely useful for:

Streamlined conditional assignments
Returning different function results based on conditional tests
Rapid prototyping and experimentation
Anywhere you need a quick if-else decision but don’t want to break your code into separate lines or blocks

Some key properties of the ternary operator:

The condition must evaluate to a boolean (True or False)
value_if_true and value_if_false can be any valid expressions
The else clause is required (otherwise it‘s a missing value, not a ternary operator)

Now that you understand the basics of what the ternary operator is and does, let‘s go over some terminology.

Ternary Operator Terminology

The ternary operator gets its name because it takes three arguments: the condition, the “true” value, and the “false” value. These three parts can be referred to as follows:

condition – The conditional test that gets evaluated, which must return a boolean value. This determines which path the ternary operator takes.

true_value (aka value_if_true) – The value that gets returned or executed if condition evaluates to True.

false_value (aka value_if_false) – The value gets returned or executed if condition evaluates to False.

These distinct names for the three parts of the ternary operator are sometimes used interchangeably, but they refer to the same components:

The conditional test
The result if that test passes
The result if that test fails

Understanding this terminology will help you read and discuss ternary operators more easily.

With the fundamentals covered, let‘s move on to some examples!

Basic Examples of the Ternary Operator in Python

In most cases, the Python ternary operator gets used for conditional assignments or function returns.

Let‘s start with some basic examples:

Conditional Assignment

Here we assign the value of x based on a condition:

Since 2 does indeed exceed 1, the condition evaluates to True. x is therefore set to the "true" value of 1.

Here‘s another example:

Because age is under 18, the conditional fails, setting adult to the "false" value of False.

Conditional Function Returns

Similarly, we can return different results from a function based on a conditional test:

Based on the input grade, status() will return either "Passing" or "Failing".

We can make this return value even more dynamic:

Now calc_bonus() will return a $1000 bonus if sales exceed the $10k target, or $0 otherwise.

Assignment Inside Conditionals

A common pattern is to assign a variable value inside the ternary operator itself:

Here we calculate output based on the input, assign it inside the condition, then return either that output or 0 based on a further conditional test.

This allows us to reuse that mid-calculation output value in the true portion without having to calculate it twice. Nifty!

The ternary operator handles all sorts of datatypes too:

Here we print the first value if the list has any contents, or "Neither" if it does not.

As you can see, there are many useful applications for brief "if X then Y else Z" logic in Python.

Now let‘s talk about…

Chaining Multiple Ternary Operators in Python

You can actually chain multiple ternary conditionals back-to-back:

Here we set nested true/false values for under 16, under 18, and 18+, with drive being assigned based on the first True condition.

While this can work, heavily nested ternary chains get very hard to read! In general try to avoid more than 2 or 3 chained ternaries.

We can tweak the above to be a little cleaner with if/elif logic:

This checks each condition explicitly, which is often preferable for complex conditional logic.

Nonetheless, the capability to chain ternaries exists if you want to wield it responsibly!

Now that we‘ve covered ternary basics and some simple use cases, let‘s examine some key differences between ternaries and standard if-else statements.

Ternary Operator vs If-Else Statements in Python

While the ternary operator can function as shorthand for if-else decisions, there are some notable differences:

Length: Ternary expressions are almost always shorter/more condensed than equivalent if-else blocks.

Readability: More complex conditional logic generally reads better with if-else spanning separate lines/blocks.

Expandability: It’s easier to tweak, expand, or enhance if-else statements without heavily restructuring code.

Flow: Code execution cannot break or continue out of a ternary operator, unlike standalone if-else blocks.

Nesting: Ternaries can be chained to mimic elif-style logic, but this gets complex fast.

Returning: Ternaries are often used to conditionally return function values, whereas if-else statements execute conditional code blocks inline.

In practice ternaries tend to be most useful for:

Quick one-off conditional assignments
Returning different values from functions
Setting map/dict values based on conditions

Whereas standard if-else statements allow for:

More readable complex logic
Additional control flow like breaks
Expandable conditional code blocks
Executing conditional code inline vs returning values

So in summary:

Ternaries prioritize single-line brevity
If-else enables readability through explicit control flow

You’ll generally want to use the right tool for the job based on your specific need.

Now that you understand how ternaries differ from normal if-else logic, let‘s go over some common mistakes to avoid.

Common Mistakes When Using Ternary Operators in Python

While ternary operations seem straightforward on the surface, there are some subtle syntax issues and edge cases that can trip you up:

1. Forgetting the else clause

All ternary expressions require an else clause with an alternate value. Otherwise, you will get a SyntaxError.

2. Condition not evaluating to a boolean

The condition in your ternary must resolve to a boolean True or False result. If not, you’ll get confusing logic errors.

❌ Invalid (string isn‘t boolean):

✅ Valid (evaluates to True):

3. Assignment operator instead of comparison

It’s easy to mistakenly use a single = rather than a == comparison operator in your ternary test. Then instead of evaluating True/False, that part of the expression gets assigned.

Watch out for this common pitfall in particular!

And one lesser-known edge case to be aware of:

4. Modifying a list/dict while iterating

Mutating a collection like a list or dict while iterating in a loop can lead to weirdcontenttypes python issues. Ternaries inside loops can make this hard to spot:

❌ Dangerous (mutates list during iteration):

✅ Safer (iterates over copy of list):

In summary, watch out for:

Missing else clauses
Conditions that don‘t evaluate to booleans
Using = instead of ==
Mutating iterables in loops

Follow the guidelines above, and you‘ll sidestep the most frustrating ternary traps!

Now that you know what to watch out for, let‘s move on to some more advanced examples.

Advanced Python Ternary Usage

Once you grasp ternary basics, there are all sorts of clever ways to leverage them:

Ternaries with Dictionaries

Since dictionaries map keys to values, ternaries are handy for dynamic key/value assignment:

Here .get() lets us fallback to a default if the passed key doesn‘t exist.

We can also inline-create new dict values on the fly:

So ternaries provide flexibility when dealing with sparse dictionaries that may not contain all keys at all times.

Ternary Operator List Comprehensions

Because ternaries return values, they can be used inline with list comprehensions:

Here we output a new list with values doubled if they exceed 2 based on the ternary condition. Pretty slick!

List comprehensions can leverage ternaries to filter data based on complex criteria.

Ternaries to Simplify Logic in Loops

We can also incorporate ternaries directly inside loops:

Here we inline-print the minimum required age per movie based on its rating.

Using ternaries this way keeps our core loop logic clean and simple.

Function Argument Defaults

You can even use ternaries to define function argument default values:

Here if no timestamp gets provided, we default to the current datetime, while still allowing custom timestamps to be passed in.

All Sorts of Possibilities

Beyond these examples, ternary operators can be used creatively just about anywhere an if-else statement would work.

Some ideas:

Conditionally executing functions
Running different methods on classes
Returning early from functions
Custom error handling
Dynamic imports
Math expressions/calculations
Interactive scripts
Ternaries make great "one line if-else" replacements
They excel at simple "if X then Y else Z" tests
But avoid complex nested ternary chains!

Now you have a firm grasp on how to use the Python ternary operator.

Let’s wrap up with a quick review!

Summary of Key Points

The ternary operator evaluates a condition and returns one of two results based on a True or False outcome
It provides a compact "if X then Y else Z" syntax
Ternaries work great for quick conditional assignments and function returns
Chained ternaries mimic elif-style logic but get complex fast
Ternaries prioritize single-line brevity over complex logic
Watch out for common syntax issues like missing else clauses
Ternaries enable all sorts of advanced conditional logic techniques

The Python ternary operator gives you the power to implement "if this then that" decisions in one line. While they can‘t replace multiline if-else statements for complex logic, ternaries open up all sorts of useful possibilities.

Now that you know the ins and outs of Python ternary operators, some creative coding awaits!

Dr. Alex Mitchell is a dedicated coding instructor with a deep passion for teaching and a wealth of experience in computer science education. As a university professor, Dr. Mitchell has played a pivotal role in shaping the coding skills of countless students, helping them navigate the intricate world of programming languages and software development.

Beyond the classroom, Dr. Mitchell is an active contributor to the freeCodeCamp community, where he regularly shares his expertise through tutorials, code examples, and practical insights. His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner.

Dr. Mitchell’s approach to teaching blends academic rigor with real-world applications, ensuring that his students not only understand the theory but also how to apply it effectively. His commitment to education and his ability to simplify complex topics have made him a respected figure in both the university and online learning communities.

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Python Conditional Assignment Operator in Python 3

Python is a versatile programming language that offers a wide range of features and functionalities. One such feature is the conditional assignment operator, which allows programmers to assign a value to a variable based on a condition. This operator, also known as the “ternary operator,” provides a concise and elegant way to write conditional statements in Python.

The conditional assignment operator in Python follows the syntax: value_if_true if condition else value_if_false . It evaluates the condition and returns the value_if_true if the condition is true, otherwise it returns the value_if_false.

This operator can be particularly useful when you want to assign a value to a variable based on a simple condition without the need for an if-else statement. It allows you to write more compact and readable code, reducing the number of lines and improving code efficiency.

Let’s consider a simple example to understand the usage of the conditional assignment operator:

In this example, we assign the value “Adult” to the variable “status” if the age is greater than or equal to 18. Otherwise, we assign the value “Minor”. The output of this code will be “Adult” since the age is 25.

The conditional assignment operator can also be used in more complex scenarios. For instance, consider the following example:

In this example, we assign the sum of x and y to the variable “result” if x is greater than y. Otherwise, we assign the difference between x and y. The output of this code will be 15 since x is greater than y.

Related Evidence

The conditional assignment operator is a widely used feature in Python programming. It provides a concise and efficient way to assign values based on conditions. Many Python developers appreciate its simplicity and readability.

Furthermore, the conditional assignment operator is supported in Python 3 and later versions. It is an integral part of the language’s syntax and is widely documented in Python’s official documentation and various programming resources.

Overall, the conditional assignment operator in Python is a powerful tool that allows programmers to write more concise and efficient code. Its usage can greatly enhance code readability and reduce the complexity of conditional statements.

The Python Conditional Assignment Operator, also known as the “walrus operator,” was introduced in Python 3.8. It allows you to assign a value to a variable based on a condition in a single line of code. This operator is denoted by “:=” and can be used in various scenarios to simplify your code and make it more readable.

In the first example, we use the conditional assignment operator to assign the value True to the variable is_adult if the age is greater than or equal to 18. Otherwise, it assigns False. This simplifies the code and makes it more concise.

In the second example, we assign a default value “John Doe” to the variable name if it is None. This is achieved using the conditional assignment operator in combination with the logical OR operator. If the variable name is None, the expression evaluates to True, and the default_name value is assigned to name.

The third example demonstrates how the conditional assignment operator can be used to assign a value from a function call if the current value of the variable is None. This can be useful when you want to assign a default value from a function only if it hasn’t been set previously.

Overall, the Python Conditional Assignment Operator is a powerful tool that allows you to write more concise and readable code. It simplifies assignments based on conditions and reduces the need for multiple lines of code. However, it should be used judiciously to maintain code clarity and avoid excessive complexity.

Reference Links:

Python 3.8 Documentation – Assignment Expressions
GeeksforGeeks – Assignment Expressions in Python
Real Python – Assignment Expressions (The Walrus Operator)

In conclusion, the Python Conditional Assignment Operator is a valuable addition to the language that simplifies assignments based on conditions. It allows you to write more concise and readable code by reducing the need for multiple lines of code. However, it is important to use it judiciously and maintain code clarity to avoid excessive complexity. The operator has been well-received by the Python community and has become a popular feature in Python 3.8 and later versions.

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6. Expressions
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6. Expressions ¶

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

and no semantics are given, the semantics of this form of name are the same as for othername .

6.1. Arithmetic conversions ¶

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

If either argument is a complex number, the other is converted to complex;

otherwise, if either argument is a floating-point number, the other is converted to floating point;

otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

6.2. Atoms ¶

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

6.2.1. Identifiers (Names) ¶

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

6.2.1.1. Private name mangling ¶

When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class.

The class specifications .

More precisely, private names are transformed to a longer form before code is generated for them. If the transformed name is longer than 255 characters, implementation-defined truncation may happen.

The transformation is independent of the syntactical context in which the identifier is used but only the following private identifiers are mangled:

Any name used as the name of a variable that is assigned or read or any name of an attribute being accessed.

The __name__ attribute of nested functions, classes, and type aliases is however not mangled.

The name of imported modules, e.g., __spam in import __spam . If the module is part of a package (i.e., its name contains a dot), the name is not mangled, e.g., the __foo in import __foo.bar is not mangled.

The name of an imported member, e.g., __f in from spam import __f .

The transformation rule is defined as follows:

The class name, with leading underscores removed and a single leading underscore inserted, is inserted in front of the identifier, e.g., the identifier __spam occurring in a class named Foo , _Foo or __Foo is transformed to _Foo__spam .

If the class name consists only of underscores, the transformation is the identity, e.g., the identifier __spam occurring in a class named _ or __ is left as is.

6.2.2. Literals ¶

Python supports string and bytes literals and various numeric literals:

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating-point number, complex number) with the given value. The value may be approximated in the case of floating-point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

6.2.3. Parenthesized forms ¶

A parenthesized form is an optional expression list enclosed in parentheses:

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries ¶

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

either the container contents are listed explicitly, or

they are computed via a set of looping and filtering instructions, called a comprehension .

Common syntax elements for comprehensions are:

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)] .

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator . A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions.

If a comprehension contains async for clauses, or if it contains await expressions or other asynchronous comprehensions anywhere except the iterable expression in the leftmost for clause, it is called an asynchronous comprehension . An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530 .

Added in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

6.2.5. List displays ¶

A list display is a possibly empty series of expressions enclosed in square brackets:

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays ¶

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with {} ; this literal constructs an empty dictionary.

6.2.7. Dictionary displays ¶

A dictionary display is a possibly empty series of dict items (key/value pairs) enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of dict items is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding value. This means that you can specify the same key multiple times in the dict item list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking . Its operand must be a mapping . Each mapping item is added to the new dictionary. Later values replace values already set by earlier dict items and earlier dictionary unpackings.

Added in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448 .

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy . (To summarize, the key type should be hashable , which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last value (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572 .

6.2.8. Generator expressions ¶

A generator expression is a compact generator notation in parentheses:

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)) .

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression . An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators ).

Added in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

6.2.9. Yield expressions ¶

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions .

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None . Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError , while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration , or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

The proposal for adding generators and the yield statement to Python.

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

6.2.9.1. Generator-iterator methods ¶

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None . The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__() ’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

6.2.9.2. Examples ¶

Here is a simple example that demonstrates the behavior of generators and generator functions:

For examples using yield from , see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

6.2.9.3. Asynchronous generator functions ¶

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None . Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break , the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks() . When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py .

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

6.2.9.4. Asynchronous generator-iterator methods ¶

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration , or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

6.3. Primaries ¶

Primaries represent the most tightly bound operations of the language. Their syntax is:

6.3.1. Attribute references ¶

An attribute reference is a primary followed by a period and a name:

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. The type and value produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

This production can be customized by overriding the __getattribute__() method or the __getattr__() method. The __getattribute__() method is called first and either returns a value or raises AttributeError if the attribute is not available.

If an AttributeError is raised and the object has a __getattr__() method, that method is called as a fallback.

6.3.2. Subscriptions ¶

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__() . When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__ , see __class_getitem__ versus __getitem__ .

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__() :

Mappings. If the primary is a mapping , the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.

Sequences. If the primary is a sequence , the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str , list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences . However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x . The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters . A character is not a separate data type but a string of exactly one character.

6.3.3. Slicings ¶

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy ) whose start , stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

6.3.4. Calls ¶

A call calls a callable object (e.g., a function ) with a possibly empty series of arguments :

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None , it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable . Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4) , if y evaluates to a sequence y1 , …, yM , this is equivalent to a call with M+4 positional arguments x1 , x2 , y1 , …, yM , x3 , x4 .

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping , the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings ( * ), and keyword arguments may follow dictionary unpackings ( ** ). Originally proposed by PEP 448 .

A call always returns some value, possibly None , unless it raises an exception. How this value is computed depends on the type of the callable object.

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions . When the code block executes a return statement, this specifies the return value of the function call.

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

A new instance of that class is returned.

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

The class must define a __call__() method; the effect is then the same as if that method was called.

6.4. Await expression ¶

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function .

Added in version 3.5.

6.5. The power operator ¶

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1 .

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100 , but 10**-2 returns 0.01 .

Raising 0.0 to a negative power results in a ZeroDivisionError . Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError .)

This operation can be customized using the special __pow__() and __rpow__() methods.

6.6. Unary arithmetic and bitwise operations ¶

All unary arithmetic and bitwise operations have the same priority:

The unary - (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

The unary ~ (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1) . It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

6.7. Binary arithmetic operations ¶

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

This operation can be customized using the special __matmul__() and __rmatmul__() methods.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

The division operation can be customized using the special __truediv__() and __rtruediv__() methods. The floor division operation can be customized using the special __floordiv__() and __rfloordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating-point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34 .) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand [ 1 ] .

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y) . Floor division and modulo are also connected with the built-in function divmod() : divmod(x, y) == (x//y, x%y) . [ 2 ] .

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting .

The modulo operation can be customized using the special __mod__() and __rmod__() methods.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating-point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The - (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() and __rsub__() methods.

6.8. Shifting operations ¶

The shifting operations have lower priority than the arithmetic operations:

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

The left shift operation can be customized using the special __lshift__() and __rlshift__() methods. The right shift operation can be customized using the special __rshift__() and __rrshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n) . A left shift by n bits is defined as multiplication with pow(2,n) .

6.9. Binary bitwise operations ¶

Each of the three bitwise operations has a different priority level:

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

6.10. Comparisons ¶

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

Comparisons yield boolean values: True or False . Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a , b , c , …, y , z are expressions and op1 , op2 , …, opN are comparison operators, then a op1 b op2 c ... y opN z is equivalent to a op1 b and b op2 c and ... y opN z , except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c , so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

6.10.1. Value comparisons ¶

The operators < , > , == , >= , <= , and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object , they inherit the default comparison behavior from object . Types can customize their comparison behavior by implementing rich comparison methods like __lt__() , described in Basic customization .

The default behavior for equality comparison ( == and != ) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y ).

A default order comparison ( < , > , <= , and >= ) is not provided; an attempt raises TypeError . A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

Numbers of built-in numeric types ( Numeric Types — int, float, complex ) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

The not-a-number values float('NaN') and decimal.Decimal('NaN') are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float('NaN') , 3 < x , x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not , never the equality operators.

Binary sequences (instances of bytes or bytearray ) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.

Strings (instances of str ) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord() ) of their characters. [ 3 ]

Strings and binary sequences cannot be directly compared.

Sequences (instances of tuple , list , or range ) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError .

Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

Lexicographical comparison between built-in collections works as follows:

For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).

Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y ). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).

Mappings (instances of dict ) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

Order comparisons ( < , > , <= , and >= ) raise TypeError .

Sets (instances of set or frozenset ) can be compared within and across their types.

They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets {1,2} and {2,3} are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min() , max() , and sorted() produce undefined results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

Equality comparison should be reflexive. In other words, identical objects should compare equal:

x is y implies x == y

Comparison should be symmetric. In other words, the following expressions should have the same result:

x == y and y == x x != y and y != x x < y and y > x x <= y and y >= x

Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

x > y and y > z implies x > z x < y and y <= z implies x < z

Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

x == y and not x != y x < y and not x >= y (for total ordering) x > y and not x <= y (for total ordering)

The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

6.10.2. Membership test operations ¶

The operators in and not in test for membership. x in s evaluates to True if x is a member of s , and False otherwise. x not in s returns the negation of x in s . All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y) .

For the string and bytes types, x in y is True if and only if x is a substring of y . An equivalent test is y.find(x) != -1 . Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True .

For user-defined classes which define the __contains__() method, x in y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__() , x in y is True if some value z , for which the expression x is z or x == z is true, is produced while iterating over y . If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__() , x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i] , and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in .

6.10.3. Identity comparisons ¶

The operators is and is not test for an object’s identity: x is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. [ 4 ]

6.11. Boolean operations ¶

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False , None , numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x ; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x ; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True , but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or 'foo' yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not 'foo' produces False rather than '' .)

6.12. Assignment expressions ¶

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier , while also returning the value of the expression .

One common use case is when handling matched regular expressions:

Or, when processing a file stream in chunks:

Assignment expressions must be surrounded by parentheses when used as expression statements and when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert , with , and assignment statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

Added in version 3.8: See PEP 572 for more details about assignment expressions.

6.13. Conditional expressions ¶

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x . If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

6.14. Lambdas ¶

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

6.15. Expression lists ¶

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking . Its operand must be an iterable . The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

Added in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448 .

A trailing comma is required only to create a one-item tuple, such as 1, ; it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: () .)

6.16. Evaluation order ¶

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

6.17. Operator precedence ¶

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

Operator	Description
, , value...},	Binding or parenthesized expression, list display, dictionary display, set display
, , ,	Subscription, slicing, call, attribute reference
x	Await expression
	Exponentiation 5]
, ,	Positive, negative, bitwise NOT
, , , ,	Multiplication, matrix multiplication, division, floor division, remainder 6]
,	Addition and subtraction
,	Shifts
	Bitwise AND
	Bitwise XOR
	Bitwise OR
, in, , not, , , , , ,	Comparisons, including membership tests and identity tests
x	Boolean NOT
	Boolean AND
	Boolean OR
–	Conditional expression
	Lambda expression
	Assignment expression

6.1. Arithmetic conversions
6.2.1.1. Private name mangling
6.2.2. Literals
6.2.3. Parenthesized forms
6.2.4. Displays for lists, sets and dictionaries
6.2.5. List displays
6.2.6. Set displays
6.2.7. Dictionary displays
6.2.8. Generator expressions
6.2.9.1. Generator-iterator methods
6.2.9.2. Examples
6.2.9.3. Asynchronous generator functions
6.2.9.4. Asynchronous generator-iterator methods
6.3.1. Attribute references
6.3.2. Subscriptions
6.3.3. Slicings
6.3.4. Calls
6.4. Await expression
6.5. The power operator
6.6. Unary arithmetic and bitwise operations
6.7. Binary arithmetic operations
6.8. Shifting operations
6.9. Binary bitwise operations
6.10.1. Value comparisons
6.10.2. Membership test operations
6.10.3. Identity comparisons
6.11. Boolean operations
6.12. Assignment expressions
6.13. Conditional expressions
6.14. Lambdas
6.15. Expression lists
6.16. Evaluation order
6.17. Operator precedence

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The Walrus Operator: Python's Assignment Expressions

Table of Contents

Hello, Walrus!

Implementation, lists and dictionaries, list comprehensions, while loops, witnesses and counterexamples, walrus operator syntax, walrus operator pitfalls.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Assignment Expressions and Using the Walrus Operator

Each new version of Python adds new features to the language. Back when Python 3.8 was released, the biggest change was the addition of assignment expressions . Specifically, the := operator gave you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator .

This tutorial is an in-depth introduction to the walrus operator. You’ll learn some of the motivations for the syntax update and explore examples where assignment expressions can be useful.

In this tutorial, you’ll learn how to:

Identify the walrus operator and understand its meaning
Understand use cases for the walrus operator
Avoid repetitive code by using the walrus operator
Convert between code using the walrus operator and code using other assignment methods
Use appropriate style in your assignment expressions

Note that all walrus operator examples in this tutorial require Python 3.8 or later to work.

Get Your Code: Click here to download the free sample code that shows you how to use Python’s walrus operator.

Take the Quiz: Test your knowledge with our interactive “The Walrus Operator: Python's Assignment Expressions” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

In this quiz, you'll test your understanding of the Python Walrus Operator. This operator was introduced in Python 3.8, and understanding it can help you write more concise and efficient code.

Walrus Operator Fundamentals

First, look at some different terms that programmers use to refer to this new syntax. You’ve already seen a few in this tutorial.

The := operator is officially known as the assignment expression operator . During early discussions, it was dubbed the walrus operator because the := syntax resembles the eyes and tusks of a walrus lying on its side. You may also see the := operator referred to as the colon equals operator . Yet another term used for assignment expressions is named expressions .

To get a first impression of what assignment expressions are all about, start your REPL and play around with the following code:

Line 1 shows a traditional assignment statement where the value False is assigned to walrus . Next, on line 5, you use an assignment expression to assign the value True to walrus . After both lines 1 and 5, you can refer to the assigned values by using the variable name walrus .

You might be wondering why you’re using parentheses on line 5, and you’ll learn why the parentheses are needed later on in this tutorial .

Note: A statement in Python is a unit of code. An expression is a special statement that can be evaluated to some value.

For example, 1 + 2 is an expression that evaluates to the value 3 , while number = 1 + 2 is an assignment statement that doesn’t evaluate to a value. Although running the statement number = 1 + 2 doesn’t evaluate to 3 , it does assign the value 3 to number .

In Python, you often see simple statements like return statements and import statements , as well as compound statements like if statements and function definitions . These are all statements, not expressions.

There’s a subtle—but important—difference between the two types of assignments with the walrus variable. An assignment expression returns the value, while a traditional assignment doesn’t. You can see this in action when the REPL doesn’t print any value after walrus = False on line 1 but prints out True after the assignment expression on line 5.

You can see another important aspect about walrus operators in this example. Though it might look new, the := operator does not do anything that isn’t possible without it. It only makes certain constructs more convenient and can sometimes communicate the intent of your code more clearly.

Now you have a basic idea of what the := operator is and what it can do. It’s an operator used in assignment expressions, which can return the value being assigned, unlike traditional assignment statements. To get deeper and really learn about the walrus operator, continue reading to see where you should and shouldn’t use it.

Like most new features in Python, assignment expressions were introduced through a Python Enhancement Proposal (PEP). PEP 572 describes the motivation for introducing the walrus operator, the details of the syntax, and examples where the := operator can be used to improve your code.

This PEP was originally written by Chris Angelico in February 2018. Following some heated discussion, PEP 572 was accepted by Guido van Rossum in July 2018.

Since then, Guido announced that he was stepping down from his role as benevolent dictator for life (BDFL) . Since early 2019, the Python language has been governed by an elected steering council instead.

The walrus operator was implemented by Emily Morehouse , and made available in the first alpha release of Python 3.8.

In many languages, including C and its derivatives, assignment statements are also expressions. This can be both very powerful and a source of confusing bugs. For example, the following code is valid C but doesn’t execute as intended:

Here, if (x = y) will evaluate to true, and the code snippet will print out x and y are equal (x = 8, y = 8) . Is this the result you were expecting? You were trying to compare x and y . How did the value of x change from 3 to 8 ?

The problem is that you’re using the assignment operator ( = ) instead of the equality comparison operator ( == ). In C, x = y is an expression that evaluates to the value of y . In this example, x = y is evaluated as 8 , which is considered truthy in the context of the if statement.

Take a look at a corresponding example in Python. This code causes a SyntaxError :

Unlike the C example, this Python code gives you an explicit error instead of a bug.

The distinction between assignment statements and assignment expressions in Python is useful in order to avoid these kinds of hard-to-find bugs. PEP 572 argues that Python is better suited to having different syntax for assignment statements and expressions instead of turning the existing assignment statements into expressions.

One design principle underpinning the walrus operator is that there are no identical code contexts where both an assignment statement using the = operator and an assignment expression using the := operator would be valid. For example, you can’t do a plain assignment with the walrus operator:

In many cases, you can add parentheses ( () ) around the assignment expression to make it valid Python:

Writing a traditional assignment statement with = isn’t allowed inside such parentheses. This helps you catch potential bugs.

Later on in this tutorial , you’ll learn more about situations where the walrus operator isn’t allowed, but first you’ll learn about the situations where you might want to use it.

Walrus Operator Use Cases

In this section, you’ll see several examples where the walrus operator can simplify your code. A general theme in all these examples is that you’ll avoid different kinds of repetition:

Repeated function calls can make your code slower than necessary.
Repeated statements can make your code hard to maintain.
Repeated calls that exhaust iterators can make your code overly complex.

You’ll see how the walrus operator can help in each of these situations.

Arguably one of the best use cases for the walrus operator is when debugging complex expressions. Say that you want to find the distance between two locations along the earth’s surface. One way to do this is to use the haversine formula :

ϕ represents the latitude, and λ represents the longitude of each location. To demonstrate this formula, you can calculate the distance between Oslo (59.9°N 10.8°E) and Vancouver (49.3°N 123.1°W) as follows:

As you can see, the distance from Oslo to Vancouver is just under 7,200 kilometers.

Note: Python source code is typically written using UTF-8 Unicode . This allows you to use Greek letters like ϕ and λ in your code, which may be useful when translating mathematical formulas. Wikipedia shows some alternatives for using Unicode on your system.

While UTF-8 is supported (in string literals, for instance), Python’s variable names use a more limited character set . For example, you can’t use emojis while naming your variables. That’s a good restriction !

Now, say that you need to double-check your implementation and want to see how much the haversine terms contribute to the final result. You could copy and paste the term from your main code to evaluate it separately. However, you could also use the := operator to give a name to the subexpression that you’re interested in:

The advantage of using the walrus operator here is that you calculate the value of the full expression and keep track of the value of ϕ_hav at the same time. This allows you to confirm that you didn’t introduce any errors while debugging.

Lists are powerful data structures in Python that often represent a series of related attributes. Similarly, dictionaries are used all over Python and are great for structuring information.

Sometimes when setting up these data structures, you end up performing the same operation several times. As a first example, calculate some basic descriptive statistics of a list of numbers and store them in a dictionary:

Note that both the sum and the length of the numbers list are calculated twice. The consequences are not too bad in this simple example, but if the list were larger or the calculations were more complicated, you might want to optimize the code. To do this, you can first move the function calls out of the dictionary definition:

The variables num_length and num_sum are only used to optimize the calculations inside the dictionary. By using the walrus operator, you can make this role clearer:

You’ve now defined num_length and num_sum inside the definition of description . This is a clear hint to anybody reading this code that these variables are just used to optimize these calculations and aren’t used again later.

Note: The scope of the num_length and num_sum variables is the same in the example with the walrus operator and in the example without. This means that in both examples, the variables are available after the definition of description .

Even though both examples are very similar functionally, a benefit of using the assignment expressions is that the := operator communicates the intent of these variables as throwaway optimizations.

In the next example, you’ll work with a bare-bones implementation of the wc utility for counting lines, words, and characters in a text file:

This script can read one or several text files and report how many lines, words, and characters each of them contains. Here’s a breakdown of what’s happening in the code:

Line 4 loops over each filename provided by the user. The sys.argv list contains each argument given on the command line, starting with the name of your script. For more information about sys.argv , you can check out Python Command Line Arguments .
Line 5 converts each filename string to a pathlib.Path object . Storing a filename in a Path object allows you to conveniently read the text file in the next lines.
Lines 6 to 10 construct a tuple of counts to represent the number of lines, words, and characters in one text file.
Line 7 reads a text file and calculates the number of lines by counting newlines.
Line 8 reads a text file and calculates the number of words by splitting on whitespace.
Line 9 reads a text file and calculates the number of characters by finding the length of the string.
Line 11 prints all three counts together with the filename to the console. The *counts syntax unpacks the counts tuple. In this case, the print() statement is equivalent to print(counts[0], counts[1], counts[2], path) .

To see wc.py in action, you can use the script on itself as follows:

In other words, the wc.py file consists of 11 lines, 32 words, and 307 characters.

If you look closely at this implementation, then you’ll notice that it’s far from optimal. In particular, it repeats the call to path.read_text() three times. That means that the program reads each text file three times. You can use the walrus operator to avoid the repetition:

You assign the contents of the file to text , which you reuse in the next two calculations. Note the placement of parentheses that help scope that text will refer to the text in the file and not the number of lines.

The program still functions the same, although the word and character counts have changed:

As in the earlier examples, an alternative approach is to define text before the definition of counts :

While this is one line longer than the previous implementation, it probably provides the best balance between readability and efficiency. The := assignment expression operator isn’t always the most readable solution even when it makes your code more concise.

List comprehensions are great for constructing and filtering lists. They clearly state the intent of the code and will usually run quite fast.

There’s one list comprehension use case where the walrus operator can be particularly useful. Say that you want to apply some computationally expensive function, slow() , to the elements in your list and filter on the resulting values. You could do something like the following:

Here, you filter the numbers list and leave the positive results from applying slow() . The problem with this code is that this expensive function is called twice.

A very common solution for this type of situation is rewriting your code to use an explicit for loop:

This will only call slow() once. Unfortunately, the code is now more verbose, and the intent of the code is harder to understand. The list comprehension had clearly signaled that you were creating a new list, while this is more hidden in the explicit for loop since several lines of code separate the list creation and the use of .append() . Additionally, a list comprehension runs faster than the repeated calls to .append() .

You can code some other solutions by using a filter() expression or a kind of double list comprehension:

The good news is that there’s only one call to slow() for each number. The bad news is that the code’s readability has suffered in both expressions.

Figuring out what’s actually happening in the double list comprehension takes a fair amount of head-scratching. Essentially, the second for statement is used only to give the name value to the return value of slow(num) . Fortunately, that sounds like something that you can accomplish with an assignment expression!

You can rewrite the list comprehension using the walrus operator as follows:

Note that the parentheses around value := slow(num) are required. This version is effective and readable, and it communicates the intent of the code well.

Note: You need to add the assignment expression on the if clause of the list comprehension. If you try to define value with the other call to slow() , then it won’t work:

This will raise a NameError because the if clause is evaluated before the expression at the beginning of the comprehension.

Next, look at a slightly more involved and practical example. Say that you want to use the Real Python feed to find the titles of the last episodes of the Real Python Podcast .

You can use the Real Python Feed Reader to download information about the latest Real Python publications. In order to find the podcast episode titles, you’ll use the third-party Parse package. Start by creating a virtual environment and installing both packages:

You can now read the latest titles published by Real Python:

Podcast titles start with "The Real Python Podcast" , so here you can create a pattern that Parse can use to identify them:

Compiling the pattern beforehand speeds up later comparisons, especially when you want to match the same pattern over and over. You can check if a string matches your pattern using either pattern.parse() or pattern.search() :

Note that Parse is able to pick out the podcast episode number and the episode name. The episode number is converted to an integer data type because you used the :d format specifier .

Time to get back to the task at hand. In order to list all the recent podcast titles, you need to check whether each string matches your pattern and then parse out the episode title. A first attempt may look something like this:

Though it works, you might notice the same problem you saw earlier. You’re parsing each title twice because you filter out titles that match your pattern and then use that same pattern to pick out the episode title.

Like you did earlier, you can avoid the double work by rewriting the list comprehension using either an explicit for loop or a double list comprehension. Using the walrus operator, however, is even more straightforward:

Assignment expressions work well to simplify these kinds of list comprehensions. They keep your code readable and save you from doing a potentially expensive operation twice.

Note: The Real Python Podcast has its own separate RSS feed , which you should use if you want to play around with information about the podcast only. You can get all the episode titles with the following code:

See The Real Python Podcast for options to listen to it using your podcast player.

In this section, you’ve focused on examples where you can rewrite list comprehensions using the walrus operator. The same principles also apply if you see that you need to repeat an operation in a dictionary comprehension , a set comprehension , or a generator expression .

The following example uses a generator expression to calculate the average length of episode titles that are over 50 characters long:

The generator expression uses an assignment expression to avoid calculating the length of each episode title twice.

Python has two different loop constructs: for loops and while loops . You typically use a for loop when you need to iterate over a known sequence of elements. A while loop, on the other hand, is for when you don’t know beforehand how many times you’ll need to repeat the loop.

In while loops, you need to define and check the ending condition at the top of the loop. This sometimes leads to some awkward code when you need to do some setup before performing the check. Here’s a snippet from a multiple-choice quiz program that asks the user to answer a question with one of several valid answers:

This works but has an unfortunate repetition of two identical input() lines. It’s necessary to get at least one answer from the user before checking whether it’s valid or not. You then have a second call to input() inside the while loop to ask for a second answer in case the original user_answer wasn’t valid.

If you want to make your code more maintainable, it’s quite common to rewrite this kind of logic with a while True loop. Instead of making the check part of the main while statement, the check is performed later in the loop together with an explicit break :

This has the advantage of avoiding the repetition. However, the actual check is now harder to spot.

Assignment expressions can simplify these kinds of loops. In this example, you can now put the check back together with while where it makes more sense:

The while statement is a bit denser, but the code now communicates the intent more clearly without repeated lines or seemingly infinite loops.

You can expand the box below to see the full code of the multiple-choice quiz program and try a couple of questions about the walrus operator yourself.

Full source code of multiple-choice quiz program Show/Hide

This script runs a multiple-choice quiz. You’ll be asked each of the questions in order, but the order of answers will be shuffled each time:

Note that the first answer is assumed to be the correct one, while the others serve as distractors. You can add more questions to the quiz yourself. Feel free to share your questions with the community in the comments section below the tutorial!

See Build a Quiz Application With Python if you want to dive deeper into using Python to quiz yourself or your friends. You can also quiz yourself on your knowledge of the walrus operator:

You can often simplify while loops by using assignment expressions. The original PEP shows an example from the standard library that makes the same point.

In the examples you’ve seen so far, the := assignment expression operator does essentially the same job as the = assignment operator in your old code. You’ve seen how to simplify code, and now you’ll learn about a different type of use case that this operator makes possible.

In this section, you’ll learn how you can find witnesses when calling any() by using a clever trick that isn’t immediately possible without using the walrus operator. A witness, in this context, is an element that satisfies the check and causes any() to return True .

By applying similar logic, you’ll also learn how you can find counterexamples when working with all() . A counterexample, in this context, is an element that doesn’t satisfy the check and causes all() to return False .

In order to have some data to work with, define the following list of city names:

You can use any() and all() to answer questions about your data:

In each of these cases, any() and all() give you plain True or False answers. What if you’re also interested in seeing an example or a counterexample of the city names? It could be nice to see what’s causing your True or False result:

Does any city name start with "B" ?

Yes, because "Berlin" starts with "B" .

Do all city names start with "B" ?

No, because "Oslo" doesn’t start with "B" .

In other words, you want a witness or a counterexample to justify the answer.

Capturing a witness to an any() expression has not been intuitive in earlier versions of Python. If you were calling any() on a list and then realized you also wanted a witness, you’d typically need to rewrite your code:

Here, you first capture all city names that start with "B" . Then, if there’s at least one such city name, you print out the first city name starting with "B" . Note that here you’re actually not using any() even though you’re doing a similar operation with the list comprehension.

By using the := operator, you can find witnesses directly in your any() expressions:

You can capture a witness inside the any() expression. The reason this works is a bit subtle and relies on any() and all() using short-circuit evaluation : they only check as many items as necessary to determine the result.

Note: If you want to check whether all city names start with the letter "B" , then you can look for a counterexample by replacing any() with all() and updating the print() functions to report the first item that doesn’t pass the check.

You can more clearly see what’s happening by wrapping .startswith("B") in a function that also prints out which item is being checked:

Note that any() doesn’t actually check all the items in cities . It only checks items until it finds one that satisfies the condition. Combining the := operator and any() works by iteratively assigning each item that is being checked to witness . However, only the last such item survives and shows which item was last checked by any() .

Even when any() returns False , a witness is found:

However, in this case, witness doesn’t give any insight. 'Belgrade' doesn’t contain ten or more characters. The witness only shows which item happened to be evaluated last.

One of the main reasons assignments weren’t expressions in Python from the beginning is that the visual similarity of the assignment operator ( = ) and the equality comparison operator ( == ) could potentially lead to bugs.

When introducing assignment expressions, the developers put a lot of thought into how to avoid similar bugs with the walrus operator. As mentioned earlier , one important feature is that the := operator is never allowed as a direct replacement for the = operator, and vice versa.

As you saw at the beginning of this tutorial, you can’t use a plain assignment expression to assign a value:

It’s syntactically legal to use an assignment expression to only assign a value, but you need to add parentheses:

Even though it’s possible, this is a prime example of where you should stay away from the walrus operator and use a traditional assignment statement instead.

PEP 572 shows several other examples where the := operator is either illegal or discouraged. The following examples all cause a SyntaxError :

In all these cases, you’re better served using = instead. The next examples are similar and are all legal code. However, the walrus operator doesn’t improve your code in any of these cases:

None of these examples make your code more readable. You should instead do the extra assignment separately by using a traditional assignment statement. See PEP 572 for more details about the reasoning.

There’s one use case where the := character sequence is already valid Python. In f-strings , you use a colon ( : ) to separate values from their format specification . For example:

The := in this case does look like a walrus operator, but the effect is quite different. To interpret x:=8 inside the f-string, the expression is broken into three parts: x , : , and =8 .

Here, x is the value, : acts as a separator, and =8 is a format specification. According to Python’s Format Specification Mini-Language , in this context = specifies an alignment option. In this case, the value is padded with spaces in a field of width 8 .

To use assignment expressions inside f-strings, you need to add parentheses:

This updates the value of x as expected. However, you’re probably better off using traditional assignments outside of your f-strings instead.

Now, look at some other situations where assignment expressions are illegal:

Attribute and item assignment: You can only assign to simple names, not dotted or indexed names:

This fails with a descriptive error message. There’s no straightforward workaround.

Iterable unpacking: You can’t unpack when using the walrus operator:

If you add parentheses around the whole expression, then Python will interpret it as a 3-tuple with the three elements lat , 59.9 , and 10.8 .

Augmented assignment: You can’t use the walrus operator combined with augmented assignment operators like += . This raises a SyntaxError :

The easiest workaround would be to do the augmentation explicitly. You could, for example, do (count := count + 1) . PEP 577 originally described how to add augmented assignment expressions to Python, but the proposal was withdrawn.

When you’re using the walrus operator, it’ll behave similarly to traditional assignment statements in many respects:

The scope of the assignment target is the same as for assignments. It’ll follow the LEGB rule . Typically, the assignment will happen in the local scope, but if the target name is already declared global or nonlocal , that declaration is honored.

The precedence of the walrus operator can cause some confusion. It binds less tightly than all other operators except the comma, so you might need parentheses to delimit the expression that you’re assigning. As an example, note what happens when you don’t use parentheses:

square is bound to the whole expression number ** 2 > 5 . In other words, square gets the value True and not the value of number ** 2 , which was the intention. In this case, you can delimit the expression with parentheses:

The parentheses make the if statement both clearer and actually correct.

There’s one final gotcha. When assigning a tuple using the walrus operator, you always need to use parentheses around the tuple. Compare the following assignments:

Note that in the second example, walrus takes the value 3.8 and not the whole tuple 3.8, True . That’s because the := operator binds more tightly than the comma. This may seem a bit annoying. However, if the := operator bound less tightly than the comma, then it wouldn’t be possible to use the walrus operator in function calls with more than one argument.

The style recommendations for the walrus operator are mostly the same as for the = operator used for assignment. First, always add spaces around the := operator in your code. Second, use parentheses around the expression as necessary, but avoid adding extra parentheses that you don’t need.

The general design of assignment expressions is to make them easy to use when they’re helpful but to avoid overusing them when they might clutter up your code.

The walrus operator is a newer syntax that’s only available in Python 3.8 and later. This means that any code you write that uses the := syntax will only work on these versions of Python.

If you need to support legacy versions of Python, then you can’t ship code that uses assignment expressions. As you’ve learned in this tutorial, you can always write code without the walrus operator and stay compatible with older versions.

Experience with the walrus operator indicates that := will not revolutionize Python. Instead, using assignment expressions where they’re useful can help you make several small improvements to your code that could benefit your work overall.

You’ll run into several situations where it’s possible for you to use the walrus operator, but it won’t necessarily improve the readability or efficiency of your code. In those cases, you’re better off writing your code in a more traditional manner.

You now know how the walrus operator works and how you can use it in your own code. By using the := syntax, you can avoid different kinds of repetition in your code and make your code both more efficient and easier to read and maintain. At the same time, you shouldn’t use assignment expressions everywhere. They’ll only help you in specific use cases.

In this tutorial, you learned how to:

To learn more about the details of assignment expressions, see PEP 572 . You can also check out the PyCon 2019 talk PEP 572: The Walrus Operator , where Dustin Ingram gives an overview of both the walrus operator and the discussion around the PEP.

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Geir Arne is an avid Pythonista and a member of the Real Python tutorial team.

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How to Use Conditional Statements in Python – Examples of if, else, and elif

By Oluseye Jeremiah

Conditional statements are an essential part of programming in Python. They allow you to make decisions based on the values of variables or the result of comparisons.

In this article, we'll explore how to use if, else, and elif statements in Python, along with some examples of how to use them in practice.

How to Use the if Statement in Python

The if statement allows you to execute a block of code if a certain condition is true. Here's the basic syntax:

The condition can be any expression that evaluates to a Boolean value (True or False). If the condition is True, the code block indented below the if statement will be executed. If the condition is False, the code block will be skipped.

Here's an example of how to use an if statement to check if a number is positive:

In this example, we use the > operator to compare the value of num to 0. If num is greater than 0, the code block indented below the if statement will be executed, and the message "The number is positive." will be printed.

How to Use the else Statement in Python

The else statement allows you to execute a different block of code if the if condition is False. Here's the basic syntax:

If the condition is True, the code block indented below the if statement will be executed, and the code block indented below the else statement will be skipped.

If the condition is False, the code block indented below the else statement will be executed, and the code block indented below the if statement will be skipped.

Here's an example of how to use an if-else statement to check if a number is positive or negative:

In this example, we use an if-else statement to check if num is greater than 0. If it is, the message "The number is positive." is printed. If it is not (that is, num is negative or zero), the message "The number is negative." is printed.

How to Use the elif Statement in Python

The elif statement allows you to check multiple conditions in sequence, and execute different code blocks depending on which condition is true. Here's the basic syntax:

The elif statement is short for "else if", and can be used multiple times to check additional conditions.

Here's an example of how to use an if-elif-else statement to check if a number is positive, negative, or zero:

Use Cases For Conditional Statements

Example 1: checking if a number is even or odd..

In this example, we use the modulus operator (%) to check if num is evenly divisible by 2.

If the remainder of num divided by 2 is 0, the condition num % 2 == 0 is True, and the code block indented below the if statement will be executed. It will print the message "The number is even."

If the remainder is not 0, the condition is False, and the code block indented below the else statement will be executed, printing the message "The number is odd."

Example 2: Assigning a letter grade based on a numerical score

In this example, we use an if-elif-else statement to assign a letter grade based on a numerical score.

The if statement checks if the score is greater than or equal to 90. If it is, the grade is set to "A". If not, the first elif statement checks if the score is greater than or equal to 80. If it is, the grade is set to "B". If not, the second elif statement checks if the score is greater than or equal to 70, and so on. If none of the conditions are met, the else statement assigns the grade "F".

Example 3: Checking if a year is a leap year

In this example, we use nested if statements to check if a year is a leap year. A year is a leap year if it is divisible by 4, except for years that are divisible by 100 but not divisible by 400.

The outer if statement checks if year is divisible by 4. If it is, the inner if statement checks if it is also divisible by 100. If it is, the innermost if statement checks if it is divisible by 400. If it is, the code block indented below that statement will be executed, printing the message "is a leap year."

If it is not, the code block indented below the else statement inside the inner if statement will be executed, printing the message "is not a leap year.".

If the year is not divisible by 4, the code block indented below the else statement of the outer if statement will be executed, printing the message "is not a leap year."

Example 4: Checking if a string contains a certain character

In this example, we use the in operator to check if the character char is present in the string string. If it is, the condition char in string is True, and the code block indented below the if statement will be executed, printing the message "The string contains the character" followed by the character itself.

If char is not present in string, the condition is False, and the code block indented below the else statement will be executed, printing the message "The string does not contain the character" followed by the character itself.

Conditional statements (if, else, and elif) are fundamental programming constructs that allow you to control the flow of your program based on conditions that you specify. They provide a way to make decisions in your program and execute different code based on those decisions.

In this article, we have seen several examples of how to use these statements in Python, including checking if a number is even or odd, assigning a letter grade based on a numerical score, checking if a year is a leap year, and checking if a string contains a certain character.

By mastering these statements, you can create more powerful and versatile programs that can handle a wider range of tasks and scenarios.

It is important to keep in mind that proper indentation is crucial when using conditional statements in Python, as it determines which code block is executed based on the condition.

With practice, you will become proficient in using these statements to create more complex and effective Python programs.

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Assignment Operators in Python

The Python Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, and bitwise computations. The value the operator operates on is known as the Operand. Here, we will cover Different Assignment operators in Python .


=	Assign the value of the right side of the expression to the left side operand	c = a + b
+=	Add right side operand with left side operand and then assign the result to left operand	a += b
-=	Subtract right side operand from left side operand and then assign the result to left operand	a -= b
*=	Multiply right operand with left operand and then assign the result to the left operand	a *= b
/=	Divide left operand with right operand and then assign the result to the left operand	a /= b
%=	Divides the left operand with the right operand and then assign the remainder to the left operand	a %= b
//=	Divide left operand with right operand and then assign the value(floor) to left operand	a //= b
**=	Calculate exponent(raise power) value using operands and then assign the result to left operand	a **= b
&=	Performs Bitwise AND on operands and assign the result to left operand	a &= b
\|=	Performs Bitwise OR on operands and assign the value to left operand	a \|= b
^=	Performs Bitwise XOR on operands and assign the value to left operand	a ^= b
>>=	Performs Bitwise right shift on operands and assign the result to left operand	a >>= b
<<=	Performs Bitwise left shift on operands and assign the result to left operand	a <<= b
:=	Assign a value to a variable within an expression	a := exp

Here are the Assignment Operators in Python with examples.

Assignment Operator

Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand.

Addition Assignment Operator

The Addition Assignment Operator is used to add the right-hand side operand with the left-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the addition assignment operator which will first perform the addition operation and then assign the result to the variable on the left-hand side.

S ubtraction Assignment Operator

The Subtraction Assignment Operator is used to subtract the right-hand side operand from the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the subtraction assignment operator which will first perform the subtraction operation and then assign the result to the variable on the left-hand side.

M ultiplication Assignment Operator

The Multiplication Assignment Operator is used to multiply the right-hand side operand with the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the multiplication assignment operator which will first perform the multiplication operation and then assign the result to the variable on the left-hand side.

D ivision Assignment Operator

The Division Assignment Operator is used to divide the left-hand side operand with the right-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the division assignment operator which will first perform the division operation and then assign the result to the variable on the left-hand side.

M odulus Assignment Operator

The Modulus Assignment Operator is used to take the modulus, that is, it first divides the operands and then takes the remainder and assigns it to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the modulus assignment operator which will first perform the modulus operation and then assign the result to the variable on the left-hand side.

F loor Division Assignment Operator

The Floor Division Assignment Operator is used to divide the left operand with the right operand and then assigs the result(floor value) to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the floor division assignment operator which will first perform the floor division operation and then assign the result to the variable on the left-hand side.

Exponentiation Assignment Operator

The Exponentiation Assignment Operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the exponentiation assignment operator which will first perform exponent operation and then assign the result to the variable on the left-hand side.

Bitwise AND Assignment Operator

The Bitwise AND Assignment Operator is used to perform Bitwise AND operation on both operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise AND assignment operator which will first perform Bitwise AND operation and then assign the result to the variable on the left-hand side.

Bitwise OR Assignment Operator

The Bitwise OR Assignment Operator is used to perform Bitwise OR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise OR assignment operator which will first perform bitwise OR operation and then assign the result to the variable on the left-hand side.

Bitwise XOR Assignment Operator

The Bitwise XOR Assignment Operator is used to perform Bitwise XOR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise XOR assignment operator which will first perform bitwise XOR operation and then assign the result to the variable on the left-hand side.

Bitwise Right Shift Assignment Operator

The Bitwise Right Shift Assignment Operator is used to perform Bitwise Right Shift Operation on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise right shift assignment operator which will first perform bitwise right shift operation and then assign the result to the variable on the left-hand side.

Bitwise Left Shift Assignment Operator

The Bitwise Left Shift Assignment Operator is used to perform Bitwise Left Shift Opertator on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise left shift assignment operator which will first perform bitwise left shift operation and then assign the result to the variable on the left-hand side.

Walrus Operator

The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression.

Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop . The operator will solve the expression on the right-hand side and assign the value to the left-hand side operand ‘x’ and then execute the remaining code.

Assignment Operators in Python – FAQs

What are assignment operators in python.

Assignment operators in Python are used to assign values to variables. These operators can also perform additional operations during the assignment. The basic assignment operator is = , which simply assigns the value of the right-hand operand to the left-hand operand. Other common assignment operators include += , -= , *= , /= , %= , and more, which perform an operation on the variable and then assign the result back to the variable.

What is the := Operator in Python?

The := operator, introduced in Python 3.8, is known as the “walrus operator”. It is an assignment expression, which means that it assigns values to variables as part of a larger expression. Its main benefit is that it allows you to assign values to variables within expressions, including within conditions of loops and if statements, thereby reducing the need for additional lines of code. Here’s an example: # Example of using the walrus operator in a while loop while (n := int(input("Enter a number (0 to stop): "))) != 0: print(f"You entered: {n}") This loop continues to prompt the user for input and immediately uses that input in both the condition check and the loop body.

What is the Assignment Operator in Structure?

In programming languages that use structures (like C or C++), the assignment operator = is used to copy values from one structure variable to another. Each member of the structure is copied from the source structure to the destination structure. Python, however, does not have a built-in concept of ‘structures’ as in C or C++; instead, similar functionality is achieved through classes or dictionaries.

What is the Assignment Operator in Python Dictionary?

In Python dictionaries, the assignment operator = is used to assign a new key-value pair to the dictionary or update the value of an existing key. Here’s how you might use it: my_dict = {} # Create an empty dictionary my_dict['key1'] = 'value1' # Assign a new key-value pair my_dict['key1'] = 'updated value' # Update the value of an existing key print(my_dict) # Output: {'key1': 'updated value'}

What is += and -= in Python?

The += and -= operators in Python are compound assignment operators. += adds the right-hand operand to the left-hand operand and assigns the result to the left-hand operand. Conversely, -= subtracts the right-hand operand from the left-hand operand and assigns the result to the left-hand operand. Here are examples of both: # Example of using += a = 5 a += 3 # Equivalent to a = a + 3 print(a) # Output: 8 # Example of using -= b = 10 b -= 4 # Equivalent to b = b - 4 print(b) # Output: 6 These operators make code more concise and are commonly used in loops and iterative data processing.

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Assignment operators are used to assign values to variables:

Operator	Example	Same As
=	x = 5	x = 5
+=	x += 3	x = x + 3
-=	x -= 3	x = x - 3
*=	x *= 3	x = x * 3
/=	x /= 3	x = x / 3
%=	x %= 3	x = x % 3
//=	x //= 3	x = x // 3
**=	x **= 3	x = x ** 3
&=	x &= 3	x = x & 3
\|=	x \|= 3	x = x \| 3
^=	x ^= 3	x = x ^ 3
>>=	x >>= 3	x = x >> 3
<<=	x <<= 3	x = x << 3

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Is it possible to make multiple assignments using a conditional expression?

Here is the example:

Is it possible? Something like:

I am still having problems with understanding ternary operator in Python.

ternary-operator
conditional-operator

The syntax you requested is not possible. The conditional expression syntax is true-expression if condition else false-expression . – Terry Jan Reedy Commented Jun 1, 2017 at 21:55
@TerryJanReedy Well, you can actually bind names with expressions. globals().__setitem__('st', 'Kid') , for example. – wim Commented Jun 1, 2017 at 22:03
Just use the if-else statement. There is no good reason to try to cram your code into a single line, especially if it requires weird, unidiomatic usage of language constructs not meant to achieve that. – juanpa.arrivillaga Commented Jun 1, 2017 at 22:17

3 Answers 3

Assignment statements support multiple targets :

is there another possible way??? assuming that if kid < 20, if less than 20 ONLY assign "rejected=True" and for adult assign st="Adult" and Rejected=False – metazord Commented Jun 1, 2017 at 21:55
2 Technically possible, but ugly. Just use an if statement and stop trying to be so fancy. – wim Commented Jun 1, 2017 at 21:59

You could do it like this:

is there another possible way??? assuming that if kid < 20, if less than 20 ONLY assign "rejected=True" and for adult st="Adult" and Rejected=False – metazord Commented Jun 1, 2017 at 21:53
other than st, reject = (None, True) if age < 20 else ('Adult', False) i can't think of anything off the top of my head – rammelmueller Commented Jun 1, 2017 at 21:58

You can use:

When you use:

You are doing the same as:

And when you use:

Your are doing the same as:

If you want with this define multiples variables you have to make a tuple () with the variables and they will be unpacked.

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COMMENTS

Python Conditional Assignment (in 3 Ways)
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.
Python conditional assignment operator
There is conditional assignment in Python 2.5 and later - the syntax is not very obvious hence it's easy to miss. Here's how you do it: x = true_value if condition else false_value For further reference, check out the Python 2.5 docs.
Python's Assignment Operator: Write Robust Assignments
Use Python's assignment operator to write assignment statements; Take advantage of augmented assignments in Python; ... Because the assignment expression returns the sample size anyway, the conditional can check whether that size equals 0 or not and then take a certain course of action depending on the result of this check.
Conditional Statements in Python
In the form shown above: <expr> is an expression evaluated in a Boolean context, as discussed in the section on Logical Operators in the Operators and Expressions in Python tutorial. <statement> is a valid Python statement, which must be indented. (You will see why very soon.) If <expr> is true (evaluates to a value that is "truthy"), then <statement> is executed.
Ternary Operator in Python
The ternary operator in Python is a one-liner conditional expression that assigns a value based on a condition. It is written as value_if_true if condition else value_if_false. a = 5 ... A ternary is a concise way to perform a conditional assignment in a single line. In the context of programming, it typically refers to a ternary operator ...
Conditional Statements in Python
The most common conditional statements in Python are if, elif, and else. These allow the program to react differently depending on whether a condition (or a series of conditions) is true or false. x = 10. if x > 5: print("x is greater than 5") elif x == 5: print("x is equal to 5") else: print("x is less than 5")
Python Ternary: How to Use It and Why It's Useful (with Examples)
If the standard syntax for the Python ternary operator is a if condition else b, here we would re-write it as (b, a)[condition], like this: t = 90 ('Water is not boiling', 'Water is boiling')[t >= 100] 'Water is not boiling'. In the syntax above, the first item of the tuple is the value that will be returned if the condition evaluates to False ...
Conditional Assignment Operator in Python
Meaning of ||= Operator in Ruby. x ||= y. The basic meaning of this operator is to assign the value of the variable y to variable x if variable x is undefined or is falsy value, otherwise no assignment operation is performed. But this operator is much more complex and confusing than other simpler conditional operators like +=, -= because ...
Conditional expression (ternary operator) in Python
Basics of the conditional expression (ternary operator) In Python, the conditional expression is written as follows. The condition is evaluated first. If condition is True, X is evaluated and its value is returned, and if condition is False, Y is evaluated and its value is returned. If you want to switch the value based on a condition, simply ...
Conditional Statements
In its simplest form, a conditional statement requires only an if clause. else and elif clauses can only follow an if clause. # A conditional statement consisting of # an "if"-clause, only. x = -1 if x < 0: x = x ** 2 # x is now 1. Similarly, conditional statements can have an if and an else without an elif:
Python Conditional Operator : Syntax and Examples
Here are some more examples of Nested Python conditional operators with explanation and code: Example 1: Check if a number is divisible by both 2 and 3. Below is the code implementation and explanation. num = 6. result = "Divisible by both" if num % 2 == 0 and num % 3 == 0 else "Not divisible by both".
Does Python have a ternary conditional operator?
This means you can't use assignment statements or pass or other statements within a conditional expression. Walrus operator in Python 3.8. After the walrus operator was introduced in Python 3.8, something changed. (a := 3) if True else (b := 5) gives a = 3 and b is not defined, (a := 3) if False else (b := 5) gives a is not defined and b = 5, and
Python Ternary Operator
Basic Examples of the Ternary Operator in Python. In most cases, the Python ternary operator gets used for conditional assignments or function returns. Let's start with some basic examples: Conditional Assignment. Here we assign the value of x based on a condition: x = 1 if 2 > 1 else 0 print(x) # Prints 1
Python Conditional Assignment Operator in Python 3
The Python Conditional Assignment Operator, also known as the "walrus operator," was introduced in Python 3.8. It allows you to assign a value to a variable based on a condition in a single line of code. This operator is denoted by ":=" and can be used in various scenarios to simplify your code and make it more readable. ...
6. Expressions
Conditional expressions (sometimes called a "ternary operator") have the lowest priority of all Python operations. The expression x if C else y first evaluates the condition, C rather than x . If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.
The Walrus Operator: Python's Assignment Expressions
Each new version of Python adds new features to the language. Back when Python 3.8 was released, the biggest change was the addition of assignment expressions.Specifically, the := operator gave you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator.. This tutorial is an in-depth introduction to the walrus operator.
How to Use Conditional Statements in Python
In this example, we use the > operator to compare the value of num to 0. If num is greater than 0, ... It is important to keep in mind that proper indentation is crucial when using conditional statements in Python, as it determines which code block is executed based on the condition.
Assignment Operators in Python
The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression. Syntax: a := expression. Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop.
python
Starting Python 3.8, and the introduction of assignment expressions (PEP 572) (:= operator), it's now possible to capture the condition value (isBig(y)) as a variable (x) in order to re-use it within the body of the condition: if x := isBig(y): return x. edited Jan 8, 2023 at 14:33. answered Apr 27, 2019 at 15:37. Xavier Guihot. 60.4k 24 310 198.
Python Assignment Operators
Python Assignment Operators. Assignment operators are used to assign values to variables: Operator. Example. Same As. Try it. =. x = 5. x = 5.
python
SyntaxError: cannot use assignment expressions with conditional expression Can assignment expressions not be used in this way? If not, what is wrong with my assumption: I was under the impression the idea is to pre-assign a dummy value to an expensive operation in a single expression. ... Python conditional assignment operator-1. Assignment in ...
python
Python conditional assignment operator. 1. python ternary operator with assignment. 10. Using statements on either side of a python ternary conditional. 0. if as a ternary operator python. 6. Assign two variables with ternary operator. 3. Conditional expression/ternary operator. 0.