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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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Home » Python Basics » Python Ternary Operator

Python Ternary Operator

Summary : in this tutorial, you’ll learn about the Python ternary operator and how to use it to make your code more concise.

Introduction to Python Ternary Operator

The following program prompts you for your age and determines the ticket price based on it:

Here is the output when you enter 18:

In this example, the following if...else statement assigns 20 to the ticket_price if the age is greater than or equal to 18. Otherwise, it assigns the ticket_price 5:

To make it more concise, you can use an alternative syntax like this:

In this statement, the left side of the assignment operator ( = ) is the variable ticket_price .

The expression on the right side returns 20 if the age is greater than or equal to 18 or 5 otherwise.

The following syntax is called a ternary operator in Python:

The ternary operator evaluates the condition . If the result is True , it returns the value_if_true . Otherwise, it returns the value_if_false .

The ternary operator is equivalent to the following if...else statement:

Note that you have been programming languages such as C# or Java, and you’re familiar with the following ternary operator syntax:

However, Python doesn’t support this ternary operator syntax.

The following program uses the ternary operator instead of the if statement:

• The Python ternary operator is value_if_true if condition else value_if_false .
• Use the ternary operator to make your code more concise.

Conditional Statements in Python

Table of Contents

Introduction to the if Statement

Python: it’s all about the indentation, what do other languages do, which is better, the else and elif clauses, one-line if statements, conditional expressions (python’s ternary operator), the python pass statement.

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: Conditional Statements in Python (if/elif/else)

From the previous tutorials in this series, you now have quite a bit of Python code under your belt. Everything you have seen so far has consisted of sequential execution , in which statements are always performed one after the next, in exactly the order specified.

But the world is often more complicated than that. Frequently, a program needs to skip over some statements, execute a series of statements repetitively, or choose between alternate sets of statements to execute.

That is where control structures come in. A control structure directs the order of execution of the statements in a program (referred to as the program’s control flow ).

Here’s what you’ll learn in this tutorial: You’ll encounter your first Python control structure, the if statement.

In the real world, we commonly must evaluate information around us and then choose one course of action or another based on what we observe:

If the weather is nice, then I’ll mow the lawn. (It’s implied that if the weather isn’t nice, then I won’t mow the lawn.)

In a Python program, the if statement is how you perform this sort of decision-making. It allows for conditional execution of a statement or group of statements based on the value of an expression.

The outline of this tutorial is as follows:

• First, you’ll get a quick overview of the if statement in its simplest form.
• Next, using the if statement as a model, you’ll see why control structures require some mechanism for grouping statements together into compound statements or blocks . You’ll learn how this is done in Python.
• Lastly, you’ll tie it all together and learn how to write complex decision-making code.

Ready? Here we go!

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

Interactive Quiz

Test your understanding of Python conditional statements

We’ll start by looking at the most basic type of if statement. In its simplest form, it looks like this:

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. If <expr> is false, then <statement> is skipped over and not executed.

Note that the colon ( : ) following <expr> is required. Some programming languages require <expr> to be enclosed in parentheses, but Python does not.

Here are several examples of this type of if statement:

Note: If you are trying these examples interactively in a REPL session, you’ll find that, when you hit Enter after typing in the print('yes') statement, nothing happens.

Because this is a multiline statement, you need to hit Enter a second time to tell the interpreter that you’re finished with it. This extra newline is not necessary in code executed from a script file.

Grouping Statements: Indentation and Blocks

So far, so good.

But let’s say you want to evaluate a condition and then do more than one thing if it is true:

If the weather is nice, then I will: Mow the lawn Weed the garden Take the dog for a walk (If the weather isn’t nice, then I won’t do any of these things.)

In all the examples shown above, each if <expr>: has been followed by only a single <statement> . There needs to be some way to say “If <expr> is true, do all of the following things.”

The usual approach taken by most programming languages is to define a syntactic device that groups multiple statements into one compound statement or block . A block is regarded syntactically as a single entity. When it is the target of an if statement, and <expr> is true, then all the statements in the block are executed. If <expr> is false, then none of them are.

Virtually all programming languages provide the capability to define blocks, but they don’t all provide it in the same way. Let’s see how Python does it.

Python follows a convention known as the off-side rule , a term coined by British computer scientist Peter J. Landin. (The term is taken from the offside law in association football.) Languages that adhere to the off-side rule define blocks by indentation. Python is one of a relatively small set of off-side rule languages .

Recall from the previous tutorial on Python program structure that indentation has special significance in a Python program. Now you know why: indentation is used to define compound statements or blocks. In a Python program, contiguous statements that are indented to the same level are considered to be part of the same block.

Thus, a compound if statement in Python looks like this:

Here, all the statements at the matching indentation level (lines 2 to 5) are considered part of the same block. The entire block is executed if <expr> is true, or skipped over if <expr> is false. Either way, execution proceeds with <following_statement> (line 6) afterward.

Notice that there is no token that denotes the end of the block. Rather, the end of the block is indicated by a line that is indented less than the lines of the block itself.

Note: In the Python documentation, a group of statements defined by indentation is often referred to as a suite . This tutorial series uses the terms block and suite interchangeably.

Consider this script file foo.py :

Running foo.py produces this output:

The four print() statements on lines 2 to 5 are indented to the same level as one another. They constitute the block that would be executed if the condition were true. But it is false, so all the statements in the block are skipped. After the end of the compound if statement has been reached (whether the statements in the block on lines 2 to 5 are executed or not), execution proceeds to the first statement having a lesser indentation level: the print() statement on line 6.

Blocks can be nested to arbitrary depth. Each indent defines a new block, and each outdent ends the preceding block. The resulting structure is straightforward, consistent, and intuitive.

Here is a more complicated script file called blocks.py :

The output generated when this script is run is shown below:

Note: In case you have been wondering, the off-side rule is the reason for the necessity of the extra newline when entering multiline statements in a REPL session. The interpreter otherwise has no way to know that the last statement of the block has been entered.

Perhaps you’re curious what the alternatives are. How are blocks defined in languages that don’t adhere to the off-side rule?

The tactic used by most programming languages is to designate special tokens that mark the start and end of a block. For example, in Perl blocks are defined with pairs of curly braces ( {} ) like this:

C/C++, Java , and a whole host of other languages use curly braces in this way.

Other languages, such as Algol and Pascal, use keywords begin and end to enclose blocks.

Better is in the eye of the beholder. On the whole, programmers tend to feel rather strongly about how they do things. Debate about the merits of the off-side rule can run pretty hot.

On the plus side:

• Python’s use of indentation is clean, concise, and consistent.
• In programming languages that do not use the off-side rule, indentation of code is completely independent of block definition and code function. It’s possible to write code that is indented in a manner that does not actually match how the code executes, thus creating a mistaken impression when a person just glances at it. This sort of mistake is virtually impossible to make in Python.
• Use of indentation to define blocks forces you to maintain code formatting standards you probably should be using anyway.

On the negative side:

• Many programmers don’t like to be forced to do things a certain way. They tend to have strong opinions about what looks good and what doesn’t, and they don’t like to be shoehorned into a specific choice.
• Some editors insert a mix of space and tab characters to the left of indented lines, which makes it difficult for the Python interpreter to determine indentation levels. On the other hand, it is frequently possible to configure editors not to do this. It generally isn’t considered desirable to have a mix of tabs and spaces in source code anyhow, no matter the language.

Like it or not, if you’re programming in Python, you’re stuck with the off-side rule. All control structures in Python use it, as you will see in several future tutorials.

For what it’s worth, many programmers who have been used to languages with more traditional means of block definition have initially recoiled at Python’s way but have gotten comfortable with it and have even grown to prefer it.

Now you know how to use an if statement to conditionally execute a single statement or a block of several statements. It’s time to find out what else you can do.

Sometimes, you want to evaluate a condition and take one path if it is true but specify an alternative path if it is not. This is accomplished with an else clause:

If <expr> is true, the first suite is executed, and the second is skipped. If <expr> is false, the first suite is skipped and the second is executed. Either way, execution then resumes after the second suite. Both suites are defined by indentation, as described above.

In this example, x is less than 50 , so the first suite (lines 4 to 5) are executed, and the second suite (lines 7 to 8) are skipped:

Here, on the other hand, x is greater than 50 , so the first suite is passed over, and the second suite executed:

There is also syntax for branching execution based on several alternatives. For this, use one or more elif (short for else if ) clauses. Python evaluates each <expr> in turn and executes the suite corresponding to the first that is true. If none of the expressions are true, and an else clause is specified, then its suite is executed:

An arbitrary number of elif clauses can be specified. The else clause is optional. If it is present, there can be only one, and it must be specified last:

At most, one of the code blocks specified will be executed. If an else clause isn’t included, and all the conditions are false, then none of the blocks will be executed.

Note: Using a lengthy if / elif / else series can be a little inelegant, especially when the actions are simple statements like print() . In many cases, there may be a more Pythonic way to accomplish the same thing.

Here’s one possible alternative to the example above using the dict.get() method:

Recall from the tutorial on Python dictionaries that the dict.get() method searches a dictionary for the specified key and returns the associated value if it is found, or the given default value if it isn’t.

An if statement with elif clauses uses short-circuit evaluation, analogous to what you saw with the and and or operators. Once one of the expressions is found to be true and its block is executed, none of the remaining expressions are tested. This is demonstrated below:

The second expression contains a division by zero, and the third references an undefined variable var . Either would raise an error, but neither is evaluated because the first condition specified is true.

It is customary to write if <expr> on one line and <statement> indented on the following line like this:

But it is permissible to write an entire if statement on one line. The following is functionally equivalent to the example above:

There can even be more than one <statement> on the same line, separated by semicolons:

But what does this mean? There are two possible interpretations:

If <expr> is true, execute <statement_1> .

Then, execute <statement_2> ... <statement_n> unconditionally, irrespective of whether <expr> is true or not.

If <expr> is true, execute all of <statement_1> ... <statement_n> . Otherwise, don’t execute any of them.

Python takes the latter interpretation. The semicolon separating the <statements> has higher precedence than the colon following <expr> —in computer lingo, the semicolon is said to bind more tightly than the colon. Thus, the <statements> are treated as a suite, and either all of them are executed, or none of them are:

Multiple statements may be specified on the same line as an elif or else clause as well:

While all of this works, and the interpreter allows it, it is generally discouraged on the grounds that it leads to poor readability, particularly for complex if statements. PEP 8 specifically recommends against it.

As usual, it is somewhat a matter of taste. Most people would find the following more visually appealing and easier to understand at first glance than the example above:

If an if statement is simple enough, though, putting it all on one line may be reasonable. Something like this probably wouldn’t raise anyone’s hackles too much:

Python supports one additional decision-making entity called a conditional expression. (It is also referred to as a conditional operator or ternary operator in various places in the Python documentation.) Conditional expressions were proposed for addition to the language in PEP 308 and green-lighted by Guido in 2005.

In its simplest form, the syntax of the conditional expression is as follows:

This is different from the if statement forms listed above because it is not a control structure that directs the flow of program execution. It acts more like an operator that defines an expression. In the above example, <conditional_expr> is evaluated first. If it is true, the expression evaluates to <expr1> . If it is false, the expression evaluates to <expr2> .

Notice the non-obvious order: the middle expression is evaluated first, and based on that result, one of the expressions on the ends is returned. Here are some examples that will hopefully help clarify:

Note: Python’s conditional expression is similar to the <conditional_expr> ? <expr1> : <expr2> syntax used by many other languages—C, Perl and Java to name a few. In fact, the ?: operator is commonly called the ternary operator in those languages, which is probably the reason Python’s conditional expression is sometimes referred to as the Python ternary operator.

You can see in PEP 308 that the <conditional_expr> ? <expr1> : <expr2> syntax was considered for Python but ultimately rejected in favor of the syntax shown above.

A common use of the conditional expression is to select variable assignment. For example, suppose you want to find the larger of two numbers. Of course, there is a built-in function, max() , that does just this (and more) that you could use. But suppose you want to write your own code from scratch.

You could use a standard if statement with an else clause:

But a conditional expression is shorter and arguably more readable as well:

Remember that the conditional expression behaves like an expression syntactically. It can be used as part of a longer expression. The conditional expression has lower precedence than virtually all the other operators, so parentheses are needed to group it by itself.

In the following example, the + operator binds more tightly than the conditional expression, so 1 + x and y + 2 are evaluated first, followed by the conditional expression. The parentheses in the second case are unnecessary and do not change the result:

If you want the conditional expression to be evaluated first, you need to surround it with grouping parentheses. In the next example, (x if x > y else y) is evaluated first. The result is y , which is 40 , so z is assigned 1 + 40 + 2 = 43 :

If you are using a conditional expression as part of a larger expression, it probably is a good idea to use grouping parentheses for clarification even if they are not needed.

Conditional expressions also use short-circuit evaluation like compound logical expressions. Portions of a conditional expression are not evaluated if they don’t need to be.

In the expression <expr1> if <conditional_expr> else <expr2> :

• If <conditional_expr> is true, <expr1> is returned and <expr2> is not evaluated.
• If <conditional_expr> is false, <expr2> is returned and <expr1> is not evaluated.

As before, you can verify this by using terms that would raise an error:

In both cases, the 1/0 terms are not evaluated, so no exception is raised.

Conditional expressions can also be chained together, as a sort of alternative if / elif / else structure, as shown here:

It’s not clear that this has any significant advantage over the corresponding if / elif / else statement, but it is syntactically correct Python.

Occasionally, you may find that you want to write what is called a code stub: a placeholder for where you will eventually put a block of code that you haven’t implemented yet.

In languages where token delimiters are used to define blocks, like the curly braces in Perl and C, empty delimiters can be used to define a code stub. For example, the following is legitimate Perl or C code:

Here, the empty curly braces define an empty block. Perl or C will evaluate the expression x , and then even if it is true, quietly do nothing.

Because Python uses indentation instead of delimiters, it is not possible to specify an empty block. If you introduce an if statement with if <expr>: , something has to come after it, either on the same line or indented on the following line.

Consider this script foo.py :

If you try to run foo.py , you’ll get this:

The Python pass statement solves this problem. It doesn’t change program behavior at all. It is used as a placeholder to keep the interpreter happy in any situation where a statement is syntactically required, but you don’t really want to do anything:

Now foo.py runs without error:

With the completion of this tutorial, you are beginning to write Python code that goes beyond simple sequential execution:

• You were introduced to the concept of control structures . These are compound statements that alter program control flow —the order of execution of program statements.
• You learned how to group individual statements together into a block or suite .
• You encountered your first control structure, the if statement, which makes it possible to conditionally execute a statement or block based on evaluation of program data.

All of these concepts are crucial to developing more complex Python code.

The next two tutorials will present two new control structures: the while statement and the for statement. These structures facilitate iteration , execution of a statement or block of statements repeatedly.

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Python Ternary Operator - If Else One Line

In this tutorial, we will learn about the Python ternary operator. Moreover, we will look at its syntax, usage, and examples.

Table Of Contents - Python Ternary Operator

Ternary operator syntax

• python ternary operator example
• python ternary assignment
• Nested ternary operator
• python ternary operator multiple conditions
• python ternary operator without else

Ternary Operator In Python

The ternary operator in Python is a conditional operator that has three operands. The ternary operator is used to evaluate a condition and return either of the two values depending on the condition.

The ternary operator is just like a python if else statement but more clean, concise, and easy to understand.

The syntax of Python ternary operator is as follows:

The 'condition' is the condition that is to be evaluated. The 'expression1' and 'expression2' are the expressions that are evaluated based on the condition.

The first condition is evaluated. If the condition is true, then the 'expression1' is evaluated and the value is returned. Otherwise, the 'expression2' is evaluated and the value is returned.

Note : Python ternary operator is the same as condition ? expression1 : expression2 in other programming languages like C, C++, JavaScript, etc.

Python Ternary Operator Example

The following example shows the usage of the ternary operator in python and also compares its code with the if-else statement.

• Ternary Operator

Here is another simple example of a ternary operator.

a is less than b

The above example compares the value of a and b, if a is greater than b then it prints "a is greater than b" otherwise it prints "a is less than b".

Python Ternary Assignment

The ternary operator is mostly used in assigning values to variables . When you have to decide different values of a variable based on the condition, then you can use the ternary operator.

Using a ternary operator for assigning values also makes the code more readable and concise.

Nested Ternary Operator

The ternary operator can also be nested. This means you can have a ternary operator inside another ternary operator.

A nested ternary operator is used to evaluate results based on multiple conditions.

Code explanation: Return the value of a if a is greater than b [ a if a > b else (b if a < b else a) ] , else return the value of b if a is less than b, else return the value of a [ b if a < b else a ].

Here is an example of 3 nested ternary operators.

Python Ternary Operator Multiple Conditions

You can also use multiple conditions in a ternary operator by using logical operator keywords like and , or , not

Separate as many conditions with logical operators like and , or , not and wrap them in parenthesis.

The above code checks if a and b are divisible by 2, 3, and 5 using multiple conditions. If all conditions are true then it prints "Yes" otherwise it prints "No".

Python Ternary Operator Without Else

Python does not allow using ternary operator without else.

But you can create similar functionality by using the and operator.

Here is an example of that:

Code explanation

• The above code has the format of True/False and string
• If the first statement is false then the and operator will return False and the second statement will not be executed.
• If the first statement is true then the and operator will return second statement.

Python Ternary Operator Conclusion

Ternary operator in python is an alternate way of writing if-else which is concise and simple.

It first evaluates the given condition, then executes expression based on the condition then returns the value.

The ternary operator is not always useful. It is helpful when you have only one statement to execute after the condition is evaluated.

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Python Ternary Operator: How and Why You Should Use It

Writing concise, practical, organized, and intelligible code should be a top priority for any Python developer. Enter, the Python ternary operator.

What is this? I hear you ask. Well, you can use the ternary operator to test a condition and then execute a conditional assignment with only one line of code. And why is this so great? Well, this means we can replace those ever-popular if-else statements with a quicker and more practical way to code conditional assignments.

Now don’t get me wrong, we still need if-else statements (check out our comprehensive guide and Python cheat sheet for the best way to use conditional statements). But, it’s always good practice to make your code more Pythonic when there’s a simpler way to get the same result, and that’s exactly what you get with the ternary operator. 

• Why Use the Python Ternary Operator?

Ternary operators (also known as conditional operators) in Python perform evaluations and assignments after checking whether a given condition is true or false. This means that the ternary operator is a bit like a simplified, one-line if-else statement. Cool, right?

When used correctly, this operator reduces code size and improves readability .

Ternary Operator Syntax

A ternary operator takes three operands: 

• condition:  A Boolean expression that evaluates to true or false
• true_value:  A value or expression to be assigned if the condition evaluates to true
• false_value:  A value or expression to be assigned if the condition evaluates to false

This is how it should look when we put it all together:

So, this means that the variable "var" on the left side of the assignment operator (=), will be assigned either:

• true_value if the Boolean expression evaluates to true
• false_value if the Boolean expression evaluates to false
• How to Use the Ternary Operator Instead of If-Else

To better understand how we replace an if-else statement with the ternary operator in Python, let’s write a simple program with each approach. In this first example, we’ll assume the user enters an integer number when prompted, and we’ll start with the familiar if-else.

Program Using an If-Else Statement

Here’s what we get when the user enters 22 at the input prompt:

In this example, the if-else statement assigns “Yes, you are old enough to watch this movie!” to the movie_acess variable if the user enters an age that is greater than or equal to 18, otherwise, it assigns "Sorry, you aren't old enough to watch this movie yet!”. Finally, we use an f-string to print out the result to the screen.

Now, let's use the ternary operator syntax to make the program much more concise.

Program Using a Ternary Operator

In this version of the program, the variable that receives the result of the conditional statement (movie_access) is to the left of the assignment operator (=).

The ternary operator evaluates the condition:

If the condition evaluates to true, the program assigns “Yes, you are old enough to watch this movie!” to movie_access, else it assigns “Sorry, you aren't old enough to watch this movie yet!”.

Let’s take a look at another example that uses the Python ternary operator and if-else to achieve the same goal. This time, we’ll write a simple Python code snippet to check whether a given integer is even or odd.

In this program, we’ve used a ternary operator and an if-else statement block to determine whether the given integer produces a zero remainder when using modulo divide by 2 (if you’re unfamiliar with this operator, take a look at our cheat sheet). If the remainder is zero, we have an even number, otherwise, we have an odd number. 

Right away, we can tell what the if-else statement will do after evaluating the condition. With the ternary operator snippet, we can see the following:

• "Even" is assigned to msg if the condition (given_int % 2 == 0) is true
• "Odd" is assigned to msg if the condition (given_int % 2 == 0) is false

And, as we’ve set the given integer to an even number, the program will print “Even” to the screen. 

So, we can see again that a ternary conditional statement is a much cleaner and more concise way to achieve the same outcome as an if-else statement. We simply evaluate the ternary expression from left to right, then assign the return value for the true or false condition.

• Key Considerations for Python Ternary Statements

The ternary operator is not always suitable to replace if-else in your code.

If for example, we have more than two conditional branches with an if-elif-else statement, we can’t replace this with a single ternary operator. The clue here is in the name, as ternary refers to the number ‘three’, meaning that it expects three operands. But, if we try to replace an if-elif-else statement with a ternary operator, we’ll have four operands to handle (or possibly more if we have many ‘elif’ branches).

So what do we do here? Well, we need to chain together (or nest) multiple ternary operators (we cover this in the FAQs), but sometimes this can get a little messy, and it may be cleaner to use if-elif-else if the code becomes difficult to read.

Another thing to keep in mind is that we’re meant to use the ternary operator for conditional assignment. What does this mean? Well, we can’t use the ternary approach if we want to execute blocks of code after evaluating a conditional expression: in these situations, it’s best to stick with if-else.

But, we can use the ternary operator if we are assigning something to a variable after checking against a condition. See, it’s easy if we remember that we’re supposed to be assigning something to something else.

Distinct Features of Python Ternary Statements

• Returns A or B depending on the Boolean result of the conditional expression (a < b)
• Compared with C-type languages(C/C++), it uses a different ordering of the provided arguments
• Conditional expressions have the lowest priority of all Python operations

That's all. We did our best to fill you in on everything there is to know about the Python ternary operator. We also covered how to use ternary statements in your Python code, and we compared them to their close relatives, the ever-popular if-else statement.

If you’re a beginner that’s looking for an easy start to your Python programming career, check out our learning guide along with our up-to-date list of Python courses where you can easily enroll online.

Lastly, don’t forget that you can also check out our comprehensive Python cheat sheet which covers various topics, including Python basics, flow control, Modules in Python, functions, exception handling, lists, dictionaries, and data structures, sets, the itertools module, comprehensions, lambda functions, string formatting, the ternary conditional operator, and much more.

• Frequently Asked Questions

1. What are Ternary Operators (including an example)?

Programmers like to use the concise ternary operator for conditional assignments instead of lengthy if-else statements.

The ternary operator takes three arguments:

• Firstly, the comparison argument
• Secondly, the value (or result of an expression) to assign if the comparison is true
• Thirdly, the value (or result of an expression) to assign if the comparison is false

Simple Ternary Operator Example:

If we run this simple program, the "(m < n)" condition evaluates to true, which means that "m" is assigned to "o" and the print statement outputs the integer 10.

The conditional return values of "m" and "n" must not be whole statements, but rather simple values or the result of an expression.

2. How Do You Write a 3-Condition Ternary Operator?

Say you have three conditions to check against and you want to use a ternary conditional statement. What do you do? The answer is actually pretty simple, chain together multiple ternary operators. The syntax below shows the general format for this:

If we break this syntax down, it is saying:

• If condition_1 is true, return value_1 and assign this to var
• Else, check if condition_2 is true. If it is, return value_2 and assign it to var
• If neither conditions are true, return value_3 and assign this to var

We’ve now created a one-line version of an if-elif-else statement using a chain of ternary operators. 

The equivalent if-elif-else statement would be:

One thing to consider before chaining together ternary statements is whether it makes the code more difficult to read. If so, then it’s probably not very Pythonic and you’d be better off with an if-elif-else statement.

3. How Do You Code a Ternary Operator Statement?

We can easily write a ternary expression in Python if we follow the general form shown below:

So, we start by choosing a condition to evaluate against. Then, we either return the true_value or the false_value  and then we assign this to results_var . 

4. Is the Ternary Operator Faster Than If-Else?

If we consider that a ternary operator is a single-line statement and an if-else statement is a block of code, then it makes sense that if-else will take longer to complete, which means that yes, the ternary operator is faster.

But, if we think about speed in terms of Big-O notation (if I’ve lost you here, don’t worry, head on over to our Big-O cheat sheet ), then they are in fact equally fast. How can this be? This is because conditional statements are viewed as constant time operations when the size of our problem grows very large.

So, the answer is both yes and no depending on the type of question you’re asking. 

Jenna Inouye currently works at Google and has been a full-stack developer for two decades, specializing in web application design and development. She is a tech expert with a B.S. in Information & Computer Science and MCITP certification. For the last eight years, she has worked as a news and feature writer focusing on technology and finance, with bylines in Udemy, SVG, The Gamer, Productivity Spot, and Spreadsheet Point.

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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

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• Python »
• 3.10.13 Documentation »
• The Python Language Reference »
• 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.

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. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier __spam occurring in a class named Ham will be transformed to _Ham__spam . This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done.

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 operator. 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 either async for clauses or await expressions 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 .

New in version 3.6: Asynchronous comprehensions were introduced.

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

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 key/datum pairs enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of key/datum pairs 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 datum. This means that you can specify the same key multiple times in the key/datum 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 key/datum pairs and earlier dictionary unpackings.

New 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 datum (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 ).

New 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.

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. This production can be customized by overriding the __getattr__() method. If this attribute is not available, the exception AttributeError is raised. Otherwise, the type and value of the object produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

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 value of 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 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 .

New 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__() method.

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.

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.

This operation can be customized using the special __truediv__() and __floordiv__() 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__() method.

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__() method.

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.

This operation can be customized using the special __lshift__() and __rshift__() 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 sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert and with statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

New 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.

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

The trailing comma is required only to create a single tuple (a.k.a. a singleton ); 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, which groups 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.

While abs(x%y) < abs(y) is true mathematically, for floats it may not be true numerically due to roundoff. For example, and assuming a platform on which a Python float is an IEEE 754 double-precision number, in order that -1e-100 % 1e100 have the same sign as 1e100 , the computed result is -1e-100 + 1e100 , which is numerically exactly equal to 1e100 . The function math.fmod() returns a result whose sign matches the sign of the first argument instead, and so returns -1e-100 in this case. Which approach is more appropriate depends on the application.

If x is very close to an exact integer multiple of y, it’s possible for x//y to be one larger than (x-x%y)//y due to rounding. In such cases, Python returns the latter result, in order to preserve that divmod(x,y)[0] * y + x % y be very close to x .

The Unicode standard distinguishes between code points (e.g. U+0041) and abstract characters (e.g. “LATIN CAPITAL LETTER A”). While most abstract characters in Unicode are only represented using one code point, there is a number of abstract characters that can in addition be represented using a sequence of more than one code point. For example, the abstract character “LATIN CAPITAL LETTER C WITH CEDILLA” can be represented as a single precomposed character at code position U+00C7, or as a sequence of a base character at code position U+0043 (LATIN CAPITAL LETTER C), followed by a combining character at code position U+0327 (COMBINING CEDILLA).

The comparison operators on strings compare at the level of Unicode code points. This may be counter-intuitive to humans. For example, "\u00C7" == "\u0043\u0327" is False , even though both strings represent the same abstract character “LATIN CAPITAL LETTER C WITH CEDILLA”.

To compare strings at the level of abstract characters (that is, in a way intuitive to humans), use unicodedata.normalize() .

Due to automatic garbage-collection, free lists, and the dynamic nature of descriptors, you may notice seemingly unusual behaviour in certain uses of the is operator, like those involving comparisons between instance methods, or constants. Check their documentation for more info.

The power operator ** binds less tightly than an arithmetic or bitwise unary operator on its right, that is, 2**-1 is 0.5 .

The % operator is also used for string formatting; the same precedence applies.

Table of Contents

• 6.1. Arithmetic conversions
• 6.2.1. Identifiers (Names)
• 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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Python Ternary Operator

Syntax of ternary operator.

• A simple example for Ternary Operator
• Print statements in ternary operator
• Nested Ternary Operator

Python Ternary operator is used to select one of the two values based on a condition. It is a miniature of if-else statement that assigns one of the two values to a variable.

In this tutorial, we will learn how to use Ternary Operator in Python, with the help of examples.

The syntax of Ternary Operator in Python is

value_1 is selected if expression evaluates to True . Or if the expression evaluates to False , value_2 is selected.

You can either provide a value, variable, expression, or statement, for the value_1 and value_2 .

In the following examples, we will see how to use Ternary Operator in selection one of the two values based on a condition, or executing one of the two statements based on a condition. We shall take a step further and look into nested Ternary Operator as well.

1. A simple example for Ternary Operator

In this example, we find out the maximum of given two numbers, using ternary operator.

The ternary operator in the following program selects a or b based on the condition a>b evaluating to True or False respectively.

Python Program

Run the program. As a>b returns False, b is selected.

You may swap the values of a and b , and run the program. The condition would evaluate to True and a would be selected.

2. Print statements in ternary operator

In this example, we will write print statements in the ternary operator. Based on the return value of condition, Python executes one of the print statements.

Run the program. As a>b returns False, second print statement is executed.

This example demonstrates that you can run any Python function inside a Ternary Operator.

3. Nested Ternary Operator

You can nest a ternary operator in another statement with ternary operator.

In the following example, we shall use nested ternary operator and find the maximum of three numbers.

After the first else keyword, that is another ternary operator.

Change the values for a, b and c, and try running the nested ternary operator.

In this tutorial of Python Examples , we learned what Ternary Operator is in Python, how to use it in programs in different scenarios like basic example; executing statements inside Ternary Operator; nested Ternary Operator; etc., with the help of well detailed Python programs.

Related Tutorials

Python Ternary Operator – Conditional Operators in Python

You can use conditional operators in Python to execute code based on a predefined condition(s).

In this article, you'll learn how to use the ternary operator in Python. You'll see its syntax along with some practical examples.

What Is the Ternary Operator Used for in Python?

The ternary operator in Python is simply a shorter way of writing an if and if...else statements.

Here's what an if...else statement looks like in Python:

In the code above, we created a variable user_score with a value of 90.

We then printed either of two statements based on a predefined condition — if user_score > 50 .

So if the user_score variable is greater than 50, we print "Next level". If it's less than user_score , we print "Repeat level".

You can shorten the if...else statement using the ternary operator syntax.

Python Ternary Operator Example

In the last example, we saw how to use an if...else statement in Python.

You can shorten it using the ternary operator. Here's what the syntax looks like:  

In the syntax above, option1 will be executed if the condition is true. If the condition is false then option2 will be executed.

In other words, the ternary operator is just a shorthand of the if and if...else statements. You can use it in just a single line of code.

Here's a more practical example:

In the code above, "Next level" will be printed out because the condition is true.

In this article, we talked about the ternary operator in Python. It's a shorter way of writing if and if...else statements.

You can use ternary operators to execute code based on predefined conditions.

Happy coding! I also write about Python on my blog .

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Python is among the most user-friendly computer programming languages with few grammatical complexities and is quickly becoming one of the world’s fastest-growing computer languages. A vibrant community of Python users and developers contributes to the language’s improvement and growth. Python has always been quite dynamic from its birth in 1991, and it is continually developing with time, with new upgrades being added and old features being discarded.

A Python developer’s priority should always be to write short, clean, efficient, and understandable Python code. Ternary operators, which allow for a more concise manner of writing conditional statements in Python, can be utilized to do this. It was added as an enhancement in Python 2.5.

Today’s topic is Python Ternary Operator. In addition, we will go through an example and syntax of the Ternary Operator in Python. We will also learn about before and nested Python Ternary Operators. Finally, we will go over how to implement Ternary operators in Python.

Introduction to Python Conditional Statements

In Python, conditional statements conduct alternative computations or actions based on whether a given Boolean constraint evaluates to true or false. The if…else statement is how you execute this type of decision-making in a Python program. It enables the execution of a statement or collection of statements conditionally based on the value of an expression.

Assume we are developing an application that determines whether a customer is entitled to a 30% discount at a medical store. If the buyer is 65 or older, a discount should be given; otherwise, no discount should be granted. This program could be written using an if…else expression.

What is Python Ternary Operator?

In the Python programming language, the Ternary Operator is a condition expression that allows developers to evaluate statements. The Ternary Operators perform an action based on whether the statement is True or False. As a result, these operators are shorter than a standard if-else statement.

Syntax of Python Ternary Operator with Example

Python’s ternary operators, as the name implies, require three operands to function. The Python ternary statement has the following syntax:

The three operands are as follows:

1. condition: A Boolean expression must be evaluated to determine whether it is true or false.

2. true_value: A value assigned if the condition is true.

3. false_value: A value to be assigned if the condition is false.

Ternary Operators are commonly used to determine the value of a variable. If the condition is True, the variable takes on the value “true value,” else it takes on the value “false value.”

Example for Ternary Operator Implementation

Let’s return to the earlier-mentioned example, where we wish to provide a consumer at the medical store a discount if they are 65 or older. Customers who are not 65 or older are not eligible for a discount.

Code and Output for if-else Implementation

To grasp the difference between the Python ternary operator and the if-else statement approach, let’s start with the if-else statement.

Output if 67 is the input:

Code and Output for Ternary Operator Implementation We can now use the syntax of the ternary expression to make this program much more compact.

Output if 78 is the input:

Enter Your Age : 78

Yay! 30% Discount!

Ways to implement Python Ternary Operator

Now that we understand how Python’s ternary operator works let’s look at how it pertains to Tuples, Dictionary, and Lambda.

Before we get there, let’s start with a simple program that finds the smaller of two user-input numbers. Using the ternary approach with Python’s Tuples, Dictionary, and Lambda functions helps to have a clear picture of what we want to achieve.

Here is a simple and easy Python program that uses the ternary operator method to find the smaller of two values.

Output if a = 78 and b = 56 is the input:

Code and Output for Python ternary operator with Tuples

Let’s look at how ternary conditions are applied to Tuples now. The syntax to remember when using the ternary operator with Tuples is:

(false_value,true_value)[condition]

Output if a = 74 and b = 86 is the input:

Code and Output for Python ternary operator with Dictionary

Let’s look at how ternary conditions are applied to the Dictionary data structure.

If [a<b] is True, the True key’s value will be written in this scenario. Otherwise, if the condition is False, the value of the False key is printed.

Output if a = 44 and b = 86 is the input:

Code and Output for Python ternary operator with the Lambda function

Surprisingly, using the Lambda function to build the ternary operator is more efficient than the other two techniques. This is because Lambda guarantees that just one expression will be evaluated. In the case of Tuple or Dictionary, however, both expressions are evaluated.

Output if a = 94 and b = 66 is the input:

Implementation of Nested Ternary Operators

The term “nested” ternaries is a bit misleading because ternaries are so easy to write in a straight line that you never need to nest them with indent levels at all. They just read from top to bottom in a straight line, returning a value whenever they come across a true condition or the fallback.

There is no nesting to parse if you write ternaries correctly. It’s difficult to get lost when you’re following a straight line. Instead, we should term them “chained ternaries.”

Let’s make things easy to understand through an easy example.

Output if n = 90 is the input:

Here, we check for the value of no (given by the user). If it falls shorter than 0, we print “Negative Number”; if its value equals 0, we print “Number is Zero.” Else, we print “Positive Number.” Take note of how we nested them.

Before the Birth of Ternary Operators

Before Ternary Operators were introduced, programmers used to use alternatives to the operators. Look at the example code below.

The program checks for two conditions, i.e., (a>b) and (a or b). Let’s understand both conditions one after another.

Condition 1: a>b

The condition will return “True” if the value of a>value of b, otherwise it returns “False.”

Condition 2: a or b

We already know that when “or” is applied to two numbers, it will always return the first number.

Now, this might look like b will never come as an answer. But don’t forget the “and” in between the two conditions. If a>b comes out to be “False,” then (a>b and a) comes out as “False,” and the value of b is returned as an answer.

Suppose we consider the value of “a” as 45 and “b” as 89. The first condition becomes “False” as 45<89. Then “False and a” becomes “False” along with this, the final condition “False or b” will return b as the answer (which is the larger value).

Limitations of Ternary Operators

Here are examples of the ternary operator’s restrictions, as stated below: 1. While Ternary Operators can replace the if-else statement, they are only valid for a single if-else statement.

2. Ternary Operators are not used for numerous if-else expressions.

3. Ternary Operator can be utilized as a nested if-else; however, as seen above, that’s only possible for a condition with three possible values.

Python Interview Questions on Ternary Operator

Q1. Is there a Ternary operator in Python?

Ans. Yes, it has been included in version 2.5. The syntax of the expression is as follows: an if condition else b. The condition is first assessed, and then exactly one of a or b is evaluated and returned based on the condition’s Boolean value.

Q2. What exactly is the Python ternary operator symbol?

Ans. Many C-like programming languages provide a ternary operator?:, which defines a conditional statement. This operator is recognized as the conditional operator in some languages. In Python, the ternary operator is simply a one-line version of the if-else expression. There is no such thing as a symbol.

The Python ternary operator provides a quick and easy way to build if-else sentences. It first analyses the supplied condition and then returns a value based on whether that condition is True or False. In the following post, we addressed Ternary Operator, one of Python’s most effective tools, which has decreased code size by replacing typical if-else expressions, resulting in better code readability. The various applications of Ternary operators and their syntax and examples have been thoroughly addressed.

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Table of contents - Python ternary operators

Python ternary operators - How to use them?

Ancil Eric D'Silva

Software Developer

Published on  Fri Mar 11 2022

In this short tutorial, let us look at how we can write code using the Python ternary operator. One can make their Python code more concise using the ternary operator.

What are ternary operators in Python?

Here, Python’s ternary or conditional operators are operators that evaluate something based on a condition being true or false. They are also known as conditional expressions . To further simplify it, ternary operators can be thought of as one-line versions of the if-else statements.

As the name suggests, Python’s ternary operators need three operands to run. The three operands are: - condition: A boolean expression that needs to evaluate whether true or false. - val_true: A value that is to be assigned if the condition evaluates to be true. - val_false: A value that is to be assigned if the condition evaluates to be false. When it’s all put together this is how it should look like: some_var = val_ true if [condition] else val_ false

The variable some_var that you see on the left side of the equal-to sign “=” ( assignment operator ) will be assigned either one of the following: - val_true if the boolean expression evaluates to be true. Or - val_false if the boolean expression evaluates to be false.

Simple example of the Python ternary operator in a program

Let’s write a simple Python program that helps us understand the ternary operator’s usage. To understand the difference between the Python ternary operator and the if-else statement method, let's first write the program using the if-else statement.

The program using the "if-else" method:

And here is the output if 20 is the input:

In this example, the if-else statement assigns “Yes, you can drive!” to the driving_permit variable if the age entered is greater than or equal to 18. Otherwise, it assigns Sorry, you can’t drive yet!” to driving_permit.

Now, to make this program a lot more concise, we can make use of the syntax of the ternary expression.

The program using the ternary operator method

In this statement, the left side of the assignment operator (=) is the variable driving_permit . The ternary operator evaluates the condition which is if int(your_age) > = 18 . If the result is true, then it returns the val_true , which in this case is “Yes, you can drive!” . Else it returns val_false , which is Sorry, you can’t drive yet!”

Python ternary operator with Tuples, Dictionary and Lambda

As we now have an understanding of how Python’s ternary operator works, let’s see how it applies with Tuples, Dictionary and Lambda. Before that, let’s begin with a simple program on finding the greatest of 2 numbers. It helps with having a clear picture of what we want to accomplish when we apply the ternary method with Python’s Tuples, Dictionary and Lambda function.

Sample program of Python ternary operator.

Here is a simple Python program where we find the greatest of 2 numbers using the ternary operator method.

If [x>y] is true it returns 1, so the element with 1 index will be printed. Otherwise if [x>y] is false it will return 0, so the element with 0 index will be printed.

Python ternary operator with Tuples

Let’s now see how ternary conditions are used with Tuples. The syntax to be considered during the usage of the ternary operator with Tuples is ( if _ check _ is _f alse, if _ check _ is _ true)[check]

Python ternary operator with Dictionary

Let’s now see how ternary conditions are used with the Dictionary data structure.

In this case, if [x > y] is True then the value of the True key will be printed. Else if [x>y] is False then the value of the False key will be printed

Python ternary operator with the Lambda function

Interestingly, implementing the ternary operator with the Lambda function is more efficient than the other two methods. This is because with Lambda we are assured that only one expression will be evaluated. But in the instance of Tuple or Dictionary, both the expressions are evaluated.

Closing thoughts

The Python ternary operator is a concise and simple way of implementing if-else statements. It first evaluates the given condition, then returns a specific value depending on whether that condition turns out to be True or False .

About the author

Ancil Eric D'Silva is a software development engineer known for his proficiency in full-stack development and his ability to integrate complex systems with ease, enhancing product functionality and user experience.

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Master the Potential of Python Ternary Operator

January 9, 2024

Welcome to this comprehensive guide on Python ternary operator. Whether you're a beginner just starting out with Python, or an experienced developer looking to deepen your understanding, this article aims to provide you with the knowledge you need to make the most of this powerful language feature.

So, what exactly is a ternary operator? In simple terms, it's a concise way to perform conditional operations in Python . Instead of writing out a full if-else block, you can condense it into a single line of code using the ternary operator. Not only does this make your code shorter, but it can also make it more readable, and even slightly more efficient in some cases.

In the sections to follow, we will delve into the syntax, use-cases, advantages, and limitations of using the ternary operator in Python. We'll also discuss best practices, common pitfalls to avoid, and answer some frequently asked questions. Let's get started!

Definition of Python Ternary Operator

The ternary operator is a concise way of executing conditional statements in Python. It allows you to evaluate an expression and return a value based on whether the expression evaluates to True or False . Unlike conventional if-else statements that can span multiple lines, a ternary operator can accomplish the same in a single line of code. The basic syntax is:

Here's how it works:

• condition : This is the Boolean expression that the operator evaluates.
• value_if_true : This is the value that result will take if the condition is True .
• value_if_false : This is the value that result will take if the condition is False .

For example:

In this example, y will be assigned the value "Even" because x % 2 == 0 is True.

In Python, you may sometimes encounter the ternary operator used in combination with other Pythonic structures like tuple unpacking or even lambda functions , but the core syntax remains the same.

Simple Examples

Let's look at some simple, straightforward examples to help you get a better understanding of how the Python ternary operator works:

Checking if a number is positive, negative, or zero:

String formatting based on condition:

In list comprehensions:

Brief Comparison with Traditional If-Else Statements

Although both Python ternary operators and traditional if-else statements are used for conditional logic, there are key differences between them:

The ternary operator is more concise, allowing for shorter code. For example, consider this if-else block:

Using a Python ternary operator, the same logic can be condensed into a single line:

The ternary operator can make the code more readable when the conditional logic is simple. However, for more complex conditions, using a traditional if-else block might be more readable.

Traditional if-else statements are more versatile and can handle more complex logic with multiple conditions using elif. Ternary operators are best suited for straightforward conditions that result in a single outcome for True and False cases.

Use Cases for Beginners

Value Assignment

One of the most straightforward uses of the Python ternary operator is assigning a value to a variable based on a condition:

Simple Conditionals

The Python ternary operator shines in scenarios where you need to make a quick, simple decision within your code. For example, setting a flag based on user input:

It allows for more concise and often more readable code for simple conditional checks.

How Python Ternary Operator Works?

Once you've grasped the basics of the Python ternary operator, understanding its internals can offer you a deeper level of insight. This section aims to elucidate how the ternary operator works under the hood, focusing on evaluation order, return values, and the type of expressions allowed.

1. Evaluation Order

One of the key aspects to understand about the ternary operator is the order in which it evaluates its components. The general syntax, as a reminder, is:

Here's how the evaluation order works:

• First , the condition is evaluated.
• Next , based on whether the condition is True or False , either value_if_true or value_if_false is evaluated and returned. The other value is not evaluated at all, making the ternary operator a "short-circuit" operator.

In this example, because y != 0 evaluates to False , Python directly goes to the value_if_false , i.e., "Division by zero," without attempting to evaluate x / y , thus avoiding a runtime error.

2. Return Values

The return value of a Python ternary operation is the value that corresponds to the evaluated condition. Therefore, it could either be value_if_true or value_if_false , depending on the condition. This makes the Python ternary operator quite flexible in the types of operations it can perform and the types of data it can return.

3. Type of Expressions Allowed

The Python ternary operator is quite flexible when it comes to the types of expressions you can use for condition , value_if_true , and value_if_false . However, there are some considerations:

• Condition : Must evaluate to a Boolean value ( True or False ). It can be a comparison, logical operation, or any expression that returns a Boolean.
• value_if_true and value_if_false : Can be of any data type, even different types from each other. However, it's best practice to keep them of the same type for readability and predictability.
• Complex Expressions : You can use more complex expressions, like function calls or mathematical operations, but it may hamper readability.

Example with different types:

Example with function calls:

Advanced Usage

Here we'll look at chaining and nesting Python ternary operators, its use with functions and lambda expressions, as well as its application in data structures like lists, tuples, and dictionaries.

1. Using Ternary Operator in Function Calls

The Python ternary operator can be directly embedded in function calls to make the code more concise while still being readable. The key is to maintain the balance between brevity and readability.

Basic Example:

With Multiple Arguments:

You can use the Python ternary operator for one or more arguments in a function call.

Inline Decision Making:

The Python ternary operator can help you make inline decisions while calling a function, like choosing between different functions or methods to call.

2. Lambda Functions and Ternary Operator

Lambda functions in Python are anonymous functions defined using the lambda keyword. Since they are limited to a single expression, using the ternary operator within lambda functions can be very useful for simple conditional logic.

Basic Usage:

Here's a simple example that uses the Python ternary operator within a lambda function :

Multiple Conditions:

You can even chain Python ternary operations inside a lambda function for handling multiple conditions, although this can hurt readability if overused.

Use in Higher-Order Functions:

Lambda functions often find use in higher-order functions like map , filter , and sorted . The Python ternary operator can be quite useful in such cases.

In this example, the squared_or_cubed list will contain the squares of even numbers and cubes of odd numbers from the numbers list.

3. Chaining Ternary Operators

Chaining multiple Python ternary operators can help you represent more complex logic in a single line. While it provides brevity, be careful not to compromise readability.

Basic Chaining:

Extended Chaining:

4. Nested Ternary Operators

Nested ternary operators involve placing one or more ternary expressions inside another. While this can make your code more concise, it can also make it less readable and harder to debug if not used carefully.

Imagine you're choosing what to wear based on the weather. If it's sunny, you'll wear sunglasses. If it's not sunny but cloudy, you'll grab an umbrella just in case. If it's neither sunny nor cloudy, you decide to stay indoors. A Python ternary operator helps you make this decision in one go, and if you have to make another decision based on these conditions, you can "nest" another decision inside the first one. This is called a "nested ternary operator."

Example 1: Choosing a Drink

You go to a café. If they have orange juice, you'll take it. If they don't but have apple juice, you'll take that. If they have neither, you'll settle for water.

In Python, you could represent this decision like so:

Here, the decision about apple juice is "nested" inside the decision about orange juice. If drink_available is "orange", choice becomes "orange juice". Otherwise, another ternary operation is evaluated.

Example 2: Weather Example

You're deciding whether to go outside based on the weather. If it's sunny, you'll go to the beach. If it's cloudy but not raining, you'll go to a park. Otherwise, you'll stay home.

Here's how you could do that in Python:

If weather is "sunny", activity will be "beach". If weather is "cloudy", activity will be "park". For all other weather types, activity will be "home".

5. Ternary Operator with Lists, Tuples, and Dictionaries

You can use the ternary operator to conditionally construct or modify these data types.

List Comprehension:

Tuple Construction:

Dictionary Construction:

Performance Considerations

While the Python ternary operator provides a more compact way of writing conditionals, it's essential to consider its performance impact. This section will delve into the speed comparison with traditional if-else statements, its usage within class definitions, interoperability with Python's Walrus operator, and memory considerations.

1. Speed Comparison with If-Else

Generally speaking, the ternary operator tends to perform slightly faster than an if-else block for simple conditionals because it is optimized for such scenarios. However, the performance difference is often negligible and should not be the primary reason for choosing one over the other.

From the results, it appears that using_ternary is slightly faster than using_if_else . However, the difference is quite small (in the order of milliseconds for a million iterations), so in most real-world applications, you likely won't notice a performance difference between the two.

It's worth noting that while the ternary operator can be faster for simple conditions, the primary reason to use it is for code readability and brevity for straightforward conditions. For complex conditions or multi-step operations, a traditional if-else statement is usually more readable and should be preferred.

2. Ternary Operator in Class Definitions

Using the ternary operator within class definitions can lead to cleaner, more Pythonic code.

3. Using with Python’s Walrus Operator

Python 3.8 introduced the Walrus Operator ( := ), which allows assignment and evaluation in a single statement. You can use it in conjunction with the ternary operator to both evaluate and use a value conditionally.

4. Memory Usage

Memory usage generally isn't a major concern when using the ternary operator compared to traditional if-else statements for simple conditions. Both approaches are quite efficient in that regard. However, if the ternary operator's expressions involve creating large data structures or other memory-intensive operations, then memory usage could be a consideration.

Memory-Intensive Example:

In the above example, regardless of whether some_condition is True or False, a list with 1 million elements will be created, taking up a significant amount of memory.

Common Mistakes, Limitations, and Pitfalls

While the ternary operator in Python can make your code more concise, it's not without its drawbacks and potential for misuse. This section will highlight some common mistakes, limitations, and pitfalls you should be aware of.

When Not to Use Ternary Operators

• Complex Conditions : If the condition involves multiple and/or/nor logic, it's better to stick to if-else blocks for clarity.
• Multiple Actions : If you need to perform more than one action based on the condition, the ternary operator is not suitable.
• Long Expressions : If the expressions for value_if_true or value_if_false are long and complicated, they can make the ternary statement hard to read.

Overusing Ternary Operators

• Chaining : Excessive chaining of ternary operators can make your code difficult to understand and debug.
• Nesting : While nesting is possible, it often leads to unreadable code.

Type-related Mistakes

Type Inconsistency : Using different types for value_if_true and value_if_false can lead to unexpected behavior. For example:

In this example, x could either be an integer or a string, depending on some_condition . This can create issues later in the code.

Implicit Type Conversion : Python's dynamic typing can sometimes result in implicit type conversions, which might not be what you expect.

In this example, result could be either an integer or a float , which could lead to precision issues in calculations.

Error Handling

While the Python ternary operator simplifies conditional logic, it's not entirely immune to issues that can lead to errors or bugs. Understanding the kinds of errors that might occur and how to debug them is crucial. This section covers syntax errors, logical errors, and offers some debugging tips.

Syntax Errors

Syntax errors are mistakes in the language structure that the interpreter can catch before your program runs.

Incorrect Ordering : The ternary operator has a specific order: value_if_true if condition else value_if_false .

Missing Components : Omitting any part of the ternary operator will result in a syntax error.

Logical Errors

Logical errors occur when your program runs without crashing but doesn't produce the expected output.

Inverted Condition : Sometimes, you might accidentally invert the true and false parts of the ternary operator.

Chained Confusion : When chaining multiple ternary operators, keeping track of conditions can get confusing, leading to logical errors

Debugging Tips

Break It Down : If you're chaining or nesting ternary operators and encountering issues, break them down into separate if-else blocks for easier debugging.

Print Statements : Inserting print statements can help debug the flow of conditional logic. For example:

Code Formatting : Sometimes, simply formatting the code clearly can help identify errors in your ternary logic.

Comparison with Other Languages

The ternary conditional operator exists in many programming languages, although its syntax and capabilities can vary. Understanding these differences can be especially useful if you are transitioning from one language to another or working in a multi-language environment.

Ternary in C, C++, Java, etc.

The syntax for the ternary operator in languages like C, C++, and Java is usually in the form of condition ? value_if_true : value_if_false .

Example in C:

Example in Java:

Common Features:

• Type Safety : In statically typed languages like C++ and Java, the types of value_if_true and value_if_false usually must be compatible.
• Short-circuiting : Just like in Python, these languages also evaluate the condition and only one of the value_if_true or value_if_false , not both.

Uniqueness in Python

Python's syntax is somewhat more readable and fits better with its overall syntax style. Here's how the Python ternary operator is unique:

In Python, the ternary operator takes the form of value_if_true if condition else value_if_false .

Top 10 Frequently Asked Questions

What is the Python Ternary Operator?

The Python ternary operator is a shorthand way of writing an if-else statement. It allows you to return a value based on a condition, all in a single line.

How is the Ternary Operator Different from Traditional If-Else Statements?

The ternary operator is more concise than traditional if-else statements and is often used for simple, straightforward conditions. However, it is not suitable for complex conditions or multiple actions based on a condition.

Can I Nest Ternary Operators?

Yes, you can nest ternary operators, but it can make your code hard to read and understand. It's generally not recommended for complex conditions.

Is the Ternary Operator Faster Than If-Else Statements?

For simple conditions, the ternary operator can be slightly faster, but the performance difference is generally negligible for most applications.

Can I Use the Ternary Operator with Functions?

Yes, you can use the ternary operator within function calls or even within the definition of a function, as long as you adhere to its syntax and limitations.

What Types of Expressions Can I Use with the Ternary Operator?

You can use any expression that returns a value, including function calls, arithmetic operations, or even other ternary operations, as long as they fit within the syntax requirements.

Can I Use the Ternary Operator in List Comprehensions?

Yes, the ternary operator can be used in list comprehensions for conditional value assignment.

Are There Memory or Performance Concerns with the Ternary Operator?

Memory and performance are generally not major concerns for the ternary operator when compared to traditional if-else statements. However, be cautious when the expressions involved are memory-intensive or computationally heavy.

What Are Common Mistakes to Avoid?

Common mistakes include inverting the true and false parts of the operator, omitting parts of the syntax, or using it in situations where an if-else statement would be more appropriate due to complexity.

Can I Chain Multiple Ternary Operators Together?

Yes, you can chain multiple ternary operators, but doing so can make your code harder to read and debug. Use this feature sparingly and consider breaking down complex chains into simpler parts or using traditional if-else statements.

The Python ternary operator serves as a shorthand for conditional if-else statements , allowing for more concise and sometimes more readable code. While it offers various benefits, such as brevity and some performance advantages, it's essential to understand its limitations, syntax quirks, and best-use cases to leverage it effectively.

Key Takeaways

• Syntax is King : Ensure you understand the value_if_true if condition else value_if_false structure.
• Readability Over Brevity : Always prioritize code readability. If a ternary operator complicates understanding, consider using a traditional if-else block.
• Limited to Simple Cases : The ternary operator is best for simple, straightforward conditions and should not replace complex if-else statements.
• Dynamic and Flexible : Due to Python’s dynamic typing, you can use various types of expressions, but be cautious to maintain consistency.
• Debugging Challenges : Though concise, ternary operators can be tricky to debug, especially when nested or chained.
• Performance : While there can be slight performance benefits, they are often negligible for most real-world applications.

Additional Resources

For further reading and more in-depth understanding, you may consult the Python official documentation on conditional expressions .

Bashir Alam

He is a Computer Science graduate from the University of Central Asia, currently employed as a full-time Machine Learning Engineer at uExel. His expertise lies in Python, Java, Machine Learning, OCR, text extraction, data preprocessing, and predictive models. You can connect with him on his LinkedIn profile.

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Python Numerical Methods

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists , the content is also available at Berkeley Python Numerical Methods .

The copyright of the book belongs to Elsevier. We also have this interactive book online for a better learning experience. The code is released under the MIT license . If you find this content useful, please consider supporting the work on Elsevier or Amazon !

< 4.1 If-Else Statements | Contents | 4.3 Summary and Problems >

Ternary Operators ¶

Most programming languages have ternary operators , which usually known as conditional expressions . It provides a way that we can use one-line code to evaluate the first expression if the condition is true, otherwise it evaluates the second expression. Python has its way to implement the ternary operator, which can be constructed as below:

CONSTRUCTION : ternary operator in Python

EXAMPLE: Ternary operator

From the above example, we can see this one-line code is equivalent to the following block of codes.

Ternary operator provides a simple way for branching, and it can make our codes concise. Besides, in the next chapter, you will also see it commonly be used in list comprehensions, which is quite useful.

Does Python Have a Ternary Conditional Operator?

• python basics

Are you a Python programmer who loves writing clean and concise code? If so, you may wonder if Python has a ternary conditional operator. We’ll answer your question in this article.

Other programming languages have a ternary operator – a programming feature that lets you compare three conditions in one line. With Python’s insistence on code that’s clear, clean, and concise, you’d expect Python to have a ternary conditional operator as well. And that’s what we’ll be talking about in this article.

If you are not confident using Python yet, our Python Programming Track will help you get started. It’s a set of 5 interactive courses that will teach you Python's fundamentals: data types, functions, methods, control flow statements, and more. By the end of the track, you will be able to write your own functions and pursue the Python programming journey of your choice – whether that’s general programming, data science, or something else.

By the way, if data science interests you, check out our 12 Python tips and tricks that every data scientist should know .

But let's go back to our topic! Whether you're a seasoned Python developer or just starting out, this article will provide you with valuable insights into one of Python's most useful features. So, let's dive in!

What Is a Python Ternary Conditional Operator?

A ternary or conditional operator is a shorthand way of writing an if-else statement in a single line of code. It's often used in programming to make code more concise, especially when dealing with simple conditional expressions.

In C, a simple if-else statement could be written as:

Using the ternary operator, the same expression can be written as:

As you can see, the code is much shorter, especially when you’re dealing with simple conditional expressions.

Now, the question remains: does Python have a ternary operator?

The answer is yes ; it is called a conditional expression rather than a ternary operator.

Indeed, Python does have a ternary conditional operator in the form of a conditional expression using if and else in a single line .

The syntax for a Python ternary conditional operator is:

This code evaluates the condition. If it's true, the code returns the value of value_if_true ; if it's false, it returns the value of value_if_false . It's important to note that the if and else are mandatory.

Let's look at an example to see how the Python conditional expression works:

The Python Conditional Expression at Work

In this example, we use the Python conditional expression to assign the value of even to y if x is even and the value of odd if x is odd.

Note that we could write the same thing using an if-els e statement, but it would be much longer.

Python Ternary Operator with Multiple Conditions

We can also use a Python ternary operator with multiple conditions . In the case of a standard if-else statement, we would need to use elif :

To write the equivalent as a one-liner Python ternary operator, we need to ditch the elif keyword and use else instead; we are, in fact, writing a nested ternary operator. Therefore, the code would be:

And here we go! We have checked multiple conditions in one line of code with a Python ternary conditional operator!

Efficient Python Conditional Expressions

As you might realize by now, one of the main advantages of using Python's conditional expression syntax is that it can significantly reduce the amount of code needed to write a simple if-else statement. We can also use list comprehension to make the code even more efficient.

Let's say we need to print a list of items based on a particular condition. Here's an example of using Python's ternary operator to print a list of even and odd numbers with list comprehensions.

In this example, we use list comprehension to create two separate lists of even and odd numbers from a given list of integers. We then use conditional expressions to print the even and odd numbers if they exist or print a message if no even or odd numbers are found.

Using Python's ternary operator, we can write more concise and readable code compared to standard if-else statements. This example demonstrates how conditional expressions can be a valuable tool in simplifying your code and making it more efficient. This is especially true when dealing with simple conditional expressions or nested conditions. Instead of writing a multi-line if-else statement, we can write a single-line conditional expression that achieves the same result.

Also, note that Python always skips the else statement when the if evaluates to True; this decreases the code execution's time .

Leveraging Python’s Ternary Conditional Operator

In summary, Python's conditional expression syntax can save a significant amount of space when dealing with simple conditional expressions. Still, it's essential to consider readability and efficiency when using nested or complex conditions.

Now that you've learned about Python's ternary operator, it's important to practice using it in your code. By incorporating conditional expressions into your code, you can make your code more concise and efficient.

A great way to practice using Python's ternary operator is by trying it out in our Built-In Algorithms in Python course . You can also look at existing code and identify areas where a ternary operator could be used instead of a standard if-else statement.

Remember, while Python's ternary operator can make your code more concise, it's important to prioritize readability and maintainability. So, use it appropriately and consider alternative options for more complex conditions.

With practice, using Python's ternary operator can become second nature and significantly shorten your code. Adding knowledge of VS Code extensions for Python and useful Python packages to your toolbox will bring you to the next level!

See you soon on LearnPython.com for more Python tips!

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In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators. 

• OPERATORS: These are the special symbols. Eg- + , * , /, etc.
• OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators and Membership Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient. 

There are two types of division operators: 

Float division

• Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

• P – Parentheses
• E – Exponentiation
• M – Multiplication (Multiplication and division have the same precedence)
• D – Division
• A – Addition (Addition and subtraction have the same precedence)
• S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

• x % 10 -> yields the last digit
• x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

• Logical not
• logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

• Bitwise NOT
• Bitwise Shift
• Bitwise AND
• Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical. 

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5. 

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax :   [on_true] if [expression] else [on_false] 

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python . 

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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Operators are symbols that perform operations on variables and values. For example, + is an operator used for addition, while * is also an operator used for multiplication.

Operators in Java can be classified into 5 types:

• Arithmetic Operators
• Assignment Operators
• Relational Operators
• Logical Operators
• Unary Operators
• Bitwise Operators

1. Java Arithmetic Operators

Arithmetic operators are used to perform arithmetic operations on variables and data. For example,

Here, the + operator is used to add two variables a and b . Similarly, there are various other arithmetic operators in Java.

Example 1: Arithmetic Operators

In the above example, we have used + , - , and * operators to compute addition, subtraction, and multiplication operations.

/ Division Operator

Note the operation, a / b in our program. The / operator is the division operator.

If we use the division operator with two integers, then the resulting quotient will also be an integer. And, if one of the operands is a floating-point number, we will get the result will also be in floating-point.

% Modulo Operator

The modulo operator % computes the remainder. When a = 7 is divided by b = 4 , the remainder is 3 .

Note : The % operator is mainly used with integers.

2. Java Assignment Operators

Assignment operators are used in Java to assign values to variables. For example,

Here, = is the assignment operator. It assigns the value on its right to the variable on its left. That is, 5 is assigned to the variable age .

Let's see some more assignment operators available in Java.

Example 2: Assignment Operators

3. java relational operators.

Relational operators are used to check the relationship between two operands. For example,

Here, < operator is the relational operator. It checks if a is less than b or not.

It returns either true or false .

Example 3: Relational Operators

Note : Relational operators are used in decision making and loops.

4. Java Logical Operators

Logical operators are used to check whether an expression is true or false . They are used in decision making.

Example 4: Logical Operators

Working of Program

• (5 > 3) && (8 > 5) returns true because both (5 > 3) and (8 > 5) are true .
• (5 > 3) && (8 < 5) returns false because the expression (8 < 5) is false .
• (5 < 3) || (8 > 5) returns true because the expression (8 > 5) is true .
• (5 > 3) || (8 < 5) returns true because the expression (5 > 3) is true .
• (5 < 3) || (8 < 5) returns false because both (5 < 3) and (8 < 5) are false .
• !(5 == 3) returns true because 5 == 3 is false .
• !(5 > 3) returns false because 5 > 3 is true .

5. Java Unary Operators

Unary operators are used with only one operand. For example, ++ is a unary operator that increases the value of a variable by 1 . That is, ++5 will return 6 .

Different types of unary operators are:

• Increment and Decrement Operators

Java also provides increment and decrement operators: ++ and -- respectively. ++ increases the value of the operand by 1 , while -- decrease it by 1 . For example,

Here, the value of num gets increased to 6 from its initial value of 5 .

Example 5: Increment and Decrement Operators

In the above program, we have used the ++ and -- operator as prefixes (++a, --b) . We can also use these operators as postfix (a++, b++) .

There is a slight difference when these operators are used as prefix versus when they are used as a postfix.

To learn more about these operators, visit increment and decrement operators .

6. Java Bitwise Operators

Bitwise operators in Java are used to perform operations on individual bits. For example,

Here, ~ is a bitwise operator. It inverts the value of each bit ( 0 to 1 and 1 to 0 ).

The various bitwise operators present in Java are:

These operators are not generally used in Java. To learn more, visit Java Bitwise and Bit Shift Operators .

Other operators

Besides these operators, there are other additional operators in Java.

The instanceof operator checks whether an object is an instanceof a particular class. For example,

Here, str is an instance of the String class. Hence, the instanceof operator returns true . To learn more, visit Java instanceof .

The ternary operator (conditional operator) is shorthand for the if-then-else statement. For example,

Here's how it works.

• If the Expression is true , expression1 is assigned to the variable .
• If the Expression is false , expression2 is assigned to the variable .

Let's see an example of a ternary operator.

In the above example, we have used the ternary operator to check if the year is a leap year or not. To learn more, visit the Java ternary operator .

Now that you know about Java operators, it's time to know about the order in which operators are evaluated. To learn more, visit Java Operator Precedence .

Table of Contents

• Introduction
• Java Arithmetic Operators
• Java Assignment Operators
• Java Relational Operators
• Java Logical Operators
• Java Unary Operators
• Java Bitwise Operators

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• Hatch : The hatch project not only handles project setup and management, but also provides a build system, tools for packaging projects for redistribution on PyPI, test handling, and many other useful functions.
• uv : The experimental uv project is written by the same folks who make the ruff Python linting tool. It aims to replace pip , venv , and several other command-line Python tools at once. It's written in Rust for speed (like ruff ), and many of its commands resemble those of pip and other tools it replaces, making it relatively easy to learn.

When creating new projects that are meant to be worked on in a team environment or distributed to others (e.g., via PyPI), be sure to use the modern pyproject.toml format for your requirements and project configuration, along with the project layout used with it. You can still use the older requirements.txt file side-by-side with pyproject.toml , but the latter covers a wider range of use cases and makes your projects forward-compatible.

Python’s evolution has meant many new additions to the language itself. The last few versions of Python have added useful syntactical constructions that allow for more powerful and succinct progamming. While they aren't mandatory, newer third-pary modules may use them, so they're worth getting to know at least casually.

Three recent syntax additions are especially notable.

Pattern matching

The biggest recent addition, structural pattern matching , which arrived in Python 3.10, is more than just “ switch/case for Python” as it has sometimes been described. Structural pattern matching lets you make control-flow decisions based on the contents or structure of objects. In short, it's a way to match based on types or the shapes of types (a list with an int and a string , for instance) rather than values .

The ‘walrus operator’

So named for its appearance ( := ), the walrus operator, added in Python 3.8, introduces assignment expressions , a way to assign a value to a variable and then apply a test to the variable in a single step. It makes for less verbose code in many common situations, such as checking a function’s return value while also preserving the results.

Positional-only parameters

A minor but useful recent addition to Python’s syntax, positional-only parameters , lets you indicate which function parameters must be specified as positional ones, never as keyword arguments. This feature is generally intended to improve the clarity and ease the future development of a codebase, goals that many of Python’s other new features also focus on.

Writing tests for a codebase is like flossing daily: Everyone agrees it’s a good thing, few of us actually do it, and even fewer do it properly. Modern Python codebases deserve to have test suites, and the current tooling for testing makes creating test suites easier than ever.

Python has its own built-in testing framework, unittest . It isn't bad as a default, but its design and behaviors are dated. The Pytest framework has risen to prominence as a common substitute. It’s more flexible (you can declare tests in any part of your code, not just a subset) and requires writing far less boilerplate. Plus, Pytest has plenty of add-ons to expand its functionality (e.g., for testing asynchronous code).

Another important adjunct to testing is code coverage, determining how much of one’s codebase the tests actually cover. The module Coverage has you covered for this (as the name suggests) and Pytest even comes with a plug-in to work with it.

Next read this:

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Serdar Yegulalp is a senior writer at InfoWorld, focused on machine learning, containerization, devops, the Python ecosystem, and periodic reviews.

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Lock-free reference-counting a TLS slot using atomics, part 1

Raymond Chen

June 12th, 2024 6 0

Some time ago, we spent time looking at various lock-free algorithms, one of which is the lock-free singleton constructor . But suppose you want your singleton to be reference-counted?

To make things concrete, let’s suppose that we want a class which manages a TLS slot, allocating it on demand, and freeing it when there are no longer any users.

Let’s start with a sketch of how we want this to work, but without worrying about atomicity yet.

The idea here is that a Tls­Manager is the object that manages access to a TLS slot. You call Acquire to start using the TLS slot (allocating it on demand), and you can use that slot until you call Release . When the last consumer of a slot calls Release , the slot is freed.

Instead of talking directly to the Tls­Manager , you use a Tls­Usage , which is an RAII type that deals with the acquire/release protocol for you.

To make the Tls­Manager thread-safe, we can add locks:

Now, in practice, this might end up being efficient enough if Tls­Usage objects are not frequently created and destroyed. But you might be in a case where your program is constantly creating and destroying Widget objects, and each Widget needs a Tls­Usage . That lock might end up being a bottleneck. We’ll try to address this next time.

Update : TlsUsage move constructor and assignment fixed.

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Rant: To those who read the comment section, I highly recommend wrapping a std::unique_ptr with a custom deleter instead of hand-rolling your own RAII type. Correctly implementing all the methods an RAII type needs is very verbose and error-prone.

Edit: The incorrect code below was in the article before it was edited, but I didn’t expect it to stir up this much confusion among commenters. Please do not follow up with more off-topic comments. (Please, DevBlogs, support more HTML tags…)

As an example, if your move assignment operator is TlsUsage& operator=(TlsUsage&& other) { std::swap(*this, other); } it actually results in an infinite recursion, because std::swap is implemented in terms of the move assignment operator. (It’s also not noexcept , which mostly defeats the purpose of having a move assignment operator at all.)

My normal implementation of such methods looks like this: `void operator=(TlsUsage& other);`

Thus trying to call it is a link time error; which is fine because it should never be called.

It gets its standard constructor, its copy constructor (which is implemented as move), its destructor, and its link error assignment operator and nothing else. And half the time the copy constructor can be a link error copy constructor too.

The “fixed” move assignment operator now leaks by abandoning the original held state and overwriting it with the incoming state. Funny enough this same bug is in MSVC’s std::experimental::generator, so it seems to be a common mistake to make. Using std::swap instead was originally correct, it just wasn’t being passed the correct parameters. EDIT: Seems to have been updated to properly use std::swap now, yay 🙂

Confused (as of Jun 14 11pm PST) TlsUsage::operator=() calls std::move, and it looks correct. Were there two updates?

The move assignment operator in the fixed code doesn’t seem wrong to me, it’s using std::swap correctly this time, and it doesn’t call std::move . I suspect both of you confused the move assignment operator with the move constructor .

Seems it was again updated after my comment, when I wrote my comment the move assignment operator had the same implementation as the move constructor, using std::exchange. It is now correctly using std::swap.

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Are multiple variable assignments in a ternary operator in Python possible?

Just out of curiosity. I wonder if it is possible to make multiple assignments with the ternary operator in Python. I was thinking of something like this

On the other hand I wonder why it is not possible to write the code as above. Why wouldn't one implement the Syntax this way? (Sorry if this question is too meta)

to clarify. I was just wrting some bisection function https://en.wikipedia.org/wiki/Bisection_method :

Thanks for any ideas!

• Since that is not valid, it's unclear what you want it to mean. –  Kelly Bundy Commented Feb 11, 2020 at 23:28
• 1 That syntax seems confusing. Starting with rval = would suggest setting the variable rval but that wouldn't be the case if your condition isn't met. That is, lval is set instead. –  busybear Commented Feb 11, 2020 at 23:28
• sure it is no vlaid code, but I wonder if there is a way to realise my example. like using sets in this article stackoverflow.com/questions/394809/… second Post. is kind of out of the box thinking in my opinion. –  user2853437 Commented Feb 11, 2020 at 23:30
• Like I said: It's unclear what you want it to mean. So how are we supposed to tell a way to realise what you want if we don't know what you want? –  Kelly Bundy Commented Feb 11, 2020 at 23:32
• @HeapOverflow Sorry. Does the Edit clarify what I was thinking about? –  user2853437 Commented Feb 11, 2020 at 23:43

2 Answers 2

You could do this:

But the normal way with an if-else statement is clearer (at least for now, while we're still new to the := operator) and more appropriate (since you're assigning here, which better is a normal statement than an expression with side effects).

• In which Python version is this valid? –  user2853437 Commented Feb 11, 2020 at 23:33
• @user2853437 In 3.8. –  Kelly Bundy Commented Feb 11, 2020 at 23:34
• Thank you. That's exactly what I was wondering. So it is possible but starting from version 3.8 –  user2853437 Commented Feb 11, 2020 at 23:40
• So a kind of better approach would be rival, lival = (m,lival) if (fl*fm) < 0 else (rival,m) ? –  user2853437 Commented Feb 12, 2020 at 0:16
• @user2853437 Hmm... I'd say that's better in some way and worse in another. I'd just stick to the normal way that you have in your question. –  Kelly Bundy Commented Feb 12, 2020 at 0:18

The Zen of Python

While the thought behind the question should be genuinely appreciated and applauded, along with the clever and bleeding edge solution by @Heap Overflow - this seems a dangerous path to travel.

You're right, I'm not the 'Python Police'; however the Zen of Python speaks for itself regarding this implementation.

Explicit is better than implicit. Simple is better than complex. Readability counts. There should be one-- and preferably only one --obvious way to do it. If the implementation is hard to explain, it's a bad idea.

To make this post an answer rather than a rambling, I'll refer to the quote above. Although the thought is certainly interesting ...

"It's a bad idea" in Python.

• @HeapOverflow @s3dev I was kind of aware of the Zen, when asking the question, but readability is kind of ambigous to me. Since tenary operator is a shorthand, it already makes it 'more difficult' to read. The question would more relate to There should be one-- and preferably only one --obvious way to do it. So I wonder when would one use the := operator with a tenary operator? –  user2853437 Commented Feb 12, 2020 at 11:32

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