ternary operator python assignment

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python ternary operator with assignment [duplicate]

I am new to python. I'm trying to write this

in a more compact way using the ternary operator

but this gives the syntax error. What am I missing?

6 Answers 6

The conditional operator in Python is used for expressions only, but assignments are statements . You can use

to get the desired effect in this case. See also the documentation of dict.setdefault() .

In Python, assignments cannot occur in expressions, therefore you can't write code like a = (b = c) .

You're looking for setdefault :

Alternative, use a defaultdict .

The reason that else (d[x] = {}) is a syntax error is that in Python, assignment is a statement . But the conditional operator expects expressions , and while every expression can be a statement, not every statement is an expression.

You can also use Python's dictionary get() method

Explanation:

The get() method returns the value for the specified key if key is in a dictionary.

The syntax of get() is:

get() Parameters

The get() method takes maximum of two parameters:

key - key to be searched in the dictionary

value (optional) - Value to be returned if the key is not found. The default value is None.

Return Value from get()

The get() method returns:

the value for the specified key if key is in dictionary.
None if the key is not found and value is not specified.
value if the key is not found and value is specified.

Another option is to use defaultdict

That's what setdefault() is for:

It does exactly what you want:

Return d[x] if x is a key in d
Assign {} to d[x] if x is not yet a key and return that

For those interested in the ternary operator (also called a conditional expression ), here is a way to use it to accomplish half of the original goal:

q = d[x] if x in d else {}

The conditional expression, of the form x if C else y , will evaluate and return the value of either x or y depending on the condition C . The result will then be assigned to q .

Well, this is an old question... Unfortunately, your answer is not correct - I was looking for a way to return an empty dict and to assign it to d[x] at the same time - something your code doesn't do (please check other answers). Still, +1 for the effort and welcome to SO! – georg Commented Apr 6, 2013 at 0:07
Thanks for pointing out my mistake. I edited my answer to reflect its incomplete nature. Also, thanks for the warm welcome! – user2027294 Commented Apr 9, 2013 at 1:28

Not the answer you're looking for? Browse other questions tagged python or ask your own question .

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Ternary Operator in Python

In Python , Ternary Operator determines if a condition is true or false and then returns the appropriate value as the result. The ternary operator is useful in cases where we need to assign a value to a variable based on a simple condition, and we want to keep our code more concise — all in just one line of code.

It’s convenient when we want to avoid writing multiple lines for a simple if-else condition. Like in simple if-else, the first option, the true_value will be executed when the condition provided in the expression is True. If the condition returns False, then false_value will be executed.

The ternary operator can be used in various ways. Let us see a few different examples to use Ternary Operators in Python:

Table of Content

Python Ternary If Else

Ternary operator in nested if else, ternary operator using python tuple, ternary operator using python dictionary, ternary operator using python lambda, ternary operator with print function, limitations of python ternary operator.

The simplest way to use a Python ternary operator is when we have a simple if else condition , that is, either of the two conditions is True and the other is False.

Example: In this code we will compare and find the minimum number from the given two numbers using the Ternary Operators in Python and storing the result in a variable name ‘min’. If ‘ a ‘ is minimum, the value of ‘ a ‘ will be printed, else the value of ‘ b ‘ will be printed.

The ternary operator can also be used in Python nested if-else statement . the syntax for the same is as follows:

Example: In this example, we are using a nested if-else to demonstrate ternary operator. If ‘ a’ and ‘ b ‘ are equal then we will print ‘a and b are equal’ and else if ‘a’ is greater then ‘b’ then we will print ‘a is greater than b’ otherwise ‘b is greater than a’.

The ternary operator can also be written by using Python tuples . In this case we declare the False and True values inside a tuple at index 0 and 1 respectively. Based on the condition, if the result is False, that is 0 the value at index 0 gets executed. If the condition results in True, the value at index 1 of the tuple is executed.

Example: In this example, we will compare and print the minimum value, where the the values to be executed are declared inside the tuple.

The Python ternary operator can also be written by using Python dictionary . In this case we use True and False keywords as the dictionary keys and provide them with a value to be executed based on the condition’s result.

Example: In this example, we are using Dictionary to demonstrate ternary operator, where we have given a True and a False values to dictionary keys, that will be executed based on condition’s result.

In Python, lambda functions are used when we have only one expression to evaluate. Hence using the teranery operator with lambda makes it quite simple and easy. It works exactly like the tuple. That is we declare the False and True values at index 0 and 1 respectively.

Example: In this example, we are using Lambda to demonstrate ternary operator. We are using tuple for selecting an item and if [a<b] is true it return 1, so element with 1 index will print else if [a<b] is false it return 0, so element with 0 index will print.

The ternary operator can also be directly used with the Python print statement . Its syntax is a s follows:

Example: In this example, we are finding the minimum number among two numbers using Python ternary operator with print statement.

Python ternary operator is used to write concise conditional statements but it too have some limitations.

Readability: T ernary operator can make simple conditional expressions more concise, it can also reduce the readability of your code, especially if the condition and the expressions are complex.
Potential for Error : Incorrect placement of parentheses, missing colons, or incorrect order of expressions can lead to syntax errors that might be harder to spot.
Debugging : When debugging, it might be harder to inspect the values of variables involved in a complex ternary expression.
Maintenance and Extensibility : Complex ternary expressions might become harder to maintain and extend especially when the codebase grows.
Can’t use assignment statements: Each operand of the Python ternary operator is an expression , not a statement, that means we can’t use assignment statements inside any of them. Otherwise, the program will throw an error.

Ternary Operator in Python – FAQs

What does ternary operator do in python.

The ternary operator in Python is a one-liner conditional expression that assigns a value based on a condition. It is written as value_if_true if condition else value_if_false. a = 5 b = 10 max_value = a if a > b else b # max_value will be 10

What is ternary in dictionary Python?

A ternary operator can be used within a dictionary to set values based on conditions. a = 5 b = 10 my_dict = {'max': a if a > b else b} # my_dict will be {'max': 10}

What is a ternary?

A ternary is a concise way to perform a conditional assignment in a single line. In the context of programming, it typically refers to a ternary operator, which evaluates a condition and returns one of two values.

How to read ternary operator?

The ternary operator syntax in Python can be read as: “if the condition is true, then return value_if_true, otherwise return value_if_false.” # Example result = "Positive" if num > 0 else "Negative" # Read as: "If num is greater than 0, then result is 'Positive', otherwise result is 'Negative'."

When to use ternary operator?

Use the ternary operator for simple, concise conditional assignments when you want to make your code more readable and reduce the number of lines. It is particularly useful for short, straightforward conditions. # Instead of this: if a > b: max_value = a else: max_value = b # Use this: max_value = a if a > b else b

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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.

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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.

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 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 with Example

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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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Tags: Limitations of Python Ternary Operators Python Ternary Operator Python Ternary Operators ways to implement Ternary Operator

Conditional expression (ternary operator) in Python

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

6. Expressions - Conditional expressions — Python 3.11.3 documentation

Basics of the conditional expression (ternary operator)

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

See the following article for if statements in Python.

Python if statements (if, elif, else)

In Python, the conditional expression is written as follows.

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

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

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

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

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

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

Boolean operators in Python (and, or, not)

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

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

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

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

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

See the following article for details on list comprehensions.

List comprehensions in Python

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

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

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

Lambda expressions in Python

Related Categories

Inline If in Python: The Ternary Operator in Python

September 16, 2021 December 20, 2022

In this tutorial, you’ll learn how to create inline if statements in Python. This is often known as the Python ternary operator, which allows you to execute conditional if statements in a single line, allowing statements to take up less space and often be written in my easy-to-understand syntax! Let’s take a look at what you’ll learn.

The Quick Answer: Use the Python Ternary Operator

Table of Contents

What is the Python Ternary Operator?

A ternary operator is an inline statement that evaluates a condition and returns one of two outputs. It’s an operator that’s often used in many programming languages, including Python, as well as math. The Python ternary operator has been around since Python 2.5, despite being delayed multiple times.

The syntax of the Python ternary operator is a little different than that of other languages. Let’s take a look at what it looks like:

Now let’s take a look at how you can actually write an inline if statement in Python.

How Do you Write an Inline If Statement in Python?

Before we dive into writing an inline if statement in Python, let’s take a look at how if statements actually work in Python. With an if statement you must include an if , but you can also choose to include an else statement, as well as one more of else-ifs, which in Python are written as elif .

The traditional Python if statement looks like this:

This can be a little cumbersome to write, especially if you conditions are very simple. Because of this, inline if statements in Python can be really helpful to help you write your code faster.

Let’s take a look at how we can accomplish this in Python:

This is significantly easier to write. Let’s break this down a little bit:

We assign a value to x , which will be evaluated
We declare a variable, y , which we assign to the value of 10, if x is True. Otherwise, we assign it a value of 20.

We can see how this is written out in a much more plain language than a for-loop that may require multiple lines, thereby wasting space.

Tip! This is quite similar to how you’d written a list comprehension. If you want to learn more about Python List Comprehensions, check out my in-depth tutorial here . If you want to learn more about Python for-loops, check out my in-depth guide here .

Now that you know how to write a basic inline if statement in Python, let’s see how you can simplify it even further by omitting the else statement.

How To Write an Inline If Statement Without an Else Statement

Now that you know how to write an inline if statement in Python with an else clause, let’s take a look at how we can do this in Python.

Before we do this, let’s see how we can do this with a traditional if statement in Python

You can see that this still requires you to write two lines. But we know better – we can easily cut this down to a single line. Let’s get started!

We can see here that really what this accomplishes is remove the line break between the if line and the code it executes.

Now let’s take a look at how we can even include an elif clause in our inline if statements in Python!

Check out some other Python tutorials on datagy.io, including our complete guide to styling Pandas and our comprehensive overview of Pivot Tables in Pandas !

How to Write an Inline If Statement With an Elif Statement

Including an else-if, or elif , in your Python inline if statement is a little less intuitive. But it’s definitely doable! So let’s get started. Let’s imagine we want to write this if-statement:

Let’s see how we can easily turn this into an inline if statement in Python:

This is a bit different than what we’ve seen so far, so let’s break it down a bit:

First, we evaluate is x == 1. If that’s true, the conditions end and y = 10.
Otherwise, we create another condition in brackets
First we check if x == 20, and if that’s true, then y = 20. Note that we did not repeated y= here.
Finally, if neither of the other decisions are true, we assign 30 to y

This is definitely a bit more complex to read, so you may be better off creating a traditional if statement.

In this post, you learned how to create inline if statement in Python! You learned about the Python ternary operator and how it works. You also learned how to create inline if statements with else statements, without else statements, as well as with else if statements.

To learn more about Python ternary operators, check out the official documentation here .

Nik Piepenbreier

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

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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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.

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

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.

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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What are ternary operators in Python?

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.

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 .

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One line if statement in Python (ternary conditional operator)

In the real world, there are specific classifications and conditions on every action that occurs around us. A twelve-year-old person is a kid, whereas a thirteen-year-old person is a teenager. If the weather is pleasant, you can make plans for an outing. But if it isn’t, you will have to cancel your plans. These conditions control the coding world as well. You will encounter various coding problems where you will have to print the output based on some conditions.

Luckily, Python has a straightforward command and syntax to solve such kinds of problems. These are called conditional statements. So let’s begin our discussion on conditional statements, their syntax, and their applications.

Basic if Statement (Ternary Operator)

Many programming languages have a ternary operator , which defines a conditional expression. The most common usage is to make a terse, simple dependent assignment statement. In other words, it offers a one-line code to evaluate the first expression if the condition is true; otherwise, it considers the second expression. Programming languages derived from C usually have the following syntax:

The Python BDFL (creator of Python, Guido van Rossum) rejected it as non-Pythonic since it is hard to understand for people not used to C. Moreover, the colon already has many uses in Python. So, when PEP 308 was approved, Python finally received its shortcut conditional expression:

It first evaluates the condition; if it returns True , the compiler will consider expression1 to give the result, otherwise expression2 . Evaluation is lazy, so only one expression will be executed.

Let's take a look at this example:

Here we have defined the age variable whose value is fifteen. Now we use the if-else command to print if the kid is an adult or not. The condition for being an adult is that the person’s age should be eighteen or greater than that. We have mentioned this condition in the if-else command. Now let’s see what the output is:

As we can see, we have obtained the output as “kid” based on the value of the age variable.

We can chain the ternary operators as well:

Here we have incorporated multiple conditions. This form is the chained form of ternary operators. Let’s check the output:

This command is the same as the program given below :

The compiler evaluates conditions from left to right, which is easy to double-check with something like the pprint module:

Alternatives To The Ternary Operator

For Python versions lower than 2.5, programmers developed several tricks that somehow emulate the behavior of the ternary conditional operator. They are generally discouraged, but it's good to know how they work:

These are various ways to impose conditions in your code :

We can see that for various inputs, the same output is obtained for the exact value of the variable.

The problem of such an approach is that both expressions will be evaluated no matter what the condition is. As a workaround, lambdas can help:

We obtain the output as follows :

Another approach is using 'and' or 'or' statements:

Yes, most of the workarounds look ugly. Nevertheless, there are situations when it's better to use 'and' or 'or' logic than the ternary operator. For example, when your condition is the same as one of the expressions, you probably want to avoid evaluating it twice:

Indentations And Blocks

Python is very careful of the syntax of programming statements. We have to maintain proper indentation and blocks while we write composite statements like if-else . The correct indentation syntax of the if-else statement is given as follows:

The statements under 'if' are considered a part of one 'block.' The other statements are not part of the if block and are not considered when statements of 'if' are evaluated.

Python will automatically change the text color if you deviate from this indentation and display an error when you run your code. Let's take an example where we intentionally differ from the proper indentation:

We can see here that Python delivers an error message as: "Expected an indented block ."

Also, note the color of 'print' in line 3. All the other text is green, while 'print' has the color red. The color variation happens because of the abrupt indentation of 'print.'

Now let us correct the indentation :

When we have maintained the indentation of Python, we get the output hassle-free.

The else And elif Clauses

Suppose your ‘ if ’ condition is false and you have an alternative statement ready for execution. Then you can easily use the else clause. Now, suppose you have multiple if conditions and an alternative for each one. Then, you can use the elif clause and specify any number of situations. Now let us take an example for each case :

Use of else clause:

The syntax of the if-else statement is straightforward and has been used multiple times in this tutorial. Let us take a fundamental problem: There is a football team selection. The most critical condition for a candidate's eligibility is that he should be seventeen years or older. If his age is greater than or equal to seventeen, the output will be " You are eligible." If the boy is younger than seventeen years of age, the result will be " Sorry. You are not eligible."

Now let’s look at the code for this problem :

Let’s run this code and see what the output is :

The program first asks for the user input of age. We first enter the age as sixteen.

Now let us enter the age as eighteen and observe the output.

Thus we can see that the code assesses the input entered("age") and checks the value against the if-else conditions. If the condition is true, the compiler considers the statement under 'if ' and other statements are ignored. If the condition is false, the compiler executes the statement under 'else ,' and all the other statements are ignored.

Use of elif clause :

We use this clause when we have multiple conditions to check before printing the output. The word elif is compact for ‘ else-if .' When we use the elif clause, the else clause is optional. But if we want to use else clause, there has to be only one clause and that too at the end of the program.

Let us take a problem. We ask the user to enter a number between one and seven, and we display the corresponding weekday name. Let's look at the program for this problem.

The above-given code has elif as well as else clause.

Now let’s check the output:

The program first asks for user input of a number. Let’s enter four.

Now, let's check the output for the input value twelve.

Thus, the code works for any user-entered input value.

Conditions dominate every aspect of our real-life situations. To simulate these real-life conditions properly in our virtual world of coding, we, the programmers, need a good grasp on the control statements like if-else . We hope that this article helped you in understanding conditional statements and their syntax in Python. The various problems discussed in this article will help you understand the fundamental concepts of if-else statements and their applications.

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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 .

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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Ternary Operator in Python

Python Course for Beginners With Certification: Mastering the Essentials

The ternary operator in Python is a concise way of writing simple if/else statements in a single line. It returns a true or false value by evaluating a boolean condition. It is shorter and more readable than simple if/else statements. We can also implement ternary operators with tuples, lists, dictionaries, or lambda functions. Ternary operators can also be nested by chaining them.

What is Ternary Operator in Python?

The ternary operator (also known as a conditional expression) is a concise way of writing conditional statements in Python. It returns a value based on the result of a boolean condition.

Sometimes writing traditional if/else statements can become hectic. For instance, using if/else to simply print something:

It took 4 lines for such a simple task, we can achieve the same result with ternary operators in a single line.

In the following sections, we will see how to use the ternary operator in Python.

Takeaway The ternary operator is a way of writing simple if/else statements in a single line.

Syntax of Ternary Operator

Let's use the ternary operator to simplify the if/else statements written before.

Done! In just one line.

This conditional operator is given the name "ternary" because it is composed of three parts. Let's see each one of them in detail.

The ternary operator is composed of three operands.

Three Operands of Python Ternary Operator

The ternary operator includes three operands:

Condition: A boolean expression that evaluates to either True or False .
True value: A value (any Python object) to return if the condition is evaluated to True .
False value: A value (any Python object) to return if the condition is evaluated to False .
The ternary operator includes a condition to evaluate, a true value, and a false value to return based on that condition.

Example of Ternary Operator in Python

We have already seen how the ternary operator can be used to print different values. Let's see a couple more examples.

Example 1: Assigning Variable Using the Ternary Operator

The ternary operator allows us to assign one value to the variable if the condition is true, and another value if the condition is false.

Suppose we want to assign a variable action according to the value of the variable signal :

Set action to "move" if the value of the signal is "green light" otherwise set the action to "stop".

We can do this in a single line using the ternary operator:

Explanation:

First, the condition signal == "green light" is evaluated. As the value of the signal is "green light", the condition is evaluated to True and the first value of the ternary operator "move" is returned and then assigned to the variable action .

Example 2: Returning Values Using the Ternary Operator

Let's write a function that returns different fee amounts by checking if a customer is a member or not:

As the condition is_member is False this time, the second value is returned.

We can assign different values to a variable with the ternary operator depending on a condition.
We can use the ternary operator to return a value from a function depending on a condition.

Various Ways to Implement Ternary Operator in Python

Although we have the simple method of defining ternary operators as described above, we can also implement ternary operators using:

Lambda Functions

1. Implementing Ternary Operator with Tuples

We can implement the ternary operator with tuples:

Tuples are immutable data structures defined with parenthesis that stores an ordered sequence of values. For example: (1, 2, 3) is a tuple with three values. As tuples are immutable, you can not change the values inside it after it is defined.

We first created a tuple - ("stop", "move") with values for both true and false conditions. Then we used indexing - [signal == "green light"] to select one of the values.

The reason this works is that the condition will return either False or True which will be converted to 0 or 1 respectively when indexing.

Thus, the first value (0th index) corresponds to False and the second value(1st index) corresponds to True.

As in our case, the condition evaluates to True, thus "move" is returned.

2. Implementing Ternary Operator with Lists

We can implement the ternary operator with lists:

Lists are mutable data structure defined with square brackets that stores an ordered sequence of values.

For example: [1, 2, 3] is a list with three values.

Lists are similar to tuples, but they are mutable which means we can change the values inside of it.

This example is similar to tuple one. We just used a list instead of a tuple, the working remains the same.

3. Implementing Ternary Operator with Dictionaries

We can implement the ternary operator with dictionaries:

A dictionary is a collection of key: value pairs where each value is accessed using a unique key. It is defined using curly braces. For example: {"name": "harry", "age": 11} is a dictionary with two keys. Values are accessed using keys instead of indexes as in lists.

In this case, also a dictionary is created first and then the condition is evaluated and matched against the keys of the dictionary which are either True or False , and the corresponding value is returned.

Unlike tuples or lists, the order of true or false values doesn't matter with the dictionary.

4. Implementing Ternary Operator with Lambda Functions

We can implement the ternary operator with lambda functions:

A lambda function is an anonymous function that is defined without a name using the lambda keyword instead of the usual def keyword. For example: double = lambda x: x * 2 is a lambda function that takes one argument x .

We can also provide a lambda function inside of the tuple instead of hard-coded values as done before. One of the lambda functions will be called based on the condition.

You can also implement ternary operators with a tuple, list, dictionary, or lambda functions.

Ternary Operator vs if/else

If we have done the same example using traditional if/else statements it would take 4 lines:

Ternary operator is a better choice for simple if/else statements.

Nested Ternary Operator in Python

We can also nest ternary operators, here is an example:

This is equivalent to the following if/else statements:

Remember with great powers come great responsibility, just because you can nest ternary operators doesn't mean you should always do it. Nesting ternary operators may hurt readability and cause issues in development down the line.

We can nest ternary operators by chaining them.
We should avoid nesting as it may hurt readability.

Also, check out this article to learn about Operators in Python.

The ternary operator is a concise way of writing simple if/else statements in a single line.
Syntax: true_value if condition else false_value .
False value
You can assign variables with ternary operators.
Ternary operators are shorter and more readable than simple if/else statements.
We can nest ternary operators by chaining them to write complex if/else statements. This hurts readability and should be avoided.

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6. Ternary Operators
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6. Ternary Operators ¶

Ternary operators are more commonly known as conditional expressions in Python. These operators evaluate something based on a condition being true or not. They became a part of Python in version 2.4

Here is a blueprint and an example of using these conditional expressions.

It allows to quickly test a condition instead of a multiline if statement. Often times it can be immensely helpful and can make your code compact but still maintainable.

Another more obscure and not widely used example involves tuples. Here is some sample code:

This works simply because True == 1 and False == 0, and so can be done with lists in addition to tuples.

The above example is not widely used and is generally disliked by Pythonistas for not being Pythonic. It is also easy to confuse where to put the true value and where to put the false value in the tuple.

Another reason to avoid using a tupled ternery is that it results in both elements of the tuple being evaluated, whereas the if-else ternary operator does not.

This happens because with the tupled ternary technique, the tuple is first built, then an index is found. For the if-else ternary operator, it follows the normal if-else logic tree. Thus, if one case could raise an exception based on the condition, or if either case is a computation-heavy method, using tuples is best avoided.

ShortHand Ternary

In python there is also the shorthand ternary tag which is a shorter version of the normal ternary operator you have seen above.

Syntax was introduced in Python 2.5 and can be used in python 2.5 or greater.

The first statement ( True or “Some” ) will return True and the second statement ( False or “Some” ) will return Some .

This is helpful in case where you quickly want to check for the output of a function and give a useful message if the output is empty:

Or as a simple way to define function parameters with dynamic default values:

JavaScript Operators and Operator Precedence – Beginner‘s Guide

JavaScript makes heavy use of operators to manipulate values and variables in code. As a beginner, grasping how these operators work and their order of evaluation can be confusing initially. In this comprehensive 2650+ words guide, we will cover all the key concepts you need to know about operators in JavaScript.

What are Operators in JavaScript?

Operators are special symbols or keywords that perform operations on operands (values and variables).

For example:

The value that the operator works on is called the operand . Operands can be literal values, variables, or more complex expressions that resolve to a value.

According to Code Frequency analysis of open-source JavaScript projects on Github, some of the most commonly used operators are:

Operator	Frequency of Use
.	#1 most used
=	#3 most used
+	#6 most used
++	#8 most used
*	#11 most used

This shows operators are extensively used in real-world JavaScript code.

Some broad categories of JavaScript operators include:

Arithmetic operators
Assignment operators
Comparison operators
Logical operators
Bitwise operators

Later we will explain the most popular operators falling under these categories that every JavaScript developer should know.

Operator Precedence in JavaScript

Operator precedence determines the order in which operations get performed in expressions with multiple operators.

Certain operators have higher precedence than others. For example, the multiplication operator has a higher precedence than the addition operator.

Consider this example:

Here, the multiplication happens first before the addition because it has higher precedence. So 3 * 4 equals 12, then 2 gets added giving 14.

The overall order is:

Parentheses
Exponential
Multiplication & Division (left-to-right)
Addition & Subtraction (left-to-right)

Following this exact precedence order, JavaScript evaluates each expression from start to finish.

Understanding this flow helps avoid errors in complex statements with multiple operators. Getting it wrong can lead to unintended behavior.

Now let‘s dive deeper into the most popular JavaScript operator categories.

Types of Operators in JavaScript

JavaScript includes a diverse range of operators to perform different types of actions and operations. We will focus on the operators you need to know as a beginner.

1. Arithmetic Operators

These operators are used to perform common mathematical calculations:

This covers basic math operations like addition/subtraction to more complex ones like modulus, exponential, increment and decrement.

According to Code Frequency stats, the + and ++ operators appear in the top 10 most used operators in JavaScript code on Github.

Arithmetic operators take numerical values (either literals or variables) as their operands. By combining them, we can execute complex math in JavaScript.

2. Assignment Operators

The most basic assignment operator is = that assigns a value to a variable.

Some other assignment operators:

These operators first perform the arithmetic operation on current and new value, and assign the result back to the variable.

Assignment operators rank high in usage frequency – the basic = assignment operator is #3 most used across JavaScript projects per Code Frequency.

3. Comparison Operators

Comparison operators compare two operand values or expressions and return a boolean true / false result based on that condition.

The comparison operators supported in JavaScript are:

Equal to (==)
Strict equal to (===)
Not equal to (!=)
Strict not equal (!==)
Greater than (>)
Greater than or equal to (>=)
Less than (<)
Less than or equal to (<=)

Equal to (==) compares values only, while strict equal to (===) checks the value AND matches data type.

Using === avoids unexpected type coercions.

4. Logical Operators

Logical operators evaluate an expression and return a boolean true or false.

The logical operators are:

Logical AND (&&)
Logical OR (||)
Logical NOT (!)

These allow creating complex boolean logic conditions in code.

5. Ternary Operator

The ternary or conditional operator assigns a value to a variable based on a condition.

It adheres to this syntax:

This acts as a shorthand for if-else statement in assigning values based on conditions.

Operator Precedence Revisited

We learned about different JavaScript operators. But what happens when an expression contains multiple operators? Operator precedence rules decide the order of evaluation.

Here, multiplication occurs before addition because it has higher precedence. So x equals 25 instead of 35.

The complete JavaScript operator precedence order is:

Grouping – parentheses ()
Negation/Increment operators (!, –, ++)
Exponential (**)
Multiplication/Division (*, /)
Addition/Subtraction (+, -)
Relational operators (<, <=, >, >=)
Equality operators (==, !=, ===, !==)
Assignment operators (=, +=, -= etc.)
Comma Operator (,)

This precise precedence hierarchy is applied while evaluating expressions with multiple operators.

Understanding this order allows you to correctly reason about complex statements. Getting it wrong will lead to unintended behavior.

Examples of Operator Precedence

Let‘s breakdown a few examples in detail:

These examples clearly show how operator precedence can dramatically affect results.

Familiarity with these rules will let you interpret and debug complex code much more easily.

Getting precedence wrong is a common source of unexpected behavior and bugs in JavaScript code.

Common Operator Precedence Errors

Some typical errors that occur due to incorrect assumptions about operator precedence:

1. Incorrect string concatenation

2. Mathematical calculations go wrong

3. Incorrect boolean logic

These kinds of errors can be avoided by properly applying the operator precedence rules.

Using parentheses also helps in overriding default precedence when unsure.

Associativity of Operators

Associativity refers to the direction in which operators of the same precedence are evaluated.

Most JavaScript operators are left-to-right associative – they group left-to-right in the absence of parentheses.

But a few operators show right-to-left associativity – they group right-to-left direction.

For instance, the exponentiation operator (**) is right-to-left associative:

This behavior can cause mistakes if we assume left-to-right evaluation for everything.

So be aware of associativity rules to accurately reason about complex expressions.

Recommended Practices

When working extensively with operators, adopt these best practices:

Use parentheses to explicitly dictate order, improves readability
Break down large expressions into smaller components
Use descriptive variable/function names
Follow a consistent formatting style guide
Add comments explaining complex logic flow and operations
Use strict equality checks (===) instead of loose equality (==)
Use template literals for string manipulation instead of concatenation
Prefer increment/decrement operators (++/–) when possible

Cultivating these good habits early on will help avoid lots of pitfalls later in complex JavaScript applications.

New and Experimental Operators

JavaScript continues to evolve with newer ECMAScript standards introducing experimental features.

Some operators added recently:

1. Nullish Coalescing Operator (??)

The ?? operator returns the right-hand value when the left one is null or undefined.

2. Optional Chaining Operator (?.)

The ?. operator stops evaluation if trying to access non-existent property.

These experimental operators open new possibilities and use cases going forward.

In this comprehensive 2600+ words guide, we started by answering:

What operators are and how they evaluate operands
Different categories of operators like arithmetic, logical and more
Operator precedence determines order of evaluation
Examples showed precedence dramatically affects results

We then covered specifics of the most popular operators used in JavaScript:

Arithmetic operators for math calculations
Assignment operators to assign values
Comparison operators return boolean result
Logical operators combine boolean logic
Ternary operator shorthands if-else conditional

And some key concepts like:

Operator precedence order – which operator gets evaluated first
Common mistakes due to incorrect assumptions
Associativity rules – left-to-right or right-to-left
Best practices for clean and optimized code

JavaScript operators provide the building blocks that power complex logic. Mastering them early on gives a major boost for becoming an efficient JavaScript programmer.

Of all the operator concepts covered, which one did you find most useful and why? Let me know in the comments!

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

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

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

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CS 2110: Object-Oriented Programming and Data Structures

Assignment 1.

A1 consists of a series of exercises to help you transition to procedural programming in the Java language. The problem-solving elements are at the level of lab exercises from CS 1110/1112. The assignment comes bundled with a thorough test suite, so you will know when you have implemented each method’s specifications correctly.

You must work on this assignment independently (no partners)—we want to ensure that every student can write, test, and submit Java code on their own. For this reason, we are also grading this assignment for mastery ; if a grader identifies mistakes that you didn’t catch yourself, you may resubmit once to correct them without penalty.

Learning objectives

Author Java code in the IntelliJ IDEA IDE.
Employ operations on Java’s primitive types ( boolean , int , double ) and strings to solve high-level problems.
Employ Java control structures ( if , for , while ) to implement algorithms for solving high-level problems.
Select relevant mathematical functions from Java’s standard library by referencing JavaDoc pages.
Verify the correctness of implementations by running JUnit test cases.
Adopt good programming style to facilitate readability and maintenance.
Witness examples of precise method specifications that separate “what” from “how”.

If you are worried that these exercises seem a bit dry and mathematical, that is a consequence of restricting ourselves to Java’s primitive types (which are mostly numbers). Once we get to object-oriented programming in A2, the problem domains will become richer.

Collaboration policy

This assignment is to be completed as an individual. You may talk with others to discuss Java syntax, debugging tips, or navigating the IntelliJ IDE, but you should refrain from discussing algorithms that might be used to solve the problems, and you must never show your in-progress or completed code to another student. Consulting hours are the best way to get individualized assistance at the source code level.

Frequently asked questions

There is a pinned post on Ed where we will post any clarifications for this assignment. Please review it before asking a new question in case your concern has already been addressed. You should also review the FAQ before submitting to see whether there are any new ideas that might help you to improve your solution.

I. Getting started

You already know at least one procedural language (e.g. Python) with which you should be able to solve the problems on this assignment. If that language is not Java, then the goal is for you to become comfortable with procedural Java syntax, as it compares with what you already know, by practicing it in targeted problems. Start by reading transition to Java on the course website, which provides a focused translation guide between Python, MATLAB, and Java.

Download the release code from the CMSX assignment page; it is a ZIP file named “a1-release.zip”. Decide where on your computer’s disk you want your project to be stored (we recommend a “CS2110” directory under your home or documents folder), then extract the contents of the ZIP file to that directory. Find the folder simply named “a1” (depending on your operating system, this may be under another folder named “a1-release”); its contents should look like this:

We assume you have already followed the setup instructions for IntelliJ, possibly in your first discussion section. It is important that JDK 21 has been downloaded.

In IntelliJ, select File | Open , then browse to the location of this “a1” directory, highlight “a1”, and click OK . IntelliJ may ask whether you want to open the project in the same window or a new one; this is up to you, noting that if you choose the same window, whatever project you previously had open (e.g. a discussion activity or old assignment) will be closed.

Please keep track of how much time you spend on this assignment. There is a place in “reflection.txt” for reporting this time, along with asserting authorship.

II. Working with the provided code

Specifications and preconditions.

A recurring theme in this course is distinguishing between the roles of client and implementer . For methods, a client is someone who calls a method, and the implementer is someone who writes the method’s body. Although you might act both as client and implementer in a small project, ideally the two roles have no knowledge of one another, so you should keep track of which role you are currently in and “split your brain” accordingly. For most of this assignment you will be in the implementer role relative to the assignment’s methods.

Each method is accompanied by a specification in a JavaDoc comment. As the implementer, your job is to write a method body that fulfills that specification. Specifications may include a precondition phrased as a “requires” clause. As the implementer, you can assume that the precondition is already satisfied. You do not have to attempt to check the precondition or to handle invalid arguments in any special way. It would be the responsibility of clients to ensure that they do not violate such conditions.

For example, if a specification contains the precondition “ nTerms is non-negative”, then the client is never permitted to pass negative arguments to the function. If the client nonetheless does so, the specification promises nothing about the result. Specifically, the specification does not promise that the function will check for non-negativity, and the specification does not promise that any kind of error will be produced. That means the implementer has an easy job: they can simply ignore the possibility of negative arguments.

You might have been taught in previous programming classes that implementers must always check preconditions, or must always produce an error when a precondition is violated. Those are useful and important defensive programming techniques! (And we will see how to employ them in Java soon.) But the point we are making here is that they are not mandated by a “requires” clause. So in this assignment, you do not have to use such techniques, and it’s likely to be easier for you to omit them entirely.

Replacing method “stubs”

The body of each method initially looks like this:

This is a placeholder —it allows the method to compile even though it doesn’t return a value yet, but it will cause any tests of the method to fail. You should delete these lines as the first step when implementing each method. (We’ll discuss the meaning of these lines later in the course when we cover exceptions, objects, and so forth.)

Use the TODO comments to guide you to work that still needs to be done. As you complete each task, remove the comment line with the TODO since it doesn’t need doing anymore! Temporary comment prefixes like TODO and FIXME are a convenient way to keep track of your progress when writing and debugging code; IntelliJ will even track them for you in its TODO window .

Finally, some method bodies contain comments with “implementation constraints.” These are not part of the specification, as they do not affect potential clients. Instead, they are requirements of the assignment to ensure that you get practice with the necessary skills. Violating these constraints will not cause unit tests to fail, but points will be deducted by your grader, so make sure you obey them.

Test cases for each method are in “tests/cs2110/A1Test.java”. You are encouraged to read the test suites to see what corner cases are considered, but you do not need to add any tests of your own for this assignment.

To run a test case, click the green arrow to the left of the method name, then select “Run”; the results will be shown at the bottom of the screen. To run all test cases, use the arrow to the left of the class name ( A1Test ). If a test fails with a yellow “X”, that means it returned the wrong result. By reading the messages in the output window, you should be able to determine which case failed and what your implementation computed instead. If it fails with a red “!”, that means it encountered an error (or you forgot to delete the placeholder throw line).

Take note of the style of these tests. While you may not understand all of the Java syntax yet, you should see that related tests are grouped together and given descriptive names. This makes it easier to debug your code, since your IDE will clearly show which scenarios are behaving as expected and which are not. You are expected to adopt this style for your own tests on future assignments.

III. Assignment walkthrough

Implement the methods one at a time. As soon as you implement one, run the corresponding test case to verify your work. Do not modify any method signatures (or return types or throws clauses)—not only would that change the class type we provided, but it would make it impossible for our autograder to interoperate with your code. And do not leave print statements in your final submission unless the method specification mentions printing output as a side effect.

Each of the numbered exercises below references a section of the Supplement 1 chapter of the primary course textbook, Data Structures and Abstraction with Java , 5th edition, to which you should have access in Canvas through the “Course Materials” link. That supplement is designed to help students who know how to program, but are new to Java. It is a great resource for making the transition to Java.

1. Regular polygons

[Textbook: S1.29: The Class Math ]

The area of a regular polygon with n sides of length s is given by the formula:

$$A = \frac{1}{4} s^2 \frac{n}{\tan(\pi/n)}$$

Implement this formula. You will need one or more math functions and/or constants from Java’s standard library. Skim the JavaDoc page for the static methods in the Math class to learn which functions are available. Remember that a Math. prefix is required when calling them (so to compute the absolute value of -5, you would write Math.abs(-5) ).

Take some time to explore these functions and get a feel for how Java’s standard library is documented. The functions you are most likely to use later in the course include: abs() , min() , max() , sqrt() , pow() , and the trigonometric functions.

Note: methods dealing with dimensionful quantities (like length and area) should always say something about what units (e.g. meters, acres) those quantities are measured in. In this case, the formula makes no assumptions about units, so the specification simply tells the client that the units of the output are compatible with the units of the input.

2. Collatz sequence

[Textbook: S1.59: The while Statement]

The Collatz conjecture is a fun piece of mathematical trivia: by repeatedly performing one of two simple operations on a positive integer (depending on whether it is even or odd), you always seem to get back to 1. The rules for determining the next number in the sequence are:

If the last number was even, divide it by 2.
If the last number was odd, multiply it by 3 and add 1.

Our objective is to sum all of the terms in the sequence starting from a given “seed” number until we get to 1. This involves indefinite iteration , and you should use a while loop for this.

This is also a chance to practice problem decomposition and defining new methods. Declare and implement a method named nextCollatz() that takes one int argument and returns an int value according to the given specification. When you have done this, remove the TODO and uncomment the relevant test case in A1Test (it was commented out because the test case will not compile unless that method is at least declared, preventing you from running tests for other methods in the suite). Tip: there is an IntelliJ keyboard shortcut for commenting and uncommenting whole selections of code; can you find it?

3. Median of three

[Textbook: S1.38: The if-else Statement]

The median value of a collection of numbers is the value that would be in the middle if the collection were sorted. A special case is median-of-three voting , which is used in fault-tolerant systems to decide how to proceed when not all components agree. This small function is actually one of the most commonly-run procedures in SpaceX’s flight software, helping it determine which sensors and commands to trust dozens of times per second.

You need to develop an algorithm for determining which of three numbers is the middle value. The numbers could be in any order, and there could be duplicates. Use a chain of conditional statements ( if / else ), possibly nested, to find the middle value.

4. Interval overlaps

[Textbook: S1.41: Boolean Expressions]

Intervals are a useful abstraction when working with schedules. For example, if class meeting times are represented as intervals over the seconds of a day, then an overlap would imply that two classes conflict.

This exercise is designed to help you avoid a common “anti-pattern” among new programmers:

(here, expr is a Boolean expression, like x > 0 ). Remember that the conditions used in if statements are expressions that yield a boolean value and can be used anywhere a boolean value like true or false could be used. Therefore, the above code can (and should) be rewritten as:

Keeping this in mind, implement intervalsOverlap() using a single return statement.

5. Estimating pi

[Textbook: S1.61: The for Statement]

The Madhava-Leibniz series is an infinite sum of numbers that is related to π:

$$\frac{\pi}{4} = 1 - \frac{1}{3} + \frac{1}{5} - \frac{1}{7} + \frac{1}{9} - \ldots$$

Observe that the denominators of the terms are the sequence of odd integers and that the sign alternates between plus and minus.

By truncating this series after a finite number of terms, we get an approximation for π (though this particular formula requires many terms for even modest accuracy). Use a for -loop to evaluate this approximation for a specified number of terms (this is an example of definite iteration ). Recall that integer division in Java rounds down to another integer, so you may need to cast some numbers to a floating-point type when evaluating the fractions.

As a corner case, note that the sum of zero terms is 0.

6. Palindromes

[Textbook: S1.67: The Class String ]

With control structures and primitive types out of the way, it’s time to get some experience with our first aggregate type : String (an aggregate type is one that groups together multiple values, like how a String contains multiple char s). Strings get some special treatment in Java; while they are objects , they are immutable (their contents can’t be changed), which means they behave much like primitive values. And unlike other objects, they have their own literals and even an operator ( + for concatenation). But as a sequence of characters they are like arrays, giving you some early practice with algorithms that iterate over data.

A palindrome has the same sequence of characters when written backwards as when written normally. To examine the i th character in string s , use s.charAt(i) . The total number of characters in s is given by s.length() . In Java, the index of the first character is 0 , and the index of the last character is s.length() - 1 . The specifications for these methods are in the API documentation for String ; by calling them, you are now in the client role with respect to the String class.

7. Formatting messages

[Textbook: S1.70: Concatenation of Strings]

A common task in computing systems is to format information to be read by humans. The system may need to support translations in multiple languages, and sometimes words will change depending on the data being explained (e.g. singular vs. plural nouns, “a” vs. “an” depending on the following word, etc.). To manage this potential complexity, it is a good idea to move formatting logic into its own function (Java actually provides a sophisticated infrastructure for managing this in large applications, but that is beyond the scope of this course).

This exercise requires you to concatenate strings and to format numerical data as a string. There are several approaches you can take; the + operator is probably most convenient, but if you have used a format or printf feature in another language, you might be interested in String ’s format() method. This is also a good opportunity to get practice with Java’s ternary operator ?: , an awkward-to-read but extremely useful bit of syntax; use this to decide between the singular and plural forms of “item” without using an if -statement.

8. Making a program

Now it’s time to step into the client role. Add a main() method so that the A1 class can be run as a program. Find a way to use at least four of the methods in A1 in combination, then print the final result. Is is okay to be silly here! Ideas include:

Compute the sums of three Collatz sequences of your choice, then take their median; use this as the number of sides of a polygon. For the length of the polygon’s sides, use an estimate of π. Print the polygon’s area.
Compute the median of three numbers of your choice, then find the next number in the Collatz sequence after it. Use this as the number of items in an order, then determine whether that order’s confirmation message is a palindrome.

You can be creative here and provide arbitrary inputs as necessary, but all four methods must contribute somehow to the final result. For this assignment, you should hard-code your inputs, rather than expecting program arguments (in other words, ignore args ).

Your program’s printed output should be a complete sentence written in English. It should describe what final operation was performed, what its inputs were, and what the result was (the inputs and outputs should not be baked into the printed text; instead, you should print the values of program variables or expressions). For example, “The area of a polygon with 4 sides of length 0.5 m is 0.25 m^2.” When you run your program, make sure this is the only output (i.e., that there are no “debugging prints” in the other methods you are calling).

9. Reflecting on your work

Stepping back and thinking about how you approached an assignment (a habit called metacognition ) will help you make new mental connections and better retain the skills you just practiced. Therefore, each assignment will ask you to write a brief reflection in a file called “reflection.txt”. This file is typically divided into three sections:

Submitter metadata: Assert authorship over your work, and let us know how long the assignment took you to complete.
Verification questions: These will ask for a result that is easily obtained by running your assignment. (Do not attempt to answer these using analysis; the intent is always for you to run your program, possibly provide it with some particular input, and copy its output.)
Reflection questions: These will ask you write about your experience completing the assignment. We’re not looking for an essay, but we do generally expect a few complete sentences.

Respond to the four TODOs in “reflection.txt” (you can edit this file in IntelliJ).

IV. Scoring

This assignment is evaluated in the following categories (note: weights are approximate and may be adjusted slightly in final grading):

Submitted and compiles (25%)
Fulfills specifications (41%)
Complies with implementation constraints (25%)
Exhibits good code style (5%)
Responds to reflection questions (4%)

You can maximize the “fulfilling specifications” portion of your score by passing all of the included unit tests (don’t forget to uncomment the ones for nextCollatz() ). The smoketester will also run these tests for you when you submit so you can be sure that your code works as well for us as it does for you.

Formatting is a subset of style. To be on the safe side, ensure that our style scheme is installed and that you activate “Reformat Code” before submitting. Graders will deduct for obvious violations that detract from readability, including improper indentation and misaligned braces.

But beyond formatting, choose meaningful local variable names, follow Java’s capitalization conventions (camelCase), and look for ways to simplify your logic. If the logic is subtle or the intent of a statement is not obvious, clarify with an implementation comment.

V. Submission

Upload your “A1.java” and “reflection.txt” files to CMSX before the deadline. If you forgot where your project is saved on your computer, you can right-click on “A1.java” in IntelliJ’s project browser and select “Open In”, then your file explorer (e.g. “Explorer” for Windows, “Finder” for Mac). Be careful to only submit “.java” files, not files with other extensions (e.g. “.class”).

After you submit, CMSX will automatically send your submission to a smoketester , which is a separate system that runs your solution against the same tests that we provided to you in the release code. The purpose of the smoketester is to give you confidence that you submitted correctly. You should receive an email from the smoketester shortly after submitting. Read it carefully, and if it doesn’t match your expectations, confirm that you uploaded the intended version of your file (it will be attached to the smoketester feedback). Be aware that these emails occasionally get misclassified as spam, so check your spam folder. It is also possible that the smoketester may fall behind when lots of students are submitting at once. Remember that the smoketester is just running the same tests that you are running in IntelliJ yourself, so don’t panic if its report gets lost—we will grade all work that is submitted to CMSX, whether or not you receive the email.

(Note: it may take us a day after the assignment is released before the smoketester is up and running.)

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COMMENTS

python ternary operator with assignment
1. For those interested in the ternary operator (also called a conditional expression ), here is a way to use it to accomplish half of the original goal: q = d [x] if x in d else {} The conditional expression, of the form x if C else y, will evaluate and return the value of either x or y depending on the condition C.
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The ternary operator can also be used in Python nested if-else statement. the syntax for the same is as follows: Syntax: true_value if condition1 else (true_value if condition2 else false_value) Example: In this example, we are using a nested if-else to demonstrate ternary operator. If 'a' and 'b' are equal then we will print 'a and b ...
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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: ... 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 ...
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1. Using Ternary Operator. The ternary operator is very special operator in Python, it is used to assign a value to a variable based on some condition. It goes like this: variable = condition ? value_if_true : value_if_false. Here, the value of variable will be value_if_true if the condition is true, else it will be value_if_false.
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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: value_if_true if condition else value_if_false Code language: Python (python) The ternary operator evaluates the condition. If the result is True, it returns the value_if_true.
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The syntax of Ternary Operator in Python is. [value_1] if [expression] else [value_2] 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.
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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. Example 3
Python's Ternary Operator: A Quick and Efficient Decision-Making Tool
In this tutorial, we will explore the ternary conditional expression in Python, how it works, and provide examples to illustrate its usage. Table of Contents. 1. Basic Syntax. 2. How the Ternary Operator Works. 3. Examples. Simple Ternary Expression; Using the Ternary Operator for Assignment; Nested Ternary Expressions; 4. Benefits and Use ...
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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.
Conditional Statements in Python
Conditional Expressions (Python's Ternary Operator) ... 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 ...
Python Ternary Operator with Example
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 ...
Conditional expression (ternary operator) in Python
Basics of the conditional expression (ternary operator) In Python, the conditional expression is written as follows. The condition is evaluated first. If condition is True, X is evaluated and its value is returned, and if condition is False, Y is evaluated and its value is returned. If you want to switch the value based on a condition, simply ...
Inline If in Python: The Ternary Operator in Python • datagy
A ternary operator is an inline statement that evaluates a condition and returns one of two outputs. It's an operator that's often used in many programming languages, including Python, as well as math. The Python ternary operator has been around since Python 2.5, despite being delayed multiple times.
Python Ternary Operator
Here's what the syntax looks like: [option1] if [condition] else [option2] 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.
6. Expressions
comprehension::= assignment_expression comp_for comp_for ::= ["async"] ... operator is intended to be used for matrix multiplication. No builtin Python types implement this operator. New in version 3.5. The / ... (sometimes called a "ternary operator") have the lowest priority of all Python operations. ...
Python ternary operators
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. Learn how to use Python ternary operators efficiently with examples and explanations.
One line if statement in Python (ternary conditional operator)
Many programming languages have a ternary operator, which defines a conditional expression. The most common usage is to make a terse, simple dependent assignment statement. In other words, it offers a one-line code to evaluate the first expression if the condition is true; otherwise, it considers the second expression.
Master the Potential of Python Ternary Operator
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.
Ternary Operator in Python
The ternary operator (also known as a conditional expression) is a concise way of writing conditional statements in Python. It returns a value based on the result of a boolean condition. Sometimes writing traditional if/else statements can become hectic. For instance, using if/else to simply print something:
6. Ternary Operators
6. Ternary Operators. Edit on GitHub. 6. Ternary Operators ¶. Ternary operators are more commonly known as conditional expressions in Python. These operators evaluate something based on a condition being true or not. They became a part of Python in version 2.4. Here is a blueprint and an example of using these conditional expressions.
Learn JavaScript Operators
Assignment Operators. ... Ternary Operator. The conditional ternary operator provides a compact shorthand for if-else statements. Here is the syntax: ... His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next.js, and React, which he presents in an accessible and engaging manner. ...
JavaScript Operators and Operator Precedence
Assignment operators to assign values; ... Ternary operator shorthands if-else conditional; And some key concepts like: Operator precedence order - which operator gets evaluated first; Common mistakes due to incorrect assumptions; ... His teaching repertoire includes a wide range of languages and frameworks, such as Python, JavaScript, Next ...
EECS 201
layout: true <div class=bot-bar> Python </div> --- class: center, middle # Python #### `import tensorflow as tf` --- # Overview * High level scripting * What is ...
Assignment 1 (CS 2110 Fall 2024)
This is also a good opportunity to get practice with Java's ternary operator ?:, an awkward-to-read but extremely useful bit of syntax; use this to decide between the singular and plural forms of "item" without using an if-statement. 8. Making a program. Now it's time to step into the client role.

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