python if condition with assignment

Python Conditional Assignment

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

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

1. Using Ternary Operator

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

It goes like this:

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

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

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

2. Using if-else statement

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

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

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

3. Using Logical Short Circuit Evaluation

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

The format of logical short circuit evaluation is:

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

Let's see an example:

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

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

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

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

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

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

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

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

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

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

Happy coding! 😊

How to Write the Python if Statement in one Line

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Have you ever heard of writing a Python if statement in a single line? Here, we explore multiple ways to do exactly that, including using conditional expressions in Python.

The if statement is one of the most fundamental statements in Python. In this article, we learn how to write the Python if in one line.

The if is a key piece in writing Python code. It allows developers to control the flow and logic of their code based on information received at runtime. However, many Python developers do not know they may reduce the length and complexity of their if statements by writing them in a single line.

For this article, we assume you’re somewhat familiar with Python conditions and comparisons. If not, don’t worry! Our Python Basics Course will get you up to speed in no time. This course is included in the Python Basics Track , a full-fledged Python learning track designed for complete beginners.

We start with a recap on how Python if statements work. Then, we explore some examples of how to write if statements in a single line. Let’s get started!

How the if Statement Works in Python

Let’s start with the basics. An if statement in Python is used to determine whether a condition is True or False . This information can then be used to perform specific actions in the code, essentially controlling its logic during execution.

The structure of the basic if statement is as follows:

The <expression> is the code that evaluates to either True or False . If this code evaluates to True, then the code below (represented by <perform_action> ) executes.

Python uses whitespaces to indicate which lines are controlled by the if statement. The if statement controls all indented lines below it. Typically, the indentation is set to four spaces (read this post if you’re having trouble with the indentation ).

As a simple example, the code below prints a message if and only if the current weather is sunny:

The if statement in Python has two optional components: the elif statement, which executes only if the preceding if/elif statements are False ; and the else statement, which executes only if all of the preceding if/elif statements are False. While we may have as many elif statements as we want, we may only have a single else statement at the very end of the code block.

Here’s the basic structure:

Here’s how our previous example looks after adding elif and else statements. Change the value of the weather variable to see a different message printed:

How to Write a Python if in one Line

Writing an if statement in Python (along with the optional elif and else statements) uses a lot of whitespaces. Some people may find it confusing or tiresome to follow each statement and its corresponding indented lines.

To overcome this, there is a trick many Python developers often overlook: write an if statement in a single line !

Though not the standard, Python does allow us to write an if statement and its associated action in the same line. Here’s the basic structure:

As you can see, not much has changed. We simply need to “pull” the indented line <perform_action> up to the right of the colon character ( : ). It’s that simple!

Let’s check it with a real example. The code below works as it did previously despite the if statement being in a single line. Test it out and see for yourself:

Writing a Python if Statement With Multiple Actions in one Line

That’s all well and good, but what if my if statement has multiple actions under its control? When using the standard indentation, we separate different actions in multiple indented lines as the structure below shows:

Can we do this in a single line? The surprising answer is yes! We use semicolons to separate each action in the same line as if placed in different lines.

Here’s how the structure looks:

And an example of this functionality:

Have you noticed how each call to the print() function appears in its own line? This indicates we have successfully executed multiple actions from a single line. Nice!

By the way, interested in learning more about the print() function? We have an article on the ins and outs of the print() function .

Writing a Full Python if/elif/else Block Using Single Lines

You may have seen this coming, but we can even write elif and else statements each in a single line. To do so, we use the same syntax as writing an if statement in a single line.

Here’s the general structure:

Looks simple, right? Depending on the content of your expressions and actions, you may find this structure easier to read and understand compared to the indented blocks.

Here’s our previous example of a full if/elif/else block, rewritten as single lines:

Using Python Conditional Expressions to Write an if/else Block in one Line

There’s still a final trick to writing a Python if in one line. Conditional expressions in Python (also known as Python ternary operators) can run an if/else block in a single line.

A conditional expression is even more compact! Remember it took at least two lines to write a block containing both if and else statements in our last example.

In contrast, here’s how a conditional expression is structured:

The syntax is somewhat harder to follow at first, but the basic idea is that <expression> is a test. If the test evaluates to True , then <value_if_true> is the result. Otherwise, the expression results in <value_if_false> .

As you can see, conditional expressions always evaluate to a single value in the end. They are not complete replacements for an if/elif/else block. In fact, we cannot have elif statements in them at all. However, they’re most helpful when determining a single value depending on a single condition.

Take a look at the code below, which determines the value of is_baby depending on whether or not the age is below five:

This is the exact use case for a conditional expression! Here’s how we rewrite this if/else block in a single line:

Much simpler!

Go Even Further With Python!

We hope you now know many ways to write a Python if in one line. We’ve reached the end of the article, but don’t stop practicing now!

If you do not know where to go next, read this post on how to get beyond the basics in Python . If you’d rather get technical, we have a post on the best code editors and IDEs for Python . Remember to keep improving!

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Python One Line Conditional Assignment

Problem : How to perform one-line if conditional assignments in Python?

Example : Say, you start with the following code.

You want to set the value of x to 42 if boo is True , and do nothing otherwise.

Let’s dive into the different ways to accomplish this in Python. We start with an overview:

Exercise : Run the code. Are all outputs the same?

Next, you’ll dive into each of those methods and boost your one-liner superpower !

Method 1: Ternary Operator

The most basic ternary operator x if c else y returns expression x if the Boolean expression c evaluates to True . Otherwise, if the expression c evaluates to False , the ternary operator returns the alternative expression y .

Let’s go back to our example problem! You want to set the value of x to 42 if boo is True , and do nothing otherwise. Here’s how to do this in a single line:

While using the ternary operator works, you may wonder whether it’s possible to avoid the ...else x part for clarity of the code? In the next method, you’ll learn how!

If you need to improve your understanding of the ternary operator, watch the following video:

You can also read the related article:

• Python One Line Ternary

Method 2: Single-Line If Statement

Like in the previous method, you want to set the value of x to 42 if boo is True , and do nothing otherwise. But you don’t want to have a redundant else branch. How to do this in Python?

The solution to skip the else part of the ternary operator is surprisingly simple— use a standard if statement without else branch and write it into a single line of code :

To learn more about what you can pack into a single line, watch my tutorial video “If-Then-Else in One Line Python” :

Method 3: Ternary Tuple Syntax Hack

A shorthand form of the ternary operator is the following tuple syntax .

Syntax : You can use the tuple syntax (x, y)[c] consisting of a tuple (x, y) and a condition c enclosed in a square bracket. Here’s a more intuitive way to represent this tuple syntax.

In fact, the order of the <OnFalse> and <OnTrue> operands is just flipped when compared to the basic ternary operator. First, you have the branch that’s returned if the condition does NOT hold. Second, you run the branch that’s returned if the condition holds.

Clever! The condition boo holds so the return value passed into the x variable is the <OnTrue> branch 42 .

Don’t worry if this confuses you—you’re not alone. You can clarify the tuple syntax once and for all by studying my detailed blog article.

Related Article : Python Ternary — Tuple Syntax Hack

Python One-Liners Book: Master the Single Line First!

Python programmers will improve their computer science skills with these useful one-liners.

Python One-Liners will teach you how to read and write “one-liners”: concise statements of useful functionality packed into a single line of code. You’ll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert.

The book’s five chapters cover (1) tips and tricks, (2) regular expressions, (3) machine learning, (4) core data science topics, and (5) useful algorithms.

Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills . You’ll learn about advanced Python features such as list comprehension , slicing , lambda functions , regular expressions , map and reduce functions, and slice assignments .

You’ll also learn how to:

• Leverage data structures to solve real-world problems , like using Boolean indexing to find cities with above-average pollution
• Use NumPy basics such as array , shape , axis , type , broadcasting , advanced indexing , slicing , sorting , searching , aggregating , and statistics
• Calculate basic statistics of multidimensional data arrays and the K-Means algorithms for unsupervised learning
• Create more advanced regular expressions using grouping and named groups , negative lookaheads , escaped characters , whitespaces, character sets (and negative characters sets ), and greedy/nongreedy operators
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By the end of the book, you’ll know how to write Python at its most refined , and create concise, beautiful pieces of “Python art” in merely a single line.

Get your Python One-Liners on Amazon!!

One line if without else in Python

This tutorial shows you examples of writing intuitive one line if statements in practice.

1. One line if statement

2. ternary operator syntax without else.

Using a regular if statement has less repetition and easier to read.

3. Shorthand syntax with the and operator

Take your skills to the next level ⚡️.

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.

About The Author

Anton Caceres

• Python Tips and Tricks

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Conditional expression (ternary operator) in Python

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

• 6. Expressions - Conditional expressions — Python 3.11.3 documentation

Basics of the conditional expression (ternary operator)

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

See the following article for if statements in Python.

• Python if statements (if, elif, else)

In Python, the conditional expression is written as follows.

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

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

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

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

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

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

• Boolean operators in Python (and, or, not)

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

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

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

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

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

See the following article for details on list comprehensions.

• List comprehensions in Python

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

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

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

• Lambda expressions in Python

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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

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

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

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

These conditional tasks can be achieved using the if statement.

• Python if Statement

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

Here, condition is a boolean expression, such as number > 5 , that evaluates to either True or False .

• If condition evaluates to True , the body of the if statement is executed.
• If condition evaluates to False , the body of the if statement will be skipped from execution.

Let's look at an example.

• Example: Python if Statement

Sample Output 1

If user enters 10 , the condition number > 0 evaluates to True . Therefore, the body of if is executed.

Sample Output 2

If user enters -2 , the condition number > 0 evaluates to False . Therefore, the body of if is skipped from execution.

Indentation in Python

Python uses indentation to define a block of code, such as the body of an if statement. For example,

Here, the body of if has two statements. We know this because two statements (immediately after if ) start with indentation.

We usually use four spaces for indentation in Python, although any number of spaces works as long as we are consistent.

You will get an error if you write the above code like this:

Here, we haven't used indentation after the if statement. In this case, Python thinks our if statement is empty, which results in an error.

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

Here, if the condition inside the if statement evaluates to

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

• Example: Python if…else Statement

If user enters 10 , the condition number > 0 evalutes to True . Therefore, the body of if is executed and the body of else is skipped.

If user enters 0 , the condition number > 0 evalutes to False . Therefore, the body of if is skipped and the body of else is executed.

• Python if…elif…else Statement

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

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

• Example: Python if…elif…else Statement

Here, the first condition, number > 0 , evaluates to False . In this scenario, the second condition is checked.

The second condition, number < 0 , evaluates to True . Therefore, the statements inside the elif block is executed.

In the above program, it is important to note that regardless the value of number variable, only one block of code will be executed.

• Python Nested if Statements

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

Here's how this program works.

More on Python if…else Statement

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

This code can be compactly written as

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

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

can be written as

If needed, we can use logical operators such as and and or to create complex conditions to work with an if statement.

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

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

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

Table of Contents

• Introduction

Before we wrap up, let’s put your knowledge of Python if else to the test! Can you solve the following challenge?

Write a function to check whether a student passed or failed his/her examination.

• Assume the pass marks to be 50 .
• Return Passed if the student scored more than 50. Otherwise, return Failed .

Video: Python if...else Statement

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Python Tutorial

How to use python if else in one line with examples

December 31, 2023

How do I write a simple python if else in one line? What are ternary operator in Python? Can we use one liner for complex if and else statements?

In this tutorial I will share different examples to help you understand and learn about usage of ternary operator in one liner if and else condition with Python. Conditional expressions (sometimes called a “ ternary operator ”) have the lowest priority of all Python operations. Programmers coming to Python from C, C++, or Perl sometimes miss the so-called ternary operator ?:. It’s most often used for avoiding a few lines of code and a temporary variable for simple decisions.

I will not go into details of generic ternary operator as this is used across Python for loops and control flow statements. Here we will concentrate on learning python if else in one line using ternary operator

Python if else in one line

The general syntax of single if and else statement in Python is:

Now if we wish to write this in one line using ternary operator, the syntax would be:

In this syntax, first of all the else condition is evaluated.

• If condition returns True then value_when_true is returned
• If condition returns False then value_when_false is returned

Similarly if you had a variable assigned in the general if else block based on the condition

The same can be written in single line:

Here as well, first of all the condition is evaluated.

• if condition returns True then true-expr is assigned to value object
• if condition returns False then false-expr is assigned to value object

For simple cases like this, I find it very nice to be able to express that logic in one line instead of four. Remember, as a coder, you spend much more time reading code than writing it, so Python's conciseness is invaluable.

Some important points to remember:

• You can use a ternary expression in Python, but only for expressions , not for statements
• You cannot use Python if..elif..else block in one line.
• The name " ternary " means there are just 3 parts to the operator: condition , then , and else .
• Although there are hacks to modify if..elif..else block into if..else block and then use it in single line but that can be complex depending upon conditions and should be avoided
• With if-else blocks , only one of the expressions will be executed.
• While it may be tempting to always use ternary expressions to condense your code, realise that you may sacrifice readability if the condition as well as the true and false expressions are very complex.

Python Script Example

This is a simple script where we use comparison operator in our if condition

• First collect user input in the form of integer and store this value into b
• If b is greater than or equal to 0 then return " positive " which will be True condition
• If b returns False i.e. above condition was not success then return " negative "
• The final returned value i.e. either " positive " or " negative " is stored in object a
• Lastly print the value of value a

The multi-line form of this code would be:

Python if..elif..else in one line

Now as I told this earlier, it is not possible to use if..elif..else block in one line using ternary expressions. Although we can hack our way into this but make sure the maximum allowed length of a line in Python is 79 as per PEP-8 Guidelines

We have this if..elif..else block where we return expression based on the condition check:

We can write this if..elif..else block in one-line using this syntax:

In this syntax,

• First of all condition2 is evaluated, if return True then expr2 is returned
• If condition2 returns False then condition1 is evaluated, if return True then expr1 is returned
• If condition1 also returns False then else is executed and expr is returned

As you see, it was easier if we read this in multi-line if..elif..else block while the same becomes hard to understand for beginners.

We can add multiple if else block in this syntax, but we must also adhere to PEP-8 guidelines

Python Script Example-1

In this sample script we collect an integer value from end user and store it in " b ". The order of execution would be:

• If the value of b is less than 0 then " neg " is returned
• If the value of b is greater than 0 then " pos " is returned.
• If both the condition return False , then " zero " is returned

The multi-line form of the code would be:

Output(when if condition is True )

Output(when if condition is False and elif condition is True )

Output(when both if and elif condition are False )

Python script Example-2

We will add some more else blocks in this sample script, the order of the check would be in below sequence :

• Collect user input for value b which will be converted to integer type
• If value of b is equal to 100 then return " equal to 100 ", If this returns False then next if else condition would be executed
• If value of b is equal to 50 then return " equal to 50 ", If this returns False then next if else condition would be executed
• If value of b is equal to 40 then return " equal to 40 ", If this returns False then next if else condition would be executed
• If value of b is greater than 100 then return " greater than 100 ", If this returns False then next go to else block
• Lastly if all the condition return False then return " less than hundred "

The multi-line form of this example would be:

Python nested if..else in one line

We can also use ternary expression to define nested if..else block on one line with Python.

If you have a multi-line code using nested if else block , something like this:

The one line syntax to use this nested if else block in Python would be:

Here, we have added nested if..elif..else inside the else block using ternary expression. The sequence of the check in the following order

• If condition1 returns True then expr1 is returned, if it returns False then next condition is checked
• If condition-m returns True then expr-m is returned, if it returns False then else block with nested if..elif..else is checked
• If condition3 returns True then expr3 is returned, if it returns False then next condition inside the nested block is returned
• If condition-n returns True then expr-n is returned, if it returns False then expr5 is returned from the else condition

In this example I am using nested if else inside the else block of our one liner. The order of execution will be in the provided sequence:

• First of all collect integer value of b from the end user
• If the value of b is equal to 100 then the if condition returns True and " equal to 100 " is returned
• If the value of b is equal to 50 then the elif condition returns True and " equal to 50 " is returned
• If both if and elif condition returns False then the else block is executed where we have nested if and else condition
• Inside the else block , if b is greater than 100 then it returns " greater than 100 " and if it returns False then " less than 100 " is returned

In this tutorial we learned about usage of ternary operator in if else statement to be able to use it in one line. Although Python does not allow if..elif..else statement in one line but we can still break it into if else and then use it in single line form. Similarly we can also use nested if with ternary operator in single line. I shared multiple examples to help you understand the concept of ternary operator with if and else statement of Python programming language

Lastly I hope this tutorial guide on python if else one line was helpful. So, let me know your suggestions and feedback using the comment section.

Deepak Prasad

He is the founder of GoLinuxCloud and brings over a decade of expertise in Linux, Python, Go, Laravel, DevOps, Kubernetes, Git, Shell scripting, OpenShift, AWS, Networking, and Security. With extensive experience, he excels in various domains, from development to DevOps, Networking, and Security, ensuring robust and efficient solutions for diverse projects. You can connect with him on his LinkedIn profile.

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Basic Statements in Python

Table of contents, what is a statement in python, statement set, multi-line statements, simple statements, expression statements, the assert statement, the try statement.

In Python, statements are instructions or commands that you write to perform specific actions or tasks. They are the building blocks of a Python program.

A statement is a line of code that performs a specific action. It is the smallest unit of code that can be executed by the Python interpreter.

Assignment Statement

In this example, the value 10 is assigned to the variable x using the assignment statement.

Conditional Statement

In this example, the if-else statement is used to check the value of x and print a corresponding message.

By using statements, programmers can instruct the computer to perform a variety of tasks, from simple arithmetic operations to complex decision-making processes. Proper use of statements is crucial to writing efficient and effective Python code.

Here's a table summarizing various types of statements in Python:

Please note that this table provides a brief overview of each statement type, and there may be additional details and variations for each statement.

Multi-line statements are a convenient way to write long code in Python without making it cluttered. They allow you to write several lines of code as a single statement, making it easier for developers to read and understand the code. Here are two examples of multi-line statements in Python:

• Using backslash:
• Using parentheses:

Simple statements are the smallest unit of execution in Python programming language and they do not contain any logical or conditional expressions. They are usually composed of a single line of code and can perform basic operations such as assigning values to variables , printing out values, or calling functions .

Examples of simple statements in Python:

Simple statements are essential to programming in Python and are often used in combination with more complex statements to create robust programs and applications.

Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result.

In this example, we assign the value 5 to the variable x , then add 3 to x and assign the result ( 8 ) to the variable y . Finally, we print the value of y .

In this example, we define a function square that takes one argument ( x ) and returns its square. We then call the function with the argument 5 and assign the result ( 25 ) to the variable result . Finally, we print the value of result .

Overall, expression statements are an essential part of Python programming and allow for the execution of mathematical and computational operations.

The assert statement in Python is used to test conditions and trigger an error if the condition is not met. It is often used for debugging and testing purposes.

Where condition is the expression that is tested, and message is the optional error message that is displayed when the condition is not met.

In this example, the assert statement tests whether x is equal to 5 . If the condition is met, the statement has no effect. If the condition is not met, an error will be raised with the message x should be 5 .

In this example, the assert statement tests whether y is not equal to 0 before performing the division. If the condition is met, the division proceeds as normal. If the condition is not met, an error will be raised with the message Cannot divide by zero .

Overall, assert statements are a useful tool in Python for debugging and testing, as they can help catch errors early on. They are also easily disabled in production code to avoid any unnecessary overhead.

The try statement in Python is used to catch exceptions that may occur during the execution of a block of code. It ensures that even when an error occurs, the code does not stop running.

Examples of Error Processing

Dive deep into the topic.

• Match Statements
• Operators in Python Statements
• The IF Statement

Contribute with us!

Do not hesitate to contribute to Python tutorials on GitHub: create a fork, update content and issue a pull request.

Python's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

The assignment operator, assignments and variables, other assignment syntax, initializing and updating variables, making multiple variables refer to the same object, updating lists through indices and slices, adding and updating dictionary keys, doing parallel assignments, unpacking iterables, providing default argument values, augmented mathematical assignment operators, augmented assignments for concatenation and repetition, augmented bitwise assignment operators, annotated assignment statements, assignment expressions with the walrus operator, managed attribute assignments, define or call a function, work with classes, import modules and objects, use a decorator, access the control variable in a for loop or a comprehension, use the as keyword, access the _ special variable in an interactive session, built-in objects, named constants.

Python’s assignment operators allow you to define assignment statements . This type of statement lets you create, initialize, and update variables throughout your code. Variables are a fundamental cornerstone in every piece of code, and assignment statements give you complete control over variable creation and mutation.

Learning about the Python assignment operator and its use for writing assignment statements will arm you with powerful tools for writing better and more robust Python code.

In this tutorial, you’ll:

• Use Python’s assignment operator to write assignment statements
• Take advantage of augmented assignments in Python
• Explore assignment variants, like assignment expressions and managed attributes
• Become aware of illegal and dangerous assignments in Python

You’ll dive deep into Python’s assignment statements. To get the most out of this tutorial, you should be comfortable with several basic topics, including variables , built-in data types , comprehensions , functions , and Python keywords . Before diving into some of the later sections, you should also be familiar with intermediate topics, such as object-oriented programming , constants , imports , type hints , properties , descriptors , and decorators .

Free Source Code: Click here to download the free assignment operator source code that you’ll use to write assignment statements that allow you to create, initialize, and update variables in your code.

Assignment Statements and the Assignment Operator

One of the most powerful programming language features is the ability to create, access, and mutate variables . In Python, a variable is a name that refers to a concrete value or object, allowing you to reuse that value or object throughout your code.

To create a new variable or to update the value of an existing one in Python, you’ll use an assignment statement . This statement has the following three components:

• A left operand, which must be a variable
• The assignment operator ( = )
• A right operand, which can be a concrete value , an object , or an expression

Here’s how an assignment statement will generally look in Python:

Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal —or an expression that evaluates to a value.

To execute an assignment statement like the above, Python runs the following steps:

• Evaluate the right-hand expression to produce a concrete value or object . This value will live at a specific memory address in your computer.
• Store the object’s memory address in the left-hand variable . This step creates a new variable if the current one doesn’t already exist or updates the value of an existing variable.

The second step shows that variables work differently in Python than in other programming languages. In Python, variables aren’t containers for objects. Python variables point to a value or object through its memory address. They store memory addresses rather than objects.

This behavior difference directly impacts how data moves around in Python, which is always by reference . In most cases, this difference is irrelevant in your day-to-day coding, but it’s still good to know.

The central component of an assignment statement is the assignment operator . This operator is represented by the = symbol, which separates two operands:

• A value or an expression that evaluates to a concrete value

Operators are special symbols that perform mathematical , logical , and bitwise operations in a programming language. The objects (or object) on which an operator operates are called operands .

Unary operators, like the not Boolean operator, operate on a single object or operand, while binary operators act on two. That means the assignment operator is a binary operator.

Note: Like C , Python uses == for equality comparisons and = for assignments. Unlike C, Python doesn’t allow you to accidentally use the assignment operator ( = ) in an equality comparison.

Equality is a symmetrical relationship, and assignment is not. For example, the expression a == 42 is equivalent to 42 == a . In contrast, the statement a = 42 is correct and legal, while 42 = a isn’t allowed. You’ll learn more about illegal assignments later on.

The right-hand operand in an assignment statement can be any Python object, such as a number , list , string , dictionary , or even a user-defined object. It can also be an expression. In the end, expressions always evaluate to concrete objects, which is their return value.

Here are a few examples of assignments in Python:

The first two sample assignments in this code snippet use concrete values, also known as literals , to create and initialize number and greeting . The third example assigns the result of a math expression to the total variable, while the last example uses a Boolean expression.

Note: You can use the built-in id() function to inspect the memory address stored in a given variable.

Here’s a short example of how this function works:

The number in your output represents the memory address stored in number . Through this address, Python can access the content of number , which is the integer 42 in this example.

If you run this code on your computer, then you’ll get a different memory address because this value varies from execution to execution and computer to computer.

Unlike expressions, assignment statements don’t have a return value because their purpose is to make the association between the variable and its value. That’s why the Python interpreter doesn’t issue any output in the above examples.

Now that you know the basics of how to write an assignment statement, it’s time to tackle why you would want to use one.

The assignment statement is the explicit way for you to associate a name with an object in Python. You can use this statement for two main purposes:

• Creating and initializing new variables
• Updating the values of existing variables

When you use a variable name as the left operand in an assignment statement for the first time, you’re creating a new variable. At the same time, you’re initializing the variable to point to the value of the right operand.

On the other hand, when you use an existing variable in a new assignment, you’re updating or mutating the variable’s value. Strictly speaking, every new assignment will make the variable refer to a new value and stop referring to the old one. Python will garbage-collect all the values that are no longer referenced by any existing variable.

Assignment statements not only assign a value to a variable but also determine the data type of the variable at hand. This additional behavior is another important detail to consider in this kind of statement.

Because Python is a dynamically typed language, successive assignments to a given variable can change the variable’s data type. Changing the data type of a variable during a program’s execution is considered bad practice and highly discouraged. It can lead to subtle bugs that can be difficult to track down.

Unlike in math equations, in Python assignments, the left operand must be a variable rather than an expression or a value. For example, the following construct is illegal, and Python flags it as invalid syntax:

In this example, you have expressions on both sides of the = sign, and this isn’t allowed in Python code. The error message suggests that you may be confusing the equality operator with the assignment one, but that’s not the case. You’re really running an invalid assignment.

To correct this construct and convert it into a valid assignment, you’ll have to do something like the following:

In this code snippet, you first import the sqrt() function from the math module. Then you isolate the hypotenuse variable in the original equation by using the sqrt() function. Now your code works correctly.

Now you know what kind of syntax is invalid. But don’t get the idea that assignment statements are rigid and inflexible. In fact, they offer lots of room for customization, as you’ll learn next.

Python’s assignment statements are pretty flexible and versatile. You can write them in several ways, depending on your specific needs and preferences. Here’s a quick summary of the main ways to write assignments in Python:

Up to this point, you’ve mostly learned about the base assignment syntax in the above code snippet. In the following sections, you’ll learn about multiple, parallel, and augmented assignments. You’ll also learn about assignments with iterable unpacking.

Read on to see the assignment statements in action!

Assignment Statements in Action

You’ll find and use assignment statements everywhere in your Python code. They’re a fundamental part of the language, providing an explicit way to create, initialize, and mutate variables.

You can use assignment statements with plain names, like number or counter . You can also use assignments in more complicated scenarios, such as with:

• Qualified attribute names , like user.name
• Indices and slices of mutable sequences, like a_list[i] and a_list[i:j]
• Dictionary keys , like a_dict[key]

This list isn’t exhaustive. However, it gives you some idea of how flexible these statements are. You can even assign multiple values to an equal number of variables in a single line, commonly known as parallel assignment . Additionally, you can simultaneously assign the values in an iterable to a comma-separated group of variables in what’s known as an iterable unpacking operation.

In the following sections, you’ll dive deeper into all these topics and a few other exciting things that you can do with assignment statements in Python.

The most elementary use case of an assignment statement is to create a new variable and initialize it using a particular value or expression:

All these statements create new variables, assigning them initial values or expressions. For an initial value, you should always use the most sensible and least surprising value that you can think of. For example, initializing a counter to something different from 0 may be confusing and unexpected because counters almost always start having counted no objects.

Updating a variable’s current value or state is another common use case of assignment statements. In Python, assigning a new value to an existing variable doesn’t modify the variable’s current value. Instead, it causes the variable to refer to a different value. The previous value will be garbage-collected if no other variable refers to it.

Consider the following examples:

These examples run two consecutive assignments on the same variable. The first one assigns the string "Hello, World!" to a new variable named greeting .

The second assignment updates the value of greeting by reassigning it the "Hi, Pythonistas!" string. In this example, the original value of greeting —the "Hello, World!" string— is lost and garbage-collected. From this point on, you can’t access the old "Hello, World!" string.

Even though running multiple assignments on the same variable during a program’s execution is common practice, you should use this feature with caution. Changing the value of a variable can make your code difficult to read, understand, and debug. To comprehend the code fully, you’ll have to remember all the places where the variable was changed and the sequential order of those changes.

Because assignments also define the data type of their target variables, it’s also possible for your code to accidentally change the type of a given variable at runtime. A change like this can lead to breaking errors, like AttributeError exceptions. Remember that strings don’t have the same methods and attributes as lists or dictionaries, for example.

In Python, you can make several variables reference the same object in a multiple-assignment line. This can be useful when you want to initialize several similar variables using the same initial value:

In this example, you chain two assignment operators in a single line. This way, your two variables refer to the same initial value of 0 . Note how both variables hold the same memory address, so they point to the same instance of 0 .

When it comes to integer variables, Python exhibits a curious behavior. It provides a numeric interval where multiple assignments behave the same as independent assignments. Consider the following examples:

To create n and m , you use independent assignments. Therefore, they should point to different instances of the number 42 . However, both variables hold the same object, which you confirm by comparing their corresponding memory addresses.

Now check what happens when you use a greater initial value:

Now n and m hold different memory addresses, which means they point to different instances of the integer number 300 . In contrast, when you use multiple assignments, both variables refer to the same object. This tiny difference can save you small bits of memory if you frequently initialize integer variables in your code.

The implicit behavior of making independent assignments point to the same integer number is actually an optimization called interning . It consists of globally caching the most commonly used integer values in day-to-day programming.

Under the hood, Python defines a numeric interval in which interning takes place. That’s the interning interval for integer numbers. You can determine this interval using a small script like the following:

This script helps you determine the interning interval by comparing integer numbers from -10 to 500 . If you run the script from your command line, then you’ll get an output like the following:

This output means that if you use a single number between -5 and 256 to initialize several variables in independent statements, then all these variables will point to the same object, which will help you save small bits of memory in your code.

In contrast, if you use a number that falls outside of the interning interval, then your variables will point to different objects instead. Each of these objects will occupy a different memory spot.

You can use the assignment operator to mutate the value stored at a given index in a Python list. The operator also works with list slices . The syntax to write these types of assignment statements is the following:

In the first construct, expression can return any Python object, including another list. In the second construct, expression must return a series of values as a list, tuple, or any other sequence. You’ll get a TypeError if expression returns a single value.

Note: When creating slice objects, you can use up to three arguments. These arguments are start , stop , and step . They define the number that starts the slice, the number at which the slicing must stop retrieving values, and the step between values.

Here’s an example of updating an individual value in a list:

In this example, you update the value at index 2 using an assignment statement. The original number at that index was 7 , and after the assignment, the number is 3 .

Note: Using indices and the assignment operator to update a value in a tuple or a character in a string isn’t possible because tuples and strings are immutable data types in Python.

Their immutability means that you can’t change their items in place :

You can’t use the assignment operator to change individual items in tuples or strings. These data types are immutable and don’t support item assignments.

It’s important to note that you can’t add new values to a list by using indices that don’t exist in the target list:

In this example, you try to add a new value to the end of numbers by using an index that doesn’t exist. This assignment isn’t allowed because there’s no way to guarantee that new indices will be consecutive. If you ever want to add a single value to the end of a list, then use the .append() method.

If you want to update several consecutive values in a list, then you can use slicing and an assignment statement:

In the first example, you update the letters between indices 1 and 3 without including the letter at 3 . The second example updates the letters from index 3 until the end of the list. Note that this slicing appends a new value to the list because the target slice is shorter than the assigned values.

Also note that the new values were provided through a tuple, which means that this type of assignment allows you to use other types of sequences to update your target list.

The third example updates a single value using a slice where both indices are equal. In this example, the assignment inserts a new item into your target list.

In the final example, you use a step of 2 to replace alternating letters with their lowercase counterparts. This slicing starts at index 1 and runs through the whole list, stepping by two items each time.

Updating the value of an existing key or adding new key-value pairs to a dictionary is another common use case of assignment statements. To do these operations, you can use the following syntax:

The first construct helps you update the current value of an existing key, while the second construct allows you to add a new key-value pair to the dictionary.

For example, to update an existing key, you can do something like this:

In this example, you update the current inventory of oranges in your store using an assignment. The left operand is the existing dictionary key, and the right operand is the desired new value.

While you can’t add new values to a list by assignment, dictionaries do allow you to add new key-value pairs using the assignment operator. In the example below, you add a lemon key to inventory :

In this example, you successfully add a new key-value pair to your inventory with 100 units. This addition is possible because dictionaries don’t have consecutive indices but unique keys, which are safe to add by assignment.

The assignment statement does more than assign the result of a single expression to a single variable. It can also cope nicely with assigning multiple values to multiple variables simultaneously in what’s known as a parallel assignment .

Here’s the general syntax for parallel assignments in Python:

Note that the left side of the statement can be either a tuple or a list of variables. Remember that to create a tuple, you just need a series of comma-separated elements. In this case, these elements must be variables.

The right side of the statement must be a sequence or iterable of values or expressions. In any case, the number of elements in the right operand must match the number of variables on the left. Otherwise, you’ll get a ValueError exception.

In the following example, you compute the two solutions of a quadratic equation using a parallel assignment:

In this example, you first import sqrt() from the math module. Then you initialize the equation’s coefficients in a parallel assignment.

The equation’s solution is computed in another parallel assignment. The left operand contains a tuple of two variables, x1 and x2 . The right operand consists of a tuple of expressions that compute the solutions for the equation. Note how each result is assigned to each variable by position.

A classical use case of parallel assignment is to swap values between variables:

The highlighted line does the magic and swaps the values of previous_value and next_value at the same time. Note that in a programming language that doesn’t support this kind of assignment, you’d have to use a temporary variable to produce the same effect:

In this example, instead of using parallel assignment to swap values between variables, you use a new variable to temporarily store the value of previous_value to avoid losing its reference.

For a concrete example of when you’d need to swap values between variables, say you’re learning how to implement the bubble sort algorithm , and you come up with the following function:

In the highlighted line, you use a parallel assignment to swap values in place if the current value is less than the next value in the input list. To dive deeper into the bubble sort algorithm and into sorting algorithms in general, check out Sorting Algorithms in Python .

You can use assignment statements for iterable unpacking in Python. Unpacking an iterable means assigning its values to a series of variables one by one. The iterable must be the right operand in the assignment, while the variables must be the left operand.

Like in parallel assignments, the variables must come as a tuple or list. The number of variables must match the number of values in the iterable. Alternatively, you can use the unpacking operator ( * ) to grab several values in a variable if the number of variables doesn’t match the iterable length.

Here’s the general syntax for iterable unpacking in Python:

Iterable unpacking is a powerful feature that you can use all around your code. It can help you write more readable and concise code. For example, you may find yourself doing something like this:

Whenever you do something like this in your code, go ahead and replace it with a more readable iterable unpacking using a single and elegant assignment, like in the following code snippet:

The numbers list on the right side contains four values. The assignment operator unpacks these values into the four variables on the left side of the statement. The values in numbers get assigned to variables in the same order that they appear in the iterable. The assignment is done by position.

Note: Because Python sets are also iterables, you can use them in an iterable unpacking operation. However, it won’t be clear which value goes to which variable because sets are unordered data structures.

The above example shows the most common form of iterable unpacking in Python. The main condition for the example to work is that the number of variables matches the number of values in the iterable.

What if you don’t know the iterable length upfront? Will the unpacking work? It’ll work if you use the * operator to pack several values into one of your target variables.

For example, say that you want to unpack the first and second values in numbers into two different variables. Additionally, you would like to pack the rest of the values in a single variable conveniently called rest . In this case, you can use the unpacking operator like in the following code:

In this example, first and second hold the first and second values in numbers , respectively. These values are assigned by position. The * operator packs all the remaining values in the input iterable into rest .

The unpacking operator ( * ) can appear at any position in your series of target variables. However, you can only use one instance of the operator:

The iterable unpacking operator works in any position in your list of variables. Note that you can only use one unpacking operator per assignment. Using more than one unpacking operator isn’t allowed and raises a SyntaxError .

Dropping away unwanted values from the iterable is a common use case for the iterable unpacking operator. Consider the following example:

In Python, if you want to signal that a variable won’t be used, then you use an underscore ( _ ) as the variable’s name. In this example, useful holds the only value that you need to use from the input iterable. The _ variable is a placeholder that guarantees that the unpacking works correctly. You won’t use the values that end up in this disposable variable.

Note: In the example above, if your target iterable is a sequence data type, such as a list or tuple, then it’s best to access its last item directly.

To do this, you can use the -1 index:

Using -1 gives you access to the last item of any sequence data type. In contrast, if you’re dealing with iterators , then you won’t be able to use indices. That’s when the *_ syntax comes to your rescue.

The pattern used in the above example comes in handy when you have a function that returns multiple values, and you only need a few of these values in your code. The os.walk() function may provide a good example of this situation.

This function allows you to iterate over the content of a directory recursively. The function returns a generator object that yields three-item tuples. Each tuple contains the following items:

• The path to the current directory as a string
• The names of all the immediate subdirectories as a list of strings
• The names of all the files in the current directory as a list of strings

Now say that you want to iterate over your home directory and list only the files. You can do something like this:

This code will issue a long output depending on the current content of your home directory. Note that you need to provide a string with the path to your user folder for the example to work. The _ placeholder variable will hold the unwanted data.

In contrast, the filenames variable will hold the list of files in the current directory, which is the data that you need. The code will print the list of filenames. Go ahead and give it a try!

The assignment operator also comes in handy when you need to provide default argument values in your functions and methods. Default argument values allow you to define functions that take arguments with sensible defaults. These defaults allow you to call the function with specific values or to simply rely on the defaults.

As an example, consider the following function:

This function takes one argument, called name . This argument has a sensible default value that’ll be used when you call the function without arguments. To provide this sensible default value, you use an assignment.

Note: According to PEP 8 , the style guide for Python code, you shouldn’t use spaces around the assignment operator when providing default argument values in function definitions.

Here’s how the function works:

If you don’t provide a name during the call to greet() , then the function uses the default value provided in the definition. If you provide a name, then the function uses it instead of the default one.

Up to this point, you’ve learned a lot about the Python assignment operator and how to use it for writing different types of assignment statements. In the following sections, you’ll dive into a great feature of assignment statements in Python. You’ll learn about augmented assignments .

Augmented Assignment Operators in Python

Python supports what are known as augmented assignments . An augmented assignment combines the assignment operator with another operator to make the statement more concise. Most Python math and bitwise operators have an augmented assignment variation that looks something like this:

Note that $ isn’t a valid Python operator. In this example, it’s a placeholder for a generic operator. This statement works as follows:

• Evaluate expression to produce a value.
• Run the operation defined by the operator that prefixes the = sign, using the previous value of variable and the return value of expression as operands.
• Assign the resulting value back to variable .

In practice, an augmented assignment like the above is equivalent to the following statement:

As you can conclude, augmented assignments are syntactic sugar . They provide a shorthand notation for a specific and popular kind of assignment.

For example, say that you need to define a counter variable to count some stuff in your code. You can use the += operator to increment counter by 1 using the following code:

In this example, the += operator, known as augmented addition , adds 1 to the previous value in counter each time you run the statement counter += 1 .

It’s important to note that unlike regular assignments, augmented assignments don’t create new variables. They only allow you to update existing variables. If you use an augmented assignment with an undefined variable, then you get a NameError :

Python evaluates the right side of the statement before assigning the resulting value back to the target variable. In this specific example, when Python tries to compute x + 1 , it finds that x isn’t defined.

Great! You now know that an augmented assignment consists of combining the assignment operator with another operator, like a math or bitwise operator. To continue this discussion, you’ll learn which math operators have an augmented variation in Python.

An equation like x = x + b doesn’t make sense in math. But in programming, a statement like x = x + b is perfectly valid and can be extremely useful. It adds b to x and reassigns the result back to x .

As you already learned, Python provides an operator to shorten x = x + b . Yes, the += operator allows you to write x += b instead. Python also offers augmented assignment operators for most math operators. Here’s a summary:

The Example column provides generic examples of how to use the operators in actual code. Note that x must be previously defined for the operators to work correctly. On the other hand, y can be either a concrete value or an expression that returns a value.

Note: The matrix multiplication operator ( @ ) doesn’t support augmented assignments yet.

Consider the following example of matrix multiplication using NumPy arrays:

Note that the exception traceback indicates that the operation isn’t supported yet.

To illustrate how augmented assignment operators work, say that you need to create a function that takes an iterable of numeric values and returns their sum. You can write this function like in the code below:

In this function, you first initialize total to 0 . In each iteration, the loop adds a new number to total using the augmented addition operator ( += ). When the loop terminates, total holds the sum of all the input numbers. Variables like total are known as accumulators . The += operator is typically used to update accumulators.

Note: Computing the sum of a series of numeric values is a common operation in programming. Python provides the built-in sum() function for this specific computation.

Another interesting example of using an augmented assignment is when you need to implement a countdown while loop to reverse an iterable. In this case, you can use the -= operator:

In this example, custom_reversed() is a generator function because it uses yield . Calling the function creates an iterator that yields items from the input iterable in reverse order. To decrement the control variable, index , you use an augmented subtraction statement that subtracts 1 from the variable in every iteration.

Note: Similar to summing the values in an iterable, reversing an iterable is also a common requirement. Python provides the built-in reversed() function for this specific computation, so you don’t have to implement your own. The above example only intends to show the -= operator in action.

Finally, counters are a special type of accumulators that allow you to count objects. Here’s an example of a letter counter:

To create this counter, you use a Python dictionary. The keys store the letters. The values store the counts. Again, to increment the counter, you use an augmented addition.

Counters are so common in programming that Python provides a tool specially designed to facilitate the task of counting. Check out Python’s Counter: The Pythonic Way to Count Objects for a complete guide on how to use this tool.

The += and *= augmented assignment operators also work with sequences , such as lists, tuples, and strings. The += operator performs augmented concatenations , while the *= operator performs augmented repetition .

These operators behave differently with mutable and immutable data types:

Note that the augmented concatenation operator operates on two sequences, while the augmented repetition operator works on a sequence and an integer number.

Consider the following examples and pay attention to the result of calling the id() function:

Mutable sequences like lists support the += augmented assignment operator through the .__iadd__() method, which performs an in-place addition. This method mutates the underlying list, appending new values to its end.

Note: If the left operand is mutable, then x += y may not be completely equivalent to x = x + y . For example, if you do list_1 = list_1 + list_2 instead of list_1 += list_2 above, then you’ll create a new list instead of mutating the existing one. This may be important if other variables refer to the same list.

Immutable sequences, such as tuples and strings, don’t provide an .__iadd__() method. Therefore, augmented concatenations fall back to the .__add__() method, which doesn’t modify the sequence in place but returns a new sequence.

There’s another difference between mutable and immutable sequences when you use them in an augmented concatenation. Consider the following examples:

With mutable sequences, the data to be concatenated can come as a list, tuple, string, or any other iterable. In contrast, with immutable sequences, the data can only come as objects of the same type. You can concatenate tuples to tuples and strings to strings, for example.

Again, the augmented repetition operator works with a sequence on the left side of the operator and an integer on the right side. This integer value represents the number of repetitions to get in the resulting sequence:

When the *= operator operates on a mutable sequence, it falls back to the .__imul__() method, which performs the operation in place, modifying the underlying sequence. In contrast, if *= operates on an immutable sequence, then .__mul__() is called, returning a new sequence of the same type.

Note: Values of n less than 0 are treated as 0 , which returns an empty sequence of the same data type as the target sequence on the left side of the *= operand.

Note that a_list[0] is a_list[3] returns True . This is because the *= operator doesn’t make a copy of the repeated data. It only reflects the data. This behavior can be a source of issues when you use the operator with mutable values.

For example, say that you want to create a list of lists to represent a matrix, and you need to initialize the list with n empty lists, like in the following code:

In this example, you use the *= operator to populate matrix with three empty lists. Now check out what happens when you try to populate the first sublist in matrix :

The appended values are reflected in the three sublists. This happens because the *= operator doesn’t make copies of the data that you want to repeat. It only reflects the data. Therefore, every sublist in matrix points to the same object and memory address.

If you ever need to initialize a list with a bunch of empty sublists, then use a list comprehension :

This time, when you populate the first sublist of matrix , your changes aren’t propagated to the other sublists. This is because all the sublists are different objects that live in different memory addresses.

Bitwise operators also have their augmented versions. The logic behind them is similar to that of the math operators. The following table summarizes the augmented bitwise operators that Python provides:

The augmented bitwise assignment operators perform the intended operation by taking the current value of the left operand as a starting point for the computation. Consider the following example, which uses the & and &= operators:

Programmers who work with high-level languages like Python rarely use bitwise operations in day-to-day coding. However, these types of operations can be useful in some situations.

For example, say that you’re implementing a Unix-style permission system for your users to access a given resource. In this case, you can use the characters "r" for reading, "w" for writing, and "x" for execution permissions, respectively. However, using bit-based permissions could be more memory efficient:

You can assign permissions to your users with the OR bitwise operator or the augmented OR bitwise operator. Finally, you can use the bitwise AND operator to check if a user has a certain permission, as you did in the final two examples.

You’ve learned a lot about augmented assignment operators and statements in this and the previous sections. These operators apply to math, concatenation, repetition, and bitwise operations. Now you’re ready to look at other assignment variants that you can use in your code or find in other developers’ code.

Other Assignment Variants

So far, you’ve learned that Python’s assignment statements and the assignment operator are present in many different scenarios and use cases. Those use cases include variable creation and initialization, parallel assignments, iterable unpacking, augmented assignments, and more.

In the following sections, you’ll learn about a few variants of assignment statements that can be useful in your future coding. You can also find these assignment variants in other developers’ code. So, you should be aware of them and know how they work in practice.

In short, you’ll learn about:

• Annotated assignment statements with type hints
• Assignment expressions with the walrus operator
• Managed attribute assignments with properties and descriptors
• Implicit assignments in Python

These topics will take you through several interesting and useful examples that showcase the power of Python’s assignment statements.

PEP 526 introduced a dedicated syntax for variable annotation back in Python 3.6 . The syntax consists of the variable name followed by a colon ( : ) and the variable type:

Even though these statements declare three variables with their corresponding data types, the variables aren’t actually created or initialized. So, for example, you can’t use any of these variables in an augmented assignment statement:

If you try to use one of the previously declared variables in an augmented assignment, then you get a NameError because the annotation syntax doesn’t define the variable. To actually define it, you need to use an assignment.

The good news is that you can use the variable annotation syntax in an assignment statement with the = operator:

The first statement in this example is what you can call an annotated assignment statement in Python. You may ask yourself why you should use type annotations in this type of assignment if everybody can see that counter holds an integer number. You’re right. In this example, the variable type is unambiguous.

However, imagine what would happen if you found a variable initialization like the following:

What would be the data type of each user in users ? If the initialization of users is far away from the definition of the User class, then there’s no quick way to answer this question. To clarify this ambiguity, you can provide the appropriate type hint for users :

Now you’re clearly communicating that users will hold a list of User instances. Using type hints in assignment statements that initialize variables to empty collection data types—such as lists, tuples, or dictionaries—allows you to provide more context about how your code works. This practice will make your code more explicit and less error-prone.

Up to this point, you’ve learned that regular assignment statements with the = operator don’t have a return value. They just create or update variables. Therefore, you can’t use a regular assignment to assign a value to a variable within the context of an expression.

Python 3.8 changed this by introducing a new type of assignment statement through PEP 572 . This new statement is known as an assignment expression or named expression .

Note: Expressions are a special type of statement in Python. Their distinguishing characteristic is that expressions always have a return value, which isn’t the case with all types of statements.

Unlike regular assignments, assignment expressions have a return value, which is why they’re called expressions in the first place. This return value is automatically assigned to a variable. To write an assignment expression, you must use the walrus operator ( := ), which was named for its resemblance to the eyes and tusks of a walrus lying on its side.

The general syntax of an assignment statement is as follows:

This expression looks like a regular assignment. However, instead of using the assignment operator ( = ), it uses the walrus operator ( := ). For the expression to work correctly, the enclosing parentheses are required in most use cases. However, there are certain situations in which these parentheses are superfluous. Either way, they won’t hurt you.

Assignment expressions come in handy when you want to reuse the result of an expression or part of an expression without using a dedicated assignment to grab this value beforehand.

Note: Assignment expressions with the walrus operator have several practical use cases. They also have a few restrictions. For example, they’re illegal in certain contexts, such as lambda functions, parallel assignments, and augmented assignments.

For a deep dive into this special type of assignment, check out The Walrus Operator: Python’s Assignment Expressions .

A particularly handy use case for assignment expressions is when you need to grab the result of an expression used in the context of a conditional statement. For example, say that you need to write a function to compute the mean of a sample of numeric values. Without the walrus operator, you could do something like this:

In this example, the sample size ( n ) is a value that you need to reuse in two different computations. First, you need to check whether the sample has data points or not. Then you need to use the sample size to compute the mean. To be able to reuse n , you wrote a dedicated assignment statement at the beginning of your function to grab the sample size.

You can avoid this extra step by combining it with the first use of the target value, len(sample) , using an assignment expression like the following:

The assignment expression introduced in the conditional computes the sample size and assigns it to n . This way, you guarantee that you have a reference to the sample size to use in further computations.

Because the assignment expression returns the sample size anyway, the conditional can check whether that size equals 0 or not and then take a certain course of action depending on the result of this check. The return statement computes the sample’s mean and sends the result back to the function caller.

Python provides a few tools that allow you to fine-tune the operations behind the assignment of attributes. The attributes that run implicit operations on assignments are commonly referred to as managed attributes .

Properties are the most commonly used tool for providing managed attributes in your classes. However, you can also use descriptors and, in some cases, the .__setitem__() special method.

To understand what fine-tuning the operation behind an assignment means, say that you need a Point class that only allows numeric values for its coordinates, x and y . To write this class, you must set up a validation mechanism to reject non-numeric values. You can use properties to attach the validation functionality on top of x and y .

Here’s how you can write your class:

In Point , you use properties for the .x and .y coordinates. Each property has a getter and a setter method . The getter method returns the attribute at hand. The setter method runs the input validation using a try … except block and the built-in float() function. Then the method assigns the result to the actual attribute.

Here’s how your class works in practice:

When you use a property-based attribute as the left operand in an assignment statement, Python automatically calls the property’s setter method, running any computation from it.

Because both .x and .y are properties, the input validation runs whenever you assign a value to either attribute. In the first example, the input values are valid numbers and the validation passes. In the final example, "one" isn’t a valid numeric value, so the validation fails.

If you look at your Point class, you’ll note that it follows a repetitive pattern, with the getter and setter methods looking quite similar. To avoid this repetition, you can use a descriptor instead of a property.

A descriptor is a class that implements the descriptor protocol , which consists of four special methods :

• .__get__() runs when you access the attribute represented by the descriptor.
• .__set__() runs when you use the attribute in an assignment statement.
• .__delete__() runs when you use the attribute in a del statement.
• .__set_name__() sets the attribute’s name, creating a name-aware attribute.

Here’s how your code may look if you use a descriptor to represent the coordinates of your Point class:

You’ve removed repetitive code by defining Coordinate as a descriptor that manages the input validation in a single place. Go ahead and run the following code to try out the new implementation of Point :

Great! The class works as expected. Thanks to the Coordinate descriptor, you now have a more concise and non-repetitive version of your original code.

Another way to fine-tune the operations behind an assignment statement is to provide a custom implementation of .__setitem__() in your class. You’ll use this method in classes representing mutable data collections, such as custom list-like or dictionary-like classes.

As an example, say that you need to create a dictionary-like class that stores its keys in lowercase letters:

In this example, you create a dictionary-like class by subclassing UserDict from collections . Your class implements a .__setitem__() method, which takes key and value as arguments. The method uses str.lower() to convert key into lowercase letters before storing it in the underlying dictionary.

Python implicitly calls .__setitem__() every time you use a key as the left operand in an assignment statement. This behavior allows you to tweak how you process the assignment of keys in your custom dictionary.

Implicit Assignments in Python

Python implicitly runs assignments in many different contexts. In most cases, these implicit assignments are part of the language syntax. In other cases, they support specific behaviors.

Whenever you complete an action in the following list, Python runs an implicit assignment for you:

• Define or call a function
• Define or instantiate a class
• Use the current instance , self
• Import modules and objects
• Use a decorator
• Use the control variable in a for loop or a comprehension
• Use the as qualifier in with statements , imports, and try … except blocks
• Access the _ special variable in an interactive session

Behind the scenes, Python performs an assignment in every one of the above situations. In the following subsections, you’ll take a tour of all these situations.

When you define a function, the def keyword implicitly assigns a function object to your function’s name. Here’s an example:

From this point on, the name greet refers to a function object that lives at a given memory address in your computer. You can call the function using its name and a pair of parentheses with appropriate arguments. This way, you can reuse greet() wherever you need it.

If you call your greet() function with fellow as an argument, then Python implicitly assigns the input argument value to the name parameter on the function’s definition. The parameter will hold a reference to the input arguments.

When you define a class with the class keyword, you’re assigning a specific name to a class object . You can later use this name to create instances of that class. Consider the following example:

In this example, the name User holds a reference to a class object, which was defined in __main__.User . Like with a function, when you call the class’s constructor with the appropriate arguments to create an instance, Python assigns the arguments to the parameters defined in the class initializer .

Another example of implicit assignments is the current instance of a class, which in Python is called self by convention. This name implicitly gets a reference to the current object whenever you instantiate a class. Thanks to this implicit assignment, you can access .name and .job from within the class without getting a NameError in your code.

Import statements are another variant of implicit assignments in Python. Through an import statement, you assign a name to a module object, class, function, or any other imported object. This name is then created in your current namespace so that you can access it later in your code:

In this example, you import the sys module object from the standard library and assign it to the sys name, which is now available in your namespace, as you can conclude from the second call to the built-in dir() function.

You also run an implicit assignment when you use a decorator in your code. The decorator syntax is just a shortcut for a formal assignment like the following:

Here, you call decorator() with a function object as an argument. This call will typically add functionality on top of the existing function, func() , and return a function object, which is then reassigned to the func name.

The decorator syntax is syntactic sugar for replacing the previous assignment, which you can now write as follows:

Even though this new code looks pretty different from the above assignment, the code implicitly runs the same steps.

Another situation in which Python automatically runs an implicit assignment is when you use a for loop or a comprehension. In both cases, you can have one or more control variables that you then use in the loop or comprehension body:

The memory address of control_variable changes on each iteration of the loop. This is because Python internally reassigns a new value from the loop iterable to the loop control variable on each cycle.

The same behavior appears in comprehensions:

In the end, comprehensions work like for loops but use a more concise syntax. This comprehension creates a new list of strings that mimic the output from the previous example.

The as keyword in with statements, except clauses, and import statements is another example of an implicit assignment in Python. This time, the assignment isn’t completely implicit because the as keyword provides an explicit way to define the target variable.

In a with statement, the target variable that follows the as keyword will hold a reference to the context manager that you’re working with. As an example, say that you have a hello.txt file with the following content:

You want to open this file and print each of its lines on your screen. In this case, you can use the with statement to open the file using the built-in open() function.

In the example below, you accomplish this. You also add some calls to print() that display information about the target variable defined by the as keyword:

This with statement uses the open() function to open hello.txt . The open() function is a context manager that returns a text file object represented by an io.TextIOWrapper instance.

Since you’ve defined a hello target variable with the as keyword, now that variable holds a reference to the file object itself. You confirm this by printing the object and its memory address. Finally, the for loop iterates over the lines and prints this content to the screen.

When it comes to using the as keyword in the context of an except clause, the target variable will contain an exception object if any exception occurs:

In this example, you run a division that raises a ZeroDivisionError . The as keyword assigns the raised exception to error . Note that when you print the exception object, you get only the message because exceptions have a custom .__str__() method that supports this behavior.

There’s a final detail to remember when using the as specifier in a try … except block like the one in the above example. Once you leave the except block, the target variable goes out of scope , and you can’t use it anymore.

Finally, Python’s import statements also support the as keyword. In this context, you can use as to import objects with a different name:

In these examples, you use the as keyword to import the numpy package with the np name and pandas with the name pd . If you call dir() , then you’ll realize that np and pd are now in your namespace. However, the numpy and pandas names are not.

Using the as keyword in your imports comes in handy when you want to use shorter names for your objects or when you need to use different objects that originally had the same name in your code. It’s also useful when you want to make your imported names non-public using a leading underscore, like in import sys as _sys .

The final implicit assignment that you’ll learn about in this tutorial only occurs when you’re using Python in an interactive session. Every time you run a statement that returns a value, the interpreter stores the result in a special variable denoted by a single underscore character ( _ ).

You can access this special variable as you’d access any other variable:

These examples cover several situations in which Python internally uses the _ variable. The first two examples evaluate expressions. Expressions always have a return value, which is automatically assigned to the _ variable every time.

When it comes to function calls, note that if your function returns a fruitful value, then _ will hold it. In contrast, if your function returns None , then the _ variable will remain untouched.

The next example consists of a regular assignment statement. As you already know, regular assignments don’t return any value, so the _ variable isn’t updated after these statements run. Finally, note that accessing a variable in an interactive session returns the value stored in the target variable. This value is then assigned to the _ variable.

Note that since _ is a regular variable, you can use it in other expressions:

In this example, you first create a list of values. Then you call len() to get the number of values in the list. Python automatically stores this value in the _ variable. Finally, you use _ to compute the mean of your list of values.

Now that you’ve learned about some of the implicit assignments that Python runs under the hood, it’s time to dig into a final assignment-related topic. In the following few sections, you’ll learn about some illegal and dangerous assignments that you should be aware of and avoid in your code.

Illegal and Dangerous Assignments in Python

In Python, you’ll find a few situations in which using assignments is either forbidden or dangerous. You must be aware of these special situations and try to avoid them in your code.

In the following sections, you’ll learn when using assignment statements isn’t allowed in Python. You’ll also learn about some situations in which using assignments should be avoided if you want to keep your code consistent and robust.

You can’t use Python keywords as variable names in assignment statements. This kind of assignment is explicitly forbidden. If you try to use a keyword as a variable name in an assignment, then you get a SyntaxError :

Whenever you try to use a keyword as the left operand in an assignment statement, you get a SyntaxError . Keywords are an intrinsic part of the language and can’t be overridden.

If you ever feel the need to name one of your variables using a Python keyword, then you can append an underscore to the name of your variable:

In this example, you’re using the desired name for your variables. Because you added a final underscore to the names, Python doesn’t recognize them as keywords, so it doesn’t raise an error.

Note: Even though adding an underscore at the end of a name is an officially recommended practice , it can be confusing sometimes. Therefore, try to find an alternative name or use a synonym whenever you find yourself using this convention.

For example, you can write something like this:

In this example, using the name booking_class for your variable is way clearer and more descriptive than using class_ .

You’ll also find that you can use only a few keywords as part of the right operand in an assignment statement. Those keywords will generally define simple statements that return a value or object. These include lambda , and , or , not , True , False , None , in , and is . You can also use the for keyword when it’s part of a comprehension and the if keyword when it’s used as part of a ternary operator .

In an assignment, you can never use a compound statement as the right operand. Compound statements are those that require an indented block, such as for and while loops, conditionals, with statements, try … except blocks, and class or function definitions.

Sometimes, you need to name variables, but the desired or ideal name is already taken and used as a built-in name. If this is your case, think harder and find another name. Don’t shadow the built-in.

Shadowing built-in names can cause hard-to-identify problems in your code. A common example of this issue is using list or dict to name user-defined variables. In this case, you override the corresponding built-in names, which won’t work as expected if you use them later in your code.

Consider the following example:

The exception in this example may sound surprising. How come you can’t use list() to build a list from a call to map() that returns a generator of square numbers?

By using the name list to identify your list of numbers, you shadowed the built-in list name. Now that name points to a list object rather than the built-in class. List objects aren’t callable, so your code no longer works.

In Python, you’ll have nothing that warns against using built-in, standard-library, or even relevant third-party names to identify your own variables. Therefore, you should keep an eye out for this practice. It can be a source of hard-to-debug errors.

In programming, a constant refers to a name associated with a value that never changes during a program’s execution. Unlike other programming languages, Python doesn’t have a dedicated syntax for defining constants. This fact implies that Python doesn’t have constants in the strict sense of the word.

Python only has variables. If you need a constant in Python, then you’ll have to define a variable and guarantee that it won’t change during your code’s execution. To do that, you must avoid using that variable as the left operand in an assignment statement.

To tell other Python programmers that a given variable should be treated as a constant, you must write your variable’s name in capital letters with underscores separating the words. This naming convention has been adopted by the Python community and is a recommendation that you’ll find in the Constants section of PEP 8 .

In the following examples, you define some constants in Python:

The problem with these constants is that they’re actually variables. Nothing prevents you from changing their value during your code’s execution. So, at any time, you can do something like the following:

These assignments modify the value of two of your original constants. Python doesn’t complain about these changes, which can cause issues later in your code. As a Python developer, you must guarantee that named constants in your code remain constant.

The only way to do that is never to use named constants in an assignment statement other than the constant definition.

You’ve learned a lot about Python’s assignment operators and how to use them for writing assignment statements . With this type of statement, you can create, initialize, and update variables according to your needs. Now you have the required skills to fully manage the creation and mutation of variables in your Python code.

In this tutorial, you’ve learned how to:

• Write assignment statements using Python’s assignment operators
• Work with augmented assignments in Python
• Explore assignment variants, like assignment expression and managed attributes
• Identify illegal and dangerous assignments in Python

Learning about the Python assignment operator and how to use it in assignment statements is a fundamental skill in Python. It empowers you to write reliable and effective Python code.

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• The Walrus Operator: Python's Assignment Expressions
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Python's Assignment Operator: Write Robust Assignments (Source Code)

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• Conditionals  > 

Now that we understand how to use Boolean values in our programs, it’s time to put those values to use. One way to think of the result of a Boolean expression is that it helps us make a decision in our programs. For example, if we want to do something special in our program when the user inputs the value $ 42 $ into the variable x , then we can write the Boolean expression x == 42 to help us decide if the user input the correct value.

Once we have that decision made, we need some way to tell our program to run a different piece of code based on the outcome of that decision. In Python, as well as most other programming languages, we can use a special construct called an if statement to do this. If statements are one example of a conditional statement in programming.

These conditional statements allow us to affect the control flow of our programs. Effectively, we can use a Boolean expression to determine whether a particular piece of code should be executed or not. In an if statement , we include a Boolean expression and a block of statements. If the Boolean expression evaluates to True , then we execute the code in the block of statements. If it is False , then we skip the block and continue with the rest of the program.

The structure of an if statement in Python is shown below:

In this structure, we have a <boolean expression> that is evaluated. After the Boolean expression is a colon : .

Then, the <block of statements> is included below the if statement’s first line, and it must be indented one level. In Python, the <block of statements> must include at least one line of code, otherwise Python won’t be able to understand it.

Indentation in Python

Let’s briefly discuss indentation in Python, since it is very important and is usually something that trips up new Python developers. Most programming languages use symbols such as curly braces {} to surround blocks of statements in code, making it easy to tell where one block ends and another begins. Similarly, those languages also use symbols such as semicolons ; at the end of each line of code, indicating the end of a particular statement.

These programming languages use those symbols to make it clear to both the developer and the computer where a particular line or block of code begins and ends, and it makes it very easy for tools to understand and run the code. As an interesting side effect, it also means that the code doesn’t need to follow any particular structure beyond the use of those symbols - many of the languages allow developers to place multiple statements, or even entire programs, on a single line of code. Likewise, indentation is completely optional, and only done to help make the code more readable.

Python takes a different approach. Instead of using symbols like semicolons ; and curly braces {} to show the structure of the code, Python uses newlines and indentation for this purpose. By doing so, Python is simultaneously simpler (since it has fewer symbols to learn) and more complex (the indentation has meaning) than other languages. It’s really a tradeoff, though most Python programmers will admit that not having to deal with special symbols in Python is well worth the effort of making sure that the indentation is correct, especially since most other languages follow the same conventions anyway, even if it isn’t strictly required.

So, how can we indent code in Python? Typically, we use four consecutive spaces for each level of indentation. So, below the conditional statement if <boolean expression>: shown above, we would place four spaces before the first line of the <block of statements> , and then continue to do so for each line below that should be included in the block of code.

Thankfully, most text editors used for programming, such as Codio, Atom, Visual Studio Code, and more, are all configured to convert tabs to spaces. So, we can simply press the Tab key on the keyboard to insert the correct amount of spaces for one level of indentation. Likewise, we can usually use Shift+Tab to remove four spaces.

Finally, it is worth noting that there is a special symbol that actually is a tab in text, which is represented as \t in a string. Like the newline symbol, we can’t see it in our UI in most cases, but it is there. Some non-programming text editors, such as Notepad in Windows, will insert that symbol instead of four spaces when we press the Tab key. If we try to run a program that contains those symbols, the Python interpreter may not be able to read our program and will give us an error about inconsistent use of tabs and spaces. If that happens, we’ll need to make sure our program is consistently using only tabs or spaces for indentation. Most editors used for programming have a special function for converting tabs to spaces, and there are lots of online tools available for this as well.

Code Tracing Example - False

Let’s go through a couple of code tracing examples in Python Tutor to see how an if statement works in code.

Consider this program in Python:

We can see this example by clicking on this Python Tutor link. At first, our window should look something like this:

When we step through the program, the first line of code will ask the user to input a number, so Python Tutor will show an input box at the bottom of the window:

For this first time through the program, let’s assume the user inputs the string "42" as input:

So, when we click Submit, we’ll see the integer value $ 42 $ stored in the variable x in the Global frame:

At this point, we’ve reached the if statement. The first step is to evaluate the Boolean expression x == 7 . Since x is actually storing the value $ 42 $ , this statement will evaluate to False . So, when we click the Next button on the state below:

We’ll see that the program arrow jumps past the block of statements in the if statement. So, the next line will simply print the goodbye message:

The entire process is shown in the animation below:

As we can see, when the Boolean expression evaluates to False , we’ll just skip the block of statements inside of the if statement.

Code Tracing Example - True

Now let’s see what happens when the Boolean expression evaluates to True instead. To see that, we can go back to the point where our program is asking for input, as shown below:

This time, we’ll assume the user inputs the string "7" as input.

So, when we click Submit, we’ll see the integer value $ 7 $ stored in the variable x in the Global frame:

This time, when we reach the if statement, we’ll see that the Boolean expression x == 7 will evaluate to True . So, when we click the Next button, we’ll be taken to the block of statements inside of the if statement:

Here, we’ll print the special message for finding a lucky number:

Then, we’ll print the program’s goodbye message:

The entire process can be seen in this animation:

So, when the Boolean expression evaluates to True , we’ll run the code inside the if statement. If it is False , then we’ll just skip past the if statement and continue with the rest of our program.

If Statement Flowchart

Another way to visualize an if statement is using a flowchart. Consider the following Python code:

This Python code can also be represented as the flowchart shown below:

When we read flowcharts like this, we typically go from top to bottom, starting with the circle labeled “START”. The first step is to read input from the user, and store that value in the variable x .

Then, we reach a decision node, which uses a diamond shape. This node asks if the value in the variable x is greater than 0. Notice that there are two lines coming from the decision node: one labeled “True” and another labeled “False”. They correspond to the possible values that can result from evaluating the Boolean expression x > 0 . So, if the result of that expression is True , then we’ll follow the path that exits the diamond from the side labeled “True”, and we’ll output the value x to the terminal. Once that is done, we can follow that path around to the next box that will output the string "Goodbye" .

If that result is False , then we’ll follow the downward path labeled “False” and skip the path to the side. Instead, we’ll just reach the line that prints "Goodbye" and then end the program.

As we learn how to use if statements in our code, we might find it easier to use flowcharts or other tools to help understand exactly what our program will do. The path that a program takes through a flowchart like this is called the control flow of the program. We’ll discuss that topic a bit more toward the end of this lab.

Last modified by: Russell Feldhausen Jun 27, 2024

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Assign within if statement Python

Is there a simpler alternative than

One which combines the assignment and if conditional into one statement?

• if-statement

• The first way you have is fine. Just be sure to use if res is None if the returned value can be some other valid but falsey values (e.g. empty string). –  wim Commented May 6, 2015 at 2:52
• What you're really looking for here is C-style if res=spam(): or if (ch=getch()) != EOF: , and Python doesn't have that. Python has good reasons for that; Swift has different good reasons for it; if you're designing a new language and, saving one line or 5 characters may well be worth if it there are no compelling reasons otherwise, but you may have compelling reasons otherwise. –  abarnert Commented May 8, 2015 at 22:00

2 Answers 2

For Python 3.8+, PEP 572 introduces Assignment Expressions

This allows assigning to variables within an expression using the notation NAME := expr . It can be used within if statements, for example:

• 3 You forgot to mention the informal name given to this new operator, the "walrus operator". –  Mark Ransom Commented Jul 23, 2021 at 18:53
• this assigns match to the result of pattern.search(data). Would you be able to assign match to data in one line? –  Vincent Commented Feb 7, 2023 at 8:45

Often, what you have is already the best option.

You can always create a new scope to bind the value to a variable:

But that's certainly not going to be more readable, or more Pythonic.

If you just want to save one line, you can do this:

This is sometimes more readable, but usually it's a net loss.

Another way to deal with this would be to change do_something_with to accept None and do nothing. This isn't as common in Python as it is in, say, Smalltalk or Swift, but it definitely has its place sometimes.

It's hard to see why with a toy example like this, but if you're calling these functions 70 times, putting the check in one place, inside the function, instead of in 70 places, everywhere it's called, is an obvious win. (Especially since you'd probably put it in 68 places and forget the other 2.)

Last, but not least, in many cases the right answer is exceptions. Your do_something_with probably already raises if you pass None . And you could surely change returns_value_or_none to returns_value_or_raises .

Again, in this toy example, it'll just look like more code. But in real-life code, it often makes sense to put a whole block of code inside a try / except , and handle all the errors at once down at the end. Or even to let the exceptions percolate up to a higher level where they're easier to deal with.

Of course that isn't appropriate in every case; if you're expecting None to be a frequent and perfectly reasonable response, and just want to skip one step rather than abort the whole chain of operations, checking or passing through None is going to make a lot more sense than littering your code with small try blocks.

• Thanks, I guess I was optimistic about some syntactic sugar. The problem with allowing the called functions to take None is that, in other contexts, I want the same function to raise an error if None or another invalid type is received. Plus, in most cases, it's something like if re.match(some_pattern, text): do_something_with(re.match(some_pattern, text).group(1)) . If forget the if condition and re.match() returns None the interpreter will let me know soon enough. –  Peter Commented May 8, 2015 at 21:49
• @Peter: Usually there's nothing wrong with letting the interpreter let you know (whether "you" means the except handler in your code, or the programmer who didn't consider the possibility that the value could be None and therefore didn't code for it). –  abarnert Commented May 8, 2015 at 21:56
• @Peter: Meanwhile, if you only show us a meaningless toy example, you're only going to get "here are a half-dozen ways to do similar things; which one is appropriate in your real code depends on that real code"; if you show us a couple of real examples, we can show you the idiomatic way to deal with those examples. –  abarnert Commented May 8, 2015 at 22:01

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