python if in 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! 😊

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

Related Articles

• Introduction to Python Classes (Part 1 of 2)
• How to Sort a List, Tuple or Object (with sorted) in Python
• Best Text Editors for Python development
• Introduction to SQLite in Python
• Introductory Tutorial of Python’s SQLAlchemy

Signup for new content

Thank you for joining our mailing list!

Latest Articles

• Building AI-Powered Analytics Pipelines with Python
• Key Factors in Custom Software Development and Their Impact on Pricing
• Software Development With Python: How Good Is It?
• 6 Benefits of Python Development in the Healthcare Sector
• Role of Document Scanning in Document Management: With Python Script Bonus
• Data Structure
• csv in python
• logging in python
• Python Counter
• python subprocess
• numpy module
• Python code generators
• python tutorial
• csv file python
• python logging
• Counter class
• Python assert
• numbers_list
• binary search
• Insert Node
• Python tips
• python dictionary
• Python's Built-in CSV Library
• logging APIs
• Constructing Counters
• Matplotlib Plotting
• any() Function
• linear search
• Python tools
• python update
• logging module
• Concatenate Data Frames
• python comments
• Recursion Limit
• Data structures
• installation
• python function
• pandas installation
• Zen of Python
• concatenation
• Echo Client
• NumPy Pad()
• install python
• how to install pandas
• Philosophy of Programming
• concat() function
• Socket State
• Python YAML
• remove a node
• function scope
• Tuple in Python
• pandas groupby
• socket programming
• Python Modulo
• Dictionary Update()
• datastructure
• bubble sort
• find a node
• calling function
• GroupBy method
• Np.Arange()
• Modulo Operator
• Python Or Operator
• Python salaries
• pyenv global
• NumPy arrays
• insertion sort
• in place reversal
• learn python
• python packages
• zeros() function
• Scikit Learn
• HTML Parser
• circular queue
• effiiciency
• python maps
• Num Py Zeros
• Python Lists
• HTML Extraction
• selection sort
• Programming
• install python on windows
• reverse string
• python Code Editors
• pandas.reset_index
• Infinite Numbers in Python
• Python Readlines()
• Programming language
• remove python
• concatenate string
• Code Editors
• reset_index()
• Train Test Split
• Local Testing Server
• Python Input
• priority queue
• web development
• uninstall python
• python string
• code interface
• round numbers
• train_test_split()
• Flask module
• Linked List
• machine learning
• compare string
• pandas dataframes
• arange() method
• Singly Linked List
• python scripts
• learning python
• python bugs
• ZipFunction
• plus equals
• np.linspace
• SQLAlchemy advance

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
• Understand a wide range of computer science topics , including anagrams , palindromes , supersets , permutations , factorials , prime numbers , Fibonacci numbers, obfuscation , searching , and algorithmic sorting

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!!

While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.

To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer , and owner of one of the top 10 largest Python blogs worldwide.

His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.

• Module 2: The Essentials of Python »
• Conditional Statements
• View page source

Conditional Statements 

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

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

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

Comparison Operations 

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

The first six of these operators are familiar from mathematics:

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

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

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

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

Use is not to check if two objects are distinct:

bool and Truth Values of Non-Boolean Objects 

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

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

and non-zero Python integers are associated with True :

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

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

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

Empty dictionaries and sets

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

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

if , else , and elif 

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

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

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

In practice this can look like:

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

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

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

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

Reading Comprehension: Conditional statements

Assume my_list is a list. Given the following code:

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

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

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

"code.py" \(\rightarrow\) "code"

"doc2.pdf" \(\rightarrow\) "doc2"

"hello_world" \(\rightarrow\) "hello_world"

Inline if-else statements 

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

can be written in a single line as:

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

The inline if-else statement :

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

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

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

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

Short-Circuiting Logical Expressions 

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

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

To demonstrate this behavior, consider the following example:

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

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

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

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

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

Reading Comprehension: short-circuited expressions

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

Links to Official Documentation 

Truth testing

Boolean operations

Comparisons

‘if’ statements

Reading Comprehension Exercise Solutions: 

Conditional statements

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

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

Short-circuited expressions

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

How to use python if else in one line with examples

December 31, 2023

Python Ternary Operator , Python

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.

Can't find what you're searching for? Let us assist you.

Enter your query below, and we'll provide instant results tailored to your needs.

If my articles on GoLinuxCloud has helped you, kindly consider buying me a coffee as a token of appreciation.

For any other feedbacks or questions you can send mail to [email protected]

Thank You for your support!!

Leave a Comment Cancel reply

Save my name and email in this browser for the next time I comment.

Notify me via e-mail if anyone answers my comment.

We try to offer easy-to-follow guides and tips on various topics such as Linux, Cloud Computing, Programming Languages, Ethical Hacking and much more.

Recent Comments

Popular posts, 7 tools to detect memory leaks with examples, 100+ linux commands cheat sheet & examples, tutorial: beginners guide on linux memory management, top 15 tools to monitor disk io performance with examples, overview on different disk types and disk interface types, 6 ssh authentication methods to secure connection (sshd_config), how to check security updates list & perform linux patch management rhel 6/7/8, 8 ways to prevent brute force ssh attacks in linux (centos/rhel 7).

Privacy Policy

HTML Sitemap

• Subscription

Tutorial: Using If Statements in Python

Our life is full of conditions even if we don’t notice them most of the time. Let’s look at a few examples:

If tomorrow it doesn't rain, I’ll go out with my friends in the park. Otherwise, I’ll stay home with a cup of hot tea and watch TV.

If tomorrow it isn't too hot, I’ll go to the sea, but if it is, I’ll have a walk in the forest. However, if it rains, I’ll stay home.

You get the idea. Let’s see how conditions work in computers. You may already know that programs in Python are executed line by line. However, sometimes, we need to skip some code and execute only some of it only if certain conditions are met. This is where control structures become useful. Conditional statements in Python are built on these control structures. They will guide the computer in the execution of a program.

In this tutorial, you'll learn how to use conditional statements. This guide is for beginners in Python, but you'll need to know some basics of coding in Python. If you don’t, then check this free Python Fundamentals course .

Basic if Statement

In Python, if statements are a starting point to implement a condition. Let’s look at the simplest example:

When <condition> is evaluated by Python, it’ll become either True or False (Booleans). Thus, if the condition is True (i.e, it is met), the <expression> will be executed, but if <condition> is False (i.e., it is not met), the <expression> won’t be executed.

We are pretty free to decide what conditions and expressions can be because Python is very flexible.

Let’s look at a concrete example.

First of all, we define two variables, x and y . Then we say that if variable x is smaller than variable y , print out x is smaller than y ). Indeed, if we execute this code, we’ll print out this output because 3 is smaller than 10.

Output: x is smaller than y.

Let’s look at a more complex example.

In this case, if the condition is met, then a value of 13 will be assigned to the variable z . Then Variable z is now 13. will be printed out (note that the print statement can be used both outside and inside the if statement).

As you can see, we aren't restrained in the choice of an expression to execute. You can now practice more by writing more complex code.

Let’s know see what happens if we execute the following code:

Here we changed the direction of the comparison symbol (it was less than , and now it’s greater than ). Can you guess the output?

There will be no output! This happened because the condition hadn't been met. 3 is not greater than 10, so the condition evaluated to False , and the expression wasn’t executed. How do we solve this problem? With the else statement.

else Statement

What if we want to execute some code if the condition isn't met? We add an else statement below the if statement. Let’s look at an example.

Here, Python first executes the if condition and checks if it’s True . Since 3 is not greater than 10, the condition isn't met, so we don’t print out “x is greater than y.” Then we say that in all other cases we should execute the code under the else statement: x is smaller than y.

Let’s get back to our first example of a conditional statement:

Here the else statement is “Otherwise.”

What happens if the condition is met?

In this case, Python just prints out the first sentence as before.

What if x is equal to y ?

The output is clearly wrong because 3 is equal to 3! We have another condition outside the greater or less than comparison symbols; thus, we have to use the elif statement.

elif Statement

Let’s rewrite the above example and add an elif statement.

Output: x is equal to y .

Python first checks if the condition x < y is met. It isn't, so it goes on to the second condition, which in Python, we write as elif , which is short for else if . If the first condition isn't met, check the second condition, and if it’s met, execute the expression. Else, do something else. The output is “x is equal to y.”

Let’s now get back to one of our first examples of conditional statements:

Here, our first condition is that tomorrow it’s not too hot ( if statement). If this condition isn't met, then we go for a walk in the forest ( elif statement). Finally, if neither condition is met, we’ll stay home ( else statement).

Now let’s translate this sentence into Python.

In this example, we're going to use strings instead of integers to demonstrate the flexibility of the if condition in Python.

Python first checks if the variable tomorrow is equal to “warm” and if it is, then it prints out I'll go to the sea. and stops the execution. What happens if the first condition isn't met?

In this case, Python evaluates the first condition to False and goes to the second condition. This condition is True , so it prints out I'll go to the forest. and stops the execution.

If neither of the two conditions is met, then it’ll print out I’ll stay home.

Of course, you can use whatever number of elif statements you want. Let’s add more conditions and also change what is printed out under the else statement to Weather not recognized. (for example, if tomorrow is “f”, we don’t know what it means).

Guess what’s printed out?

Multiple Conditions

Let’s now add some complexity. What if we want to meet multiple conditions in one if statement?

Let’s say we want to predict a biome (i.e., desert or tropical forest) based on two climate measurements: temperature and humidity. For example, if it’s hot and dry, then it’s a hot desert, but if it’s cold and dry, then it’s an arctic desert. You can see that we cannot classify these two biomes based only on their humidity (they are both dry) so we also have to add the temperature measure.

In Python, we can use logical operators (i.e., and, or) to use multiple conditions in the same if statement.

Look at the code below.

The output will be It's a hot desert. because only when humidity is low and temperature is high, the combined condition is True . It’s not sufficient to have only one of the conditions to be True .

Formally, Python checks if the first condition of humidity is True (indeed, it is), then it checks if the second condition of temperature is True (and it is) and only in this case the combined condition is True . If at least one of these conditions isn't met, then the combined condition evaluates to False .

What if we want either of two (or more) conditions is met? In this case we should use the or logical operator.

Let’s look at an example. Say you have a list of numbers from 1 to 14 (inclusive), and you want to extract all the numbers that are smaller than 3 or greater or equal to 10. You can achieve the result using an or operator!

Output: [1, 2, 10, 11, 12, 13, 14]

Here Python checks whether the current number in the for loop is less than 3, and if it’s True , then the combined if statement evaluates to True . The same happens if the current number is equal to or greater than 10. If the combined if statement is True , then the expression is executed and the current number is appended to the list nums_less_3_greater_equal_10 .

For the sake of experiment, let’s change or to and .

In this case, the current number should be simultaneously smaller than 3 and greater or equal to 10, which is clearly not possible so the combined if statement evaluates to False and the expression isn't executed.

To make things even more clear, look at this print statement.

Output: True

Here Python evaluates the combination of False and True , and since we have the or logical operator, it’s sufficient that at least one of these Booleans is True to evaluate the combined statement to True .

Now, what happens if we change or to and ?

Output: False

Both Booleans should be True to evaluate the combined condition to True . Since one of them is False , the combined condition is also False . This is what happens in the example with numbers.

You can even combine multiple logical operators in one expression. Let’s use the same list of numbers, but now, we want to find all the numbers that are either smaller than 3 or greater or equal to 10 and simultaneously are even numbers.

Output: [2, 10, 12, 14]

Why is the first number of the output 2? In the second for loop, 2 is evaluated in the first condition in parentheses. It is smaller than 3, so the combined condition in parentheses is True . 2 is also divisible by 2 with the remainder 0, so the second condition is also True . Both conditions are True , so this number is appended to the list.

Why do we use parentheses? It’s because of the operator precedence in Python. What if we remove them?

Output: [1, 2, 10, 12, 14]

We have 1 in the list! In Python, all operators are evaluated in a precise order. For example, the and operator takes precedence over the or operator. But if we place the or operator in parentheses, it’ll take precedence over the and operator.

First we evaluate the conditions on both sides of the and operator (it has precedence). 1 is neither greater than 10 nor yields 0 if divided by 2, so the combined condition is False . We are left with the condition if num < 3 or False . 1 is smaller than 3, so the first condition is True . The condition becomes True or False . We have an or operator, so the combined condition evaluates to True , and 1 is appended to the list. Practice by checking what happens with the other numbers.

Finally, have a look at this truth table to understand how logical operators work. Here, we will describe only the and and or logical operators, but in Python, we also have the not operator. We invite you to learn more about it and practice using it inside if statements.

We have two inputs, A and B, that can be either True or False . For example, in the second row, A is True , while B is False ; thus, A AND B evaluate to False but A OR B evaluate to True . The rest of the table is read in the same way. Take a minute to understand what it tells you.

Nested if Statements

Python is a very flexible programming language, and it allows you to use if statements inside other if statements, so called nested if statements . Let’s look at an example.

Output: Well done!

Here, if the mark is between 60 and 100, the expression under the if statement is executed. But then we have other conditions that are also evaluated. So, our mark is 85, which is between 60 and 100. However, 85 is smaller than 90, so the first nested if condition is False , and the first nested expression isn't executed. But 85 is higher than 80, so the second expression is executed and “Well done!” is printed out.

Of course, we also have elif statements outside the expression below the first if statement. For example, what if the mark is higher than 100? If the first condition (number between 60 and 100) is False , then we go directly to the elif statement mark > 100 and print out This mark is too low. .

Try to assign different numbers to the mark variable to understand the logic of this code.

Pattern Matching in Python 3.10

The pattern matching was added in Python 3.10, released in October, 2021. In short, it can be seen a different syntax for if..elif statements. Let's look at an example by rewrting a previous example using the pattern matching.

We can see similarities between using the if..elif statements and the match..case syntax. First, we define what variable we want to match , and when we define the cases (or values this variable can take). The rest of the code is similar. If a case is matched (that's equivalent of a double equal sign), then the print expression is executed.

Note the last case statement, it's the _ case, which is equivalent to else : if no cases are matched, then we print Weather not recognized .

pass Statement

As you start writing more complex code, you may find yourself in the situation where you have to use a placeholder instead of the code you want to implement later. The pass statement is this placeholder. Let’s look at an example with and without the pass statement.

Python expects some code under the if statement, but you are yet to implement it! You can write pass there and solve this problem.

Output: I'll write this code later.

If instead you place pass in the if statement, Python won’t throw any error and will pass to any code you have below the if statement. This works even if you have other conditions below the first if statement.

Output: The variable num is 4.

Conclusions

In Python, if statements are used all the time, and you’ll find yourself using them in basically any project or script you're building, so it's essential to understand the logic behind them. In this article, we’ve covered the most important aspects of if conditions in Python:

• Creating basic if statements
• Adding complexity by using else and elif statements
• Combining multiple conditions in one if statement using logical operators ( or , and )
• Using nested if statements
• Using pass statements as placeholders

With this knowledge, you can now start working with conditional statements in Python.

Feel free to connect with me on LinkedIn and GitHub . Happy coding!

More learning resources

Tutorial: advanced for loops in python, how i built a python bot to help me find an apartment in san francisco.

Learn data skills 10x faster

Join 1M+ learners

Enroll for free

• Data Analyst (Python)
• Gen AI (Python)
• Business Analyst (Power BI)
• Business Analyst (Tableau)
• Machine Learning
• Data Analyst (R)

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

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

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

How to Use the if Statement in Python

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

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

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

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

How to Use the else Statement in Python

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

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

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

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

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

How to Use the elif Statement in Python

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

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

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

Use Cases For Conditional Statements

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

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

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

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

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

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

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

Example 3: Checking if a year is a leap year

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

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

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

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

Example 4: Checking if a string contains a certain character

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

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

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

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

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

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

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

Let’s connect on Twitter and Linkedin .

Data Scientist||Machine Learning Engineer|| Data Analyst|| Microsoft Student Learn Ambassador

If you read this far, thank the author to show them you care. Say Thanks

Learn to code for free. freeCodeCamp's open source curriculum has helped more than 40,000 people get jobs as developers. Get started

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 3.8 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.

🐍 Python Tricks 💌

Get a short & sweet Python Trick delivered to your inbox every couple of days. No spam ever. Unsubscribe any time. Curated by the Real Python team.

About Leodanis Pozo Ramos

Leodanis is an industrial engineer who loves Python and software development. He's a self-taught Python developer with 6+ years of experience. He's an avid technical writer with a growing number of articles published on Real Python and other sites.

Each tutorial at Real Python is created by a team of developers so that it meets our high quality standards. The team members who worked on this tutorial are:

Master Real-World Python Skills With Unlimited Access to Real Python

Join us and get access to thousands of tutorials, hands-on video courses, and a community of expert Pythonistas:

Join us and get access to thousands of tutorials, hands-on video courses, and a community of expert Pythonistas:

What Do You Think?

What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment below and let us know.

Commenting Tips: The most useful comments are those written with the goal of learning from or helping out other students. Get tips for asking good questions and get answers to common questions in our support portal . Looking for a real-time conversation? Visit the Real Python Community Chat or join the next “Office Hours” Live Q&A Session . Happy Pythoning!

Keep Learning

Related Topics: intermediate best-practices python

Keep reading Real Python by creating a free account or signing in:

Already have an account? Sign-In

Almost there! Complete this form and click the button below to gain instant access:

Python's Assignment Operator: Write Robust Assignments (Source Code)

🔒 No spam. We take your privacy seriously.

Learn Python practically and Get Certified .

Popular Tutorials

Popular examples, reference materials, learn python interactively, python introduction.

• Get Started With Python
• Your First Python Program
• Python Comments

Python Fundamentals

• Python Variables and Literals
• Python Type Conversion
• Python Basic Input and Output
• Python Operators

Python Flow Control

• Python if...else Statement
• Python for Loop

Python while Loop

Python break and continue

Python pass Statement

Python Data types

• Python Numbers and Mathematics
• Python List
• Python Tuple
• Python String
• Python Sets
• Python Dictionary
• Python Functions
• Python Function Arguments
• Python Variable Scope
• Python Global Keyword
• Python Recursion
• Python Modules
• Python Package
• Python Main function

Python Files

• Python Directory and Files Management
• Python CSV: Read and Write CSV files
• Reading CSV files in Python
• Writing CSV files in Python
• Python Exception Handling
• Python Exceptions
• Python Custom Exceptions

Python Object & Class

• Python Objects and Classes
• Python Inheritance
• Python Multiple Inheritance
• Polymorphism in Python
• Python Operator Overloading

Python Advanced Topics

• List comprehension
• Python Lambda/Anonymous Function
• Python Iterators
• Python Generators
• Python Namespace and Scope
• Python Closures
• Python Decorators
• Python @property decorator
• Python RegEx

Python Date and Time

• Python datetime
• Python strftime()
• Python strptime()
• How to get current date and time in Python?
• Python Get Current Time
• Python timestamp to datetime and vice-versa
• Python time Module
• Python sleep()

Additional Topic

• Precedence and Associativity of Operators in Python
• Python Keywords and Identifiers
• Python Asserts
• Python Json
• Python *args and **kwargs

Python Tutorials

Python Assert Statement

• List of Keywords in Python

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

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

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

These conditional tasks can be achieved using the if statement.

• Python if Statement

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

Here, if the condition of the if statement is:

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

Let's look at an example.

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

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

• Example: Python if Statement

Sample Output 1

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

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

Sample Output 2

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

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

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

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

Here, if the condition inside the if statement evaluates to

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

• Example: Python if…else Statement

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

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

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

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

• Python if…elif…else Statement

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

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

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

• Example: Python if…elif…else Statement

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

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

• Python Nested if Statements

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

Here's how this program works.

More on Python if…else Statement

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

This code can be compactly written as

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

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

can be written as

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

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

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

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

Table of Contents

• Introduction

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

Sorry about that.

Related Tutorials

Python Tutorial

Python Enhancement Proposals

• Python »
• PEP Index »

PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

• In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

• Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
• Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
• Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
• Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
• Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
• Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

• for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
• due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

• Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
• Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

• Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
• Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

• Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
• Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

• Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
• Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

• Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
• Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

• In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
• import foo as bar
• except Exc as var
• with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

• NAME = EXPR
• if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

• If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
• If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

• Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
• Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

• Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

• Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

• Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
• Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

• Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
• Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

• Python Basics
• Interview Questions
• Python Quiz
• Popular Packages
• Python Projects
• Practice Python
• AI With Python
• Learn Python3
• Python Automation
• Python Web Dev
• DSA with Python
• Python OOPs
• Dictionaries
• Python Operators
• Precedence and Associativity of Operators in Python
• Python Arithmetic Operators
• Difference between / vs. // operator in Python
• Python - Star or Asterisk operator ( * )
• What does the Double Star operator mean in Python?
• Division Operators in Python
• Modulo operator (%) in Python
• Python Logical Operators
• Python OR Operator
• Difference between 'and' and '&' in Python
• not Operator in Python | Boolean Logic
• Ternary Operator in Python
• Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

• Walrus Operator in Python 3.8
• Increment += and Decrement -= Assignment Operators in Python
• Merging and Updating Dictionary Operators in Python 3.9
• New '=' Operator in Python3.8 f-string

Python Relational Operators

• Comparison Operators in Python
• Python NOT EQUAL operator
• Difference between == and is operator in Python
• Chaining comparison operators in Python
• Python Membership and Identity Operators
• Difference between != and is not operator in Python

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

Here are the Assignment Operators in Python with examples.

Assignment Operator

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

Addition Assignment Operator

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

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

S ubtraction Assignment Operator

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

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

M ultiplication Assignment Operator

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

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

D ivision Assignment Operator

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

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

M odulus Assignment Operator

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

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

F loor Division Assignment Operator

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

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

Exponentiation Assignment Operator

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

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

Bitwise AND Assignment Operator

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

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

Bitwise OR Assignment Operator

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

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

Bitwise XOR Assignment Operator 

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

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

Bitwise Right Shift Assignment Operator

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

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

Bitwise Left Shift Assignment Operator

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

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

Walrus Operator

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

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

Please Login to comment...

Similar reads.

• Python-Operators

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

Assignments  »  Conditional Structures  »  Set1

Conditional Structures

1. Write a program that prompts the user to input a number and display if the number is even or odd. Solution

2. Write a Python program that takes an age as input and determines whether a person is eligible to vote. If the age is 18 or above, print "You are eligible to vote." Otherwise, print "You are not eligible to vote yet.". Solution

3. Write a program that prompts the user to input two integers and outputs the largest. Solution

4. Write a program that prompts the user to enter a number and determines whether it is positive, negative, or zero. The program should print "Positive" if the number is greater than 0, "Negative" if the number is less than 0, and "Zero" if the number is 0. Solution

5. Write a program that prompts the user to enter their age and prints the corresponding age group. The program should use the following age groups:

6. Write a program that prompts the user to input a number from 1 to 7. The program should display the corresponding day for the given number. For example, if the user types 1, the output should be Sunday. If the user types 7, the output should be Saturday. If the number is not between 1 to 7 user should get error message as shown in sample output. Solution

7. Write a program that prompts the user to enter their weight (in kilograms) and height (in meters). The program should calculate the Body Mass Index (BMI) using the formula: BMI = weight / (height * height). The program should then classify the BMI into one of the following categories:

8. The marks obtained by a student in 3 different subjects are input by the user. Your program should calculate the average of subjects and display the grade. The student gets a grade as per the following rules:

Write a program that prompts the user to input the value of a (the coefficient of x 2 ), b (the coefficient of x), and c (the constant term) and outputs the roots of the quadratic equation. Solution

10. Write a program that prompts the user to enter three numbers and sorts them in ascending order. The program should print the sorted numbers. Solution

11. Write a program that prompts the user to input three integers and outputs the largest. Solution

12. Write a program that prompts the user to input a character and determine the character is vowel or consonant. Solution

13. Write a program that prompts the user to input a year and determine whether the year is a leap year or not. Leap Years are any year that can be evenly divided by 4. A year that is evenly divisible by 100 is a leap year only if it is also evenly divisible by 400. Example :

14. Write a program that prompts the user to input number of calls and calculate the monthly telephone bills as per the following rule: Minimum Rs. 200 for up to 100 calls. Plus Rs. 0.60 per call for next 50 calls. Plus Rs. 0.50 per call for next 50 calls. Plus Rs. 0.40 per call for any call beyond 200 calls. Solution

10 if-else Practice Problems in Python

• learn python
• python programming

If you're trying to learn Python, you need to practice! These ten if-else Python practice problems provide you some hands-on experience. And don’t worry – we’ve provided full code solutions and detailed explanations!

Python is particularly good for beginners to learn. Its clear syntax can be read almost as clearly as a normal sentence. The if-else statement is a good example of this; it lets you build logic into your programs. This article will walk you through ten i f-else practice exercises in Python. Each one is specifically designed for beginners, helping you hone your understanding of if-else statements.

The exercises in this article are taken directly from our courses, including Python Basics: Part 1 . This course helps absolute beginners understand computer programming with Python; if you’ve never written a line of code, this course is a great place to start.

As the saying goes, practice makes perfect. Learning Python is no exception. As we discuss in Different Ways to Practice Python , doing online courses is a great way to practice a variety of topics. The more you practice writing code, the more comfortable and confident you'll become in your abilities. So – whether you aim to build your own software, delve into data science, or automate mundane tasks – dedicating time to practice is key.

The Python if-else Statement

Before we dive into the exercises, let's briefly discuss the workings of if-else statements in Python. An if-else statement allows your program to make decisions based on certain conditions. The syntax is straightforward:

• You provide a condition to evaluate within the if
• If that condition is true, the corresponding block of code is executed.
• If the condition is false, the code within the optional else block is executed instead.

Here's a simple example:

In this example, if the value of x is greater than 5, the program will print "x is greater than 5"; otherwise, it will print "x is not greater than 5".

This concept forms the backbone of decision-making in Python programs.

To get the most benefit from this article, attempt the problems before reading the solutions. Some of these exercises will have several possible solutions, so also try to think about alternative ways to solve the same problem. Let’s get started.

10 if-else Python Practice Exercises

Exercise 1: are you tall.

Exercise: Write a program that will ask the user for their height in centimeters. Use the input() built-in function to do this. If the height is more than 185 centimeters, print the following line of code:

Explanation: This is a similar syntax to the above template example. The height variable stores the user input as an integer and tests if it’s greater than 185 cm. If so, the message is printed. Notice there is no else; this means if the height is less than or equal to 185, nothing happens.

Exercise 2: More Options

Exercise: Write a program that will ask the user the following question:

• If the user answers y , print: Wrong! Canberra is the capital!
• If the user answers n , print: Correct!
• If the user answers anything else, print: I do not understand your answer!

Explanation: The if statement can be extended with an elif (i.e. else if) to check any answer that returns False to the first condition. If the elif statement also returns False , the indented block under the else is executed.

Exercise 3: Check If a Character Is in a String

Exercise: Write a program to ask the user to do the following:

• Provide the name of a country that does not contain any lowercase a or e letters. (Use the prompt: The country is : )
• If the user provides a correct string (i.e. one with no a or e inside it), print: You won... unless you made this name up!
• Otherwise, print: You lost!

(By the way, possible answers include Hong Kong, Cyprus, and Togo.)

Explanation: This code prompts the user to input a country name using the built-in input() function. Then, it checks if the letters 'a' or 'e' is present in the inputted country name using the in operator. If either letter is found, it prints "You lost!". Otherwise, it prints "You won... unless you made this name up!" using the else statement.

Exercise 4: Count Letter Frequency in a String

Exercise: The letter e is said to be the most frequent letter in the English language. Count and print how many times this letter appears in the poem below:

John Knox was a man of wondrous might, And his words ran high and shrill, For bold and stout was his spirit bright, And strong was his stalwart will. Kings sought in vain his mind to chain, And that giant brain to control, But naught on plain or stormy main Could daunt that mighty soul. John would sit and sigh till morning cold Its shining lamps put out, For thoughts untold on his mind lay hold, And brought but pain and doubt. But light at last on his soul was cast, Away sank pain and sorrow, His soul is gay, in a fair to-day, And looks for a bright to-morrow. (Source: "Unidentified," in Current Opinion, July 1888)

Explanation: The solution starts by initializing a counter with the value of 0. Then a for loop is used to loop through every letter in the string variable poem. The if statement tests if the current letter is an e; if it is, the counter is incremented by 1. Finally, the result of the counter is printed. Note that there is no else needed in this solution, since nothing should happen if the letter is not an e.

Exercise 5: Format Recipes

Exercise: Anne loves cooking and she tries out various recipes from the Internet. Sometimes, though, she finds recipes that are written as a single paragraph of text. For instance:

When you're in the middle of cooking, such a paragraph is difficult to read. Anne would prefer an ordered list, like this:

• Cut a slit into the chicken breast.
• Stuff it with mustard, mozzarella and cheddar.
• Secure the whole thing with rashers of bacon.
• Roast for 20 minutes at 200C.

Write a Python script that accepts a recipe string from the user ('Paste your recipe: ') and prints an ordered list of sentences, just like in the example above.

Each sentence of both the input and output should end with a single period.

Explanation: The solution takes a recipe input from the user with input() , then iterates through each character in the recipe. Whenever it encounters a period ( '.' ), it increments a counter, adds a new line character ( '\n' ) and the incremented counter followed by a period to an ordered list. If it encounters any other character, it simply adds that character to the ordered list. Finally, it calculates the length of the counter, removes the last part of the ordered list to exclude the unnecessary counter, and prints the modified ordered list, which displays the recipe with numbered steps.

Exercise 6: Nested if Statements

Exercise: A leap year is a year that consists of 366 (not 365) days. It occurs roughly every four years. More specifically, a year is considered leap if it is either divisible by 4 but not by 100 or it is divisible by 400.

Write a program that asks the user for a year and replies with either leap year or not a leap year.

Explanation: This program takes a year from the user as input. It checks if the year is divisible by 4. If it is, it then checks if the year is divisible by 100. If it is, it further checks if the year is divisible by 400. If it is, it prints 'leap year' because it satisfies all the conditions for a leap year. This is all achieved with nesting.

If it's divisible by 4, but not by 100, or if it’s not divisible by 400, the program prints 'not a leap year'. If the year is not divisible by 100, it directly prints 'leap year' because it satisfies the basic condition for a leap year (being divisible by 4). If the year is not divisible by 4, it prints 'not a leap year'.

Exercise 7: Scrabble

Exercise: Create a function called can_build_word(letters_list, word) that takes a list of letters and a word. The function should return True if the word uses only the letters from the list.

Like in real Scrabble, you cannot use one tile twice in the same word. For example, if a list of letters contains only one 'a', ‘banana’ returns False. The function should also return False if the word contains any letters not in the list.

For example …

… should return True. On the other hand …

… should return False because there is only one 'f'.

One thing to keep in mind: Neither the provided letter list nor the word should be changed by your function. It may be tempting to remove letters from the letters list to remove the used tiles. This should not be the case. Instead, you should copy the passed list. Simply use the list() function and pass it another list:

This way we can safely operate on a copy of the list without changing the contents of the original list.

Explanation: The function starts by making a copy of the list of letters to avoid modifying the original. Then, it iterates through each letter in the word. For each letter, it checks if that letter exists in the list copy. If it does, it removes that letter from the copy, which ensures each letter is used only once.

If the letter doesn't exist in the copy of the letters list, it means the word cannot be formed using the available letters; it returns False. If all the letters in the word can be found and removed from the list copy, it means the word can indeed be formed; it returns True .

If you are looking for additional material to help you learn how to work with Python lists, our article 12 Beginner-Level Python List Exercises with Solutions has got you covered.

Exercise 8: for Loops and if Statements

Exercise: Write a program that asks for a positive integer and prints all of its divisors, one by one, on separate lines in ascending order.

A divisor is a number that divides a particular number with no remainder. For example, the divisors of 4 are 1, 2, and 4.

To check if a is a divisor of b , you can use the modulo operator %. a % b returns the remainder of dividing a by b . For example, 9 % 2 is 1 because 9 = (4 * 2) + 1, and 9 % 3 is 0 because 9 divides into 3 with no remainder. If a % b == 0 , then b is a divisor of a .

Explanation: This solution takes an input number from the user and then iterates through numbers from 1 to that input number (inclusive). For each number i in this range, it checks if the input number is divisible by i with no remainder ( number % i == 0 ). If the condition is satisfied, it means that i is a factor of the input number.

Looping is another important skill in Python. This problem could also be solved with a while loop. Take a look at 10 Python Loop Exercises with Solutions to learn more about for and while loops.

Exercise 9: if Statements and Data Structures

Exercise: Write a function named get_character_frequencies() that takes a string as input and returns a dictionary of lowercase characters as keys and the number of their occurrences as values. Ignore the case of a character.

Explanation: This function initializes an empty dictionary ( freq_dict ) to store the frequencies of characters. It iterates through each character in the input word using a for loop. Within the loop, it converts each character to lowercase using the lower() method; this ensures case-insensitive counting.

The program checks if the lowercase character is already a key in the dictionary. If it's not, it adds the character as a key and sets its frequency to 1. If it is already a key, it increments the frequency count for that character by 1 and returns the dictionary.

If you’re not familiar with how Python dictionaries work, Top 10 Python Dictionary Exercises for Beginners will give you experience working with this important data structure.

Exercise 10: if Statements and Multiple Returns

Exercise: Write a function named return_bigger(a, b) that takes two numbers and returns the bigger one. If the numbers are equal, return 0.

Explanation: When using if statements in a function, you can define a return value in the indented block of code under the if-else statement. This makes your code simpler and more readable – another great example of the clear syntax which makes Python such an attractive tool for all programmers.

Do You Want More Python Practice Exercises?

Learning anything new is a challenge. Relying on a variety of resources is a good way to get a broad background. Read some books and blogs to get a theoretical understanding, watch videos online, take advantage of the active Python community on platforms like Stack Overflow . And most importantly, do some hands-on practice. We discuss these points in detail in What’s the Best Way to Practice Python?

The Python practice exercises and solutions shown here were taken directly from several of our interactive online courses, including Python Basics Practice , Python Practice: Word Games , and Working with Strings in Python . The courses build up your skills and knowledge, giving you exposure to a variety of topics.

And if you’re already motivated to take your skills to the next level, have a go at another 10 Python Practice Exercises for Beginners with Solutions . Happy coding!

You may also like

How Do You Write a SELECT Statement in SQL?

What Is a Foreign Key in SQL?

Enumerate and Explain All the Basic Elements of an SQL Query

New to python assignments

Hi ! I’m new to python and all of this, if anyone could help me understand what to do i’d appreciate it Assignment: You will be developing a Python program to process an unknown number of exam scores, using a loop.

The exam scores are entered by the user at the keyboard, one score at a time

Exam scores need to be at least zero and at most 100

• if an exam score is at least 90, increment A_count by 1
• if it’s at least 80 and less than 90, increment B_count by 1
• if it is at least 70, and less than 80, increment C_count by 1
• if it’s at least 60 and less than 70, increment D_count by 1
• if it’s at least 0 and less than 60, increment F_count by 1

Once an exam score less than 0 or greater than 100 is entered, then report each of the above counts, one per line, and each along with an appropriate message

Stop the program

I don’t follow; what exactly is unclear about the assignment?

would I just be typing this into the program? Sorry there was more to the assignment but it wouldn’t let me upload it I just assumed that was the gist.

The program should ask you for the exam scores, which you type in one at a time. You indicate when you’ve finished all by typing in a score that is less than 0 or greater than 100.

here are some basic pointers:

You will be developing a Python program to process an unknown number of exam scores, using a loop.

• This implies a need for a while loop; i.e., you do not know the number of scores entered beforehand so we need to keep running until a condition is met. The condition being when you enter anything above 100 or below 0.

The exam scores are entered by the user at the keyboard, one score at a time.

This implies an input such as this: score_entered = int(input('Enter score: '))

Note that you have to append the prefix ‘int’ to the input statement as input outputs type strings. The int converts a string to a type integer which we need for comparison purposes.

if an exam score is at least 90, increment A_count by 1 if it’s at least 80 and less than 90, increment B_count by 1 if it is at least 70, and less than 80, increment C_count by 1 if it’s at least 60 and less than 70, increment D_count by 1 if it’s at least 0 and less than 60, increment F_count by 1

• This implies creating five different variables for each potential conditional statement. Be sure to initialize them to zero before the program starts to start a fresh count. Increment each variable by 1 each time that a conditional statement is met.

Once an exam score less than 0 or greater than 100 is entered, then report each of the above counts, one per line, and each along with an appropriate message.

This is the conditional statement by which your program will exit the program. This ties to the very first item described above. Thus, there should be a total of six conditional statements.

Once this conditional statement is satisfied, it will exit the program and print out the results. This is where the print statements come in as a summary to tabulate the results for the user.

Make sure to read the assignment requirements carefully and compare it with the topics that are currently being discussed in the course as well as with material that has been covered up to this point. You should be able to put two and two together as they say and quickly come up with a potential solution.

It’s hard to understand what you mean. First off, I can’t guess what you’re referring to by “this”. But more importantly: we aren’t in your class, so we don’t know what you’re expected to have learned by now; and we aren’t you, so we don’t know what you actually have learned so far.

So - thus far, have you been expected to write any Python programs, at all? What programs did you write, and what difficulties did you encounter with them? Before this assignment, what lessons were provided? Did you find anything specific confusing about the material?

If exit() the last statement, it’s not needed, because the program will stop at the end anyway.

Related Topics

• Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers
• Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand
• OverflowAI GenAI features for Teams
• OverflowAPI Train & fine-tune LLMs
• Labs The future of collective knowledge sharing
• About the company Visit the blog

Collectives™ on Stack Overflow

Find centralized, trusted content and collaborate around the technologies you use most.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

Get early access and see previews of new features.

Python if-else short-hand [duplicate]

Suppose I have some code like:

Is there a simpler way to write it? Does Python offer syntax similar to this?

• @MartijnPieters: the second part certainly is a duplicate, but I'm not sure about the first one. –  DSM Commented Jan 22, 2013 at 15:24
• @DSM: well, the first part won't be needed once more pythonic structures and loops are discovered by the OP.. Are you going to try and write a full introduction into iterators? –  Martijn Pieters ♦ Commented Jan 22, 2013 at 15:25
• @DSM: I also don't see anyone below addressing that part. ;-) –  Martijn Pieters ♦ Commented Jan 22, 2013 at 15:26
• 5 @Martijn Pieters , while I am thankful for all participations, your comment is rather cheap. If you have an answer for the first part, post it. Ridicule is not reasoning. –  learner Commented Jan 22, 2013 at 15:29
• @user1612593: I'm sorry, I don't mean to ridicule you. It takes time to get used to a new language and it's idioms. There is too little context here to give a concise and meaningful answer; you mostly do not encounter situations like yours in idiomatic Python. –  Martijn Pieters ♦ Commented Jan 22, 2013 at 15:31

2 Answers 2

The most readable way is

but you can use and and or , too:

The "Zen of Python" says that "readability counts", though, so go for the first way.

Also, the and-or trick will fail if you put a variable instead of 10 and it evaluates to False .

However, if more than the assignment depends on this condition, it will be more readable to write it as you have:

unless you put i and j in a container. But if you show us why you need it, it may well turn out that you don't.

• Thanks. This works for the second part of my question. Up vote! –  learner Commented Jan 22, 2013 at 15:25
• I have edited to show that I need j and i outside of the while-loop. –  learner Commented Jan 22, 2013 at 15:42
• 1 x = a > b and 10 or 11 also works correctly in a specific set of problems, because anything that evaluates to false instead of 10 would make it fail, for example, a String. –  Ihor Husar Commented Nov 9, 2015 at 16:52
• 1 [i<2 and i or i-4 for i in range(4)] returns: [-4, 1, -2, -1] . It trolled me for two days and I had to actually read code line by line with my professor to figure out why our algorithm doesn't work. Thanks. –  Ch3shire Commented Jan 17, 2017 at 10:59
• How does this work internally: x = a > b and 10 or 11 –  variable Commented Nov 9, 2019 at 5:18

This is a sample of execution:

• 35 10 if a > b else 11 . No need to resort to boolean operators. –  Martijn Pieters ♦ Commented Jan 22, 2013 at 15:23
• Thanks. This works for the second part of my question. Up vote! –  learner Commented Jan 22, 2013 at 15:26
• How does this work internally: x = a > b and 10 or 11 –  variable Commented Nov 9, 2019 at 5:19

Not the answer you're looking for? Browse other questions tagged python python-2.7 or ask your own question .

• The Overflow Blog
• How to build open source apps in a highly regulated industry
• Community Products Roadmap Update, July 2024
• Featured on Meta
• We spent a sprint addressing your requests — here’s how it went
• Upcoming initiatives on Stack Overflow and across the Stack Exchange network...
• Policy: Generative AI (e.g., ChatGPT) is banned
• The [lib] tag is being burninated
• What makes a homepage useful for logged-in users

Hot Network Questions

• mirrorlist.centos.org no longer resolve?
• Inversion naming conventions
• Hölder continuity in time of heat semigroup
• What does a letter "R" means in a helipad?
• How well does the following argument work as a counter towards unfalsifiable supernatural claims?
• Can someone explain the Trump immunity ruling?
• Is there a drawback to using Heart's blood rote repeatedly?
• How many steps are needed to turn one "a" into at least 100,000 "a"s using only the three functions of "select all", "copy" and "paste"?
• Imagining Graham's number in your head collapses your head to a Black hole
• How much time do I need on my Passport expiry date to leave Australia for South Africa?
• What does Athena mean in this passage of book 3 of the Odyssey?
• lme4 Inconsistency
• How to maintain dependencies shared among microservices?
• Did the BBC censor a non-binary character in Transformers: EarthSpark?
• Con permiso to enter your own house?
• Position where last x halfmoves are determined
• Travel to Mexico from India, do I need to pay a fee?
• I can't mount my external hard drive in Linux
• Why is a game's minor update on Steam (e.g., New World) ~15 GB to download?
• Are inductors in resonant circuits dangerous? They produce very high voltages
• Why should the Vce be half the value of the supply source?
• E flat tuning - adjust bass?
• Is there any other reason to stockpile minerals aside preparing for war?
• I want to leave my current job during probation but I don't want to tell the next interviewer I am currently working