python if else variable assignment

Python Conditional Assignment

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

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

1. Using Ternary Operator

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

It goes like this:

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

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

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

2. Using if-else statement

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

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

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

3. Using Logical Short Circuit Evaluation

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

The format of logical short circuit evaluation is:

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

Let's see an example:

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

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

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

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

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

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

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

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

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

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

Happy coding! 😊

How to Write the Python if Statement in one Line

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

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

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

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

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

How the if Statement Works in Python

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

The structure of the basic if statement is as follows:

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

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

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

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

Here’s the basic structure:

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

How to Write a Python if in one Line

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

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

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

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

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

Writing a Python if Statement With Multiple Actions in one Line

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

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

Here’s how the structure looks:

And an example of this functionality:

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

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

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

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

Here’s the general structure:

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

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

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

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

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

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

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

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

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

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

Much simpler!

Go Even Further With Python!

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

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

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

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

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

These conditional tasks can be achieved using the if statement.

• Python if Statement

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

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

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

Let's look at an example.

• Example: Python if Statement

Sample Output 1

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

Sample Output 2

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

Indentation in Python

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

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

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

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

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

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

Here, if the condition inside the if statement evaluates to

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

• Example: Python if…else Statement

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

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

• Python if…elif…else Statement

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

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

• Example: Python if…elif…else Statement

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

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

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

• Python Nested if Statements

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

Here's how this program works.

More on Python if…else Statement

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

This code can be compactly written as

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

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

can be written as

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

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

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

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

Table of Contents

• Introduction

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

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

Video: Python if...else Statement

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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.

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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!

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Tutorial: web scraping with python using beautiful soup, python if else tutorial: control the flow of your code.

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Python One Line Conditional Assignment

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

Example : Say, you start with the following code.

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

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

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

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

Method 1: Ternary Operator

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

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

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

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

You can also read the related article:

• Python One Line Ternary

Method 2: Single-Line If Statement

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

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

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

Method 3: Ternary Tuple Syntax Hack

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

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

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

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

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

Related Article : Python Ternary — Tuple Syntax Hack

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

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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 .

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• 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

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Python's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

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

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

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

In this tutorial, you’ll:

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

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

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

Assignment Statements and the Assignment Operator

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are a few examples of assignments in Python:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Read on to see the assignment statements in action!

Assignment Statements in Action

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

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

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

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

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

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

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

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

Consider the following examples:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

As an example, consider the following function:

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

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

Here’s how the function works:

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

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

Augmented Assignment Operators in Python

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Consider the following example of matrix multiplication using NumPy arrays:

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

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

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

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

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

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

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

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

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

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

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

These operators behave differently with mutable and immutable data types:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Other Assignment Variants

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

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

In short, you’ll learn about:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The general syntax of an assignment statement is as follows:

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

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

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

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

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

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

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

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

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

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

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

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

Here’s how you can write your class:

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

Here’s how your class works in practice:

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

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

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

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

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

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

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

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

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

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

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

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

Implicit Assignments in Python

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The same behavior appears in comprehensions:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Illegal and Dangerous Assignments in Python

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

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

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

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

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

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

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

For example, you can write something like this:

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

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

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

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

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

Consider the following example:

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

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

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

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

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

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

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

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

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

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

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

In this tutorial, you’ve learned how to:

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

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

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

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Python If Else Statements – Conditional Statements

In both real life and programming, decision-making is crucial. We often face situations where we need to make choices, and based on those choices, we determine our next actions. Similarly, in programming, we encounter scenarios where we must make decisions to control the flow of our code.

Conditional statements in Python play a key role in determining the direction of program execution. Among these, If-Else statements are fundamental, providing a way to execute different blocks of code based on specific conditions. As the name suggests, If-Else statements offer two paths, allowing for different outcomes depending on the condition evaluated.

Types of Control Flow in Python

Python If Statement

Python if else statement, python nested if statement, python elif, ternary statement | short hand if else statement.

The if statement is the most simple decision-making statement. It is used to decide whether a certain statement or block of statements will be executed or not.

Flowchart of If Statement

Let’s look at the flow of code in the Python If statements.

Flowchart of Python if statement

Syntax of If Statement in Python

Here, the condition after evaluation will be either true or false. if the statement accepts boolean values – if the value is true then it will execute the block of statements below it otherwise not.

As we know, Python uses indentation to identify a block. So the block under the Python if statements will be identified as shown in the below example:  

Example of Python if Statement

As the condition present in the if statements in Python is false. So, the block below the if statement is executed.

The if statement alone tells us that if a condition is true it will execute a block of statements and if the condition is false it won’t. But if we want to do something else if the condition is false, we can use the else statement with the if statement Python to execute a block of code when the Python if condition is false. 

Flowchart of If Else Statement

Let’s look at the flow of code in an if else Python statement.

Syntax of If Else in Python

Example of python if else statement.

The block of code following the else if in Python, the statement is executed as the condition present in the if statement is false after calling the statement which is not in the block(without spaces).

If Else in Python using List Comprehension

In this example, we are using an Python else if statement in a list comprehension with the condition that if the element of the list is odd then its digit sum will be stored else not.

A nested if is an if statement that is the target of another if statement. Nested if statements mean an if statement inside another if statement.

Yes, Python allows us to nest if statements within if statements. i.e., we can place an if statement inside another if statement.

Flowchart of Python Nested if Statement

Flowchart of Python Nested if statement

Example of Python Nested If Statement

In this example, we are showing nested if conditions in the code, All the If condition in Python will be executed one by one.

Here, a user can decide among multiple options. The if statements are executed from the top down.

As soon as one of the conditions controlling the if is true, the statement associated with that if is executed, and the rest of the ladder is bypassed. If none of the conditions is true, then the final “else” statement will be executed.

Flowchart of Elif Statement in Python

Let’s look at the flow of control in if-elif-else ladder:

Flowchart of if-elif-else ladder

Example of Python if-elif-else ladder

In the example, we are showing single if in Python, multiple elif conditions, and single else condition.

Whenever there is only a single statement to be executed inside the if block then shorthand if can be used. The statement can be put on the same line as the if statement. 

Example of Python If shorthand

In the given example, we have a condition that if the number is less than 15, then further code will be executed.

Example of Short Hand If Else Statements

This can be used to write the if-else statements in a single line where only one statement is needed in both the if and else blocks. 

In the given example, we are printing True if the number is 15, or else it will print False.

Similar Reads:

• Python3 – if , if..else, Nested if, if-elif statements
• Using Else Conditional Statement With For loop in Python
• How to use if, else & elif in Python Lambda Functions

Python If Else Statements – Conditional Statements – FAQs

What is the conditional statement of if-else.

The if-else statement in Python is used to control the flow of the program based on a condition. It has the following syntax: if condition: # Execute this block if condition is True else: # Execute this block if condition is False For example: x = 10 if x > 5: print("x is greater than 5") else: print("x is not greater than 5")

How many else statements can a single if condition have in Python?

A single if condition can have at most one else statement. However, you can have multiple elif (else if) statements to check additional conditions if needed: x = 10 if x > 15: print("x is greater than 15") elif x > 5: print("x is greater than 5 but not greater than 15") else: print("x is 5 or less")

What are the different types of control statements in Python?

In Python, control statements are used to alter the flow of execution based on specific conditions or looping requirements. The main types of control statements are: Conditional statements : if , else , elif Looping statements : for , while Control flow statements : break , continue , pass , return

What are the two types of control statements?

The two primary types of control statements in Python are: Conditional statements : Used to execute code based on certain conditions ( if , else , elif ). Looping statements : Used to execute code repeatedly until a condition is met ( for , while ).

Are control statements and conditional statements the same?

No, control statements and conditional statements are not exactly the same. Conditional statements ( if , else , elif ) specifically deal with checking conditions and executing code based on whether those conditions are True or False . Control statements encompass a broader category that includes both conditional statements ( if , else , elif ) and looping statements ( for , while ), as well as other statements ( break , continue , pass , return ) that control the flow of execution in a program.

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Python - if, elif, else Conditions

By default, statements in the script are executed sequentially from the first to the last. If the processing logic requires so, the sequential flow can be altered in two ways:

Python uses the if keyword to implement decision control. Python's syntax for executing a block conditionally is as below:

Any Boolean expression evaluating to True or False appears after the if keyword. Use the : symbol and press Enter after the expression to start a block with an increased indent. One or more statements written with the same level of indent will be executed if the Boolean expression evaluates to True .

To end the block, decrease the indentation. Subsequent statements after the block will be executed out of the if condition. The following example demonstrates the if condition.

In the above example, the expression price < 100 evaluates to True , so it will execute the block. The if block starts from the new line after : and all the statements under the if condition starts with an increased indentation, either space or tab. Above, the if block contains only one statement. The following example has multiple statements in the if condition.

Above, the if condition contains multiple statements with the same indentation. If all the statements are not in the same indentation, either space or a tab then it will raise an IdentationError .

The statements with the same indentation level as if condition will not consider in the if block. They will consider out of the if condition.

The following example demonstrates multiple if conditions.

Notice that each if block contains a statement in a different indentation, and that's valid because they are different from each other.

else Condition

Along with the if statement, the else condition can be optionally used to define an alternate block of statements to be executed if the boolean expression in the if condition evaluates to False .

As mentioned before, the indented block starts after the : symbol, after the boolean expression. It will get executed when the condition is True . We have another block that should be executed when the if condition is False . First, complete the if block by a backspace and write else , put add the : symbol in front of the new block to begin it, and add the required statements in the block.

In the above example, the if condition price >= 100 is False , so the else block will be executed. The else block can also contain multiple statements with the same indentation; otherwise, it will raise the IndentationError .

Note that you cannot have multiple else blocks, and it must be the last block.

elif Condition

Use the elif condition is used to include multiple conditional expressions after the if condition or between the if and else conditions.

The elif block is executed if the specified condition evaluates to True .

In the above example, the elif conditions are applied after the if condition. Python will evalute the if condition and if it evaluates to False then it will evalute the elif blocks and execute the elif block whose expression evaluates to True . If multiple elif conditions become True , then the first elif block will be executed.

The following example demonstrates if, elif, and else conditions.

All the if, elif, and else conditions must start from the same indentation level, otherwise it will raise the IndentationError .

Nested if, elif, else Conditions

Python supports nested if, elif, and else condition. The inner condition must be with increased indentation than the outer condition, and all the statements under the one block should be with the same indentation.

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Python If Else Statement – Conditional Statements Explained

There are many cases where you don't want all of your code to be executed in your programs.

Instead, you might want certain code to run only when a specific condition is met, and a different set of code to run when the condition is not satisified.

That's where conditional statements come in handy.

Conditional statements allow you to control the logical flow of programs in a clean and compact way.

They are branches – like forks in the road – that modify how code is executed and handle decision making.

This tutorial goes over the basics of if , if..else , and elif statements in the Python programming language, using examples along the way.

Let's get started!

The syntax of a basic if statement

An if statement in Python essentially says:

"If this expression evaluates to True, then run once the code that follows the exprerssion. If it isn't True, then don't run the block of code that follows."

The general syntax for a basic if statement looks something like this:

An if statement consists of:

• The if keyword, which starts the if statement.
• Then comes a condition. A condition can evaluate to either True or False. Parentheses ( () ) surrounding the condition are optional, but they do help improve the readability of the code when more than one condition is present.
• A colon : that separates the condition from the executable statement that follows.
• A new line.
• A level of indentation of 4 spaces, which is a Python convention. The level of indentation is associated with the body of the statement that follows.
• Lastly comes the body of the statement. This is the code that will run only if the statement evaluated to True. We can have multiple lines in the body that can be executed, and in that case we need to be careful they all have the same level of indentation.

Let's take the following example:

In the example above, we created two variables, a and b , and assigned them the values 1 and 2 , respectively.

The phrase in the print statement does in fact get printed to the console because the condition b > a evaluated to True, so the code that followed it ran. If it wasn't True, nothing would have happend. No code would have run.

If we had instead done this:

No code would have been executed and nothing would have been printed to the console.

How do Python if..else statements work?

An if statement runs code only when a condition is met. Nothing happens otherwise.

What if we also want code to run when the condition is not met? That's where the else part comes in.

The syntax of an if..else statement looks like this:

An if..else statement in Python means:

"When the if expression evaluates to True, then execute the code that follows it. But if it evalates to False, then run the code that follows the else statement"

The else statement is written on a new line after the last line of indented code and it can't be written by itself. An else statement is part of an if statement.

The code following it also needs to be indented with 4 spaces to show it is part of the else clause.

The code following the else statement gets executed if and only if the if statement is False. If your if statement is True and therefore the code ran, then the code in the else block will never run.

Here, the line of code following the else statement, print("a is in fact bigger than b") , will never run. The if statement that came before it is True so only that code runs instead.

The else block runs when:

Be aware that you can't write any other code between if and else . You'll get a SyntaxError if you do so:

How does elif work in Python?

What if we want to have more than just two options?

Instead of saying: "If the first condition is true do this, otherwise do that instead", now we say "If this isn't True, try this instead, and if all conditions fail to be True, resort to doing this".

elif stands for else, if.

The basic syntax looks like this:

We can use more than one elif statement. This gives us more conditions and more options.

For example:

In this example, the if statement tests a specific condition, the elif blocks are two alternatives, and the else block is the last solution when all the previous conditions have not been met.

Be aware of the order in which you write your elif statements.

In the previous example, if you had written:

The line x is less than 20! would have been executed because it came first.

The elif statement makes code easier to write. You can use it instead of keeping track of if..else statements as programs get more complex and grow in size.

If all the elif statements are not considered and are False, then and only then as the last resort will the code following the else statement run.

For example, here's a case when the else statement would run:

And that's it!

Those are the basic principles of if , if..else and elif in Python to get you started with conditional statements.

From here the statements can get more advanced and complex.

Conditional statements can be nested inside of other conditional statements, depending on the problem you're trying to solve and the logic behind the solution.

Thanks for reading and happy coding!

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4. More Control Flow Tools ¶

As well as the while statement just introduced, Python uses a few more that we will encounter in this chapter.

4.1. if Statements ¶

Perhaps the most well-known statement type is the if statement. For example:

There can be zero or more elif parts, and the else part is optional. The keyword ‘ elif ’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages.

If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements .

4.2. for Statements ¶

The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):

Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection:

4.3. The range() Function ¶

If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:

The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):

To iterate over the indices of a sequence, you can combine range() and len() as follows:

In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques .

A strange thing happens if you just print a range:

In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.

We say such an object is iterable , that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum() :

Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures , we will discuss in more detail about list() .

4.4. break and continue Statements, and else Clauses on Loops ¶

The break statement breaks out of the innermost enclosing for or while loop.

A for or while loop can include an else clause.

In a for loop, the else clause is executed after the loop reaches its final iteration.

In a while loop, it’s executed after the loop’s condition becomes false.

In either kind of loop, the else clause is not executed if the loop was terminated by a break .

This is exemplified in the following for loop, which searches for prime numbers:

(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)

When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions .

The continue statement, also borrowed from C, continues with the next iteration of the loop:

4.5. pass Statements ¶

The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:

This is commonly used for creating minimal classes:

Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:

4.6. match Statements ¶

A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals:

Note the last block: the “variable name” _ acts as a wildcard and never fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using | (“or”):

Patterns can look like unpacking assignments, and can be used to bind variables:

Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject ( point ). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point .

If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables:

You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable):

A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to.

Patterns can be arbitrarily nested. For example, if we have a short list of Points, with __match_args__ added, we could match it like this:

We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:

Several other key features of this statement:

Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings.

Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, *rest) work similar to unpacking assignments. The name after * may also be _ , so (x, y, *_) matches a sequence of at least two items without binding the remaining items.

Mapping patterns: {"bandwidth": b, "latency": l} captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.)

Subpatterns may be captured using the as keyword:

will capture the second element of the input as p2 (as long as the input is a sequence of two points)

Most literals are compared by equality, however the singletons True , False and None are compared by identity.

Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable:

For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format.

4.7. Defining Functions ¶

We can create a function that writes the Fibonacci series to an arbitrary boundary:

The keyword def introduces a function definition . It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.

The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring . (More about docstrings can be found in the section Documentation Strings .) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.

The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference , not the value of the object). [ 1 ] When a function calls another function, or calls itself recursively, a new local symbol table is created for that call.

A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be used to access the function:

Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print() :

It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:

This example, as usual, demonstrates some new Python features:

The return statement returns with a value from a function. return without an expression argument returns None . Falling off the end of a function also returns None .

The statement result.append(a) calls a method of the list object result . A method is a function that ‘belongs’ to an object and is named obj.methodname , where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes , see Classes ) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a] , but more efficient.

4.8. More on Defining Functions ¶

It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.

4.8.1. Default Argument Values ¶

The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:

This function can be called in several ways:

giving only the mandatory argument: ask_ok('Do you really want to quit?')

giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2)

or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')

This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.

The default values are evaluated at the point of function definition in the defining scope, so that

will print 5 .

Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls:

This will print

If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:

4.8.2. Keyword Arguments ¶

Functions can also be called using keyword arguments of the form kwarg=value . For instance, the following function:

accepts one required argument ( voltage ) and three optional arguments ( state , action , and type ). This function can be called in any of the following ways:

but all the following calls would be invalid:

In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:

When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. ( *name must occur before **name .) For example, if we define a function like this:

It could be called like this:

and of course it would print:

Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call.

4.8.3. Special parameters ¶

By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword.

A function definition may look like:

where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters.

4.8.3.1. Positional-or-Keyword Arguments ¶

If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword.

4.8.3.2. Positional-Only Parameters ¶

Looking at this in a bit more detail, it is possible to mark certain parameters as positional-only . If positional-only , the parameters’ order matters, and the parameters cannot be passed by keyword. Positional-only parameters are placed before a / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters.

Parameters following the / may be positional-or-keyword or keyword-only .

4.8.3.3. Keyword-Only Arguments ¶

To mark parameters as keyword-only , indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter.

4.8.3.4. Function Examples ¶

Consider the following example function definitions paying close attention to the markers / and * :

The first function definition, standard_arg , the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword:

The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition:

The third function kwd_only_args only allows keyword arguments as indicated by a * in the function definition:

And the last uses all three calling conventions in the same function definition:

Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key:

There is no possible call that will make it return True as the keyword 'name' will always bind to the first parameter. For example:

But using / (positional only arguments), it is possible since it allows name as a positional argument and 'name' as a key in the keyword arguments:

In other words, the names of positional-only parameters can be used in **kwds without ambiguity.

4.8.3.5. Recap ¶

The use case will determine which parameters to use in the function definition:

As guidance:

Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords.

Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed.

For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future.

4.8.4. Arbitrary Argument Lists ¶

Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences ). Before the variable number of arguments, zero or more normal arguments may occur.

Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments.

4.8.5. Unpacking Argument Lists ¶

The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the * -operator to unpack the arguments out of a list or tuple:

In the same fashion, dictionaries can deliver keyword arguments with the ** -operator:

4.8.6. Lambda Expressions ¶

Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b . Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:

The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:

4.8.7. Documentation Strings ¶

Here are some conventions about the content and formatting of documentation strings.

The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.

If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).

Here is an example of a multi-line docstring:

4.8.8. Function Annotations ¶

Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information).

Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal -> , followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated:

4.9. Intermezzo: Coding Style ¶

Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style . Most languages can be written (or more concise, formatted ) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.

For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:

Use 4-space indentation, and no tabs.

4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.

Wrap lines so that they don’t exceed 79 characters.

This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.

Use blank lines to separate functions and classes, and larger blocks of code inside functions.

When possible, put comments on a line of their own.

Use docstrings.

Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4) .

Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).

Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case.

Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.

Table of Contents

• 4.1. if Statements
• 4.2. for Statements
• 4.3. The range() Function
• 4.4. break and continue Statements, and else Clauses on Loops
• 4.5. pass Statements
• 4.6. match Statements
• 4.7. Defining Functions
• 4.8.1. Default Argument Values
• 4.8.2. Keyword Arguments
• 4.8.3.1. Positional-or-Keyword Arguments
• 4.8.3.2. Positional-Only Parameters
• 4.8.3.3. Keyword-Only Arguments
• 4.8.3.4. Function Examples
• 4.8.3.5. Recap
• 4.8.4. Arbitrary Argument Lists
• 4.8.5. Unpacking Argument Lists
• 4.8.6. Lambda Expressions
• 4.8.7. Documentation Strings
• 4.8.8. Function Annotations
• 4.9. Intermezzo: Coding Style

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3. An Informal Introduction to Python

5. Data Structures

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Python supports the usual logical conditions from mathematics:

• Equals: a == b
• Not Equals: a != b
• Less than: a < b
• Less than or equal to: a <= b
• Greater than: a > b
• Greater than or equal to: a >= b

These conditions can be used in several ways, most commonly in "if statements" and loops.

An "if statement" is written by using the if keyword.

If statement:

In this example we use two variables, a and b , which are used as part of the if statement to test whether b is greater than a . As a is 33 , and b is 200 , we know that 200 is greater than 33, and so we print to screen that "b is greater than a".

Indentation

If statement, without indentation (will raise an error):

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The elif keyword is Python's way of saying "if the previous conditions were not true, then try this condition".

In this example a is equal to b , so the first condition is not true, but the elif condition is true, so we print to screen that "a and b are equal".

The else keyword catches anything which isn't caught by the preceding conditions.

In this example a is greater than b , so the first condition is not true, also the elif condition is not true, so we go to the else condition and print to screen that "a is greater than b".

You can also have an else without the elif :

Short Hand If

If you have only one statement to execute, you can put it on the same line as the if statement.

One line if statement:

Short Hand If ... Else

If you have only one statement to execute, one for if, and one for else, you can put it all on the same line:

One line if else statement:

This technique is known as Ternary Operators , or Conditional Expressions .

You can also have multiple else statements on the same line:

One line if else statement, with 3 conditions:

The and keyword is a logical operator, and is used to combine conditional statements:

Test if a is greater than b , AND if c is greater than a :

The or keyword is a logical operator, and is used to combine conditional statements:

Test if a is greater than b , OR if a is greater than c :

The not keyword is a logical operator, and is used to reverse the result of the conditional statement:

Test if a is NOT greater than b :

You can have if statements inside if statements, this is called nested if statements.

The pass Statement

if statements cannot be empty, but if you for some reason have an if statement with no content, put in the pass statement to avoid getting an error.

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Evaluate expressions in workflows and actions

You can evaluate expressions in workflows and actions.

In this article

About expressions.

You can use expressions to programmatically set environment variables in workflow files and access contexts. An expression can be any combination of literal values, references to a context, or functions. You can combine literals, context references, and functions using operators. For more information about contexts, see " Accessing contextual information about workflow runs ."

Expressions are commonly used with the conditional if keyword in a workflow file to determine whether a step should run. When an if conditional is true , the step will run.

You need to use specific syntax to tell GitHub to evaluate an expression rather than treat it as a string.

${{ <expression> }}

Note : The exception to this rule is when you are using expressions in an if clause, where, optionally, you can usually omit ${{ and }} . For more information about if conditionals, see " Workflow syntax for GitHub Actions ."

Warning: When creating workflows and actions, you should always consider whether your code might execute untrusted input from possible attackers. Certain contexts should be treated as untrusted input, as an attacker could insert their own malicious content. For more information, see " Security hardening for GitHub Actions ."

Example setting an environment variable

As part of an expression, you can use boolean , null , number , or string data types.

Note that in conditionals, falsy values ( false , 0 , -0 , "" , '' , null ) are coerced to false and truthy ( true and other non-falsy values) are coerced to true .

Example of literals

GitHub ignores case when comparing strings.

• steps.<step_id>.outputs.<output_name> evaluates as a string. You need to use specific syntax to tell GitHub to evaluate an expression rather than treat it as a string. For more information, see " Accessing contextual information about workflow runs ."
• For numerical comparison, the fromJSON() function can be used to convert a string to a number. For more information on the fromJSON() function, see " fromJSON ."

GitHub performs loose equality comparisons.

If the types do not match, GitHub coerces the type to a number. GitHub casts data types to a number using these conversions:

When NaN is one of the operands of any relational comparison ( > , < , >= , <= ), the result is always false . For more information, see the " NaN Mozilla docs ."

Objects and arrays are only considered equal when they are the same instance.

GitHub offers ternary operator like behaviour that you can use in expressions. By using a ternary operator in this way, you can dynamically set the value of an environment variable based on a condition, without having to write separate if-else blocks for each possible option.

In this example, we're using a ternary operator to set the value of the MY_ENV_VAR environment variable based on whether the GitHub reference is set to refs/heads/main or not. If it is, the variable is set to value_for_main_branch . Otherwise, it is set to value_for_other_branches . It is important to note that the first value after the && must be truthy. Otherwise, the value after the || will always be returned.

GitHub offers a set of built-in functions that you can use in expressions. Some functions cast values to a string to perform comparisons. GitHub casts data types to a string using these conversions:

contains( search, item )

Returns true if search contains item . If search is an array, this function returns true if the item is an element in the array. If search is a string, this function returns true if the item is a substring of search . This function is not case sensitive. Casts values to a string.

Example using a string

contains('Hello world', 'llo') returns true .

Example using an object filter

contains(github.event.issue.labels.*.name, 'bug') returns true if the issue related to the event has a label "bug".

For more information, see " Object filters ."

Example matching an array of strings

Instead of writing github.event_name == "push" || github.event_name == "pull_request" , you can use contains() with fromJSON() to check if an array of strings contains an item .

For example, contains(fromJSON('["push", "pull_request"]'), github.event_name) returns true if github.event_name is "push" or "pull_request".

startsWith( searchString, searchValue )

Returns true when searchString starts with searchValue . This function is not case sensitive. Casts values to a string.

Example of startsWith

startsWith('Hello world', 'He') returns true .

endsWith( searchString, searchValue )

Returns true if searchString ends with searchValue . This function is not case sensitive. Casts values to a string.

Example of endsWith

endsWith('Hello world', 'ld') returns true .

format( string, replaceValue0, replaceValue1, ..., replaceValueN)

Replaces values in the string , with the variable replaceValueN . Variables in the string are specified using the {N} syntax, where N is an integer. You must specify at least one replaceValue and string . There is no maximum for the number of variables ( replaceValueN ) you can use. Escape curly braces using double braces.

Example of format

Returns 'Hello Mona the Octocat'.

Example escaping braces

Returns '{Hello Mona the Octocat!}'.

join( array, optionalSeparator )

The value for array can be an array or a string. All values in array are concatenated into a string. If you provide optionalSeparator , it is inserted between the concatenated values. Otherwise, the default separator , is used. Casts values to a string.

Example of join

join(github.event.issue.labels.*.name, ', ') may return 'bug, help wanted'

toJSON(value)

Returns a pretty-print JSON representation of value . You can use this function to debug the information provided in contexts.

Example of toJSON

toJSON(job) might return { "status": "success" }

fromJSON(value)

Returns a JSON object or JSON data type for value . You can use this function to provide a JSON object as an evaluated expression or to convert any data type that can be represented in JSON or JavaScript, such as strings, booleans, null values, arrays, and objects.

Example returning a JSON object

This workflow sets a JSON matrix in one job, and passes it to the next job using an output and fromJSON .

Example returning a JSON data type

This workflow uses fromJSON to convert environment variables from a string to a Boolean or integer.

The workflow uses the fromJSON() function to convert the environment variable continue from a string to a boolean, allowing it to determine whether to continue-on-error or not. Similarly, it converts the time environment variable from a string to an integer, setting the timeout for the job in minutes.

hashFiles(path)

Returns a single hash for the set of files that matches the path pattern. You can provide a single path pattern or multiple path patterns separated by commas. The path is relative to the GITHUB_WORKSPACE directory and can only include files inside of the GITHUB_WORKSPACE . This function calculates an individual SHA-256 hash for each matched file, and then uses those hashes to calculate a final SHA-256 hash for the set of files. If the path pattern does not match any files, this returns an empty string. For more information about SHA-256, see " SHA-2 ."

You can use pattern matching characters to match file names. Pattern matching for hashFiles follows glob pattern matching and is case-insensitive on Windows. For more information about supported pattern matching characters, see the Patterns section in the @actions/glob documentation.

Example with a single pattern

Matches any package-lock.json file in the repository.

hashFiles('**/package-lock.json')

Example with multiple patterns

Creates a hash for any package-lock.json and Gemfile.lock files in the repository.

hashFiles('**/package-lock.json', '**/Gemfile.lock')

Status check functions

You can use the following status check functions as expressions in if conditionals. A default status check of success() is applied unless you include one of these functions. For more information about if conditionals, see " Workflow syntax for GitHub Actions " and " Metadata syntax for GitHub Actions ".

Returns true when all previous steps have succeeded.

Example of success

Causes the step to always execute, and returns true , even when canceled. The always expression is best used at the step level or on tasks that you expect to run even when a job is canceled. For example, you can use always to send logs even when a job is canceled.

Warning: Avoid using always for any task that could suffer from a critical failure, for example: getting sources, otherwise the workflow may hang until it times out. If you want to run a job or step regardless of its success or failure, use the recommended alternative: if: ${{ !cancelled() }}

Example of always

Returns true if the workflow was canceled.

Example of cancelled

Returns true when any previous step of a job fails. If you have a chain of dependent jobs, failure() returns true if any ancestor job fails.

Example of failure

Failure with conditions.

You can include extra conditions for a step to run after a failure, but you must still include failure() to override the default status check of success() that is automatically applied to if conditions that don't contain a status check function.

Example of failure with conditions

Object filters.

You can use the * syntax to apply a filter and select matching items in a collection.

For example, consider an array of objects named fruits .

The filter fruits.*.name returns the array [ "apple", "orange", "pear" ] .

You may also use the * syntax on an object. For example, suppose you have an object named vegetables .

The filter vegetables.*.ediblePortions could evaluate to:

Since objects don't preserve order, the order of the output cannot be guaranteed.

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Using if else statement to assign a variable in python [closed]

I'm trying to make a program telling you how to order a specific number of nuggets, using if and else statements to assign variables. My problem is if one of the first if statements is true, then the program throws an error, because I called a variable that doesn't exist. How can I make My program skip over the rest of the if/else statements if the first one is found to be true?

• if-statement

• 1 Are you familiar with elif ? And perhaps with varying indentation levels? –  TigerhawkT3 Commented Jan 22, 2016 at 0:19
• If you're just trying to find out whether the user's number matches one of several numbers, this is the wrong way to do it. If that's not what you want to do, I have no idea what it is. –  TigerhawkT3 Commented Jan 22, 2016 at 0:35

This is terrible practice, but you could use the arrow of doom approach.

That said, this is all a bit silly isn't it? You're basically implementing a change-maker, so use one of the thousands of approaches that already exist, none of which use random variables that may or may not exist. My favorite is a dictionary.

This example leaves:

• divmod isn't a commonly used builtin, but you can find the docs here . It basically returns the tuple (a//b, a%b) –  Adam Smith Commented Jan 22, 2016 at 0:30

Not the answer you're looking for? Browse other questions tagged python python-3.x if-statement or ask your own question .

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