python assignment multiple variables

Multiple assignment in Python: Assign multiple values or the same value to multiple variables

In Python, the = operator is used to assign values to variables.

You can assign values to multiple variables in one line.

Assign multiple values to multiple variables

Assign the same value to multiple variables.

You can assign multiple values to multiple variables by separating them with commas , .

You can assign values to more than three variables, and it is also possible to assign values of different data types to those variables.

When only one variable is on the left side, values on the right side are assigned as a tuple to that variable.

If the number of variables on the left does not match the number of values on the right, a ValueError occurs. You can assign the remaining values as a list by prefixing the variable name with * .

For more information on using * and assigning elements of a tuple and list to multiple variables, see the following article.

• Unpack a tuple and list in Python

You can also swap the values of multiple variables in the same way. See the following article for details:

• Swap values ​​in a list or values of variables in Python

You can assign the same value to multiple variables by using = consecutively.

For example, this is useful when initializing multiple variables with the same value.

After assigning the same value, you can assign a different value to one of these variables. As described later, be cautious when assigning mutable objects such as list and dict .

You can apply the same method when assigning the same value to three or more variables.

Be careful when assigning mutable objects such as list and dict .

If you use = consecutively, the same object is assigned to all variables. Therefore, if you change the value of an element or add a new element in one variable, the changes will be reflected in the others as well.

If you want to handle mutable objects separately, you need to assign them individually.

after c = []; d = [] , c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d .) 3. Data model — Python 3.11.3 documentation

You can also use copy() or deepcopy() from the copy module to make shallow and deep copies. See the following article.

• Shallow and deep copy in Python: copy(), deepcopy()

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Python allows you to assign values to multiple variables in one line:

Note: Make sure the number of variables matches the number of values, or else you will get an error.

One Value to Multiple Variables

And you can assign the same value to multiple variables in one line:

Unpack a Collection

If you have a collection of values in a list, tuple etc. Python allows you to extract the values into variables. This is called unpacking .

Unpack a list:

Learn more about unpacking in our Unpack Tuples Chapter.

Video: Python Variable Names

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Mastering Multiple Variable Assignment in Python

Python's ability to assign multiple variables in a single line is a feature that exemplifies the language's emphasis on readability and efficiency. In this detailed blog post, we'll explore the nuances of assigning multiple variables in Python, a technique that not only simplifies code but also enhances its readability and maintainability.

Introduction to Multiple Variable Assignment

Python allows the assignment of multiple variables simultaneously. This feature is not only a syntactic sugar but a powerful tool that can make your code more Pythonic.

What is Multiple Variable Assignment?

• Simultaneous Assignment : Python enables the initialization of several variables in a single line, thereby reducing the number of lines of code and making it more readable.
• Versatility : This feature can be used with various data types and is particularly useful for unpacking sequences.

Basic Multiple Variable Assignment

The simplest form of multiple variable assignment in Python involves assigning single values to multiple variables in one line.

Syntax and Examples

Parallel Assignment : Assign values to several variables in parallel.

• Clarity and Brevity : This form of assignment is clear and concise.
• Efficiency : Reduces the need for multiple lines when initializing several variables.

Unpacking Sequences into Variables

Python takes multiple variable assignment a step further with unpacking, allowing the assignment of sequences to individual variables.

Unpacking Lists and Tuples

Direct Unpacking : If you have a list or tuple, you can unpack its elements into individual variables.

Unpacking Strings

Character Assignment : You can also unpack strings into variables with each character assigned to one variable.

Using Underscore for Unwanted Values

When unpacking, you may not always need all the values. Python allows the use of the underscore ( _ ) as a placeholder for unwanted values.

Ignoring Unnecessary Values

Discarding Values : Use _ for values you don't intend to use.

Swapping Variables Efficiently

Multiple variable assignment can be used for an elegant and efficient way to swap the values of two variables.

Swapping Variables

No Temporary Variable Needed : Swap values without the need for an additional temporary variable.

Advanced Unpacking Techniques

Python provides even more advanced ways to handle multiple variable assignments, especially useful with longer sequences.

Extended Unpacking

Using Asterisk ( * ): Python 3 introduced a syntax for extended unpacking where you can use * to collect multiple values.

Best Practices and Common Pitfalls

While multiple variable assignment is a powerful feature, it should be used judiciously.

• Readability : Ensure that your use of multiple variable assignments enhances, rather than detracts from, readability.
• Matching Lengths : Be cautious of the sequence length. The number of elements must match the number of variables being assigned.

Multiple variable assignment in Python is a testament to the language’s design philosophy of simplicity and elegance. By understanding and effectively utilizing this feature, you can write more concise, readable, and Pythonic code. Whether unpacking sequences or swapping values, multiple variable assignment is a technique that can significantly improve the efficiency of your Python programming.

• Python Tutorial for Beginners to Advanced

Assign Multiple Variables in Python

By James L.

In this article, we will discuss the following topics:

Assign the same value to multiple variables

• Assign multiple values to multiple variables (Tuple Unpacking)

We can assign the same value to multiple variables in Python by using the assignment operator ( = ) consecutively between the variable names and assigning a single value at the end.

For example:

a = b = c = 200

The above code is equivalent to 

In the above example, all three variables a , b , and c are assigned a value of 200 .

If we have assigned the same value to multiple variables in one line, then we must be very careful when modifying or updating any of the variables.

Lots of beginners get this wrong. Keep in mind that everything in Python is an object and some objects in Python are mutable and some are immutable.

Immutable objects are objects whose value cannot be changed or modified after they have been assigned a value.

Mutable objects are objects whose value can be changed or modified even after they have been assigned a value.

For immutable objects like numeric types (int, float, bool, and complex), str, and tuple, if we update the value of any of the variables, it will not affect others.

In the above example, I initially assigned a value of 200 to all three variables a , b , and c . Later I changed the value of variable b to 500 . When I printed all three variables, we can see that only the value of variable b is changed to 500 while the values of variables a and c are still 200 .

Note : In Python, immutable data types and immutable objects are the same. Also, mutable data types and mutable objects are the same. This may not be the case in other programming languages.

For mutable objects like list, dict, and set, if we update the value of any one of the variables. The value of other variables also gets changed.

In the above example, I initially assigned [1, 2, 3, 4] to all three lists a , b , and c . Later I appended a value of 10 to the list b . When I print all three lists to the console, we can see that the value of 10 is not just appended to list b , it is appended to all three lists a , b , and c .

Assigning the same value of mutable data types to multiple variables in a single line may not be a good idea. The whole purpose of having a mutable object in Python is so that we can update the values. 

If you are sure that you won’t be updating the values of mutable data types, then you can assign values of mutable data types to multiple variables in a single line. Otherwise, you should assign them separately.

a = [1, 2, 3, 4]

b = [1, 2, 3, 4]

c = [1, 2, 3, 4]

The value of immutable objects cannot be changed doesn’t mean the variable assigned with an immutable object cannot be reassigned to a different value. It simply means the value cannot be changed but the same variable can be assigned a new value. 

We can see this behavior of an object by printing the id of the object by using the id() function. The id is the memory address of the object. In Python, all objects have a unique id.

The ID of immutable object:

In the above example, I initially assigned a value of 100 to variable a . When I printed the id of variable a to the console, it is 1686573813072 . Later I changed the value of a to 200 . Now when I print the id of variable a , it is 1686573816272 . Since the id of variable a in the beginning and after I changed the value is different, we can conclude that variable a was pointing to a different memory address in the beginning and now it is pointing to a different memory address. It means that the previous object with its value is replaced by the new object with its new value.

Note : The id will be different from my output id for your machine. May even change every time you run the program.

The ID of mutable object:

In the above example, I have assigned [1, 2, 3, 4] to list a . When I printed the id of list a , it is 1767333244416 . Later, I appended a value of 10 to the list a . When I print the id of the list a , it is still 1767333244416 . Since the id of the list a in the beginning and after I appended a value is the same, we can conclude that list a is still pointing to the same memory address. This means that the value of list a is modified. 

Assign multiple variables to the multiple values (Tuple Unpacking)

In Python, we can assign multiple values separated by commas to multiple variables separated by commas in one line using the assignment operator ( = ) .

a, b, c = 100, 200, 300

In the above example, variable a is assigned a value of 100 , variable b is assigned a value of 200 , and variable c is assigned a value of 300 respectively.

We have to make sure that the number of variables is equal to the number of values. Otherwise, it will throw an error.

We can also assign values of different data types to multiple variables in one line.

a, b, c = 2.5, 400, "Hello World"

In the above example, variable a is assigned a value of float data type, variable b is assigned a value of integer data type, and c is assigned a value of string data type.

If the number of values is more than the number of variables, we can still assign those values to multiple variables by placing an asterisk (*) before the variable.

Notice the variable with an asterisk (*) is assigned a list.

This method of assigning multiple variables to multiple values is also called tuple unpacking. In the above examples, what we are doing is, we are unpacking the elements of a tuple and assigning them to multiple separate variables.

In Python, we can create tuples with or without parenthesis.

E.g. a = 1, 2, 3 and a = (1, 2, 3) both are the same thing.

Our first example of assigning multiple variables to multiple values can also be written as:

We can also assign tuples to multiple variables directly.

a, b, c = (100, 200, 300)

We can also unpack other iterable objects like lists, sets, and dictionaries.

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Assigning multiple or the same value to multiple variables in Python

In Python, the = operator is used to assign values to variables.

As in the example above, you can assign values to multiple variables at once instead of one at a time, which is convenient because it requires only one simple line of code to write.

The following two cases are described.

Assign multiple values to multiple variables

Assign the same value to multiple variables.

Multiple values can be assigned to multiple variables simultaneously by separating variables and values with commas.

Three or more variables, each of a different type, are acceptable.

If there is one variable on the left side, it is assigned as a tuple.

If the number of variables on the left-hand side does not match the number of values on the right-hand side, a ValueError error will result, but the rest can be assigned as a list by adding an asterisk to the variable.

For more information about asterisks and how to assign elements of a tuple or list to multiple variables, see the following article.

• RELATED: Unpack (expand and assign to multiple variables) tuples and lists in Python

The same value can be assigned to multiple variables by using = consecutively. This is useful for initializing multiple variables to the same value.

More than 3 pieces are acceptable.

After assigning the same value, another value can be assigned to one of them.

Be careful when assigning mutable objects such as lists and dictionary types, rather than immutable (unchangeable) objects such as integers, floating point numbers, and strings.

Using = consecutively means that all variables point to the same object, so if you change the value of one element or add a new element, the other will change as well.

Same as below.

If you wish to process them separately, simply assign to each.

after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.) 3. Data model — Python 3.10.4 Documentation

There are also methods to generate shallow and deep copies with copy() and deepcopy() in the copy module.

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Python Variables - Assign Multiple Values

Python Variables: Assign Multiple Values with Ease

Are you ready to take your Python skills to the next level? Understanding how to assign multiple values to variables is one of the key concepts of programming, and mastering it can significantly improve the efficiency and readability of your code. In this article, we'll dive deep into Python variable assignments, covering everything from the basics of multiple assignments to advanced topics such as tuple and list unpacking. Whether you're a beginner just starting or an experienced developer looking to brush up on your knowledge, this article is for you. So please grab a cup of coffee, and let's get started!

What is Multiple Assignment?

What is multiple assignment in python.

In Python, multiple assignments is a technique that allows you to assign multiple values to multiple variables in one line of code. Use the assignment operator (=) to accomplish it and separate the variables and values with commas.

Why use Multiple Assignments in Python?

Multiple assignments are a powerful technique that can significantly improve the efficiency and readability of your code. It allows you to assign multiple values to multiple variables in one line of code, reducing the amount of code needed and making it easier to understand. It can also be used to swap the values of two variables in one line of code.

Basic Multiple Assignment

How to assign multiple values to multiple variables in python.

You can give multiple values to various variables in Python using the assignment operator (=) and separate the variables and values with commas. For example:

x, y, z = 1, 2, 3

Best Practices for Basic Multiple Assignment

When using multiple basic assignments, ensuring the number of variables and values match is important. Also, use meaningful and descriptive variable names to make your code more readable.

Tuple Unpacking

What is tuple unpacking in python.

In Python, tuple unpacking is a technique that allows you to assign the elements of a tuple to multiple variables. You can do it by using the assignment operator (=) and placing the tuple on the right side of the operator.

How to Use Tuple Unpacking in Python?

To use tuple unpacking in Python, you can assign the elements of a tuple to multiple variables using the assignment operator (=) and placing the tuple on the right side of the operator. For example:

x, y, z = (1, 2, 3)

List Unpacking

What is list unpacking in python.

In Python, list unpacking is a technique that allows you to assign the elements of a list to multiple variables. You can use the assignment operator (=) and place the list on the right side of the operator.

How to Use List Unpacking in Python?

To use list unpacking in Python, you can assign the elements of a list to multiple variables using the assignment operator (=) and placing the list on the right side of the operator. For example:

x, y, z = [1, 2, 3]

Advanced Multiple Assignment

How to use star operator for unpacking in python.

In Python, you can use the star operator (*) to unpack the elements of a list or tuple and assign them to multiple variables. For example:

x, *y, z = [1, 2, 3, 4]

How to Use Advanced Multiple Assignment for Swapping Variables?

In Python, you can use multiple assignments to swap the values of two or more variables easily. For example:

x, y = y, x

Additional Topics

How to use multiple assignments with dictionaries.

In Python, you can use multiple assignments to unpack the keys and values of a dictionary and assign them to separate variables. For example:

x, y = {'key1': 'value1', 'key2': 'value2'}.items()

How to Use Multiple Assignments with Iterators?

In Python, you can use multiple assignments to unpack an iterator's elements and assign them to multiple variables. It can be helpful when working with large datasets or performing complex operations.

In this article, we've covered the basics of Python variable assignment, from multiple basic assignments to advanced techniques such as tuple and list unpacking. We've also discussed best practices for working with multiple assignments, such as ensuring the number of variables and values match and using meaningful and descriptive variable names.

Following this article's best practices, you can write straightforward, efficient, and easy-to-understand code. Keep honing your skills and experimenting with various multiple-assignment strategies; you'll be a Python multiple-assignment master in no time!

This technique can also be used with other Python concepts, such as loops and data structures, for even more powerful and efficient code.

To further improve your Python skills, consider experimenting with different use cases and applications for multiple assignments and reading more about advanced topics such as lists and dictionary comprehension. With continued practice and learning, you'll be able to master the art of Python variable assignment and take your skills to the next level.

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Assign Multiple Values

Assign values to multiple variables.

Python is a popular programming language used for a variety of applications, from web development to data analysis. One of the essential features of Python is the ability to assign multiple variables simultaneously. In this guide, we will explore how to use multiple variables in Python and their benefits.

Understanding Python Multiple Variables

Python allows you to assign multiple variables in a single line of code. The syntax for assigning multiple variables is as follows:

The above code assigns three variables, variable1 , variable2 , and variable3 , with values value1 , value2 , and value3 , respectively.

Using multiple variables in Python can simplify your code and make it more readable. Instead of declaring and assigning variables separately, you can assign them all in a single line of code, saving you time and reducing the chances of errors.

Advantages of Using Multiple Variables in Python

Using multiple variables in Python has several advantages, including:

Simplify Code

When you need to assign multiple variables, you can use a single line of code instead of multiple lines. This makes your code more concise and easier to read, reducing the chances of errors.

Improve Readability

Multiple variables make your code more readable, as you can quickly identify the variables and their corresponding values. This is especially useful when working with large datasets.

Reduce Memory Usage

When you assign multiple variables in a single line of code, you save memory, as you do not need to declare separate variables.

Faster Execution

Using multiple variables can make your code run faster, as it reduces the number of lines of code and memory usage.

Examples of Using Multiple Variables in Python

Here are some examples of how to use multiple variables in Python:

Example 1: Assigning Multiple Variables

Example 2: swapping variables.

In this guide, we have explored the benefits of using multiple variables in Python, as well as provided some examples to help you get started. Using multiple variables can make your code more concise, easier to read, and faster to execute. By incorporating multiple variables in your Python code, you can improve its overall efficiency and effectiveness.

To help you visualize the concept of multiple variables, we have provided a diagram below:

We hope this guide has been useful, and we wish you success in your programming journey!

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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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Multiple Assignment in Python

Assigning multiple variables in one line in python.

In this video, we will explore how to assign multiple variables in one line in Python. This technique allows for concise and readable code, especially when you need to initialize multiple variables simultaneously. This tutorial is perfect for students, professionals, or anyone interested in enhancing their Python programming skills.

Why Assign Multiple Variables in One Line?

Assigning multiple variables in one line helps to:

• Write more concise and readable code.
• Initialize multiple variables simultaneously.
• Simplify code maintenance and debugging.

Key Concepts

1. Multiple Assignment:

• The ability to assign values to multiple variables in a single line of code.

2. Tuple Unpacking:

• A technique that allows you to assign values from a tuple to multiple variables in one line.

How to Assign Multiple Variables in One Line

1. Basic Multiple Assignment:

• Assign values to multiple variables separated by commas.
• Use tuples to assign multiple variables in a single line.

3. List Unpacking:

• Similar to tuple unpacking, but using lists.

Practical Examples

Example 1: Basic Multiple Assignment

• Assign values to multiple variables in one line.
• Example: a, b, c = 1, 2, 3

Example 2: Tuple Unpacking

• Use tuple unpacking to assign values.
• Example: x, y = (4, 5)

Example 3: List Unpacking

• Use list unpacking to assign values.
• Example: m, n, o = [6, 7, 8]

Practical Applications

Data Initialization:

• Initialize multiple variables with values in one line for cleaner and more concise code.

Function Returns:

• Assign multiple return values from a function call to separate variables in one line.

Swapping Variables:

• Swap values of two variables in one line using tuple unpacking.
• Example: a, b = b, a

• This Website
• Chapter 1: Introduction
• Additional Problems
• Additional Examples
• Additional Notes
• Book (2nd ed.)
• Chapter 2: The Core Python Language I

Assigning multiple variables in one statement

In an assignment using the ' = ' operator the right hand side expression is evaluated first. This provides a convenient way to swap the values of two variables using tuples:

Here, the righthand side is packed into a tuple object, which is then unpacked into the variables assigned on the lefthand side. This is a more convenient than using a temporary variable:

Multiple Assignment Syntax in Python

• python-tricks

The multiple assignment syntax, often referred to as tuple unpacking or extended unpacking, is a powerful feature in Python. There are several ways to assign multiple values to variables at once.

Let's start with a first example that uses extended unpacking . This syntax is used to assign values from an iterable (in this case, a string) to multiple variables:

a : This variable will be assigned the first element of the iterable, which is 'D' in the case of the string 'Devlabs'.

*b : The asterisk (*) before b is used to collect the remaining elements of the iterable (the middle characters in the string 'Devlabs') into a list: ['e', 'v', 'l', 'a', 'b']

c : This variable will be assigned the last element of the iterable: 's'.

The multiple assignment syntax can also be used for numerous other tasks:

Swapping Values

This swaps the values of variables a and b without needing a temporary variable.

Splitting a List

first will be 1, and rest will be a list containing [2, 3, 4, 5] .

Assigning Multiple Values from a Function

This assigns the values returned by get_values() to x, y, and z.

Ignoring Values

Here, you're ignoring the first value with an underscore _ and assigning "Hello" to the important_value . In Python, the underscore is commonly used as a convention to indicate that a variable is being intentionally ignored or is a placeholder for a value that you don't intend to use.

Unpacking Nested Structures

This unpacks a nested structure (Tuple in this example) into separate variables. We can use similar syntax also for Dictionaries:

In this case, we first extract the 'person' dictionary from data, and then we use multiple assignment to further extract values from the nested dictionaries, making the code more concise.

Extended Unpacking with Slicing

first will be 1, middle will be a list containing [2, 3, 4], and last will be 5.

Split a String into a List

*split, is used for iterable unpacking. The asterisk (*) collects the remaining elements into a list variable named split . In this case, it collects all the characters from the string.

The comma , after *split is used to indicate that it's a single-element tuple assignment. It's a syntax requirement to ensure that split becomes a list containing the characters.

Python Programming

Python Variables

Updated on:  August 31, 2021 | 19 Comments

A variable is a reserved memory area (memory address) to store value . For example, we want to store an employee’s salary. In such a case, we can create a variable and store salary using it. Using that variable name, you can read or modify the salary amount.

In other words, a variable is a value that varies according to the condition or input pass to the program. Everything in Python is treated as an object so every variable is nothing but an object in Python.

A variable can be either mutable or immutable . If the variable’s value can change, the object is called mutable, while if the value cannot change, the object is called immutable. We will learn the difference between mutable and immutable types in the later section of this article.

Also, Solve :

• Python variables and data type Quiz
• Basic Python exercise for beginners

Table of contents

Creating a variable, changing the value of a variable, integer variable, float variable, complex type, string variable, list type variable, get the data type of variable, delete a variable, variable’s case-sensitive, assigning a value to a constant in python, rules and naming convention for variables and constants, assigning a single value to multiple variables, assigning multiple values to multiple variables, local variable, global variable, object reference, unpack a collection into a variable.

Python programming language is dynamically typed, so there is no need to declare a variable before using it or declare the data type of variable like in other programming languages. The declaration happens automatically when we assign a value to the variable.

Creating a variable and assigning a value

We can assign a value to the variable at that time variable is created. We can use the assignment operator = to assign a value to a variable.

The operand, which is on the left side of the assignment operator, is a variable name. And the operand, which is the right side of the assignment operator, is the variable’s value.

In the above example, “John”, 25, 25800.60 are values that are assigned to name , age , and salary respectively.

Many programming languages are statically typed languages where the variable is initially declared with a specific type, and during its lifetime, it must always have that type.

But in Python, variables are dynamically typed  and not subject to the data type restriction. A variable may be assigned to a value of  one type , and then later, we can also re-assigned a value of a different type . Let’s see the example.

Create Number, String, List variables

We can create different types of variables as per our requirements. Let’s see each one by one.

A number is a data type to store numeric values. The object for the number will be created when we assign a value to the variable. In Python3, we can use the following three data types to store numeric values.

The  int is a data type that returns integer type values (signed integers); they are also called  ints or integers . The integer value can be positive or negative without a decimal point.

Note : We used the built-in Python method type() to check the variable type.

Floats are the values with the decimal point dividing the integer and the fractional parts of the number.  Use float data type to store decimal values.

In the above example, the variable salary assigned to value 10800.55, which is a float value.

The complex is the numbers that come with the real and imaginary part. A complex number is in the form of a+bj, where a and b contain integers or floating-point values.

In Python, a string is a set of characters  represented in quotation marks. Python allows us to define a string in either pair of  single  or  double quotation marks. For example, to store a person’s name we can use a string type.

To retrieve a piece of string from a given string, we can use to slice operator [] or [:] . Slicing provides us the subset of a string with an index starting from index 0 to index end-1.

To concatenate the string, we can use  the addition (+) operator.

If we want to represent  a group of elements (or value) as a single entity, we should go for the list variable type. For example, we can use them to store student names. In the list, the insertion order of elements is preserved. That means, in which order elements are inserted in the list, the order will be intact.

Read : Complete Guide on Python lists

The list can be accessed in two ways, either positive or negative index.  The list has the following characteristics:

• In the list insertion order of elements is preserved.
• Heterogeneous (all types of data types int , float , string ) elements are allowed.
• Duplicates elements are permitted.
• The list is mutable(can change).
• Growable in nature means based on our requirement, we can increase or decrease the list’s size.
• List elements should be enclosed within square brackets [] .

No matter what is stored in a variable (object), a variable can be any type like int , float , str , list , tuple , dict , etc. There is a built-in function called type() to get the data type of any variable.

The type() function has a simple and straightforward syntax.

Syntax of type() :

If we want to get the name of the datatype only as output, then we can use the __name__ attribute along with the type() function. See the following example where __name__ attribute is used.

Use the del keyword to delete the variable. Once we delete the variable, it will not be longer accessible and eligible for the garbage collector.

Now, let’s delete var1 and try to access it again.

Python is a case-sensitive language. If we define a variable with names a = 100 and A =200 then, Python differentiates between a and A . These variables are treated as two different variables (or objects).

Constant is a variable or value that does not change, which means it remains the same and cannot be modified. But in the case of Python, the constant concept is  not applicable . By convention, we can use only uppercase characters to define the constant variable if we don’t want to change it.

 Example

It is just convention, but we can change the value of MAX_VALUE variable.

As we see earlier, in the case of Python, the constant concept is not applicable. But if we still want to implement it, we can do it using the following way.

The declaration and assignment of constant in Python done with the module. Module means Python file ( .py ) which contains variables, functions, and packages.

So let’s create two modules, constant.py  and main.py , respectively.

• In the constant.py file, we will declare two constant variables,  PI and TOTAL_AREA .
• import constant module In main.py file.

To create a constant module write the below code in the constant.py file.

Constants are declared with uppercase later variables and separating the words with an underscore.

Create a  main.py  and write the below code in it.

Note : Constant concept is not available in Python. By convention, we define constants in an uppercase letter to differentiate from variables. But it does not prevent reassignment, which means we can change the value of a constant variable.

A name in a Python program is called an identifier. An identifier can be a variable name, class name, function name, and module name.

There are some rules to define variables in Python.

In Python, there are some conventions and rules to define variables and constants that should follow.

Rule 1 : The name of the variable and constant should have a combination of letters, digits, and underscore symbols.

• Alphabet/letters i.e., lowercase (a to z) or uppercase (A to Z)
• Digits(0 to 9)
• Underscore symbol (_)

Example 

Rule 2 : The variable name and constant name should make sense.

Note: we should always create a meaningful variable name so it will be easy to understand. That is, it should be meaningful.

It above example variable x does not make more sense, but student_name  is a meaningful variable.

Rule 3: Don’t’ use special symbols in a variable name

For declaring variable and constant, we cannot use special symbols like $, #, @, %, !~, etc. If we try to declare names with a special symbol, Python generates an error

Rule 4:  Variable and constant should not start with digit letters.

You will receive an error if you start a variable name with a digit. Let’s verify this using a simple example.

Here Python will generate a syntax error at 1studnet . instead of this, you can declare a variable like studnet_1 = "Jessa"

Rule 5:  Identifiers are case sensitive.

Here, Python makes a difference between these variables that is uppercase and lowercase, so that it will create three different variables total , Total , TOTAL .

Rule 6:  To declare constant should use capital letters.

Rule 6: Use an underscore symbol for separating the words in a variable name

If we want to declare variable and constant names having two words, use an underscore symbol for separating the words.

Multiple assignments

In Python, there is no restriction to declare a variable before using it in the program. Python allows us to create a variable as and when required.

We can do multiple assignments in two ways, either by assigning a single value to multiple variables  or assigning  multiple values to multiple variables .

we can assign a single value to multiple variables simultaneously using the assignment operator = .

Now, let’s create an example to assign the single value 10 to all three variables a , b , and c .

In the above example, two integer values 10 and 70 are assigned to variables roll_no and marks , respectively, and string literal, “Jessa,” is assigned to the variable name .

Variable scope

Scope : The scope of a variable refers to the places where we can access a variable.

Depending on the scope, the variable can categorize into two types  local variable and the global variable.

A local variable is a variable that is accessible inside a block of code only where it is declared. That means, If we declare a variable inside a method, the scope of the local variable is limited to the method only. So it is not accessible from outside of the method. If we try to access it, we will get an error.

In the above example, we created a function with the name test1 . Inside it, we created a local variable price. Similarly, we created another function with the name test2 and tried to access price, but we got an error "price is not defined" because its scope is limited to function test1() . This error occurs because we cannot access the local variable from outside the code block.

A Global variable is a variable that is defined outside of the method (block of code). That is accessible anywhere in the code file.

In the above example, we created a global variable price and tried to access it in test1 and test2 . In return, we got the same value because the global variable is accessible in the entire file.

Note : You must declare the global variable outside function.

Object/Variable identity and references

In Python, whenever we create an object, a number is given to it and uniquely identifies it. This number is nothing but a memory address of a variable’s value. Once the object is created, the identity of that object never changes.

No two objects will have the same identifier. The Object is for eligible garbage collection when deleted. Python has a built-in function id() to get the memory address of a variable.

For example, consider a library with many books (memory addresses) and many students (objects). At the beginning(when we start The Python program), all books are available. When a new student comes to the library (a new object created), the librarian gives him a book. Every book has a unique number (identity), and that id number tells us which book is delivered to the student (object)

It returns the same address location because both variables share the same value. But if we assign m to some different value, it points to a different object with a different identity.

See the following example

For m = 500 , Python created an integer object with the value 500 and set m as a reference to it. Similarly, n is assigned to an integer object with the value 400 and sets n as a reference to it. Both variables have different identities.

In Python, when we assign a value to a variable, we create an object and reference it.

For example, a=10 , here, an object with the value  10 is created in memory, and reference a now points to the memory address where the object is stored.

Suppose we created a=10 , b=10 , and  c=10 , the value of the three variables is the same. Instead of creating three objects, Python creates only one object as  10  with references such as  a , b , c .

We can access the memory addresses of these variables using the id() method. a , b refers to the same address  in memory, and c , d , e refers to the same address. See the following example for more details.

Here, an object in memory initialized with the value 10 and reference added to it, the reference count increments by ‘1’.

When Python executes the next statement that is b=10 , since it is the same value 10, a new object will not be created because the same object in memory with value 10 available, so it has created another reference, b . Now, the reference count for value 10 is ‘2’.

Again for  c=20 , a new object is created with reference ‘c’ since it has a unique value (20). Similarly, for d , and e  new objects will not be created because the object ’20’ already available. Now, only the reference count will be incremented.

We can check the reference counts of every object by using the getrefcount function of a  sys module. This function takes the object as an input and returns the number of references.

We can pass variable, name, value, function, class as an input to getrefcount() and in return, it will give a reference count for a given object.

See the following image for more details.

In the above picture, a , b pointing to the same memory address location (i.e., 140722211837248), and c , d , e pointing to the same memory address (i.e., 140722211837568 ). So reference count will be 2 and 3 respectively.

• In Python, we can create a tuple (or list) by packing a group of variables.
• Packing can be used when we want to collect multiple values in a single variable. Generally, this operation is referred to as tuple packing.

Here a , b , c , d  are packed in the tuple tuple1 .

Tuple unpacking  is the reverse operation of tuple packing . We can unpack tuple and assign tuple values to different variables.

Note: When we are performing unpacking, the number of variables and the number of values should be the same. That is, the number of variables on the left side of the tuple must exactly match a number of values on the right side of the tuple. Otherwise, we will get a ValueError .

Also, See :

• Class Variables
• Instance variables

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About Vishal

I’m  Vishal Hule , the Founder of PYnative.com. As a Python developer, I enjoy assisting students, developers, and learners. Follow me on  Twitter .

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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! 😊

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Python for absolute beginners, next episode, variables and assignment.

Overview Teaching: 15 min Exercises: 15 min Questions How can I store data in programs? Objectives Write scripts that assign values to variables and perform calculations with those values. Correctly trace value changes in scripts that use assignment.

Use variables to store values

Variables are one of the fundamental building blocks of Python. A variable is like a tiny container where you store values and data, such as filenames, words, numbers, collections of words and numbers, and more.

The variable name will point to a value that you “assign” it. You might think about variable assignment like putting a value “into” the variable, as if the variable is a little box 🎁

(In fact, a variable is not a container as such but more like an adress label that points to a container with a given value. This difference will become relevant once we start talking about lists and mutable data types.)

You assign variables with an equals sign ( = ). In Python, a single equals sign = is the “assignment operator.” (A double equals sign == is the “real” equals sign.)

• Variables are names for values.
• In Python the = symbol assigns the value on the right to the name on the left.
• The variable is created when a value is assigned to it.
• Here, Python assigns an age to a variable age and a name in quotation marks to a variable first_name :

Variable names

Variable names can be as long or as short as you want, but there are certain rules you must follow.

• Cannot start with a digit.
• Cannot contain spaces, quotation marks, or other punctuation.
• May contain an underscore (typically used to separate words in long variable names).
• Having an underscore at the beginning of a variable name like _alistairs_real_age has a special meaning. So we won’t do that until we understand the convention.
• The standard naming convention for variable names in Python is the so-called “snake case”, where each word is separated by an underscore. For example my_first_variable . You can read more about naming conventions in Python here .

Use meaningful variable names

Python doesn’t care what you call variables as long as they obey the rules (alphanumeric characters and the underscore). As you start to code, you will almost certainly be tempted to use extremely short variables names like f . Your fingers will get tired. Your coffee will wear off. You will see other people using variables like f . You’ll promise yourself that you’ll definitely remember what f means. But you probably won’t.

So, resist the temptation of bad variable names! Clear and precisely-named variables will:

• Make your code more readable (both to yourself and others).
• Reinforce your understanding of Python and what’s happening in the code.
• Clarify and strengthen your thinking.

Use meaningful variable names to help other people understand what the program does. The most important “other person” is your future self!

Python is case-sensitive

Python thinks that upper- and lower-case letters are different, so Name and name are different variables. There are conventions for using upper-case letters at the start of variable names so we will use lower-case letters for now.

Off-Limits Names

The only variable names that are off-limits are names that are reserved by, or built into, the Python programming language itself — such as print , True , and list . Some of these you can overwrite into variable names (not ideal!), but Jupyter Lab (and many other environments and editors) will catch this by colour coding your variable. If your would-be variable is colour-coded green, rethink your name choice. This is not something to worry too much about. You can get the object back by resetting your kernel.

Use print() to display values

We can check to see what’s “inside” variables by running a cell with the variable’s name. This is one of the handiest features of a Jupyter notebook. Outside the Jupyter environment, you would need to use the print() function to display the variable.

You can run the print() function inside the Jupyter environment, too. This is sometimes useful because Jupyter will only display the last variable in a cell, while print() can display multiple variables. Additionally, Jupyter will display text with \n characters (which means “new line”), while print() will display the text appropriately formatted with new lines.

• Python has a built-in function called print() that prints things as text.
• Provide values to the function (i.e., the things to print) in parentheses.
• To add a string to the printout, wrap the string in single or double quotations.
• The values passed to the function are called ‘arguments’ and are separated by commas.
• When using the print() function, we can also separate with a ‘+’ sign. However, when using ‘+’ we have to add spaces in between manually.
• print() automatically puts a single space between items to separate them.
• And wraps around to a new line at the end.

Variables must be created before they are used

If a variable doesn’t exist yet, or if the name has been misspelled, Python reports an error (unlike some languages, which “guess” a default value).

The last line of an error message is usually the most informative. This message lets us know that there is no variable called eye_color in the script.

Variables Persist Between Cells Variables defined in one cell exist in all other cells once executed, so the relative location of cells in the notebook do not matter (i.e., cells lower down can still affect those above). Notice the number in the square brackets [ ] to the left of the cell. These numbers indicate the order, in which the cells have been executed. Cells with lower numbers will affect cells with higher numbers as Python runs the cells chronologically. As a best practice, we recommend you keep your notebook in chronological order so that it is easier for the human eye to read and make sense of, as well as to avoid any errors if you close and reopen your project, and then rerun what you have done. Remember: Notebook cells are just a way to organize a program! As far as Python is concerned, all of the source code is one long set of instructions.

Variables can be used in calculations

• We can use variables in calculations just as if they were values. Remember, we assigned 42 to age a few lines ago.

This code works in the following way. We are reassigning the value of the variable age by taking its previous value (42) and adding 3, thus getting our new value of 45.

Use an index to get a single character from a string

• The characters (individual letters, numbers, and so on) in a string are ordered. For example, the string ‘AB’ is not the same as ‘BA’. Because of this ordering, we can treat the string as a list of characters.
• Each position in the string (first, second, etc.) is given a number. This number is called an index or sometimes a subscript.
• Indices are numbered from 0 rather than 1.
• Use the position’s index in square brackets to get the character at that position.

Use a slice to get a substring

A part of a string is called a substring. A substring can be as short as a single character. A slice is a part of a string (or, more generally, any list-like thing). We take a slice by using [start:stop] , where start is replaced with the index of the first element we want and stop is replaced with the index of the element just after the last element we want. Mathematically, you might say that a slice selects [start:stop] . The difference between stop and start is the slice’s length. Taking a slice does not change the contents of the original string. Instead, the slice is a copy of part of the original string.

Use the built-in function len() to find the length of a string

The built-in function len() is used to find the length of a string (and later, of other data types, too).

Note that the result is 6 and not 7. This is because it is the length of the value of the variable (i.e. 'helium' ) that is being counted and not the name of the variable (i.e. element )

Also note that nested functions are evaluated from the inside out, just like in mathematics. Thus, Python first reads the len() function, then the print() function.

Choosing a Name Which is a better variable name, m , min , or minutes ? Why? Hint: think about which code you would rather inherit from someone who is leaving the library: ts = m * 60 + s tot_sec = min * 60 + sec total_seconds = minutes * 60 + seconds Solution minutes is better because min might mean something like “minimum” (and actually does in Python, but we haven’t seen that yet).

Swapping Values Draw a table showing the values of the variables in this program after each statement is executed. In simple terms, what do the last three lines of this program do? x = 1.0 y = 3.0 swap = x x = y y = swap Solution swap = x # x->1.0 y->3.0 swap->1.0 x = y # x->3.0 y->3.0 swap->1.0 y = swap # x->3.0 y->1.0 swap->1.0 These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom.

Predicting Values What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.) initial = "left" position = initial initial = "right" Solution initial = "left" # Initial is assigned the string "left" position = initial # Position is assigned the variable initial, currently "left" initial = "right" # Initial is assigned the string "right" print(position) left The last assignment to position was “left”

Can you slice integers? If you assign a = 123 , what happens if you try to get the second digit of a ? Solution Numbers are not stored in the written representation, so they can’t be treated like strings. a = 123 print(a[1]) TypeError: 'int' object is not subscriptable

Slicing What does the following program print? library_name = 'social sciences' print('library_name[1:3] is:', library_name[1:3]) If thing is a variable name, low is a low number, and high is a high number: What does thing[low:high] do? What does thing[low:] (without a value after the colon) do? What does thing[:high] (without a value before the colon) do? What does thing[:] (just a colon) do? What does thing[number:negative-number] do? Solution library_name[1:3] is: oc It will slice the string, starting at the low index and ending an element before the high index It will slice the string, starting at the low index and stopping at the end of the string It will slice the string, starting at the beginning on the string, and ending an element before the high index It will print the entire string It will slice the string, starting the number index, and ending a distance of the absolute value of negative-number elements from the end of the string

Key Points Use variables to store values. Use meaningful variable names. Python is case-sensitive. Use print() to display values. Variables must be created before they are used. Variables persist between cells. Variables can be used in calculations. Use an index to get a single character from a string. Use a slice to get a substring. Use the built-in function len to find the length of a string.

What Is a Variable in Python?

It might seem like a straightforward concept, but variables are more interesting than you think. In an effort to expand our concept map, we’re here to cover one of the most basic programming concepts: variables in Python.

Table of Contents

Concept overview, where can variables be used, what if i don’t need a variable, is the equal sign the only way to create a variable, how should i name my variables, changing over time.

Most likely at some point in your life, you’ve heard the term “variable” used. Unfortunately, it has a lot of different definitions depending on the context. For example, in algebra, you might be familiar with variables like x and y , which are meant to represent sets of possible numbers. Meanwhile, in science, you might be familiar with concepts like dependent and independent variables (i.e., aspects of an experiment which can be manipulated to observe some outcome).

In the world of programming, variables are probably most closely associated with the kinds of variables you’ve seen in algebra. However, programming variables tend to have some unique features. For example, programming variables are not restricted to just numbers. We can use variables to “store” any type of data we want, from simpler data types like numbers and characters to more complex data types like entire data tables or objects.

In addition, programming variables are more “real” than mathematical variables in the sense that the data has to be stored somewhere. As a result, programming variables often don’t store data directly but rather store the address to where that data is stored. Of course, this varies from language to language.

That said, it’s very easy to assume that programming variables are similar to mathematical variables because the syntax often looks so familiar. For example, here’s how you might define your own variable in Python:

Given the use of the equal sign, it’s tempting to assume that all the rules from algebra apply. For instance, you might imagine that this statement can be rearranged as follows:

However, this is not legal code because the equal sign is not defining a relationship between x and 37. Instead, the equal sign is often called the assignment operator . In other words, x can be assigned the value 37, but 37 cannot be assigned the value x . Because of this confusion, some programming languages choose different symbols for their assignment operator, such as the walrus operator (i.e., := ) or an arrow (i.e., <- ).

Of course, as I said previously, we’re not restricted to storing numbers in our variables in Python. We can store whatever we like, including data structures and functions. In fact, as long as there is an expression on the right side of the equal sign, Python will be happy. As a result, all of the following lines of code are valid variable definitions in Python:

How cool is that? Now, in the remainder of this article, we’ll answer some of your questions about variables?

Variables are probably one of the most fundamental features of Python, so you will see them everywhere. For example, in a normal Python program, you might see a variable defined based on user input:

Likewise, you will also see variables used as function parameters. For example, the input function above accepts a prompt as an argument. That argument, is then saved in a variable for the function to use. Here’s what that might look like:

In fact, variables are so ubiquitous in Python, that most of the features wouldn’t work without them. For example, you can’t really use a while loop without a variable in the condition :

It’s possible to make the code above work using a function, but the function argument is just another variable. A similar argument can be made for if statements and other more complex structures like list comprehensions and pattern matching.

Because variables are so ubiquitous, there are times where you will receive one that you don’t need, such as part of a return value from a function. In Python, we have a fun convention for dealing with these situations—the underscore. For example, you might have a list of values from which you only want the first and last. In Python, we can use iterable unpacking :

In this example, the underscore is basically a dummy variable, which we dump blue and green into. It acts like any other variable, but it signals to the reader that we don’t care about its value.

Typically, we create variables using the assignment operator. However, it’s possible to create variables in more recent versions of Python using the walrus operator . It functions almost identically to the regular assignment operator, but it can only be used in certain scenarios. For example, you might be familiar with the traditional while loop for reading lines from a file:

Code like this is straightforward but some folks really don’t like duplicate code, so they opt for something like this:

This code only works when using the walrus operator. You cannot swap the walrus operator out for the usual assignment operator. Likewise, the following is not legal code:

The short explanation is that if you want to save and use a value at the same time, then you can use the walrus operator (e.g., in the context of a loop condition). Otherwise, you must use the regular assignment operator.

At their core, variables are just names given to data. As a result, you can name them whatever you like, aside from a few exceptions like starting with a number, including spaces, or using the name of a reserved keyword.

However, developers tend to agree that constraints are good for reducing complexity, so we often subscribe to a variety of conventions. For variables, the typical naming convention is to use snake_case , which constrains variable names to lowercase letters and underscores.

You may impose further naming conventions on your variables, so it is clearer what they contain. For example, there are a bunch of interesting conventions for distinguishing classes from interfaces (e.g., Fruit vs. IFruit ). Because you don’t have to specify types in Python, you might also include some type information in the name—though I would just use type hints.

Like variables, this series is changing. With the addition of this article on variables, I think next time we’ll look at expressions. Let’s keep the concept map growing!

With that said, it’s time to call it today. Below you’ll find some related articles:

• The Haters Guide to Python
• Abusing Python’s Operator Overloading Feature
• 5 Things You Should Know Before You Pick Up Python

Likewise, here are some resources to help you learn more about Python (#ad):

Finally, feel free to check out my list of ways to grow the site . Otherwise, take care!

An activity I regularly do with my students is a concept map. Typically, we do it at the start and end of each semester to get an idea of how well our understanding of the material as matured over time. Naturally, I had the idea to extend this concept into its own series, just to see how deeply I can explore my own knowledge of Python. It should be a lot of fun, and I hope you enjoy it!

Jeremy Grifski

Jeremy grew up in a small town where he enjoyed playing soccer and video games, practicing taekwondo, and trading Pokémon cards. Once out of the nest, he pursued a Bachelors in Computer Engineering with a minor in Game Design. After college, he spent about two years writing software for a major engineering company. Then, he earned a master's in Computer Science and Engineering. Today, he pursues a PhD in Engineering Education in order to ultimately land a teaching gig. In his spare time, Jeremy enjoys spending time with his wife and kid, playing Overwatch 2, Lethal Company, and Baldur's Gate 3, reading manga, watching Penguins hockey, and traveling the world.

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• Instructor View

Variables and Types

Last updated on 2024-09-03 | Edit this page

• How can I store data in Python?
• What are some types of data that I can work with in Python?
• Write Python to assign values to variables.
• Print outputs to a Jupyter notebook.
• Use indexing to manipulate string elements.
• View and convert the data types of Python objects.
• Use variables to store values.

Variables are names given to certain values. In Python the = symbol assigns a value to a variable. Here, Python assigns the number 42 to the variable age and the name Ahmed in single quote to a variable name .

Naming variables

Variable names:

• cannot start with a digit
• cannot contain spaces, quotation marks, or other punctuation
• may contain an underscore (typically used to separate words in long variable names)
• are case sensitive. name and Name would be different variables.

Use print() to display values.

You can print Python objects to the Jupyter notebook output using the built-in function, print() . Inside of the parentheses we can add the objects that we want print, which are known as the print() function’s arguments.

In Jupyter notebooks, you can leave out the print() function for objects – such as variables – that are on the last line of a cell. If the final line of Jupyter cell includes the name of a variable, its value will display in the notebook when you run the cell.

Format output with f-strings

F-strings provide a concise and readable way to format strings by embedding Python expressions within them. You can format variables as text strings in your output using an f-string. To do so, start a string with f before the open single (or double) quote. Then add any replacement fields, such as variable names, between curly braces {} . (Note the f string syntax can only be used with Python 3.6 or higher.)

• Variables must be created before they are used.

If a variable doesn’t exist yet, or if the name has been misspelled, Python reports an error called a NameError .

The last line of an error message is usually the most informative. In this case it tells us that the eye_color variable is not defined. NameErrors often refer to variables that haven’t been created or assigned yet.

• Variables can be used in calculations.

We can use variables in calculations as if they were values. We assigned 42 to age a few lines ago, so we can reference that value within a new variable assignment.

Every Python object has a type.

Everything in Python is some type of object and every Python object will be of a specific type. Understanding an object’s type will help you know what you can and can’t do with that object.

You can use the built-in Python function type() to find out an object’s type.

• 140.2 is an example of a floating point number or float . These are fractional numbers.
• The value of the age variable is 45, which is a whole number, or integer ( int ).
• The name variable refers to the string ( str ) of ‘Ahmed’.
• The built-in Python function print() is also an object with a type, in this case it’s a builtin_function_or_method . Built-in functions refer to those that are included in the core Python library.

Types control what operations (or methods) can be performed on objects.

An object’s type determines what the program can do with it.

We get an error if we try to subtract a letter from a string:

• Use an index to get a single character from a string.

We can reference the specific location of a character (individual letters, numbers, and so on) in a string by using its index position. In Python, each character in a string (first, second, etc.) is given a number, which is called an index. Indexes begin from 0 rather than 1. We can use an index in square brackets to refer to the character at that position.

Use a slice to get multiple characters from a string.

A slice is a part of a string that we can reference using [start:stop] , where start is the index of the first character we want and stop is the last character. Referencing a string slice does not change the contents of the original string. Instead, the slice returns a copy of the part of the original string we want.

Note that in the example above, library[0:3] begins with zero, which refers to the first element in the string, and ends with a 3. When working with slices the end point is interpreted as going up to, but not including the index number provided. In other words, the character in the index position of 3 in the string Alexandria is x , so the slice [0:3] will go up to but not include that character, and therefore give us Ale .

• Use the built-in function len to find the length of a string.

The len() function will tell us the length of an item. In the case of a string, it will tell us how many characters are in the string.

• Variables only change value when something is assigned to them.

Once a Python variable is assigned it will not change value unless the code is run again. The value of older_age below does not get updated when we change the value of age to 50 , for example:

A variable in Python is analogous to a sticky note with a name written on it: assigning a value to a variable is like putting a sticky note on a particular value. When we assigned the variable older_age , it was like we put a sticky note with the name older_age on the value of 45 . Remember, 45 was the result of age + 3 because age at that point in the code was equal to 42 . The older_age sticky note (variable) was never attached to (assigned to) another value, so it doesn’t change when the age variable is updated to be 50 .

F-string Syntax

Use an f-string to construct output in Python by filling in the blanks with variables and f-string syntax to tell Christina how old she will be in 10 years.

Tip: You can combine variables and mathematical expressions in an f-string in the same way you can in variable assignment. We’ll see more examples of dynamic f-string output as we go through the lesson.

Show me the solution

Swapping values.

Draw a table showing the values of the variables in this program after each statement is executed. In simple terms, what do the last three lines of this program do?

These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom.

Predicting Values

What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.)

The last assignment to position was “left”

Can you slice integers?

If you assign a = 123 , what happens if you try to get the second digit of a ?

Numbers are not stored in the written representation, so they can’t be treated like strings.

We know how to slice using an explicit start and end point:

But we can also use implicit and negative index values when we define a slice. Try the following (replacing low and high with index positions of your choosing) to figure out how these different forms of slicing work:

• What does library_name[low:] (without a value after the colon) do?
• What does library_name[:high] (without a value before the colon) do?
• What does library_name[:] (just a colon) do?
• What does library_name[number:negative-number] do?
• It will slice the string, starting at the low index and stopping at the end of the string.
• It will slice the string, starting at the beginning on the string, and ending an element before the high index.
• It will print the entire string.
• It will slice the string, starting the number index, and ending a distance of the absolute value of negative-number elements from the end of the string.

What type of value is 3.4? How can you find out?

It is a floating-point number (often abbreviated “float”).

Automatic Type Conversion

What type of value is 3.25 + 4?

It is a float: integers are automatically converted to floats as necessary.

• Use print to display values.
• Format output with f-strings.
• Variables persist between cells.
• Use a slice to get a portion of a string.
• Python is case-sensitive.
• Every object has a type.
• Use the built-in function type to find the type of an object.
• Types control what operations can be done on objects.

Python Static Variable Explained: When and How to Use Them

Static variables are useful when you want the same piece of data shared across all instances of a class. Unlike regular variables that belong to each object, static variables—also called class variables—are shared by every instance of the class. Python handles this with class-level attributes, which work a bit differently from languages like C++ or Java.

In this article, we’ll explain how static variables work in Python, why they’re helpful, and give you some easy examples. Whether you’re new to Python or want to improve your coding, understanding static variables can make your programs simpler and more efficient.

What Is a Static Variable in Python?

Table of Contents

In Python, a static variable, also called a class variable, is a value shared by all instances of a class. Unlike instance variables, those are unique to each object, static variables are set at the class level and are the same for every object created from that class.

How Static Variables Work in Python

• Static variables are set up inside the class but outside of any methods. They hold a value that is used by all instances of the class.

class MyClass:

    static_variable = 0  # This is a static variable

• You can get the value of a static variable using either the class name or an instance of the class. It’s usually clearer to use the class name.

print(MyClass.static_variable)  # Access using class name

obj = MyClass()

print(obj.static_variable)  # Access using instance

• Changing a static variable affects all instances of the class. If you update it using the class or any instance, the change will be seen by all objects.

MyClass.static_variable = 10  # Change the static variable

obj1 = MyClass()

obj2 = MyClass()

print(obj1.static_variable)  # Output: 10

print(obj2.static_variable)  # Output: 10

• Static variables are useful when you need a value that should be the same for every instance of a class. For example, use them to count how many objects have been created or to store a setting that all objects should share.

class Counter:

    count = 0  # Static variable to count instances

    def __init__(self):

        Counter.count += 1

obj1 = Counter()

obj2 = Counter()

print(Counter.count)  # Output: 2

In summary, static variables in Python are shared across all instances of a class. They help keep data consistent for every object created from the class.

Example of Static Variables in Python

Here’s a simple example to show how static variables (class variables) work in Python.  

import random

    # This static variable counts how many Car instances are created

    total_cars = 0

    def __init__(self, make, model):

        self.make = make

        self.model = model

        # Each time a new Car is created, increase the total_cars count

        Car.total_cars += 1

        # Give each car a random ID

        self.car_id = random.randint(1000, 9999)

# Create some Car objects

car1 = Car(“Toyota”, “Corolla”)

car2 = Car(“Honda”, “Civic”)

car3 = Car(“Ford”, “Mustang”)

# Print out the total number of cars

print(“Total number of cars created:”, Car.total_cars)  # Output: Total number of cars created: 3

# Print out each car’s random ID

print(“Car 1 ID:”, car1.car_id)  # Output: Car 1 ID: <random number>

print(“Car 2 ID:”, car2.car_id)  # Output: Car 2 ID: <random number>

print(“Car 3 ID:”, car3.car_id)  # Output: Car 3 ID: <random number>

Explanation

• total_cars is a static variable in the Car class that keeps track of how many Car objects have been made.
• Every time a new Car is created, the __init__ method adds 1 to total_cars.
• Each car is given a random ID between 1000 and 9999.
• We can see the total number of cars created with Car.total_cars.
• Each car has its own unique car_id.

This example shows how static variables track shared information for all instances of a class while each instance can have its own unique details.

Use Cases for Static Variables in Python

Static variables (or class variables) in Python can be really useful for different tasks. Here’s how they can be used:

1. Counting Instances

Static variables are perfect for counting the number of objects of a class that have been created. This helps keep track of objects or set limits.

class Employee:

    total_employees = 0  # Counts all employees

    def __init__(self, name):

        self.name = name

        Employee.total_employees += 1  # Increase count for each new employee

# Create some employee instances

e1 = Employee(“Michael”)

e2 = Employee(“Dwight”)

print(“Total number of employees:”, Employee.total_employees)  # Output: Total number of employees: 2

2. Storing Shared Settings

Static variables can store settings or constants that should be the same for all instances. This ensures consistency and avoids repeating data.

class Config:

    max_connections = 100  # Maximum allowed connections

# Access the setting

print(“Max connections:”, Config.max_connections)  # Output: Max connections: 100

3. Caching Data

Static variables are used to save data that doesn’t change between instances. This makes your program faster by avoiding repeated calculations or data fetching.

class Fibonacci:

    cache = {}  # Stores Fibonacci numbers

    @staticmethod

    def fib(n):

        if n in Fibonacci.cache:

            return Fibonacci.cache[n]

        if n <= 1:

            return n

        result = Fibonacci.fib(n – 1) + Fibonacci.fib(n – 2)

        Fibonacci.cache[n] = result

        return result

# Calculate and cache Fibonacci numbers

print(Fibonacci.fib(10))  # Output: 55

4. Managing Shared Resources

Static variables are useful for managing shared resources, such as a pool of database connections. This ensures that all instances use the same resources.

class DatabaseConnection:

    connection_pool = []  # Pool of connections

    def get_connection():

        if DatabaseConnection.connection_pool:

            return DatabaseConnection.connection_pool.pop()

        else:

            return “New Connection”  # Create a new connection if none are available

# Get connections from the pool

conn1 = DatabaseConnection.get_connection()

conn2 = DatabaseConnection.get_connection()

print(conn1, conn2)  # Output: New Connection New Connection

5. Tracking Class-Wide Status

Static variables can track the status or state of the class as a whole, not just individual objects.

class Game:

    is_active = True  # Shows if the game is active

    @classmethod

    def end_game(cls):

        cls.is_active = False

# End the game

Game.end_game()

print(“Is the game active?”, Game.is_active)  # Output: Is the game active? False

Static variables in Python help manage data and settings that should be shared across all instances of a class. They are great for:

• Counting the number of objects created
• Storing common settings
• Caching data for faster access
• Managing shared resources
• Tracking the overall status of the class

They keep data consistent and easy to access, making your code simpler and more organized.

Limitations and Considerations of Static Variables

Static variables in Python are handy, but they have some limitations. Here’s a simple guide to understanding these:

1. Shared Data Among All Instances

All instances of a class share static variables. If one instance changes a static variable, all other instances see the same change. This can sometimes cause unexpected results if you’re not careful.

    count = 0

obj1.count = 10

print(obj2.count)  # Output: 10 (unexpectedly changed)

2. Thread Safety Issues

In programs that use multiple threads , static variables can cause problems. Different threads might try to use or change the same variable at the same time, which can lead to errors. To fix this, you should use locks to keep things in order.

import threading

    lock = threading.Lock()

    def increment():

        with Counter.lock:

            Counter.count += 1

threads = [threading.Thread(target=Counter.increment) for _ in range(100)]

for thread in threads:

    thread.start()

    thread.join()

print(Counter.count)  # Should be 100 if threads are managed properly

3. Managing Global State

Static variables act like global variables within a class. Using them a lot can make your code harder to understand because it’s not always clear where and how the data is being changed.

4. Testing Problems

Testing code that uses static variables can be tricky. Since these variables keep their values between tests, one test might affect another. Make sure to set up and clean up properly to keep tests separate.

5. Memory Usage

Static variables stay in memory as long as the class is around. If the class is around for a long time, these variables can use up more memory, especially if they hold a lot of data.

6. Less Flexibility

Static variables can’t have different values for different instances. This limits their use if each instance needs its separate data.

7. Debugging Difficulties

Finding bugs related to static variables can be harder because changes in one part of the code can affect other parts in unexpected ways. This makes debugging more difficult.

8. Encapsulation Issues

Static variables can expose internal class data, breaking the principle of encapsulation. This can lead to code that is more fragile and harder to manage.

Final Words

In short, static variables in Python are great for sharing the same information across all objects of a class. They help you keep track of things like how many objects have been created, store settings, or save data that doesn’t change. This makes sure all objects have the same info and avoids repeating data.

Static variables also make your code work better by handling shared resources and speeding things up. They help keep your code simple and organized by putting important details in one place. Using static variables correctly makes your Python programs more efficient and easier to manage.

Are static variables safe to use in multi-threaded programs?

Static variables are not automatically safe for use with multiple threads. If several threads access or modify static variables at the same time, it can lead to problems. You may need to use tools like locks to keep things in order.

How are static variables different from instance variables?

Static variables are the same for every instance of a class, while instance variables are unique to each object. This means that all the cases share the same static variable, but each object can have different instance variables.

Can static variables be set inside methods?

Static variables should be set outside of methods, directly in the class body. Setting them inside methods can cause issues, as they might be reset every time the process is called.

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Mastering Global Variables in Python: An Expert Guide

As a Python developer, understanding global variables – variables defined at the top-level outside of functions – is crucial for architecting programs. Global variables enable convenient state sharing between functions, but can also lead to confusing bugs if used carelessly.

In this comprehensive 3K word guide, you’ll gain an expert full-stack developer’s perspective on effectively using global variables in Python.

Here‘s what we‘ll cover:

Variable Scope in Python Explained

• Visualization of Scope

Defining Global Variables in Python

• Using the global Keyword to Modify Globals
• Common Mistakes with Globals in Functions
• Mutable vs Immutable Global Variable Differences

Expert Insights on Global Variable Usage

• Caching and Config Examples
• The Pros and Cons of Global State

Best Practices for Leveraging Global Variables

So let‘s dive in and master global variables in Python!

Before covering specifics on global variables, we need to understand how variable scope works in Python.

In Python, variable scope refers to where in a program a variable is visible/accessible. Python has the following rules for variable scope:

Local scope: Variables defined inside function bodies have local scope – they only exist within the function they are declared and created in.

Global scope: Variables declared outside functions, at the top level of a module, have module-wide global scope. Globals can be accessed inside functions.

Enclosing/nonlocal scope: Used in nested functions for non-global scopes.

Now let‘s visualize variable scope further using code.

Visualization of Local vs Global Scope

Here is some simple Python code to demonstrate how variable scoping works:

In this code:

• a is a globally scoped variable
• b and c are locally scoped variables inside the enclose() and enclosed() functions respectively.
• a is accessible anywhere
• b is only available in enclose()
• c only exists within enclosed()

Being aware of these scope differences will save you debugging time down the road! Next let‘s clarify some key differences between local and global variables in Python.

Local vs Global Variable Differences

Local variables:

• Declared inside function bodies
• Created anew each time function is called
• Scope limited to the enclosing function
• Undefined outside that function

Global variables:

• Declared at top-level module scope
• Persist in memory the entire program lifetime
• Directly accessible by all functions
• Modifiable by all (mutable case)

As you can see, the scopes have very different implications. Now that you are clear on scope rules, let‘s properly define global variables.

Declaring global variables correctly in Python is straightforward:

To recap, remember these rules about global variable creation:

✅ Define at top-level outside any functions/classes

✅ Accessible across the entire code file/module

❌ Avoid using the same name as a Python built-in/keyword

❌ Minimize use of mutable globals when possible

Also avoid initializing globals to mutable types like lists or dictionaries if the global will be mutated anywhere – since this can introduce hard to trace bugs.

Now let‘s examine modifying globals inside functions.

Modifying Global Variables via the global Keyword

In Python, attempting to reassign a global variable inside a function by default actually redefines it as a local variable:

To indicate we want to modify the global count , use the global keyword:

Some key points about the global keyword:

✅ Signals that a variable is global and not local

✅ Must be declared before variable is accessed in function

❌ No need to use global when just reading values

This clears up a very common source of confusion around modifying state in functions. Now let‘s cover some other expert tips for avoiding issues when using globals.

Common Mistakes with Global Variables in Functions

Here are some other typical yet subtle bugs that can occur when handling globals inside functions:

1. Forgetting to declare global

Fix by declaring global count at start.

2. Variable hoisting issues

In languages like JavaScript, variable declarations get hoisted/raised to the top. But in Python, the global keyword only affects references:

So global must be declared before referencing.

3. Introducing side-effects

Modifying global mutable types like lists can produce unexpected results:

While convenient, mutating globals risks confusing behavior. Understanding these nuances will help you debug and architect robust Python programs.

Mutable vs Immutable Global Variables

An important aspect relating to global variables is whether the object assigned is mutable or immutable.

Mutable global variable values can be modified, like lists, dicts, sets, objects, etc. This means that if any function mutates it, the change is reflected globally.

For example:

Whereas immutable globals like numbers, strings, tuples etc. cannot be changed – so functions modifying them will not cause side effects:

Understanding this distinction helps avoid unexpected behavior when using mutable globals.

Now that you are clear on scope rules, let‘s examine some Python project data on real-world global variable usage…

To provide expert commentary on global variable usage, I analyzed top Python projects on GitHub and surveyed developers:

Global Variable Usage Statistics

After scanning over 500k lines of Python code across popular projects like Django, Flask, Pandas, etc – global variable usage broke down as:

Key things to note:

• Over half of globals are constants/config, demonstrating good practice
• But 36% were state related which poses issues if mutated unexpectedly

Developer Survey Data

Additionally, I surveyed over 100 Python developers on their opinions regarding global state:

• 67% try to avoid using globals where possible
• 49% said confusing bugs caused by globals are common
• 23% admitted to frequent overuse of global mutable state

This aligns with my recommendation to minimize global state, especially mutable state.

So when is using global variables appropriate?

Common Use Cases for Sharing State

While limiting globals is best practice in Python, here are three cases where they shine:

1. Centralized application config

Globals keep configs convenient and consistent:

2. Cached computation results

Avoiding recomputing expensive operations using globals:

3. User session data

Tracking logged in user via global variable:

There are definitely more use cases, but you should always balance convenience vs complexity.

Recommended Global Usage: Caching and Configuration

Based on my analysis, caching and configuration are prime examples of recommended global variable usage:

• Usually immutable values so avoids side effects
• Centralizes app config logic for simplicity
• Performance gains from cached computations

Use globals judiciously for these purposes. However, utilizing globals extensively for mutable program state is risky…

Now that you understand proper usage, let‘s examine downsides to be aware of.

Balancing Pros and Cons of Global Variables

While enabling convenient state sharing between functions, globals also have drawbacks to consider:

✅ Accessible anywhere so less parameter passing

✅ Conveniently persist values between calls

✅ Simplifies caching common resources

✅ Centralizes configurations

❌ Name clashes possible with local variables

❌ Dependency masking – hard to track where value is modified

❌ Mutable globals can cause unexpected side effects

❌ Excess usage leads to confusing implicit coupling

As software expert Robert C. Martin notes:

Global variables are a potential danger in software systems. Use them sparingly, if ever.

To mitigate issues, here are some expert best practices…

Based on pitfalls we reviewed about global variables in Python, here are some key best practices I recommend as a senior full stack developer:

✅ Use constants for immutable program configuration – Minimizes issues with central config object.

✅ Utilize globals sparingly – Resist overusing global mutable state and debt accumulates.

✅ Understand scope rules – Declare global to modify rather than global x = to assign.

✅ Minimize mutable global variables – Sidesteps odd behavior from functions mutating silently.

✅ Split components with too much hidden global dependence – Avoid tangled component coupling.

Adopting these global variable best practices will help you become a more effective Python programmer.

And there you have it – a comprehensive 3k word guide all about mastering global variables in Python as an expert developer would!

• Variable scoping fundamentals
• Key local vs global differences
• Properly defining and modifying globals
• Common mistakes and edge cases
• Mutable vs immutable considerations
• Real-world usage statistics
• When to leverage globals appropriately
• Downsides to balance
• And most importantly – expert best practices

You‘re now equipped with insider knowledge of global variables in Python – how to utilize them judiciously and steer clear of pitfalls.

This global variable guide serves as a definitive reference to level up your skills!

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

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

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

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Help Creating a Function

I have the following list:

people = [(‘Jake’, 36, ‘M’),(‘Rose’, 24, ‘F’),(‘Derek’, 78, ‘F’),(‘Alan’, 17, ‘M’),(‘Sarah’, 14, ‘F’)]

I want to create a function where it will only bring back a list of names for those who are >= 18 years of age. I currently have the following but I’m struggling to complete the code:

You are on the right track, but a few things are missing:

• You try to append something to a variable name that does not yet exist. I think you mean to append to people instead. Similarly, you wish to append name[0] when you mean entry[0] .
• The return statement is not right: you should return people .
• This is possibly a side-effect of pasting, but the indentation isn’t quite right.

A fixed version would be:

This can actually be achieved in much less code using a list comprehension. It may be more difficult for you to read at first if you are a beginner, but it will be very helpful to get acquainted with them:

I see you already have a reply, including the list comprehension that may be ahead of what you are expected to use now.

I will try another approach and hopefully one that is at a lower level, if that is what you need. If not, then you alredy know enough.

First, perhaps as an artifact, I am getting the wrong type of quotes in the one-liner you sent to populate the file as it was not in the ```python format of the rest of your code.

I had to replace all the open/close single quotes to get:

It is a list of tuples containing what seems to be a name/age/gender and perhaps could have been written a tad more clearly as:

The above is not important but it helps to see the internal structure so a different approach is possible.

If you loop over this, you can do what you did and get one tuple at a time and then use something like entry[1] to refer to the age in what is the second part of each tuple, but just for the heck of it, consider this code in which you deconstruct each tuple:

I am assuming from your code that you only want the names, not the full tuples. When I run the code, I get this result:

Note that if you wanted to return the entire tuples that matched, use this instead:

This version returns a subset of the list:

If you really don’t want the gender, some people would replace it with a single underscore to mean you don’t care.

The above is not a better method but just an alternate to what Alex showed. He went with your method of using subscripting and that is fine. The method I am showing may be more readable to some, or more confusing if they do not understand how python supports multiple assignments as shown.

If you have learned how to use a list comprehension, I supply my variant of what Alex suggests, again, not as better but as another way of looking at it. It only cares about name and age and ignores gender and is really internally seen as automatically building a list and returning it in a more compressed and hopefully readable way:

But note that this function is so short as a one-liner that it may not be needed at all if you use it only once. You could just have:

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Vulnerability Summary for the Week of August 26, 2024

The CISA Vulnerability Bulletin provides a summary of new vulnerabilities that have been recorded by the  National Institute of Standards and Technology  (NIST)  National Vulnerability Database  (NVD) in the past week. In some cases, the vulnerabilities in the bulletin may not yet have assigned CVSS scores. Please visit NVD for updated vulnerability entries, which include CVSS scores once they are available.

Vulnerabilities are based on the  Common Vulnerabilities and Exposures  (CVE) vulnerability naming standard and are organized according to severity, determined by the  Common Vulnerability Scoring System  (CVSS) standard. The division of high, medium, and low severities correspond to the following scores:

• High : vulnerabilities with a CVSS base score of 7.0–10.0
• Medium : vulnerabilities with a CVSS base score of 4.0–6.9
• Low : vulnerabilities with a CVSS base score of 0.0–3.9

Entries may include additional information provided by organizations and efforts sponsored by CISA. This information may include identifying information, values, definitions, and related links. Patch information is provided when available. Please note that some of the information in the bulletin is compiled from external, open-source reports and is not a direct result of CISA analysis.  

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Multiple assignment and evaluation order in Python

Suppose we have:

If we try assigning both values at once like so:

Then we get a different result than if we did the assignments separately:

Why does this happen?

See also Multiple assignment semantics regarding the effect and purpose of parentheses on the left-hand side of a multiple assignment.

See also Understand Python swapping: why is a, b = b, a not always equivalent to b, a = a, b? for more complex cases, where the order of assignment matters.

• variable-assignment
• assignment-operator
• multiple-assignment

11 Answers 11

In an assignment statement, the right-hand side is always evaluated fully before doing the actual setting of variables. So,

evaluates y (let's call the result ham ), evaluates x + y (call that spam ), then sets x to ham and y to spam . I.e., it's like

By contrast,

sets x to y , then sets y to x (which == y ) plus y , so it's equivalent to

• To relate to similar syntax one may find in a codebase x,y = y,x+y is identical to (x,y) = y,x+y , therefore the parenthesis are unnecessary. Does that sound about right? –  jxramos Commented Sep 17, 2018 at 23:13
• @jxramos Yes, the parentheses are unnecessary, but that's more of a question about tuples. –  wjandrea Commented Jun 13, 2020 at 2:51

It is explained in the docs in the section entitled "Evaluation order" :

... while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

The first expression:

• Creates a temporary tuple with value y,x+y
• Assigned in to another temporary tuple
• Extract the tuple to variables x and y

The second statement is actually two expressions, without the tuple usage.

The surprise is, the first expression is actually:

You can learn more about the usage of comma in " Parenthesized forms ".

An observation regarding the left-hand side as well: the order of assignments is guaranteed to be the order of their appearance, in other words:

is equivalent functionally to precisely (besides t creation):

This matters in cases like object attribute assignment, e.g.:

This also implies that you can do things like object creation and access using one-liners, e.g.:

• 1 Thx for this,gpt4o actually told me that "cur, cur.next = cur.next, None" and "cur.next, cur = None, cur.next" are equivalent –  J7bits Commented Jul 26 at 7:56

I've recently started using Python and this "feature" baffled me. Although there are many answers given, I'll post my understanding anyway.

If I want to swap the values of two variables, in JavaScipt, I'd do the following:

I'd need a third variable to temporarily hold one of the values. A very straightforward swap wouldn't work, because both of the variables would end up with the same value.

Imagine having two different (red and blue) buckets and having two different liquids (water and oil) in them, respectively. Now, try to swap the buckets/liquids (water in blue, and oil in red bucket). You can't do it unless you have an extra bucket.

Python deals with this with a "cleaner" way/solution: Tuple Assignment .

I guess, this way Python is creating the "temp" variables automatically and we don't have to worry about them.

• Like the link mentions "All the expressions on the right side are evaluated before any of the assignments". Thank you for the this clarity. –  Harsh Goyal Commented Jul 31, 2019 at 15:11

Other answers have already explained how it works, but I want to add a really concrete example.

In the last line, first the names are dereferenced like this:

Then the expression is evaluated:

Then the tuples are expanded and then the assignment happens, equivalent to:

In the second case, you assign x+y to x

In the first case, the second result ( x+y ) is assigned to y

This is why you obtain different results.

After your edit

This happen because, in the statement

all variables at the right member are evaluated and, then, are stored in the left members. So first proceed with right member, and second with the left member.

In the second statement

yo first evaluated y and assign it to x ; in that way, the sum of x+y is equivalent to a sum of y+y and not of x+x wich is the first case.

The first one is a tuple-like assignment:

Where x is the first element of the tuple, and y is the second element, thus what you are doing is:

Wheras the second is doing a straight assign:

For newbies, I came across this example that can help explain this:

With the multiple assignment, set initial values as a=0, b=1. In the while loop, both elements are assigned new values (hence called 'multiple' assignment). View it as (a,b) = (b,a+b). So a = b, b = a+b at each iteration of the loop. This continues while a<10.

RESULTS: 0 1 1 2 3 5 8

Let's grok the difference.

x, y = y, x + y It's x tuple xssignment, mexns (x, y) = (y, x + y) , just like (x, y) = (y, x)

Stxrt from x quick example:

When comes to (x, y) = (y, x + y) ExFP, have x try directly

The result is different from the first try.

Thx's because Python firstly evaluates the right-hand x+y So it equivxlent to:

In summary, x, y = y, x+y means, x exchanges to get old_value of y , y exchanges to get the sum of old value x and old value y ,

the variables a and b simultaneously get the new values 0 and 1 , the same a, b = b, a+b , a and b are assigned simultaneously.

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