assignment operators symbols

Python's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

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

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

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

In this tutorial, you’ll:

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

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

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

Assignment Statements and the Assignment Operator

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are a few examples of assignments in Python:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Read on to see the assignment statements in action!

Assignment Statements in Action

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

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

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

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

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

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

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

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

Consider the following examples:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

As an example, consider the following function:

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

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

Here’s how the function works:

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

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

Augmented Assignment Operators in Python

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Consider the following example of matrix multiplication using NumPy arrays:

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

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

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

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

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

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

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

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

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

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

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

These operators behave differently with mutable and immutable data types:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Other Assignment Variants

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

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

In short, you’ll learn about:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The general syntax of an assignment statement is as follows:

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

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

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

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

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

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

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

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

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

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

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

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

Here’s how you can write your class:

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

Here’s how your class works in practice:

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

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

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

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

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

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

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

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

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

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

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

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

Implicit Assignments in Python

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The same behavior appears in comprehensions:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Illegal and Dangerous Assignments in Python

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

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

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

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

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

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

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

For example, you can write something like this:

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

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

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

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

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

Consider the following example:

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

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

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

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

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

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

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

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

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

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

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

In this tutorial, you’ve learned how to:

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

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

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

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cppreference.com

Assignment operators.

Assignment operators modify the value of the object.

[ edit ] Definitions

Copy assignment replaces the contents of the object a with a copy of the contents of b ( b is not modified). For class types, this is performed in a special member function, described in copy assignment operator .

For non-class types, copy and move assignment are indistinguishable and are referred to as direct assignment .

Compound assignment replace the contents of the object a with the result of a binary operation between the previous value of a and the value of b .

[ edit ] Assignment operator syntax

The assignment expressions have the form

• ↑ target-expr must have higher precedence than an assignment expression.
• ↑ new-value cannot be a comma expression, because its precedence is lower.

[ edit ] Built-in simple assignment operator

For the built-in simple assignment, the object referred to by target-expr is modified by replacing its value with the result of new-value . target-expr must be a modifiable lvalue.

The result of a built-in simple assignment is an lvalue of the type of target-expr , referring to target-expr . If target-expr is a bit-field , the result is also a bit-field.

[ edit ] Assignment from an expression

If new-value is an expression, it is implicitly converted to the cv-unqualified type of target-expr . When target-expr is a bit-field that cannot represent the value of the expression, the resulting value of the bit-field is implementation-defined.

If target-expr and new-value identify overlapping objects, the behavior is undefined (unless the overlap is exact and the type is the same).

In overload resolution against user-defined operators , for every type T , the following function signatures participate in overload resolution:

For every enumeration or pointer to member type T , optionally volatile-qualified, the following function signature participates in overload resolution:

For every pair A1 and A2 , where A1 is an arithmetic type (optionally volatile-qualified) and A2 is a promoted arithmetic type, the following function signature participates in overload resolution:

[ edit ] Built-in compound assignment operator

The behavior of every built-in compound-assignment expression target-expr   op   =   new-value is exactly the same as the behavior of the expression target-expr   =   target-expr   op   new-value , except that target-expr is evaluated only once.

The requirements on target-expr and new-value of built-in simple assignment operators also apply. Furthermore:

• For + = and - = , the type of target-expr must be an arithmetic type or a pointer to a (possibly cv-qualified) completely-defined object type .
• For all other compound assignment operators, the type of target-expr must be an arithmetic type.

In overload resolution against user-defined operators , for every pair A1 and A2 , where A1 is an arithmetic type (optionally volatile-qualified) and A2 is a promoted arithmetic type, the following function signatures participate in overload resolution:

For every pair I1 and I2 , where I1 is an integral type (optionally volatile-qualified) and I2 is a promoted integral type, the following function signatures participate in overload resolution:

For every optionally cv-qualified object type T , the following function signatures participate in overload resolution:

[ edit ] Example

Possible output:

[ edit ] Defect reports

The following behavior-changing defect reports were applied retroactively to previously published C++ standards.

[ edit ] See also

Operator precedence

Operator overloading

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Python Assignment Operator

The = (equal to) symbol is defined as assignment operator in Python. The value of Python expression on its right is assigned to a single variable on its left. The = symbol as in programming in general (and Python in particular) should not be confused with its usage in Mathematics, where it states that the expressions on the either side of the symbol are equal.

Example of Assignment Operator in Python

Consider following Python statements −

At the first instance, at least for somebody new to programming but who knows maths, the statement "a=a+b" looks strange. How could a be equal to "a+b"? However, it needs to be reemphasized that the = symbol is an assignment operator here and not used to show the equality of LHS and RHS.

Because it is an assignment, the expression on right evaluates to 15, the value is assigned to a.

In the statement "a+=b", the two operators "+" and "=" can be combined in a "+=" operator. It is called as add and assign operator. In a single statement, it performs addition of two operands "a" and "b", and result is assigned to operand on left, i.e., "a".

Augmented Assignment Operators in Python

In addition to the simple assignment operator, Python provides few more assignment operators for advanced use. They are called cumulative or augmented assignment operators. In this chapter, we shall learn to use augmented assignment operators defined in Python.

Python has the augmented assignment operators for all arithmetic and comparison operators.

Python augmented assignment operators combines addition and assignment in one statement. Since Python supports mixed arithmetic, the two operands may be of different types. However, the type of left operand changes to the operand of on right, if it is wider.

The += operator is an augmented operator. It is also called cumulative addition operator, as it adds "b" in "a" and assigns the result back to a variable.

The following are the augmented assignment operators in Python:

• Augmented Addition Operator
• Augmented Subtraction Operator
• Augmented Multiplication Operator
• Augmented Division Operator
• Augmented Modulus Operator
• Augmented Exponent Operator
• Augmented Floor division Operator

Augmented Addition Operator (+=)

Following examples will help in understanding how the "+=" operator works −

It will produce the following output −

Augmented Subtraction Operator (-=)

Use -= symbol to perform subtract and assign operations in a single statement. The "a-=b" statement performs "a=a-b" assignment. Operands may be of any number type. Python performs implicit type casting on the object which is narrower in size.

Augmented Multiplication Operator (*=)

The "*=" operator works on similar principle. "a*=b" performs multiply and assign operations, and is equivalent to "a=a*b". In case of augmented multiplication of two complex numbers, the rule of multiplication as discussed in the previous chapter is applicable.

Augmented Division Operator (/=)

The combination symbol "/=" acts as divide and assignment operator, hence "a/=b" is equivalent to "a=a/b". The division operation of int or float operands is float. Division of two complex numbers returns a complex number. Given below are examples of augmented division operator.

Augmented Modulus Operator (%=)

To perform modulus and assignment operation in a single statement, use the %= operator. Like the mod operator, its augmented version also is not supported for complex number.

Augmented Exponent Operator (**=)

The "**=" operator results in computation of "a" raised to "b", and assigning the value back to "a". Given below are some examples −

Augmented Floor division Operator (//=)

For performing floor division and assignment in a single statement, use the "//=" operator. "a//=b" is equivalent to "a=a//b". This operator cannot be used with complex numbers.

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Assignment operators (C# reference)

• 11 contributors

The assignment operator = assigns the value of its right-hand operand to a variable, a property , or an indexer element given by its left-hand operand. The result of an assignment expression is the value assigned to the left-hand operand. The type of the right-hand operand must be the same as the type of the left-hand operand or implicitly convertible to it.

The assignment operator = is right-associative, that is, an expression of the form

is evaluated as

The following example demonstrates the usage of the assignment operator with a local variable, a property, and an indexer element as its left-hand operand:

The left-hand operand of an assignment receives the value of the right-hand operand. When the operands are of value types , assignment copies the contents of the right-hand operand. When the operands are of reference types , assignment copies the reference to the object.

This is called value assignment : the value is assigned.

ref assignment

Ref assignment = ref makes its left-hand operand an alias to the right-hand operand, as the following example demonstrates:

In the preceding example, the local reference variable arrayElement is initialized as an alias to the first array element. Then, it's ref reassigned to refer to the last array element. As it's an alias, when you update its value with an ordinary assignment operator = , the corresponding array element is also updated.

The left-hand operand of ref assignment can be a local reference variable , a ref field , and a ref , out , or in method parameter. Both operands must be of the same type.

Compound assignment

For a binary operator op , a compound assignment expression of the form

is equivalent to

except that x is only evaluated once.

Compound assignment is supported by arithmetic , Boolean logical , and bitwise logical and shift operators.

Null-coalescing assignment

You can use the null-coalescing assignment operator ??= to assign the value of its right-hand operand to its left-hand operand only if the left-hand operand evaluates to null . For more information, see the ?? and ??= operators article.

Operator overloadability

A user-defined type can't overload the assignment operator. However, a user-defined type can define an implicit conversion to another type. That way, the value of a user-defined type can be assigned to a variable, a property, or an indexer element of another type. For more information, see User-defined conversion operators .

A user-defined type can't explicitly overload a compound assignment operator. However, if a user-defined type overloads a binary operator op , the op= operator, if it exists, is also implicitly overloaded.

C# language specification

For more information, see the Assignment operators section of the C# language specification .

• C# operators and expressions
• ref keyword
• Use compound assignment (style rules IDE0054 and IDE0074)

Additional resources

Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise

Operators are special symbols that perform some operation on operands and returns the result. For example, 5 + 6 is an expression where + is an operator that performs arithmetic add operation on numeric left operand 5 and the right side operand 6 and returns a sum of two operands as a result.

Python includes the operator module that includes underlying methods for each operator. For example, the + operator calls the operator.add(a,b) method.

Above, expression 5 + 6 is equivalent to the expression operator.add(5, 6) and operator.__add__(5, 6) . Many function names are those used for special methods, without the double underscores (dunder methods). For backward compatibility, many of these have functions with the double underscores kept.

Python includes the following categories of operators:

Arithmetic Operators

Assignment operators, comparison operators, logical operators, identity operators, membership test operators, bitwise operators.

Arithmetic operators perform the common mathematical operation on the numeric operands.

The arithmetic operators return the type of result depends on the type of operands, as below.

• If either operand is a complex number, the result is converted to complex;
• If either operand is a floating point number, the result is converted to floating point;
• If both operands are integers, then the result is an integer and no conversion is needed.

The following table lists all the arithmetic operators in Python:

The assignment operators are used to assign values to variables. The following table lists all the arithmetic operators in Python:

The comparison operators compare two operands and return a boolean either True or False. The following table lists comparison operators in Python.

The logical operators are used to combine two boolean expressions. The logical operations are generally applicable to all objects, and support truth tests, identity tests, and boolean operations.

The identity operators check whether the two objects have the same id value e.i. both the objects point to the same memory location.

The membership test operators in and not in test whether the sequence has a given item or not. For the string and bytes types, x in y is True if and only if x is a substring of y .

Bitwise operators perform operations on binary operands.

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Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

Python divides the operators in the following groups:

• Arithmetic operators
• Assignment operators
• Comparison operators
• Logical operators
• Identity operators
• Membership operators
• Bitwise operators

Python Arithmetic Operators

Arithmetic operators are used with numeric values to perform common mathematical operations:

Python Assignment Operators

Assignment operators are used to assign values to variables:

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Python Comparison Operators

Comparison operators are used to compare two values:

Python Logical Operators

Logical operators are used to combine conditional statements:

Python Identity Operators

Identity operators are used to compare the objects, not if they are equal, but if they are actually the same object, with the same memory location:

Python Membership Operators

Membership operators are used to test if a sequence is presented in an object:

Python Bitwise Operators

Bitwise operators are used to compare (binary) numbers:

Operator Precedence

Operator precedence describes the order in which operations are performed.

Parentheses has the highest precedence, meaning that expressions inside parentheses must be evaluated first:

Multiplication * has higher precedence than addition + , and therefor multiplications are evaluated before additions:

The precedence order is described in the table below, starting with the highest precedence at the top:

If two operators have the same precedence, the expression is evaluated from left to right.

Addition + and subtraction - has the same precedence, and therefor we evaluate the expression from left to right:

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What are the differences between "=" and "<-" assignment operators?

What are the differences between the assignment operators = and <- in R?

I know that operators are slightly different, as this example shows

But is this the only difference?

• assignment-operator

• 68 As noted here the origins of the <- symbol come from old APL keyboards that actually had a single <- key on them. –  joran Commented Dec 12, 2014 at 17:35

9 Answers 9

The difference in assignment operators is clearer when you use them to set an argument value in a function call. For example:

In this case, x is declared within the scope of the function, so it does not exist in the user workspace.

In this case, x is declared in the user workspace, so you can use it after the function call has been completed.

There is a general preference among the R community for using <- for assignment (other than in function signatures) for compatibility with (very) old versions of S-Plus. Note that the spaces help to clarify situations like

Most R IDEs have keyboard shortcuts to make <- easier to type. Ctrl + = in Architect, Alt + - in RStudio ( Option + - under macOS), Shift + - (underscore) in emacs+ESS.

If you prefer writing = to <- but want to use the more common assignment symbol for publicly released code (on CRAN, for example), then you can use one of the tidy_* functions in the formatR package to automatically replace = with <- .

The answer to the question "Why does x <- y = 5 throw an error but not x <- y <- 5 ?" is "It's down to the magic contained in the parser". R's syntax contains many ambiguous cases that have to be resolved one way or another. The parser chooses to resolve the bits of the expression in different orders depending on whether = or <- was used.

To understand what is happening, you need to know that assignment silently returns the value that was assigned. You can see that more clearly by explicitly printing, for example print(x <- 2 + 3) .

Secondly, it's clearer if we use prefix notation for assignment. So

The parser interprets x <- y <- 5 as

We might expect that x <- y = 5 would then be

but actually it gets interpreted as

This is because = is lower precedence than <- , as shown on the ?Syntax help page.

• 14 This is also mentioned in chapter 8.2.26 of The R Inferno by Patrick Burns (Not me but a recommendation anyway) –  Uwe Commented Jun 14, 2016 at 9:17
• 6 I just realised that your explanation of how x <- x = 5 gets interpreted is slightly wrong: In reality, R interprets it as ​`<-<-`(x, y = 5, value = 5) (which itself is more or less equivalent to tmp <- x; x <- `<-<-`(tmp, y = 5, value = 5) ). Yikes! –  Konrad Rudolph Commented Jul 27, 2018 at 11:25
• 14 … And I just realised that the very first part of this answer is incorrect and, unfortunately, quite misleading because it perpetuates a common misconception: The way you use = in a function call does not perform assignment , and isn’t an assignment operator. It’s an entirely distinct parsed R expression, which just happens to use the same character. Further, the code you show does not “declare” x in the scope of the function. The function declaration performs said declaration. The function call doesn’t (it gets a bit more complicated with named ... arguments). –  Konrad Rudolph Commented Apr 12, 2019 at 10:33
• 1 @ClarkThomborson The semantics are fundamentally different because in R assignment is a regular operation which is performed via a function call to an assignment function. However, this is not the case for = in an argument list. In an argument list, = is an arbitrary separator token which is no longer present after parsing. After parsing f(x = 1) , R sees (essentially) call("f", 1) . Whereas for x = 1 R sees call("=", "x", 1) . It's true that in both cases name binding also happens but, for the assignment operator, it happens after calling the assignment operator function. –  Konrad Rudolph Commented Jan 12, 2023 at 15:35
• 2 @JAQuent, the intention wasn't that the expression median(x = 1:10) errors. It is to contrast it to side-effect of median(x <- 1:10) , which creates an assignment in the parent frame. Perhaps a bit subtle, but after the median(...) statements there is an x and that is what generates the error. After running median(x = 1:10) there is no x in the global environment (in this case), so evaluating x errors. –  LMc Commented Jun 28 at 20:56

As your example shows, = and <- have slightly different operator precedence (which determines the order of evaluation when they are mixed in the same expression). In fact, ?Syntax in R gives the following operator precedence table, from highest to lowest:

… ‘-> ->>’ rightwards assignment ‘<- <<-’ assignment (right to left) ‘=’ assignment (right to left) …

Since you were asking about the assignment operators : yes, that is the only difference. However, you would be forgiven for believing otherwise. Even the R documentation of ?assignOps claims that there are more differences:

The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

Let’s not put too fine a point on it: the R documentation is wrong . This is easy to show: we just need to find a counter-example of the = operator that isn’t (a) at the top level, nor (b) a subexpression in a braced list of expressions (i.e. {…; …} ). — Without further ado:

Clearly we’ve performed an assignment, using = , outside of contexts (a) and (b). So, why has the documentation of a core R language feature been wrong for decades?

It’s because in R’s syntax the symbol = has two distinct meanings that get routinely conflated (even by experts, including in the documentation cited above):

• The first meaning is as an assignment operator . This is all we’ve talked about so far.
• The second meaning isn’t an operator but rather a syntax token that signals named argument passing in a function call. Unlike the = operator it performs no action at runtime, it merely changes the way an expression is parsed.

So how does R decide whether a given usage of = refers to the operator or to named argument passing? Let’s see.

In any piece of code of the general form …

… the = is the token that defines named argument passing: it is not the assignment operator. Furthermore, = is entirely forbidden in some syntactic contexts:

Any of these will raise an error “unexpected '=' in ‹bla›”.

In any other context, = refers to the assignment operator call. In particular, merely putting parentheses around the subexpression makes any of the above (a) valid, and (b) an assignment . For instance, the following performs assignment:

Now you might object that such code is atrocious (and you may be right). But I took this code from the base::file.copy function (replacing <- with = ) — it’s a pervasive pattern in much of the core R codebase.

The original explanation by John Chambers , which the the R documentation is probably based on, actually explains this correctly:

[ = assignment is] allowed in only two places in the grammar: at the top level (as a complete program or user-typed expression); and when isolated from surrounding logical structure, by braces or an extra pair of parentheses.

In sum, by default the operators <- and = do the same thing. But either of them can be overridden separately to change its behaviour. By contrast, <- and -> (left-to-right assignment), though syntactically distinct, always call the same function. Overriding one also overrides the other. Knowing this is rarely practical but it can be used for some fun shenanigans .

• 5 About the precedence, and errors in R's doc, the precedence of ? is actually right in between = and <- , which has important consequences when overriding ? , and virtually none otherwise. –  moodymudskipper Commented Jan 10, 2020 at 0:12
• 2 @Moody_Mudskipper that’s bizarre! You seem to be right, but according to the source code ( main/gram.y ), the precedence of ? is correctly documented, and is lower than both = and <- . –  Konrad Rudolph Commented Jan 10, 2020 at 10:13
• 2 I like your explanation of R semantics... which I'd rephrase as follows. The "=" operator is overloaded. Its base semantics is to bind a formal name to an actual parameter in the arglist of a function call. In most (but not all!) contexts outside a function call, it has the same semantics as "<-": it binds a name to an existing object (or to a constant value), with copy-on-write semantics, with the side-effect of defining this name if it is currently undefined. In a few contexts, it is bound to stop() to warn naive or careless users who confuse it with the "==" operator. –  Clark Thomborson Commented Nov 24, 2022 at 2:50
• 2 @ClarkThomborson I don't agree with calling one of the meanings the "base" semantics, because this implies a hierarchy that doesn't exist. And I think it's confusing to call = an overloaded operator, as well: the term "operator" in R has (until R 4.0, at least!) a specific meaning referring to a function call with special syntactic rules. This isn't what = is doing when used to bind a name to an parameter name inside a function call argument list. There's no call happening, so = in this context is just a syntactic token (like ; ), not an operator. –  Konrad Rudolph Commented Nov 24, 2022 at 10:17
• 2 @ClarkThomborson Regardless of whether you find it helpful, that's precisely what it is: the syntactic = token for named parameters completely vanishes after the parsing phase, it isn't represented in the parse tree at all, nor is it evaluated. Name binding in assignment and in function calling happens fundamentally differently in R (and, to varying extents, in other languages), it isn't merely a "temporal distinction", nor is it due to operator precedence. –  Konrad Rudolph Commented Nov 24, 2022 at 15:33

Google's R style guide simplifies the issue by prohibiting the "=" for assignment. Not a bad choice.

https://google.github.io/styleguide/Rguide.xml

The R manual goes into nice detail on all 5 assignment operators.

http://stat.ethz.ch/R-manual/R-patched/library/base/html/assignOps.html

• 14 Note that any non-0 is considered TRUE by R. So if you intend to test if x is less than -y , you might write if (x<-y) which will not warn or error, and appear to work fine. It'll only be FALSE when y=0 , though. –  Matt Dowle Commented Jun 8, 2012 at 15:21
• 53 Why hurt your eyes and finger with <- if you can use = ? In 99.99% of times = is fine. Sometimes you need <<- though, which is a different history. –  Fernando Commented Oct 9, 2013 at 1:22
• 2 Besides the 0.01% which it won't work is just bad unreadable, hacky shortcut code for anyone who is not R only. It is just like doing bitshift to do a half division just to look smart (but you are not). My opinion is that R should deprecate the <- operator. –  caiohamamura Commented Oct 31, 2023 at 13:36

x = y = 5 is equivalent to x = (y = 5) , because the assignment operators "group" right to left, which works. Meaning: assign 5 to y , leaving the number 5; and then assign that 5 to x .

This is not the same as (x = y) = 5 , which doesn't work! Meaning: assign the value of y to x , leaving the value of y ; and then assign 5 to, umm..., what exactly?

When you mix the different kinds of assignment operators, <- binds tighter than = . So x = y <- 5 is interpreted as x = (y <- 5) , which is the case that makes sense.

Unfortunately, x <- y = 5 is interpreted as (x <- y) = 5 , which is the case that doesn't work!

See ?Syntax and ?assignOps for the precedence (binding) and grouping rules.

• This was a succinct answer that hit the nail on the head! –  BroVic Commented Jun 4, 2023 at 16:03

According to John Chambers, the operator = is only allowed at "the top level," which means it is not allowed in control structures like if , making the following programming error illegal.

As he writes, "Disallowing the new assignment form [=] in control expressions avoids programming errors (such as the example above) that are more likely with the equal operator than with other S assignments."

You can manage to do this if it's "isolated from surrounding logical structure, by braces or an extra pair of parentheses," so if ((x = 0)) 1 else x would work.

See http://developer.r-project.org/equalAssign.html

From the official R documentation :

The operators <- and = assign into the environment in which they are evaluated. The operator <- can be used anywhere, whereas the operator = is only allowed at the top level (e.g., in the complete expression typed at the command prompt) or as one of the subexpressions in a braced list of expressions.

• 12 I think "top level" means at the statement level, rather than the expression level. So x <- 42 on its own is a statement; in if (x <- 42) {} it would be an expression, and isn't valid. To be clear, this has nothing to do with whether you are in the global environment or not. –  Steve Pitchers Commented Sep 16, 2014 at 9:58
• 1 This: “the operator = is only allowed at the top level” is a widely held misunderstanding and completely wrong. –  Konrad Rudolph Commented Mar 2, 2017 at 14:20
• This is not true - for example, this works, even though assignment is not a complete expression: 1 + (x = 2) –  Pavel Minaev Commented Mar 5, 2017 at 22:52
• 1 To clarify the comments by KonradRudolph and PavelMinaev, I think it's too strong to say that it's completely wrong, but there is an exception, which is when it's "isolated from surrounding logical structure, by braces or an extra pair of parentheses." –  Aaron - mostly inactive Commented Oct 15, 2019 at 13:57
• 1 Or in function() x = 1 , repeat x = 1 , if (TRUE) x = 1 .... –  moodymudskipper Commented Jan 10, 2020 at 0:18

This may also add to understanding of the difference between those two operators:

For the first element R has assigned values and proper name, while the name of the second element looks a bit strange.

R version 3.3.2 (2016-10-31); macOS Sierra 10.12.1

I am not sure if Patrick Burns book R inferno has been cited here where in 8.2.26 = is not a synonym of <- Patrick states "You clearly do not want to use '<-' when you want to set an argument of a function.". The book is available at https://www.burns-stat.com/documents/books/the-r-inferno/

• 2 Yup, it has been mentioned . But the question is about the assignment operator , whereas your excerpt concerns the syntax for passing arguments. It should be made clear (because there’s substantial confusion surrounding this point) that this is not the assignment operator. –  Konrad Rudolph Commented Sep 6, 2021 at 17:27

There are some differences between <- and = in the past version of R or even the predecessor language of R (S language). But currently, it seems using = only like any other modern language (python, java) won't cause any problem. You can achieve some more functionality by using <- when passing a value to some augments while also creating a global variable at the same time but it may have weird/unwanted behavior like in

Highly recommended! Try to read this article which is the best article that tries to explain the difference between those two: Check https://colinfay.me/r-assignment/

Also, think about <- as a function that invisibly returns a value.

See: https://adv-r.hadley.nz/functions.html

• Unfortuantely Colin Fay’s article (and now your answer) repeats the common misconception about the alleged difference between = and <- . The explanation is therefore incorrect. See my answer for an exhaustive correction of this pernicious falsehood. To make it explicit: you can rewrite your first code to use = instead of <- without changing its meaning: df <- data.frame(a = rnorm(10), (b = rnorm(10))) . And just like <- , =` is a function that invisibly returns a value. –  Konrad Rudolph Commented Jan 14, 2023 at 19:04
• @Konrad Rudolph R uses some rules/principles when designing the language and code interpretation for efficiency and usability that not saw in other languages. I believe most people who ask the difference between = and <- is curious about why R has more than one assignment operator compared with other popular Science/math language such as Python. And whether I can safely just only use one = like in other languages. Besides, ( is also a function in R so technically (b = rnorm(10)) is not the same as b <- rnorm(10) since you can override the meaning of ( function in codes. –  Chunhui Gu Commented Jan 15, 2023 at 0:42
• Yes that’s all true but what does this have to do with my comment? The answer to “why” is: “purely historical”, and the answer to “can I just use = ” is “yes”. Everything else, in particular your claim that = can’t be used in some cases, is incorrect . Yes, of course you can override ( , just like you can override = and <- so, yes, technically you can redefine them so that they are no longer identical. But surely you agree that this is a pure distraction. –  Konrad Rudolph Commented Jan 15, 2023 at 11:27

Not the answer you're looking for? Browse other questions tagged r assignment-operator r-faq or ask your own question .

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• C++ Multimap
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STL - Iterators & Algorithms

• C++ Iterators
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C++ Bitwise Operators

• C++ Buffers
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C++ Tutorials

C++ Operator Precedence and Associativity

• Subtract Complex Number Using Operator Overloading
• Increment ++ and Decrement -- Operator Overloading in C++ Programming

Operators are symbols that perform operations on variables and values. For example, + is an operator used for addition, while - is an operator used for subtraction.

Operators in C++ can be classified into 6 types:

• Arithmetic Operators
• Assignment Operators
• Relational Operators
• Logical Operators
• Bitwise Operators
• Other Operators

1. C++ Arithmetic Operators

Arithmetic operators are used to perform arithmetic operations on variables and data. For example,

Here, the + operator is used to add two variables a and b . Similarly there are various other arithmetic operators in C++.

Example 1: Arithmetic Operators

Here, the operators + , - and * compute addition, subtraction, and multiplication respectively as we might have expected.

/ Division Operator

Note the operation (a / b) in our program. The / operator is the division operator.

As we can see from the above example, if an integer is divided by another integer, we will get the quotient. However, if either divisor or dividend is a floating-point number, we will get the result in decimals.

% Modulo Operator

The modulo operator % computes the remainder. When a = 9 is divided by b = 4 , the remainder is 1 .

Note: The % operator can only be used with integers.

• Increment and Decrement Operators

C++ also provides increment and decrement operators: ++ and -- respectively.

• ++ increases the value of the operand by 1
• -- decreases it by 1

For example,

Here, the code ++num; increases the value of num by 1 .

Example 2: Increment and Decrement Operators

In the above program, we have used the ++ and -- operators as prefixes (++a and --b) . However, we can also use these operators as postfix (a++ and b--) .

To learn more, visit increment and decrement operators .

2. C++ Assignment Operators

In C++, assignment operators are used to assign values to variables. For example,

Here, we have assigned a value of 5 to the variable a .

Example 3: Assignment Operators

3. c++ relational operators.

A relational operator is used to check the relationship between two operands. For example,

Here, > is a relational operator. It checks if a is greater than b or not.

If the relation is true , it returns 1 whereas if the relation is false , it returns 0 .

Example 4: Relational Operators

Note : Relational operators are used in decision-making and loops.

4. C++ Logical Operators

Logical operators are used to check whether an expression is true or false . If the expression is true , it returns 1 whereas if the expression is false , it returns 0 .

In C++, logical operators are commonly used in decision making. To further understand the logical operators, let's see the following examples,

Example 5: Logical Operators

Explanation of logical operator program

• (3 != 5) && (3 < 5) evaluates to 1 because both operands (3 != 5) and (3 < 5) are 1 (true).
• (3 == 5) && (3 < 5) evaluates to 0 because the operand (3 == 5) is 0 (false).
• (3 == 5) && (3 > 5) evaluates to 0 because both operands (3 == 5) and (3 > 5) are 0 (false).
• (3 != 5) || (3 < 5) evaluates to 1 because both operands (3 != 5) and (3 < 5) are 1 (true).
• (3 != 5) || (3 > 5) evaluates to 1 because the operand (3 != 5) is 1 (true).
• (3 == 5) || (3 > 5) evaluates to 0 because both operands (3 == 5) and (3 > 5) are 0 (false).
• !(5 == 2) evaluates to 1 because the operand (5 == 2) is 0 (false).
• !(5 == 5) evaluates to 0 because the operand (5 == 5) is 1 (true).

5. C++ Bitwise Operators

In C++, bitwise operators are used to perform operations on individual bits. They can only be used alongside char and int data types.

To learn more, visit C++ bitwise operators .

6. Other C++ Operators

Here's a list of some other common operators available in C++. We will learn about them in later tutorials.

• C++ Operators Precedence and Associativity

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What are Operators in Programming?

Operators in programming are essential symbols that perform operations on variables and values , enabling tasks like arithmetic calculations, logical comparisons, and bitwise manipulations. In this article, we will learn about the basics of operators and their types.

Operators in Programming

Table of Content

• Types of Operators in Programming
• Operator Precedence and Associativity in Programming
• Frequently Asked Questions (FAQs) related to Programming Operators

Operators in programming are symbols or keywords that represent computations or actions performed on operands. Operands can be variables , constants , or values , and the combination of operators and operands form expressions. Operators play a crucial role in performing various tasks, such as arithmetic calculations, logical comparisons, bitwise operations, etc.

Types of Operators in Programming:

Here are some common types of operators:

• Arithmetic Operators: Perform basic arithmetic operations on numeric values. Examples: + (addition), – (subtraction), * (multiplication), / (division), % (modulo).
• Comparison Operators: Compare two values and return a Boolean result (true or false). Examples: == (equal to), != (not equal to), < (less than), > (greater than), <= (less than or equal to), >= (greater than or equal to).
• Logical Operators: Perform logical operations on Boolean values. Examples: && (logical AND), || (logical OR), ! (logical NOT).
• Assignment Operators: Assign values to variables. Examples: = (assign), += (add and assign), -=, *= (multiply and assign), /=, %=.
• Increment and Decrement Operators: Increase or decrease the value of a variable by 1. Examples: ++ (increment), — (decrement).
• Bitwise Operators: Perform operations on individual bits of binary representations of numbers. Examples: & (bitwise AND), | (bitwise OR), ^ (bitwise XOR), ~ (bitwise NOT), << (left shift), >> (right shift).

These operators provide the building blocks for creating complex expressions and performing diverse operations in programming languages. Understanding their usage is crucial for writing efficient and expressive code.

Arithmetic Operators in Programming:

Arithmetic operators in programming are fundamental components of programming languages, enabling the manipulation of numeric values for various computational tasks. Here’s an elaboration on the key arithmetic operators:

Comparison Operators in Programming:

Comparison operators in programming are used to compare two values or expressions and return a Boolean result indicating the relationship between them. These operators play a crucial role in decision-making and conditional statements. Here are the common comparison operators:

These operators are extensively used in conditional statements, loops, and decision-making constructs to control the flow of a program based on the relationship between variables or values. Understanding comparison operators is crucial for creating logical and effective algorithms in programming.

Logical Operators in Programming:

Logical operators in programming are used to perform logical operations on Boolean values . These operators are crucial for combining or manipulating conditions and controlling the flow of a program based on logical expressions. Here are the common logical operators:

These logical operators are frequently used in conditional statements (if, else if, else), loops, and decision-making constructs to create complex conditions based on multiple Boolean expressions. Understanding how to use logical operators is essential for designing effective and readable control flow in programming.

Assignment Operators in Programming:

Assignment operators in programming are used to assign values to variables. They are essential for storing and updating data within a program. Here are common assignment operators:

Assignment operators are fundamental for updating variable values, especially in loops and mathematical computations, contributing to the dynamic nature of programming. Understanding how to use assignment operators is essential for effective variable manipulation in a program.

Increment and Decrement Operators in Programming:

Increment and decrement operators in programming are used to increase or decrease the value of a variable by 1, respectively. They are shorthand notations for common operations and are particularly useful in loops. Here are the two types:

These operators are frequently employed in loops, especially for iterating through arrays or performing repetitive tasks. Their concise syntax enhances code readability and expressiveness.

Bitwise Operators in Programming:

Bitwise operators in programming perform operations at the bit level , manipulating individual bits of binary representations of numbers. These operators are often used in low-level programming, such as embedded systems and device drivers. Here are the common bitwise operators:

Bitwise operators are useful in scenarios where direct manipulation of binary representations or specific bit patterns is required, such as optimizing certain algorithms or working with hardware interfaces. Understanding bitwise operations is essential for low-level programming tasks.

Operator Precedence and Associativity in Programming :

Operator Precedence is a rule that determines the order in which operators are evaluated in an expression. It defines which operators take precedence over others when they are combined in the same expression. Operators with higher precedence are evaluated before operators with lower precedence. Parentheses can be used to override the default precedence and explicitly specify the order of evaluation.

Operator Associativity is a rule that determines the grouping of operators with the same precedence in an expression when they appear consecutively. It specifies the direction in which operators of equal precedence are evaluated. The two common associativities are:

• Left to Right (Left-Associative): Operators with left associativity are evaluated from left to right. For example, in the expression a + b + c , the addition operators have left associativity, so the expression is equivalent to (a + b) + c .
• Right to Left (Right-Associative): Operators with right associativity are evaluated from right to left. For example, in the expression a = b = c , the assignment operator = has right associativity, so the expression is equivalent to a = (b = c) .

Frequently Asked Questions (FAQs) related to Programming Operators :

Here are some frequently asked questions (FAQs) related to programming operators:

Q1: What are operators in programming?

A: Operators in programming are symbols that represent computations or actions to be performed on operands. They can manipulate data, perform calculations, and facilitate various operations in a program.

Q2: How are operators categorized?

A: Operators are categorized based on their functionality. Common categories include arithmetic operators (for mathematical operations), assignment operators (for assigning values), comparison operators (for comparing values), logical operators (for logical operations), and bitwise operators (for manipulating individual bits).

Q3: What is the difference between unary and binary operators?

A: Unary operators operate on a single operand, while binary operators operate on two operands. For example, the unary minus -x negates the value of x , while the binary plus a + b adds the values of a and b .

Q4: Can operators be overloaded in programming languages?

A: Yes, some programming languages support operator overloading, allowing developers to define custom behaviors for operators when applied to user-defined types. This is commonly seen in languages like C++.

Q5: How do logical AND ( && ) and logical OR ( || ) operators work?

A: The logical AND ( && ) operator returns true if both of its operands are true. The logical OR ( || ) operator returns true if at least one of its operands is true. These operators are often used in conditional statements and expressions.

Q6: What is the purpose of the ternary operator ( ?: )?

A: The ternary operator is a shorthand for an if-else statement. It evaluates a condition and returns one of two values based on whether the condition is true or false. It is often used for concise conditional assignments.

Q7: How does the bitwise XOR operator ( ^ ) work?

A: The bitwise XOR ( ^ ) operator performs an exclusive OR operation on individual bits. It returns 1 for bits that are different and 0 for bits that are the same. This operator is commonly used in bit manipulation tasks.

Q8: What is the difference between = and == ?

A: The = operator is an assignment operator used to assign a value to a variable. The == operator is a comparison operator used to check if two values are equal. It is important not to confuse the two, as = is used for assignment, and == is used for comparison.

Q9: How do increment ( ++ ) and decrement ( -- ) operators work?

A: The increment ( ++ ) operator adds 1 to the value of a variable, while the decrement ( -- ) operator subtracts 1. These operators can be used as prefix ( ++x ) or postfix ( x++ ), affecting the order of the increment or decrement operation.

Q10: Are operators language-specific?

A: While many operators are common across programming languages, some languages may introduce unique operators or have variations in their behavior. However, the fundamental concepts of arithmetic, logical, and bitwise operations are prevalent across various languages.

Q11: What is the modulus operator ( % ) used for?

A: The modulus operator ( % ) returns the remainder when one number is divided by another. It is often used to check divisibility or to cycle through a sequence of values. For example, a % 2 can be used to determine if a is an even or odd number.

Q12: Can the same operator have different meanings in different programming languages?

A: Yes, the same symbol may represent different operators or operations in different programming languages. For example, the + operator is used for addition in most languages, but in some languages (like JavaScript), it is also used for string concatenation.

Q13: What is short-circuit evaluation in the context of logical operators?

A: Short-circuit evaluation is a behavior where the second operand of a logical AND ( && ) or logical OR ( || ) operator is not evaluated if the outcome can be determined by the value of the first operand alone. This can lead to more efficient code execution.

Q14: How are bitwise left shift ( << ) and right shift ( >> ) operators used?

A: The bitwise left shift ( << ) operator shifts the bits of a binary number to the left, effectively multiplying the number by 2. The bitwise right shift ( >> ) operator shifts the bits to the right, effectively dividing the number by 2.

Q15: Can you provide an example of operator precedence in programming?

A: Operator precedence determines the order in which operators are evaluated. For example, in the expression a + b * c , the multiplication ( * ) has higher precedence than addition ( + ), so b * c is evaluated first.

Q16: How does the ternary operator differ from an if-else statement?

A: The ternary operator ( ?: ) is a concise way to express a conditional statement with a single line of code. It returns one of two values based on a condition. An if-else statement provides a more extensive code block for handling multiple statements based on a condition.

Q17: Are there any operators specifically designed for working with arrays or collections?

A: Some programming languages provide specific operators or methods for working with arrays or collections. For example, in Python, the in operator is used to check if an element is present in a list.

Q18: How can bitwise operators be used for efficient memory management?

A: Bitwise operators are often used for efficient memory management by manipulating individual bits. For example, bitwise AND can be used to mask specific bits, and bitwise OR can be used to set particular bits.

Q19: Can operators be overloaded in all programming languages?

A: No, not all programming languages support operator overloading. While some languages like C++ allow developers to redefine the behavior of operators for user-defined types, others, like Java, do not permit operator overloading.

Q20: How do you handle operator precedence in complex expressions?

A: Parentheses are used to explicitly specify the order of operations in complex expressions, ensuring that certain operations are performed before others. For example, (a + b) * c ensures that addition is performed before multiplication.

In conclusion, operators in programming are essential tools that enable the manipulation, comparison, and logical operations on variables and values. They form the building blocks of expressions and play a fundamental role in controlling program flow, making decisions, and performing calculations. From arithmetic and comparison operators for numerical tasks to logical operators for decision-making, and bitwise operators for low-level bit manipulation, each type serves specific purposes in diverse programming scenarios.

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