assignment statement syntax

Python's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

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

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

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

In this tutorial, you’ll:

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

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

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

Assignment Statements and the Assignment Operator

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are a few examples of assignments in Python:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Read on to see the assignment statements in action!

Assignment Statements in Action

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

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

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

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

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

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

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

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

Consider the following examples:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

As an example, consider the following function:

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

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

Here’s how the function works:

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

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

Augmented Assignment Operators in Python

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Consider the following example of matrix multiplication using NumPy arrays:

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

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

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

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

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

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

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

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

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

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

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

These operators behave differently with mutable and immutable data types:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Other Assignment Variants

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

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

In short, you’ll learn about:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The general syntax of an assignment statement is as follows:

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

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

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

For a deep dive into this special type of assignment, check out The Walrus Operator: Python 3.8 Assignment Expressions .

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

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

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

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

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

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

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

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

Here’s how you can write your class:

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

Here’s how your class works in practice:

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

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

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

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

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

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

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

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

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

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

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

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

Implicit Assignments in Python

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The same behavior appears in comprehensions:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Illegal and Dangerous Assignments in Python

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

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

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

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

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

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

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

For example, you can write something like this:

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

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

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

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

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

Consider the following example:

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

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

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

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

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

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

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

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

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

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

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

In this tutorial, you’ve learned how to:

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

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

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

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Different Forms of Assignment Statements in Python

• Statement, Indentation and Comment in Python
• Conditional Statements in Python
• Assignment Operators in Python
• Loops and Control Statements (continue, break and pass) in Python
• Different Ways of Using Inline if in Python
• Difference between "__eq__" VS "is" VS "==" in Python
• Augmented Assignment Operators in Python
• Nested-if statement in Python
• How to write memory efficient classes in Python?
• Difference Between List and Tuple in Python
• A += B Assignment Riddle in Python
• Difference between List VS Set VS Tuple in Python
• Assign Function to a Variable in Python
• Python pass Statement
• Python If Else Statements - Conditional Statements
• Data Classes in Python | Set 5 (post-init)
• Assigning multiple variables in one line in Python
• Assignment Operators in Programming
• What is the difference between = (Assignment) and == (Equal to) operators

We use Python assignment statements to assign objects to names. The target of an assignment statement is written on the left side of the equal sign (=), and the object on the right can be an arbitrary expression that computes an object.

There are some important properties of assignment in Python :-

• Assignment creates object references instead of copying the objects.
• Python creates a variable name the first time when they are assigned a value.
• Names must be assigned before being referenced.
• There are some operations that perform assignments implicitly.

Assignment statement forms :-

1. Basic form:

This form is the most common form.

2. Tuple assignment:

When we code a tuple on the left side of the =, Python pairs objects on the right side with targets on the left by position and assigns them from left to right. Therefore, the values of x and y are 50 and 100 respectively.

3. List assignment:

This works in the same way as the tuple assignment.

4. Sequence assignment:

In recent version of Python, tuple and list assignment have been generalized into instances of what we now call sequence assignment – any sequence of names can be assigned to any sequence of values, and Python assigns the items one at a time by position.

5. Extended Sequence unpacking:

It allows us to be more flexible in how we select portions of a sequence to assign.

Here, p is matched with the first character in the string on the right and q with the rest. The starred name (*q) is assigned a list, which collects all items in the sequence not assigned to other names.

This is especially handy for a common coding pattern such as splitting a sequence and accessing its front and rest part.

6. Multiple- target assignment:

In this form, Python assigns a reference to the same object (the object which is rightmost) to all the target on the left.

7. Augmented assignment :

The augmented assignment is a shorthand assignment that combines an expression and an assignment.

There are several other augmented assignment forms:

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What kind of Experience do you want to share?

• Assignment Statement

An Assignment statement is a statement that is used to set a value to the variable name in a program .

Assignment statement allows a variable to hold different types of values during its program lifespan. Another way of understanding an assignment statement is, it stores a value in the memory location which is denoted by a variable name.

The symbol used in an assignment statement is called as an operator . The symbol is ‘=’ .

Note: The Assignment Operator should never be used for Equality purpose which is double equal sign ‘==’.

The Basic Syntax of Assignment Statement in a programming language is :

variable = expression ;

variable = variable name

expression = it could be either a direct value or a math expression/formula or a function call

Few programming languages such as Java, C, C++ require data type to be specified for the variable, so that it is easy to allocate memory space and store those values during program execution.

data_type variable_name = value ;

In the above-given examples, Variable ‘a’ is assigned a value in the same statement as per its defined data type. A data type is only declared for Variable ‘b’. In the 3 rd line of code, Variable ‘a’ is reassigned the value 25. The 4 th line of code assigns the value for Variable ‘b’.

Assignment Statement Forms

This is one of the most common forms of Assignment Statements. Here the Variable name is defined, initialized, and assigned a value in the same statement. This form is generally used when we want to use the Variable quite a few times and we do not want to change its value very frequently.

Tuple Assignment

Generally, we use this form when we want to define and assign values for more than 1 variable at the same time. This saves time and is an easy method. Note that here every individual variable has a different value assigned to it.

(Code In Python)

Sequence Assignment

(Code in Python)

Multiple-target Assignment or Chain Assignment

In this format, a single value is assigned to two or more variables.

Augmented Assignment

In this format, we use the combination of mathematical expressions and values for the Variable. Other augmented Assignment forms are: &=, -=, **=, etc.

Browse more Topics Under Data Types, Variables and Constants

• Concept of Data types
• Built-in Data Types
• Constants in Programing Language 
• Access Modifier
• Variables of Built-in-Datatypes
• Declaration/Initialization of Variables
• Type Modifier

Few Rules for Assignment Statement

Few Rules to be followed while writing the Assignment Statements are:

• Variable names must begin with a letter, underscore, non-number character. Each language has its own conventions.
• The Data type defined and the variable value must match.
• A variable name once defined can only be used once in the program. You cannot define it again to store other types of value.
• If you assign a new value to an existing variable, it will overwrite the previous value and assign the new value.

FAQs on Assignment Statement

Q1. Which of the following shows the syntax of an  assignment statement ?

• variablename = expression ;
• expression = variable ;
• datatype = variablename ;
• expression = datatype variable ;

Answer – Option A.

Q2. What is an expression ?

• Same as statement
• List of statements that make up a program
• Combination of literals, operators, variables, math formulas used to calculate a value
• Numbers expressed in digits

Answer – Option C.

Q3. What are the two steps that take place when an  assignment statement  is executed?

• Evaluate the expression, store the value in the variable
• Reserve memory, fill it with value
• Evaluate variable, store the result
• Store the value in the variable, evaluate the expression.

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How To Use Assignment Expressions in Python

DavidMuller and Kathryn Hancox

The author selected the COVID-19 Relief Fund to receive a donation as part of the Write for DOnations program.

Introduction

Python 3.8 , released in October 2019, adds assignment expressions to Python via the := syntax. The assignment expression syntax is also sometimes called “the walrus operator” because := vaguely resembles a walrus with tusks.

Assignment expressions allow variable assignments to occur inside of larger expressions. While assignment expressions are never strictly necessary to write correct Python code, they can help make existing Python code more concise. For example, assignment expressions using the := syntax allow variables to be assigned inside of if statements , which can often produce shorter and more compact sections of Python code by eliminating variable assignments in lines preceding or following the if statement.

In this tutorial, you will use assignment expressions in several examples to produce concise sections of code.

Prerequisites

To get the most out of this tutorial, you will need:

Python 3.8 or above. Assignment expressions are a new feature added starting in Python 3.8. You can view the How To Install Python 3 and Set Up a Programming Environment on an Ubuntu 18.04 Server tutorial for help installing and upgrading Python.

The Python Interactive Console. If you would like to try out the example code in this tutorial you can use the How To Work with the Python Interactive Console tutorial.

Some familiarity with while loops, if statements, list comprehensions, and functions in Python 3 is useful, but not necessary. You can review our How To Code in Python 3 tutorial series for background knowledge.

Using Assignment Expressions in if Statements

Let’s start with an example of how you can use assignment expressions in an if statement.

Consider the following code that checks the length of a list and prints a statement:

If you run the previous code, you will receive the following output:

You initialize a list named some_list that contains three elements. Then, the if statement uses the assignment expression ((list_length := len(some_list)) to bind the variable named list_length to the length of some_list . The if statement evaluates to True because list_length is greater than 2 . You print a string using the list_length variable, which you bound initially with the assignment expression, indicating the the three-element list is too long.

Note: Assignment expressions are a new feature introduced in Python 3.8 . To run the examples in this tutorial, you will need to use Python 3.8 or higher.

Had we not used assignment expression, our code might have been slightly longer. For example:

This code sample is equivalent to the first example, but this code requires one extra standalone line to bind the value of list_length to len(some_list) .

Another equivalent code sample might just compute len(some_list) twice: once in the if statement and once in the print statement. This would avoid incurring the extra line required to bind a variable to the value of len(some_list) :

Assignment expressions help avoid the extra line or the double calculation.

Note: Assignment expressions are a helpful tool, but are not strictly necessary. Use your judgement and add assignment expressions to your code when it significantly improves the readability of a passage.

In the next section, we’ll explore using assignment expressions inside of while loops.

Using Assignment Expressions in while Loops

Assignment expressions often work well in while loops because they allow us to fold more context into the loop condition.

Consider the following example that embeds a user input function inside the while loop condition:

If you run this code, Python will continually prompt you for text input from your keyboard until you type the word stop . One example session might look like:

The assignment expression (directive := input("Enter text: ")) binds the value of directive to the value retrieved from the user via the input function. You bind the return value to the variable directive , which you print out in the body of the while loop. The while loop exits whenever the you type stop .

Had you not used an assignment expression, you might have written an equivalent input loop like:

This code is functionally identical to the one with assignment expressions, but requires four total lines (as opposed to two lines). It also duplicates the input("Enter text: ") call in two places. Certainly, there are many ways to write an equivalent while loop, but the assignment expression variant introduced earlier is compact and captures the program’s intention well.

So far, you’ve used assignment expression in if statements and while loops. In the next section, you’ll use an assignment expression inside of a list comprehension.

Using Assignment Expressions in List Comprehensions

We can also use assignment expressions in list comprehensions . List comprehensions allow you to build lists succinctly by iterating over a sequence and potentially adding elements to the list that satisfy some condition. Like list comprehensions, we can use assignment expressions to improve readability and make our code more concise.

Consider the following example that uses a list comprehension and an assignment expression to build a list of multiplied integers:

If you run the previous code, you will receive the following:

You define a function named slow_calculation that multiplies the given number x with itself. A list comprehension then iterates through 0 , 1 , and 2 returned by range(3) . An assignment expression binds the value result to the return of slow_calculation with i . You add the result to the newly built list as long as it is greater than 0. In this example, 0 , 1 , and 2 are all multiplied with themselves, but only the results 1 ( 1 * 1 ) and 4 ( 2 * 2 ) satisfy the greater than 0 condition and become part of the final list [1, 4] .

The slow_calculation function isn’t necessarily slow in absolute terms, but is meant to illustrate an important point about effeciency. Consider an alternate implementation of the previous example without assignment expressions:

Running this, you will receive the following output:

In this variant of the previous code, you use no assignment expressions. Instead, you call slow_calculation up to two times: once to ensure slow_calculation(i) is greater than 0 , and potentially a second time to add the result of the calculation to the final list. 0 is only multiplied with itself once because 0 * 0 is not greater than 0 . The other results, however, are doubly calculated because they satisfy the greater than 0 condition, and then have their results recalculated to become part of the final list [1, 4] .

You’ve now combined assignment expressions with list comprehensions to create blocks of code that are both efficient and concise.

In this tutorial, you used assignment expressions to make compact sections of Python code that assign values to variables inside of if statements, while loops, and list comprehensions.

For more information on other assignment expressions, you can view PEP 572 —the document that initially proposed adding assignment expressions to Python.

You may also want to check out our other Python content on our topic page .

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Assignment Statement

The assignment statement is an instruction that stores a value in a variable . You use this instruction any time you want to update the value of a variable.

The assignment statement performs two actions. First, it calculates the value of the expression (calculation) on the right-hand side of the assignment operator (the = ). Once it has the value, it stores the value (assigns it) to the variable on the left-hand side of the assignment operator.

Assignment Statement — when, why, and how

When you create a variable, you have identified a piece of information that you want to be able to change as your program runs. Whenever you need to give a variable an initial or new value, you use an assignment statement .

The assignment statement uses the assignment operator = . Whatever is on the right-hand side of = represents the value to be assigned. This could be a literal , a method call , or any other expression . On the left-hand side you write the identifier of the variable you want to store this value in.

For example, you might decide to ask the user for their name. First, you need a variable to store the value. You might decide to call this variable name . Then, the assignment statement lets you read a response from the user and store it in that variable. In this case, the right-hand side of the assignment would be a call to ReadLine , which reads input from standard in and returns it to you. The left-hand side would be the identifier of our variable, name .

It is important to remember that every assignment statement has 2 actions :

• Calculate the value on the right-hand side
• Store it in the variable on the left-hand side.

The ordering of these actions allow you to update the value of a variable using an expression involving the variable being updated. This can be very useful. For example, you might want to update the value of a variable storing the number of steps you have taken today.

In C# the assignment operator is = . Most assignment statements are written using = , with an identifier on the left-hand side and an expression on the right-hand side. The assignment operator can optionally be modified with + , - , * , or / , which are shorthands for adding to, subtracting from, multiplying, and dividing the variable identified on the left-hand side of the statement.

Some assignment statements are written without = . These are assignment statements using increment ( ++ ) or decrement ( -- ), which allow you to add or remove one from a variable’s current value.

For example, x = x - 1 , x -= 1 , and x-- are all assignment statements which do the same thing — assign the variable x a new value that is one lower than its current value.

Basic assignment statement

In this example we use ReadLine to get input from the user and store it in a name variable.

Shorthand assignment statements

The following code shows an example of how to use some of the shorthand assignment statements.

If you ran the above code and entered 17 as the start count you should get this output:

You do not always need to store values in variables. Sometimes you can just use the value and then forget it. For example, in the above code, we read the initial count from the user. This requires us to read it as text, and then convert that text to a number. Given that we do not ever use the details in line again, we do not need to create this variable in the first place. Instead, we could pass the value to the convert function directly as shown below.

Assignment statement up close

The following sliders show how the assignment statement works in detail. These are both relatively simple programs, but notice how much is going on behind the scenes!

Assigning an int division result to an int variable

Assigning an int division result to a double variable.

Assignment Statements

Learn about assignment statements in Python.

• Assignment shortcuts
• Walrus operator

Assignment statements consist of a variable , an equal sign, and an expression .

Here’s an example:

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Assignment Statements

Assignment statements in a program come in two forms – with and without mutations. Assignments without mutation are those that give a value to a variable without using the old value of that variable. Assignments with mutation are variable assignments that use the old value of a variable to calculate a value for the variable.

For example, an increment statement like x = x + 1 MUTATES the value of x by updating its value to be one bigger than it was before. In order to make sense of such a statement, we need to know the previous value of x .

In contrast, a statement like y = x + 1 assigns to y one more than the value in x . We do not need to know the previous value of y , as we are not using it in the assignment statement. (We do need to know the value of x ).

Assignments without mutation

We have already seen the steps necessary to process assignment statements that do not involve variable mutation. Recall that we can declare as a Premise any assignment statement or claim from a previous proof block involving variables that have not since changed.

For example, suppose we want to verify the following program so the assert statement at the end will hold (this example again eliminates the Logika mode notation and the necessary import statements, which we will continue to do in subsequent examples):

Since none of the statements involve variable mutation, we can do the verification in a single proof block:

Note that we did need to do AndI so that the last claim was y == z ∧ y == 6 , even though we had previously established the claims y == z and y == 6 . In order for an assert to hold (at least until we switch Logika modes in chapter 10), we need to have established EXACTLY the claim in the assert in a previous proof block.

Assignments with mutation

Assignments with mutation are trickier – we need to know the old value of a variable in order to reason about its new value. For example, if we have the following program:

Then we might try to add the following proof blocks:

…but then we get stuck in the second proof block. There, x is supposed to refer to the CURRENT value of x (after being incremented), but both our attempted claims are untrue. The current value of x is not one more than itself (this makes no sense!), and we can tell from reading the code that x is now 5, not 4.

To help reason about changing variables, Logika has a special Old(varName) function that refers to the OLD value of a variable called varName , just before the latest update. In the example above, we can use Old(x) in the second proof block to refer to x ’s value just before it was incremented. We can now change our premises and finish the verification as follows:

By the end of the proof block following a variable mutation, we need to express everything we know about the variable’s current value WITHOUT using the Old terminology, as its scope will end when the proof block ends. Moreover, we only ever have one Old value available in a proof block – the variable that was most recently changed. This means we will need proof blocks after each variable mutation to process the changes to any related facts.

Variable swap example

Suppose we have the following program:

We can see that this program gets two user input values, x and y , and then swaps their values. So if x was originally 4 and y was originally 6, then at the end of the program x would be 6 and y would be 4.

We would like to be able to assert what we did – that x now has the original value from y , and that y now has the original value from x . To do this, we might invent dummy constants called xOrig and yOrig that represent the original values of those variables. Then we can add our assert:

We can complete the verification by adding proof blocks after assignment statements, being careful to update all we know (without using the Old value) by the end of each block:

Notice that in each proof block, we express as much as we can about all variables/values in the program. In the first proof block, even though xOrig and yOrig were not used in the previous assignment statement, we still expressed how the current values our other variables compared to xOrig and yOrig . It helps to think about what you are trying to claim in the final assert – since our assert involved xOrig and yOrig , we needed to relate the current values of our variables to those values as we progressed through the program.

Last modified by: Julie Thornton Apr 25, 2024

On-line Guides How To Guides        Statement   Chapter 6. Variables, Assignment and Input   The Assignment Statement Assignment is fundamental to Python; it is how the objects created by an expression are preserved. We'll look at the basic assignment statement, plus the augmented assignment statement. Later, in Multiple Assignment Statement , we'll look at multiple assignment. Basic Assignment We create and change variables primarily with the assignment statement. This statement provides an expression and a variable name which will be used to label the value of the expression. variable = expression Here's a short script that contains some examples of assignment statements. Example 6.1. example3.py We have an object, the number 150 , which we assign to the variable shares . We have an expression 3+5.0/8.0 , which creates a floating-point number, which we save in the variable price . We have another expression, shares * price , which creates a floating-point number; we save this in value so that we can print it. This script created three new variables. Since this file is new, we'll need to do the chmod +x example3.py once, after we create this file. Then, when we run this progam, we see the following. Augmented Assignment Any of the usual arithmetic operations can be combined with assignment to create an augmented assignment statement. For example, look at this augmented assignment statement: This statement is a shorthand that means the same thing as the following: Here's a larger example Example 6.2. portfolio.py First, we'll do the chmod +x portfolio.py on this file. Then, when we run this progam, we see the following. The other basic math operations can be used similarly, although the purpose gets obscure for some operations. These include -=, *=, /=, %=, &=, ^=, |=, <<= and >>=. Here's a lengthy example. This is an extension of Craps Odds in the section called “Numeric Types and Expressions” . In craps, the first roll of the dice is called the “ come out roll ”. This roll can be won immediately if one rolls 7 or 11. It can be lost immediately if one roll 2, 3 or 12. The remaining numbers establish a point and the game continues. Example 6.3. craps.py There's a 22.2% chance of winning, and a 11.1% chance of losing. What's the chance of establishing a point? One way is to figure that it's what's left after winning or loosing. The total of all probabilities always add to 1. Subtract the odds of winning and the odds of losing and what's left is the odds of setting a point. Here's another way to figure the odds of rolling 4, 5, 6, 8, 9 or 10. By the way, you can add the statement print win + lose + point to confirm that these odds all add to 1. This means that we have defined all possible outcomes for the come out roll in craps. Tracing Execution We can trace the execution of a program by simply following the changes of value of all the variables in the program. Here's an example for Example 6.3, “craps.py” win: 0 , 0.16 , 0.22 list: 0 , 0.027 , 0.083 , 0.111 point: 1 , 0.77 , 0.66 As with many things Python, there is some additional subtlety to this, but we'll cover those topics later. For example, multiple-assignment statement is something we'll look into in more deeply in Chapter 13, Tuples .     Chapter 6. Variables, Assignment and Input   Input Functions   Published under the terms of the Open Publication License    Python Tutorial File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python assignment operators. Assignment operators are used to assign values to variables: Operator Example Same As Try it = x = 5 x = 5 += x += 3 x = x + 3 -= x -= 3 x = x - 3 *= x *= 3 x = x * 3 /= x /= 3 x = x / 3 %= x %= 3 x = x % 3 //= x //= 3 x = x // 3 **= x **= 3 x = x ** 3 &= x &= 3 x = x & 3 |= x |= 3 x = x | 3 ^= x ^= 3 x = x ^ 3 >>= x >>= 3 x = x >> 3 <<= x <<= 3 x = x << 3 Related Pages COLOR PICKER Contact Sales If you want to use W3Schools services as an educational institution, team or enterprise, send us an e-mail: [email protected] Report Error If you want to report an error, or if you want to make a suggestion, send us an e-mail: [email protected] Top Tutorials Top references, top examples, get certified. Python Enhancement Proposals Python » PEP Index » PEP 572 – Assignment Expressions The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics. This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr . As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value). During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally). Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts. Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer. During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other. The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”. The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”). Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below. However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code. Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines. Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing: they would write: Another example illustrates that programmers sometimes do more work to save an extra level of indentation: This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been: Syntax and semantics In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier. The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value: There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion: This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid. Again, this rule is included to avoid two visually similar ways of saying the same thing. This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already. This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet). The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly. This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code. This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended. An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose. There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope. The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example: Second, it allows a compact way of updating mutable state from a comprehension, for example: However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead. For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too: While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour. This restriction applies even if the assignment expression is never executed: For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions. Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword): A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError : (The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.) See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code. The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used. The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument. Some examples to clarify what’s technically valid or invalid: Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable: This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.) In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order: In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.) Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions. Conversely, assignment expressions don’t support the advanced features found in assignment statements: Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0))) Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = [] Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1 Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax) The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released: for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3] due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases. Examples from the Python standard library env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block. Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base Avoid nested if and remove one indentation level. Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.) Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls ) Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls ) tz is only used for s += tz , moving its assignment inside the if helps to show its scope. Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand. Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0 Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0 A list comprehension can map and filter efficiently by capturing the condition: Similarly, a subexpression can be reused within the main expression, by giving it a name on first use: Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ). Assignment expressions can be used to good effect in the header of an if or while statement: Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value. An example from the low-level UNIX world: Rejected alternative proposals Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above. A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions. Broadly the same semantics as the current proposal, but spelled differently. Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements). (Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .) Additional reasons to prefer := over this spelling include: In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y . import foo as bar except Exc as var with ctxmgr() as var To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well. NAME = EXPR if NAME := EXPR reinforces the visual recognition of assignment expressions. This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition. This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder. Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ). This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative. One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value: This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions. Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements. Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions. This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library). As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own. As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax. Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein. There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it: Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful. While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma). Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent: (With the current version of the proposal, the latter would be equivalent to ((point := x), y) .) However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence: The less confusing option is to make := bind more tightly than comma. It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant: Frequently Raised Objections C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction. The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts. Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore. (The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] ) As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested. If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent. If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead. The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation. Appendix A: Tim Peters’s findings Here’s a brief essay Tim Peters wrote on the topic. I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of: instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled: is a vast improvement over the briefer: The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane. In other cases, combining related logic made it harder to understand, such as rewriting: as the briefer: The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way. But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form: I find that clearer, and certainly a bit less typing and pattern-matching reading, as: It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me! There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py : The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”: Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it. A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level: That if is about as long as I want my lines to get, but remains easy to follow. So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment. I have another example that quite impressed me at the time. Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration): It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding. If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression. To my eyes, the original form is harder to understand: This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding. Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code. Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope). Let’s start with a reminder of what code is generated for a generator expression without assignment expression. Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ] Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE ))) Let’s add a simple assignment expression. Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ] Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE ))) Let’s add a global TARGET declaration in f() . Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ] Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE ))) Or instead let’s add a nonlocal TARGET declaration in f() . Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ] Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE ))) Finally, let’s nest two comprehensions. Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2 Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET ) Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example: This document has been placed in the public domain. Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst Last modified: 2023-10-11 12:05:51 GMT Python » 3.12.4 Documentation » The Python Tutorial » 3. An Informal Introduction to Python Theme Auto Light Dark | 3. An Informal Introduction to Python ¶ In the following examples, input and output are distinguished by the presence or absence of prompts ( >>> and … ): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command. You can toggle the display of prompts and output by clicking on >>> in the upper-right corner of an example box. If you hide the prompts and output for an example, then you can easily copy and paste the input lines into your interpreter. Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, # , and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples. Some examples: 3.1. Using Python as a Calculator ¶ Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>> . (It shouldn’t take long.) 3.1.1. Numbers ¶ The interpreter acts as a simple calculator: you can type an expression at it and it will write the value. Expression syntax is straightforward: the operators + , - , * and / can be used to perform arithmetic; parentheses ( () ) can be used for grouping. For example: The integer numbers (e.g. 2 , 4 , 20 ) have type int , the ones with a fractional part (e.g. 5.0 , 1.6 ) have type float . We will see more about numeric types later in the tutorial. Division ( / ) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use % : With Python, it is possible to use the ** operator to calculate powers [ 1 ] : The equal sign ( = ) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt: If a variable is not “defined” (assigned a value), trying to use it will give you an error: There is full support for floating point; operators with mixed type operands convert the integer operand to floating point: In interactive mode, the last printed expression is assigned to the variable _ . This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example: This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior. In addition to int and float , Python supports other types of numbers, such as Decimal and Fraction . Python also has built-in support for complex numbers , and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j ). 3.1.2. Text ¶ Python can manipulate text (represented by type str , so-called “strings”) as well as numbers. This includes characters “ ! ”, words “ rabbit ”, names “ Paris ”, sentences “ Got your back. ”, etc. “ Yay! :) ”. They can be enclosed in single quotes ( '...' ) or double quotes ( "..." ) with the same result [ 2 ] . To quote a quote, we need to “escape” it, by preceding it with \ . Alternatively, we can use the other type of quotation marks: In the Python shell, the string definition and output string can look different. The print() function produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters: If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote: There is one subtle aspect to raw strings: a raw string may not end in an odd number of \ characters; see the FAQ entry for more information and workarounds. String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...''' . End of lines are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. The following example: produces the following output (note that the initial newline is not included): Strings can be concatenated (glued together) with the + operator, and repeated with * : Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated. This feature is particularly useful when you want to break long strings: This only works with two literals though, not with variables or expressions: If you want to concatenate variables or a variable and a literal, use + : Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one: Indices may also be negative numbers, to start counting from the right: Note that since -0 is the same as 0, negative indices start from -1. In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring: Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced. Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s : One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n , for example: The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j , respectively. For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2. Attempting to use an index that is too large will result in an error: However, out of range slice indexes are handled gracefully when used for slicing: Python strings cannot be changed — they are immutable . Therefore, assigning to an indexed position in the string results in an error: If you need a different string, you should create a new one: The built-in function len() returns the length of a string: Strings are examples of sequence types , and support the common operations supported by such types. Strings support a large number of methods for basic transformations and searching. String literals that have embedded expressions. Information about string formatting with str.format() . The old formatting operations invoked when strings are the left operand of the % operator are described in more detail here. 3.1.3. Lists ¶ Python knows a number of compound data types, used to group together other values. The most versatile is the list , which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type. Like strings (and all other built-in sequence types), lists can be indexed and sliced: Lists also support operations like concatenation: Unlike strings, which are immutable , lists are a mutable type, i.e. it is possible to change their content: You can also add new items at the end of the list, by using the list.append() method (we will see more about methods later): Simple assignment in Python never copies data. When you assign a list to a variable, the variable refers to the existing list . Any changes you make to the list through one variable will be seen through all other variables that refer to it.: All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list: Assignment to slices is also possible, and this can even change the size of the list or clear it entirely: The built-in function len() also applies to lists: It is possible to nest lists (create lists containing other lists), for example: 3.2. First Steps Towards Programming ¶ Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows: This example introduces several new features. The first line contains a multiple assignment : the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right. The while loop executes as long as the condition (here: a < 10 ) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to). The body of the loop is indented : indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount. The print() function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this: The keyword argument end can be used to avoid the newline after the output, or end the output with a different string: Table of Contents 3.1.1. Numbers 3.1.2. Text 3.1.3. Lists 3.2. First Steps Towards Programming Previous topic 2. Using the Python Interpreter 4. More Control Flow Tools Report a Bug Show Source 1.7 Java | Assignment Statements & Expressions An assignment statement designates a value for a variable. An assignment statement can be used as an expression in Java. After a variable is declared, you can assign a value to it by using an assignment statement . In Java, the equal sign = is used as the assignment operator . The syntax for assignment statements is as follows: An expression represents a computation involving values, variables, and operators that, when taking them together, evaluates to a value. For example, consider the following code: You can use a variable in an expression. A variable can also be used on both sides of the =  operator. For example: In the above assignment statement, the result of x + 1  is assigned to the variable x . Let’s say that x is 1 before the statement is executed, and so becomes 2 after the statement execution. To assign a value to a variable, you must place the variable name to the left of the assignment operator. Thus the following statement is wrong: Note that the math equation  x = 2 * x + 1  ≠ the Java expression x = 2 * x + 1 Which is equivalent to: And this statement is equivalent to: Note: The data type of a variable on the left must be compatible with the data type of a value on the right. For example, int x = 1.0 would be illegal, because the data type of x is int (integer) and does not accept the double value 1.0 without Type Casting . ◄◄◄BACK | NEXT►►► What's Your Opinion? Cancel reply Enhance your Brain Subscribe to Receive Free Bio Hacking, Nootropic, and Health Information HTML for Simple Website Customization My Personal Web Customization Personal Insights DISCLAIMER | Sitemap | ◘ HTML for Simple Website Customization My Personal Web Customization Personal Insights SEO Checklist Publishing Checklist My Tools Top Posts & Pages JavaHyperText and Data Structures Java HyperText Style Guide 0. Create project 1. Show line numbers 2. Import preferences for automatic formatting 3. Reset layout 4. Syntax help 5. Open 2 files 6. ToDO comments 7. JUnit testing 8. Assert statement 9. Generating javadoc 10. Red square: terminating programs 11. Remove/add imports 12. Use UTF-8 character encoding 1. API documentation 3. Using Strings Introduction 1. Assignment statement 2. New-expression 3. Method calls 2. Try-statement 1. Output of thrown exceptions 2. Throwable objects 3. Try-stmt/propagation 4. Throw-statement 5. Throws-clause Abstract classes interfaces Iterator & Iterable Program correctness 1. Introduction 2. Developing loops 3. Finding an invariant Stepwise refinement Intro to recursion Recursion on linked lists Recursion on trees Backtracking Shortest path 1. How to execute the assignment statement 1. explaining how to execute the assignment statement. 2. Homework assignment HW1 The first question is easy ---you can copy from the first video or its transcript on the pdf file. Questions 2 and 3 ask about the if-statement and if-else statement. These are the same as in just about any programming language, except for the syntax, of course. So use your knowledge of these statements in whatever programming language you know. 1. Write the algorithm for executing the Java assignment statement <variable>= <expression>; 2. Write the algorithm for executing the Java if-statement               if (<boolean-expression>) <statement 1> 3. Write the algorithm for executing the Java if-else-statement               if (<boolean-expression>) <statement 1> else <statement 2> 4. Tell us in a few words what you thought of the videos on presenting algorithms in English and executing the assignment statement, their message, and the homework. Python Programming Python Statements Updated on:  September 1, 2021 | 20 Comments In this tutorial, you will learn Python statements. Also, you will learn simple statements and compound statements. Table of contents Multi-line statements, python compound statements, expression statements, the pass statement. The del statement The return statement The import statement The continue and break statement What is a statement in Python? A statement is an instruction that a Python interpreter can execute . So, in simple words, we can say anything written in Python is a statement. Python statement ends with the token NEWLINE character. It means each line in a Python script is a statement. For example, a = 10 is an assignment statement. where a is a variable name and 10 is its value. There are other kinds of statements such as if statement, for statement, while statement, etc., we will learn them in the following lessons. There are mainly four types of statements in Python, print statements, Assignment statements, Conditional statements , Looping statements . The print and assignment statements are commonly used. The result of a print statement is a value. Assignment statements don’t produce a result it just assigns a value to the operand on its left side. A Python script usually contains a sequence of statements. If there is more than one statement, the result appears only one time when all statements execute. As you can see, we have used three statements in our program. Also, we have added the comments in our code. In Python, we use the hash ( # ) symbol to start writing a comment. In Python, comments describe what code is doing so other people can understand it. We can add multiple statements on a single line separated by semicolons, as follows: Python statement ends with the token NEWLINE character. But we can extend the statement over multiple lines using line continuation character ( \ ). This is known as an explicit continuation. Implicit continuation : We can use parentheses () to write a multi-line statement. We can add a line continuation statement inside it. Whatever we add inside a parentheses () will treat as a single statement even it is placed on multiple lines. As you see, we have removed the the line continuation character ( \ ) if we are using the parentheses () . We can use square brackets [] to create a list . Then, if required, we can place each list item on a single line for better readability. Same as square brackets, we can use the curly { } to create a dictionary with every key-value pair on a new line for better readability. Compound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way. The compound statement includes the conditional and loop statement. if statement: It is a control flow statement that will execute statements under it if the condition is true. Also kown as a conditional statement. while statements: The while loop statement repeatedly executes a code block while a particular condition is true. Also known as a looping statement. for statement: Using for loop statement, we can iterate any sequence or iterable variable. The sequence can be string, list, dictionary, set, or tuple. Also known as a looping statement. try statement: specifies exception handlers . with statement: Used to cleanup code for a group of statements, while the with statement allows the execution of initialization and finalization code around a block of code. Simple Statements Apart from the declaration and calculation statements, Python has various simple statements for a specific purpose. Let’s see them one by one. If you are an absolute beginner, you can move to the other beginner tutorials and then come back to this section. Expression statements are used to compute and write a value. An expression statement evaluates the expression list and calculates the value. To understand this, you need to understand an expression is in Python. An expression is a combination of values, variables , and operators . A single value all by itself is considered an expression. Following are all legal expressions (assuming that the variable x has been assigned a value): If your type the expression in an interactive python shell, you will get the result. So here x + 20 is the expression statement which computes the final value if we assume variable x has been assigned a value (10). So final value of the expression will become 30. But in a script, an expression all by itself doesn’t do anything! So we mostly assign an expression to a variable, which becomes a statement for an interpreter to execute. pass is a null operation. Nothing happens when it executes. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed. For example, you created a function for future releases, so you don’t want to write a code now. In such cases, we can use a pass statement. The  del  statement The Python del statement is used to delete objects/variables. The target_list contains the variable to delete separated by a comma. Once the variable is deleted, we can’t access it. The  return  statement We create a function in Python to perform a specific task. The function can return a value that is nothing but an output of function execution. Using a return statement, we can return a value from a function when called. The  import  statement The import statement is used to import modules . We can also import individual classes from a module. Python has a huge list of built-in modules which we can use in our code. For example, we can use the built-in module DateTime to work with date and time. Example : Import datetime module The continue and break statement break Statement: The break statement is used inside the loop to exit out of the loop. continue Statement: The continue statement skip the current iteration and move to the next iteration. We use break, continue statements to alter the loop’s execution in a certain manner. Read More : Break and Continue in Python Did you find this page helpful? Let others know about it. Sharing helps me continue to create free Python resources. About Vishal I’m  Vishal Hule , the Founder of PYnative.com. As a Python developer, I enjoy assisting students, developers, and learners. Follow me on  Twitter . Related Tutorial Topics: Python exercises and quizzes. Free coding exercises and quizzes cover Python basics, data structure, data analytics, and more. 15+ Topic-specific Exercises and Quizzes Each Exercise contains 10 questions Each Quiz contains 12-15 MCQ Loading comments... Please wait. About PYnative PYnative.com is for Python lovers. Here, You can get Tutorials, Exercises, and Quizzes to practice and improve your Python skills . Explore Python Learn Python Python Basics Python Databases Python Exercises Python Quizzes Online Python Code Editor Python Tricks To get New Python Tutorials, Exercises, and Quizzes Legal Stuff We use cookies to improve your experience. While using PYnative, you agree to have read and accepted our Terms Of Use , Cookie Policy , and Privacy Policy . Copyright © 2018–2024 pynative.com CS101: Introduction to Computer Science I (2019.A.01) Enrollment options. Time: 52 hours College Credit Recommended ($25 Proctor Fee) --> Free Certificate What Is an Assignment Statement in Java? Java programs store data values in variables. When a programmer creates a variable in a Java application, he declares the type and name of the variable, then assigns a value to it. The value of a variable can be altered at subsequent points in execution using further assignment operations. The assignment statement in Java involves using the assignment operator to set the value of a variable. The exact syntax depends on the type of variable receiving a value. Advertisement Video of the Day In Java, variables are strongly typed. This means that when you declare a variable in a Java program, you must declare its type, followed by its name. The following sample Java code demonstrates declaring two variables, one of primitive-type integer and one of an object type for a class within the application: int num; ApplicationHelper myHelp; Once a program contains a variable declaration, the kind of value assigned to the variable must be suited to the type declared. These variable declarations could be followed by assignment statements on subsequent lines. However, the assignment operation could also take place on the same line as the declaration. Assignment in Java is the process of giving a value to a primitive-type variable or giving an object reference to an object-type variable. The equals sign acts as assignment operator in Java, followed by the value to assign. The following sample Java code demonstrates assigning a value to a primitive-type integer variable, which has already been declared: num = 5; The assignment operation could alternatively appear within the same line of code as the declaration of the variable, as follows: int num = 5; The value of the variable can be altered again in subsequent processing as in this example: num++; This code increments the variable value, adding a value of one to it. Instantiation When the assignment statement appears with object references, the assignment operation may also involve object instantiation. When Java code creates a new object instance of a Java class in an application, the "new" keyword causes the constructor method of the class to execute, instantiating the object. The following sample code demonstrates instantiating an object variable: myHelp = new ApplicationHelper(); This could also appear within the same line as the variable declaration as follows: ApplicationHelper myHelp = new ApplicationHelper(); When this line of code executes, the class constructor method executes, returning an instance of the class, a reference to which is stored by the variable. Referencing Once a variable has been declared and assigned a value, a Java program can refer to the variable in subsequent processing. For primitive-type variables, the variable name refers to a stored value. For object types, the variable refers to the location of the object instance in memory. This means that two object variables can point to the same instance, as in the following sample code: ApplicationHelper myHelp = new ApplicationHelper(); ApplicationHelper sameHelp = myHelp; This syntax appears commonly when programs pass object references as parameters to class methods. Oracle: The Java Tutorials - Variables Oracle: The Java Tutorials - Assignment, Arithmetic, and Unary Operators Oracle: The Java Tutorials - Primitive Data Types Oracle: The Java Tutorials - Creating Objects Oracle: The Java Tutorials - What Is an Object? Oracle: The Java Tutorials - Summary of Variables Java Language Specification; Types, Values, and Variables; 2000 Oracle: The Java Tutorials - Understanding Instance and Class Members Report an Issue Screenshot loading... Stack Overflow Public questions & answers Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers Talent Build your employer brand Advertising Reach developers & technologists worldwide Labs The future of collective knowledge sharing About the company Collectives™ on Stack Overflow Find centralized, trusted content and collaborate around the technologies you use most. Q&A for work Connect and share knowledge within a single location that is structured and easy to search. Get early access and see previews of new features. Error: Syntax in assignment statement l-value This is the Verilog code. I'm getting the following errors. Can you help me understand how to fix them? There are a few syntax errors with your code. Do not use the assign keyword to make an assignment to a reg ( F1 ). Do not place a module instance ( sub ) inside an always block. Use an instance name for your sub module instance. Do not create a task with the same name as a module ( sub ). There is no need for the task/endtask in this case. Here is a version which compiles cleanly for me: Your Answer Reminder: Answers generated by artificial intelligence tools are not allowed on Stack Overflow. Learn more Sign up or log in Post as a guest. Required, but never shown By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy . Not the answer you're looking for? Browse other questions tagged verilog or ask your own question . 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As you write JavaScript code, you'll refer to these pages often (thus the title "JavaScript reference"). The JavaScript language is intended to be used within some larger environment, be it a browser, server-side scripts, or similar. For the most part, this reference attempts to be environment-agnostic and does not target a web browser environment. If you are new to JavaScript, start with the guide . Once you have a firm grasp of the fundamentals, you can use the reference to get more details on individual objects and language constructs. JavaScript standard built-in objects , along with their methods and properties. Value properties Function properties. parseFloat() decodeURI() decodeURIComponent() encodeURI() encodeURIComponent() escape() Deprecated unescape() Deprecated Fundamental objects Error objects. AggregateError ReferenceError SyntaxError InternalError Non-standard Numbers and dates Text processing, indexed collections. Uint8ClampedArray Uint16Array Uint32Array BigInt64Array BigUint64Array Float16Array Float32Array Float64Array Keyed collections Structured data. ArrayBuffer SharedArrayBuffer Managing memory FinalizationRegistry Control abstraction objects AsyncIterator GeneratorFunction AsyncGeneratorFunction AsyncGenerator AsyncFunction Internationalization Intl.Collator Intl.DateTimeFormat Intl.DisplayNames Intl.DurationFormat Intl.ListFormat Intl.Locale Intl.NumberFormat Intl.PluralRules Intl.RelativeTimeFormat Intl.Segmenter JavaScript statements and declarations Control flow try...catch Declaring variables Functions and classes. async function async function* for await...of Expression statement with Deprecated Expressions and operators JavaScript expressions and operators . Primary expressions Left-hand-side expressions. Property accessors import.meta Increment and decrement Unary operators, arithmetic operators, relational operators. < (Less than) > (Greater than) Equality operators Bitwise shift operators. >>> Binary bitwise operators Binary logical operators, conditional (ternary) operator. (condition ? ifTrue : ifFalse) Assignment operators >>>= &&= [a, b] = arr , { a, b } = obj Yield operators Spread syntax, comma operator. JavaScript functions. Arrow Functions Default parameters Rest parameters Method definitions JavaScript classes. constructor Private properties Public class fields Static initialization blocks Additional reference pages Lexical grammar Data types and data structures Iteration protocols Trailing commas Strict mode Deprecated features Hunter Biden’s conviction shows there’s not a two-tier system of justice Four Israeli hostages are finally free. Now for peace talks to break through. How much should Hunter Biden’s conviction matter? 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Read our guide to writing a guest opinion Submit a guest opinion Submit a letter to the editor 159 COMMENTS Python's Assignment Operator: Write Robust Assignments The Assignment Statement Syntax. 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 Different Forms of Assignment Statements in Python Multiple- target assignment: x = y = 75. print(x, y) In this form, Python assigns a reference to the same object (the object which is rightmost) to all the target on the left. OUTPUT. 75 75. 7. Augmented assignment : The augmented assignment is a shorthand assignment that combines an expression and an assignment. What are Assignment Statement: Definition, Assignment Statement ... An Assignment statement is a statement that is used to set a value to the variable name in a program.. Assignment statement allows a variable to hold different types of values during its program lifespan. Another way of understanding an assignment statement is, it stores a value in the memory location which is denoted by a variable name.. Syntax Assignment Statement in Python Learn the basics of assignment statements in Python in this tutorial. We'll cover the syntax and usage of the assignment operator, including multiple assignm... Assignment (computer science) Assignment (computer science) In computer programming, an assignment statement sets and/or re-sets the value stored in the storage location (s) denoted by a variable name; in other words, it copies a value into the variable. In most imperative programming languages, the assignment statement (or expression) is a fundamental construct. How To Use Assignment Expressions in Python For example, assignment expressions using the := syntax allow variables to be assigned inside of if statements, which can often produce shorter and more compact sections of Python code by eliminating variable assignments in lines preceding or following the if statement. In this tutorial, you will use assignment expressions in several examples ... 7. Simple statements The syntax for an expression statement is: ... An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right. ... Assignment Statement For example, x = x - 1, x -= 1, and x--are all assignment statements which do the same thing — assign the variable x a new value that is one lower than its current value. Examples Basic assignment statement. In this example we use ReadLine to get input from the user and store it in a name variable. Assignment Statements Syntax. Assignment statements consist of a variable, an equal sign, and an expression. Here's an example: Get hands-on with 1200+ tech skills courses. Assignment Statements :: CIS 301 Textbook Assignment Statements. Assignment statements in a program come in two forms - with and without mutations. Assignments without mutation are those that give a value to a variable without using the old value of that variable. ... For example, an increment statement like x = x + 1 MUTATES the value of x by updating its value to be one bigger than ... PDF The assignment statement The assignment statement. The assignment statement is used to store a value in a variable. As in most programming languages these days, the assignment statement has the form: <variable>= <expression>; For example, once we have an int variable j, we can assign it the value of expression 4 + 6: int j; j= 4+6; As a convention, we always place a ... Python This statement provides an expression and a variable name which will be used to label the value of the expression. variable = expression. Here's a short script that contains some examples of assignment statements. Example 6.1. example3.py. #!/usr/bin/env python. # Computer the value of a block of stock. shares= 150. price= 3 + 5.0/8.0. Python Assignment Operators Python Assignment Operators. Assignment operators are used to assign values to variables: Operator. Example. Same As. Try it. =. x = 5. x = 5. PEP 572 Unparenthesized assignment expressions are prohibited at the top level of the right hand side of an assignment statement. Example: y0 = y1 := f(x) # INVALID y0 = (y1 := f(x)) # Valid, though discouraged. Again, this rule is included to avoid two visually similar ways of saying the same thing. 3. An Informal Introduction to Python This example introduces several new features. The first line contains a multiple assignment: the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. 1.7 Java An assignment statement designates a value for a variable. An assignment statement can be used as an expression in Java. After a variable is declared, you can assign a value to it by using an assignment statement. In Java, the equal sign = is used as the assignment operator. The syntax for assignment statements is as follows: variable ... 1. How to execute the assignment statement Questions 2 and 3 ask about the if-statement and if-else statement. These are the same as in just about any programming language, except for the syntax, of course. So use your knowledge of these statements in whatever programming language you know. 1. Write the algorithm for executing the Java assignment statement <variable>= <expression>; 2. CS105: Variables and Assignment Statements The assignment operator = is used to associate a variable name with a given value. For example, type the command: a=3.45. in the command line window. This command assigns the value 3.45 to the variable named a. Next, type the command: a. in the command window and hit the enter key. You should see the value contained in the variable a echoed to ... Python Statements With Examples- PYnative A statement is an instruction that a Python interpreter can execute. So, in simple words, we can say anything written in Python is a statement. Python statement ends with the token NEWLINE character. It means each line in a Python script is a statement. For example, a = 10 is an assignment statement. where a is a variable name and Variables and Assignment Statements: Assignment Statements So far, the example programs have been using the value initially put into a variable. Programs can change the value in a variable. An assignment statement changes the value that is held in a variable. The program uses an assignment statement. The assignment statement puts the value 123 into the variable. CS101 (2019.A.01) Free Certificate. Explore the basic concepts, nomenclature, and historical perspective of computers and computing, and principles of software development and Object-Oriented Programming. Self enrollment (Student) Guests cannot access this course. What Is an Assignment Statement in Java? The assignment statement in Java involves using the assignment operator to set the value of a variable. The exact syntax depends on the type of variable receiving a value. Advertisement Variables Video of the Day In Java, variables are strongly typed. This means that when you declare a variable in a Java program, you must declare its type ... verilog 0. You have 2 types of syntax errors. You need to declare new_content as a reg since you make a procedural assignment to it in an always block. You need to place @ to the left of the ( in your always line. This code compiles with no errors for me: module IF_ID(new_content, instruction, newPC, clk, pwrite1); input pwrite1, clk; Error: Syntax in assignment statement l-value 0. There are a few syntax errors with your code. Do not use the assign keyword to make an assignment to a reg ( F1 ). Do not place a module instance ( sub) inside an always block. Use an instance name for your sub module instance. Do not create a task with the same name as a module ( sub ). There is no need for the task/endtask in this case. JavaScript reference JavaScript reference. The JavaScript reference serves as a repository of facts about the JavaScript language. The entire language is described here in detail. As you write JavaScript code, you'll refer to these pages often (thus the title "JavaScript reference"). The JavaScript language is intended to be used within some larger environment, be ... Opinions The Washington Post Opinions section features opinion articles, op-eds, editorials by the Editorial Board, global opinions and letters to the editor on the issues of the day. Offerings include ... assignment business plan essay writing homework paper writing research paper review writing