assignment statement for

• 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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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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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.

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

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2.3: Arithmetic Operations and Assignment Statements

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• Page ID 206261

• Robert Belford
• University of Arkansas at Little Rock

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hypothes.is tag:  s20iostpy03ualr Download Assignment:  S2020py03

Learning Objectives

Students will be able to:

• Explain each Python arithmetic operator
• Explain the meaning and use of an  assignment statement
• Explain the use of "+"  and "*" with strings and numbers
• Use the  int()   and  float()  functions to convert string input to numbers for computation
• Incorporate numeric formatting into print statements
• Recognize the four main operations of a computer within a simple Python program
• Create  input  statements in Python
• Create  Python  code that performs mathematical and string operations
• Create  Python  code that uses assignment statements
• Create  Python   code that formats numeric output

Prior Knowledge

• Understanding of Python print and input statements
• Understanding of mathematical operations
• Understanding of flowchart input symbols

Further Reading

• https://en.wikibooks.org/wiki/Non-Programmer%27s_Tutorial_for_Python_3/Hello,_World
• https://en.wikibooks.org/wiki/Non-Programmer%27s_Tutorial_for_Python_3/Who_Goes_There%3F

Model 1: Arithmetic Operators in  Python

Python includes several arithmetic operators: addition, subtraction, multiplication, two types of division, exponentiation and  mod .

Critical Thinking Questions:

1.  Draw a line between each flowchart symbol and its corresponding line of Python code. Make note of any problems.

2. Execute the print statements in the previous Python program

    a.  Next to each print statement above, write the output.     b.  What is the value of the following line of code?

    c.  Predict the values of 17%3 and 18%3 without using your computer.

3.  Explain the purpose of each arithmetic operation:

a.               +          ____________________________

b.               -           ____________________________

c.               *          ____________________________

d.               **        ____________________________

e.               /           ____________________________

f.                //          ____________________________

g.                %         ____________________________

An  assignment statement  is a line of code that uses a "=" sign. The statement stores the result of an operation performed on the right-hand side of the sign into the variable memory location on the left-hand side.

4.         Enter and execute the following lines of Python code in the editor window of your IDE (e.g. Thonny):

 a.  What are the variables in the above python program?    b.  What does the  assignment statement :  MethaneMolMs = 16  do?    c.  What happens if you replace the comma (,) in the print statements with a plus sign (+) and execute the code again?  Why does this happen?

5.    What is stored in memory after each assignment statement is executed?

Note: Concatenating Strings in python

The "+"  concatenates  the two strings stored in the variables into one string.    "+" can only be used when both operators are strings.

6.         Run the following program in the editor window of your IDE (e.g. Thonny) to see what happens if you try to use the "+" with strings instead of numbers?

   a.  The third line of code contains an assignment statement. What is stored in  fullName   when the line is executed?    b.  What is the difference between the two output lines?    c.  How could you alter your assignment statements so that  print(fullName)  gives the same output as  print(firstName,lastName)    d. Only one of the following programs will work. Which one will work, and why doesn’t the other work? Try doing this without running the programs!

   e.  Run the programs above and see if you were correct.    f.  The program that worked above results in no space between the number and the street name. How can you alter the code so that it prints properly while using a concatenation operator?

7.  Before entering the following code into the Python interpreter (Thonny IDE editor window), predict the output of this program.

Now execute it.  What is the actual output?  Is this what you thought it would do?  Explain.

8.   Let’s take a look at a python program that prompts the user for two numbers and subtracts them. 

            Execute the following code by entering it in the editor window of Thonny.

      a.   What output do you expect?       b.   What is the actual output       c.   Revise the program in the following manner:

• Between lines two and three add the following lines of code:       num1 = int(firstNumber)      num2 = int(secondNumber)
• Next, replace the statement:     difference = firstNumber – secondNumber with the statement:     difference = num1 – num2
• Execute the program again. What output did you get?

     d.  Explain the purpose of the function  int().      e.  Explain how the changes in the program produced the desired output.

Model 3: Formatting Output in  Python

There are multiple ways to format output in python. The old way is to use the string modulo %, and the new way is with a format method function.

9.  Look closely at the output for python program 7.

    a. How do you indicate the number of decimals to display using

the string modulo (%) ______________________________________________________

the format function ________________________________________________________

     b. What happens to the number if you tell it to display less decimals than are in the number, regardless of formatting method used?

     c. What type of code allows you to right justify your numbers?

10.       Execute the following code by entering it in the editor window of Thonny.

a.  Does the output look like standard output for something that has dollars and cents associated with it?

b.  Replace the last line of code with the following:

print("Total cost of laptops: $%.2f" % price)   

print("Total cost of laptops:" ,format(price, '.2f.))

                Discuss the change in the output.

c.  Replace the last line of code with the following:

print("Total cost of laptops: $",   format(price,'.2f') print("Total cost of laptops: $" ,format(price, '.2f.))

              Discuss the change in the output.

d.  Experiment with the number ".2" in the ‘0.2f’ of the print above statement by substituting the following numbers and explain the results.

                     .4         ___________________________________________________

                     .0         ___________________________________________________

                     .1         ___________________________________________________

                     .8         ___________________________________________________

e.  Now try the following numbers in the same print statement. These numbers contain a whole number and a decimal. Explain the output for each number.

                     02.5     ___________________________________________________

                     08.2     ___________________________________________________

                     03.1     ___________________________________________________

f.  Explain what each part of the format function:  format(variable,  "%n.nf")  does in a print statement where n.n represents a number.

variable ____________________________           First n _________________________

Second n_______________________                      f    _________________________

g.          Revise the print statement by changing the "f" to "d" and  laptopCost = 600 . Execute the statements and explain the output format.

            print("Total cost of laptops: %2d" % price)             print("Total cost of laptops: %10d" % price)

h.         Explain how the function  format(var,'10d')  formats numeric data.  var  represents a whole number.

11.    Use the following program and output to answer the questions below.

a.   From the code and comments in the previous program, explain how the four main operations are implemented in this program. b.  There is one new function in this sample program.  What is it? From the corresponding output, determine what it does.

Application Questions: Use the Python Interpreter to check your work

• 8 to the 4 th  power
• The sum of 5 and 6 multiplied by the quotient of 34 and 7 using floating point arithmetic  
• Write an assignment statement that stores the remainder obtained from dividing 87 and 8 in the variable  leftover  
• Assume:  

courseLabel = "CHEM" courseNumber = "3350"

Write a line of Python code that concatenates the label with the number and stores the result in the variable  courseName . Be sure that there is a space between the course label and the course number when they are concatenated.

• Write one line of Python code that will print the word "Happy!" one hundred times.  
• Write one line of code that calculates the cost of 15 items and stores the result in the variable  totalCost
• Write one line of code that prints the total cost with a label, a dollar sign, and exactly two decimal places.  Sample output:  Total cost: $22.5  
• Assume: 

height1 = 67850 height2 = 456

Use Python formatting to write two print statements that will produce the following output exactly at it appears below:

Homework Assignment: s2020py03

Download the assignment from the website, fill out the word document, and upload to your Google Drive folder the completed assignment along with the two python files.

1. (5 pts)  Write a Python program that prompts the user for two numbers, and then gives the sum and product of those two numbers. Your sample output should look like this:

Enter your first number:10 Enter your second number:2 The sum of these numbers is: 12 The product of these two numbers is: 20

• Your program must contain documentation lines that include your name, the date, a line that states "Py03 Homework question 1" and a description line that indicates what the program is supposed to do. 
• Paste the code this word document and upload to your Google drive when the assignment is completed, with file name [your last name]_py03_HWQ1
• Save the program as a python file (ends with .py), with file name [your last name]_py03Q1_program and upload that to the Google Drive.

2. (10 pts) Write a program that calculates the molarity of a solution. Molarity is defined as numbers of moles per liter solvent. Your program will calculate molarity and must ask for the substance name, its molecular weight, how many grams of substance you are putting in solution, and the total volume of the solution. Report your calculated value of molarity to 3 decimal places. Your output should also be separated from the input with a line containing 80 asterixis.

Assuming you are using sodium chloride, your input and output should look like:

• Your program must contain documentation lines that include your name, the date, a line that states "Py03 Homework question 2" and a description line that indicates what the program is supposed to do. 
• Paste the code to question two below
• Save the program as a python file (ends with .py), with file name [your last name]_py03Q2_program and upload that to the Google Drive.

3. (4 pts) Make two hypothes.is annotations dealing with external open access resources on formatting with the format function method of formatting.  These need the tag of s20iostpy03ualr .

Copyright Statement

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 .

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• 7. Simple statements
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7. Simple statements ¶

A simple statement is comprised within a single logical line. Several simple statements may occur on a single line separated by semicolons. The syntax for simple statements is:

7.1. Expression statements ¶

Expression statements are used (mostly interactively) to compute and write a value, or (usually) to call a procedure (a function that returns no meaningful result; in Python, procedures return the value None ). Other uses of expression statements are allowed and occasionally useful. The syntax for an expression statement is:

An expression statement evaluates the expression list (which may be a single expression).

In interactive mode, if the value is not None , it is converted to a string using the built-in repr() function and the resulting string is written to standard output on a line by itself (except if the result is None , so that procedure calls do not cause any output.)

7.2. Assignment statements ¶

Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects:

(See section Primaries for the syntax definitions for attributeref , subscription , and slicing .)

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 is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy ).

Assignment of an object to a target list, optionally enclosed in parentheses or square brackets, is recursively defined as follows.

If the target list is a single target with no trailing comma, optionally in parentheses, the object is assigned to that target.

If the target list contains one target prefixed with an asterisk, called a “starred” target: The object must be an iterable with at least as many items as there are targets in the target list, minus one. The first items of the iterable are assigned, from left to right, to the targets before the starred target. The final items of the iterable are assigned to the targets after the starred target. A list of the remaining items in the iterable is then assigned to the starred target (the list can be empty).

Else: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as follows.

If the target is an identifier (name):

If the name does not occur in a global or nonlocal statement in the current code block: the name is bound to the object in the current local namespace.

Otherwise: the name is bound to the object in the global namespace or the outer namespace determined by nonlocal , respectively.

The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called.

If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, TypeError is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily AttributeError ).

Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the right-hand side expression, a.x can access either an instance attribute or (if no instance attribute exists) a class attribute. The left-hand side target a.x is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of a.x do not necessarily refer to the same attribute: if the right-hand side expression refers to a class attribute, the left-hand side creates a new instance attribute as the target of the assignment:

This description does not necessarily apply to descriptor attributes, such as properties created with property() .

If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated.

If the primary is a mutable sequence object (such as a list), the subscript must yield an integer. If it is negative, the sequence’s length is added to it. The resulting value must be a nonnegative integer less than the sequence’s length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, IndexError is raised (assignment to a subscripted sequence cannot add new items to a list).

If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping’s key type, and the mapping is then asked to create a key/value pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed).

For user-defined objects, the __setitem__() method is called with appropriate arguments.

If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence’s length. The bounds should evaluate to integers. If either bound is negative, the sequence’s length is added to it. The resulting bounds are clipped to lie between zero and the sequence’s length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the target sequence allows it.

CPython implementation detail: In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages.

Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘simultaneous’ (for example a, b = b, a swaps two variables), overlaps within the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints [0, 2] :

The specification for the *target feature.

7.2.1. Augmented assignment statements ¶

Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement:

(See section Primaries for the syntax definitions of the last three symbols.)

An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once.

An augmented assignment expression like x += 1 can be rewritten as x = x + 1 to achieve a similar, but not exactly equal effect. In the augmented version, x is only evaluated once. Also, when possible, the actual operation is performed in-place , meaning that rather than creating a new object and assigning that to the target, the old object is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-hand side before evaluating the right-hand side. For example, a[i] += f(x) first looks-up a[i] , then it evaluates f(x) and performs the addition, and lastly, it writes the result back to a[i] .

With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place behavior, the binary operation performed by augmented assignment is the same as the normal binary operations.

For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments.

7.2.2. Annotated assignment statements ¶

Annotation assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement:

The difference from normal Assignment statements is that only a single target is allowed.

For simple names as assignment targets, if in class or module scope, the annotations are evaluated and stored in a special class or module attribute __annotations__ that is a dictionary mapping from variable names (mangled if private) to evaluated annotations. This attribute is writable and is automatically created at the start of class or module body execution, if annotations are found statically.

For expressions as assignment targets, the annotations are evaluated if in class or module scope, but not stored.

If a name is annotated in a function scope, then this name is local for that scope. Annotations are never evaluated and stored in function scopes.

If the right hand side is present, an annotated assignment performs the actual assignment before evaluating annotations (where applicable). If the right hand side is not present for an expression target, then the interpreter evaluates the target except for the last __setitem__() or __setattr__() call.

The proposal that added syntax for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments.

The proposal that added the typing module to provide a standard syntax for type annotations that can be used in static analysis tools and IDEs.

Changed in version 3.8: Now annotated assignments allow the same expressions in the right hand side as regular assignments. Previously, some expressions (like un-parenthesized tuple expressions) caused a syntax error.

7.3. The assert statement ¶

Assert statements are a convenient way to insert debugging assertions into a program:

The simple form, assert expression , is equivalent to

The extended form, assert expression1, expression2 , is equivalent to

These equivalences assume that __debug__ and AssertionError refer to the built-in variables with those names. In the current implementation, the built-in variable __debug__ is True under normal circumstances, False when optimization is requested (command line option -O ). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace.

Assignments to __debug__ are illegal. The value for the built-in variable is determined when the interpreter starts.

7.4. The pass statement ¶

pass is a null operation — when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example:

7.5. The del statement ¶

Deletion is recursively defined very similar to the way assignment is defined. Rather than spelling it out in full details, here are some hints.

Deletion of a target list recursively deletes each target, from left to right.

Deletion of a name removes the binding of that name from the local or global namespace, depending on whether the name occurs in a global statement in the same code block. If the name is unbound, a NameError exception will be raised.

Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a slicing is in general equivalent to assignment of an empty slice of the right type (but even this is determined by the sliced object).

Changed in version 3.2: Previously it was illegal to delete a name from the local namespace if it occurs as a free variable in a nested block.

7.6. The return statement ¶

return may only occur syntactically nested in a function definition, not within a nested class definition.

If an expression list is present, it is evaluated, else None is substituted.

return leaves the current function call with the expression list (or None ) as return value.

When return passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the function.

In a generator function, the return statement indicates that the generator is done and will cause StopIteration to be raised. The returned value (if any) is used as an argument to construct StopIteration and becomes the StopIteration.value attribute.

In an asynchronous generator function, an empty return statement indicates that the asynchronous generator is done and will cause StopAsyncIteration to be raised. A non-empty return statement is a syntax error in an asynchronous generator function.

7.7. The yield statement ¶

A yield statement is semantically equivalent to a yield expression . The yield statement can be used to omit the parentheses that would otherwise be required in the equivalent yield expression statement. For example, the yield statements

are equivalent to the yield expression statements

Yield expressions and statements are only used when defining a generator function, and are only used in the body of the generator function. Using yield in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

For full details of yield semantics, refer to the Yield expressions section.

7.8. The raise statement ¶

If no expressions are present, raise re-raises the exception that is currently being handled, which is also known as the active exception . If there isn’t currently an active exception, a RuntimeError exception is raised indicating that this is an error.

Otherwise, raise evaluates the first expression as the exception object. It must be either a subclass or an instance of BaseException . If it is a class, the exception instance will be obtained when needed by instantiating the class with no arguments.

The type of the exception is the exception instance’s class, the value is the instance itself.

A traceback object is normally created automatically when an exception is raised and attached to it as the __traceback__ attribute. You can create an exception and set your own traceback in one step using the with_traceback() exception method (which returns the same exception instance, with its traceback set to its argument), like so:

The from clause is used for exception chaining: if given, the second expression must be another exception class or instance. If the second expression is an exception instance, it will be attached to the raised exception as the __cause__ attribute (which is writable). If the expression is an exception class, the class will be instantiated and the resulting exception instance will be attached to the raised exception as the __cause__ attribute. If the raised exception is not handled, both exceptions will be printed:

A similar mechanism works implicitly if a new exception is raised when an exception is already being handled. An exception may be handled when an except or finally clause, or a with statement, is used. The previous exception is then attached as the new exception’s __context__ attribute:

Exception chaining can be explicitly suppressed by specifying None in the from clause:

Additional information on exceptions can be found in section Exceptions , and information about handling exceptions is in section The try statement .

Changed in version 3.3: None is now permitted as Y in raise X from Y .

Added the __suppress_context__ attribute to suppress automatic display of the exception context.

Changed in version 3.11: If the traceback of the active exception is modified in an except clause, a subsequent raise statement re-raises the exception with the modified traceback. Previously, the exception was re-raised with the traceback it had when it was caught.

7.9. The break statement ¶

break may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop.

It terminates the nearest enclosing loop, skipping the optional else clause if the loop has one.

If a for loop is terminated by break , the loop control target keeps its current value.

When break passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the loop.

7.10. The continue statement ¶

continue may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop. It continues with the next cycle of the nearest enclosing loop.

When continue passes control out of a try statement with a finally clause, that finally clause is executed before really starting the next loop cycle.

7.11. The import statement ¶

The basic import statement (no from clause) is executed in two steps:

find a module, loading and initializing it if necessary

define a name or names in the local namespace for the scope where the import statement occurs.

When the statement contains multiple clauses (separated by commas) the two steps are carried out separately for each clause, just as though the clauses had been separated out into individual import statements.

The details of the first step, finding and loading modules, are described in greater detail in the section on the import system , which also describes the various types of packages and modules that can be imported, as well as all the hooks that can be used to customize the import system. Note that failures in this step may indicate either that the module could not be located, or that an error occurred while initializing the module, which includes execution of the module’s code.

If the requested module is retrieved successfully, it will be made available in the local namespace in one of three ways:

If the module name is followed by as , then the name following as is bound directly to the imported module.

If no other name is specified, and the module being imported is a top level module, the module’s name is bound in the local namespace as a reference to the imported module

If the module being imported is not a top level module, then the name of the top level package that contains the module is bound in the local namespace as a reference to the top level package. The imported module must be accessed using its full qualified name rather than directly

The from form uses a slightly more complex process:

find the module specified in the from clause, loading and initializing it if necessary;

for each of the identifiers specified in the import clauses:

check if the imported module has an attribute by that name

if not, attempt to import a submodule with that name and then check the imported module again for that attribute

if the attribute is not found, ImportError is raised.

otherwise, a reference to that value is stored in the local namespace, using the name in the as clause if it is present, otherwise using the attribute name

If the list of identifiers is replaced by a star ( '*' ), all public names defined in the module are bound in the local namespace for the scope where the import statement occurs.

The public names defined by a module are determined by checking the module’s namespace for a variable named __all__ ; if defined, it must be a sequence of strings which are names defined or imported by that module. The names given in __all__ are all considered public and are required to exist. If __all__ is not defined, the set of public names includes all names found in the module’s namespace which do not begin with an underscore character ( '_' ). __all__ should contain the entire public API. It is intended to avoid accidentally exporting items that are not part of the API (such as library modules which were imported and used within the module).

The wild card form of import — from module import * — is only allowed at the module level. Attempting to use it in class or function definitions will raise a SyntaxError .

When specifying what module to import you do not have to specify the absolute name of the module. When a module or package is contained within another package it is possible to make a relative import within the same top package without having to mention the package name. By using leading dots in the specified module or package after from you can specify how high to traverse up the current package hierarchy without specifying exact names. One leading dot means the current package where the module making the import exists. Two dots means up one package level. Three dots is up two levels, etc. So if you execute from . import mod from a module in the pkg package then you will end up importing pkg.mod . If you execute from ..subpkg2 import mod from within pkg.subpkg1 you will import pkg.subpkg2.mod . The specification for relative imports is contained in the Package Relative Imports section.

importlib.import_module() is provided to support applications that determine dynamically the modules to be loaded.

Raises an auditing event import with arguments module , filename , sys.path , sys.meta_path , sys.path_hooks .

7.11.1. Future statements ¶

A future statement is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of Python where the feature becomes standard.

The future statement is intended to ease migration to future versions of Python that introduce incompatible changes to the language. It allows use of the new features on a per-module basis before the release in which the feature becomes standard.

A future statement must appear near the top of the module. The only lines that can appear before a future statement are:

the module docstring (if any),

blank lines, and

other future statements.

The only feature that requires using the future statement is annotations (see PEP 563 ).

All historical features enabled by the future statement are still recognized by Python 3. The list includes absolute_import , division , generators , generator_stop , unicode_literals , print_function , nested_scopes and with_statement . They are all redundant because they are always enabled, and only kept for backwards compatibility.

A future statement is recognized and treated specially at compile time: Changes to the semantics of core constructs are often implemented by generating different code. It may even be the case that a new feature introduces new incompatible syntax (such as a new reserved word), in which case the compiler may need to parse the module differently. Such decisions cannot be pushed off until runtime.

For any given release, the compiler knows which feature names have been defined, and raises a compile-time error if a future statement contains a feature not known to it.

The direct runtime semantics are the same as for any import statement: there is a standard module __future__ , described later, and it will be imported in the usual way at the time the future statement is executed.

The interesting runtime semantics depend on the specific feature enabled by the future statement.

Note that there is nothing special about the statement:

That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions.

Code compiled by calls to the built-in functions exec() and compile() that occur in a module M containing a future statement will, by default, use the new syntax or semantics associated with the future statement. This can be controlled by optional arguments to compile() — see the documentation of that function for details.

A future statement typed at an interactive interpreter prompt will take effect for the rest of the interpreter session. If an interpreter is started with the -i option, is passed a script name to execute, and the script includes a future statement, it will be in effect in the interactive session started after the script is executed.

The original proposal for the __future__ mechanism.

7.12. The global statement ¶

The global statement is a declaration which holds for the entire current code block. It means that the listed identifiers are to be interpreted as globals. It would be impossible to assign to a global variable without global , although free variables may refer to globals without being declared global.

Names listed in a global statement must not be used in the same code block textually preceding that global statement.

Names listed in a global statement must not be defined as formal parameters, or as targets in with statements or except clauses, or in a for target list, class definition, function definition, import statement, or variable annotation.

CPython implementation detail: The current implementation does not enforce some of these restrictions, but programs should not abuse this freedom, as future implementations may enforce them or silently change the meaning of the program.

Programmer’s note: global is a directive to the parser. It applies only to code parsed at the same time as the global statement. In particular, a global statement contained in a string or code object supplied to the built-in exec() function does not affect the code block containing the function call, and code contained in such a string is unaffected by global statements in the code containing the function call. The same applies to the eval() and compile() functions.

7.13. The nonlocal statement ¶

When the definition of a function or class is nested (enclosed) within the definitions of other functions, its nonlocal scopes are the local scopes of the enclosing functions. The nonlocal statement causes the listed identifiers to refer to names previously bound in nonlocal scopes. It allows encapsulated code to rebind such nonlocal identifiers. If a name is bound in more than one nonlocal scope, the nearest binding is used. If a name is not bound in any nonlocal scope, or if there is no nonlocal scope, a SyntaxError is raised.

The nonlocal statement applies to the entire scope of a function or class body. A SyntaxError is raised if a variable is used or assigned to prior to its nonlocal declaration in the scope.

The specification for the nonlocal statement.

Programmer’s note: nonlocal is a directive to the parser and applies only to code parsed along with it. See the note for the global statement.

7.14. The type statement ¶

The type statement declares a type alias, which is an instance of typing.TypeAliasType .

For example, the following statement creates a type alias:

This code is roughly equivalent to:

annotation-def indicates an annotation scope , which behaves mostly like a function, but with several small differences.

The value of the type alias is evaluated in the annotation scope. It is not evaluated when the type alias is created, but only when the value is accessed through the type alias’s __value__ attribute (see Lazy evaluation ). This allows the type alias to refer to names that are not yet defined.

Type aliases may be made generic by adding a type parameter list after the name. See Generic type aliases for more.

type is a soft keyword .

Added in version 3.12.

Introduced the type statement and syntax for generic classes and functions.

Table of Contents

• 7.1. Expression statements
• 7.2.1. Augmented assignment statements
• 7.2.2. Annotated assignment statements
• 7.3. The assert statement
• 7.4. The pass statement
• 7.5. The del statement
• 7.6. The return statement
• 7.7. The yield statement
• 7.8. The raise statement
• 7.9. The break statement
• 7.10. The continue statement
• 7.11.1. Future statements
• 7.12. The global statement
• 7.13. The nonlocal statement
• 7.14. The type statement

Previous topic

6. Expressions

8. Compound statements

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1.4. Expressions and Assignment Statements ¶

In this lesson, you will learn about assignment statements and expressions that contain math operators and variables.

1.4.1. Assignment Statements ¶

Remember that a variable holds a value that can change or vary. Assignment statements initialize or change the value stored in a variable using the assignment operator = . An assignment statement always has a single variable on the left hand side of the = sign. The value of the expression on the right hand side of the = sign (which can contain math operators and other variables) is copied into the memory location of the variable on the left hand side.

Figure 1: Assignment Statement (variable = expression) ¶

Instead of saying equals for the = operator in an assignment statement, say “gets” or “is assigned” to remember that the variable on the left hand side gets or is assigned the value on the right. In the figure above, score is assigned the value of 10 times points (which is another variable) plus 5.

The following video by Dr. Colleen Lewis shows how variables can change values in memory using assignment statements.

As we saw in the video, we can set one variable to a copy of the value of another variable like y = x;. This won’t change the value of the variable that you are copying from.

Click on the Show CodeLens button to step through the code and see how the values of the variables change.

The program is supposed to figure out the total money value given the number of dimes, quarters and nickels. There is an error in the calculation of the total. Fix the error to compute the correct amount.

Calculate and print the total pay given the weekly salary and the number of weeks worked. Use string concatenation with the totalPay variable to produce the output Total Pay = $3000 . Don’t hardcode the number 3000 in your print statement.

Assume you have a package with a given height 3 inches and width 5 inches. If the package is rotated 90 degrees, you should swap the values for the height and width. The code below makes an attempt to swap the values stored in two variables h and w, which represent height and width. Variable h should end up with w’s initial value of 5 and w should get h’s initial value of 3. Unfortunately this code has an error and does not work. Use the CodeLens to step through the code to understand why it fails to swap the values in h and w.

1-4-7: Explain in your own words why the ErrorSwap program code does not swap the values stored in h and w.

Swapping two variables requires a third variable. Before assigning h = w , you need to store the original value of h in the temporary variable. In the mixed up programs below, drag the blocks to the right to put them in the right order.

The following has the correct code that uses a third variable named “temp” to swap the values in h and w.

The code is mixed up and contains one extra block which is not needed in a correct solution. Drag the needed blocks from the left into the correct order on the right, then check your solution. You will be told if any of the blocks are in the wrong order or if you need to remove one or more blocks.

After three incorrect attempts you will be able to use the Help Me button to make the problem easier.

Fix the code below to perform a correct swap of h and w. You need to add a new variable named temp to use for the swap.

1.4.2. Incrementing the value of a variable ¶

If you use a variable to keep score you would probably increment it (add one to the current value) whenever score should go up. You can do this by setting the variable to the current value of the variable plus one (score = score + 1) as shown below. The formula looks a little crazy in math class, but it makes sense in coding because the variable on the left is set to the value of the arithmetic expression on the right. So, the score variable is set to the previous value of score + 1.

Click on the Show CodeLens button to step through the code and see how the score value changes.

1-4-11: What is the value of b after the following code executes?

• It sets the value for the variable on the left to the value from evaluating the right side. What is 5 * 2?
• Correct. 5 * 2 is 10.

1-4-12: What are the values of x, y, and z after the following code executes?

• x = 0, y = 1, z = 2
• These are the initial values in the variable, but the values are changed.
• x = 1, y = 2, z = 3
• x changes to y's initial value, y's value is doubled, and z is set to 3
• x = 2, y = 2, z = 3
• Remember that the equal sign doesn't mean that the two sides are equal. It sets the value for the variable on the left to the value from evaluating the right side.
• x = 1, y = 0, z = 3

1.4.3. Operators ¶

Java uses the standard mathematical operators for addition ( + ), subtraction ( - ), multiplication ( * ), and division ( / ). Arithmetic expressions can be of type int or double. An arithmetic operation that uses two int values will evaluate to an int value. An arithmetic operation that uses at least one double value will evaluate to a double value. (You may have noticed that + was also used to put text together in the input program above – more on this when we talk about strings.)

Java uses the operator == to test if the value on the left is equal to the value on the right and != to test if two items are not equal. Don’t get one equal sign = confused with two equal signs == ! They mean different things in Java. One equal sign is used to assign a value to a variable. Two equal signs are used to test a variable to see if it is a certain value and that returns true or false as you’ll see below. Use == and != only with int values and not doubles because double values are an approximation and 3.3333 will not equal 3.3334 even though they are very close.

Run the code below to see all the operators in action. Do all of those operators do what you expected? What about 2 / 3 ? Isn’t surprising that it prints 0 ? See the note below.

When Java sees you doing integer division (or any operation with integers) it assumes you want an integer result so it throws away anything after the decimal point in the answer, essentially rounding down the answer to a whole number. If you need a double answer, you should make at least one of the values in the expression a double like 2.0.

With division, another thing to watch out for is dividing by 0. An attempt to divide an integer by zero will result in an ArithmeticException error message. Try it in one of the active code windows above.

Operators can be used to create compound expressions with more than one operator. You can either use a literal value which is a fixed value like 2, or variables in them. When compound expressions are evaluated, operator precedence rules are used, so that *, /, and % are done before + and -. However, anything in parentheses is done first. It doesn’t hurt to put in extra parentheses if you are unsure as to what will be done first.

In the example below, try to guess what it will print out and then run it to see if you are right. Remember to consider operator precedence .

1-4-15: Consider the following code segment. Be careful about integer division.

What is printed when the code segment is executed?

• 0.666666666666667
• Don't forget that division and multiplication will be done first due to operator precedence.
• Yes, this is equivalent to (5 + ((a/b)*c) - 1).
• Don't forget that division and multiplication will be done first due to operator precedence, and that an int/int gives an int result where it is rounded down to the nearest int.

1-4-16: Consider the following code segment.

What is the value of the expression?

• Dividing an integer by an integer results in an integer
• Correct. Dividing an integer by an integer results in an integer
• The value 5.5 will be rounded down to 5

1-4-17: Consider the following code segment.

• Correct. Dividing a double by an integer results in a double
• Dividing a double by an integer results in a double

1-4-18: Consider the following code segment.

• Correct. Dividing an integer by an double results in a double
• Dividing an integer by an double results in a double

1.4.4. The Modulo Operator ¶

The percent sign operator ( % ) is the mod (modulo) or remainder operator. The mod operator ( x % y ) returns the remainder after you divide x (first number) by y (second number) so 5 % 2 will return 1 since 2 goes into 5 two times with a remainder of 1. Remember long division when you had to specify how many times one number went into another evenly and the remainder? That remainder is what is returned by the modulo operator.

Figure 2: Long division showing the whole number result and the remainder ¶

In the example below, try to guess what it will print out and then run it to see if you are right.

The result of x % y when x is smaller than y is always x . The value y can’t go into x at all (goes in 0 times), since x is smaller than y , so the result is just x . So if you see 2 % 3 the result is 2 .

1-4-21: What is the result of 158 % 10?

• This would be the result of 158 divided by 10. modulo gives you the remainder.
• modulo gives you the remainder after the division.
• When you divide 158 by 10 you get a remainder of 8.

1-4-22: What is the result of 3 % 8?

• 8 goes into 3 no times so the remainder is 3. The remainder of a smaller number divided by a larger number is always the smaller number!
• This would be the remainder if the question was 8 % 3 but here we are asking for the reminder after we divide 3 by 8.
• What is the remainder after you divide 3 by 8?

1.4.5. FlowCharting ¶

Assume you have 16 pieces of pizza and 5 people. If everyone gets the same number of slices, how many slices does each person get? Are there any leftover pieces?

In industry, a flowchart is used to describe a process through symbols and text. A flowchart usually does not show variable declarations, but it can show assignment statements (drawn as rectangle) and output statements (drawn as rhomboid).

The flowchart in figure 3 shows a process to compute the fair distribution of pizza slices among a number of people. The process relies on integer division to determine slices per person, and the mod operator to determine remaining slices.

Figure 3: Example Flow Chart ¶

A flowchart shows pseudo-code, which is like Java but not exactly the same. Syntactic details like semi-colons are omitted, and input and output is described in abstract terms.

Complete the program based on the process shown in the Figure 3 flowchart. Note the first line of code declares all 4 variables as type int. Add assignment statements and print statements to compute and print the slices per person and leftover slices. Use System.out.println for output.

1.4.6. Storing User Input in Variables ¶

Variables are a powerful abstraction in programming because the same algorithm can be used with different input values saved in variables.

Figure 4: Program input and output ¶

A Java program can ask the user to type in one or more values. The Java class Scanner is used to read from the keyboard input stream, which is referenced by System.in . Normally the keyboard input is typed into a console window, but since this is running in a browser you will type in a small textbox window displayed below the code. The code below shows an example of prompting the user to enter a name and then printing a greeting. The code String name = scan.nextLine() gets the string value you enter as program input and then stores the value in a variable.

Run the program a few times, typing in a different name. The code works for any name: behold, the power of variables!

Run this program to read in a name from the input stream. You can type a different name in the input window shown below the code.

Try stepping through the code with the CodeLens tool to see how the name variable is assigned to the value read by the scanner. You will have to click “Hide CodeLens” and then “Show in CodeLens” to enter a different name for input.

The Scanner class has several useful methods for reading user input. A token is a sequence of characters separated by white space.

Run this program to read in an integer from the input stream. You can type a different integer value in the input window shown below the code.

A rhomboid (slanted rectangle) is used in a flowchart to depict data flowing into and out of a program. The previous flowchart in Figure 3 used a rhomboid to indicate program output. A rhomboid is also used to denote reading a value from the input stream.

Figure 5: Flow Chart Reading User Input ¶

Figure 5 contains an updated version of the pizza calculator process. The first two steps have been altered to initialize the pizzaSlices and numPeople variables by reading two values from the input stream. In Java this will be done using a Scanner object and reading from System.in.

Complete the program based on the process shown in the Figure 5 flowchart. The program should scan two integer values to initialize pizzaSlices and numPeople. Run the program a few times to experiment with different values for input. What happens if you enter 0 for the number of people? The program will bomb due to division by zero! We will see how to prevent this in a later lesson.

The program below reads two integer values from the input stream and attempts to print the sum. Unfortunately there is a problem with the last line of code that prints the sum.

Run the program and look at the result. When the input is 5 and 7 , the output is Sum is 57 . Both of the + operators in the print statement are performing string concatenation. While the first + operator should perform string concatenation, the second + operator should perform addition. You can force the second + operator to perform addition by putting the arithmetic expression in parentheses ( num1 + num2 ) .

More information on using the Scanner class can be found here https://www.w3schools.com/java/java_user_input.asp

1.4.7. Programming Challenge : Dog Years ¶

In this programming challenge, you will calculate your age, and your pet’s age from your birthdates, and your pet’s age in dog years. In the code below, type in the current year, the year you were born, the year your dog or cat was born (if you don’t have one, make one up!) in the variables below. Then write formulas in assignment statements to calculate how old you are, how old your dog or cat is, and how old they are in dog years which is 7 times a human year. Finally, print it all out.

Calculate your age and your pet’s age from the birthdates, and then your pet’s age in dog years. If you want an extra challenge, try reading the values using a Scanner.

1.4.8. Summary ¶

Arithmetic expressions include expressions of type int and double.

The arithmetic operators consist of +, -, * , /, and % (modulo for the remainder in division).

An arithmetic operation that uses two int values will evaluate to an int value. With integer division, any decimal part in the result will be thrown away, essentially rounding down the answer to a whole number.

An arithmetic operation that uses at least one double value will evaluate to a double value.

Operators can be used to construct compound expressions.

During evaluation, operands are associated with operators according to operator precedence to determine how they are grouped. (*, /, % have precedence over + and -, unless parentheses are used to group those.)

An attempt to divide an integer by zero will result in an ArithmeticException to occur.

The assignment operator (=) allows a program to initialize or change the value stored in a variable. The value of the expression on the right is stored in the variable on the left.

During execution, expressions are evaluated to produce a single value.

The value of an expression has a type based on the evaluation of the expression.

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 .

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Assignment Operators in Programming

• Binary Operators in Programming
• Operator Associativity in Programming
• C++ Assignment Operator Overloading
• What are Operators in Programming?
• Assignment Operators In C++
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• Coding for Everyone Course

Assignment operators in programming are symbols used to assign values to variables. They offer shorthand notations for performing arithmetic operations and updating variable values in a single step. These operators are fundamental in most programming languages and help streamline code while improving readability.

Table of Content

What are Assignment Operators?

• Types of Assignment Operators
• Assignment Operators in C++
• Assignment Operators in Java
• Assignment Operators in C#
• Assignment Operators in Javascript
• Application of Assignment Operators

Assignment operators are used in programming to  assign values  to variables. We use an assignment operator to store and update data within a program. They enable programmers to store data in variables and manipulate that data. The most common assignment operator is the equals sign ( = ), which assigns the value on the right side of the operator to the variable on the left side.

Types of Assignment Operators:

• Simple Assignment Operator ( = )
• Addition Assignment Operator ( += )
• Subtraction Assignment Operator ( -= )
• Multiplication Assignment Operator ( *= )
• Division Assignment Operator ( /= )
• Modulus Assignment Operator ( %= )

Below is a table summarizing common assignment operators along with their symbols, description, and examples:

Assignment Operators in C:

Here are the implementation of Assignment Operator in C language:

Assignment Operators in C++:

Here are the implementation of Assignment Operator in C++ language:

Assignment Operators in Java:

Here are the implementation of Assignment Operator in java language:

Assignment Operators in Python:

Here are the implementation of Assignment Operator in python language:

Assignment Operators in C#:

Here are the implementation of Assignment Operator in C# language:

Assignment Operators in Javascript:

Here are the implementation of Assignment Operator in javascript language:

Application of Assignment Operators:

• Variable Initialization : Setting initial values to variables during declaration.
• Mathematical Operations : Combining arithmetic operations with assignment to update variable values.
• Loop Control : Updating loop variables to control loop iterations.
• Conditional Statements : Assigning different values based on conditions in conditional statements.
• Function Return Values : Storing the return values of functions in variables.
• Data Manipulation : Assigning values received from user input or retrieved from databases to variables.

Conclusion:

In conclusion, assignment operators in programming are essential tools for assigning values to variables and performing operations in a concise and efficient manner. They allow programmers to manipulate data and control the flow of their programs effectively. Understanding and using assignment operators correctly is fundamental to writing clear, efficient, and maintainable code in various programming languages.

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Understanding Assignments

What this handout is about.

The first step in any successful college writing venture is reading the assignment. While this sounds like a simple task, it can be a tough one. This handout will help you unravel your assignment and begin to craft an effective response. Much of the following advice will involve translating typical assignment terms and practices into meaningful clues to the type of writing your instructor expects. See our short video for more tips.

Basic beginnings

Regardless of the assignment, department, or instructor, adopting these two habits will serve you well :

• Read the assignment carefully as soon as you receive it. Do not put this task off—reading the assignment at the beginning will save you time, stress, and problems later. An assignment can look pretty straightforward at first, particularly if the instructor has provided lots of information. That does not mean it will not take time and effort to complete; you may even have to learn a new skill to complete the assignment.
• Ask the instructor about anything you do not understand. Do not hesitate to approach your instructor. Instructors would prefer to set you straight before you hand the paper in. That’s also when you will find their feedback most useful.

Assignment formats

Many assignments follow a basic format. Assignments often begin with an overview of the topic, include a central verb or verbs that describe the task, and offer some additional suggestions, questions, or prompts to get you started.

An Overview of Some Kind

The instructor might set the stage with some general discussion of the subject of the assignment, introduce the topic, or remind you of something pertinent that you have discussed in class. For example:

“Throughout history, gerbils have played a key role in politics,” or “In the last few weeks of class, we have focused on the evening wear of the housefly …”

The Task of the Assignment

Pay attention; this part tells you what to do when you write the paper. Look for the key verb or verbs in the sentence. Words like analyze, summarize, or compare direct you to think about your topic in a certain way. Also pay attention to words such as how, what, when, where, and why; these words guide your attention toward specific information. (See the section in this handout titled “Key Terms” for more information.)

“Analyze the effect that gerbils had on the Russian Revolution”, or “Suggest an interpretation of housefly undergarments that differs from Darwin’s.”

Additional Material to Think about

Here you will find some questions to use as springboards as you begin to think about the topic. Instructors usually include these questions as suggestions rather than requirements. Do not feel compelled to answer every question unless the instructor asks you to do so. Pay attention to the order of the questions. Sometimes they suggest the thinking process your instructor imagines you will need to follow to begin thinking about the topic.

“You may wish to consider the differing views held by Communist gerbils vs. Monarchist gerbils, or Can there be such a thing as ‘the housefly garment industry’ or is it just a home-based craft?”

These are the instructor’s comments about writing expectations:

“Be concise”, “Write effectively”, or “Argue furiously.”

Technical Details

These instructions usually indicate format rules or guidelines.

“Your paper must be typed in Palatino font on gray paper and must not exceed 600 pages. It is due on the anniversary of Mao Tse-tung’s death.”

The assignment’s parts may not appear in exactly this order, and each part may be very long or really short. Nonetheless, being aware of this standard pattern can help you understand what your instructor wants you to do.

Interpreting the assignment

Ask yourself a few basic questions as you read and jot down the answers on the assignment sheet:

Why did your instructor ask you to do this particular task?

Who is your audience.

• What kind of evidence do you need to support your ideas?

What kind of writing style is acceptable?

• What are the absolute rules of the paper?

Try to look at the question from the point of view of the instructor. Recognize that your instructor has a reason for giving you this assignment and for giving it to you at a particular point in the semester. In every assignment, the instructor has a challenge for you. This challenge could be anything from demonstrating an ability to think clearly to demonstrating an ability to use the library. See the assignment not as a vague suggestion of what to do but as an opportunity to show that you can handle the course material as directed. Paper assignments give you more than a topic to discuss—they ask you to do something with the topic. Keep reminding yourself of that. Be careful to avoid the other extreme as well: do not read more into the assignment than what is there.

Of course, your instructor has given you an assignment so that they will be able to assess your understanding of the course material and give you an appropriate grade. But there is more to it than that. Your instructor has tried to design a learning experience of some kind. Your instructor wants you to think about something in a particular way for a particular reason. If you read the course description at the beginning of your syllabus, review the assigned readings, and consider the assignment itself, you may begin to see the plan, purpose, or approach to the subject matter that your instructor has created for you. If you still aren’t sure of the assignment’s goals, try asking the instructor. For help with this, see our handout on getting feedback .

Given your instructor’s efforts, it helps to answer the question: What is my purpose in completing this assignment? Is it to gather research from a variety of outside sources and present a coherent picture? Is it to take material I have been learning in class and apply it to a new situation? Is it to prove a point one way or another? Key words from the assignment can help you figure this out. Look for key terms in the form of active verbs that tell you what to do.

Key Terms: Finding Those Active Verbs

Here are some common key words and definitions to help you think about assignment terms:

Information words Ask you to demonstrate what you know about the subject, such as who, what, when, where, how, and why.

• define —give the subject’s meaning (according to someone or something). Sometimes you have to give more than one view on the subject’s meaning
• describe —provide details about the subject by answering question words (such as who, what, when, where, how, and why); you might also give details related to the five senses (what you see, hear, feel, taste, and smell)
• explain —give reasons why or examples of how something happened
• illustrate —give descriptive examples of the subject and show how each is connected with the subject
• summarize —briefly list the important ideas you learned about the subject
• trace —outline how something has changed or developed from an earlier time to its current form
• research —gather material from outside sources about the subject, often with the implication or requirement that you will analyze what you have found

Relation words Ask you to demonstrate how things are connected.

• compare —show how two or more things are similar (and, sometimes, different)
• contrast —show how two or more things are dissimilar
• apply—use details that you’ve been given to demonstrate how an idea, theory, or concept works in a particular situation
• cause —show how one event or series of events made something else happen
• relate —show or describe the connections between things

Interpretation words Ask you to defend ideas of your own about the subject. Do not see these words as requesting opinion alone (unless the assignment specifically says so), but as requiring opinion that is supported by concrete evidence. Remember examples, principles, definitions, or concepts from class or research and use them in your interpretation.

• assess —summarize your opinion of the subject and measure it against something
• prove, justify —give reasons or examples to demonstrate how or why something is the truth
• evaluate, respond —state your opinion of the subject as good, bad, or some combination of the two, with examples and reasons
• support —give reasons or evidence for something you believe (be sure to state clearly what it is that you believe)
• synthesize —put two or more things together that have not been put together in class or in your readings before; do not just summarize one and then the other and say that they are similar or different—you must provide a reason for putting them together that runs all the way through the paper
• analyze —determine how individual parts create or relate to the whole, figure out how something works, what it might mean, or why it is important
• argue —take a side and defend it with evidence against the other side

More Clues to Your Purpose As you read the assignment, think about what the teacher does in class:

• What kinds of textbooks or coursepack did your instructor choose for the course—ones that provide background information, explain theories or perspectives, or argue a point of view?
• In lecture, does your instructor ask your opinion, try to prove their point of view, or use keywords that show up again in the assignment?
• What kinds of assignments are typical in this discipline? Social science classes often expect more research. Humanities classes thrive on interpretation and analysis.
• How do the assignments, readings, and lectures work together in the course? Instructors spend time designing courses, sometimes even arguing with their peers about the most effective course materials. Figuring out the overall design to the course will help you understand what each assignment is meant to achieve.

Now, what about your reader? Most undergraduates think of their audience as the instructor. True, your instructor is a good person to keep in mind as you write. But for the purposes of a good paper, think of your audience as someone like your roommate: smart enough to understand a clear, logical argument, but not someone who already knows exactly what is going on in your particular paper. Remember, even if the instructor knows everything there is to know about your paper topic, they still have to read your paper and assess your understanding. In other words, teach the material to your reader.

Aiming a paper at your audience happens in two ways: you make decisions about the tone and the level of information you want to convey.

• Tone means the “voice” of your paper. Should you be chatty, formal, or objective? Usually you will find some happy medium—you do not want to alienate your reader by sounding condescending or superior, but you do not want to, um, like, totally wig on the man, you know? Eschew ostentatious erudition: some students think the way to sound academic is to use big words. Be careful—you can sound ridiculous, especially if you use the wrong big words.
• The level of information you use depends on who you think your audience is. If you imagine your audience as your instructor and they already know everything you have to say, you may find yourself leaving out key information that can cause your argument to be unconvincing and illogical. But you do not have to explain every single word or issue. If you are telling your roommate what happened on your favorite science fiction TV show last night, you do not say, “First a dark-haired white man of average height, wearing a suit and carrying a flashlight, walked into the room. Then a purple alien with fifteen arms and at least three eyes turned around. Then the man smiled slightly. In the background, you could hear a clock ticking. The room was fairly dark and had at least two windows that I saw.” You also do not say, “This guy found some aliens. The end.” Find some balance of useful details that support your main point.

You’ll find a much more detailed discussion of these concepts in our handout on audience .

The Grim Truth

With a few exceptions (including some lab and ethnography reports), you are probably being asked to make an argument. You must convince your audience. It is easy to forget this aim when you are researching and writing; as you become involved in your subject matter, you may become enmeshed in the details and focus on learning or simply telling the information you have found. You need to do more than just repeat what you have read. Your writing should have a point, and you should be able to say it in a sentence. Sometimes instructors call this sentence a “thesis” or a “claim.”

So, if your instructor tells you to write about some aspect of oral hygiene, you do not want to just list: “First, you brush your teeth with a soft brush and some peanut butter. Then, you floss with unwaxed, bologna-flavored string. Finally, gargle with bourbon.” Instead, you could say, “Of all the oral cleaning methods, sandblasting removes the most plaque. Therefore it should be recommended by the American Dental Association.” Or, “From an aesthetic perspective, moldy teeth can be quite charming. However, their joys are short-lived.”

Convincing the reader of your argument is the goal of academic writing. It doesn’t have to say “argument” anywhere in the assignment for you to need one. Look at the assignment and think about what kind of argument you could make about it instead of just seeing it as a checklist of information you have to present. For help with understanding the role of argument in academic writing, see our handout on argument .

What kind of evidence do you need?

There are many kinds of evidence, and what type of evidence will work for your assignment can depend on several factors–the discipline, the parameters of the assignment, and your instructor’s preference. Should you use statistics? Historical examples? Do you need to conduct your own experiment? Can you rely on personal experience? See our handout on evidence for suggestions on how to use evidence appropriately.

Make sure you are clear about this part of the assignment, because your use of evidence will be crucial in writing a successful paper. You are not just learning how to argue; you are learning how to argue with specific types of materials and ideas. Ask your instructor what counts as acceptable evidence. You can also ask a librarian for help. No matter what kind of evidence you use, be sure to cite it correctly—see the UNC Libraries citation tutorial .

You cannot always tell from the assignment just what sort of writing style your instructor expects. The instructor may be really laid back in class but still expect you to sound formal in writing. Or the instructor may be fairly formal in class and ask you to write a reflection paper where you need to use “I” and speak from your own experience.

Try to avoid false associations of a particular field with a style (“art historians like wacky creativity,” or “political scientists are boring and just give facts”) and look instead to the types of readings you have been given in class. No one expects you to write like Plato—just use the readings as a guide for what is standard or preferable to your instructor. When in doubt, ask your instructor about the level of formality they expect.

No matter what field you are writing for or what facts you are including, if you do not write so that your reader can understand your main idea, you have wasted your time. So make clarity your main goal. For specific help with style, see our handout on style .

Technical details about the assignment

The technical information you are given in an assignment always seems like the easy part. This section can actually give you lots of little hints about approaching the task. Find out if elements such as page length and citation format (see the UNC Libraries citation tutorial ) are negotiable. Some professors do not have strong preferences as long as you are consistent and fully answer the assignment. Some professors are very specific and will deduct big points for deviations.

Usually, the page length tells you something important: The instructor thinks the size of the paper is appropriate to the assignment’s parameters. In plain English, your instructor is telling you how many pages it should take for you to answer the question as fully as you are expected to. So if an assignment is two pages long, you cannot pad your paper with examples or reword your main idea several times. Hit your one point early, defend it with the clearest example, and finish quickly. If an assignment is ten pages long, you can be more complex in your main points and examples—and if you can only produce five pages for that assignment, you need to see someone for help—as soon as possible.

Tricks that don’t work

Your instructors are not fooled when you:

• spend more time on the cover page than the essay —graphics, cool binders, and cute titles are no replacement for a well-written paper.
• use huge fonts, wide margins, or extra spacing to pad the page length —these tricks are immediately obvious to the eye. Most instructors use the same word processor you do. They know what’s possible. Such tactics are especially damning when the instructor has a stack of 60 papers to grade and yours is the only one that low-flying airplane pilots could read.
• use a paper from another class that covered “sort of similar” material . Again, the instructor has a particular task for you to fulfill in the assignment that usually relates to course material and lectures. Your other paper may not cover this material, and turning in the same paper for more than one course may constitute an Honor Code violation . Ask the instructor—it can’t hurt.
• get all wacky and “creative” before you answer the question . Showing that you are able to think beyond the boundaries of a simple assignment can be good, but you must do what the assignment calls for first. Again, check with your instructor. A humorous tone can be refreshing for someone grading a stack of papers, but it will not get you a good grade if you have not fulfilled the task.

Critical reading of assignments leads to skills in other types of reading and writing. If you get good at figuring out what the real goals of assignments are, you are going to be better at understanding the goals of all of your classes and fields of study.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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• How to Write a Thesis Statement | 4 Steps & Examples

How to Write a Thesis Statement | 4 Steps & Examples

Published on January 11, 2019 by Shona McCombes . Revised on August 15, 2023 by Eoghan Ryan.

A thesis statement is a sentence that sums up the central point of your paper or essay . It usually comes near the end of your introduction .

Your thesis will look a bit different depending on the type of essay you’re writing. But the thesis statement should always clearly state the main idea you want to get across. Everything else in your essay should relate back to this idea.

You can write your thesis statement by following four simple steps:

• Start with a question
• Write your initial answer
• Develop your answer
• Refine your thesis statement

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Table of contents

What is a thesis statement, placement of the thesis statement, step 1: start with a question, step 2: write your initial answer, step 3: develop your answer, step 4: refine your thesis statement, types of thesis statements, other interesting articles, frequently asked questions about thesis statements.

A thesis statement summarizes the central points of your essay. It is a signpost telling the reader what the essay will argue and why.

The best thesis statements are:

• Concise: A good thesis statement is short and sweet—don’t use more words than necessary. State your point clearly and directly in one or two sentences.
• Contentious: Your thesis shouldn’t be a simple statement of fact that everyone already knows. A good thesis statement is a claim that requires further evidence or analysis to back it up.
• Coherent: Everything mentioned in your thesis statement must be supported and explained in the rest of your paper.

Prevent plagiarism. Run a free check.

The thesis statement generally appears at the end of your essay introduction or research paper introduction .

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts and among young people more generally is hotly debated. For many who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its many benefits for education: the internet facilitates easier access to information, exposure to different perspectives, and a flexible learning environment for both students and teachers.

You should come up with an initial thesis, sometimes called a working thesis , early in the writing process . As soon as you’ve decided on your essay topic , you need to work out what you want to say about it—a clear thesis will give your essay direction and structure.

You might already have a question in your assignment, but if not, try to come up with your own. What would you like to find out or decide about your topic?

For example, you might ask:

After some initial research, you can formulate a tentative answer to this question. At this stage it can be simple, and it should guide the research process and writing process .

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See an example

Now you need to consider why this is your answer and how you will convince your reader to agree with you. As you read more about your topic and begin writing, your answer should get more detailed.

In your essay about the internet and education, the thesis states your position and sketches out the key arguments you’ll use to support it.

The negatives of internet use are outweighed by its many benefits for education because it facilitates easier access to information.

In your essay about braille, the thesis statement summarizes the key historical development that you’ll explain.

The invention of braille in the 19th century transformed the lives of blind people, allowing them to participate more actively in public life.

A strong thesis statement should tell the reader:

• Why you hold this position
• What they’ll learn from your essay
• The key points of your argument or narrative

The final thesis statement doesn’t just state your position, but summarizes your overall argument or the entire topic you’re going to explain. To strengthen a weak thesis statement, it can help to consider the broader context of your topic.

These examples are more specific and show that you’ll explore your topic in depth.

Your thesis statement should match the goals of your essay, which vary depending on the type of essay you’re writing:

• In an argumentative essay , your thesis statement should take a strong position. Your aim in the essay is to convince your reader of this thesis based on evidence and logical reasoning.
• In an expository essay , you’ll aim to explain the facts of a topic or process. Your thesis statement doesn’t have to include a strong opinion in this case, but it should clearly state the central point you want to make, and mention the key elements you’ll explain.

If you want to know more about AI tools , college essays , or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

• Ad hominem fallacy
• Post hoc fallacy
• Appeal to authority fallacy
• False cause fallacy
• Sunk cost fallacy

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A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, August 15). How to Write a Thesis Statement | 4 Steps & Examples. Scribbr. Retrieved June 11, 2024, from https://www.scribbr.com/academic-essay/thesis-statement/

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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 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

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Excel IF statement between two numbers or dates

The tutorial shows how to use an Excel IF formula to see if a given number or date falls between two values.

To check if a given value is between two numeric values, you can use the AND function with two logical tests. To return your own values when both expressions evaluate to TRUE, nest AND inside the IF function. Detailed examples follow below.

Excel formula: if between two numbers

To test if a given number is between two numbers that you specify, use the AND function with two logical tests:

• Use the greater then (>) operator to check if the value is higher than a smaller number.
• Use the less than (<) operator to check if the value is lower than a larger number.

The generic If between formula is:

To include the boundary values, use the greater than or equal to (>=) and less than or equal to (<=) operators:

For example, to see if a number in A2 falls between 10 and 20, not including the boundary values, the formula in B2, copied down, is:

=AND(A2>10, A2<20)

To check if A2 is between 10 and 20, including the threshold values, the formula in C2 takes this form:

=AND(A2>=10, A2<=20)

If between two numbers then

In case you want to return a custom value if a number is between two values, then place the AND formula in the logical test of the IF function.

For example, to return "Yes" if the number in A2 is between 10 and 20, "No" otherwise, use one of these IF statements:

If between 10 and 20:

=IF(AND(A2>10, A2<20), "Yes", "No")

If between 10 and 20, including the boundaries:

Suppose you have a set of values in column A and wish to know which of the values fall between the numbers in columns B and C in the same row. Assuming a smaller number is always in column B and a larger number is in column C, the task can be accomplished with this formula:

=IF(AND(A2>B2, A2<C2), "Yes", "No")

Including the boundaries:

And here is a variation of the If between statement that returns a value itself if TRUE, some text or an empty string if FALSE:

=IF(AND(A2>10, A2<20), A2, "Invalid")

If boundary values are in different columns

When smaller and larger numbers you are comparing against may appear in different columns (i.e. number 1 is not always smaller than number 2), use a slightly more complex version of the formula.

Here, we first test if the target value is higher than a smaller of the two numbers returned by the MIN function, and then check if it is lower than a larger of the two numbers returned by the MAX function.

To include the threshold numbers, adjust the logic as follows:

For example, to find out if a number in A2 falls between two numbers in B2 and C2, use one of these formulas:

Excluding boundaries:

=AND(A2>MIN(B2, C2), A2<MAX(B2, C2))

Including boundaries:

=AND(A2>=MIN(B2, C2), A2<=MAX(B2, C2))

To return your own values instead of TRUE and FALSE, use the following Excel IF statement between two numbers:

=IF(AND(A2>MIN(B2, C2), A2<MAX(B2, C2)), "Yes", "No")

Excel formula: if between two dates

The If between dates formula in Excel is essentially the same as If between numbers .

To check whether a given date is within a certain range, the generic formula is:

Not including the boundary dates:

However, there is a caveat: IF does recognize dates supplied directly to its arguments and regards them as text strings. For IF to recognize a date, it should be wrapped in the DATEVALUE function.

For example, to test if a date in A2 falls between 1-Jan-2022 and 31-Dec-2022 inclusive, you can use this formula:

In case, the start and end dates are in predefined cells, the formula becomes much simpler:

=IF(AND(A2>=$E$2, A2<=$E$3), "Yes", "No")

If date is within next N days

To test if a date is within the next n days of today's date, use the TODAY function to determine the start and end dates. Inside the AND statement, the first logical test checks if the target date is greater than today's date, while the second logical test checks if it is less than or equal to the current date plus n days:

For example, to test if a date in A2 occurs in the next 7 days, the formula is:

If date is within last N days

To test if a given date is within the last n days of today's date, you again use IF together with the AND and TODAY functions. The first logical test of AND checks if a tested date is greater than or equal to today's date minus n days, and the second logical test checks if the date is less than today:

For example, to determine if a date in A2 occurred in the last 7 days, the formula is:

Hopefully, our examples have helped you understand how to use the If between formula in Excel efficiently. I thank you for reading and hope to see you on our blog next week!

Practice workbook

You may also be interested in.

• SUMIF between two numbers in Excel and Google Sheets
• Sum if between two dates in Excel
• Excel IF function with multiple conditions

Table of contents

146 comments

I am working with a large amount of financial data and trying to separate into Financial Statement type by GL#. I have entered the following formula:

IF((G2400000,G2500000, "Expenses")))

Balance sheet accounts GL# is less than 500000, Revenues are in the 400000 range, and expenses in the 500000. Excel is telling me this is an invalid formula. What am I doing wrong here?

Hi! Based on your description, it is hard to completely understand your task. I can assume that you have two conditions. So try using the IF AND formula. Look for the example formulas here: IF AND in Excel: nested formula, multiple statements, and more . If this does not help, explain the problem in detail.

Hi Alex, What formula should I use if I want to check If my Renewal End Date :4/27/2024 and close date is 4/4/2024 & I want to check if close date is less than or equal to renewal end date ?

Hi! Have you tried any of the methods described in this blog post? You can also use these instructions to compare two dates and display the desired message: Using IF function with dates . Simply compare the two cells and determine which one is larger.

How do i check if a cell is between two dates, and return one of three values.

Does A2 have a date between 01/01/2024 - 31/01/2024. If yes, return Yes. If no, return no. If A2 has nothing in the cell, return as blank.

Hi! Have you tried the methods described in this blog post? The formula might look like this:

=IF(A2<>"",IF(AND(A2>D1, A2<D2), "Yes", "No"),"")

Read more: Nested IF in Excel – formula with multiple conditions .

Hi Alexander, thank you! I was trying that for hours, and yes i had read the blog post. I look at Excel formulas maybe one every 9/10 months, so i just don't hold the information very well!

It's really apprecaited.

Hi, I would like to use IF function to calculate the age to a certain date and turn Yes the result is >=16 or No if it is <16. How can I dothat?

Hi! The following tutorial should help: How to calculate age in Excel from birthday . Calculate the age and use it in the IF formula as described in the article above.

=IF(OR(C3>=DATEVALUE("2/19/2024"), C3<=DATEVALUE("2/25/2024")),K3, 0)

I am trying to use a date range to select data on the same spread sheet in a different cell. If the reference cell meets the date range then the value in the "true" referenced cell, if not then enter a zero. I have tried the above formular however, it doesn't work. Any suggestions?

Hi! "2/19/2024" may not match your computer's date format. Also, note in the article above that you should use the AND operator, not the OR operator, for the date range. You can also try to set the date by using the DATE function . For example:

=IF(AND(C3>=DATE(2024,2,19), C3<=DATE(2024,2,25)),K3, 0)

I would very much appreciate if someone could explain to me how to make excel calculate the following:

Date Range using the if formula

My spreadsheet is comprised of names dates and then blank boxes for the 12 months of the year. I would like if excel could use the dates and place an x in each months box that corresponds to the date range. Example date range 1/12023 - 3/30/2021 it would place an x in January February and march and leave all the other boxes blank.

Hi! Your sample date range is text. Therefore, you cannot perform any calculations with it, including calculating months.

i want to assign a new value , if the number falls in between two numbers . there are ten such two number in 10 ten row

Hi! Have you tried the ways described in this blog post? If they don’t work for you, then please describe your task in detail.

Looking for a little help I want to make a cell auto fill a number The formula I have now is =IF(AND(C19>=150,C19<=149.99),"$1500","$1000")

What I am looking for is if greater than 150 - "$1500", if between 120 &149.99 "$1000". if between 95 and 119.99 "$500" and if <94.99"$0" can anyone help please?

Hi! The answer to your question can be found in this article: Nested IF in Excel – formula with multiple conditions . Read Example 1.

Hi Any help on this...

I need to calculate a number between the range of 2 numbers and I need to return 50% of the above value of whatever number has been input.

i.e. Between 60,000 and 100,000 I need to return 50% of whatever number is input..... so if they input 75,000 the answer needs to be 7500 which is 50% of the value of 75000 minus 60000 = 15000 = ANSWER 7500

Hi! If you have two conditions, use an IF AND expression as described in these instructions: Excel IF: greater than AND less than . I also recommend that you read the first paragraph of the article above carefully. Your description of the calculations in the second paragraph contradicts the first paragraph. But I think you can write the calculation formula yourself.

I've tried IF, AND, MATCH, XMATCH, and I can't seem to get my formula to work. In one sheet I have Move Out Date & Fiscal Year. In another sheet, I have the dates of the fiscal year (start & end) and the Fiscal Year name.

Move Out Date Fiscal Year 09/01/2011 09/05/2011 09/10/2011

Start End Fiscal Year 9/1/2011 8/31/2012 FY2012 9/1/2012 8/31/2013 FY2013 9/1/2013 8/31/2014 FY2014

Depending on the date in the Move out Date cell, I would like for it to determine what the Fiscal Year is for that move out date. In other words, if column a, cell 1 is dated 9/1/2011, I want it to return "FY2012" into a column. Can anyone help me? Thank you.

Hi! To find Fiscal Year by two conditions, use the INDEX MATCH functions and these instructions: Excel INDEX MATCH with multiple criteria - formula examples . I believe the following formula will help you solve your task:

=INDEX(Sheet2!C1:C10,MATCH(1,(Sheet2!A1:A10<Sheet1!A1)*(Sheet2!B1:B10>Sheet1!A1),0))

Hi! Need your help please to get the data I want using the compound "If" function to get the vacation leave balance based on the hiring data. Below are the details:

A1: Hiring Date

Conditions: * If hired between 1/1/2023 - 6/30/2023 = vacation leave should be 11 days * If hired between 7/1/2023 - 7/31/2023 = vacation leave should be 6 days * If hired between 8/1/2023 - 8/31/2023 = vacation leave should be 5 days * If hired between 9/1/2023 - 9/30/2023 = vacation leave should be 4 days * If hired between 10/1/2023 - 10/31/2023 = vacation leave should be 3 days * If hired between 11/1/2023 - 11/30/2023 = vacation leave should be 2 days * If hired between 12/1/2023 - 12/31/2023 = vacation leave should be 1 day

Really appreciate if you can help me build a formula based on the above. Thanks in advance!

Hi! I recommend reading this guide: Excel Nested IF statement: examples, best practices and alternatives . Try to use CHOOSE function instead of nested IF formula:

=CHOOSE(MONTH(A1),11,10,9,8,7,6,5,4,3,2,1)

Define the month number using the MONTH function .

if A1 gives current date and B1 says W then what is if formulae to show the actual date seen in A1 (not update it).

Hi! Based on your description, it is hard to completely understand your task. However, I’ll try to guess and offer you the following guide: How to insert today date & current time as unchangeable time stamp .

sorry it was not clear, i'll try again as your solution didn't give me the result i needed. thank you anyway A1 IS POPULATED WITH A DATE..... B1 IS EITHER D or W...... C1 needs to give the result.

so what is the formula to be put into C! if B2 is W ....to replicate the date seen in A1. (it needs to be the same date that is there and not update it

Hi! Your description of the problem is not very clear. I will try to assume such a formula for cell C1:

=IF(B2="w",A1,"")

This will work if the date in A1 is written manually. If the date in A1 is entered using the TODAY function, use the method I recommended earlier.

thank you so much, that's worked a treat. Thank you for your patience too. sorry for the lack of clarity.

Hi and thank you in advance, I'm trying to automate functional lab evaluations. So far, I've been able to use a nested conditional formatting: =IF(N7>=7.5,"Acute infection",IF(N7<=5,"Chronic infection"))

The problem I'm having is that when a result is functionally in range it returns the word FALSE. I don't want anything to appear in a cell where the lab result is in the functional range (I have to build this because the functional range is much tighter than the reference range printed on the lab report--it just takes a lot of time to evaluate).

How do I prevent the word FALSE and just show a blank cell. Otherwise my report looks extremely cluttered and is hard to go over with patients.

Appreciate it!

Hi! Read the IF function manual carefully. Define value_if_false argument.

=IF(N7>=7.5,"Acute infection",IF(N7<=5,"Chronic infection",""))

Hi there, looking to extract data during a one month period (eg. May 30 - June 30) across a 30 year period in one set of data. Is there a way to do this without having to repeat a formula 30 times?

Hi! Use the DAY and MONTH functions to specify the desired dates. For example:

=AND(DATE(2023,MONTH(A1),DAY(A1))>=DATE(2023,5,30),DATE(2023,MONTH(A1),DAY(A1))<=DATE(2023,6,30))

Hello! I need help with a formula.

Columns are: Column I - End Date: 5/31/2023 Column M - Sign On Payout: 7/30/2023 Column K - Amount: 1000

I was hoping to write a formula that says if the end date is before or equal to the payout date then "N/A", if not the amount.

=IF(AND(I5>=M5,I5<=M5),"n/a",K5) is what I wrote and its not working.

Hi! Remove the AND condition from the formula

=IF(I5<=M5,"n/a",K5)

I did this and its still not working. I even tried formatting the cells for the dates to be the same. Any advice?

Hi! The formula matches your question. Check your data. I also recommend studying the article above carefully.

I need excel formula for below example.

Sheet 1 Date Part Code Qty Rate 10-01-2023 ABC 10 25 Rate should be come through formula from "sheet 2" - First match "part code" & Date match with "sheet 2" between date 12-01-2023 ABC 20 30 11-01-2023 XYZ 5 12

Sheet 2 Part Code Valid from Valid To Rate ABC 01-01-2023 05-01-2023 20 ABC 06-01-2023 10-01-2023 25 ABC 11-01-2023 15-01-2023 30 XYZ 01-01-2023 10-01-2023 10 XYZ 10-01-2023 12-01-2023 12

Hi! If I understand your task correctly, this article may be helpful: Excel INDEX MATCH with multiple criteria . Try this formula:

=INDEX(Sheet2!D2:D6,MATCH(1,(Sheet1!A1>=Sheet2!B2:B6)*(Sheet1!A1<=Sheet2!C2:C6)*(Sheet1!B1=Sheet2!A2:A6),0))

Hi, I'm trying to pull through certain data from a sheet based on a name if the dates in column B on that sheet falls between a certain month only.

=IF(('PC 156598'!B:B>=DATEVALUE("11/1/2023"),'PC 156598!'B:B<=DATEVALUE("11/30/2023")),VLOOKUP(C2,'PC 156598'!$A$2:$C$64,3,0),0)

Excel is saying there's something wrong with the second tab reference 'PC 156598!' Have I nested a vlookup within the IF formula incorrectly?

Please help! Thank you

Hi! I can assume that you wanted to use an IF formula with two conditions. However, you do not have an AND or OR operator to combine the conditions. For example,

=IF(AND('PC 156598'!B:B>=DATEVALUE("11/1/2023"),'PC 156598!'B:B<=DATEVALUE("11/30/2023")),VLOOKUP(C2,'PC 156598'!$A$2:$C$64,3,0),0)

For more information, please visit: Excel IF function with multiple conditions .

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Tax Examiner 1 – Unclaimed Property and Contractor’s Gross Receipts – 24141681

June 7, 2024

Job Overview:

This position resides in the  Business & Income Taxes Division of the Department of Revenue . The Business and Income Tax Division ensures secure handling and processing of taxpayer information and data. Duties include conducting technical level examinations on assigned tax returns that involve research, verifying data, adjusting returns, providing communications to taxpayers and their representatives, and participating in the appeal process. Tax examiner I’s assist with development of audit programs and procedures, draft summary reports of audit findings, and testify as an expert witness. They also assist other tax examiners in their responsibilities and provide a range of support services to taxpayers and other department programs and employees. The position does not supervise other staff.

Knowledge of:

• Tax accounting
• Auditing standards, principles, and practices
• Financial analysis
• Investigative methods and procedures
• State and federal tax laws, rules, and regulations
• Business practices
• Computer applications related to taxes and finance
• Research and analysis
• Cost benefit analysis
• Accuracy and attention to detail
• Mathematics
• Conflict resolution & customer service
• Microsoft programs and other data base applications
• Written, verbal, and interpersonal communication

You would be a great fit for this position if you have:

• Self-motivated
• Strong work ethic
• Follow instructions
• Provide timely and accurate customer service
• Work under pressure
• Be open minded and think creatively

REMOTE/TELEWORK:  This position may be eligible to work from an approved worksite within the state of Montana. This position would be required to report to a Department of Revenue office assigned by the supervisor. Employees must meet and sustain Department of Revenue telework eligibility requirements and supervisor's approval to participate in the DOR Telework Program.

*This is an incomplete list of job duties. For a complete job description please contact Human Resources at [email protected] or (406) 444-9858.

To be considered for a Department of Revenue position, successful applicants are required to successfully pass tax compliance and criminal background check(s). DOR is an equal opportunity employer. Women, minorities, and people with disabilities are encouraged to apply.

Education and Experience

The above competencies are typically acquired through a combination of education and experience equivalent to:

• Bachelor’s degree in accounting or finance, business administration, or public administration supplemented by college coursework in accounting or closely related field.
• One year of job-related work experience in auditing, tax examination, or accounting practices.

Other combinations of education and experience will be evaluated on an individual basis.

*If you have documented postsecondary education, please attach your transcripts to your application for it to be considered in the evaluation process.

Applicant Pool Statement

If another department vacancy occurs in this job title within six months, the same applicant pool may be used for the selection.

Training Assignment Available

This agency may use a training assignment. Employees in training assignments may be paid below the base pay established by the agency pay rules. Conditions of the training assignment will be stated in writing at the time of hire.

Job Overview

Apply For This Position

Citizen Services Call Center

Contact Us Online

Phone: (406) 444-6900

TDD: Montana Relay 711

Email: [email protected]

We’re available Monday through Thursday, 9:00 a.m. until 4:00 pm and Friday, 9:00 a.m. until 1:00 p.m.

Taxpayer Advocate

If you need help working with the department or figuring out our audit, appeals, or relief processes, the Taxpayer Advocate can help.

The Education Donations Portal 2.0 is Now Available!

You may now register to receive donations as a Montana Public School District or Student Scholarship Organization.

For more information on tax credits for qualified education contributions, please see our guide .

Payment and Filing Options

Payment options, filing options.

The Department of Revenue works hard to ensure we process everyone’s return as securely and quickly as possible.

Unfortunately, it can take up to 90 days to issue your refund  and we may need to ask you to  verify your return .

We encourage all Montanans to file early and electronically. This is the easiest and most secure way to file and get your refund as quickly as possible.

Remember, we are here to help. Please contact us if you need additional assistance.

Where's My Refund?

The My Revenue portal will no longer be available after July 23, 2021. Department of Revenue forms will be made available on MTRevenue.gov.

If you have records currently saved in My Revenue, we ask you to log into your My Revenue account and download them before July 23, 2021.

Log into My Revenue

We understand COVID-19 impacts all aspects of our community. Throughout this event, we will work hard to keep you updated on the impact COVID-19 has on taxation, alcoholic beverage control, and property assessment.

We serve the people of Montana and are here to help you through this time of crisis.

If you have been impacted by COVID-19 and are facing hardships, we will work with you to find a solution.

To see how other agencies have been impacted by COVID-19 go to COVID19.mt.gov .

We are continually reviewing due dates and deadlines. Please check back regularly or subscribe for COVID-19 updates to receive notifications for future changes.

Information Regarding COVID-19 Stimulus Payments

Stimulus payments are being issued by the IRS.

The Montana Department of Revenue is unable to assist in securing your stimulus payment.

You can check on the status of your COVID-19 Stimulus payment at IRS.gov/Coronavirus/Get-My-Payment .

Subscribe for Updates on COVID-19’s Impact to the Department of Revenue

Other COVID-19 Resources

Title Transfers and Changes

To prove vehicle ownership, it’s important to have a valid, up-to-date, and accurate California Certificate of Title. Here’s how you can transfer and change a title. 

Transfer your Title online!

You can now transfer a title online. Learn more about the steps and get started.

How to Transfer a Title

Anytime there’s a change to a vehicle or vessel’s registered owner or lienholder, that change must be updated in DMV’s records within 10 days and the California Certificate of Title must be transferred to the new owner.

A change in ownership is usually due to:

• Sale, gift, or donation
• Adding or deleting the name of an owner
• Inheritance
• Satisfaction of lien (full payment of car loan)

To transfer a title, you will need:

• Either the California Certificate of Title or an Application for Replacement or Transfer of Title (REG 227) (if the title is missing). 
• The signature(s) of seller(s) and lienholder (if any).
• The signature(s) of buyer(s).
• A transfer fee .

Depending on the type of transfer, you might need to complete and submit additional forms. See below for other title transfers and title transfer forms.

Submit your title transfer paperwork and fee (if any) to a DMV office or by mail to: 

DMV PO Box 942869 Sacramento, CA 94269

Rush Title Processing

If you need us to expedite your title processing, you can request rush title processing for an additional fee.

Transfer Fees

Depending on the type of transfer, you may need to pay the following fees:

• Replacement title
• Use tax, based on the buyer’s county of residence
• Registration

See the full list of fees .

Renewal fees and parking/toll violation fees don’t need to be paid to issue a replacement California Certificate of Title.

Title Transfer Forms

These forms may be required when transferring ownership of a vehicle or vessel:  Application for Replacement or Transfer of Title (REG 227) Vehicle/Vessel Transfer and Reassignment (REG 262) form (call the DMV’s automated voice system at 1-800-777-0133 to have a form mailed to you) Statement of Facts (REG 256) Lien Satisfied/Title Holder Release (REG 166) Notice of Transfer and Release of Liability Smog certification Vehicle Emission System Statement (Smog) (REG 139) Declaration of Gross Vehicle Weight (GVW)/Combined Gross Vehicle Weight (CGW) (REG 4008) Affidavit for Transfer without Probate (REG 5) Bill of Sale (REG 135) Verification of Vehicle (REG 31)

Other Title Transfers

When you’re buying a new car or a used car from a dealership, the dealer will handle the paperwork and you’ll receive your title from DMV in the mail.

When vehicle ownership is transferred between two private parties, it’s up to them to transfer the title. If you have the California Certificate of Title for the vehicle , the seller signs the title to release ownership of the vehicle. The buyer should then bring the signed title to a DMV office to apply for transfer of ownership. 

If you don’t have the California Certificate of Title , you need to use an Application for Replacement or Transfer of Title (REG 227) to transfer ownership. The lienholder’s release, if any, must be notarized. The buyer should then bring the completed form to a DMV office and we will issue a new registration and title.

Make sure you have all signatures on the proper lines to avoid delays.

Other Steps for the Seller When Vehicle Ownership is Transferred

• 10 years old or older.
• Commercial with a GVW or CGW of more than 16,000 pounds.
• New and being transferred prior to its first retail sale by a dealer.
• Complete a Notice of Transfer and Release of Liability (NRL) within 5 days of releasing ownership and keep a copy for your records.

Once the seller gives the buyer all required documentation and DMV receives the completed NRL, the seller’s part of the transaction is complete.

*If the vehicle has been sold more than once with the same title, a REG 262 is required from each seller.

Other Steps for the Buyer When Vehicle Ownership is Transferred

• Current registered owner(s), how names are joined (“and/or”), and lienholder/legal owner (if any).
• License plate number, vehicle identification number (VIN), make, model, year, and registration expiration date.
• Title brands (if any).
• Words “Nontransferable/No California Title Issued,” indicating a California title was not issued and a REG 227 cannot be used (see FAQs).
• Get a smog inspection (if applicable).

Once the buyer has provided the DMV with all the proper documents and fees, the vehicle record is updated to reflect the change of ownership and a registration card is issued.

A new title is issued from DMV headquarters within 60 calendar days.

To transfer a vehicle between family members, submit the following:

• The California Certificate of Title properly signed or endorsed on line 1 by the registered owner(s) shown on the title. Complete the new owner information on the back of the title and sign it.
• A Statement of Facts (REG 256) for use tax and smog exemption (if applicable).
• Odometer disclosure for vehicles less than 10 years old.
• Transfer fee .

You may transfer a vehicle from an individual to the estate of that individual without signatures on the Certificate of Title.

Submit the following:

• The California Certificate of Title. On the back of the title, the new owner section must show “Estate of (name of individual)” and their address. Any legal owner/lienholder named on the front of the title must be re-entered on the back of the title.
• A Statement of Facts (REG 256) confirming the owner is deceased and Letters Testamentary have not been issued. The person completing the statement must indicate their relationship to the deceased.

Use tax and a smog certification are not required.

Vehicle ownership can be transferred to a deceased owner’s heir 40 days after the owner’s death, as long as the value of the deceased’s property in California does not exceed:

• $150,000 if the deceased died before 1/1/20.
• $166,250 if the deceased died on or after 1/1/20.

If the heir will be the new owner, submit the following to a DMV office:

• The California Certificate of Title. The heir must sign the deceased registered owner’s name and countersign on line 1. The heir should complete and sign the back of the title.
• Affidavit for Transfer without Probate (REG 5) , completed and signed by the heir.
• An original or certified copy of the death certificate of all deceased owners.

If the heir prefers to sell the vehicle, the buyer also needs (in addition to the items above):

• Bill of Sale (REG 135) from the heir to the buyer.
• Transfer fee (two transfer fees are due in this case).

To transfer vessel ownership, submit the following:

• The California Certificate of Ownership. The registered owner signs line 1. The legal owner/lienholder (if any) signs line 2. Complete the new owner information on the back of the certificate and sign it.
• Bill(s) of sale, if needed to establish a complete chain of ownership.
• A Vessel Registration Fee .
• Use tax based on the tax rate percentage for your county of residence.

After you sell a vessel, complete a Notice of Transfer and Release of Liability (NRL) within five days of releasing ownership and keep a copy for your records.

How to Update or Change a Title

Because a California Certificate of Title is a legal document, it is important to keep it accurate and up-to-date. Here’s how you can update or change a title. 

Order a Replacement California Certificate of Title

You must order a replacement California Certificate of Title when the original is lost, stolen, damaged, illegible, or not received. 

To order a replacement title, submit the following:

• Application for Replacement or Transfer of Title (REG 227) .
• The original title (if you have it).
• California photo driver license (if submitting form in person).
• Replacement title fee .
• If another replacement title was issued in the past 90 days, a Verification of Vehicle (REG 31) completed by the California Highway Patrol (CHP). This requirement only applies if the registered owner’s name or address doesn’t match DMV records*.

You can submit your application either in-person* at a DMV office or by mail:

Department of Motor Vehicles Registration Operations PO Box 942869 Sacramento, California 94269-0001

If you’re submitting your form to a DMV office, we recommend you make an appointment so you can avoid any lines. 

You’ll receive your title by mail 15-30 calendar days from the date you submit the replacement title application.

*If you’re applying for a replacement title and the registered owner’s name or address doesn’t match DMV records (except for obvious typographical errors), you must submit your application in person with proof of ownership (e.g. registration card) and an acceptable photo ID (e.g. driver’s license/ID card).

Online Replacement Title Request

Visit our Virtual Office to request a replacement title online.

Change or Correct a Name on a Title

Your true full name must appear on your vehicle or vessel California Certificate of Title and registration card. If your name is misspelled, changes (e.g as a result of marriage or divorce), or is legally changed, you need to correct your name on your title.

To change or correct your name, submit:

• California Certificate of Title with your correct name printed or typed in the “New Registered Owner” section
• A completed Name Statement in Section F of the Statement of Facts (REG 256) .

You may submit your application to any DMV office or by mail to:

Department of Motor Vehicles Vehicle Registration Operations PO Box 942869 Sacramento, CA 94269-0001

Removing Information that was Entered by Mistake

If a name or other information is entered on a title by mistake, complete a Statement to Record Ownership (REG 101) .

Frequently Asked Questions

If the vehicle has a legal owner/lienholder, then section 5 of the REG 227 needs to be notarized. If the registration does not show a legal owner/lienholder, notarization is not required.

Need help finding the lienholder on your vehicle title? We keep a listing of banks, credit unions, and financial/lending institutions that may have gone out of business, merged, changed their name, or been acquired by another financial institution.

No. You must obtain a title from the state where the vehicle was last titled.

If you’re unable to obtain a title from that state, provide documentation that they cannot issue a title. A motor vehicle bond may be required

Contact us for more information .

Need something else?

Fee calculator.

Use our fee calculator to estimate any applicable registration or title transfer fees.

Renew Your Vehicle Registration

You need to renew your vehicle registration every 1-5 years in California, depending on the vehicle. Make sure your registration is up-to-date.

Make an Appointment

Some applications can be submitted to a DMV office near you. Make an appointment so you don’t have to wait in line.

General Disclaimer

When interacting with the Department of Motor Vehicles (DMV) Virtual Assistant, please do not include any personal information.

When your chat is over, you can save the transcript. Use caution when using a public computer or device.

The DMV chatbot and live chat services use third-party vendors to provide machine translation. Machine translation is provided for purposes of information and convenience only. The DMV is unable to guarantee the accuracy of any translation provided by the third-party vendors and is therefore not liable for any inaccurate information or changes in the formatting of the content resulting from the use of the translation service.

The content currently in English is the official and accurate source for the program information and services DMV provides. Any discrepancies or differences created in the translation are not binding and have no legal effect for compliance or enforcement purposes. If any questions arise related to the information contained in the translated content, please refer to the English version.

Google™ Translate Disclaimer

The Department of Motor Vehicles (DMV) website uses Google™ Translate to provide automatic translation of its web pages. This translation application tool is provided for purposes of information and convenience only. Google™ Translate is a free third-party service, which is not controlled by the DMV. The DMV is unable to guarantee the accuracy of any translation provided by Google™ Translate and is therefore not liable for any inaccurate information or changes in the formatting of the pages resulting from the use of the translation application tool.

The web pages currently in English on the DMV website are the official and accurate source for the program information and services the DMV provides. Any discrepancies or differences created in the translation are not binding and have no legal effect for compliance or enforcement purposes. If any questions arise related to the information contained in the translated website, please refer to the English version.

The following pages provided on the DMV website cannot be translated using Google™ Translate:

• Publications
• Field Office Locations
• Online Applications

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Google Translate is not support in your browser. To translate this page, please install the Google Toolbar (opens in new window) .