assignment expression in python

5. Assignment Expressions

By Bernd Klein . Last modified: 30 Nov 2023.

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Introduction

This section in our Python tutorial explores the "assignment expression operator" affectionately known to many as "the walrus operator. So, you can call it either the "walrus operator" or the "assignment expression operator," and people in the Python programming community will generally understand what you mean. Despite being introduced in Python version 3.8, we can still view it as a relatively recent addition to the language. The assignment expressions have been discussed in PEP 572 and this is what was written about the naming:

The purpose of this feature is not a new way to assign objects to variables, but it gives programmers a convenient way to assign variables in the middle of expressions.

You might still be curious about the origin of the term "walrus operator." It's affectionately named this way because, with a touch of imagination, the operator's characters resemble the eyes and tusks of a walrus.

We will introduce the assignment expression by comparing it first with a simple assignment statement. A simple assignment statement can also be replaced by an assignment expression, even though it looks clumsy and is definitely not the intended use case of it:

First you may wonder about the brackets, when you look at the assignment expression. It is not meant as a replacement for the simple "assignment statement". Its primary role is to be utilized within expressions

The following code shows a proper usecase:

Though you may argue that the following code might be even clearer:

Here's another Python code example that demonstrates a similar scenario. First with the walrus operator:

Now without the walrus operator:

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Beneficial applications

Now, let's explore some examples where the usage of the assignment expression is really beneficial compared to code not using it.

You can observe that if we choose to avoid using the assignment expression in the list comprehension, we would need to call the function twice. So using the assgment expression makes the code in this context more efficient.

Use Case: Regular Expressions

There is also a great advantage when we use regular expressions. Like in our previous examples we present again two similiar code snippets, the first one without and the second one with the walrus operator:

Now, the code using assignment expressions:

Assignment Expression in File Reading

Fixed-width files.

Files in which each line has the same length are often referred to as "fixed-width" or "fixed-length" files. In these files, each line (or record) is structured so that it contains a predefined number of characters, and the fields within the lines have fixed positions. The files can be read by using the read -method and the walrus operator:

Now the same in traditional coding style:

The benefits for the most recent code snippet are

• It uses a more traditional coding style, which may be more familiar to some developers.
• It assigns and uses the data variable explicitly, making the code easier to understand for those not familiar with assignment expressions.

However, a significant drawback of this approach is that it requires the explicit assignment and reassignment of the data variable, which can be seen as less concise and somewhat less elegant.

Using readline

Most people use a for loop to iterator over a text file line by line. With the walrus operator, we can also elegantly go through a text using the method readline :

Code snippet using readline but not the assignment expression:

Now with the walrus operator:

Another Use Case

In the chapter on while loops of our Python Tutorial, we had a little number guessing game:

As you can see, we had to initialize guess to zero to be able to enter the loop. We can do the initialization directly in the loop condition with an assignment expression and simplify the whole code by this:

Critiques of the Walrus Operator in Python

We said in the beginning of this page that some Python programmers longed for this construct for quite a while. One reason why it was not introduced earlier was the fact that it can also be used to write code which is less readable. In fact, the application of the walrus operator contradicts several principles highlighted in the Zen of Python. To grasp these contraventions, let's delve into the ensuing scenarios.

The Zen of Python says "Explicit is better than implicit". The walrus operator violates this requirement of the Zen of Python. This means that explicit operations are always better than implicit operations. The walrus operator assigns a value to a variable and implicitly returns the value as well. Therefore, it does not align with the concept of "Explicit is better than implicit."

The following code snippet is shows an extreme example which is not recommended to use:

What are your thoughts on the following piece of code using the Python assignment operator inside of a print function call?

These Python code snipets certainly violate two other demands of the Zen of Python:

• Beautiful is Better Than Ugly
• Complex is Better Than Complicated

The principle "There should be one and preferably only one obvious way to do it" from the Zen of Python emphasizes the importance of having a clear and singular approach. When we have the option to separate assignment and return operations into distinct statements, opting for the less readable and more complex walrus operator contradicts this principle.

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Python 101 – Assignment Expressions

Assignment expressions were added to Python in version 3.8 . The general idea is that an assignment expression allows you to assign to variables within an expression.

The syntax for doing this is:

This operator has been called the “walrus operator”, although their real name is “assignment expression”. Interestingly, the CPython internals also refer to them as “named expressions”.

You can read all about assignment expressions in PEP 572 . Let’s find out how to use assignment expressions!

Using Assignment Expressions

Assignment expressions are still relatively rare. However, you need to know about assignment expressions because you will probably come across them from time to time. PEP 572 has some good examples of assignment expressions.

In these 3 examples, you are creating a variable in the expression statement itself. The first example creates the variable match by assigning it the result of the regex pattern search. The second example assigns the variable value to the result of calling a function in the while loop’s expression. Finally, you assign the result of calling f(x) to the variable y inside of a list comprehension.

It would probably help to see the difference between code that doesn’t use an assignment expression and one that does. Here’s an example of reading a file in chunks:

This code will open up a file of indeterminate size and process it 1024 bytes at a time. You will find this useful when working with very large files as it prevents you from loading the entire file into memory. If you do, you can run out of memory and cause your application or even the computer to crash.

You can shorten this code up a bit by using an assignment expression:

Here you assign the result of the read() to data within the while loop’s expression. This allows you to then use that variable inside of the while loop’s code block. It also checks that some data was returned so you don’t have to have the if not data: break stanza.

Another good example that is mentioned in PEP 572 is taken from Python’s own site.py . Here’s how the code was originally:

And this is how it could be simplified by using an assignment expression:

You move the assignment into the conditional statement’s expression, which shortens the code up nicely.

Now let’s discover some of the situations where assignment expressions can’t be used.

What You Cannot Do With Assignment Expressions

There are several cases where assignment expressions cannot be used.

One of the most interesting features of assignment expressions is that they can be used in contexts that an assignment statement cannot, such as in a lambda or the previously mentioned comprehension. However, they do NOT support some things that assignment statements can do. For example, you cannot do multiple target assignment:

Another prohibited use case is using an assignment expression at the top level of an expression statement. Here is an example from PEP 572:

There is a detailed list of other cases where assignment expressions are prohibited or discouraged in the PEP. You should check that document out if you plan to use assignment expressions often.

Wrapping Up

Assignment expressions are an elegant way to clean up certain parts of your code. The feature’s syntax is kind of similar to type hinting a variable. Once you have the hang of one, the other should become easier to do as well.

In this article, you saw some real-world examples of using the “walrus operator”. You also learned when assignment expressions shouldn’t be used. This syntax is only available in Python 3.8 or newer, so if you happen to be forced to use an older version of Python, this feature won’t be of much use to you.

Related Reading

This article is based on a chapter from Python 101, 2nd Edition , which you can purchase on Leanpub or Amazon .

If you’d like to learn more Python, then check out these tutorials:

Python 101 – How to Work with Images

Python 101 – Documenting Your Code

Python 101: An Intro to Working with JSON

Python 101 – Creating Multiple Processes

Learning Python by doing

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Variables, Expressions, and Assignments

Variables, expressions, and assignments 1 #, introduction #.

In this chapter, we introduce some of the main building blocks needed to create programs–that is, variables, expressions, and assignments. Programming related variables can be intepret in the same way that we interpret mathematical variables, as elements that store values that can later be changed. Usually, variables and values are used within the so-called expressions. Once again, just as in mathematics, an expression is a construct of values and variables connected with operators that result in a new value. Lastly, an assignment is a language construct know as an statement that assign a value (either as a constant or expression) to a variable. The rest of this notebook will dive into the main concepts that we need to fully understand these three language constructs.

Values and Types #

A value is the basic unit used in a program. It may be, for instance, a number respresenting temperature. It may be a string representing a word. Some values are 42, 42.0, and ‘Hello, Data Scientists!’.

Each value has its own type : 42 is an integer ( int in Python), 42.0 is a floating-point number ( float in Python), and ‘Hello, Data Scientists!’ is a string ( str in Python).

The Python interpreter can tell you the type of a value: the function type takes a value as argument and returns its corresponding type.

Observe the difference between type(42) and type('42') !

Expressions and Statements #

On the one hand, an expression is a combination of values, variables, and operators.

A value all by itself is considered an expression, and so is a variable.

When you type an expression at the prompt, the interpreter evaluates it, which means that it calculates the value of the expression and displays it.

In boxes above, m has the value 27 and m + 25 has the value 52 . m + 25 is said to be an expression.

On the other hand, a statement is an instruction that has an effect, like creating a variable or displaying a value.

The first statement initializes the variable n with the value 17 , this is a so-called assignment statement .

The second statement is a print statement that prints the value of the variable n .

The effect is not always visible. Assigning a value to a variable is not visible, but printing the value of a variable is.

Assignment Statements #

We have already seen that Python allows you to evaluate expressions, for instance 40 + 2 . It is very convenient if we are able to store the calculated value in some variable for future use. The latter can be done via an assignment statement. An assignment statement creates a new variable with a given name and assigns it a value.

The example in the previous code contains three assignments. The first one assigns the value of the expression 40 + 2 to a new variable called magicnumber ; the second one assigns the value of π to the variable pi , and; the last assignment assigns the string value 'Data is eatig the world' to the variable message .

Programmers generally choose names for their variables that are meaningful. In this way, they document what the variable is used for.

Do It Yourself!

Let’s compute the volume of a cube with side \(s = 5\) . Remember that the volume of a cube is defined as \(v = s^3\) . Assign the value to a variable called volume .

Well done! Now, why don’t you print the result in a message? It can say something like “The volume of the cube with side 5 is \(volume\) ”.

Beware that there is no checking of types ( type checking ) in Python, so a variable to which you have assigned an integer may be re-used as a float, even if we provide type-hints .

Names and Keywords #

Names of variable and other language constructs such as functions (we will cover this topic later), should be meaningful and reflect the purpose of the construct.

In general, Python names should adhere to the following rules:

It should start with a letter or underscore.

It cannot start with a number.

It must only contain alpha-numeric (i.e., letters a-z A-Z and digits 0-9) characters and underscores.

They cannot share the name of a Python keyword.

If you use illegal variable names you will get a syntax error.

By choosing the right variables names you make the code self-documenting, what is better the variable v or velocity ?

The following are examples of invalid variable names.

These basic development principles are sometimes called architectural rules . By defining and agreeing upon architectural rules you make it easier for you and your fellow developers to understand and modify your code.

If you want to read more on this, please have a look at Code complete a book by Steven McConnell [ McC04 ] .

Every programming language has a collection of reserved keywords . They are used in predefined language constructs, such as loops and conditionals . These language concepts and their usage will be explained later.

The interpreter uses keywords to recognize these language constructs in a program. Python 3 has the following keywords:

False class finally is return

None continue for lambda try

True def from nonlocal while

and del global not with

as elif if or yield

assert else import pass break

except in raise

Reassignments #

It is allowed to assign a new value to an existing variable. This process is called reassignment . As soon as you assign a value to a variable, the old value is lost.

The assignment of a variable to another variable, for instance b = a does not imply that if a is reassigned then b changes as well.

You have a variable salary that shows the weekly salary of an employee. However, you want to compute the monthly salary. Can you reassign the value to the salary variable according to the instruction?

Updating Variables #

A frequently used reassignment is for updating puposes: the value of a variable depends on the previous value of the variable.

This statement expresses “get the current value of x , add one, and then update x with the new value.”

Beware, that the variable should be initialized first, usually with a simple assignment.

Do you remember the salary excercise of the previous section (cf. 13. Reassignments)? Well, if you have not done it yet, update the salary variable by using its previous value.

Updating a variable by adding 1 is called an increment ; subtracting 1 is called a decrement . A shorthand way of doing is using += and -= , which stands for x = x + ... and x = x - ... respectively.

Order of Operations #

Expressions may contain multiple operators. The order of evaluation depends on the priorities of the operators also known as rules of precedence .

For mathematical operators, Python follows mathematical convention. The acronym PEMDAS is a useful way to remember the rules:

Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3 - 1) is 4 , and (1 + 1)**(5 - 2) is 8 . You can also use parentheses to make an expression easier to read, even if it does not change the result.

Exponentiation has the next highest precedence, so 1 + 2**3 is 9 , not 27 , and 2 * 3**2 is 18 , not 36 .

Multiplication and division have higher precedence than addition and subtraction . So 2 * 3 - 1 is 5 , not 4 , and 6 + 4 / 2 is 8 , not 5 .

Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi , the division happens first and the result is multiplied by pi . To divide by 2π, you can use parentheses or write: degrees / 2 / pi .

In case of doubt, use parentheses!

Let’s see what happens when we evaluate the following expressions. Just run the cell to check the resulting value.

Floor Division and Modulus Operators #

The floor division operator // divides two numbers and rounds down to an integer.

For example, suppose that driving to the south of France takes 555 minutes. You might want to know how long that is in hours.

Conventional division returns a floating-point number.

Hours are normally not represented with decimal points. Floor division returns the integer number of hours, dropping the fraction part.

You spend around 225 minutes every week on programming activities. You want to know around how many hours you invest to this activity during a month. Use the \(//\) operator to give the answer.

The modulus operator % works on integer values. It computes the remainder when dividing the first integer by the second one.

The modulus operator is more useful than it seems.

For example, you can check whether one number is divisible by another—if x % y is zero, then x is divisible by y .

String Operations #

In general, you cannot perform mathematical operations on strings, even if the strings look like numbers, so the following operations are illegal: '2'-'1' 'eggs'/'easy' 'third'*'a charm'

But there are two exceptions, + and * .

The + operator performs string concatenation, which means it joins the strings by linking them end-to-end.

The * operator also works on strings; it performs repetition.

Speedy Gonzales is a cartoon known to be the fastest mouse in all Mexico . He is also famous for saying “Arriba Arriba Andale Arriba Arriba Yepa”. Can you use the following variables, namely arriba , andale and yepa to print the mentioned expression? Don’t forget to use the string operators.

Asking the User for Input #

The programs we have written so far accept no input from the user.

To get data from the user through the Python prompt, we can use the built-in function input .

When input is called your whole program stops and waits for the user to enter the required data. Once the user types the value and presses Return or Enter , the function returns the input value as a string and the program continues with its execution.

Try it out!

You can also print a message to clarify the purpose of the required input as follows.

The resulting string can later be translated to a different type, like an integer or a float. To do so, you use the functions int and float , respectively. But be careful, the user might introduce a value that cannot be converted to the type you required.

We want to know the name of a user so we can display a welcome message in our program. The message should say something like “Hello \(name\) , welcome to our hello world program!”.

Script Mode #

So far we have run Python in interactive mode in these Jupyter notebooks, which means that you interact directly with the interpreter in the code cells . The interactive mode is a good way to get started, but if you are working with more than a few lines of code, it can be clumsy. The alternative is to save code in a file called a script and then run the interpreter in script mode to execute the script. By convention, Python scripts have names that end with .py .

Use the PyCharm icon in Anaconda Navigator to create and execute stand-alone Python scripts. Later in the course, you will have to work with Python projects for the assignments, in order to get acquainted with another way of interacing with Python code.

This Jupyter Notebook is based on Chapter 2 of the books Python for Everybody [ Sev16 ] and Think Python (Sections 5.1, 7.1, 7.2, and 5.12) [ Dow15 ] .

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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 .

New 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

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6. Expressions

8. Compound statements

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Assignment Operators in Python

Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, bitwise computations. The value the operator operates on is known as Operand .

Here, we will cover Assignment Operators in Python. So, Assignment Operators are used to assigning values to variables. 

Now Let’s see each Assignment Operator one by one.

1) Assign: This operator is used to assign the value of the right side of the expression to the left side operand.

2) Add and Assign: This operator is used to add the right side operand with the left side operand and then assigning the result to the left operand.

Syntax: 

3) Subtract and Assign: This operator is used to subtract the right operand from the left operand and then assigning the result to the left operand.

Example –

 4) Multiply and Assign: This operator is used to multiply the right operand with the left operand and then assigning the result to the left operand.

 5) Divide and Assign: This operator is used to divide the left operand with the right operand and then assigning the result to the left operand.

 6) Modulus and Assign: This operator is used to take the modulus using the left and the right operands and then assigning the result to the left operand.

7) Divide (floor) and Assign: This operator is used to divide the left operand with the right operand and then assigning the result(floor) to the left operand.

 8) Exponent and Assign: This operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

9) Bitwise AND and Assign: This operator is used to perform Bitwise AND on both operands and then assigning the result to the left operand.

10) Bitwise OR and Assign: This operator is used to perform Bitwise OR on the operands and then assigning result to the left operand.

11) Bitwise XOR and Assign:  This operator is used to perform Bitwise XOR on the operands and then assigning result to the left operand.

12) Bitwise Right Shift and Assign: This operator is used to perform Bitwise right shift on the operands and then assigning result to the left operand.

 13) Bitwise Left Shift and Assign:  This operator is used to perform Bitwise left shift on the operands and then assigning result to the left operand.

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Python Enhancement Proposals

• Python »
• PEP Index »

PEP 379 – Adding an Assignment Expression

Motivation and summary, specification, examples from the standard library.

This PEP adds a new assignment expression to the Python language to make it possible to assign the result of an expression in almost any place. The new expression will allow the assignment of the result of an expression at first use (in a comparison for example).

Issue1714448 “if something as x:” [1] describes a feature to allow assignment of the result of an expression in an if statement to a name. It supposed that the as syntax could be borrowed for this purpose. Many times it is not the expression itself that is interesting, rather one of the terms that make up the expression. To be clear, something like this:

seems awfully limited, when this:

is probably the desired result.

See the Examples section near the end.

A new expression is proposed with the (nominal) syntax:

This single expression does the following:

• Evaluate the value of EXPR , an arbitrary expression;
• Assign the result to VAR , a single assignment target; and
• Leave the result of EXPR on the Top of Stack (TOS)

Here -> or ( RARROW ) has been used to illustrate the concept that the result of EXPR is assigned to VAR .

The translation of the proposed syntax is:

The assignment target can be either an attribute, a subscript or name:

This expression should be available anywhere that an expression is currently accepted.

All exceptions that are currently raised during invalid assignments will continue to be raised when using the assignment expression. For example, a NameError will be raised when in example 1 and 2 above if name is not previously defined, or an IndexError if index 0 was out of range.

The following two examples were chosen after a brief search through the standard library, specifically both are from ast.py which happened to be open at the time of the search.

Using assignment expression:

The examples shown below highlight some of the desirable features of the assignment expression, and some of the possible corner cases.

• Assignment in an if statement for use later: def expensive (): import time ; time . sleep ( 1 ) return 'spam' if expensive () -> res in ( 'spam' , 'eggs' ): dosomething ( res )
• Assignment in a while loop clause: while len ( expensive () -> res ) == 4 : dosomething ( res )
• Keep the iterator object from the for loop: for ch in expensive () -> res : sell_on_internet ( res )
• Corner case: for ch -> please_dont in expensive (): pass # who would want to do this? Not I.

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0379.rst

Last modified: 2023-09-09 17:39:29 GMT

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Python Operators

Harsh Pandey

Last updated on 19 Mar 2024

Python, one of the most popular programming languages today, is known for its simplicity and readability. One fundamental aspect of Python that contributes to its ease of use is its operators. Operators are the constructs which can manipulate the value of operands. In this blog, we'll explore the different types of operators available in Python and their applications.

What Are Python Operators?

Operators in Python are special symbols or keywords that are used to perform operations on one or more operands. An operand can be a value, a variable, or an expression. Operators are the building blocks of Python programs and are used to perform tasks like arithmetic calculations, logical comparisons, and memory operations.

Types Of Python Operators

The various types of operators in python are:

• Arithmetic Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Assignment Operators
• Identity Operators and Membership Operators

Arithmetic Operators In Python

Arithmetic operators in Python are used to perform basic mathematical operations and are fundamental to any kind of numerical manipulation in Python programming. These operators include addition (+), subtraction (-), multiplication (*), division (/), modulus (%), exponentiation (**), and floor division (//).

• Addition (+) Adds two operands. For example. result = 3 + 2 print(result) # Output: 5
• Subtraction (-) Subtracts the right operand from the left operand. result = 5 - 3 print(result) # Output: 2
• Multiplication (*) Multiplies two operands. result = 4 * 3 print(result) # Output: 12
• Division (/) Divides the left operand by the right operand. The result is a floating-point number. result = 8 / 2 print(result) # Output: 4.0
• Modulus (%) Returns the remainder of the division. result = 5 % 2 print(result) # Output: 1
• Exponentiation (**) Raises the first operand to the power of the second operand. result = 3 ** 2 print(result) # Output: 9
• Floor Division (//) Performs division but discards the fractional part, returning an integer result. result = 7 // 2 print(result) # Output: 3

Understanding these arithmetic operators is essential for anyone beginning with Python, as they are used widely in various types of numerical calculations and data manipulation tasks.

Comparison Operators In Python

Comparison operators in Python are used to compare values. They evaluate to either True or False based on the condition. These operators are essential in decision-making processes in Python scripts.

• Equal to (==) Checks if the values of two operands are equal. print(5 == 5) # Output: True print(5 == 3) # Output: False
• Not Equal to (!=) Checks if the values of two operands are not equal. print(5 != 3) # Output: True print(5 != 5) # Output: False
• Greater than (>) Checks if the value of the left operand is greater than the right operand. print(5 > 3) # Output: True print(3 > 5) # Output: False
• Less than (<) Checks if the value of the left operand is less than the right operand. print(3 < 5) # Output: True print(5 < 3) # Output: False
• Greater than or Equal to (>=) Checks if the left operand is greater than or equal to the right operand. print(5 >= 5) # Output: True print(4 >= 5) # Output: False
• Less than or Equal to (<=) Checks if the left operand is less than or equal to the right operand. print(3 <= 5) # Output: True print(5 <= 3) # Output: False

Understanding these comparison operators is crucial for controlling the flow of a Python program, as they are commonly used in conditional statements and loops to evaluate conditions and make decisions.

Logical Operators In Python

Logical operators in Python are used to combine conditional statements, and they are fundamental in controlling the flow of logic in Python code. These operators include and, or, and not, each serving a specific purpose in evaluating conditions.

• and Operator The and operator returns True if both the operands (conditions) are true. print((5 > 3) and (5 > 4)) # Output: True print((5 > 3) and (5 < 4)) # Output: False
• or Operator The or operator returns True if at least one of the operands is true. print((5 > 3) or (5 < 4)) # Output: True print((5 < 3) or (5 < 4)) # Output: False
• not Operator The not operator reverses the result of the condition. It returns True if the condition is false and vice versa. print(not(5 > 3)) # Output: False print(not(5 < 3)) # Output: True

Logical operators are integral in Python for building complex logical expressions, making them essential in decision-making structures like if-else statements and loops.

Bitwise Operators In Python

Bitwise operators in Python are used to perform operations on binary numbers at the bit level. These operators are essential for manipulating individual bits and performing bit-by-bit operations.

• AND Operator (&) The & operator performs a bitwise AND, where each bit of the output is 1 if both corresponding bits of the operand are 1. print(5 & 3) # Output: 1
• OR Operator (|) The | operator performs a bitwise OR, where each bit of the output is 1 if at least one of the corresponding bits of the operands is 1. print(5 | 3) # Output: 7
• XOR Operator (^) The ^ operator performs a bitwise XOR, where each bit of the output is 1 if the corresponding bits of the operands are different. print(5 ^ 3) # Output: 6
• NOT Operator (~) The ~ operator performs a bitwise NOT, inverting each bit of the number. print(~5) # Output: -6
• Shift Operators Left Shift (<<): Shifts the bits of the number to the left and fills 0 on voids left as a result. The left operand specifies the value to be shifted, and the right operand specifies the number of positions to shift. print(5 << 1) # Output: 10 Right Shift (>>): Shifts the bits of the number to the right. The left operand specifies the value to be shifted, and the right operand specifies the number of positions to shift. print(5 >> 1) # Output: 2

Bitwise operators are particularly useful in low-level programming, such as device driver writing or protocol development, where manipulation of data at the bit level is crucial.

Assignment Operator In Python

Assignment operators in Python are used to assign values to variables. These operators make the code more concise and improve its readability by combining arithmetic operations with assignment.

• The Basic Assignment Operator (=) The = operator assigns the value on the right to the variable on the left. x = 10 print(x) # Output: 10
• Compound Assignment Operators These operators combine assignment with other operations. Add and Assign (+=): Adds the right operand to the left operand and assigns the result to the left operand. x = 5 x += 3 # Same as x = x + 3 print(x) # Output: 8 Subtract and Assign (-=): Subtracts the right operand from the left operand and assigns the result to the left operand. x = 5 x -= 3 # Same as x = x - 3 print(x) # Output: 2 Multiply and Assign (*=): Multiplies the right operand with the left operand and assigns the result to the left operand. x = 5 x *= 3 # Same as x = x * 3 print(x) # Output: 15 Divide and Assign (/=): Divides the left operand by the right operand and assigns the result to the left operand. x = 10 x /= 2 # Same as x = x / 2 print(x) # Output: 5.0

Assignment operators are essential in Python for variable manipulation, simplifying expressions, and making code more efficient and easier to understand.

Identity Operators And Membership Operators In Python

Identity and Membership operators in Python are used to compare the identity of objects and check for membership within data structures, respectively.

Identity Operators

Identity operators compare the memory locations of two objects. There are two identity operators in Python: is and is not.

• is: Evaluates to true if the variables on either side of the operator point to the same object. x = ["apple", "banana"] y = x print(x is y) # Output: True
• is not: Evaluates to true if the variables on either side of the operator do not point to the same object. x = ["apple", "banana"] y = ["apple", "banana"] print(x is not y) # Output: True

Membership Operators

Membership operators test if a sequence is presented in an object. The two membership operators in Python are in and not in.

• in: Returns True if a sequence with the specified value is present in the object. x = ["apple", "banana"] print("banana" in x) # Output: True
• not in: Returns True if a sequence with the specified value is not present in the object. print("cherry" not in x) # Output: True

Identity and Membership operators are crucial in Python for validating the identity of objects and checking for the presence of elements within various data structures like lists, tuples, and strings. They are widely used in conditional statements and loops to enhance the logic and functionality of Python scripts.

Operators in Python are essential for performing various operations on data. Understanding these operators and their applications is crucial for anyone looking to master Python programming. Whether it's for data manipulation, arithmetic calculations, or logical comparisons, Python operators provide a simple yet powerful tool for developers.

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The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. More about defining functions in Python 3

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Draft PEP: Sealed decorator for static typing

Can this not be fixed?

Did I promise at some point to sponsor this PEP? I’m not currently enthusiastic about the idea, so if you do choose to submit it as a PEP, I’d prefer you find another sponsor.

I don’t think there is anything technical blocking the allowance of Union in runtime type checks. A object is an instance of a Union if it is an instance of any of its members. If there were a lot of members, this would be substantially less performant than an isinstance check on the base class unless some caching mechanism was added to each Union object, like is used in singledispatch .

Not a promise, but a probable willingness in the original discussion. In any case, that was awhile ago and I don’t want to hold you to something you aren’t interested in anymore.

Any core dev interested in being the sponsor of this PEP?

It provides only a small convenience to a small audience but would result in a net negative to millions of Python developers if it were implemented in pyright. As such, I think it represents a bad tradeoff for the Python community as a whole.

Mainly curious, is this based purely due to an assessment of pyright’s current implementation and community?

This would require a lazy type checker to analyze all class definitions in a file to determine the type and meaning of one symbol in that file.

Would this affect mypy negatively-so in the same manner as well as other type checkers? Might be worth to see others chime in.

This PEP proposes to create a new, implicit way to define a union

Agree. I do see this idea as a bigger benefit from a typing perspective though. Looking at how other languages do this, I don’t see this as such a negative runtime tradeoff over an alternative implementations. I can see how libraries like python-returns would benefit here the most, helping the community at large.

I was originally relatively positive on this, but after giving it more thought, I think the problems this attempts to solve may be better assisted by allowing case-matching a Union. I’m not sure What the semantics of that should require (that the union members share the same __match_args__ signature is probably reasonable), but this is the only place where there’s any actual need to expose the baseclass and end up with divergence, and it seems like solving this would be a much better route to fix ergonomics.

I started a discussion for your idea here: Syntactic sugar for union cases in match statements

Making Union work in a match may be nice on its own, but it does not resolve the need for sealed or something like it. An equally big problem with Union s is that inheritance does not work at all. A Union will never have abstract methods, class methods, __init_subclass__ , etc.–all the things that you can do with inheritance in Python. If you have

then Node.parse(text) cannot be made to work in any way I can think of.

or something along that line, e.g. @sealed("Leaf", "Branch") works too.

“Explicit is better than implicit.”

I think this is a fine path to go down. The difficulty is that it fundamentally requires a circular reference, which is uniquely tricky to make exist in Python even if only for typing.

Annotations are basically the only things that are evaluated lazily in Python, so the only implementation that works is probably this one:

But if were willing to add syntax for this, then this is solvable by copying the feature directly from other languages:

I would have thought there was no chance syntax would be added for this, but we just got syntax for type parameters on classes and methods, so maybe there is more appetite for nice typing syntax these days.

I dont think that’s exactly compelling.

From here, you’ve declared your functionality, shared functionality is only declared once, and shared functionality can safely be used on any object that belongs to that union

As for inheritance, your sealed example doesn’t allow other users to inherit from it, and It generally doesn’t appear to make sense to expose the actual base as a constructor if you’re expecting one of the subclasses of it.

I haven’t had the opportunity to use Python 3.10 and above too much and I don’t understand ADTs all too well so could you explain what the benefit of the proposed sealed decorator would be over using abstract base classes?

I agree with all of these arguments, but is there a way to get runtime checking too?

Override __init_subclass__ ? You could raise an error on any new subclass outside of a fixed list.

Right, but you need to expose the list to both the runtime and the type checker. And you need to be able to seal subclasses with a different set of types, right?

As I understand it, the core benefit here is to allow static verification tools (linters, type checkers) to do what is called exhaustiveness checking .

When writing a function that processes an instance of the base data type, where you need to detect the child type and act differently for each one, the type checker can enforce that you have handled every child type.

Edit: this is pretty common in languages that emphasize ADTs and structural pattern matching. In Python the match statement is still pretty new but “sealed” would probably be most helpful with that.

Runtime checking alone would not provide the main benefit of this proposal: static verification of exhaustive matching. I guess I don’t feel strongly about there being runtime behavior in addition to the static type checking, but most of the recent additions to typing in Python have leaned fully into “consenting adults”, having no runtime behavior whatsoever. This draft PEP reflects that.

As you and the draft PEP point out, you can make some of inheritance work by splitting the base class into a private base class and a public union type. But some things cannot be made to work like that, at least as far as I could figure out. It’s not the constructor I am interested in. The biggest problem comes in the class methods. SomeSumType.foo() will never work. You can’t pass SomeSumType to Pydantic or FastAPI or Typer and expect it to ever work, at least not if you want to override any of the serialization/deserialization defaults.

You have to lose some Python functionality of the base class when publicly exposing a Union . It just isn’t the same thing as a base class. A sealed decorator (or alternative syntax) on the base class adds exhaustiveness checking to the base class without compromising its other capabilities.

I don’t think you need these to work. There’s no reasonable interpretation for what class to bind in the class method. Write a function not a method.

I don’t use pydantic. Unions work fine with cattrs and msgspec, so it sounds like this is an issue that pydantic can solve without sealed

That feels like a limitation of those frameworks that they could overcome.

I’m curious if this is a case where an ordered intersection could help (specifically of the base class and the union), if anyone working on that is in this discussion. Does the resulting type have different behaviour from the simple union in a way that might help?

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