python variable assignment syntax

Python Variables

In Python, a variable is a container that stores a value. In other words, variable is the name given to a value, so that it becomes easy to refer a value later on.

Unlike C# or Java, it's not necessary to explicitly define a variable in Python before using it. Just assign a value to a variable using the = operator e.g. variable_name = value . That's it.

The following creates a variable with the integer value.

In the above example, we declared a variable named num and assigned an integer value 10 to it. Use the built-in print() function to display the value of a variable on the console or IDLE or REPL .

In the same way, the following declares variables with different types of values.

Multiple Variables Assignment

You can declare multiple variables and assign values to each variable in a single statement, as shown below.

In the above example, the first int value 10 will be assigned to the first variable x, the second value to the second variable y, and the third value to the third variable z. Assignment of values to variables must be in the same order in they declared.

You can also declare different types of values to variables in a single statement separated by a comma, as shown below.

Above, the variable x stores 10 , y stores a string 'Hello' , and z stores a boolean value True . The type of variables are based on the types of assigned value.

Assign a value to each individual variable separated by a comma will throw a syntax error, as shown below.

Variables in Python are objects. A variable is an object of a class based on the value it stores. Use the type() function to get the class name (type) of a variable.

In the above example, num is an object of the int class that contains integre value 10 . In the same way, amount is an object of the float class, greet is an object of the str class, isActive is an object of the bool class.

Unlike other programming languages like C# or Java, Python is a dynamically-typed language, which means you don't need to declare a type of a variable. The type will be assigned dynamically based on the assigned value.

The + operator sums up two int variables, whereas it concatenates two string type variables.

Object's Identity

Each object in Python has an id. It is the object's address in memory represented by an integer value. The id() function returns the id of the specified object where it is stored, as shown below.

Variables with the same value will have the same id.

Thus, Python optimize memory usage by not creating separate objects if they point to same value.

Naming Conventions

Any suitable identifier can be used as a name of a variable, based on the following rules:

• The name of the variable should start with either an alphabet letter (lower or upper case) or an underscore (_), but it cannot start with a digit.
• More than one alpha-numeric characters or underscores may follow.
• The variable name can consist of alphabet letter(s), number(s) and underscore(s) only. For example, myVar , MyVar , _myVar , MyVar123 are valid variable names, but m*var , my-var , 1myVar are invalid variable names.
• Variable names in Python are case sensitive. So, NAME , name , nAME , and nAmE are treated as different variable names.
• Variable names cannot be a reserved keywords in Python.
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Variables and Assignment ¶

When programming, it is useful to be able to store information in variables. A variable is a string of characters and numbers associated with a piece of information. The assignment operator , denoted by the “=” symbol, is the operator that is used to assign values to variables in Python. The line x=1 takes the known value, 1, and assigns that value to the variable with name “x”. After executing this line, this number will be stored into this variable. Until the value is changed or the variable deleted, the character x behaves like the value 1.

TRY IT! Assign the value 2 to the variable y. Multiply y by 3 to show that it behaves like the value 2.

A variable is more like a container to store the data in the computer’s memory, the name of the variable tells the computer where to find this value in the memory. For now, it is sufficient to know that the notebook has its own memory space to store all the variables in the notebook. As a result of the previous example, you will see the variable “x” and “y” in the memory. You can view a list of all the variables in the notebook using the magic command %whos .

TRY IT! List all the variables in this notebook

Note that the equal sign in programming is not the same as a truth statement in mathematics. In math, the statement x = 2 declares the universal truth within the given framework, x is 2 . In programming, the statement x=2 means a known value is being associated with a variable name, store 2 in x. Although it is perfectly valid to say 1 = x in mathematics, assignments in Python always go left : meaning the value to the right of the equal sign is assigned to the variable on the left of the equal sign. Therefore, 1=x will generate an error in Python. The assignment operator is always last in the order of operations relative to mathematical, logical, and comparison operators.

TRY IT! The mathematical statement x=x+1 has no solution for any value of x . In programming, if we initialize the value of x to be 1, then the statement makes perfect sense. It means, “Add x and 1, which is 2, then assign that value to the variable x”. Note that this operation overwrites the previous value stored in x .

There are some restrictions on the names variables can take. Variables can only contain alphanumeric characters (letters and numbers) as well as underscores. However, the first character of a variable name must be a letter or underscores. Spaces within a variable name are not permitted, and the variable names are case-sensitive (e.g., x and X will be considered different variables).

TIP! Unlike in pure mathematics, variables in programming almost always represent something tangible. It may be the distance between two points in space or the number of rabbits in a population. Therefore, as your code becomes increasingly complicated, it is very important that your variables carry a name that can easily be associated with what they represent. For example, the distance between two points in space is better represented by the variable dist than x , and the number of rabbits in a population is better represented by nRabbits than y .

Note that when a variable is assigned, it has no memory of how it was assigned. That is, if the value of a variable, y , is constructed from other variables, like x , reassigning the value of x will not change the value of y .

EXAMPLE: What value will y have after the following lines of code are executed?

WARNING! You can overwrite variables or functions that have been stored in Python. For example, the command help = 2 will store the value 2 in the variable with name help . After this assignment help will behave like the value 2 instead of the function help . Therefore, you should always be careful not to give your variables the same name as built-in functions or values.

TIP! Now that you know how to assign variables, it is important that you learn to never leave unassigned commands. An unassigned command is an operation that has a result, but that result is not assigned to a variable. For example, you should never use 2+2 . You should instead assign it to some variable x=2+2 . This allows you to “hold on” to the results of previous commands and will make your interaction with Python must less confusing.

You can clear a variable from the notebook using the del function. Typing del x will clear the variable x from the workspace. If you want to remove all the variables in the notebook, you can use the magic command %reset .

In mathematics, variables are usually associated with unknown numbers; in programming, variables are associated with a value of a certain type. There are many data types that can be assigned to variables. A data type is a classification of the type of information that is being stored in a variable. The basic data types that you will utilize throughout this book are boolean, int, float, string, list, tuple, dictionary, set. A formal description of these data types is given in the following sections.

Home » Python Basics » Python Variables

Python Variables

Summary : in this tutorial, you’ll learn about Python variables and how to use them effectively.

What is a variable in Python

When you develop a program, you need to manage values, a lot of them. To store values, you use variables.

In Python, a variable is a label that you can assign a value to it. And a variable is always associated with a value. For example:

In this example, message is a variable. It holds the string 'Hello, World!' . The print() function shows the message Hello, World! to the screen.

The next line assigns the string 'Good Bye!' to the message variable and print its value to the screen.

The variable message can hold various values at different times. And its value can change throughout the program.

Creating variables

To define a variable, you use the following syntax:

The = is the assignment operator. In this syntax, you assign a value to the variable_name .

The value can be anything like a number , a string , etc., that you assign to the variable.

The following defines a variable named counter and assigns the number 1 to it:

Naming variables

When you name a variable, you need to adhere to some rules. If you don’t, you’ll get an error.

The following are the variable rules that you should keep in mind:

• Variable names can contain only letters, numbers, and underscores ( _ ). They can start with a letter or an underscore ( _ ), not with a number.
• Variable names cannot contain spaces. To separate words in variables, you use underscores for example sorted_list .
• Variable names cannot be the same as keywords, reserved words, and built-in functions in Python.

The following guidelines help you define good variable names:

• Variable names should be concise and descriptive. For example, the active_user variable is more descriptive than the au .
• Use underscores (_) to separate multiple words in the variable names.
• Avoid using the letter l and the uppercase letter O because they look like the number 1 and 0 .
• A variable is a label that you can assign a value to it. The value of a variable can change throughout the program.
• Use the variable_name = value to create a variable.
• The variable names should be as concise and descriptive as possible. Also, they should adhere to Python variable naming rules.

Python Variables and Assignment

Python variables, variable assignment rules, every value has a type, memory and the garbage collector, variable swap, variable names are superficial labels, assignment = is shallow, decomp by var.

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Variables & Assignment 

There are reading-comprehension exercises included throughout the text. These are meant to help you put your reading to practice. Solutions for the exercises are included at the bottom of this page.

Variables permit us to write code that is flexible and amendable to repurpose. Suppose we want to write code that logs a student’s grade on an exam. The logic behind this process should not depend on whether we are logging Brian’s score of 92% versus Ashley’s score of 94%. As such, we can utilize variables, say name and grade , to serve as placeholders for this information. In this subsection, we will demonstrate how to define variables in Python.

In Python, the = symbol represents the “assignment” operator. The variable goes to the left of = , and the object that is being assigned to the variable goes to the right:

Attempting to reverse the assignment order (e.g. 92 = name ) will result in a syntax error. When a variable is assigned an object (like a number or a string), it is common to say that the variable is a reference to that object. For example, the variable name references the string "Brian" . This means that, once a variable is assigned an object, it can be used elsewhere in your code as a reference to (or placeholder for) that object:

Valid Names for Variables 

A variable name may consist of alphanumeric characters ( a-z , A-Z , 0-9 ) and the underscore symbol ( _ ); a valid name cannot begin with a numerical value.

var : valid

_var2 : valid

ApplePie_Yum_Yum : valid

2cool : invalid (begins with a numerical character)

I.am.the.best : invalid (contains . )

They also cannot conflict with character sequences that are reserved by the Python language. As such, the following cannot be used as variable names:

for , while , break , pass , continue

in , is , not

if , else , elif

def , class , return , yield , raises

import , from , as , with

try , except , finally

There are other unicode characters that are permitted as valid characters in a Python variable name, but it is not worthwhile to delve into those details here.

Mutable and Immutable Objects 

The mutability of an object refers to its ability to have its state changed. A mutable object can have its state changed, whereas an immutable object cannot. For instance, a list is an example of a mutable object. Once formed, we are able to update the contents of a list - replacing, adding to, and removing its elements.

To spell out what is transpiring here, we:

Create (initialize) a list with the state [1, 2, 3] .

Assign this list to the variable x ; x is now a reference to that list.

Using our referencing variable, x , update element-0 of the list to store the integer -4 .

This does not create a new list object, rather it mutates our original list. This is why printing x in the console displays [-4, 2, 3] and not [1, 2, 3] .

A tuple is an example of an immutable object. Once formed, there is no mechanism by which one can change of the state of a tuple; and any code that appears to be updating a tuple is in fact creating an entirely new tuple.

Mutable & Immutable Types of Objects 

The following are some common immutable and mutable objects in Python. These will be important to have in mind as we start to work with dictionaries and sets.

Some immutable objects

numbers (integers, floating-point numbers, complex numbers)

“frozen”-sets

Some mutable objects

dictionaries

NumPy arrays

Referencing a Mutable Object with Multiple Variables 

It is possible to assign variables to other, existing variables. Doing so will cause the variables to reference the same object:

What this entails is that these common variables will reference the same instance of the list. Meaning that if the list changes, all of the variables referencing that list will reflect this change:

We can see that list2 is still assigned to reference the same, updated list as list1 :

In general, assigning a variable b to a variable a will cause the variables to reference the same object in the system’s memory, and assigning c to a or b will simply have a third variable reference this same object. Then any change (a.k.a mutation ) of the object will be reflected in all of the variables that reference it ( a , b , and c ).

Of course, assigning two variables to identical but distinct lists means that a change to one list will not affect the other:

Reading Comprehension: Does slicing a list produce a reference to that list?

Suppose x is assigned a list, and that y is assigned a “slice” of x . Do x and y reference the same list? That is, if you update part of the subsequence common to x and y , does that change show up in both of them? Write some simple code to investigate this.

Reading Comprehension: Understanding References

Based on our discussion of mutable and immutable objects, predict what the value of y will be in the following circumstance:

Reading Comprehension Exercise Solutions: 

Does slicing a list produce a reference to that list?: Solution

Based on the following behavior, we can conclude that slicing a list does not produce a reference to the original list. Rather, slicing a list produces a copy of the appropriate subsequence of the list:

Understanding References: Solutions

Integers are immutable, thus x must reference an entirely new object ( 9 ), and y still references 3 .

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How To Assign Values To Variables In Python?

Harsh Pandey

Last updated on 16 Apr 2024

Assigning values in Python to variables is a fundamental and straightforward process. This action forms the basis for storing and manipulating Python data. The various methods for assigning values to variables in Python are given below.

Assign Values To Variables Direct Initialisation Method

Assigning values to variables in Python using the Direct Initialization Method involves a simple, single-line statement. This method is essential for efficiently setting up variables with initial values.

To use this method, you directly assign the desired value to a variable. The format follows variable_name = value . For instance, age = 21 assigns the integer 21 to the variable age.

This method is not just limited to integers. For example, assigning a string to a variable would be name = "Alice" . An example of this implementation in Python is.

Python Variables – Assign Multiple Values

In Python, assigning multiple values to variables can be done in a single, efficient line of code. This feature streamlines the initializing of several variables at once, making the code more concise and readable.

Python allows you to assign values to multiple variables simultaneously by separating each variable and value with commas. For example, x, y, z = 1, 2, 3 simultaneously assigns 1 to x, 2 to y, and 3 to z.

Additionally, Python supports unpacking a collection of values into variables. For example, if you have a list values = [1, 2, 3] , you can assign these values to a, b, c by writing a, b, c = values .

Python’s capability to assign multiple values to multiple variables in a single line enhances code efficiency and clarity. This feature is applicable across various data types and includes unpacking collections into multiple variables, as shown in the examples.

Assign Values To Variables Using Conditional Operator

Assigning values to variables in Python using a conditional operator allows for more dynamic and flexible value assignments based on certain conditions. This method employs the ternary operator, a concise way to assign values based on a condition's truth value.

The syntax for using the conditional operator in Python follows the pattern: variable = value_if_true if condition else value_if_false . For example, status = 'Adult' if age >= 18 else 'Minor' assigns 'Adult' to status if age is 18 or more, and 'Minor' otherwise.

This method can also be used with more complex conditions and various data types. For example, you can assign different strings to a variable based on a numerical comparison

Using the conditional operator for variable assignment in Python enables more nuanced and condition-dependent variable initialization. It is particularly useful for creating readable one-liners that eliminate the need for longer if-else statements, as illustrated in the examples.

Python One Liner Conditional Statement Assigning

Python allows for one-liner conditional statements to assign values to variables, providing a compact and efficient way of handling conditional assignments. This approach utilizes the ternary operator for conditional expressions in a single line of code.

The ternary operator syntax in Python is variable = value_if_true if condition else value_if_false . For instance, message = 'High' if temperature > 20 else 'Low' assigns 'High' to message if temperature is greater than 20, and 'Low' otherwise.

This method can also be applied to more complex conditions.

Using one-liner conditional statements in Python for variable assignment streamlines the process, especially when dealing with simple conditions. It replaces the need for multi-line if-else statements, making the code more concise and readable.

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File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python variables - assign multiple values, many values to multiple variables.

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

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

One Value to Multiple Variables

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

Unpack a Collection

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

Unpack a list:

Learn more about unpacking in our Unpack Tuples Chapter.

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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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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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Python Enhancement Proposals

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PEP 526 – Syntax for Variable Annotations

Notice for reviewers, global and local variable annotations, class and instance variable annotations, annotating expressions, where annotations aren’t allowed, variable annotations in stub files, preferred coding style for variable annotations, changes to standard library and documentation, other uses of annotations, rejected/postponed proposals, backwards compatibility, implementation.

This PEP has been provisionally accepted by the BDFL. See the acceptance message for more color: https://mail.python.org/pipermail/python-dev/2016-September/146282.html

This PEP was drafted in a separate repo: https://github.com/phouse512/peps/tree/pep-0526 .

There was preliminary discussion on python-ideas and at https://github.com/python/typing/issues/258 .

Before you bring up an objection in a public forum please at least read the summary of rejected ideas listed at the end of this PEP.

PEP 484 introduced type hints, a.k.a. type annotations. While its main focus was function annotations, it also introduced the notion of type comments to annotate variables:

This PEP aims at adding syntax to Python for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments:

PEP 484 explicitly states that type comments are intended to help with type inference in complex cases, and this PEP does not change this intention. However, since in practice type comments have also been adopted for class variables and instance variables, this PEP also discusses the use of type annotations for those variables.

Although type comments work well enough, the fact that they’re expressed through comments has some downsides:

• Text editors often highlight comments differently from type annotations.
• There’s no way to annotate the type of an undefined variable; one needs to initialize it to None (e.g. a = None # type: int ).
• Variables annotated in a conditional branch are difficult to read: if some_value : my_var = function () # type: Logger else : my_var = another_function () # Why isn't there a type here?
• Since type comments aren’t actually part of the language, if a Python script wants to parse them, it requires a custom parser instead of just using ast .
• Type comments are used a lot in typeshed. Migrating typeshed to use the variable annotation syntax instead of type comments would improve readability of stubs.
• In situations where normal comments and type comments are used together, it is difficult to distinguish them: path = None # type: Optional[str] # Path to module source
• It’s impossible to retrieve the annotations at runtime outside of attempting to find the module’s source code and parse it at runtime, which is inelegant, to say the least.

The majority of these issues can be alleviated by making the syntax a core part of the language. Moreover, having a dedicated annotation syntax for class and instance variables (in addition to method annotations) will pave the way to static duck-typing as a complement to nominal typing defined by PEP 484 .

While the proposal is accompanied by an extension of the typing.get_type_hints standard library function for runtime retrieval of annotations, variable annotations are not designed for runtime type checking. Third party packages will have to be developed to implement such functionality.

It should also be emphasized that Python will remain a dynamically typed language, and the authors have no desire to ever make type hints mandatory, even by convention. Type annotations should not be confused with variable declarations in statically typed languages. The goal of annotation syntax is to provide an easy way to specify structured type metadata for third party tools.

This PEP does not require type checkers to change their type checking rules. It merely provides a more readable syntax to replace type comments.

Specification

Type annotation can be added to an assignment statement or to a single expression indicating the desired type of the annotation target to a third party type checker:

This syntax does not introduce any new semantics beyond PEP 484 , so that the following three statements are equivalent:

Below we specify the syntax of type annotations in different contexts and their runtime effects.

We also suggest how type checkers might interpret annotations, but compliance to these suggestions is not mandatory. (This is in line with the attitude towards compliance in PEP 484 .)

The types of locals and globals can be annotated as follows:

Being able to omit the initial value allows for easier typing of variables assigned in conditional branches:

Note that, although the syntax does allow tuple packing, it does not allow one to annotate the types of variables when tuple unpacking is used:

Omitting the initial value leaves the variable uninitialized:

However, annotating a local variable will cause the interpreter to always make it a local:

as if the code were:

Duplicate type annotations will be ignored. However, static type checkers may issue a warning for annotations of the same variable by a different type:

Type annotations can also be used to annotate class and instance variables in class bodies and methods. In particular, the value-less notation a: int allows one to annotate instance variables that should be initialized in __init__ or __new__ . The proposed syntax is as follows:

Here ClassVar is a special class defined by the typing module that indicates to the static type checker that this variable should not be set on instances.

Note that a ClassVar parameter cannot include any type variables, regardless of the level of nesting: ClassVar[T] and ClassVar[List[Set[T]]] are both invalid if T is a type variable.

This could be illustrated with a more detailed example. In this class:

stats is intended to be a class variable (keeping track of many different per-game statistics), while captain is an instance variable with a default value set in the class. This difference might not be seen by a type checker: both get initialized in the class, but captain serves only as a convenient default value for the instance variable, while stats is truly a class variable – it is intended to be shared by all instances.

Since both variables happen to be initialized at the class level, it is useful to distinguish them by marking class variables as annotated with types wrapped in ClassVar[...] . In this way a type checker may flag accidental assignments to attributes with the same name on instances.

For example, annotating the discussed class:

As a matter of convenience (and convention), instance variables can be annotated in __init__ or other methods, rather than in the class:

The target of the annotation can be any valid single assignment target, at least syntactically (it is up to the type checker what to do with this):

Note that even a parenthesized name is considered an expression, not a simple name:

It is illegal to attempt to annotate variables subject to global or nonlocal in the same function scope:

The reason is that global and nonlocal don’t own variables; therefore, the type annotations belong in the scope owning the variable.

Only single assignment targets and single right hand side values are allowed. In addition, one cannot annotate variables used in a for or with statement; they can be annotated ahead of time, in a similar manner to tuple unpacking:

As variable annotations are more readable than type comments, they are preferred in stub files for all versions of Python, including Python 2.7. Note that stub files are not executed by Python interpreters, and therefore using variable annotations will not lead to errors. Type checkers should support variable annotations in stubs for all versions of Python. For example:

Annotations for module level variables, class and instance variables, and local variables should have a single space after corresponding colon. There should be no space before the colon. If an assignment has right hand side, then the equality sign should have exactly one space on both sides. Examples:

• Yes: code : int class Point : coords : Tuple [ int , int ] label : str = '<unknown>'
• No: code : int # No space after colon code : int # Space before colon class Test : result : int = 0 # No spaces around equality sign
• A new covariant type ClassVar[T_co] is added to the typing module. It accepts only a single argument that should be a valid type, and is used to annotate class variables that should not be set on class instances. This restriction is ensured by static checkers, but not at runtime. See the classvar section for examples and explanations for the usage of ClassVar , and see the rejected section for more information on the reasoning behind ClassVar .
• Function get_type_hints in the typing module will be extended, so that one can retrieve type annotations at runtime from modules and classes as well as functions. Annotations are returned as a dictionary mapping from variable or arguments to their type hints with forward references evaluated. For classes it returns a mapping (perhaps collections.ChainMap ) constructed from annotations in method resolution order.
• Recommended guidelines for using annotations will be added to the documentation, containing a pedagogical recapitulation of specifications described in this PEP and in PEP 484 . In addition, a helper script for translating type comments into type annotations will be published separately from the standard library.

Runtime Effects of Type Annotations

Annotating a local variable will cause the interpreter to treat it as a local, even if it was never assigned to. Annotations for local variables will not be evaluated:

However, if it is at a module or class level, then the type will be evaluated:

In addition, at the module or class level, if the item being annotated is a simple name , then it and the annotation will be stored in the __annotations__ attribute of that module or class (mangled if private) as an ordered mapping from names to evaluated annotations. Here is an example:

__annotations__ is writable, so this is permitted:

But attempting to update __annotations__ to something other than an ordered mapping may result in a TypeError:

(Note that the assignment to __annotations__ , which is the culprit, is accepted by the Python interpreter without questioning it – but the subsequent type annotation expects it to be a MutableMapping and will fail.)

The recommended way of getting annotations at runtime is by using typing.get_type_hints function; as with all dunder attributes, any undocumented use of __annotations__ is subject to breakage without warning:

Note that if annotations are not found statically, then the __annotations__ dictionary is not created at all. Also the value of having annotations available locally does not offset the cost of having to create and populate the annotations dictionary on every function call. Therefore, annotations at function level are not evaluated and not stored.

While Python with this PEP will not object to:

since it will not care about the type annotation beyond “it evaluates without raising”, a type checker that encounters it will flag it, unless disabled with # type: ignore or @no_type_check .

However, since Python won’t care what the “type” is, if the above snippet is at the global level or in a class, __annotations__ will include {'alice': 'well done', 'bob': 'what a shame'} .

These stored annotations might be used for other purposes, but with this PEP we explicitly recommend type hinting as the preferred use of annotations.

• Should we introduce variable annotations at all? Variable annotations have already been around for almost two years in the form of type comments, sanctioned by PEP 484 . They are extensively used by third party type checkers (mypy, pytype, PyCharm, etc.) and by projects using the type checkers. However, the comment syntax has many downsides listed in Rationale. This PEP is not about the need for type annotations, it is about what should be the syntax for such annotations.
• Introduce a new keyword: The choice of a good keyword is hard, e.g. it can’t be var because that is way too common a variable name, and it can’t be local if we want to use it for class variables or globals. Second, no matter what we choose, we’d still need a __future__ import.

The problem with this is that def means “define a function” to generations of Python programmers (and tools!), and using it also to define variables does not increase clarity. (Though this is of course subjective.)

• Use function based syntax : It was proposed to annotate types of variables using var = cast(annotation[, value]) . Although this syntax alleviates some problems with type comments like absence of the annotation in AST, it does not solve other problems such as readability and it introduces possible runtime overhead.

Are x and y both of type T , or do we expect T to be a tuple type of two items that are distributed over x and y , or perhaps x has type Any and y has type T ? (The latter is what this would mean if this occurred in a function signature.) Rather than leave the (human) reader guessing, we forbid this, at least for now.

• Parenthesized form (var: type) for annotations: It was brought up on python-ideas as a remedy for the above-mentioned ambiguity, but it was rejected since such syntax would be hairy, the benefits are slight, and the readability would be poor.

it is ambiguous, what should the types of y and z be? Also the second line is difficult to parse.

• Allow annotations in with and for statement: This was rejected because in for it would make it hard to spot the actual iterable, and in with it would confuse the CPython’s LL(1) parser.
• Evaluate local annotations at function definition time: This has been rejected by Guido because the placement of the annotation strongly suggests that it’s in the same scope as the surrounding code.
• Store variable annotations also in function scope: The value of having the annotations available locally is just not enough to significantly offset the cost of creating and populating the dictionary on each function call.
• Initialize variables annotated without assignment: It was proposed on python-ideas to initialize x in x: int to None or to an additional special constant like Javascript’s undefined . However, adding yet another singleton value to the language would needed to be checked for everywhere in the code. Therefore, Guido just said plain “No” to this.
• Add also InstanceVar to the typing module: This is redundant because instance variables are way more common than class variables. The more common usage deserves to be the default.
• Allow instance variable annotations only in methods: The problem is that many __init__ methods do a lot of things besides initializing instance variables, and it would be harder (for a human) to find all the instance variable annotations. And sometimes __init__ is factored into more helper methods so it’s even harder to chase them down. Putting the instance variable annotations together in the class makes it easier to find them, and helps a first-time reader of the code.
• Use syntax x: class t = v for class variables: This would require a more complicated parser and the class keyword would confuse simple-minded syntax highlighters. Anyway we need to have ClassVar store class variables to __annotations__ , so a simpler syntax was chosen.
• Forget about ClassVar altogether: This was proposed since mypy seems to be getting along fine without a way to distinguish between class and instance variables. But a type checker can do useful things with the extra information, for example flag accidental assignments to a class variable via the instance (which would create an instance variable shadowing the class variable). It could also flag instance variables with mutable defaults, a well-known hazard.
• Use ClassAttr instead of ClassVar : The main reason why ClassVar is better is following: many things are class attributes, e.g. methods, descriptors, etc. But only specific attributes are conceptually class variables (or maybe constants).
• Do not evaluate annotations, treat them as strings: This would be inconsistent with the behavior of function annotations that are always evaluated. Although this might be reconsidered in future, it was decided in PEP 484 that this would have to be a separate PEP.
• Annotate variable types in class docstring: Many projects already use various docstring conventions, often without much consistency and generally without conforming to the PEP 484 annotation syntax yet. Also this would require a special sophisticated parser. This, in turn, would defeat the purpose of the PEP – collaborating with the third party type checking tools.
• Implement __annotations__ as a descriptor: This was proposed to prohibit setting __annotations__ to something non-dictionary or non-None. Guido has rejected this idea as unnecessary; instead a TypeError will be raised if an attempt is made to update __annotations__ when it is anything other than a mapping.

the name slef should be evaluated, just so that if it is not defined (as is likely in this example :-), the error will be caught at runtime. This is more in line with what happens when there is an initial value, and thus is expected to lead to fewer surprises. (Also note that if the target was self.name (this time correctly spelled :-), an optimizing compiler has no obligation to evaluate self as long as it can prove that it will definitely be defined.)

This PEP is fully backwards compatible.

An implementation for Python 3.6 is found on GitHub repo at https://github.com/ilevkivskyi/cpython/tree/pep-526

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0526.rst

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

Python Conditional Assignment

When you want to assign a value to a variable based on some condition, like if the condition is true then assign a value to the variable, else assign some other value to the variable, then you can use the conditional assignment operator.

In this tutorial, we will look at different ways to assign values to a variable based on some condition.

1. Using Ternary Operator

The ternary operator is very special operator in Python, it is used to assign a value to a variable based on some condition.

It goes like this:

Here, the value of variable will be value_if_true if the condition is true, else it will be value_if_false .

Let's see a code snippet to understand it better.

You can see we have conditionally assigned a value to variable c based on the condition a > b .

2. Using if-else statement

if-else statements are the core part of any programming language, they are used to execute a block of code based on some condition.

Using an if-else statement, we can assign a value to a variable based on the condition we provide.

Here is an example of replacing the above code snippet with the if-else statement.

3. Using Logical Short Circuit Evaluation

Logical short circuit evaluation is another way using which you can assign a value to a variable conditionally.

The format of logical short circuit evaluation is:

It looks similar to ternary operator, but it is not. Here the condition and value_if_true performs logical AND operation, if both are true then the value of variable will be value_if_true , or else it will be value_if_false .

Let's see an example:

But if we make condition True but value_if_true False (or 0 or None), then the value of variable will be value_if_false .

So, you can see that the value of c is 20 even though the condition a < b is True .

So, you should be careful while using logical short circuit evaluation.

While working with lists , we often need to check if a list is empty or not, and if it is empty then we need to assign some default value to it.

Let's see how we can do it using conditional assignment.

Here, we have assigned a default value to my_list if it is empty.

Assign a value to a variable conditionally based on the presence of an element in a list.

Now you know 3 different ways to assign a value to a variable conditionally. Any of these methods can be used to assign a value when there is a condition.

The cleanest and fastest way to conditional value assignment is the ternary operator .

if-else statement is recommended to use when you have to execute a block of code based on some condition.

Happy coding! 😊

Variables in Strings

I keep getting “Syntax Error: Invalid Syntax” with the following code that I’m following in my textbook:

Any ideas what I’m missing here? Thanks!

I keep getting “Syntax Error: Invalid Syntax” with the following code that I’m following in my textbook: first_name = "ada" last_name = "lovelace" full_name = f"{first_name} {last_name}" print(full_name) Any ideas what I’m missing here? Thanks!

Can you show us the entire error message will all it’s accompanying lines? Also, what version of Python are you running?

To use f-strings (the f"..." form) you need Python 3.6 onwards.

To give hope, here’s your code running in Python 3.10 here in IDLE (the interactive Python prompt):

Thanks so much for the quick reply! I’m running version Python 3.12.3 and the accompanying error message lines are:

SyntaxError: invalid syntax /usr/local/bin/python3 /Users/kalvin/Desktop/python_work/full_name.py File “”, line 1 /usr/local/bin/python3 /Users/kalvin/Desktop/python_work/full_name.py ^

Is the line /usr/local/bin/python3 /Users/kalvin/Desktop/python_work/full_name.py literally in your script?

Do not put this inside the source code file (remove it if it’s already there). Instead, type it in a terminal/shell window, after saving the file. (Exactly what operating system are you using?)

… because it shouldn’t be - it’s an example instruction of how to run your script.

Hey everyone, thanks so much for the prompt replies. I tried opening a new file (I’m using a Mac with the latest version of python) and typed in the same code under a different save file and it didn’t work again. Exact same error code.

THEN I tried the code in sublime text editor and it worked. I still have no idea why it’s not working in vis studio though =/

I put visual studio python command syntax error into a search engine and the first result was:

which appears to be the exact issue you describe.

If you know what’s in your source code file and you’re getting an error message that tells you about something that isn’t in your source code file, you should suspect the IDE.

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