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Assigning a lambda expression to a variable ¶

The sole advantage that a lambda expression has over a def is that the lambda can be anonymously embedded within a larger expression. If you are going to assign a name to a lambda , you are better off just defining it as a def .

From the PEP 8 Style Guide:

The first form means that the name of the resulting function object is specifically ‘f’ instead of the generic ‘<lambda>’. This is more useful for tracebacks and string representations in general. The use of the assignment statement eliminates the sole benefit a lambda expression can offer over an explicit def statement (i.e. that it can be embedded inside a larger expression)

Anti-pattern ¶

The following code assigns a lambda function which returns the double of its input to a variable. This is functionally identical to creating a def .

Best practice ¶

Use a def for named expressions ¶.

Refactor the lambda expression into a named def expression.

References ¶

PEP 8 Style Guide - Programming Recommendations
Stack Overflow - Do not assign a lambda expression

A Guide to Python Lambda Functions, with Examples

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Explaining Python Lambda Functions

Things to understand before delving into python lambda functions, how to use python lambda functions, faqs about python lambdas.

This article introduces Python lambda functions and how write and use them.

Although Python is an object-oriented programming language, lambda functions are handy when you’re doing various kinds of functional programming.

Note: this article will assume you already understand Python programming and how to use a regular function. It’s also assumed you have Python 3.8 or above installed on your device.

In Python, functions can take in one or more positional or keyword arguments, a variable list of arguments, a variable list of keyword arguments, and so on. They can be passed into a higher-order function and returned as output. Regular functions can have several expressions and multiple statements. They also always have a name.

A Python lambda function is simply an anonymous function. It could also be called a nameless function. Normal Python functions are defined by the def keyword. Lambda functions in Python are usually composed of the lambda keyword, any number of arguments, and one expression.

Note: the terms lambda functions , lambda expressions , and lambda forms can be used interchangeably, depending on the programming language or programmer.

Lambda functions are mostly used as one-liners. They’re used very often within higher-order functions like map() and filter() . This is because anonymous functions are passed as arguments to higher-order functions, which is not only done in Python programming.

A lambda function is also very useful for handling list comprehension in Python — with various options for using Python lambda expressions for this purpose.

Lambdas are great when used for conditional rending in UI frameworks like Tkinter , wxPython , Kivy , etc. Although the workings of Python GUI frameworks aren’t covered in this article, some code snippets reveal heavy use of lambda functions to render UI based on a user’s interaction.

Because Python is an object-oriented programming language, everything is an object. Python classes, class instances, modules and functions are all handled as objects.

A function object can be assigned to a variable.

It’s not uncommon to assign variables to regular functions in Python. This behavior can also be applied to lambda functions. This is because they’re function objects, even though they’re nameless:

Higher-order functions like map(), filter(), and reduce()

It’s likely you’ll need to use a lambda function within built-in functions such as filter() and map() ,and also with reduce() — which is imported from the functools module in Python, because it’s not a built-in function. By default, higher-order functions are functions that receive other functions as arguments.

As seen in the code examples below, the normal functions can be replaced with lambdas, passed as arguments into any of these higher-order functions:

The difference between a statement and an expression

A common point of confusion amongst developers is differentiating between a statement and an expression in programming.

A statement is any piece of code that does something or performs an action — such as if or while conditions.

An expression is made of a combination of variables, values, and operators and evaluates to a new value.

This distinction is important as we explore the subject of lambda functions in Python. An expression like the one below returns a value:

A statement looks like this:

The Python style guide stipulates that every lambda function must begin with the keyword lambda (unlike normal functions, which begin with the def keyword). The syntax for a lambda function generally goes like this:

Lambda functions can take any number of positional arguments, keyword arguments, or both, followed by a colon and only one expression. There can’t be more than one expression, as it’s syntactically restricted. Let’s examine an example of a lambda expression below:

From the example above, the lambda expression is assigned to the variable add_number . A function call is made by passing arguments, which evaluates to 14.

Let’s take another example below:

As seen above, the lambda function evaluates to 728.0. A combination of positional and keyword arguments are used in the Python lambda function. While using positional arguments, we can’t alter the order outlined in the function definition. However, we can place keyword arguments at any position only after the positional arguments.

Lambda functions are always executed just like immediately invoked function expressions (IIFEs) in JavaScript. This is mostly used with a Python interpreter, as shown in the following example:

The lambda function object is wrapped within parentheses, and another pair of parentheses follows closely with arguments passed. As an IIFE, the expression is evaluated and the function returns a value that’s assigned to the variable.

Python lambda functions can also be executed within a list comprehension. A list comprehension always has an output expression, which is replaced by a lambda function. Here are some examples:

Lambda functions can be used when writing ternary expressions in Python. A ternary expression outputs a result based on a given condition. Check out the examples below:

Lambda functions within higher-order functions

The concept of higher-order functions is popular in Python, just as in other languages. They are functions that accept other functions as arguments and also return functions as output.

In Python, a higher-order function takes two arguments: a function, and an iterable. The function argument is applied to each item in the iterable object. Since we can pass a function as an argument to a higher-order function, we can equally pass in a lambda function.

Here are some examples of a lambda function used with the map() function:

Here are some lambda functions used with the filter() function:

Here are some lambda functions used with the reduce() function:

Although Python lambdas can significantly reduce the number of lines of code you write, they should be used sparingly and only when necessary. The readability of your code should be prioritized over conciseness. For more readable code, always use a normal function where suited over lambda functions, as recommended by the Python Style Guide .

Lambdas can be very handy with Python ternary expressions, but again, try not to sacrifice readability. Lambda functions really come into their own when higher-order functions are being used.

In summary:

Python lambdas are good for writing one-liner functions.
They are also used for IIFEs (immediately invoked function expression).
Lambdas shouldn’t be used when there are multiple expressions, as it makes code unreadable.
Python is an object-oriented programming language, but lambdas are a good way to explore functional programming in Python.

Lambda Functions and Anonymous Functions in Python

The def keyword is used to define a function in Python , as we have seen in the previous chapter. The lambda keyword is used to define anonymous functions in Python. Usually, such a function is meant for one-time use.

The lambda function can have zero or more arguments after the : symbol. When this function is called, the expression after : is executed.

Above, the lambda function starts with the lambda keyword followed by parameter x . An expression x * x after : returns the value of x * x to the caller. The whole lambda function lambda x : x * x is assigned to a variable square in order to call it like a named function. The variable name becomes the function name so that We can call it as a regular function, as shown below.

The above lambda function definition is the same as the following function:

The expression does not need to always return a value. The following lambda function does not return anything.

The following lambda function contains three parameters:

A lambda function can take any number of parameters by prefixing * before a parameter, as shown below:

Parameterless Lambda Function

The following is an example of the parameterless lambda function.

Anonymous Function

We can declare a lambda function and call it as an anonymous function, without assigning it to a variable.

Above, lambda x: x*x defines an anonymous function and call it once by passing arguments in the parenthesis (lambda x: x*x)(5) .

In Python, functions are the first-class citizens, which means that just as literals, functions can also be passed as arguments.

The lambda functions are useful when we want to give the function as one of the arguments to another function. We can pass the lambda function without assigning it to a variable, as an anonymous function as an argument to another function.

Above, the dosomething() function is defined with the fn parameter which is called as a function inside dosomething() . The dosomething(lambda : print('Hello World')) calls the dosomething() function with an anonymous lambda function as an argument.

Python has built-in functions that take other functions as arguments. The map() , filter() and reduce() functions are important functional programming tools. All of them take a function as their argument. The argument function can be a normal function or a lambda function.

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Lambda Functions in Python: A Comprehensive How-To Guide

Lambda functions, also known as anonymous functions, are small, inline functions that have no name. They are powerful constructs in Python that allow developers to write simple, one-line functions to achieve functionality without declaring a full function.

Lambda functions promote code simplicity and readability by reducing unnecessary syntax. They are popularly used with Python’s functional programming features like map(), filter() and reduce(). Lambda functions have a variety of applications in data science, web development, GUI programming, and more.

This comprehensive guide will provide readers with a deep understanding of lambda functions in Python. It covers the following topics in detail:

Contrast with regular functions, 1. simple single-use functions, 2. map and filter, 3. event handlers and callbacks, 4. list, dictionary and set comprehensions, 5. sorting and comparison, 6. data science and machine learning, passing functions as arguments, returning functions from other functions, assigning to variables and passing around, lazy evaluation, best practices for lambda functions, common mistakes to avoid, what are lambda functions.

Lambda functions are small, anonymous functions that are defined without a name. The lambda keyword is used to define them in Python.

The syntax for lambda functions contains only a single lambda operator with input parameters, a colon, and an expression that returns a value:

For example:

This lambda function accepts a parameter x and returns x squared.

Lambda functions work similarly to regular functions declared with def in Python. The key difference is that lambda functions are defined inline and are anonymous.

Let’s contrast lambda functions with regular functions declared with def:

As we can see, both examples yield the same output. However, there are some key differences:

Anonymous: Lambda functions have no explicit name like regular functions. They are anonymous.

Inline: Lambda functions are written inline within code, while def functions are declared and defined separately.

Concise: Lambda function syntax is more concise than a def function. No need to write return keywords.

Limited logic: Lambda functions can only contain a single expression, while def functions can hold multiple expressions and statements.

So in summary, lambda functions allow developers to declare simple, nameless functions inline without defining a full function. This promotes concise, readable code when simple or disposable functions are needed.

Use Cases and Applications

Lambda functions have many diverse use cases:

For basic functions that are only needed once or in a limited context, lambda functions avoid cluttering the code with many def function declarations.

Two commonly used built-in functions in Python - map() and filter() - accept other functions as arguments. Lambda functions work great for this.

Map example:

Filter example:

Lambda functions can be used as event handlers or callbacks that are executed when an event occurs, like a button click in GUI programming.

Comprehensions provide a concise way to generate collections in Python. Lambda functions can be leveraged inside comprehensions.

The built-in sorted() , min() , and max() functions accept key functions that can be lambda functions.

In data science and machine learning, lambda functions are widely used as part of functional programming and lazy evaluation techniques like in Numpy, Pandas, TensorFlow, PyTorch etc.

Functional Programming with Lambda Functions

Lambda functions promote a functional programming style in Python. Some key aspects:

First-class functions: Lambda functions can be assigned to variables, passed as arguments, and returned from other functions (properties of first-class functions).

Pure functions: The expression in a lambda function depends only on its input arguments and is without side effects (pure).

Lazy evaluation: Lambda functions delay evaluation until their result is needed, enabling lazy evaluation.

Let’s look at some examples of how lambda functions facilitate functional programming:

Functions like map(), filter() and sorted() accept other callable functions as arguments. Lambda functions passed in become first-class functions:

Lambda functions can be returned from other functions:

Lambda functions can be assigned to variables and passed freely like any other object:

Consider this regular function:

It will print the statement every time even if we don’t need the result.

Now consider a lambda function:

The lambda function won’t execute until we call it - enabling lazy evaluation.

When using lambda functions effectively in Python, keep these best practices in mind:

Keep the function bodies small and simple - lambdas are designed for concise, single-expression functions.

Avoid complex logic and multi-line statements - use def functions instead for longer logic.

Give lambda functions meaningful names if assigning them to variables to improve readability.

Leverage built-in functions like map(), filter(), sorted() etc. that accept lambda functions to follow functional programming principles.

Use comprehensions, closures and decorators to define stateful lambda functions that can maintain data.

Avoid common mistakes like using mutable default arguments, overusing lambdas, and readability issues.

Properly used, lambda functions promote simplicity, flexibility, and elegance in Python code.

Some common mistakes to avoid when using lambda functions:

Mutable default arguments: Lambda function arguments are defaulted to whatever value is present at function definition time. Using mutable values like lists or dictionaries leads to unexpected behavior.

Overusing lambdas: While they look clean and compact, overusing lambda functions can reduce readability. Balance with descriptive def functions.

Readability: Long or complex nested lambdas can become difficult to understand. Use good naming and spacing for clarity.

Variable scope: Lambda functions can only access variables from the enclosing scope. Name collisions can occur between lambda parameters and enclosing variables.

Performance: Complicated, long-running lambdas may run slower than a regular def function. Profile code to identify any performance impacts.

Being aware of these common pitfalls will help avoid issues when leveraging lambda functions.

Lambda functions are elegant constructs in Python that allow developers to create simple, anonymous functions inline without defining an entire function. This guide covered the syntax, use cases, functional programming applications, best practices, and common mistakes to avoid when using Python lambda functions.

Lambda functions promote code simplicity, brevity, and readability. They shine when used properly for small, non-reusable functions. Overusing lambdas can reduce code clarity, so balance them by also using descriptive def functions where applicable.

By leveraging lambda functions effectively, Python developers can write cleaner, more concise code following functional programming principles. They provide a powerful tool for any Python programmer’s toolbox.

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A lambda function is a small anonymous function.

A lambda function can take any number of arguments, but can only have one expression.

The expression is executed and the result is returned:

Add 10 to argument a , and return the result:

Lambda functions can take any number of arguments:

Multiply argument a with argument b and return the result:

Summarize argument a , b , and c and return the result:

Why Use Lambda Functions?

The power of lambda is better shown when you use them as an anonymous function inside another function.

Say you have a function definition that takes one argument, and that argument will be multiplied with an unknown number:

Use that function definition to make a function that always doubles the number you send in:

Or, use the same function definition to make a function that always triples the number you send in:

Or, use the same function definition to make both functions, in the same program:

Use lambda functions when an anonymous function is required for a short period of time.

Test Yourself With Exercises

Create a lambda function that takes one parameter ( a ) and returns it.

Start the Exercise

Lambda Functions in map() Functions

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Python Lambda Functions – How to Use Anonymous Functions with Examples

Lambda functions, also known as anonymous functions, are small, one-time-use functions in Python.

You can define them using the lambda keyword followed by the function's inputs, a colon, and the function's expression. The output of a lambda function is returned as the result of the expression, rather than a return statement.

The main purpose of lambda functions is to allow for the creation of small, throw-away functions that you can use in other parts of a program. This can be useful when you need to pass a simple function as an argument to another function, for example.

Syntax for creating lambda functions

Here is the syntax for creating a lambda function in Python:

Lambda Functions vs Named Functions Defined Using the def Keyword

When compared to named functions defined using the def keyword, lambda functions have some key differences.

Named functions can have multiple expressions and they use return statements. Lambda functions can only have one expression and the value of that expression is returned automatically.

Named functions can also be reused throughout a program, while lambda functions are used only once.

As you can see, the named function is defined using the def keyword, followed by the function name and its inputs in parentheses. The function body is indented, and the return statement is used to return the result of the expression.

Named functions can be called multiple times, making them more flexible than lambda functions for complex operations.

Here's the equivalent code using a named function defined with the lambda keyword:

How to Use Lambda Functions

This section covers the basics of how to create and use lambda functions, including their syntax and how to use them as arguments in higher-order functions such as map, filter, and reduce.

Additionally, this section explores how you can use lambda functions in sorting and in list comprehensions. By the end, you should have a foundational understanding of the basic uses of lambda functions.

How to use lambda functions as arguments in higher-order functions ( map , filter , reduce )

Lambda functions are often used as arguments in higher-order functions such as map , filter , and reduce . These functions allow you to apply a given operation to every element of a list or other iterable.

Here's an example of using a lambda function with the map function:

In this example, the lambda function takes one input x and returns the square of that value. The map function applies this operation to every element of the numbers list and returns a new list with the results.

Here's another example using the filter function:

In this example, the lambda function takes one input x and returns True if x is even, and False otherwise. The filter function applies this operation to every element of the numbers list and returns a new list with only the elements that returned True .

How to use lambda functions to return functions as values

You can also use lambda functions to return functions as values. For example:

In this example, the make_adder function takes one input x and returns a lambda function that takes one input y and returns the sum of x and y . The add5 variable is assigned the result of calling make_adder(5) , meaning it now references a lambda function that adds 5 to its input.

How to use lambda functions in sorting

You can also use lambda functions in sorting operations to specify custom sort orders. For example:

In this example, the lambda function takes one input x and returns -x , meaning that the sort order will be in descending order. The sorted function sorts the numbers list based on the values returned by the lambda function.

Limitations of Lambda Functions

While lambda functions are a convenient way to write short and simple functions, they have some limitations.

One of the main limitations is that lambda functions are limited to a single expression, meaning that they cannot contain multiple statements or complex control flow.

In addition, lambda functions cannot be referenced by name and can only be invoked when they are defined, which makes them less flexible than named functions.

Another limitation is that lambda functions do not have a name, which can make debugging more difficult and make it harder to understand the code.

In general, it's a good idea to use named functions for complex operations and only use lambda functions for short and simple operations.

Advanced Uses of Lambda Functions

This section covers how to use lambda functions with advanced functions such as reduce, filter, sorted, and key arguments. Additionally, this section provides information on using lambda functions to create anonymous functions for event handlers.

How to use lambda functions with reduce

The reduce function is a higher-order function that takes a binary function (a function that takes two arguments) and a list. It returns a single value that is the result of applying the binary function to the elements of the list in a cumulative way.

For example, to calculate the product of all elements in a list, you could use the following code:

In this example, the lambda function lambda x, y: x*y is used as the binary function in the reduce function. The reduce function starts by applying the binary function to the first two elements of the list, and then applies the result to the next element, and so on until it has processed all elements of the list.

How to use lambda functions with filter

The filter function is another higher-order function that takes a function and a list, and returns a new list that contains only the elements of the original list for which the function returns True .

For example, to filter out even numbers from a list, you could use the following code:

In this example, the lambda function lambda x: x % 2 == 0 is used as the function argument in the filter function. The filter function invokes this lambda function for each element in the numbers list and includes the element in the result list only if the lambda function returns True .

How to use lambda functions with the sorted function

The sorted function is a built-in function that sorts a list of elements. The sorted function can take an optional key argument, which is a function that takes an element of the list and returns a value that is used as the sort key.

For example, to sort a list of dictionaries by a specific key, you could use the following code:

In this example, the lambda function lambda x: x["age"] is used as the key argument in the sorted function. The sorted function uses this lambda function to extract the "age" value for each dictionary in the employees list and uses these values as the sort keys.

How to use lambda functions in the key argument of various functions

In addition to the sorted function, many other functions in Python can take a key argument, including the max , min , and sorted functions.

The key argument is a function that takes an element of the list and returns a value that is used as the sort key, as well as for comparison purposes in the case of the max and min functions.

For example, to find the employee with the highest salary in a list of employees, you could use the following code:

In this example, the lambda function lambda x: x["salary"] is used as the key argument in the max function. The max function uses this lambda function to extract the "salary" value for each employee in the employees list and uses these values to compare the employees and find the one with the highest salary.

How to use lambda functions to create anonymous functions for event handlers

You can also use lambda functions to create anonymous functions for event handlers in GUI programming or for other similar purposes.

For example, in the following code, a button click event is handled using a lambda function in Tkinter (a GUI programming toolkit for Python):

In this example, we use the lambda function lambda: print("Button clicked!") as the command argument of the Button widget in Tkinter. When the button is clicked, the lambda function is executed and the message "Button clicked!" is printed to the console.

This demonstrates the versatility and flexibility of lambda functions. You can use them in a variety of contexts where anonymous functions are required.

Best Practices for Using Lambda Functions

This section covers a range of best practices, including keeping lambda functions simple and easy to understand, avoiding complex expressions and statements, choosing the appropriate type of function for the task, and documenting lambda functions for better code readability.

We'll also highlight the importance of using descriptive variable names in lambda functions to improve the readability of your code.

Keep lambda functions simple and easy to understand

One of the best practices for using lambda functions is to keep them simple and easy to understand.

Lambda functions are intended to be small, anonymous, single-expression functions, and complex or multi-statement functions are better suited to be defined using the def keyword.

For example, the following lambda function is simple, easy to understand, and does exactly what it is intended to do:

Avoid complex expressions and statements in lambda functions

In addition to keeping lambda functions simple, it is also important to avoid complex expressions and statements in lambda functions.

Complex expressions and statements make the code harder to understand and maintain, and can lead to bugs.

For example, the following lambda function is too complex and difficult to understand:

In such cases, it is better to define a named function using the def keyword and provide a meaningful name for the function. This makes the code more readable and easier to maintain:

When to use lambda functions and when to use named functions

Lambda functions are best used in situations where you need a small, anonymous, single-expression function. They are not well suited for complex functions with multiple expressions and statements.

For example, a good use case for a lambda function is as an argument to a higher-order function such as map , filter , or reduce . A bad use case for a lambda function is a complex function with multiple expressions and statements.

In general, it is better to use named functions defined using the def keyword for functions that are complex, multi-statement, or are used multiple times in your code.

Document lambda functions for better code readability

Another best practice for using lambda functions is to document them for better code readability.

While lambda functions are often intended to be simple and easy to understand, it can still be helpful to provide a brief explanation of what the function does in the form of a docstring or a comment.

For example, the following lambda function is documented for better code readability:

Use descriptive variable names in lambda functions

Finally, it is important to use descriptive variable names in lambda functions, just as you would in any other function. Descriptive variable names make the code easier to understand and maintain.

For example, the following lambda function uses descriptive variable names:

By following these best practices, you can ensure that your lambda functions are clear, concise, and easy to understand, making your code more readable, maintainable, and error-free.

In this guide, we covered the basics of lambda functions in Python, including their definition and purpose, syntax, and basic and advanced usage in various applications.

We also discussed some best practices for using lambda functions, including keeping them simple and easy to understand, avoiding complex expressions and statements, choosing the appropriate type of function for the task, and documenting them for better code readability.

Lambda functions can be a powerful tool for writing concise, readable, and efficient code. But they have some limitations, such as being restricted to a single expression and having limited functionality compared to named functions. It's important to consider these limitations and choose the appropriate type of function for the task at hand.

In conclusion, this guide has provided an overview of lambda functions and their uses in Python, and I hope that it has been helpful in your journey to learn more about this topic. For further learning, you may want to explore the official Python documentation and practice using lambda functions in your own projects.

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Python Enhancement Proposals

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PEP Index »

PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
import foo as bar
except Exc as var
with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

NAME = EXPR
if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

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6. Expressions
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6. Expressions ¶

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

and no semantics are given, the semantics of this form of name are the same as for othername .

6.1. Arithmetic conversions ¶

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

If either argument is a complex number, the other is converted to complex;

otherwise, if either argument is a floating point number, the other is converted to floating point;

otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

6.2. Atoms ¶

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

6.2.1. Identifiers (Names) ¶

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

Private name mangling: When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier __spam occurring in a class named Ham will be transformed to _Ham__spam . This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done.

6.2.2. Literals ¶

Python supports string and bytes literals and various numeric literals:

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating point number, complex number) with the given value. The value may be approximated in the case of floating point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

6.2.3. Parenthesized forms ¶

A parenthesized form is an optional expression list enclosed in parentheses:

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries ¶

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

either the container contents are listed explicitly, or

they are computed via a set of looping and filtering instructions, called a comprehension .

Common syntax elements for comprehensions are:

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)] .

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator . A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions. If a comprehension contains either async for clauses or await expressions or other asynchronous comprehensions it is called an asynchronous comprehension . An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530 .

Added in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

6.2.5. List displays ¶

A list display is a possibly empty series of expressions enclosed in square brackets:

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays ¶

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with {} ; this literal constructs an empty dictionary.

6.2.7. Dictionary displays ¶

A dictionary display is a possibly empty series of dict items (key/value pairs) enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of dict items is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding value. This means that you can specify the same key multiple times in the dict item list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking . Its operand must be a mapping . Each mapping item is added to the new dictionary. Later values replace values already set by earlier dict items and earlier dictionary unpackings.

Added in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448 .

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy . (To summarize, the key type should be hashable , which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last value (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572 .

6.2.8. Generator expressions ¶

A generator expression is a compact generator notation in parentheses:

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)) .

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression . An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators ).

Added in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

6.2.9. Yield expressions ¶

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions .

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None . Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError , while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration , or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

The proposal for adding generators and the yield statement to Python.

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

6.2.9.1. Generator-iterator methods ¶

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None . The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__() ’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

6.2.9.2. Examples ¶

Here is a simple example that demonstrates the behavior of generators and generator functions:

For examples using yield from , see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

6.2.9.3. Asynchronous generator functions ¶

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None . Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break , the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks() . When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py .

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

6.2.9.4. Asynchronous generator-iterator methods ¶

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration , or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

6.3. Primaries ¶

Primaries represent the most tightly bound operations of the language. Their syntax is:

6.3.1. Attribute references ¶

An attribute reference is a primary followed by a period and a name:

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. The type and value produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

This production can be customized by overriding the __getattribute__() method or the __getattr__() method. The __getattribute__() method is called first and either returns a value or raises AttributeError if the attribute is not available.

If an AttributeError is raised and the object has a __getattr__() method, that method is called as a fallback.

6.3.2. Subscriptions ¶

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__() . When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__ , see __class_getitem__ versus __getitem__ .

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__() :

Mappings. If the primary is a mapping , the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.

Sequences. If the primary is a sequence , the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str , list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences . However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x . The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters . A character is not a separate data type but a string of exactly one character.

6.3.3. Slicings ¶

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy ) whose start , stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

6.3.4. Calls ¶

A call calls a callable object (e.g., a function ) with a possibly empty series of arguments :

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None , it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable . Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4) , if y evaluates to a sequence y1 , …, yM , this is equivalent to a call with M+4 positional arguments x1 , x2 , y1 , …, yM , x3 , x4 .

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping , the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings ( * ), and keyword arguments may follow dictionary unpackings ( ** ). Originally proposed by PEP 448 .

A call always returns some value, possibly None , unless it raises an exception. How this value is computed depends on the type of the callable object.

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions . When the code block executes a return statement, this specifies the return value of the function call.

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

A new instance of that class is returned.

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

The class must define a __call__() method; the effect is then the same as if that method was called.

6.4. Await expression ¶

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function .

Added in version 3.5.

6.5. The power operator ¶

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1 .

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100 , but 10**-2 returns 0.01 .

Raising 0.0 to a negative power results in a ZeroDivisionError . Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError .)

This operation can be customized using the special __pow__() method.

6.6. Unary arithmetic and bitwise operations ¶

All unary arithmetic and bitwise operations have the same priority:

The unary - (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

The unary ~ (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1) . It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

6.7. Binary arithmetic operations ¶

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

This operation can be customized using the special __truediv__() and __floordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34 .) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand [ 1 ] .

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y) . Floor division and modulo are also connected with the built-in function divmod() : divmod(x, y) == (x//y, x%y) . [ 2 ] .

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting .

The modulo operation can be customized using the special __mod__() method.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The - (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() method.

6.8. Shifting operations ¶

The shifting operations have lower priority than the arithmetic operations:

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

This operation can be customized using the special __lshift__() and __rshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n) . A left shift by n bits is defined as multiplication with pow(2,n) .

6.9. Binary bitwise operations ¶

Each of the three bitwise operations has a different priority level:

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

6.10. Comparisons ¶

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

Comparisons yield boolean values: True or False . Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a , b , c , …, y , z are expressions and op1 , op2 , …, opN are comparison operators, then a op1 b op2 c ... y opN z is equivalent to a op1 b and b op2 c and ... y opN z , except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c , so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

6.10.1. Value comparisons ¶

The operators < , > , == , >= , <= , and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object , they inherit the default comparison behavior from object . Types can customize their comparison behavior by implementing rich comparison methods like __lt__() , described in Basic customization .

The default behavior for equality comparison ( == and != ) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y ).

A default order comparison ( < , > , <= , and >= ) is not provided; an attempt raises TypeError . A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

Numbers of built-in numeric types ( Numeric Types — int, float, complex ) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

The not-a-number values float('NaN') and decimal.Decimal('NaN') are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float('NaN') , 3 < x , x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not , never the equality operators.

Binary sequences (instances of bytes or bytearray ) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.

Strings (instances of str ) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord() ) of their characters. [ 3 ]

Strings and binary sequences cannot be directly compared.

Sequences (instances of tuple , list , or range ) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError .

Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

Lexicographical comparison between built-in collections works as follows:

For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).

Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y ). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).

Mappings (instances of dict ) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

Order comparisons ( < , > , <= , and >= ) raise TypeError .

Sets (instances of set or frozenset ) can be compared within and across their types.

They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets {1,2} and {2,3} are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min() , max() , and sorted() produce undefined results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

Equality comparison should be reflexive. In other words, identical objects should compare equal:

x is y implies x == y

Comparison should be symmetric. In other words, the following expressions should have the same result:

x == y and y == x x != y and y != x x < y and y > x x <= y and y >= x

Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

x > y and y > z implies x > z x < y and y <= z implies x < z

Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

x == y and not x != y x < y and not x >= y (for total ordering) x > y and not x <= y (for total ordering)

The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

6.10.2. Membership test operations ¶

The operators in and not in test for membership. x in s evaluates to True if x is a member of s , and False otherwise. x not in s returns the negation of x in s . All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y) .

For the string and bytes types, x in y is True if and only if x is a substring of y . An equivalent test is y.find(x) != -1 . Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True .

For user-defined classes which define the __contains__() method, x in y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__() , x in y is True if some value z , for which the expression x is z or x == z is true, is produced while iterating over y . If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__() , x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i] , and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in .

6.10.3. Identity comparisons ¶

The operators is and is not test for an object’s identity: x is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. [ 4 ]

6.11. Boolean operations ¶

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False , None , numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x ; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x ; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True , but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or 'foo' yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not 'foo' produces False rather than '' .)

6.12. Assignment expressions ¶

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier , while also returning the value of the expression .

One common use case is when handling matched regular expressions:

Or, when processing a file stream in chunks:

Assignment expressions must be surrounded by parentheses when used as expression statements and when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert , with , and assignment statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

Added in version 3.8: See PEP 572 for more details about assignment expressions.

6.13. Conditional expressions ¶

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x . If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

6.14. Lambdas ¶

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

6.15. Expression lists ¶

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking . Its operand must be an iterable . The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

Added in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448 .

A trailing comma is required only to create a one-item tuple, such as 1, ; it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: () .)

6.16. Evaluation order ¶

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

6.17. Operator precedence ¶

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

6.1. Arithmetic conversions
6.2.1. Identifiers (Names)
6.2.2. Literals
6.2.3. Parenthesized forms
6.2.4. Displays for lists, sets and dictionaries
6.2.5. List displays
6.2.6. Set displays
6.2.7. Dictionary displays
6.2.8. Generator expressions
6.2.9.1. Generator-iterator methods
6.2.9.2. Examples
6.2.9.3. Asynchronous generator functions
6.2.9.4. Asynchronous generator-iterator methods
6.3.1. Attribute references
6.3.2. Subscriptions
6.3.3. Slicings
6.3.4. Calls
6.4. Await expression
6.5. The power operator
6.6. Unary arithmetic and bitwise operations
6.7. Binary arithmetic operations
6.8. Shifting operations
6.9. Binary bitwise operations
6.10.1. Value comparisons
6.10.2. Membership test operations
6.10.3. Identity comparisons
6.11. Boolean operations
6.12. Assignment expressions
6.13. Conditional expressions
6.14. Lambdas
6.15. Expression lists
6.16. Evaluation order
6.17. Operator precedence

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Lambda Expressions

A guide to programming lambda expressions in C++, C#, Java, Javascript, and Python by JoshData .

A lambda expression is a convenient syntax available in many programming languages for writing short functions. Lambda expressions do for functions what object-oriented programing does for objects: It makes a function something you can assign to a variable.

Lambda expressions are also sometimes called anonymous functions, lambda functions, and closures. The capabilities of lambda expressions differs across programming languages, and some programming languages have multiple ways of writing lambda expressions. The concept originates in philosophy.

Background Concepts

Let’s cover some general programming concepts first.

Statements versus expressions

All programming languages with lambda expressions distinguish statements from expressions :

A statement is typically a line of code. Statements control the flow of the program , like if statements, for loops, calling subroutines, and return statements in functions. An assignment to a variable (e.g. x = y ) is also a statement.
An expression is code within a statement that computes a value . Expressions are made up of constants, variables, arithmetic operators, function calls, method calls, property accesors, and so on. An expression encodes a set of instructions that results in the computation of one value.

if x > y is a statement. x > y is an expression — it computes one value, in this case either true or false. The if statement takes an action based on the value of the expression.

Statically versus dynamically typed languages

This guide covers a few common programming languages, and if you are reading it to learn about multiple languages it may be helpful to distinguish:

Statically typed languages, meaning all variables are defined with a type in source code that is known to the compiler. C, C++, C#, and Java are statically typed languages using types such as int , double , string , etc.
Dynamically typed languages, meaning any variable can hold any type of value. Variable declarations with types do not occur in these languages. Python and Javascript are dynamically typed. These languages still have types, usually the same types as in the C-family languages, but they’re only for the values computed in memory, not for variables in source code.

Lambda Syntax

A lambda expression is a function written in a shorthand syntax, and we’ll start by looking at the syntax of lambda expressions.

Here is a regular function (not a lambda function, just a normal function) that adds one to a number as it would be written in C, C++, C#, and Java (they all happen to be identical in this case) and in Javascript. We’ll look at how the same function would be written as a lambda expression.

In this example, there is a single argument x , the expression x + 1 is computed, and that expression is returned to the caller.

It can be written shorter as a lambda expression in modern versions of many languages, including C++ (starting in C++11), C# (starting in C# 9.0), Java (since Java 8), Javascript (starting in ECMAScript 6), and Python (since around Python 2.2):

C#, Java, and Javascript use an arrow symbol (made up of an equal sign or dash plus a greater than sign) to separate the arguments from the expression body. The C# and Javascript syntaxes happen to be identical in this case. (In Javascript, an “anonymous function” refers to a different but related syntactic structure — the technical name for the Javascript syntax discussed here is the “arrow function expression.”)

In the academic discipline of philosophy, the function would be written with the Greek lowercase letter lambda λ denoting the start of a lambda expression:

Here’s a function that returns the maximum value of two numbers, first written as a regular function:

and then as a lambda expression in various languages:

Lambda expressions can also have zero arguments:

Lambda expressions have most of the same components as a normal function:

An argument list: Zero or more variables separated with commas, usually in parentheses. C#, Java, and Javascript allow omitting parentheses if there is only one argument. C++ requires the arguments to have types, but the other statically typed languages allow it without making it required.
A body: Statements to execute or an expression to evaluate and return. When using statements, they are surrounded with braces, and a return statement can be used to return a value. When the body is just an expression, braces are omitted. (C++ only allows using statements and Python only allows using expressions. In C#, braces can be omitted when the body is a single statement.)

In the body of the lambda expression, some languages allow both statement blocks and expressions (C#), some only support statements (C++), and some only support expressions (Python). The add_one examples above used expressions as lambda function bodies where possible: x + 1 in C#, Python, etc. No braces are used in those languages when the body is an expression:

The max examples above used statement blocks as lambda function bodies, and when statements are used these languages require that they be surrounded in braces:

This last example could be turned into an expression-bodied lambda by using the ternary operator condition ? then : else . Expression bodies are usually easier to read because they are more compact.

The one thing missing from lambda expressions is a function name. Lambda expressions don’t have a place for a name in their syntax.

(No type is given for the return value in any of the examples above either. It is optional in C++ and not permitted in other languages. In statically typed languages, the lambda expression does have a return type but the compiler figures it out from what you return.)

Lambda Expressions are Expressions

The key difference between a regularly defined function and a lambda expression is where they occur in source code. Regular functions are normally defined either at the top-level of your source code file or in a class. All of the statements in your source code are contained within a function.

A lambda expression is exactly the opposite. It occurs not outside of statements but inside statements. It actually is an expression , which means it occurs within statements wherever an expression can be used.

Assigning a lambda expression to a variable

It can be assigned to a variable. Variable assignment normally looks like this of course:

(C++ and C# are a statically typed language so the variable must be declared first with its type, but we will omit the variable declaration for now.)

Now instead of 10 , the variable will be assigned a lambda expression:

The lambda expression is assigned to the variable f . Note that the f = at the start and the semicolon ; at the end belong to the variable assignment statement . The lambda expression appears in the middle as the expression. Since the lambda expression is multiple lines, it pushes the semicolon ; down a few lines to its end.

After the variable assignment, the variable actually holds the lambda function defined by the lambda expression.

Quick look at the type of lambda expressions in the statically typed languages

You can also make a variable declaration with an explicit type, and in C# ≤9 and Java it’s required, even though both have var keywords. The variable types to use in C++, C# and Java are:

C++: std::function<double(double, double)> (with #include <functional> )
C#: System.Func<double, double, double>
Java: java.util.function.BinaryOperator<Double>

For example, in Java:

We’ll come back to the type of lambda expressions later.

No type is needed in dynamically typed languages like Javascript and Python.

Few meaningful operators are defined for lambda expressions

Lambda expressions are expressions, but most operators don’t have any meaning with the value that a lambda expression computes. You can’t add two lambda functions together with + : It doesn’t make sense and that isn’t defined in any language.

The first operator that can be used with lambda expressions are grouping parentheses. Lambda expressions can always be wrapped in parentheses for clarity:

Be sure you see where the open and closing parens were added here.

Calling Lambda Expressions

The most important operator that works with lambda functions is the call operator. The next example starts with the same variable assignment as above, but it follows with a new statement that invokes the lambda function by calling it:

It is just like calling a function, but instead of f being the name of a function it is the name of a variable holding a lambda function.

When f(10, 20) is called, control flow moves to the lambda expression. The statements of the lambda expression are evaluated until the return statement is executed. Then control flow moves back the assignment of the value to z .

Assigning a lambda expression to a variable and then, some time later, using the call operator on the variable is the main thing lambda expressions do.

Java does not support the call operator — this is unusual for a language that has lambda expressions. To invoke the lambda function, in this example we use .apply(...) which is the correct method to use with BinaryOperator<Double>. (The method name depends on the runnable interface type that the expression is assigned to.)

In the next toy example, f is first set to a lambda expression that computes the maximum value of two arguments and then later it is set to a different lambda expression that computes the minimum value of two arguments. Although the same code f(10, 20) is called identically after each assignment, f returns a different value each time because it executes the two lambda functions:

Although f(10, 20) appears twice, it computes a different value each time. In the first call to f , control flow goes to the first lambda expression. In the second call to f , control flow goes to the second lambda expression.

In some languages, the call operator (10, 20) can occur after any expresson that evaluates to a lambda function. In C++, Javascript, and Python, it can be right after the lambda expression itself:

Make sure you see where the lambda expression begins and ends and where the call operator begins and ends.

This pattern is commonly seen in Javascript for reasons related to scope and not really about the lambda expression. It isn’t generally useful because you can always write an expression like this without the complex lambda expression syntax.

C# and Java do not allow this.

Passing lambda expressions as arguments to other functions

The last meaningful thing that you can do with lambda expressions is passing the lambda expresson as an argument to a function. In the next set of examples, a new function foo is defined that takes one argument. The program calls foo and passes a lambda expression as the argment.

Here are additional examples using the more complex lambda functions with statement bodies:

When passing lambda expressions with statement bodies in function calls, the triple of symbols brace-paren-semicolon } ); is a common line ending. The close brace belongs to the lambda expression: It is the close-brace that ends the lambda expression’s body. The close parenthesis belongs to the function call: It is the close parethesis at the end of the argument list. The semicolon marks the end of the statement as in all C++, C#, Java, and Javascript statements.

The most common way to use a lambda expression is passing it to another function and then calling it within that function. Here is the same function call example again, plus calling the lambda function within foo :

Finally, we look at a lambda expression with no return value. Lambda expressions without a return value are typically used with statement-block bodies to take an action.

This lambda expression has a void return type in C++, C#, and Java. (Javascript and Python do not support void return types — if there is no return value, the lambda expression returns undefined (Javascript) or None (Python).)

In C++, C#, Java, Javascript, and Python, any regular function name or class method can also be assigned to a variable and passed to a function, like lambda expressions. In the statically typed languages, the variable or function argument must have the right type. But in dynamically typed languages, that’s not an issue and passing around functions can be very natural:

Terminology

In this guide, lambda expression and lambda function mean slightly different things, although I can’t promise that anyone else makes this distinction:

A lambda expression is the code you type to define a short function. It is source code text that goes into the compiler and is recognized with a particular syntax. (In Javascript, technically they are called arrow function expressions/declarations.)

The expression evaluates at run time to a lambda function in memory. In memory during program execution, the f variable in the preceding examples holds a lambda function . It doesn’t hold the source code text that you typed in — that’s been compiled into some other more efficient representation. So it doesn’t hold an expression. Instead it probably holds a pointer to a memory location that has the compiled code.

The difference between a lambda expression and a lambda function is similar to the difference between a class and an instance of the class (an object). A class is a definition of a type of object. At run time, variables whose types are classes don’t hold classes: they hold pointers to objects. Similarly, variables that are assigned lambda expressions in code hold pointers to lambda functions at run time, not lambda expressions. (In fact, in many languages the lambda expression actually compiles to a new instance of a hidden class!)

Standard Library Routines that Use Lambda Functions

The standard library in each programming language has some methods that are convenient to use with lambda expressions.

Across programming languages, lambda functions are commonly used with the language’s standard library sort function to create sort orders for user-defined data types. In C++, a lambda expression can be passed to std::sort for this purpose.

std::sort ’s third argument is a function that compares two items in the list and returns whether the first item should come first. The arguments to the comparison function must be const references.

In this example, a user-defined class is sorted first by its name field and then, when there are any instances with the same name, by its value field.

std::sort will call the lambda function for each pair of elements in the list and will use its return value to sort the elements according to the order that the lambda function defines. The comparison function always looks something like this to achieve a sort order over multiple fields.

The standard library has a handful of functions that take comparison functions like sort does, including min_element / max_element — another common use of lambda functions across languages. This example finds the MyClass instance in a vector with the smallest value .

This is more compact than writing a for loop to iterate over the elements and track which one has the minimum value.

A comparison function can be used to create set and map containers for user-defined data types. (A hash code generator can also be used with unordered_set and unordered_map .)

You can also put lambda functions inside containers:

Across programming languages, lambda functions are commonly used with the language’s container sort functions to create sort orders for user-defined data types. In C#, a lambda expression can be passed to List.Sort(...) for this purpose.

List.Sort() ’s optional argument is a function that compares two items in the list and returns which should be first in the list. The comparison function returns -1 if the first item should come first, 1 if the second item should come first, or 0 if the items can have either order.

List.Sort will call the lambda function for each pair of elements in the list and will use its return value to sort the elements according to the order that the lambda function defines. The comparison function always looks something like this to achieve a sort order over multiple fields. String.CompareTo and Double.CompareTo have the same sematics as the function expected by Sort: They return -1, 0, or 1.

List.ForEach is another helpful method with a lambda expression — it simply runs the function on each element of the list. Here’s how you can print out each item in the list:

You could of course also write a regular foreach loop, but the lambda expression syntax might be clearer or cleaner in some cases.

You can also put lambda functions inside lists:

The extension methods in System.Linq.Enumerable ( reference ) provide other utility methods on collections that are helpful when used with lambda expressions. For example, Count can be used to count the items in a list that pass a test:

Lambda expressions are also commonly used with C# events, such as in System.Windows.Forms applications.

Rather than subscribing methods to event handlers (which are often hooked up by the Visual Studio designer automatically):

A lambda expression could be used instead:

Don’t subscribe to events directly with lambda expressions if the event handler (the lambda function) needs to be unsubscribed from the event later. To do that, you would need to assign the lambda expression to a variable first and then later use the variable to unsubscribe from the event.

System.Threading.Tasks.Task can be used to launch a background task that runs asynchronously on a thread pool and System.Threading.Tasks.Parallel can launch many tasks at once on the thread pool. Lambda expressions are convenient for both.

First, a single background task:

Next, a background task is launched for each item in an array:

The lambda expression is run multiple times, possibly simultaneously, for each item in the array, and the order in which the array elements are seen might be unpredictable. The first argument to ForEach can be any IEnumerable container. Unlike Task.Run which returns immediately, Parallel.ForEach waits until all of the loop iterations complete.

See https://ddc-java-10.github.io/2020/04/28/lambdas-key-functional-interfaces/ for some examples.

Lambda expressions and anonymous functions are used extensively in Javascript, in two ways:

When a lambda expression (let’s call it L) is passed as an argument to a function (let’s call it F) and the lambda L is executed by function F immediately, before the function F returns, the call to the lambda L is synchronous . All of the Javascript examples in this guide so far have been synchronous.
When the function F stores the lambda L to be called later, so that F can return before L is executed, the call to the lambda L is asynchronous . It often happens after other stored lambda functions are executed, creating a sort of concurrent execution of multiple tasks. An asynchronously executed lambda expression is often called a “callback.”

Asynchronous callbacks are so pervasive in Javascript that I can’t even begin here to provide key examples. The async package and the Promise design concept are the key places to look next.

Across programming languages, lambda functions are commonly used with the language’s list sorting function to create sort orders for user-defined classes for which the language doesn’t provide any built-in ordering. In Python, a lambda expression can be passed to list.sort() and sorted(...) for this purpose.

You might have seen this error when trying to sort user-defined classes:

MyClass , in this example, is not a sortable data type. You could implement the < operator on MyClass to make it sortable, but often the easier solution is to call list.sort with a lambda expression.

list.sort() ’s optional keyword argument key takes a function that takes an item of the list and returns a sortable value to use in place of the item . It can return anything sortable (a number, string, etc.). The items in the list will be sorted according to how their correponding return values from the key function would be sorted amongst each other.

Returning a tuple of sortable values is a convenient way to create a function that sorts on multiple fields. In this example, the user-defined class is sorted first by its name field and then, when there are any instances with the same name, by its value field.

Variable Capture

Besides a lambda expression’s arguments, a lambda expression can also access variables of the outer function that the lambda expression is contained within. This is called capture or closure.

For example:

In the above examples the captured variable is text . You can tell because it is a variable in the lambda expression that is not an argument (in these examples there are no arguments). The variable type is simply a string, but any data type including objects or other lambda functions can be captured:

If the variable holds an object, that object remains valid — it won’t be garbage-collected or destroyed — until the outer function exits and the lambda function is no longer referenced anywhere.

Lambda expressions are typically small, reusable, and self-contained, but capture makes lambda expressions less reusable and not self-contained, so excessive use of capture should be avoided.

How this works varies subtly across languages, and there are three types of variable capture that you need to be aware of.

Capture by Reference

Capture by reference means that the outer variable is shared with the lambda expression.

If the outer variable changes after the lambda expression is defined but before the lambda function is executed , the lambda function will get the updated value:

Conversely, the lambda expression can change the value of the outer variable so that when the lambda function finishes, the outer function sees the updated value:

Here’s a complex case. Can you figure out what will be printed?

In C#, Javascript, and Python, capture is always by reference.

In C++, there is no capture by default. To enable capture by reference, the & symbol can be put in the brackets that start the lambda expression. The brackets are for declaring capture.

When inside non-static methods of a class, the this variable is captured automatically in C#, Java, and Javascript and can be captured by value by adding this to the brackets in C++. That makes all of the current class instance’s fields, properties, methods, etc. available within the lambda expression as well.

In Python and Javascript, the capture rules apply to local function definitions the same as it does to lambda expressions. For example:

In Javascript, use the newer let keyword rather than var to declare variables to avoid for-loop scope mistakes.

Capture by Copy (C++)

Capture by copy means the lambda function gets a copy of the value in the capture variable at the time the lambda expression is defined . It cannot modify the outer variable and does not see changes to the outer variable.

In C++, capture can be either by reference or by copy. Using = in the capture brackets, capture is by copy.

Because a copy is made at the point where the lambda expression is declared, it will not see subsequent changes to the variable:

Variables captured by copy are const inside the lambda expression to prevent confusion about which variable is being edited:

Capture by copy is less prone to coding mistakes than capture by reference so it should be preferred, but it may come at a cost if the variable holds a complex data type that is expensive to copy.

Capture by copy should generally be used when capturing std::shared_ptr or other smart pointers because the copy will ensure the target object is not destroyed before the lambda function finishes. If that’s not a concern, capture by reference may be more efficient.

(In C#, Java, Javascript, and Python capture is always by reference (or by value in Java), so this section on capture by copy does not apply to those languages.)

Caveats and Capture by Value

It can become very difficult to track what captured variables will hold inside lambda expressions when the variable’s value changes, like in some of the examples in the previous section! It is especially hard in some specific cases:

When the variable’s value changes between the lambda expression’s definition and its invocation.
Capturing for -loop variables and variables within loops because their value may change on every iteration — in fact, they may be considered different variables on each iteration, which may or may not be what you expect.
When lambda functions are executed asynchronously, as is often the case in Javascript, because the order of execution is opposite to the source code order and may be unpredictable.

Avoid using capture by reference in these circumstances whenever possible. especially capturing for -loop variables and variables within loops.

Modifying the captured variable either inside or outside the lambda expression is not possible in Java, which requires that captured variables be final or assigned once, so that it only ever has one value. That’s nice! It prevents complex situations that are prone to error. Since the captured variable cannot be changed, capture by reference is probably not the right term: capture by value might be a more appropriate term.

To get around these issues in all of the languages, you can sometimes make the captured variable a container (e.g. a list) and modify what is inside the container. Although the variable should not (or in Java cannot) be assigned a new value, its methods can be called and its fields and properties can be modified freely.

In addition to the captured variable’s value, it is can also be very hard to track its lifetime . Objects stored in captured variables may remain in memory so long as the lambda function remains stored in a variable. The lifetime of captured variables is dependent on the lifetime of the lambda function. If the lambda function is stored in a global variable, for example, any objects in variables captured by the lambda expression may hold onto system resources indefinitely.

Or, worse, those captured objects may become invalid in some way (due to their own semantics), leading to a crash or error the next time the lambda function is called. Lambda functions must be careful that nothing intervenes between definition and execution that would violate expectations the function has about the captured variables.

Capturing loop variables

The type of lambda expressions.

If you are writing your own functions that take lambda functions as arguments in statically typed languages, or if you are using C# ≤9 or Java and want to assign a lambda expression to a variable, you will need to know the correct types to use.

The type for lambda functions in C++ is std::function<return_type(arg_type_1, arg_type_2, ...)> which is defined in the functional standard header.

The template argument return_type(arg_type_1, arg_type_2, ...) is a little unusual, but it makes sense and pretty clearly indicates what’s what.

Three types in C# are generally used with lambda expressions.

System.Action is the usual type for lambda functions that do not return anything (like a void return type function). Without any generic arguments, it is the type for lambda functions that take no arguments and have no return value.

When System.Action has generic arguments, they are the lambda function’s argument types. For example, Action<string> is the type of a lambda function that takes a string and performs an action without returning a value. Action<string,Object> is the type of a lambda function that takes a string and an object as arguments (in that order) and performs an action without returning a value.

System.Func is the usual type for lambda functions that have a return value. The first (zero or more) generic arguments are the argument types. The last generic argument is always the type of the return value of the lambda function. So, for example, Func<string> is the type of a lambda function that takes no arguments and returns a string. Func<Object,string,int> is the type of a lambda function that takes an object and a string as arguments (in that order) and returns integer.

System.Predicate is a special case of Func where the return type is bool . So, for example, Predicate<string> is the type of a lambda function that takes a string argument and returns a boolean. Predicate<Object,string,int> is the type of a lambda function that takes those three argument types and returns a bool .

These types are used throughout the built-in .NET assemblies.

Actually any delegate type can be the type of a lambda expression, but there is generally no practical reason to use any type other than the three above unless the length of the names of these types becomes too cumbersome to type or read in the source code.

The var keyword can only be used with lambda expressions starting in C# 10, and only for some lambda expressions. Otherwise, you must give a full type.

In Java, there is no fixed type that must be used with a lambda expression. The pre-defined interface classes in java.util.function ( reference ) define some commonly used types, such as Function<T,R> which can be assigned a lambda function that takes a T as an argument and returns an R -type value. The Runnable ( reference ) interface can be used for lambda expressions that take no arguments and return nothing.

Any interface with a single method (a functional interface type) can be the type of a lambda expression, so long as the method has the same return type and arguments as the lambda expression. See the language reference .

Unfortunately, to invoke the lambda function, you must know the name of the method in the particular functional interface that is being used. Consult the interface documentation for the name of the method used to invoke the lambda function.

Dynamically Typed Languages

Although dynamically typed languages like Javascript and Python still have types at runtime , the types are generally not specified in source code.

Metaprogramming

In C#, lambda expressions can be used for metaprogramming. See System.Linq.Expressions.Expression .

Language-Specific Notes

In C++, capture can also be explicit: The brackets can contain a comma-separated list of variables to capture. Variables not mentioned in the list are not captured and not available to the lambda expression. Capture is by copy by default, unless the variable name is preceded with & . The this variable can also be listed when in a non-static class member function, and it is always captured by reference. ( refreence )

Use _ as the name of lambda expression arguments to indicate they are not used, rather than dummy1, dummy2, etc. _ is a lambda discard parameter when it is the name of more than one argument.

Capture can be turned off to avoid accidental capture or if you want to have a variable the same name as a variable in the outer scope by placing the static keyword immediately before the lambda expression.

Lambda expressions can use async .

Nothing yet.

Javascript has a longer, older quasi-alternative syntax to lambda expressions (called function expressions or anonymous functions) that looks like this:

They are almost equivalent. If you don’t use this , arguments , yield , and a few other rarely used keywords, the two syntaxes are pretty much the same.

As noted previously, the capture rules apply to function expressions the same as they do with lambda expressions.

As noted earlier, the capture rules apply to local function definitions the same as they do with lambda expressions.

Because Python variable scope is determined by the nearest assignment, variables in the outer scope cannot be assigned to:

The text variable in the inner function is a different variable than the one in the outer funtion because there is an assignment to text in the inner function. A trick is to make the outer variable a contaier:

Although Python capture is always by reference, there is a trick for achieving capture by value: assigning default values to lambda expression arguments.

It’s a weird and neat trick.

C++: Lambda expressions
C#: Lambda expression
Java: Lambda expressions
Javascript: Arrow function expressions/definitions
Python: Lambda expressions

Function parameters and naming

Do I understand well that def function parameters allow the communication of values between the function scope and the rest of the code? So, for example, if I have:

I don’t need to set the my_function() param because I don’t need any parameter within print() . While if I define something like this:

{name} within the print() cannot retrieve value outside that function scope so I do have to set greeting parameter?

And why do the function parameter placeholder sometimes differ from variables in or outside the scope? For example, I define:

The first run of the outer_function does not need to retrieve any values from outside the function scope, and on the second run, just the inner_function is performed. So why is there a whatever parameter? Do we somehow use whatever ?

Hmm, did you intend to use a closure? Otherwise this function only works if input_string is a global variable.

The inner function can use the parameters of the outer function.

The way you use the terminology is a bit strange, but your understanding seems fine.

Because that’s the point . When you write a function that has a parameter, you are consciously expecting the calling code to give you the value that the code will work with. Because of this, you don’t need to know what the outside code calls it, because you won’t go looking for it - it’s given to you. You can call it whatever makes sense locally, and use that name to write the code for the function. That also means that multiple other places can use your function, and give it differently named - or even unnamed things:

This example is a lot more complicated. But first you should understand that there is no “second run” of outer_function . Instead, calling outer_function creates a new inner_function ; and when the button is pressed, Tkinter will call that separately created inner_function .

The intended technique looks like this instead:

Now, the inner_function will look up the whatever name when it runs. But when it was compiled, Python knew “oh, this function is nested, and the other function that it’s nested inside, has its own whatever name”. So Python will compile this a little differently. When the inner_function runs, it will look for a closure for whatever - a special storage area that remembers the locals from the outer_function call, even after the outer_function has finished running. (And it only creates this storage because it saw the nested function during compilation, and it only creates storage for whatever .)

The idea is, when we write

, now the outer_function can create a button callback that works with any tk.StringVar , not just the input_string global. You just have to pass it to outer_function , and it sets up this special “binding” for the inner_function , that lets it remember which tk.StringVar to use when the button is clicked.

So this means we can make multiple tk.StringVar s and multiple ttk.Button s, and use the same outer_function logic to explain how the buttons should work. When we call outer_function , we say which StringVar to use for which Button :

When we click each button, it shows the message from the corresponding entry.

Do I understand well that def function parameters allow the communication of values between the function scope and the rest of the code?

Yes. In particular, you write the code for the function to work with some fixed variable name, eg name , and the parameters assign a value to name for a particular call:

In this way the function does not need to know anything about the code which called it or what variable names that code might be using.

def greeting(name): print(f"Hi, my name is {name}.") greeting("Peter") {name} within the print() cannot retrieve value outside that function scope so I do have to set greeting parameter?

Broadly, we do not access variables from outside the function directly. We try to write “pure functions” which receive values only through their parameters and produce results only by the return statement. These functions do not have side effects, so they are easy to reason able and use safely and correctly.

However, a function has access to its outer scopes. When you use a variable name such as name in your greeting() function Python looks for the variable in the local scope i.e. the function’s local variables, which include the parameters, and then in outer scopes. Typically that outer scope is the module namespace, which is where what you think of as “global variables” live.

Here we have a function foo() with a parameter p . If you don’t supply the parameter is defaults to None and the function recognises that and sets p=DEFAULT_VALUE in that case. This kind of this is quite common. So: foo(3) would print foo: 3 and foo() would print foo: 10 .

Let’s look at the variables. When you access a variable Python looks for its name in the local variables and then in the outer scopes (which are just the module.global scope here). For p it finds it in the local variables because it is a parameter. For DEFAULT_VALUE it does not find it in the local variables but does find it in the outer scope.

You’ll have noticed that we don’t “declare” variables in Python. When you define a function Python scans the code for assignment statements. If you assign to the name anywhere in a function , that name is a local variable. The parameters are assigned to. So Python knows p is local because it’s a parameter. DEFAULT_VALUE is not assigned to, so it must be found elsewhere. Note that the assignment does not need to actually be run:

The variable x is a local variable because there’s an assignment statement in the function code. When you run the above you’ll get a UnboundLocalError because x has not yet been set, but Python still knows that it is a local variable, even if the outer scope also had an x variable.

And why do the function parameter placeholder sometimes differ from variables in or outside the scope? For example, I define: def outer_function(whatever): def inner_function(): print("Button pressed.") print(input_string.get()) return inner_function ... input_string = tk.StringVar(value = "test") entry = ttk.Entry(window, textvariable = input_string) entry.pack() ... button = ttk.Button(window, text = "Button 1", command = outer_function(input_string)) button.pack() The first run of the outer_function does not need to retrieve any values from outside the function scope, and on the second run, just the inner_function is performed. So why is there a whatever parameter? Do we somehow use whatever ?

This is more complex than you might think. And, I think, more complex than it needs to be.

To answer your question: you’re not using the whatever variable. I don’t think you need it at all. As written above . But I think the code above isn’t what might ordinarily be done.

I was thinking that Nice Zombies had missed the point, but now I think they’re spot on.

Do I gather you’ve adapted this code from some example?

So, you’re defining a button:

Here we make a button with the label Button 1 and arrange that when it is pushed it calls a function - that function is passed as the command parameter to the button setup. The idea is that pressing the button prints the current value of the input string, so the function you pass must access the object holding the input string.

Now, you’ve also defined an Entry widget which stores its stae, the input string, in a StringVar object which we refer to with the input_string variable:

So your button wants a callback function (the command parameter) which accesses the StringVar to get the current input string value. And that is when you call outer_function() : outer_function is a function which returns a function . The function it returns is what accesses and prints the value. Let’s look at how it is defined:

What does it do? It defines inner_function() , which runs a couple of print() calls. The second print directly accesses input_string to obtain the value - that’s the StringVar object. The whatever parameter is not used.

This works, but only because you’ve got exactly one Entry widget, so you get to name it directly in the function. What if you had more, for example two entries and two buttons:

As written, the call to outer_function(input_string) or outer_function(input_string2) ignores the parameter ( whatever ) and returns a function which always accesses input_string , the first one. Not so good: both buttons report on the same StringVar object.

I’d be naming these functions differently:

so that it is more clear that you have a function report() which reports on the value of some StringVar , and a function get_report_on_var() which returns a report function. So we would set up the buttons like this:

But the report() function still reaches for the hardwired input_string object. The intent of the button setups above is more clear: we want to report on input_string from button 1 and report on input_string2 from button 2. But the get_report_on_var() function ignores its argument!

What we now want is for get_report_on_var() to return a function which reports on whatever StringVar we hand it. So it needs a further change:

Now it reports on the particular StringVar object you passed in when you set up the button, and the parameter (formerly whatever ) is not ignored !

So: how does this work?

What you have above is called a “closure”, where the inner function report() has access to the variables in its immediate outer scope: those local variables of get_report_on_var() . When it accesses some_var , that is not a local variable of report() . So Python looks in the next outer scope, which is the local variables of get_report_on_var() at the time you called it . That’s important.

When you set up the buttons, you call get_report_on_var() to obtain the callback function for the button. The get_report_on_var() function defines a new function called report() :

Functions are just another type of object in Python, so this is a kind of assignment statement: the name report is bound to the function as a local variable in get_report_on_var() . And the new function you just made has access to get_report_on_var() local variables, particularly some_var , which is a local variable because it came from the parameters.

When you run a function, the local variables are new , unrelated to the ones some any earlier or later call: you get a fresh set every time.

So when you define the first button, you call get_report_on_var() once, and it returns a function which accesses some_var , which on that call is input_string .

When you define the second button, you call get_report_on_var() once again, and it returns another function which accesses some_var , which on that call is input_string2 , the other StringVar object`.

These “callback unctions” are not run until you press one of the buttons.

I think someone’s trying to teach your about closures, which is why the functions had the names outer_function and inner_function , but they’re poorly named for the programme as a whole.

Finally, returning your first question about scopes: the inner function report() (which gets defines anew on every call to get_report_on_var() ) has 3 scopes to access names:

its local variables
the local variables of get_report_on_var()
the module/global variables

A function parameter is a local variable, but one that can be defined in a special way. For example,

Both x and y are local variables, defined in the scope of foo . But while y is defined explicitly by an assignment statement, x is defined implicitly via a function call. That is,

basically performs an assignment x = 3 before entering the body of the function.

Yes, I made a mistake when retyping this segment from the video tutorial; the variable name of the second print should be whatever , so print(whatever.get()) .

I recommend to put the main code in a function, that doesn’t hide these errors.

So, as I understand it now, the outer_function parameter placeholder can differ if I am sending into the function just one value because it is clear that the function could receive just one parameter, and there is no uncertainty about which one which could be a case of more complex functions with more parameters.

So for example:

Would result in Hello Murphy . since I am sending just one parameter to the named function.

Python Functions

Python Functions is a block of statements that return the specific task. The idea is to put some commonly or repeatedly done tasks together and make a function so that instead of writing the same code again and again for different inputs, we can do the function calls to reuse code contained in it over and over again.

Some Benefits of Using Functions

Increase Code Readability
Increase Code Reusability

Python Function Declaration

The syntax to declare a function is:

Syntax of Python Function Declaration

Types of Functions in Python

Below are the different types of functions in Python :

Built-in library function: These are Standard functions in Python that are available to use.
User-defined function: We can create our own functions based on our requirements.

Creating a Function in Python

We can define a function in Python, using the def keyword. We can add any type of functionalities and properties to it as we require. By the following example, we can understand how to write a function in Python. In this way we can create Python function definition by using def keyword.

Calling a Function in Python

After creating a function in Python we can call it by using the name of the functions Python followed by parenthesis containing parameters of that particular function. Below is the example for calling def function Python.

Python Function with Parameters

If you have experience in C/C++ or Java then you must be thinking about the return type of the function and data type of arguments. That is possible in Python as well (specifically for Python 3.5 and above).

Python Function Syntax with Parameters

The following example uses arguments and parameters that you will learn later in this article so you can come back to it again if not understood.

Note: The following examples are defined using syntax 1, try to convert them in syntax 2 for practice.

Python Function Arguments

Arguments are the values passed inside the parenthesis of the function. A function can have any number of arguments separated by a comma.

In this example, we will create a simple function in Python to check whether the number passed as an argument to the function is even or odd.

Types of Python Function Arguments

Python supports various types of arguments that can be passed at the time of the function call. In Python, we have the following function argument types in Python:

Default argument
Keyword arguments (named arguments)
Positional arguments
Arbitrary arguments (variable-length arguments *args and **kwargs)

Let’s discuss each type in detail.

Default Arguments

A default argument is a parameter that assumes a default value if a value is not provided in the function call for that argument. The following example illustrates Default arguments to write functions in Python.

Like C++ default arguments, any number of arguments in a function can have a default value. But once we have a default argument, all the arguments to its right must also have default values.

Keyword Arguments

The idea is to allow the caller to specify the argument name with values so that the caller does not need to remember the order of parameters.

Positional Arguments

We used the Position argument during the function call so that the first argument (or value) is assigned to name and the second argument (or value) is assigned to age. By changing the position, or if you forget the order of the positions, the values can be used in the wrong places, as shown in the Case-2 example below, where 27 is assigned to the name and Suraj is assigned to the age.

Arbitrary Keyword Arguments

In Python Arbitrary Keyword Arguments, *args, and **kwargs can pass a variable number of arguments to a function using special symbols. There are two special symbols:

*args in Python (Non-Keyword Arguments)
**kwargs in Python (Keyword Arguments)

Example 1: Variable length non-keywords argument

Example 2: Variable length keyword arguments

The first string after the function is called the Document string or Docstring in short. This is used to describe the functionality of the function. The use of docstring in functions is optional but it is considered a good practice.

The below syntax can be used to print out the docstring of a function.

Example: Adding Docstring to the function

Python Function within Functions

A function that is defined inside another function is known as the inner function or nested function . Nested functions can access variables of the enclosing scope. Inner functions are used so that they can be protected from everything happening outside the function.

Anonymous Functions in Python

In Python, an anonymous function means that a function is without a name. As we already know the def keyword is used to define the normal functions and the lambda keyword is used to create anonymous functions.

Recursive Functions in Python

Recursion in Python refers to when a function calls itself. There are many instances when you have to build a recursive function to solve Mathematical and Recursive Problems.

Using a recursive function should be done with caution, as a recursive function can become like a non-terminating loop. It is better to check your exit statement while creating a recursive function.

Here we have created a recursive function to calculate the factorial of the number. You can see the end statement for this function is when n is equal to 0.

Return Statement in Python Function

The function return statement is used to exit from a function and go back to the function caller and return the specified value or data item to the caller. The syntax for the return statement is:

The return statement can consist of a variable, an expression, or a constant which is returned at the end of the function execution. If none of the above is present with the return statement a None object is returned.

Example: Python Function Return Statement

Pass by Reference and Pass by Value

One important thing to note is, in Python every variable name is a reference. When we pass a variable to a function Python, a new reference to the object is created. Parameter passing in Python is the same as reference passing in Java.

When we pass a reference and change the received reference to something else, the connection between the passed and received parameters is broken. For example, consider the below program as follows:

Another example demonstrates that the reference link is broken if we assign a new value (inside the function).

Exercise: Try to guess the output of the following code.

Quick Links

Quiz on Python Functions
Difference between Method and Function in Python
First Class functions in Python
Recent articles on Python Functions .

FAQs- Python Functions

Q1. what is function in python.

Python function is a block of code, that runs only when it is called. It is programmed to return the specific task. You can pass values in functions called parameters. It helps in performing repetitive tasks.

Q2. What are the 4 types of Functions in Python?

The main types of functions in Python are: Built-in function User-defined function Lambda functions Recursive functions

Q3. H ow to Write a Function in Python ?

To write a function in Python you can use the def keyword and then write the function name. You can provide the function code after using ‘:’. Basic syntax to define a function is: def function_name(): #statement

Q4. What are the parameters of a function in Python?

Parameters in Python are the variables that take the values passed as arguments when calling the functions. A function can have any number of parameters. You can also set default value to a parameter in Python.

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COMMENTS

Assignment inside lambda expression in Python
The assignment expression operator := added in Python 3.8 supports assignment inside of lambda expressions. This operator can only appear within a parenthesized (...), bracketed [...], or braced {...} expression for syntactic reasons. For example, we will be able to write the following: import sys. say_hello = lambda: (.
python
Python has setattr function to set attribute of given object. You can use it in your lambda expression. setattr is useful when you want to programmatically-correctly (sorry) set some class or class' instance. It is not used frequently because it is easier to assign variables directly with = expression. But for lambdas... It is a salvation.
How to Use Python Lambda Functions
A Python lambda function behaves like a normal function in regard to arguments. Therefore, a lambda parameter can be initialized with a default value: the parameter n takes the outer n as a default value. The Python lambda function could have been written as lambda x=n: print(x) and have the same result.
Assigning a lambda expression to a variable
If you are going to assign a name to a lambda, you are better off just defining it as a def. From the PEP 8 Style Guide: Yes: def f(x): return 2*x. No: f = lambda x: 2*x. The first form means that the name of the resulting function object is specifically 'f' instead of the generic '<lambda>'. This is more useful for tracebacks and ...
How the Python Lambda Function Works
Let's look at an example of a lambda function to see how it works. We'll compare it to a regular user-defined function. Assume I want to write a function that returns twice the number I pass it. We can define a user-defined function as follows: def f(x): return x * 2. f(3) >> 6. Now for a lambda function.
Tutorial: Lambda Functions in Python
A lambda function is an anonymous function (i.e., defined without a name) that can take any number of arguments but, unlike normal functions, evaluates and returns only one expression. A lambda function in Python has the following syntax: lambda parameters: expression. The anatomy of a lambda function includes three elements:
A Guide to Python Lambda Functions, with Examples
Let's examine an example of a lambda expression below: add_number = lambda x, y : x + y. print(add_number(10, 4)) >>>> 14. From the example above, the lambda expression is assigned to the ...
Lambda Functions and Anonymous Functions in Python
Above, the lambda function starts with the lambda keyword followed by parameter x.An expression x * x after : returns the value of x * x to the caller. The whole lambda function lambda x : x * x is assigned to a variable square in order to call it like a named function. The variable name becomes the function name so that We can call it as a regular function, as shown below.
Python workarounds for assignment in lambda
Python 3.8 introduces assignment expressions, which use := to assign a variable inline as part of expression. >>> (n:=2, n+1) (2, 3) This can be used inside a lambda , where assignments are not ordinarily allowed.
Python's Assignment Operator: Write Robust Assignments
Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value. To execute an assignment statement like the above, Python runs the following steps: Evaluate the right-hand expression to produce a concrete value or object.
Lambda Functions in Python: A Comprehensive How-To Guide
1. Simple Single-Use Functions. For basic functions that are only needed once or in a limited context, lambda functions avoid cluttering the code with many def function declarations. # Using defdef triple(x): return x*3print(triple(5))# Using lambdatriple = lambda x: x*3print(triple(5)) 2. Map and Filter.
Python Lambda
Python Variables Variable Names Assign Multiple Values Output Variables Global Variables Variable Exercises. ... A lambda function is a small anonymous function. A lambda function can take any number of arguments, but can only have one expression. Syntax. lambda arguments : expression.
Python Lambda Functions
You can also use lambda functions to return functions as values. For example: def make_adder(x): return lambda y: x + y. add5 = make_adder(5) print(add5(3)) # Output: 8. In this example, the make_adder function takes one input x and returns a lambda function that takes one input y and returns the sum of x and y.
Python lambda Tutorial
Define a lambda function echo_word using the variables word1 and echo. Replicate what the original function definition for echo_word() does above. Call echo_word() with the string argument 'hey' and the value 5, in that order. Assign the call to result. Finally, print the result variable.
PEP 572
This is a proposal for creating a way to assign to variables within an expression using the ... Unparenthesized assignment expressions are prohibited in lambda functions. ... However, this would be the only place in Python where a variable's scope is encoded into its name, making refactoring harder. Adding a where: to any statement to create ...
6. Expressions
Expressions — Python 3.12.3 documentation. 6. Expressions ¶. This chapter explains the meaning of the elements of expressions in Python. Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form.
python
You can use globals() to assign to global variables by name, but I strongly discourage you from doing so. You should reconsider your design if you find yourself thinking you need to do this. ... Python - Pass a variable to lambda if it exists. 4. How to modify a variable inside a lambda function? 5.
The Walrus Operator: Python 3.8 Assignment Expressions
Each new version of Python adds new features to the language. For Python 3.8, the biggest change is the addition of assignment expressions.Specifically, the := operator gives you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator.. This tutorial is an in-depth introduction to the walrus operator.
Lambda Expressions: A Guide
Lambda Expressions. A guide to programming lambda expressions in C++, C#, Java, Javascript, and Python by JoshData.. A lambda expression is a convenient syntax available in many programming languages for writing short functions. Lambda expressions do for functions what object-oriented programing does for objects: It makes a function something you can assign to a variable.
Function parameters and naming
Functions are just another type of object in Python, so this is a kind of assignment statement: the name report is bound to the function as a local variable in get_report_on_var(). And the new function you just made has access to get_report_on_var() local variables, particularly some_var, which is a local variable because it came from the ...
python
You say you want to implement with a lambda, but it's not working. Keep it simple and self explanatory: def a named function rather than writing an anonymous lambda. Scope of local variables within a function isn't doing you any favors.
Python Functions
Assignment Operators; Relational Operators; Logical Operators; Ternary Operators; Flow Control in Java. ... As we already know the def keyword is used to define the normal functions and the lambda keyword is used to create anonymous functions. ... in Python every variable name is a reference. When we pass a variable to a function Python, a new ...
Python: Variable assignment using "or" with lambda functions?
I think my question is different in that it deals with variable assignment rather than return values, and the application of "or" to functions, which I don't see covered in the other question. I would still like clarification on the concept of truthiness or falsey values applied to a lambda function.