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Using Python Generators and yield: A Complete Guide

February 15, 2023 February 15, 2023

In this tutorial, you’ll learn how to use generators in Python, including how to interpret the yield expression and how to use generator expressions . You’ll learn what the benefits of Python generators are and why they’re often referred to as lazy iteration. Then, you’ll learn how they work and how they’re different from normal functions.

Python generators provide you with the means to create your own iterator functions. These functions allow you to generate complex, memory-intensive operations. These operations will be executed lazily, meaning that you can better manage the memory of your Python program.

By the end of this tutorial, you’ll have learned:

What Python generators are and how to use the yield expression
How to use multiple yield keywords in a single generator
How to use generator expressions to make generators simpler to write
Some common use cases for Python generators

Table of Contents

Understanding Python Generators

Before diving into what generators are, let’s explore what iterators are. Iterators are objects that can be iterated upon, meaning that they return one action or item at a time . To be considered an iterator, objects need to implement two methods: __iter__() and __next__() . Some common examples of iterators in Python include for loops and list comprehensions .

Generators are a Pythonic implementation of creating iterators, without needing to explicitly implement a class with __iter__() and __next__() methods. Similarly, you don’t need to keep track of the object’s internal state. An important thing to note is that generators iterate over an object lazily, meaning they do not store their contents in memory .

The yield statement’s job is to control the flow of a generator function. The statement goes further to handle the state of the generator function, pausing it until it’s called again, using the next() function.

Creating a Simple Generator

In this section, you’ll learn how to create a basic generator. One of the key syntactical differences between a normal function and a generator function is that the generator function includes a yield statement .

Let’s see how we can create a simple generator function:

Let’s break down what is happening here:

We define a function, return_n_values() , which takes a single parameter, n
In the function, we first set the value of num to 0
We then enter a while loop that evaluates whether the value of num is less than our function argument, n
While that condition is True , we yield the value of num
Then, we increment the value of num using the augmented assignment operator

Immediately, there are two very interesting things that happen:

We use yield instead of return
A statement follows the yield statement, which isn’t ignored

Let’s see how we can actually use this function:

In the code above, we create a variable values , which is the result of calling our generator function with an argument of 5 passed in. When we print the value of values , a generator object is returned.

So, how do we access the values in our generator object? This is done using the next() function, which calls the internal .__iter__() method. Let’s see how this works in Python:

We can see here that the value of 0 is returned. However, intuitively, we know that the values of 0 through 4 should be returned. Because a Python generator remembers the function’s state, we can call the next() function multiple times . Let’s call it a few more times:

In this case, we’ve yielded all of the values that the while loop will accept. Let’s see what happens when we call the next() function a sixth time:

We can see in the code sample above that when the condition of our while loop is no longer True , Python will raise StopIteration .

In the next section, you’ll learn how to create a Python generator using a for loop.

Creating a Python Generator with a For Loop

In the previous example, you learned how to create and use a simple generator. However, the example above is complicated by the fact that we’re yielding a value and then incrementing it. This can often make generators much more difficult for beginners and novices to understand.

Instead, we can use a for loop, rather than a while loop, for simpler generators . Let’s rewrite our previous generator using a for loop to make the process a little more intuitive:

In the code block above, we used a for loop instead of a while loop. We used the Python range() function to create a range of values from 0 through to the end of the values. This simplifies the generator a little bit, making it more approachable to readers of your code.

Unpacking a Generator with a For Loop

In many cases, you’ll see generators wrapped inside of for loops, in order to exhaust all possible yields. In these cases, the benefit of generators is less about remembering the state (though this is used, of course, internally), and more about using memory wisely.

In the code block above, we used a for loop to loop over each iteration of the generator. This implicitly calls the __next__() method. Note that we’re using the optional end= parameter of the print function, which allows you to overwrite the default newline character .

Creating a Python Generator with Multiple Yield Statements

A very interesting difference between Python functions and generators is that a generator can actually hold more than one yield expressions ! While, theoretically, a function can have more than one return keyword, nothing after the first will execute.

Let’s take a look at an example where we define a generator with more than one yield statement:

In the code block above, our generator has more than one yield statement. When we call the first next() function, it returns only the first yielded value. We can keep calling the next() function until all the yielded values are depleted. At this point, the generator will raise a StopIteration exception.

Understanding the Performance of Python Generators

One of the key things to understand is why you’d want to use a Python generator. Because Python generators evaluate lazily, they use significantly less memory than other objects.

For example, if we created a generator that yielded the first one million numbers, the generator doesn’t actually hold the values. Meanwhile, by using a list comprehension to create a list of the first one million values, the list actually holds the values. Let’s see what this looks like:

In the code block above, we import the sys library which allows us to access the getsizeof() function. We then print the size of both the generator and the list. We can see that the list is over 75,000 times larger.

In the following section, you’ll learn how to simplify creating generators by using generator expressions.

Creating Python Generator Expressions

When you want to create one-off generators, using a function can seem redundant. Similar to list and dictionary comprehensions , Python allows you to create generator expressions. This simplifies the process of creating generators, especially for generators that you only need to use once.

In order to create a generator expression, you wrap the expression in parentheses . Say you wanted to create a generator that yields the numbers from zero through four. Then, you could write (i for i in range(5)) .

In the example above, we used a generator expression to yield values from 0 to 4. We then call the next() function five times to print out the values in the generator.

In the following section, we’ll dive further into the yield statement.

Understanding the Python yield Statement

The Python yield statement can often feel unintuitive to newcomers to generators. What separates the yield statement from the return statement is that rather than ending the process, it simply suspends the current process.

The yield statement will suspend the process and return the yielded value. When the subsequent next() function is called, the process is resumed until the following value is yielded.

What is great about this is that the state of the process is saved. This means that Python will know where to pick up its iteration, allowing it to move forward without a problem.

How to Throw Exceptions in Python Generators Using throw

Python generators have access to a special method, .throw() , which allows them to throw an exception at a specific point of iteration . This can be helpful if you know that an erroneous value may exist in the generator.

Let’s take a look at how we can use the .throw() method in a Python generator:

Let’s break down how we can use the .throw() method to throw an exception in a Python generator:

We create our generator using a generator expression
We then use a for loop to loop over each value
Within the for loop, we use an if statement to check if the value is equal to 3. If it is, we call the .throw() method, which raises an error

In some cases, you may simply want to stop a generator, rather than throwing an exception. This is what you’ll learn in the following section.

How to Stop a Python Generator Using stop

Python allows you to stop iterating over a generator by using the .close() function . This can be very helpful if you’re reading a file using a generator and you only want to read the file until a certain condition is met.

Let’s repeat our previous example, though we’ll stop the generator rather than throwing an exception:

In the code block above we used the .close() method to stop the iteration. While the example above is simple, it can be extended quite a lot. Imagine reading a file using Python – rather than reading the entire file, you may only want to read it until you find a given line.

In this tutorial, you learned how to use generators in Python, including how to interpret the yield expression and how to use generator expressions . You learned what the benefits of Python generators are and why they’re often referred to as lazy iteration. Then, you learned how they work and how they’re different from normal functions.

Additional Resources

To learn more about related topics, check out the resources below:

Understanding and Using Functions in Python for Data Science
Python: Return Multiple Values from a Function
Python Built-In Functions
Python generators: Official Documentation

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

2 thoughts on “Using Python Generators and yield: A Complete Guide”

Guides like these are highlights in the net. Thanks

Thanks so much, Vik!!

Python Yield: Create Your Generators [With Examples]

The Python yield keyword is something that at some point you will encounter as developer. What is yield? How can you use it in your programs?

The yield keyword is used to return a value to the caller of a Python function without losing the state of the function. When the function is called again its execution continues from the line after the yield expression. A function that uses the yield keyword is called generator function.

This definition might not be enough to understand yield.

That’s why we will look at some examples of how to the yield keyword in your Python code.

Let’s start coding!

Table of Contents

Regular Functions and Generator Functions

Most developers are familiar with the Python return keyword. It is used to return a value from a function and it stops the execution of that function.

When you use return in your function any information about the state of that function is lost after the execution of the return statement.

The same doesn’t happen with yield…

When you use yield the function still returns a value to the caller with the difference that the state of the function is stored in memory. This means that the execution of the function can continue from the line of code after the yield expression when the function is called again.

That sounds complicated!?!

Here is an example…

The following regular function takes as input a list of numbers and returns a new array with every value multiplied by 2.

When you execute this code you get the following output:

When the function reaches the return statement the execution of the function stops. At this point the Python interpreter doesn’t keep any details about its state in memory.

Let’s see how we can get the same result by using yield instead of return .

This new function is a lot simpler…

…here are the differences from the function that was using the return statement:

We don’t need the new double_numbers list.
We can remove the line that contains the return statement because we don’t need to return an entire list back.
Inside the for loop we can directly use yield to return values to the caller one at the time .

What output do we get this time from the print statement?

A generator function returns a generator object.

In the next section we will see how to read values from this generator object.

Read the Output of Generator Functions

Firstly let’s recap what yield does when is used in a Python function:

A function that contains the yield keyword is called generator function as opposed to a regular function that uses the return keyword to return a value to the caller. The behaviour of yield is different from return because yield returns values one at the time and pauses the execution of the function until the next call.

In the previous section we have seen that when we print the output of a generator function we get back a generator object.

But how can we get the values from the generator object in the same way we do with a regular Python list?

We can use a for loop . Remember that we were calling the generator function double(). Let’s assign the output of this function to a variable and then loop through it:

With a for loop we get back all the values from this generator object:

In the exact same way we could use this for loop to print the values in the list returned by the regular function we have defined. The one that was using the return statement.

So, what’s the difference between the two functions?

The regular function creates a list in memory and returns the full list using the return statement. The generator function doesn’t keep the full list of numbers in memory. Numbers are returned, one by one, each time the generator function is called in the for loop.

We can also get values from the generator using the next() function .

The next function returns the next item in the generator every time we pass the generator object to it.

We are expecting back a sequence of five numbers. Let’s pass the generator to the next() function six times and see what happens:

The first time we call the next() function we get back 6, then 112, then 8 and so on.

After the fifth time we call the next() function there are no more numbers to be returned by the generator. At that point we call the next() function again and we get back a StopIteration exception from the Python interpreter.

The exception is raised because no more values are available in the generator.

When you use the for loop to get the values from the generator you don’t see the StopIteration exception because the for loop handles it transparently.

Next Function and next() Generator Object Method

Using the dir() built-in function we can see that __next__ is one of the methods available for our generator object.

This is the method that is called when we pass the generator to the next() function .

Python methods whose name starts and ends with double underscores are called dunder methods .

How to Convert a Generator to a Python List

In our example of generator we have seen that when we print the value of the generator variable we get back a reference to a generator object.

But, how can we see all the values in the generator object without using a for loop or the next() function?

A way to do that is by converting the generator into a Python list using the list() function .

As you can see we got back the list of numbers in the generator as a list.

This doesn’t necessarily makes sense considering that one of the reasons you would use a generator is that generators require a lot less memory than lists.

That’s because when you use a list Python stores every single element of the list in memory while a generator returns only one value at the time. Some additional memory is required to “pause” the generator function and remember its state.

When we convert the generator into a list using the list() function we basically allocate memory required for every element returned by the generator (basically the same that happens with a regular list).

In one of the next sections we will analyse the difference in size between a list and a generator.

Generator Expressions

We have seen how to use the yield keyword to create generator function.

This is not the only way to create generators, you can also use a generator expression .

To introduce generator expression we will start from an example of list comprehension, a Python construct used to create lists based on existing lists in a one liner.

Let’s say we want to write a list comprehension that returns the same output of the functions we have defined before.

The list comprehension takes a list and returns a new list where every element is multiplied by 2.

The list comprehension starts and ends with a square bracket and in a single line does what the functions we have defined before were doing with multiple lines of code.

As you can see the value returned by the list comprehension is of type list.

Now, let’s replace the square brackets of the list comprehension with parentheses. This is a generator expression .

This time the output is slightly different…

The object returned by the new expression is a generator, it’s not a list anymore.

We can go through this generator in the same way we have seen before by using either a for loop or the next function:

To convert a list comprehension into a generator expression replace the square brackets that surround the list comprehension with parentheses.

Notice that there is a small difference in the way Python represents an object returned by a generator function and a generator expression.

Generator Function

Generator Expression

More About Using Yield in a Python Function

We have seen an example on how to use yield in a function but I want to give you another example that clearly shows the behaviour of yield.

Let’s take the generator function we have created before and add some print statements to show exactly what happens when the function is called?

When we call the next() function and pass the generator we get the following:

The first print statement and the yield statement are executed. After that the function is paused and the value in the yield expression is returned.

When we call next() again the execution of the function continues from where it left before. Here is what the Python interpreter does:

Execute the print statement after the yield expression.
Start the next iteration of the for loop.
Execute the print statement before the yield expression.
Return the yielded value and pause the function.

This gives you a better understanding of how Python pauses and resumes the state of a generator function.

How To Yield a Tuple in Python

In the examples we have seen so far we have been using the yield keyword to return a single number.

Can we apply yield to a tuple instead?

Let’s say we want to pass the following list of tuples to our function:

We can modify the previous generator function to return tuples where we multiply every element by 2.

In the same way we have done before let’s call the next() function twice and see what happens:

Second call

So, the behaviour is exactly the same.

Multiple Yield Statements in a Python Function

Can you use multiple yield statements in a single Python function?

Yes, you can!

The behaviour of the generator function doesn’t change from the scenario where you have a single yield expression.

Every time the __next__ method gets called on the generator function the execution of the function continues where it left until the next yield expression is reached.

Here is an example. Open the Python shell and create a generator function with two yield expressions. The first one returns a list and the second one returns a tuple:

When we pass the generator object gen to the next function we should get back the list first and then the tuple.

Passing the generator object to the next function is basically the same as calling the __next__ method of the generator object.

As expected the Python interpreter raises a StopIteration exception when we execute the __next__ method the third time. That’s because our generator function only contains two yield expressions.

Can I Use Yield and Return in the Same Function?

Have you wondered if you can use yield and return in the same function?

Let’s see what happens when we do that in the function we have created in the previous section.

Here we are using Python 3.8.5:

The behaviour is similar to the one of the function without the return statement. The first two times we call the next() function we get back the two values in the yield expressions.

The third time we call the next() function the Python interpreter raises a StopIteration exception. The only difference is that the string in the return statement (‘done’) becomes the exception message.

If you try to run the same code with Python 2.7 you get a SyntaxError because a return statement with argument cannot be used inside a generator function.

Let’s try to remove the return argument:

All good this time.

This is just an experiment…

In reality it might not make sense to use yield and return as part of the same generator function.

Have you found a scenario where it might be useful doing that? Let me know in the comment.

Generators and Memory Usage

One of the reasons to use generators instead of lists is to save memory.

That’s because when working with lists all the elements of a lists are stored in memory while the same doesn’t happen when working with generators.

We will generate a list made of 100,000 elements and see how much space it takes in memory using the sys module.

Let’s start by defining two functions, one regular function that returns a list of numbers and a generator function that returns a generator object for the same sequence of numbers.

Regular Function

Now, let’s get the list of numbers and the generator object back and calculate their size in bytes using the sys.getsizeof() function .

The output is:

The list takes over 7000 times the memory required by the generator!

So, there is definitely a benefit in memory allocation when it comes to using generators. At the same time using a list is faster so it’s about finding a tradeoff between memory usage and performance.

You have learned the difference between return and yield in a Python function.

So now you know how to use the yield keyword to convert a regular function into a generator function.

I have also explained how generator expressions can be used as alternative to generator functions.

Finally, we have compared generators and regular lists from a memory usage perspective and showed why you can use generators to save memory especially if you are working with big datasets.

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Python Topics

Yield Statement

Table of Contents

Understanding the Yield Statement in Python

Difference between yield and return in python, how to use the yield statement in python, practical applications of yield in python, yield in python generator expressions and functions, benefits and limitations of using yield in python, examples of yield statement in python, python yield statement in multi-threading, understanding the yield from statement in python, best practices for using yield in python.

'return' gives back a value and terminates the function, while 'yield' produces a value and suspends the function’s execution, to be resumed later.
'return' sends a specified value back to its caller whereas 'yield' can produce a sequence of values.
When a function is called, a new local scope is created for that call, and all local variables are created in that scope. In contrast, 'yield' allows the function to remember its state, so that when it's called again, the function continues where it left off, with all local variables retaining their values.
While a 'return' statement can only be used once in a function, 'yield' can be used multiple times, producing multiple results.
The 'yield' statement can only be used inside a function.
A function with a 'yield' statement is called a generator function.
Generator functions return a special iterator called a generator. You can iterate over this generator to retrieve its values.
When a generator function is called, it returns a generator object without even beginning execution of the function. When 'next()' is called for the first time, the function starts executing until it reaches the 'yield' statement, which returns the yielded value. The 'yield' keyword pauses the function and saves the local state so that it can be resumed right where it left off when 'next()' is called again.
Generator functions allow you to declare a function that behaves like an iterator, i.e., it can be used in a 'for' loop.
The difference between list comprehensions and generator expressions is that the former produces the entire list while the latter produces one item at a time, which is more memory-friendly.
Both generator functions and generator expressions are a part of Python's iterator protocol and can be used with built-in functions that accept iterators, like 'sum()', 'max()', and 'min()'.
Coroutines are a form of cooperative multitasking where the control of execution is explicitly yielded by the current task (using 'yield') to the scheduler (the 'main' function in this case).
Unlike threads, coroutines are a form of concurrency that do not run in parallel and do not require context switching, and thus they are more lightweight.
Coroutines can be used to simplify code that deals with problems which can be broken down into tasks that need to run concurrently, but do not necessarily need to run in parallel.
The 'yield from' statement is only valid in Python 3.3 and later.
'yield from' allows a generator to delegate part of its operations to another generator.
It simplifies the code by abstracting the call to the inner generator, eliminating the need for a loop in the outer generator.
'yield from' makes the code more readable and makes it easier to handle exceptions and closing of the generators.

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Python Yield – What does the yield keyword do?

September 7, 2020
Venmani A D

Adding yield keyword to a function will make the function return a generator object that can be iterated upon.

What does the yield keyword do?

Approaches to overcome generator exhaustion, how to materialize generators, how yield works, step by step, exercise 1: write a program to create a generator that generates cubes of numbers up to 1000 using yield, exercise 2: write a program to return odd number by pipelining generators, difference between yield and return.

yield in Python can be used like the return statement in a function. When done so, the function instead of returning the output, it returns a generator that can be iterated upon.

You can then iterate through the generator to extract items. Iterating is done using a for loop or simply using the next() function. But what exactly happens when you use yield ?

What the yield keyword does is as follows:

Each time you iterate, Python runs the code until it encounters a yield statement inside the function. Then, it sends the yielded value and pauses the function in that state without exiting.

When the function is invoked the next time, the state at which it was last paused is remembered and execution is continued from that point onwards. This continues until the generator is exhausted.

What does remembering the state mean?

It means, any local variable you may have created inside the function before yield was called will be available the next time you invoke the function. This is NOT the way a regular function usually behaves.

Now, how is it different from using the return keyword?

Had you used return in place of yield , the function would have returned the respective value, all the local variable values that the function had earlier computed would be cleared off and the next time the function is called, the function execution will start fresh.

Since the yield enables the function to remember its ‘state’, this function can be used to generate values in a logic defined by you. So, it function becomes a ‘generator’.

Now you can iterate through the generator object. But it works only once.

Calling the generator the second time wont give anything. Because the generator object is already exhausted and has to be re-initialized.

If you call next() over this iterator, a StopIteration error is raised

To overcome generator exhaustion, you can:

Approach 1 : Replenish the generator by recreating it again and iterate over. You just saw how to do this.
Approach 2 : Iterate by calling the function that created the generator in the first place
Approach 3 (best) : Convert it to an class that implements a __iter__() method. This creates an iterator every time, so you don’t have to worry about the generator getting exhausted.

We’ve see the first approach already. Approach 2: The second approach is to simple replace the generator with a call the the function that produced the generator, which is simple_generator() in this case. This will continue to work no matter how many times you iterate it.

Approach 3: Now, let’s try creating a class that implements a __iter__() method. It creates an iterator object every time, so you don’t have to keep recreating the generator.

We often store data in a list if you want to materialize it at some point. If you do so, the content of the list occupies tangible memory. The larger the list gets, it occupies more memory resource.

But if there is a certain logic behind producing the items that you want, you don’t have to store in a list. But rather, simply write a generator that will produce the items whenever you want them.

Let’s say, you want to iterate through squares of numbers from 1 to 10. There are at least two ways you can go about it: create the list beforehand and iterate. Or create a generator that will produce these numbers.

Let’s do the same with generators now.

Generators are memory efficient because the values are not materialized until called. And are usually faster. You will want to use a generator especially if you know the logic to produce the next number (or any object) that you want to generate.

Can a generator be materialized to a list?

Yes. You can do so easily using list comprehensions or by simply calling list() .

yield is a keyword that returns from the function without destroying the state of it’s local variables. When you replace return with yield in a function, it causes the function to hand back a generator object to its caller. In effect, yield will prevent the function from exiting, until the next time next() is called. When called, it will start executing from the point where it paused before. Output:

See that I have created a function using yield keyword. Let’s try to access the function, as we have created an object obj for the function, it will be defined as an iterator. So to access it, use the next() function. It will iterate until the next yield statement is reached.

I am going to try to create a generator function which will return the cubic of the number until the cube limit reaches 1000, one at a time using yield keyword. The memory will be alloted only to the element which is running, after the execution of output of that element, the memory will be deleted.

Multiple generators can be pipelined(one generator using another) as a series of operations in the same code. Pipelining also makes the code more efficient and easy to read. For pipeling functions, use () paranthesis to give function caller inside a function.

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In this article, we will cover the yield keyword in Python . Before starting, let’s understand the yield keyword definition.

Syntax of the Yield Keyword in Python

What does the Yield Keyword do?

yield keyword is used to create a generator function. A type of function that is memory efficient and can be used like an iterator object.

In layman terms, the yield keyword will turn any expression that is given with it into a generator object and return it to the caller. Therefore, you must iterate over the generator object if you wish to obtain the values stored there. we will see the yield python example.

Difference between return and yield Python

The yield keyword in Python is similar to a return statement used for returning values in Python which returns a generator object to the one who calls the function which contains yield, instead of simply returning a value. The main difference between them is, the return statement terminates the execution of the function. Whereas, the yield statement only pauses the execution of the function. Another difference is return statements are never executed. whereas, yield statements are executed when the function resumes its execution.

Advantages of yield:

Using yield keyword is highly memory efficient, since the execution happens only when the caller iterates over the object.
As the variables states are saved, we can pause and resume from the same point, thus saving time.

Disadvantages of yield:

Sometimes it becomes hard to understand the flow of code due to multiple times of value return from the function generator.
Calling of generator functions must be handled properly, else might cause errors in program.

Example 1: Generator functions and yield Keyword in Python

Generator functions behave and look just like normal functions, but with one defining characteristic. Instead of returning data, Python generator functions use the yield keyword. Generators’ main benefit is that they automatically create the functions __iter__() and next (). Generators offer a very tidy technique to produce data that is enormous or limitless.

Output:

Example 2: Generating an Infinite Sequence

Here, we are generating an infinite sequence of numbers with yield, yield returns the number and increments the num by + 1.

Note: Here we can observe that num+=1 is executed after yield but in the case of a return , no execution takes place after the return keyword.

Example 3: Demonstrating yield working with a list .

Here, we are extracting the even number from the list.

Example 4: Use of yield Keyword as Boolean

The possible practical application is that when handling the last amount of data and searching particulars from it, yield can be used as we don’t need to look up again from start and hence would save time. There can possibly be many applications of yield depending upon the use cases.

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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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ast — Abstract Syntax Trees ¶

Source code: Lib/ast.py

The ast module helps Python applications to process trees of the Python abstract syntax grammar. The abstract syntax itself might change with each Python release; this module helps to find out programmatically what the current grammar looks like.

An abstract syntax tree can be generated by passing ast.PyCF_ONLY_AST as a flag to the compile() built-in function, or using the parse() helper provided in this module. The result will be a tree of objects whose classes all inherit from ast.AST . An abstract syntax tree can be compiled into a Python code object using the built-in compile() function.

Abstract Grammar ¶

The abstract grammar is currently defined as follows:

Node classes ¶

This is the base of all AST node classes. The actual node classes are derived from the Parser/Python.asdl file, which is reproduced below . They are defined in the _ast C module and re-exported in ast .

There is one class defined for each left-hand side symbol in the abstract grammar (for example, ast.stmt or ast.expr ). In addition, there is one class defined for each constructor on the right-hand side; these classes inherit from the classes for the left-hand side trees. For example, ast.BinOp inherits from ast.expr . For production rules with alternatives (aka “sums”), the left-hand side class is abstract: only instances of specific constructor nodes are ever created.

Each concrete class has an attribute _fields which gives the names of all child nodes.

Each instance of a concrete class has one attribute for each child node, of the type as defined in the grammar. For example, ast.BinOp instances have an attribute left of type ast.expr .

If these attributes are marked as optional in the grammar (using a question mark), the value might be None . If the attributes can have zero-or-more values (marked with an asterisk), the values are represented as Python lists. All possible attributes must be present and have valid values when compiling an AST with compile() .

Instances of ast.expr and ast.stmt subclasses have lineno , col_offset , end_lineno , and end_col_offset attributes. The lineno and end_lineno are the first and last line numbers of the source text span (1-indexed so the first line is line 1), and the col_offset and end_col_offset are the corresponding UTF-8 byte offsets of the first and last tokens that generated the node. The UTF-8 offset is recorded because the parser uses UTF-8 internally.

Note that the end positions are not required by the compiler and are therefore optional. The end offset is after the last symbol, for example one can get the source segment of a one-line expression node using source_line[node.col_offset : node.end_col_offset] .

The constructor of a class ast.T parses its arguments as follows:

If there are positional arguments, there must be as many as there are items in T._fields ; they will be assigned as attributes of these names.

If there are keyword arguments, they will set the attributes of the same names to the given values.

For example, to create and populate an ast.UnaryOp node, you could use

or the more compact

Changed in version 3.8: Class ast.Constant is now used for all constants.

Changed in version 3.9: Simple indices are represented by their value, extended slices are represented as tuples.

Deprecated since version 3.8: Old classes ast.Num , ast.Str , ast.Bytes , ast.NameConstant and ast.Ellipsis are still available, but they will be removed in future Python releases. In the meantime, instantiating them will return an instance of a different class.

Deprecated since version 3.9: Old classes ast.Index and ast.ExtSlice are still available, but they will be removed in future Python releases. In the meantime, instantiating them will return an instance of a different class.

The descriptions of the specific node classes displayed here were initially adapted from the fantastic Green Tree Snakes project and all its contributors.

A constant value. The value attribute of the Constant literal contains the Python object it represents. The values represented can be simple types such as a number, string or None , but also immutable container types (tuples and frozensets) if all of their elements are constant.

Node representing a single formatting field in an f-string. If the string contains a single formatting field and nothing else the node can be isolated otherwise it appears in JoinedStr .

value is any expression node (such as a literal, a variable, or a function call).

conversion is an integer:

-1: no formatting

115: !s string formatting

114: !r repr formatting

97: !a ascii formatting

format_spec is a JoinedStr node representing the formatting of the value, or None if no format was specified. Both conversion and format_spec can be set at the same time.

An f-string, comprising a series of FormattedValue and Constant nodes.

A list or tuple. elts holds a list of nodes representing the elements. ctx is Store if the container is an assignment target (i.e. (x,y)=something ), and Load otherwise.

A set. elts holds a list of nodes representing the set’s elements.

A dictionary. keys and values hold lists of nodes representing the keys and the values respectively, in matching order (what would be returned when calling dictionary.keys() and dictionary.values() ).

When doing dictionary unpacking using dictionary literals the expression to be expanded goes in the values list, with a None at the corresponding position in keys .

Variables ¶

A variable name. id holds the name as a string, and ctx is one of the following types.

Variable references can be used to load the value of a variable, to assign a new value to it, or to delete it. Variable references are given a context to distinguish these cases.

A *var variable reference. value holds the variable, typically a Name node. This type must be used when building a Call node with *args .

Expressions ¶

When an expression, such as a function call, appears as a statement by itself with its return value not used or stored, it is wrapped in this container. value holds one of the other nodes in this section, a Constant , a Name , a Lambda , a Yield or YieldFrom node.

A unary operation. op is the operator, and operand any expression node.

Unary operator tokens. Not is the not keyword, Invert is the ~ operator.

A binary operation (like addition or division). op is the operator, and left and right are any expression nodes.

Binary operator tokens.

A boolean operation, ‘or’ or ‘and’. op is Or or And . values are the values involved. Consecutive operations with the same operator, such as a or b or c , are collapsed into one node with several values.

This doesn’t include not , which is a UnaryOp .

Boolean operator tokens.

A comparison of two or more values. left is the first value in the comparison, ops the list of operators, and comparators the list of values after the first element in the comparison.

Comparison operator tokens.

A function call. func is the function, which will often be a Name or Attribute object. Of the arguments:

args holds a list of the arguments passed by position.

keywords holds a list of keyword objects representing arguments passed by keyword.

When creating a Call node, args and keywords are required, but they can be empty lists. starargs and kwargs are optional.

A keyword argument to a function call or class definition. arg is a raw string of the parameter name, value is a node to pass in.

An expression such as a if b else c . Each field holds a single node, so in the following example, all three are Name nodes.

Attribute access, e.g. d.keys . value is a node, typically a Name . attr is a bare string giving the name of the attribute, and ctx is Load , Store or Del according to how the attribute is acted on.

A named expression. This AST node is produced by the assignment expressions operator (also known as the walrus operator). As opposed to the Assign node in which the first argument can be multiple nodes, in this case both target and value must be single nodes.

Subscripting ¶

A subscript, such as l[1] . value is the subscripted object (usually sequence or mapping). slice is an index, slice or key. It can be a Tuple and contain a Slice . ctx is Load , Store or Del according to the action performed with the subscript.

Regular slicing (on the form lower:upper or lower:upper:step ). Can occur only inside the slice field of Subscript , either directly or as an element of Tuple .

Comprehensions ¶

List and set comprehensions, generator expressions, and dictionary comprehensions. elt (or key and value ) is a single node representing the part that will be evaluated for each item.

generators is a list of comprehension nodes.

One for clause in a comprehension. target is the reference to use for each element - typically a Name or Tuple node. iter is the object to iterate over. ifs is a list of test expressions: each for clause can have multiple ifs .

is_async indicates a comprehension is asynchronous (using an async for instead of for ). The value is an integer (0 or 1).

Statements ¶

An assignment. targets is a list of nodes, and value is a single node.

Multiple nodes in targets represents assigning the same value to each. Unpacking is represented by putting a Tuple or List within targets .

type_comment is an optional string with the type annotation as a comment.

An assignment with a type annotation. target is a single node and can be a Name , a Attribute or a Subscript . annotation is the annotation, such as a Constant or Name node. value is a single optional node. simple is a boolean integer set to True for a Name node in target that do not appear in between parenthesis and are hence pure names and not expressions.

Augmented assignment, such as a += 1 . In the following example, target is a Name node for x (with the Store context), op is Add , and value is a Constant with value for 1.

The target attribute connot be of class Tuple or List , unlike the targets of Assign .

A raise statement. exc is the exception object to be raised, normally a Call or Name , or None for a standalone raise . cause is the optional part for y in raise x from y .

An assertion. test holds the condition, such as a Compare node. msg holds the failure message.

Represents a del statement. targets is a list of nodes, such as Name , Attribute or Subscript nodes.

A pass statement.

Other statements which are only applicable inside functions or loops are described in other sections.

An import statement. names is a list of alias nodes.

Represents from x import y . module is a raw string of the ‘from’ name, without any leading dots, or None for statements such as from . import foo . level is an integer holding the level of the relative import (0 means absolute import).

Both parameters are raw strings of the names. asname can be None if the regular name is to be used.

Control flow ¶

Optional clauses such as else are stored as an empty list if they’re not present.

An if statement. test holds a single node, such as a Compare node. body and orelse each hold a list of nodes.

elif clauses don’t have a special representation in the AST, but rather appear as extra If nodes within the orelse section of the previous one.

A for loop. target holds the variable(s) the loop assigns to, as a single Name , Tuple or List node. iter holds the item to be looped over, again as a single node. body and orelse contain lists of nodes to execute. Those in orelse are executed if the loop finishes normally, rather than via a break statement.

A while loop. test holds the condition, such as a Compare node.

The break and continue statements.

try blocks. All attributes are list of nodes to execute, except for handlers , which is a list of ExceptHandler nodes.

A single except clause. type is the exception type it will match, typically a Name node (or None for a catch-all except: clause). name is a raw string for the name to hold the exception, or None if the clause doesn’t have as foo . body is a list of nodes.

A with block. items is a list of withitem nodes representing the context managers, and body is the indented block inside the context.

A single context manager in a with block. context_expr is the context manager, often a Call node. optional_vars is a Name , Tuple or List for the as foo part, or None if that isn’t used.

Function and class definitions ¶

A function definition.

name is a raw string of the function name.

args is an arguments node.

body is the list of nodes inside the function.

decorator_list is the list of decorators to be applied, stored outermost first (i.e. the first in the list will be applied last).

returns is the return annotation.

lambda is a minimal function definition that can be used inside an expression. Unlike FunctionDef , body holds a single node.

The arguments for a function.

posonlyargs , args and kwonlyargs are lists of arg nodes.

vararg and kwarg are single arg nodes, referring to the *args, **kwargs parameters.

kw_defaults is a list of default values for keyword-only arguments. If one is None , the corresponding argument is required.

defaults is a list of default values for arguments that can be passed positionally. If there are fewer defaults, they correspond to the last n arguments.

A single argument in a list. arg is a raw string of the argument name, annotation is its annotation, such as a Str or Name node.

type_comment is an optional string with the type annotation as a comment

A return statement.

A yield or yield from expression. Because these are expressions, they must be wrapped in a Expr node if the value sent back is not used.

global and nonlocal statements. names is a list of raw strings.

A class definition.

name is a raw string for the class name

bases is a list of nodes for explicitly specified base classes.

keywords is a list of keyword nodes, principally for ‘metaclass’. Other keywords will be passed to the metaclass, as per PEP-3115 .

starargs and kwargs are each a single node, as in a function call. starargs will be expanded to join the list of base classes, and kwargs will be passed to the metaclass.

body is a list of nodes representing the code within the class definition.

decorator_list is a list of nodes, as in FunctionDef .

Async and await ¶

An async def function definition. Has the same fields as FunctionDef .

An await expression. value is what it waits for. Only valid in the body of an AsyncFunctionDef .

async for loops and async with context managers. They have the same fields as For and With , respectively. Only valid in the body of an AsyncFunctionDef .

When a string is parsed by ast.parse() , operator nodes (subclasses of ast.operator , ast.unaryop , ast.cmpop , ast.boolop and ast.expr_context ) on the returned tree will be singletons. Changes to one will be reflected in all other occurrences of the same value (e.g. ast.Add ).

ast Helpers ¶

Apart from the node classes, the ast module defines these utility functions and classes for traversing abstract syntax trees:

Parse the source into an AST node. Equivalent to compile(source, filename, mode, ast.PyCF_ONLY_AST) .

If type_comments=True is given, the parser is modified to check and return type comments as specified by PEP 484 and PEP 526 . This is equivalent to adding ast.PyCF_TYPE_COMMENTS to the flags passed to compile() . This will report syntax errors for misplaced type comments. Without this flag, type comments will be ignored, and the type_comment field on selected AST nodes will always be None . In addition, the locations of # type: ignore comments will be returned as the type_ignores attribute of Module (otherwise it is always an empty list).

In addition, if mode is 'func_type' , the input syntax is modified to correspond to PEP 484 “signature type comments”, e.g. (str, int) -> List[str] .

Also, setting feature_version to a tuple (major, minor) will attempt to parse using that Python version’s grammar. Currently major must equal to 3 . For example, setting feature_version=(3, 4) will allow the use of async and await as variable names. The lowest supported version is (3, 4) ; the highest is sys.version_info[0:2] .

If source contains a null character (‘0’), ValueError is raised.

Warning Note that successfully parsing source code into an AST object doesn’t guarantee that the source code provided is valid Python code that can be executed as the compilation step can raise further SyntaxError exceptions. For instance, the source return 42 generates a valid AST node for a return statement, but it cannot be compiled alone (it needs to be inside a function node). In particular, ast.parse() won’t do any scoping checks, which the compilation step does.

It is possible to crash the Python interpreter with a sufficiently large/complex string due to stack depth limitations in Python’s AST compiler.

Changed in version 3.8: Added type_comments , mode='func_type' and feature_version .

Unparse an ast.AST object and generate a string with code that would produce an equivalent ast.AST object if parsed back with ast.parse() .

The produced code string will not necessarily be equal to the original code that generated the ast.AST object (without any compiler optimizations, such as constant tuples/frozensets).

Trying to unparse a highly complex expression would result with RecursionError .

New in version 3.9.

Safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, and None .

This can be used for safely evaluating strings containing Python values from untrusted sources without the need to parse the values oneself. It is not capable of evaluating arbitrarily complex expressions, for example involving operators or indexing.

Changed in version 3.2: Now allows bytes and set literals.

Changed in version 3.9: Now supports creating empty sets with 'set()' .

Return the docstring of the given node (which must be a FunctionDef , AsyncFunctionDef , ClassDef , or Module node), or None if it has no docstring. If clean is true, clean up the docstring’s indentation with inspect.cleandoc() .

Changed in version 3.5: AsyncFunctionDef is now supported.

Get source code segment of the source that generated node . If some location information ( lineno , end_lineno , col_offset , or end_col_offset ) is missing, return None .

If padded is True , the first line of a multi-line statement will be padded with spaces to match its original position.

New in version 3.8.

When you compile a node tree with compile() , the compiler expects lineno and col_offset attributes for every node that supports them. This is rather tedious to fill in for generated nodes, so this helper adds these attributes recursively where not already set, by setting them to the values of the parent node. It works recursively starting at node .

Increment the line number and end line number of each node in the tree starting at node by n . This is useful to “move code” to a different location in a file.

Copy source location ( lineno , col_offset , end_lineno , and end_col_offset ) from old_node to new_node if possible, and return new_node .

Yield a tuple of (fieldname, value) for each field in node._fields that is present on node .

Yield all direct child nodes of node , that is, all fields that are nodes and all items of fields that are lists of nodes.

Recursively yield all descendant nodes in the tree starting at node (including node itself), in no specified order. This is useful if you only want to modify nodes in place and don’t care about the context.

A node visitor base class that walks the abstract syntax tree and calls a visitor function for every node found. This function may return a value which is forwarded by the visit() method.

This class is meant to be subclassed, with the subclass adding visitor methods.

Visit a node. The default implementation calls the method called self.visit_ classname where classname is the name of the node class, or generic_visit() if that method doesn’t exist.

This visitor calls visit() on all children of the node.

Note that child nodes of nodes that have a custom visitor method won’t be visited unless the visitor calls generic_visit() or visits them itself.

Don’t use the NodeVisitor if you want to apply changes to nodes during traversal. For this a special visitor exists ( NodeTransformer ) that allows modifications.

Deprecated since version 3.8: Methods visit_Num() , visit_Str() , visit_Bytes() , visit_NameConstant() and visit_Ellipsis() are deprecated now and will not be called in future Python versions. Add the visit_Constant() method to handle all constant nodes.

A NodeVisitor subclass that walks the abstract syntax tree and allows modification of nodes.

The NodeTransformer will walk the AST and use the return value of the visitor methods to replace or remove the old node. If the return value of the visitor method is None , the node will be removed from its location, otherwise it is replaced with the return value. The return value may be the original node in which case no replacement takes place.

Here is an example transformer that rewrites all occurrences of name lookups ( foo ) to data['foo'] :

Keep in mind that if the node you’re operating on has child nodes you must either transform the child nodes yourself or call the generic_visit() method for the node first.

For nodes that were part of a collection of statements (that applies to all statement nodes), the visitor may also return a list of nodes rather than just a single node.

If NodeTransformer introduces new nodes (that weren’t part of original tree) without giving them location information (such as lineno ), fix_missing_locations() should be called with the new sub-tree to recalculate the location information:

Usually you use the transformer like this:

Return a formatted dump of the tree in node . This is mainly useful for debugging purposes. If annotate_fields is true (by default), the returned string will show the names and the values for fields. If annotate_fields is false, the result string will be more compact by omitting unambiguous field names. Attributes such as line numbers and column offsets are not dumped by default. If this is wanted, include_attributes can be set to true.

If indent is a non-negative integer or string, then the tree will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the single line representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t" ), that string is used to indent each level.

Changed in version 3.9: Added the indent option.

Compiler Flags ¶

The following flags may be passed to compile() in order to change effects on the compilation of a program:

Enables support for top-level await , async for , async with and async comprehensions.

Generates and returns an abstract syntax tree instead of returning a compiled code object.

Enables support for PEP 484 and PEP 526 style type comments ( # type: <type> , # type: ignore <stuff> ).

Command-Line Usage ¶

The ast module can be executed as a script from the command line. It is as simple as:

The following options are accepted:

Show the help message and exit.

Specify what kind of code must be compiled, like the mode argument in parse() .

Don’t parse type comments.

Include attributes such as line numbers and column offsets.

Indentation of nodes in AST (number of spaces).

If infile is specified its contents are parsed to AST and dumped to stdout. Otherwise, the content is read from stdin.

Green Tree Snakes , an external documentation resource, has good details on working with Python ASTs.

ASTTokens annotates Python ASTs with the positions of tokens and text in the source code that generated them. This is helpful for tools that make source code transformations.

leoAst.py unifies the token-based and parse-tree-based views of python programs by inserting two-way links between tokens and ast nodes.

LibCST parses code as a Concrete Syntax Tree that looks like an ast tree and keeps all formatting details. It’s useful for building automated refactoring (codemod) applications and linters.

Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions (in multiple Python versions). Parso is also able to list multiple syntax errors in your python file.

Abstract Grammar
Subscripting
Comprehensions
Control flow
Function and class definitions
Async and await
ast Helpers
Compiler Flags
Command-Line Usage

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Annotation assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement: annotated_assignment_stmt::= augtarget ":" expression ["=" (starred_expression | yield_expression)] The difference from normal Assignment statements is that only a single target is allowed.
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40. The yield statement used in a function turns that function into a "generator" (a function that creates an iterator). The resulting iterator is normally resumed by calling next(). However it is possible to send values to the function by calling the method send() instead of next() to resume it: In your example this would assign the value 1 to ...
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Python. pal_gen = infinite_palindromes() for i in pal_gen: digits = len(str(i)) pal_gen.send(10 ** (digits)) With this code, you create the generator object and iterate through it. The program only yields a value once a palindrome is found. It uses len() to determine the number of digits in that palindrome.
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Let's see how we can create a simple generator function: # Creating a Simple Generator Function in Python def return_n_values ( n ): num = 0 while num < n: yield num. num += 1. Let's break down what is happening here: We define a function, return_n_values(), which takes a single parameter, n. In the function, we first set the value of num to 0.
Understanding Python Yield: A Comprehensive Guide
Now, let's dive into the 'yield from' construct. 'yield from' is a phrase used in Python to delegate part of the generator's operations to another generator or iterable. This can simplify the code in a generator function, particularly when it's dealing with nested for loops. def complex_generator(): yield from simple_generator() yield 4. yield 5.
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PEP 572
Unparenthesized assignment expressions are prohibited for the value of a keyword argument in a call. Example: foo(x = y := f(x)) # INVALID foo(x=(y := f(x))) # Valid, though probably confusing. This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.
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Node classes¶ class ast.AST¶. This is the base of all AST node classes. The actual node classes are derived from the Parser/Python.asdl file, which is reproduced below.They are defined in the _ast C module and re-exported in ast.. There is one class defined for each left-hand side symbol in the abstract grammar (for example, ast.stmt or ast.expr).In addition, there is one class defined for ...
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The generator's execution is suspended midway through execution of the line. (Remember that the value of a yield expression is not the same as the value it yields.) The x.send(10) call causes the yield expression to take value 10, and that value is what is assigned to message. Brilliant, that's the piece I was missing.
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yield is an expression. The value of the expression is the value of whatever was sent using .send, or None if nothing was sent (including if next was used instead of .send)..send is a method call and thus of course also returns a value, which is the value yielded by the generator. In other words, every time you .send, a value (which may be None) is yielded, and every time you yield, a value ...

Using Python Generators and yield: A Complete Guide

Understanding Python Generators

Creating a Simple Generator

Creating a Python Generator with a For Loop

Unpacking a Generator with a For Loop

Creating a Python Generator with Multiple Yield Statements

Understanding the Performance of Python Generators

Creating Python Generator Expressions

Understanding the Python yield Statement

How to Throw Exceptions in Python Generators Using throw

How to Stop a Python Generator Using stop

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ast — Abstract Syntax Trees ¶

Abstract Grammar ¶

Node classes ¶

Variables ¶

Expressions ¶

Subscripting ¶

Comprehensions ¶

Statements ¶

Control flow ¶

Function and class definitions ¶

Async and await ¶

ast Helpers ¶

Compiler Flags ¶

Command-Line Usage ¶

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