python assignment and check

Python Conditional Assignment

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

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

1. Using Ternary Operator

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

It goes like this:

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

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

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

2. Using if-else statement

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

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

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

3. Using Logical Short Circuit Evaluation

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

The format of logical short circuit evaluation is:

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

Let's see an example:

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

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

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

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

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

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

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

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

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

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

Happy coding! 😊

The Walrus Operator: Python's Assignment Expressions

Table of Contents

Hello, Walrus!

Implementation, lists and dictionaries, list comprehensions, while loops, witnesses and counterexamples, walrus operator syntax, walrus operator pitfalls.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Python Assignment Expressions and Using the Walrus Operator

Each new version of Python adds new features to the language. Back when Python 3.8 was released, the biggest change was the addition of assignment expressions . Specifically, the := operator gave you a new syntax for assigning variables in the middle of expressions. This operator is colloquially known as the walrus operator .

This tutorial is an in-depth introduction to the walrus operator. You’ll learn some of the motivations for the syntax update and explore examples where assignment expressions can be useful.

In this tutorial, you’ll learn how to:

• Identify the walrus operator and understand its meaning
• Understand use cases for the walrus operator
• Avoid repetitive code by using the walrus operator
• Convert between code using the walrus operator and code using other assignment methods
• Use appropriate style in your assignment expressions

Note that all walrus operator examples in this tutorial require Python 3.8 or later to work.

Get Your Code: Click here to download the free sample code that shows you how to use Python’s walrus operator.

Take the Quiz: Test your knowledge with our interactive “The Walrus Operator: Python's Assignment Expressions” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

In this quiz, you'll test your understanding of the Python Walrus Operator. This operator was introduced in Python 3.8, and understanding it can help you write more concise and efficient code.

Walrus Operator Fundamentals

First, look at some different terms that programmers use to refer to this new syntax. You’ve already seen a few in this tutorial.

The := operator is officially known as the assignment expression operator . During early discussions, it was dubbed the walrus operator because the := syntax resembles the eyes and tusks of a walrus lying on its side. You may also see the := operator referred to as the colon equals operator . Yet another term used for assignment expressions is named expressions .

To get a first impression of what assignment expressions are all about, start your REPL and play around with the following code:

Line 1 shows a traditional assignment statement where the value False is assigned to walrus . Next, on line 5, you use an assignment expression to assign the value True to walrus . After both lines 1 and 5, you can refer to the assigned values by using the variable name walrus .

You might be wondering why you’re using parentheses on line 5, and you’ll learn why the parentheses are needed later on in this tutorial .

Note: A statement in Python is a unit of code. An expression is a special statement that can be evaluated to some value.

For example, 1 + 2 is an expression that evaluates to the value 3 , while number = 1 + 2 is an assignment statement that doesn’t evaluate to a value. Although running the statement number = 1 + 2 doesn’t evaluate to 3 , it does assign the value 3 to number .

In Python, you often see simple statements like return statements and import statements , as well as compound statements like if statements and function definitions . These are all statements, not expressions.

There’s a subtle—but important—difference between the two types of assignments with the walrus variable. An assignment expression returns the value, while a traditional assignment doesn’t. You can see this in action when the REPL doesn’t print any value after walrus = False on line 1 but prints out True after the assignment expression on line 5.

You can see another important aspect about walrus operators in this example. Though it might look new, the := operator does not do anything that isn’t possible without it. It only makes certain constructs more convenient and can sometimes communicate the intent of your code more clearly.

Now you have a basic idea of what the := operator is and what it can do. It’s an operator used in assignment expressions, which can return the value being assigned, unlike traditional assignment statements. To get deeper and really learn about the walrus operator, continue reading to see where you should and shouldn’t use it.

Like most new features in Python, assignment expressions were introduced through a Python Enhancement Proposal (PEP). PEP 572 describes the motivation for introducing the walrus operator, the details of the syntax, and examples where the := operator can be used to improve your code.

This PEP was originally written by Chris Angelico in February 2018. Following some heated discussion, PEP 572 was accepted by Guido van Rossum in July 2018.

Since then, Guido announced that he was stepping down from his role as benevolent dictator for life (BDFL) . Since early 2019, the Python language has been governed by an elected steering council instead.

The walrus operator was implemented by Emily Morehouse , and made available in the first alpha release of Python 3.8.

In many languages, including C and its derivatives, assignment statements are also expressions. This can be both very powerful and a source of confusing bugs. For example, the following code is valid C but doesn’t execute as intended:

Here, if (x = y) will evaluate to true, and the code snippet will print out x and y are equal (x = 8, y = 8) . Is this the result you were expecting? You were trying to compare x and y . How did the value of x change from 3 to 8 ?

The problem is that you’re using the assignment operator ( = ) instead of the equality comparison operator ( == ). In C, x = y is an expression that evaluates to the value of y . In this example, x = y is evaluated as 8 , which is considered truthy in the context of the if statement.

Take a look at a corresponding example in Python. This code causes a SyntaxError :

Unlike the C example, this Python code gives you an explicit error instead of a bug.

The distinction between assignment statements and assignment expressions in Python is useful in order to avoid these kinds of hard-to-find bugs. PEP 572 argues that Python is better suited to having different syntax for assignment statements and expressions instead of turning the existing assignment statements into expressions.

One design principle underpinning the walrus operator is that there are no identical code contexts where both an assignment statement using the = operator and an assignment expression using the := operator would be valid. For example, you can’t do a plain assignment with the walrus operator:

In many cases, you can add parentheses ( () ) around the assignment expression to make it valid Python:

Writing a traditional assignment statement with = isn’t allowed inside such parentheses. This helps you catch potential bugs.

Later on in this tutorial , you’ll learn more about situations where the walrus operator isn’t allowed, but first you’ll learn about the situations where you might want to use it.

Walrus Operator Use Cases

In this section, you’ll see several examples where the walrus operator can simplify your code. A general theme in all these examples is that you’ll avoid different kinds of repetition:

• Repeated function calls can make your code slower than necessary.
• Repeated statements can make your code hard to maintain.
• Repeated calls that exhaust iterators can make your code overly complex.

You’ll see how the walrus operator can help in each of these situations.

Arguably one of the best use cases for the walrus operator is when debugging complex expressions. Say that you want to find the distance between two locations along the earth’s surface. One way to do this is to use the haversine formula :

ϕ represents the latitude, and λ represents the longitude of each location. To demonstrate this formula, you can calculate the distance between Oslo (59.9°N 10.8°E) and Vancouver (49.3°N 123.1°W) as follows:

As you can see, the distance from Oslo to Vancouver is just under 7,200 kilometers.

Note: Python source code is typically written using UTF-8 Unicode . This allows you to use Greek letters like ϕ and λ in your code, which may be useful when translating mathematical formulas. Wikipedia shows some alternatives for using Unicode on your system.

While UTF-8 is supported (in string literals, for instance), Python’s variable names use a more limited character set . For example, you can’t use emojis while naming your variables. That’s a good restriction !

Now, say that you need to double-check your implementation and want to see how much the haversine terms contribute to the final result. You could copy and paste the term from your main code to evaluate it separately. However, you could also use the := operator to give a name to the subexpression that you’re interested in:

The advantage of using the walrus operator here is that you calculate the value of the full expression and keep track of the value of ϕ_hav at the same time. This allows you to confirm that you didn’t introduce any errors while debugging.

Lists are powerful data structures in Python that often represent a series of related attributes. Similarly, dictionaries are used all over Python and are great for structuring information.

Sometimes when setting up these data structures, you end up performing the same operation several times. As a first example, calculate some basic descriptive statistics of a list of numbers and store them in a dictionary:

Note that both the sum and the length of the numbers list are calculated twice. The consequences are not too bad in this simple example, but if the list were larger or the calculations were more complicated, you might want to optimize the code. To do this, you can first move the function calls out of the dictionary definition:

The variables num_length and num_sum are only used to optimize the calculations inside the dictionary. By using the walrus operator, you can make this role clearer:

You’ve now defined num_length and num_sum inside the definition of description . This is a clear hint to anybody reading this code that these variables are just used to optimize these calculations and aren’t used again later.

Note: The scope of the num_length and num_sum variables is the same in the example with the walrus operator and in the example without. This means that in both examples, the variables are available after the definition of description .

Even though both examples are very similar functionally, a benefit of using the assignment expressions is that the := operator communicates the intent of these variables as throwaway optimizations.

In the next example, you’ll work with a bare-bones implementation of the wc utility for counting lines, words, and characters in a text file:

This script can read one or several text files and report how many lines, words, and characters each of them contains. Here’s a breakdown of what’s happening in the code:

• Line 4 loops over each filename provided by the user. The sys.argv list contains each argument given on the command line, starting with the name of your script. For more information about sys.argv , you can check out Python Command Line Arguments .
• Line 5 converts each filename string to a pathlib.Path object . Storing a filename in a Path object allows you to conveniently read the text file in the next lines.
• Lines 6 to 10 construct a tuple of counts to represent the number of lines, words, and characters in one text file.
• Line 7 reads a text file and calculates the number of lines by counting newlines.
• Line 8 reads a text file and calculates the number of words by splitting on whitespace.
• Line 9 reads a text file and calculates the number of characters by finding the length of the string.
• Line 11 prints all three counts together with the filename to the console. The *counts syntax unpacks the counts tuple. In this case, the print() statement is equivalent to print(counts[0], counts[1], counts[2], path) .

To see wc.py in action, you can use the script on itself as follows:

In other words, the wc.py file consists of 11 lines, 32 words, and 307 characters.

If you look closely at this implementation, then you’ll notice that it’s far from optimal. In particular, it repeats the call to path.read_text() three times. That means that the program reads each text file three times. You can use the walrus operator to avoid the repetition:

You assign the contents of the file to text , which you reuse in the next two calculations. Note the placement of parentheses that help scope that text will refer to the text in the file and not the number of lines.

The program still functions the same, although the word and character counts have changed:

As in the earlier examples, an alternative approach is to define text before the definition of counts :

While this is one line longer than the previous implementation, it probably provides the best balance between readability and efficiency. The := assignment expression operator isn’t always the most readable solution even when it makes your code more concise.

List comprehensions are great for constructing and filtering lists. They clearly state the intent of the code and will usually run quite fast.

There’s one list comprehension use case where the walrus operator can be particularly useful. Say that you want to apply some computationally expensive function, slow() , to the elements in your list and filter on the resulting values. You could do something like the following:

Here, you filter the numbers list and leave the positive results from applying slow() . The problem with this code is that this expensive function is called twice.

A very common solution for this type of situation is rewriting your code to use an explicit for loop:

This will only call slow() once. Unfortunately, the code is now more verbose, and the intent of the code is harder to understand. The list comprehension had clearly signaled that you were creating a new list, while this is more hidden in the explicit for loop since several lines of code separate the list creation and the use of .append() . Additionally, a list comprehension runs faster than the repeated calls to .append() .

You can code some other solutions by using a filter() expression or a kind of double list comprehension:

The good news is that there’s only one call to slow() for each number. The bad news is that the code’s readability has suffered in both expressions.

Figuring out what’s actually happening in the double list comprehension takes a fair amount of head-scratching. Essentially, the second for statement is used only to give the name value to the return value of slow(num) . Fortunately, that sounds like something that you can accomplish with an assignment expression!

You can rewrite the list comprehension using the walrus operator as follows:

Note that the parentheses around value := slow(num) are required. This version is effective and readable, and it communicates the intent of the code well.

Note: You need to add the assignment expression on the if clause of the list comprehension. If you try to define value with the other call to slow() , then it won’t work:

This will raise a NameError because the if clause is evaluated before the expression at the beginning of the comprehension.

Next, look at a slightly more involved and practical example. Say that you want to use the Real Python feed to find the titles of the last episodes of the Real Python Podcast .

You can use the Real Python Feed Reader to download information about the latest Real Python publications. In order to find the podcast episode titles, you’ll use the third-party Parse package. Start by creating a virtual environment and installing both packages:

You can now read the latest titles published by Real Python:

Podcast titles start with "The Real Python Podcast" , so here you can create a pattern that Parse can use to identify them:

Compiling the pattern beforehand speeds up later comparisons, especially when you want to match the same pattern over and over. You can check if a string matches your pattern using either pattern.parse() or pattern.search() :

Note that Parse is able to pick out the podcast episode number and the episode name. The episode number is converted to an integer data type because you used the :d format specifier .

Time to get back to the task at hand. In order to list all the recent podcast titles, you need to check whether each string matches your pattern and then parse out the episode title. A first attempt may look something like this:

Though it works, you might notice the same problem you saw earlier. You’re parsing each title twice because you filter out titles that match your pattern and then use that same pattern to pick out the episode title.

Like you did earlier, you can avoid the double work by rewriting the list comprehension using either an explicit for loop or a double list comprehension. Using the walrus operator, however, is even more straightforward:

Assignment expressions work well to simplify these kinds of list comprehensions. They keep your code readable and save you from doing a potentially expensive operation twice.

Note: The Real Python Podcast has its own separate RSS feed , which you should use if you want to play around with information about the podcast only. You can get all the episode titles with the following code:

See The Real Python Podcast for options to listen to it using your podcast player.

In this section, you’ve focused on examples where you can rewrite list comprehensions using the walrus operator. The same principles also apply if you see that you need to repeat an operation in a dictionary comprehension , a set comprehension , or a generator expression .

The following example uses a generator expression to calculate the average length of episode titles that are over 50 characters long:

The generator expression uses an assignment expression to avoid calculating the length of each episode title twice.

Python has two different loop constructs: for loops and while loops . You typically use a for loop when you need to iterate over a known sequence of elements. A while loop, on the other hand, is for when you don’t know beforehand how many times you’ll need to repeat the loop.

In while loops, you need to define and check the ending condition at the top of the loop. This sometimes leads to some awkward code when you need to do some setup before performing the check. Here’s a snippet from a multiple-choice quiz program that asks the user to answer a question with one of several valid answers:

This works but has an unfortunate repetition of two identical input() lines. It’s necessary to get at least one answer from the user before checking whether it’s valid or not. You then have a second call to input() inside the while loop to ask for a second answer in case the original user_answer wasn’t valid.

If you want to make your code more maintainable, it’s quite common to rewrite this kind of logic with a while True loop. Instead of making the check part of the main while statement, the check is performed later in the loop together with an explicit break :

This has the advantage of avoiding the repetition. However, the actual check is now harder to spot.

Assignment expressions can simplify these kinds of loops. In this example, you can now put the check back together with while where it makes more sense:

The while statement is a bit denser, but the code now communicates the intent more clearly without repeated lines or seemingly infinite loops.

You can expand the box below to see the full code of the multiple-choice quiz program and try a couple of questions about the walrus operator yourself.

Full source code of multiple-choice quiz program Show/Hide

This script runs a multiple-choice quiz. You’ll be asked each of the questions in order, but the order of answers will be shuffled each time:

Note that the first answer is assumed to be the correct one, while the others serve as distractors. You can add more questions to the quiz yourself. Feel free to share your questions with the community in the comments section below the tutorial!

See Build a Quiz Application With Python if you want to dive deeper into using Python to quiz yourself or your friends. You can also quiz yourself on your knowledge of the walrus operator:

You can often simplify while loops by using assignment expressions. The original PEP shows an example from the standard library that makes the same point.

In the examples you’ve seen so far, the := assignment expression operator does essentially the same job as the = assignment operator in your old code. You’ve seen how to simplify code, and now you’ll learn about a different type of use case that this operator makes possible.

In this section, you’ll learn how you can find witnesses when calling any() by using a clever trick that isn’t immediately possible without using the walrus operator. A witness, in this context, is an element that satisfies the check and causes any() to return True .

By applying similar logic, you’ll also learn how you can find counterexamples when working with all() . A counterexample, in this context, is an element that doesn’t satisfy the check and causes all() to return False .

In order to have some data to work with, define the following list of city names:

You can use any() and all() to answer questions about your data:

In each of these cases, any() and all() give you plain True or False answers. What if you’re also interested in seeing an example or a counterexample of the city names? It could be nice to see what’s causing your True or False result:

Does any city name start with "B" ?

Yes, because "Berlin" starts with "B" .

Do all city names start with "B" ?

No, because "Oslo" doesn’t start with "B" .

In other words, you want a witness or a counterexample to justify the answer.

Capturing a witness to an any() expression has not been intuitive in earlier versions of Python. If you were calling any() on a list and then realized you also wanted a witness, you’d typically need to rewrite your code:

Here, you first capture all city names that start with "B" . Then, if there’s at least one such city name, you print out the first city name starting with "B" . Note that here you’re actually not using any() even though you’re doing a similar operation with the list comprehension.

By using the := operator, you can find witnesses directly in your any() expressions:

You can capture a witness inside the any() expression. The reason this works is a bit subtle and relies on any() and all() using short-circuit evaluation : they only check as many items as necessary to determine the result.

Note: If you want to check whether all city names start with the letter "B" , then you can look for a counterexample by replacing any() with all() and updating the print() functions to report the first item that doesn’t pass the check.

You can more clearly see what’s happening by wrapping .startswith("B") in a function that also prints out which item is being checked:

Note that any() doesn’t actually check all the items in cities . It only checks items until it finds one that satisfies the condition. Combining the := operator and any() works by iteratively assigning each item that is being checked to witness . However, only the last such item survives and shows which item was last checked by any() .

Even when any() returns False , a witness is found:

However, in this case, witness doesn’t give any insight. 'Belgrade' doesn’t contain ten or more characters. The witness only shows which item happened to be evaluated last.

One of the main reasons assignments weren’t expressions in Python from the beginning is that the visual similarity of the assignment operator ( = ) and the equality comparison operator ( == ) could potentially lead to bugs.

When introducing assignment expressions, the developers put a lot of thought into how to avoid similar bugs with the walrus operator. As mentioned earlier , one important feature is that the := operator is never allowed as a direct replacement for the = operator, and vice versa.

As you saw at the beginning of this tutorial, you can’t use a plain assignment expression to assign a value:

It’s syntactically legal to use an assignment expression to only assign a value, but you need to add parentheses:

Even though it’s possible, this is a prime example of where you should stay away from the walrus operator and use a traditional assignment statement instead.

PEP 572 shows several other examples where the := operator is either illegal or discouraged. The following examples all cause a SyntaxError :

In all these cases, you’re better served using = instead. The next examples are similar and are all legal code. However, the walrus operator doesn’t improve your code in any of these cases:

None of these examples make your code more readable. You should instead do the extra assignment separately by using a traditional assignment statement. See PEP 572 for more details about the reasoning.

There’s one use case where the := character sequence is already valid Python. In f-strings , you use a colon ( : ) to separate values from their format specification . For example:

The := in this case does look like a walrus operator, but the effect is quite different. To interpret x:=8 inside the f-string, the expression is broken into three parts: x , : , and =8 .

Here, x is the value, : acts as a separator, and =8 is a format specification. According to Python’s Format Specification Mini-Language , in this context = specifies an alignment option. In this case, the value is padded with spaces in a field of width 8 .

To use assignment expressions inside f-strings, you need to add parentheses:

This updates the value of x as expected. However, you’re probably better off using traditional assignments outside of your f-strings instead.

Now, look at some other situations where assignment expressions are illegal:

Attribute and item assignment: You can only assign to simple names, not dotted or indexed names:

This fails with a descriptive error message. There’s no straightforward workaround.

Iterable unpacking: You can’t unpack when using the walrus operator:

If you add parentheses around the whole expression, then Python will interpret it as a 3-tuple with the three elements lat , 59.9 , and 10.8 .

Augmented assignment: You can’t use the walrus operator combined with augmented assignment operators like += . This raises a SyntaxError :

The easiest workaround would be to do the augmentation explicitly. You could, for example, do (count := count + 1) . PEP 577 originally described how to add augmented assignment expressions to Python, but the proposal was withdrawn.

When you’re using the walrus operator, it’ll behave similarly to traditional assignment statements in many respects:

The scope of the assignment target is the same as for assignments. It’ll follow the LEGB rule . Typically, the assignment will happen in the local scope, but if the target name is already declared global or nonlocal , that declaration is honored.

The precedence of the walrus operator can cause some confusion. It binds less tightly than all other operators except the comma, so you might need parentheses to delimit the expression that you’re assigning. As an example, note what happens when you don’t use parentheses:

square is bound to the whole expression number ** 2 > 5 . In other words, square gets the value True and not the value of number ** 2 , which was the intention. In this case, you can delimit the expression with parentheses:

The parentheses make the if statement both clearer and actually correct.

There’s one final gotcha. When assigning a tuple using the walrus operator, you always need to use parentheses around the tuple. Compare the following assignments:

Note that in the second example, walrus takes the value 3.8 and not the whole tuple 3.8, True . That’s because the := operator binds more tightly than the comma. This may seem a bit annoying. However, if the := operator bound less tightly than the comma, then it wouldn’t be possible to use the walrus operator in function calls with more than one argument.

The style recommendations for the walrus operator are mostly the same as for the = operator used for assignment. First, always add spaces around the := operator in your code. Second, use parentheses around the expression as necessary, but avoid adding extra parentheses that you don’t need.

The general design of assignment expressions is to make them easy to use when they’re helpful but to avoid overusing them when they might clutter up your code.

The walrus operator is a newer syntax that’s only available in Python 3.8 and later. This means that any code you write that uses the := syntax will only work on these versions of Python.

If you need to support legacy versions of Python, then you can’t ship code that uses assignment expressions. As you’ve learned in this tutorial, you can always write code without the walrus operator and stay compatible with older versions.

Experience with the walrus operator indicates that := will not revolutionize Python. Instead, using assignment expressions where they’re useful can help you make several small improvements to your code that could benefit your work overall.

You’ll run into several situations where it’s possible for you to use the walrus operator, but it won’t necessarily improve the readability or efficiency of your code. In those cases, you’re better off writing your code in a more traditional manner.

You now know how the walrus operator works and how you can use it in your own code. By using the := syntax, you can avoid different kinds of repetition in your code and make your code both more efficient and easier to read and maintain. At the same time, you shouldn’t use assignment expressions everywhere. They’ll only help you in specific use cases.

In this tutorial, you learned how to:

To learn more about the details of assignment expressions, see PEP 572 . You can also check out the PyCon 2019 talk PEP 572: The Walrus Operator , where Dustin Ingram gives an overview of both the walrus operator and the discussion around the new PEP.

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About Geir Arne Hjelle

Geir Arne is an avid Pythonista and a member of the Real Python tutorial team.

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Basic Statements in Python

Table of contents, what is a statement in python, statement set, multi-line statements, simple statements, expression statements, the assert statement, the try statement.

In Python, statements are instructions or commands that you write to perform specific actions or tasks. They are the building blocks of a Python program.

A statement is a line of code that performs a specific action. It is the smallest unit of code that can be executed by the Python interpreter.

Assignment Statement

In this example, the value 10 is assigned to the variable x using the assignment statement.

Conditional Statement

In this example, the if-else statement is used to check the value of x and print a corresponding message.

By using statements, programmers can instruct the computer to perform a variety of tasks, from simple arithmetic operations to complex decision-making processes. Proper use of statements is crucial to writing efficient and effective Python code.

Here's a table summarizing various types of statements in Python:

Please note that this table provides a brief overview of each statement type, and there may be additional details and variations for each statement.

Multi-line statements are a convenient way to write long code in Python without making it cluttered. They allow you to write several lines of code as a single statement, making it easier for developers to read and understand the code. Here are two examples of multi-line statements in Python:

• Using backslash:
• Using parentheses:

Simple statements are the smallest unit of execution in Python programming language and they do not contain any logical or conditional expressions. They are usually composed of a single line of code and can perform basic operations such as assigning values to variables , printing out values, or calling functions .

Examples of simple statements in Python:

Simple statements are essential to programming in Python and are often used in combination with more complex statements to create robust programs and applications.

Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result.

In this example, we assign the value 5 to the variable x , then add 3 to x and assign the result ( 8 ) to the variable y . Finally, we print the value of y .

In this example, we define a function square that takes one argument ( x ) and returns its square. We then call the function with the argument 5 and assign the result ( 25 ) to the variable result . Finally, we print the value of result .

Overall, expression statements are an essential part of Python programming and allow for the execution of mathematical and computational operations.

The assert statement in Python is used to test conditions and trigger an error if the condition is not met. It is often used for debugging and testing purposes.

Where condition is the expression that is tested, and message is the optional error message that is displayed when the condition is not met.

In this example, the assert statement tests whether x is equal to 5 . If the condition is met, the statement has no effect. If the condition is not met, an error will be raised with the message x should be 5 .

In this example, the assert statement tests whether y is not equal to 0 before performing the division. If the condition is met, the division proceeds as normal. If the condition is not met, an error will be raised with the message Cannot divide by zero .

Overall, assert statements are a useful tool in Python for debugging and testing, as they can help catch errors early on. They are also easily disabled in production code to avoid any unnecessary overhead.

The try statement in Python is used to catch exceptions that may occur during the execution of a block of code. It ensures that even when an error occurs, the code does not stop running.

Examples of Error Processing

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1.6. Variables and Assignment ¶

Each set-off line in this section should be tried in the Shell.

Nothing is displayed by the interpreter after this entry, so it is not clear anything happened. Something has happened. This is an assignment statement , with a variable , width , on the left. A variable is a name for a value. An assignment statement associates a variable name on the left of the equal sign with the value of an expression calculated from the right of the equal sign. Enter

Once a variable is assigned a value, the variable can be used in place of that value. The response to the expression width is the same as if its value had been entered.

The interpreter does not print a value after an assignment statement because the value of the expression on the right is not lost. It can be recovered if you like, by entering the variable name and we did above.

Try each of the following lines:

The equal sign is an unfortunate choice of symbol for assignment, since Python’s usage is not the mathematical usage of the equal sign. If the symbol ↤ had appeared on keyboards in the early 1990’s, it would probably have been used for assignment instead of =, emphasizing the asymmetry of assignment. In mathematics an equation is an assertion that both sides of the equal sign are already, in fact, equal . A Python assignment statement forces the variable on the left hand side to become associated with the value of the expression on the right side. The difference from the mathematical usage can be illustrated. Try:

so this is not equivalent in Python to width = 10 . The left hand side must be a variable, to which the assignment is made. Reversed, we get a syntax error . Try

This is, of course, nonsensical as mathematics, but it makes perfectly good sense as an assignment, with the right-hand side calculated first. Can you figure out the value that is now associated with width? Check by entering

In the assignment statement, the expression on the right is evaluated first . At that point width was associated with its original value 10, so width + 5 had the value of 10 + 5 which is 15. That value was then assigned to the variable on the left ( width again) to give it a new value. We will modify the value of variables in a similar way routinely.

Assignment and variables work equally well with strings. Try:

Try entering:

Note the different form of the error message. The earlier errors in these tutorials were syntax errors: errors in translation of the instruction. In this last case the syntax was legal, so the interpreter went on to execute the instruction. Only then did it find the error described. There are no quotes around fred , so the interpreter assumed fred was an identifier, but the name fred was not defined at the time the line was executed.

It is both easy to forget quotes where you need them for a literal string and to mistakenly put them around a variable name that should not have them!

Try in the Shell :

There fred , without the quotes, makes sense.

There are more subtleties to assignment and the idea of a variable being a “name for” a value, but we will worry about them later, in Issues with Mutable Objects . They do not come up if our variables are just numbers and strings.

Autocompletion: A handy short cut. Idle remembers all the variables you have defined at any moment. This is handy when editing. Without pressing Enter, type into the Shell just

Assuming you are following on the earlier variable entries to the Shell, you should see f autocompleted to be

This is particularly useful if you have long identifiers! You can press Alt-/ several times if more than one identifier starts with the initial sequence of characters you typed. If you press Alt-/ again you should see fred . Backspace and edit so you have fi , and then and press Alt-/ again. You should not see fred this time, since it does not start with fi .

1.6.1. Literals and Identifiers ¶

Expressions like 27 or 'hello' are called literals , coming from the fact that they literally mean exactly what they say. They are distinguished from variables, whose value is not directly determined by their name.

The sequence of characters used to form a variable name (and names for other Python entities later) is called an identifier . It identifies a Python variable or other entity.

There are some restrictions on the character sequence that make up an identifier:

The characters must all be letters, digits, or underscores _ , and must start with a letter. In particular, punctuation and blanks are not allowed.

There are some words that are reserved for special use in Python. You may not use these words as your own identifiers. They are easy to recognize in Idle, because they are automatically colored orange. For the curious, you may read the full list:

There are also identifiers that are automatically defined in Python, and that you could redefine, but you probably should not unless you really know what you are doing! When you start the editor, we will see how Idle uses color to help you know what identifies are predefined.

Python is case sensitive: The identifiers last , LAST , and LaSt are all different. Be sure to be consistent. Using the Alt-/ auto-completion shortcut in Idle helps ensure you are consistent.

What is legal is distinct from what is conventional or good practice or recommended. Meaningful names for variables are important for the humans who are looking at programs, understanding them, and revising them. That sometimes means you would like to use a name that is more than one word long, like price at opening , but blanks are illegal! One poor option is just leaving out the blanks, like priceatopening . Then it may be hard to figure out where words split. Two practical options are

• underscore separated: putting underscores (which are legal) in place of the blanks, like price_at_opening .
• using camel-case : omitting spaces and using all lowercase, except capitalizing all words after the first, like priceAtOpening

Use the choice that fits your taste (or the taste or convention of the people you are working with).

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Overview Teaching: 15 min Exercises: 15 min Questions How can I store data in programs? Objectives Write scripts that assign values to variables and perform calculations with those values. Correctly trace value changes in scripts that use assignment.

Use variables to store values

Variables are one of the fundamental building blocks of Python. A variable is like a tiny container where you store values and data, such as filenames, words, numbers, collections of words and numbers, and more.

The variable name will point to a value that you “assign” it. You might think about variable assignment like putting a value “into” the variable, as if the variable is a little box 🎁

(In fact, a variable is not a container as such but more like an adress label that points to a container with a given value. This difference will become relevant once we start talking about lists and mutable data types.)

You assign variables with an equals sign ( = ). In Python, a single equals sign = is the “assignment operator.” (A double equals sign == is the “real” equals sign.)

• Variables are names for values.
• In Python the = symbol assigns the value on the right to the name on the left.
• The variable is created when a value is assigned to it.
• Here, Python assigns an age to a variable age and a name in quotation marks to a variable first_name :

Variable names

Variable names can be as long or as short as you want, but there are certain rules you must follow.

• Cannot start with a digit.
• Cannot contain spaces, quotation marks, or other punctuation.
• May contain an underscore (typically used to separate words in long variable names).
• Having an underscore at the beginning of a variable name like _alistairs_real_age has a special meaning. So we won’t do that until we understand the convention.
• The standard naming convention for variable names in Python is the so-called “snake case”, where each word is separated by an underscore. For example my_first_variable . You can read more about naming conventions in Python here .

Use meaningful variable names

Python doesn’t care what you call variables as long as they obey the rules (alphanumeric characters and the underscore). As you start to code, you will almost certainly be tempted to use extremely short variables names like f . Your fingers will get tired. Your coffee will wear off. You will see other people using variables like f . You’ll promise yourself that you’ll definitely remember what f means. But you probably won’t.

So, resist the temptation of bad variable names! Clear and precisely-named variables will:

• Make your code more readable (both to yourself and others).
• Reinforce your understanding of Python and what’s happening in the code.
• Clarify and strengthen your thinking.

Use meaningful variable names to help other people understand what the program does. The most important “other person” is your future self!

Python is case-sensitive

Python thinks that upper- and lower-case letters are different, so Name and name are different variables. There are conventions for using upper-case letters at the start of variable names so we will use lower-case letters for now.

Off-Limits Names

The only variable names that are off-limits are names that are reserved by, or built into, the Python programming language itself — such as print , True , and list . Some of these you can overwrite into variable names (not ideal!), but Jupyter Lab (and many other environments and editors) will catch this by colour coding your variable. If your would-be variable is colour-coded green, rethink your name choice. This is not something to worry too much about. You can get the object back by resetting your kernel.

Use print() to display values

We can check to see what’s “inside” variables by running a cell with the variable’s name. This is one of the handiest features of a Jupyter notebook. Outside the Jupyter environment, you would need to use the print() function to display the variable.

You can run the print() function inside the Jupyter environment, too. This is sometimes useful because Jupyter will only display the last variable in a cell, while print() can display multiple variables. Additionally, Jupyter will display text with \n characters (which means “new line”), while print() will display the text appropriately formatted with new lines.

• Python has a built-in function called print() that prints things as text.
• Provide values to the function (i.e., the things to print) in parentheses.
• To add a string to the printout, wrap the string in single or double quotations.
• The values passed to the function are called ‘arguments’ and are separated by commas.
• When using the print() function, we can also separate with a ‘+’ sign. However, when using ‘+’ we have to add spaces in between manually.
• print() automatically puts a single space between items to separate them.
• And wraps around to a new line at the end.

Variables must be created before they are used

If a variable doesn’t exist yet, or if the name has been misspelled, Python reports an error (unlike some languages, which “guess” a default value).

The last line of an error message is usually the most informative. This message lets us know that there is no variable called eye_color in the script.

Variables Persist Between Cells Variables defined in one cell exist in all other cells once executed, so the relative location of cells in the notebook do not matter (i.e., cells lower down can still affect those above). Notice the number in the square brackets [ ] to the left of the cell. These numbers indicate the order, in which the cells have been executed. Cells with lower numbers will affect cells with higher numbers as Python runs the cells chronologically. As a best practice, we recommend you keep your notebook in chronological order so that it is easier for the human eye to read and make sense of, as well as to avoid any errors if you close and reopen your project, and then rerun what you have done. Remember: Notebook cells are just a way to organize a program! As far as Python is concerned, all of the source code is one long set of instructions.

Variables can be used in calculations

• We can use variables in calculations just as if they were values. Remember, we assigned 42 to age a few lines ago.

This code works in the following way. We are reassigning the value of the variable age by taking its previous value (42) and adding 3, thus getting our new value of 45.

Use an index to get a single character from a string

• The characters (individual letters, numbers, and so on) in a string are ordered. For example, the string ‘AB’ is not the same as ‘BA’. Because of this ordering, we can treat the string as a list of characters.
• Each position in the string (first, second, etc.) is given a number. This number is called an index or sometimes a subscript.
• Indices are numbered from 0 rather than 1.
• Use the position’s index in square brackets to get the character at that position.

Use a slice to get a substring

A part of a string is called a substring. A substring can be as short as a single character. A slice is a part of a string (or, more generally, any list-like thing). We take a slice by using [start:stop] , where start is replaced with the index of the first element we want and stop is replaced with the index of the element just after the last element we want. Mathematically, you might say that a slice selects [start:stop] . The difference between stop and start is the slice’s length. Taking a slice does not change the contents of the original string. Instead, the slice is a copy of part of the original string.

Use the built-in function len() to find the length of a string

The built-in function len() is used to find the length of a string (and later, of other data types, too).

Note that the result is 6 and not 7. This is because it is the length of the value of the variable (i.e. 'helium' ) that is being counted and not the name of the variable (i.e. element )

Also note that nested functions are evaluated from the inside out, just like in mathematics. Thus, Python first reads the len() function, then the print() function.

Choosing a Name Which is a better variable name, m , min , or minutes ? Why? Hint: think about which code you would rather inherit from someone who is leaving the library: ts = m * 60 + s tot_sec = min * 60 + sec total_seconds = minutes * 60 + seconds Solution minutes is better because min might mean something like “minimum” (and actually does in Python, but we haven’t seen that yet).

Swapping Values Draw a table showing the values of the variables in this program after each statement is executed. In simple terms, what do the last three lines of this program do? x = 1.0 y = 3.0 swap = x x = y y = swap Solution swap = x # x->1.0 y->3.0 swap->1.0 x = y # x->3.0 y->3.0 swap->1.0 y = swap # x->3.0 y->1.0 swap->1.0 These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom.

Predicting Values What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.) initial = "left" position = initial initial = "right" Solution initial = "left" # Initial is assigned the string "left" position = initial # Position is assigned the variable initial, currently "left" initial = "right" # Initial is assigned the string "right" print(position) left The last assignment to position was “left”

Can you slice integers? If you assign a = 123 , what happens if you try to get the second digit of a ? Solution Numbers are not stored in the written representation, so they can’t be treated like strings. a = 123 print(a[1]) TypeError: 'int' object is not subscriptable

Slicing What does the following program print? library_name = 'social sciences' print('library_name[1:3] is:', library_name[1:3]) If thing is a variable name, low is a low number, and high is a high number: What does thing[low:high] do? What does thing[low:] (without a value after the colon) do? What does thing[:high] (without a value before the colon) do? What does thing[:] (just a colon) do? What does thing[number:negative-number] do? Solution library_name[1:3] is: oc It will slice the string, starting at the low index and ending an element before the high index It will slice the string, starting at the low index and stopping at the end of the string It will slice the string, starting at the beginning on the string, and ending an element before the high index It will print the entire string It will slice the string, starting the number index, and ending a distance of the absolute value of negative-number elements from the end of the string

Key Points Use variables to store values. Use meaningful variable names. Python is case-sensitive. Use print() to display values. Variables must be created before they are used. Variables persist between cells. Variables can be used in calculations. Use an index to get a single character from a string. Use a slice to get a substring. Use the built-in function len to find the length of a string.

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Different Forms of Assignment Statements in Python

We use Python assignment statements to assign objects to names. The target of an assignment statement is written on the left side of the equal sign (=), and the object on the right can be an arbitrary expression that computes an object.

There are some important properties of assignment in Python :-

• Assignment creates object references instead of copying the objects.
• Python creates a variable name the first time when they are assigned a value.
• Names must be assigned before being referenced.
• There are some operations that perform assignments implicitly.

Assignment statement forms :-

1. Basic form:

This form is the most common form.

2. Tuple assignment:

When we code a tuple on the left side of the =, Python pairs objects on the right side with targets on the left by position and assigns them from left to right. Therefore, the values of x and y are 50 and 100 respectively.

3. List assignment:

This works in the same way as the tuple assignment.

4. Sequence assignment:

In recent version of Python, tuple and list assignment have been generalized into instances of what we now call sequence assignment – any sequence of names can be assigned to any sequence of values, and Python assigns the items one at a time by position.

5. Extended Sequence unpacking:

It allows us to be more flexible in how we select portions of a sequence to assign.

Here, p is matched with the first character in the string on the right and q with the rest. The starred name (*q) is assigned a list, which collects all items in the sequence not assigned to other names.

This is especially handy for a common coding pattern such as splitting a sequence and accessing its front and rest part.

6. Multiple- target assignment:

In this form, Python assigns a reference to the same object (the object which is rightmost) to all the target on the left.

7. Augmented assignment :

The augmented assignment is a shorthand assignment that combines an expression and an assignment.

There are several other augmented assignment forms:

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Python Enhancement Proposals

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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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Operators are special symbols that perform operations on variables and values. For example,

Here, + is an operator that adds two numbers: 5 and 6 .

• Types of Python Operators

Here's a list of different types of Python operators that we will learn in this tutorial.

• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Logical Operators
• Bitwise Operators
• Special Operators

1. Python Arithmetic Operators

Arithmetic operators are used to perform mathematical operations like addition, subtraction, multiplication, etc. For example,

Here, - is an arithmetic operator that subtracts two values or variables.

Example 1: Arithmetic Operators in Python

In the above example, we have used multiple arithmetic operators,

• + to add a and b
• - to subtract b from a
• * to multiply a and b
• / to divide a by b
• // to floor divide a by b
• % to get the remainder
• ** to get a to the power b

2. Python Assignment Operators

Assignment operators are used to assign values to variables. For example,

Here, = is an assignment operator that assigns 5 to x .

Here's a list of different assignment operators available in Python.

Example 2: Assignment Operators

Here, we have used the += operator to assign the sum of a and b to a .

Similarly, we can use any other assignment operators as per our needs.

3. Python Comparison Operators

Comparison operators compare two values/variables and return a boolean result: True or False . For example,

Here, the > comparison operator is used to compare whether a is greater than b or not.

Example 3: Comparison Operators

Note: Comparison operators are used in decision-making and loops . We'll discuss more of the comparison operator and decision-making in later tutorials.

4. Python Logical Operators

Logical operators are used to check whether an expression is True or False . They are used in decision-making. For example,

Here, and is the logical operator AND . Since both a > 2 and b >= 6 are True , the result is True .

Example 4: Logical Operators

Note : Here is the truth table for these logical operators.

5. Python Bitwise operators

Bitwise operators act on operands as if they were strings of binary digits. They operate bit by bit, hence the name.

For example, 2 is 10 in binary, and 7 is 111 .

In the table below: Let x = 10 ( 0000 1010 in binary) and y = 4 ( 0000 0100 in binary)

6. Python Special operators

Python language offers some special types of operators like the identity operator and the membership operator. They are described below with examples.

• Identity operators

In Python, is and is not are used to check if two values are located at the same memory location.

It's important to note that having two variables with equal values doesn't necessarily mean they are identical.

Example 4: Identity operators in Python

Here, we see that x1 and y1 are integers of the same values, so they are equal as well as identical. The same is the case with x2 and y2 (strings).

But x3 and y3 are lists. They are equal but not identical. It is because the interpreter locates them separately in memory, although they are equal.

• Membership operators

In Python, in and not in are the membership operators. They are used to test whether a value or variable is found in a sequence ( string , list , tuple , set and dictionary ).

In a dictionary, we can only test for the presence of a key, not the value.

Example 5: Membership operators in Python

Here, 'H' is in message , but 'hello' is not present in message (remember, Python is case-sensitive).

Similarly, 1 is key, and 'a' is the value in dictionary dict1 . Hence, 'a' in y returns False .

• Precedence and Associativity of operators in Python

Table of Contents

• Introduction
• Python Arithmetic Operators
• Python Assignment Operators
• Python Comparison Operators
• Python Logical Operators
• Python Bitwise operators
• Python Special operators

Before we wrap up, let’s put your knowledge of Python operators to the test! Can you solve the following challenge?

Write a function to split the restaurant bill among friends.

• Take the subtotal of the bill and the number of friends as inputs.
• Calculate the total bill by adding 20% tax to the subtotal and then divide it by the number of friends.
• Return the amount each friend has to pay, rounded off to two decimal places.

Video: Operators in Python

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Variables, Expressions, and Assignments

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

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

Values and Types #

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

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

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

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

Expressions and Statements #

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

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

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

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

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

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

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

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

Assignment Statements #

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

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

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

Do It Yourself!

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

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

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

Names and Keywords #

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

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

It should start with a letter or underscore.

It cannot start with a number.

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

They cannot share the name of a Python keyword.

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

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

The following are examples of invalid variable names.

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

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

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

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

False class finally is return

None continue for lambda try

True def from nonlocal while

and del global not with

as elif if or yield

assert else import pass break

except in raise

Reassignments #

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

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

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

Updating Variables #

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

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

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

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

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

Order of Operations #

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

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

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

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

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

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

In case of doubt, use parentheses!

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

Floor Division and Modulus Operators #

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

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

Conventional division returns a floating-point number.

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

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

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

The modulus operator is more useful than it seems.

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

String Operations #

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

But there are two exceptions, + and * .

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

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

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

Asking the User for Input #

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

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

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

Try it out!

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

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

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

Script Mode #

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

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

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

Python Programming

Practice Python Exercises and Challenges with Solutions

Free Coding Exercises for Python Developers. Exercises cover Python Basics , Data structure , to Data analytics . As of now, this page contains 18 Exercises.

What included in these Python Exercises?

Each exercise contains specific Python topic questions you need to practice and solve. These free exercises are nothing but Python assignments for the practice where you need to solve different programs and challenges.

• All exercises are tested on Python 3.
• Each exercise has 10-20 Questions.
• The solution is provided for every question.
• Practice each Exercise in Online Code Editor

These Python programming exercises are suitable for all Python developers. If you are a beginner, you will have a better understanding of Python after solving these exercises. Below is the list of exercises.

Select the exercise you want to solve .

Basic Exercise for Beginners

Practice and Quickly learn Python’s necessary skills by solving simple questions and problems.

Topics : Variables, Operators, Loops, String, Numbers, List

Python Input and Output Exercise

Solve input and output operations in Python. Also, we practice file handling.

Topics : print() and input() , File I/O

Python Loop Exercise

This Python loop exercise aims to help developers to practice branching and Looping techniques in Python.

Topics : If-else statements, loop, and while loop.

Python Functions Exercise

Practice how to create a function, nested functions, and use the function arguments effectively in Python by solving different questions.

Topics : Functions arguments, built-in functions.

Python String Exercise

Solve Python String exercise to learn and practice String operations and manipulations.

Python Data Structure Exercise

Practice widely used Python types such as List, Set, Dictionary, and Tuple operations in Python

Python List Exercise

This Python list exercise aims to help Python developers to learn and practice list operations.

Python Dictionary Exercise

This Python dictionary exercise aims to help Python developers to learn and practice dictionary operations.

Python Set Exercise

This exercise aims to help Python developers to learn and practice set operations.

Python Tuple Exercise

This exercise aims to help Python developers to learn and practice tuple operations.

Python Date and Time Exercise

This exercise aims to help Python developers to learn and practice DateTime and timestamp questions and problems.

Topics : Date, time, DateTime, Calendar.

Python OOP Exercise

This Python Object-oriented programming (OOP) exercise aims to help Python developers to learn and practice OOP concepts.

Topics : Object, Classes, Inheritance

Python JSON Exercise

Practice and Learn JSON creation, manipulation, Encoding, Decoding, and parsing using Python

Python NumPy Exercise

Practice NumPy questions such as Array manipulations, numeric ranges, Slicing, indexing, Searching, Sorting, and splitting, and more.

Python Pandas Exercise

Practice Data Analysis using Python Pandas. Practice Data-frame, Data selection, group-by, Series, sorting, searching, and statistics.

Python Matplotlib Exercise

Practice Data visualization using Python Matplotlib. Line plot, Style properties, multi-line plot, scatter plot, bar chart, histogram, Pie chart, Subplot, stack plot.

Random Data Generation Exercise

Practice and Learn the various techniques to generate random data in Python.

Topics : random module, secrets module, UUID module

Python Database Exercise

Practice Python database programming skills by solving the questions step by step.

Use any of the MySQL, PostgreSQL, SQLite to solve the exercise

Exercises for Intermediate developers

The following practice questions are for intermediate Python developers.

If you have not solved the above exercises, please complete them to understand and practice each topic in detail. After that, you can solve the below questions quickly.

Exercise 1: Reverse each word of a string

Expected Output

• Use the split() method to split a string into a list of words.
• Reverse each word from a list
• finally, use the join() function to convert a list into a string

Steps to solve this question :

• Split the given string into a list of words using the split() method
• Use a list comprehension to create a new list by reversing each word from a list.
• Use the join() function to convert the new list into a string
• Display the resultant string

Exercise 2: Read text file into a variable and replace all newlines with space

Given : Assume you have a following text file (sample.txt).

Expected Output :

• First, read a text file.
• Next, use string replace() function to replace all newlines ( \n ) with space ( ' ' ).

Steps to solve this question : -

• First, open the file in a read mode
• Next, read all content from a file using the read() function and assign it to a variable.
• Display final string

Exercise 3: Remove items from a list while iterating

Description :

In this question, You need to remove items from a list while iterating but without creating a different copy of a list.

Remove numbers greater than 50

Expected Output : -

• Get the list's size
• Iterate list using while loop
• Check if the number is greater than 50
• If yes, delete the item using a del keyword
• Reduce the list size

Solution 1: Using while loop

Solution 2: Using for loop and range()

Exercise 4: Reverse Dictionary mapping

Exercise 5: display all duplicate items from a list.

• Use the counter() method of the collection module.
• Create a dictionary that will maintain the count of each item of a list. Next, Fetch all keys whose value is greater than 2

Solution 1 : - Using collections.Counter()

Solution 2 : -

Exercise 6: Filter dictionary to contain keys present in the given list

Exercise 7: print the following number pattern.

Refer to Print patterns in Python to solve this question.

• Use two for loops
• The outer loop is reverse for loop from 5 to 0
• Increment value of x by 1 in each iteration of an outer loop
• The inner loop will iterate from 0 to the value of i of the outer loop
• Print value of x in each iteration of an inner loop
• Print newline at the end of each outer loop

Exercise 8: Create an inner function

Question description : -

• Create an outer function that will accept two strings, x and y . ( x= 'Emma' and y = 'Kelly' .
• Create an inner function inside an outer function that will concatenate x and y.
• At last, an outer function will join the word 'developer' to it.

Exercise 9: Modify the element of a nested list inside the following list

Change the element 35 to 3500

Exercise 10: Access the nested key increment from the following dictionary

Under Exercises: -

Python Object-Oriented Programming (OOP) Exercise: Classes and Objects Exercises

Updated on:  December 8, 2021 | 52 Comments

Python Date and Time Exercise with Solutions

Updated on:  December 8, 2021 | 10 Comments

Python Dictionary Exercise with Solutions

Updated on:  May 6, 2023 | 56 Comments

Python Tuple Exercise with Solutions

Updated on:  December 8, 2021 | 96 Comments

Python Set Exercise with Solutions

Updated on:  October 20, 2022 | 27 Comments

Python if else, for loop, and range() Exercises with Solutions

Updated on:  August 29, 2024 | 298 Comments

Updated on:  August 2, 2022 | 155 Comments

Updated on:  September 6, 2021 | 109 Comments

Python List Exercise with Solutions

Updated on:  December 8, 2021 | 201 Comments

Updated on:  December 8, 2021 | 7 Comments

Python Data Structure Exercise for Beginners

Updated on:  December 8, 2021 | 116 Comments

Python String Exercise with Solutions

Updated on:  October 6, 2021 | 221 Comments

Updated on:  March 9, 2021 | 23 Comments

Updated on:  March 9, 2021 | 51 Comments

Updated on:  July 20, 2021 | 29 Comments

Python Basic Exercise for Beginners

Updated on:  August 29, 2024 | 498 Comments

Useful Python Tips and Tricks Every Programmer Should Know

Updated on:  May 17, 2021 | 23 Comments

Python random Data generation Exercise

Updated on:  December 8, 2021 | 13 Comments

Python Database Programming Exercise

Updated on:  March 9, 2021 | 17 Comments

• Online Python Code Editor

Updated on:  June 1, 2022 |

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Defining a symbolic syntax for referring to assignment targets

FWIW, aside from the potential performance hit, mutating f_locals should work reliably in 3.13+ due to PEP 667 – Consistent views of namespaces | peps.python.org

I actually had thought about that too but the problem was that we can’t use a soft keyword for it since there is no way syntactically to distinguish the soft keyword from a variable name, and if we make it a hard keyword then it would be too much of a breaking change since the keyword would have to be a commonly used English word that likely collides with variable names in existing code base.

But now it occurred to me that we can just make the magic placeholder a dunder so no existing code base should be using it. As for the name itself, I think __target__ would be good:

The code now looks a lot less cryptic/Perlish to me.

Behind the scene though, __target__ would be a hard keyword rather than a variable name. It won’t be looked up in the namespace but is rather expanded at compilation time.

Similarlly, @'' can be made into a new hard keyword such as __target_name__ to look less cryptic.

Yup, same here. I almost wrote my own POV comparing this to walrus, but decided not to half-way through realising that it isn’t going to be of much use given this is currently at contemplative stage.

And it took me a fair bit of time to stop abusing walrus, however I am happy about it having finally learned to use it responsibly.

The notation, I think is decent - visually and semantically. And been wandering what was the reason for @ for mat_mult - never seen @ used anywhere in math before. There was quite a good example for this - matlab . Can’t do exactly the same, but retaining * might have been better, e.g. \* . So that @ could be used for something more appropriate, e.g. this. But given status quo, I don’t think it would fly. So 2 options:

• Change matrix multiplication operator. Been thinking about how hard of a sell this would be. I know that no-one would want to do it. All the backwards compatibility etc, but has anyone analysed compound cost of a bad decision? And the fact that the earlier the correction is done, the smaller the damage? I am talking in general, not only this specific case. I mean surely if the goal is best possible version of Python, (as opposed to pleasing unsatisfied and reluctant to adapt users), big breaks of backwards compatibility are inevitable…
• Pick something else for this. One idea would be to go with bash-like argument syntax, but with pythonic slicing:

Or maybe even star, given it being used in expansions:

Could also incorporate string-like formatting:

Alternatively, use * for strings, @ for values (similar to what bash does).

We have space-separated names as arguments in the standard library too, such as collections.namedtuple and enum.Enum :

Using my dunder keyword idea above, it can become:

The matrix multiplication PEP did cover that: PEP 465 – A dedicated infix operator for matrix multiplication | peps.python.org

The __target__ dunder idea is interesting, as it doesn’t provide brevity gains in most cases, but does provide the benefit of avoiding repeated evaluation.

I suspect code that assigns to identifiers would continue to be better off just using the target identifier directly, but attribute access, subscripting, slicing, and tuple assignment could still see some utility.

__target_text__ could replace @'' for both identifier assignment and tuple assignment ( __target_name__ wouldn’t match the latter use case)

I doubt you could reasonably use that form to map function parameters to local variables of the same name, though.

Sounds good indeed. With the name __target_text__ we wouldn’t have to worry about a plural form.

I don’t see why not. We can name the new keyword that forwards local variables to function parameters of the same names, __same__ . The compiler with access to AST can clearly tell which keyword argument a ast.Same node belongs to and produces bytecode that loads the local variable of that name for the argument accordingly:

This is really a completely separate proposal from the assignment target placeholder proposal though, and should be discussed about in a separate thread (perhaps in the PEP-736 thread, in which I have now made a post ).

Yeah the loss of brevity does take away the utility of this idea in cases of a single identifier, particularly as an alternative to PEP-736, where parameter names are always single identifiers.

I agree that we should focus more on its utility for more complex assignment targets.

Going further on the SymPy tangent, I can brainstorm a possible story of how to get that by more straightforward extensions of existing syntax.

The hardest hurdle in this story is the first step. Suppose Python somehow gets dict destructuring assignment:

Sympy then defines a helper whose __getitem__ gives out symbols (if it doesn’t already have it):

By this time PEP 736 has been accepted, and its syntax gets extended to dict destructuring:

(Is that Perlish?) (Is it the direction Python is going?)

People now start writing {Point2D=} = namedtuplemaker('x', 'y') … which isn’t straightforward to read today, but maybe it’s better than with @'' ?

“Whatever, we have to pick something.”

If you’re going to give the target a full identifier, you might as well provide the name rather than hardcoding it

some.nested.attribute as x = x.lower()

This doesn’t require new tokens, and the syntax mimics what we have in the match statement (which tries to overlap assignment syntax). It definitely looks more python than perl (keywords vs symbols)

Same as your proposal, this doesn’t cover argument pushing, but only double evaluation (at this point it isn’t much more than a glorified walrus). This is also missing a syntax to capture the name of the target as a str. I’m not sorry convinced on that but I think you could do (a, b, c) as (_, names) = simpy.symbols(names) .

I’m not super sold into this either, but hopefully putting this idea on the table may help someone find a better proposal

For me a big part of this feature was not needing to assign a name for this specific “some.nested.attribute”, so I’m not really sure I like the as proposal

A benefit can be that if the name persists, it can be used more times in the code that follows the assignment.

Quite like it, one issue though:

While naming tuple thing obstruct such intuitive expansion. However might not be an issue as one can always slice it on the right hand side. However, it does not seem very elegant nevertheless. Maybe something like:

This would be the 2nd method that allows extracting identifier name from identifier. First one being f{a=} . However, it would also be the second one which compulsory does more than that, while simple straight forward way to do this still does not exist, namely something similar to C# nameof , that would do exactly that without needing to evaluate anything. Thinking if I could hack this for it:

Interesting idea, for sure.

What I don’t like about it is that it encourages a coding style that binds multiple different values to the same name. Inevitably, one of those values is going to be badly named.

E.g. you might see

At point (1) , the value of sth.this_must_be_lowercase violates requirements: The value is in fact not guaranteed lowercase.

I would rather it was written such that different values have different names. Like this:

This is a fairly marginal coding style distinction, and not something I would usually make a fuss about. But I do think the latter style is marginally better, and it would be unfortunate to add syntax that encourages the former style.

I am -1 to using @ for any of the purposes outlined here, but +1 to long_name as x = foo(x) being sugar for long_name = foo('long_name') .

It should work the same for dotted names and tuples, i.e. a.b. c as x = foo(x) would be sugar for a.b. c = foo('a.b.c') and (a,*b, c) as x = foo(x) would be sugar for (a,*b, c) = foo('a, *b, c') - note my intentional inconsistent whitespace to demonstrate that whitespace ought to be normalised when generating the string representation.

Outer parens in tuple assignment would be required to make it clearly unambiguous; a, b as x would be a syntax error but might later (as a separate proposal) be allowed in order to let x be 'b' . I’m -0 on allowing this from the beginning; probably better to see how the general idea works out before adding in this complication.

I would prefer that this feature be limited to passing the string representation of some name to a function (or otherwise using it in an expression), as that’s an actually important use case (namedtuple, TypeVar). My dislike of using this for accessing the target object itself is pretty much what @AndersMunch has already explained: it’s far less confusing to just use a regular ol’ assignment for that.

“Python is not Perl” indeed!

IMO another good (also English-specific) mnemonic is “at rhymes with that”, and reading @ as “that” is a pretty natural way to speak the statement.

I suspect that I’m in the minority here but I personally found figuring out what assignment target means (i.e. whatever’s on the the left of the = operator) to be more confusing than mapping a punctuation character to the words assignment target . So perlish or not, to me eliminating @ isn’t really fixing anything.

TBH, in this version of the proposal you don’t get much for TypeVar . You are changing:

if you make up a name and copy it over you’re getting more verbosity than the original and still have repetition. Same for namedtuples. That’s whay I didn’t worry too much about the string representation when I covered this, I was more focused on Alyssa’s idea of avoiding re-evaluation of attribute accesses/slicing.

We have __set_name__ so descriptors know their own name. Have we thought of extending __set_name__ to work on objects? Or perhaps there is a reason why it’s not done? I think it would be neat if objects could capture the lhs as a name like in descriptors

(post deleted by author)

Allowing __target__ on the LFS would make the syntax ambiguous when there is also __target__ on the RHS:

Does __target__[:2] on the RHS become foo[:2] or foo[2:][:2] ?

Assignment to a name does not modify an object in-place. It merely sets the name on the LHS to a new reference to the object on the RHS. So __target__ = hex(v) would just set the local name v to a new object for that iteration rather than updating the entry in mydict , which will still hold a reference to the original value.

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Assign and check a variable in a single line in Python? [duplicate]

Sometimes, in C, I like to assign and check a conditional variable on the same line - mostly for the purposes of self-documentation while isolating a code portion (e.g. instead of just writing if ( 1 ) { ... } ), without having to write an #ifdef . Let me give an example:

This does what you'd expect: if you have if ( (mytest = true) ) { , the output of the program is:

... while if you write if ( (mytest = false) ) { , the output of the program is:

(This seems like a standard technique, considering that leaving out the inner parentheses in the if might cause " warning: using the result of an assignment as a condition without parentheses [-Wparentheses] ")

So, I was wondering if there is an equivalent syntax in Python?

The naive approach does not seem to work:

... however, for a long time I also thought Python did not have a syntax for a ternary expression, but then it turned out, it has - which is why I'm asking this question.

• 3 I think you're looking for the walrus operator := –  rdas Commented Dec 8, 2021 at 6:06

Till Python 3.8, there was no way to do this, as statements in Python don't have a return value, so it was invalid to use them where an expression was expected.

Python 3.8 introduced assignment expressions , also called walrus operator:

Not the answer you're looking for? Browse other questions tagged python syntax or ask your own question .

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