python while with variable assignment

How to use Python While with Assignment[4 Examples]

In this Python tutorial, you will learn “ How to use Python While with Assignment “, with multiple examples and different approaches.

While working on a project, I was assigned to optimise the code. In the research, I found that we can assign variables within a while loop in Python. This helps to reduce some lines of code.

Generally, we use the ” = “ assignment operator to assign the variable in Python. But we will also try the walrus operator “:= “ to assign the variable within the while loop in Python.

Let’s understand every example one by one with some practical scenarios.

Table of Contents

Python while loop with the assignment using the “=” operator

First, we will use the “=” operator, an assignment operator in Python. This operator is used to assign a value to a variable, and we will assign a variable inside the loop in Python using the ” = “ operator.

Let’s see how Python, while with assignment, works.

In the above code, we always initialize the while loop with the true condition. “ while True :” means it will iterate infinite times and then assign the variable ‘line = “Hello” inside the while loop with one default string value.

Then assigned i = 0 , and again initialize nested while loop “print(line[i],’-‘,ord(line[i])) ” to target every character one by one and print its ascii value using ord() method in Python

Python assign variables in the while condition using the walrus operator

We can also use the walrus operator ” := “, a new assignment expression operator introduced in the 3.8 version of Python. It can create a new variable inside the expression even if that variable does not exist previously.

Let’s see how we can use it in a while loop:

Python assign variable in while condition using walrus operator

In the above code, we have a list named capitals. Then we initialize a while loop and create current_capital , giving a value as capitals.pop(0) , which means removing the first element in every iteration using the walrus operator like this: ‘current_capital:= capitals.pop(0)) != “Austin” .

When it iterates at “Austin”, the condition will be False because “Austin” != “Austin” will return false, and the loop will stop iterating.

Python while with assignment by taking user input

Here, we will see how Python While with Assignment will work if we take user input with a while loop using the walrus operator in Python.

python while assignment with walrus operator in python

In the above code, we are initializing a variable with a while loop and taking user input for an integer. Then, it will check whether the number is even or odd using the % operator.

Look at how it asks the user to enter the number repeatedly because we are not giving a break statement anywhere so it will work infinite times.

Python While with Assignment by calling the function

Now, we will see how to assign a variable inside a while loop in Python by calling the function name. We will create a user-defined function so you will understand how it is working.

How Python While with Assignment can be created by calling the user-defined function.

Python While with Assignment by calling the function

In the above code, we create a function called get_ascii_value() . This function takes a string as a parameter and returns the ASCII value of each character.

Then, we initialize a while loop, taking user_input as a string. The result variable calls a function and returns a character and ASCII value in a dictionary datatype.

In this Python article, you learned how to use Python while with assignment with different approaches and examples. We tried to cover all types of scenarios, such as creating user-defined functions , assigning within a while loop, taking user input, etc.

Writing a Python While Loop with Multiple Conditions

Introduction, review of python while loops, python while loop with one condition, python while loop multiple conditions, logical or operator, evaluating multiple conditions, grouping multiple conditions.

Iteration is the process by which a program repeats a series of steps a certain number of times or while a certain condition is met. These two forms of iteration are known as definite (often implemented using a for loop and Python next function) and indefinite (often implemented using a while loop).

In this article, you'll take a more advanced look at indefinite iteration in Python. More specifically, you'll learn how to write a Python while loop with multiple conditions. Let's get started!

In Python, indefinite iteration generally takes the following form:

You initiate the loop with the while keyword, then set the condition to be any conditional expression. A conditional expression is a statement that evaluates to True or False . As long as the condition is true, the loop body (the indented block that follows) will execute any statements it contains. As soon as the condition evaluates to false, the loop terminates and no more statements will be executed.

The simplest while loops in Python are while True and while False :

In this case, the condition is always True, so the loop will run forever. In order to stop it, you'll need to close the interpreter or send Python a KeyboardInterrupt by pressing CTRL+C.

A "while True" loop is always true and runs forever. A "while False" loop, on the other hand, is never true:

Since the condition is never true to begin with, the loop body never executes, and no statement is printed.

One of the most common uses of while loops in Python is to execute a series of steps a specific number of times. Often, you'll use a “counter” variable to control how many times the loop executes. For example:

Here, you define a counter and set it to the number 5. As long as the counter is greater than zero, you want the loop body to execute, so your conditional expression is count > 0 . The output shows that the loop body successfully executes for the five iterations where the value of count is greater than zero. Each iteration prints the current value of count, then decrements count by one. On the final iteration, count is now equal to zero, and the conditional expression is now false. The while loop terminates, and the loop body stops execution.

In this example, you know the value of the counter ahead of time. However, while loops are a form of indefinite iteration, and that usually means you won't know how many times the loop body should execute beforehand. You may want to change this value depending on the goals of your code, how many records are in your database, or how soon you want your machine learning algorithm to converge. The possibilities are endless, but the underlying while loop structure is much the same.

Let's define a variable max_iterations to simulate an unknown number of iterations:

The Python random module has a method .randint() that allows you to choose any random integer between two numbers. Here, you set the max number of iterations to a random number between 0 and 10.

You don't know what the number is, but you can use a while loop to find out. To do this, set your counter to 0. The conditional expression will be "while the value of the counter is less than the maximum number of iterations." Here's what that looks like in code:

On each iteration, Python will check to see if the current value of count is less than the maximum number of iterations. If this is true, then the loop body executes. You can see here that each iteration displays the value of both variables and increments the counter by one. On the last iteration, where count is equal to max_iterations , the conditional expression becomes false and the loop terminates.

You can verify the value of max_iterations like so:

So far, you've seen how Python while loops work with one conditional expression. Python compares the two values and if the expression evaluates to true, it executes the loop body. However, it's possible to combine multiple conditional expressions in a while loop as well.

To combine two conditional expressions into one while loop, you'll need to use logical operators . This tells Python how you want all of your conditional expressions to be evaluated as a whole.

Logical AND Operator

The first logical operator is and . Here's how you would use it to combine multiple conditional expressions:

Here, the while loop has two conditional expressions, A and B, that it needs to evaluate. The and operator says to evaluate them separately and then consider their results as a whole. If (and only if) both A and B are true, then the loop body will execute. If even one of them is false, then the entire while statement evaluates to false and the loop terminates.

Let's take a look at a code example.

You define two new variables, a and b , which are equal to 5 and 10, respectively. Again, you've defined a counter and set it to zero. The while statement now contains two conditional expressions. The first checks to see if count is less than a , and the second checks to see if count is less than b . The logical operator and combines these two conditional expressions so that the loop body will only execute if both are true.

The loop runs for five iterations, incrementing count by 1 each time. In the last iteration, where the loop terminates, the value of count is now 5, which is the same value as a . This means the first conditional expression is now false (5 is not less than 5), so the while loop terminates.

Note that the second condition count < b is still true, even when count is 5. What if you wanted the loop to keep running as long as count is less than one of the variables?

Logical or is the second operator you can use to combine multiple conditional expressions:

There are still two conditional expressions, A and B, that need to be evaluated. Like logical AND, the or operator says to evaluate them separately and then consider their results as a whole. Now, however, the loop body will execute if at least one of the conditional expressions is true. The loop will only terminate if both expressions are false.

Let's take a look at the previous example. The only change is that the while loop now says or instead:

Now, the loop doesn't stop after the count variable reaches the same value as a . That's because the second condition, count < b , still holds true. Since at least one condition is true, the loop body continues to execute until count reaches a value of 10. Then, the second condition evaluates to false as well, and the loop terminates.

Logical NOT Operator

There's one other logical operator that you can use to write Python while loops with multiple conditions, and that's logical not :

This operator simply reverses the value of a given boolean expression. In other words, not True will return false, and not False will return true.

Let's add a third conditional expression to the code. Earlier, you defined the random variable max_iterations to control the execution of the loop body. Define this again to set a new random number and add it to the while statement:

This Python while loop has multiple conditions that all need to be evaluated together. The first condition checks whether count is less than a . The second condition checks whether count is less than b . The third condition uses the logical not operator to check that the value of count has not reached the maximum number of iterations. Python has set this value to a random number (here, max_iterations was 8). So, the loop runs for eight iterations and then terminates - but which condition caused this?

Python evaluates conditional expressions from left to right, comparing two expressions at a time. One can use parentheses to better visualize how this works:

Python groups the first two conditional expressions and evaluates them together. In the code block above, count is compared to a and then to b . The logical or operator that combines these two statements says to return true if either one of them is true. This is the case for all eight iterations of the loop ( count is always less than a or b ).

Next, Python combines the result of the first evaluation with the third condition:

Here, RESULT will either be true or false. Python uses the logical and operator to combine this with the third condition. If both are true, then the loop body will continue to execute. If one is false, then the loop will terminate.

Truth tables are a great tool to help visualize how a Python while loop with multiple conditions will evaluate each one.

Note that the loop terminates on the ninth iteration. This is something you may not see in your terminal output. That's because the ninth iteration causes the while statement to evaluate to false. Here, count is still less than a or b , but the maximum number of iterations has been reached. Even though the second condition is still true, the logical and requires the third condition to be true as well. Since it's false, the loop body terminates.

Parentheses can help you to see how Python evaluates while loops with multiple conditions, but they can also be used to control how Python evaluates multiple conditional expressions.

Recall the previous example:

Here, you used parentheses to visualize how Python evaluates conditional expressions from left to right. You can also use parentheses directly in your code to tell Python which expressions should be considered together first.

Let's set max_iterations to a smaller number to see this in action. Set it to 3 and start the while loop again, grouping the first two expressions in parentheses:

The parentheses tells Python to group the first two statements together, then to combine their result with whatever statement comes next. You've now reduced the multiple conditions into two groups: "count is less than a or b AND count is not greater than or equal to max_iterations." This statement evaluates to false after the third iteration, so the loop stops here.

What happens if you move the parentheses and group the last two statements together? Let's take a look:

Here, the loop runs for 5 iterations, which is far above the maximum number of 3. What happened?

Python still evaluates the conditional expressions from left to right. First, it checks to see whether count is less than a . If this is true, Python then moves to the next statement, which is a group of two conditional expressions. Python then considers this group as a whole, and combines the result with the first conditional statement. The multiple conditions are again reduced into two groups: "count is less than a OR count is less than b and not greater than or equal to max_iterations." When count is 3 - the maximum number of iterations - the expression still evaluates to true due to the logical or . The loop runs until both the expression before the logical or and the grouped expressions after it cause the while statement to evaluate to false.

In this article, you learned how to write a Python while loop with multiple conditions.

You reviewed indefinite iteration and how while statements evaluate conditional expressions. Then, you saw how to use logical operators to combine multiple conditions in while statement. Lastly, you looked at how grouping multiple conditions together in parentheses can help both to visualize statement evaluation and control loop body execution.

If you're interested in learning more about the basics of Python, coding, and software development, check out our Coding Essentials Guidebook for Developers , where we cover the essential languages, concepts, and tools that you'll need to become a professional developer.

Indefinite iteration is one of the first techniques you'll learn on your Python journey. For other beginning Python tutorials, check out our guides on functions in Python 3 , the Python min() function , and the Python floor() function .

Thanks and happy coding! We hope you enjoyed this article. If you have any questions or comments, feel free to reach out to [email protected] .

Final Notes

Recommended product: Coding Essentials Guidebook for Developers

Python 3 functions - learn python programming tutorial.

Ultimate Guide to the Python min() Function

A detailed look at the python floor() function, get the first 5 chapters of our coding essentials guidebook for developers free , the programming guide i wish i had when i started learning to code... 🚀👨‍💻📚.

Image of the cover of the Coding Essentials Guidebook for Developers

Check out our Coding Essentials Guidebook for Developers

Learn Python practically and Get Certified .

Python Flow Control

Python if...else Statement

Python for Loop

Python while Loop

Python break and continue

Python pass Statement

Python Data types

Python Numbers, Type Conversion and Mathematics
Python List
Python Tuple
Python Sets
Python Dictionary
Python Functions
Python Function Arguments
Python Variable Scope
Python Global Keyword
Python Recursion
Python Modules
Python Package
Python Main function (programiz.com)

Python Files

Python Directory and Files Management
Python CSV: Read and Write CSV files (programiz.com)
Reading CSV files in Python (programiz.com)
Writing CSV files in Python (programiz.com)
Python Exception Handling
Python Exceptions
Python Custom Exceptions

Python Object & Class

Python Objects and Classes
Python Inheritance
Python Multiple Inheritance
Polymorphism in Python(with Examples) (programiz.com)
Python Operator Overloading

Python Advanced Topics

List comprehension
Python Lambda/Anonymous Function
Python Iterators
Python Generators
Python Namespace and Scope
Python Closures
Python Decorators
Python @property decorator
Python RegEx

Python Date and Time

Python datetime
Python strftime()
Python strptime()
How to get current date and time in Python?
Python Get Current Time
Python timestamp to datetime and vice-versa
Python time Module
Python sleep()

Additional Topic

Python Keywords and Identifiers
Python Asserts
Python Json
Python *args and **kwargs (With Examples) (programiz.com)

Python Tutorials

Python Looping Techniques

Python range() Function

In Python, we use the while loop to repeat a block of code until a certain condition is met. For example,

In the above example, we have used a while loop to print the numbers from 1 to 3 . The loop runs as long as the condition number <= 3 is satisfied.

while Loop Syntax
The while loop evaluates the condition.
If the condition is true, body of while loop is executed. The condition is evaluated again.
This process continues until the condition is False .
Once the condition evaluates to False , the loop terminates.
Flowchart of Python while Loop

Example: Python while Loop

Here is how the above program works:

It asks the user to enter a number.
If the user enters a number other than 0 , it adds the number to the total and asks the user to enter a number again.
If the user enters 0 , the loop terminates and the program displays the total.
Infinite while Loop

If the condition of a while loop is always True , the loop runs for infinite times, forming an infinite while loop . For example,

The above program is equivalent to:

Video: Python while Loop

Sorry about that.

Python Enhancement Proposals

Python »
PEP Index »

PEP 572 – Assignment Expressions

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

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

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

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

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

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

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

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

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

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

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

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

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

they would write:

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

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

Syntax and semantics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Examples from the Python standard library

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

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

Avoid nested if and remove one indentation level.

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

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

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

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

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

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

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

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

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

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

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

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

An example from the low-level UNIX world:

Rejected alternative proposals

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

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

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

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

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

Additional reasons to prefer := over this spelling include:

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

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

NAME = EXPR
if NAME := EXPR

reinforces the visual recognition of assignment expressions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Frequently Raised Objections

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

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

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

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

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

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

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

Appendix A: Tim Peters’s findings

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

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

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

is a vast improvement over the briefer:

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

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

as the briefer:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Let’s add a simple assignment expression.

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

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

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

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

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

Finally, let’s nest two comprehensions.

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

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

This document has been placed in the public domain.

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

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

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! 😊

Python Variables and Assignment

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

Python Numerical Methods

This notebook contains an excerpt from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists , the content is also available at Berkeley Python Numerical Methods .

The copyright of the book belongs to Elsevier. We also have this interactive book online for a better learning experience. The code is released under the MIT license . If you find this content useful, please consider supporting the work on Elsevier or Amazon !

< 2.0 Variables and Basic Data Structures | Contents | 2.2 Data Structure - Strings >

Variables and Assignment ¶

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

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

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

TRY IT! List all the variables in this notebook

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

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

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

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

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

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

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

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

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

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

Python Tutorial

File handling, python modules, python numpy, python pandas, python matplotlib, python scipy, machine learning, python mysql, python mongodb, python reference, module reference, python how to, python examples, python variables, varia b l e s.

Variables are containers for storing data values.