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A for loop is used for iterating over a sequence (that is either a list, a tuple, a dictionary, a set, or a string).

This is less like the for keyword in other programming languages, and works more like an iterator method as found in other object-orientated programming languages.

With the for loop we can execute a set of statements, once for each item in a list, tuple, set etc.

Print each fruit in a fruit list:

The for loop does not require an indexing variable to set beforehand.

Looping Through a String

Even strings are iterable objects, they contain a sequence of characters:

Loop through the letters in the word "banana":

The break Statement

With the break statement we can stop the loop before it has looped through all the items:

Exit the loop when x is "banana":

Exit the loop when x is "banana", but this time the break comes before the print:

The continue Statement

With the continue statement we can stop the current iteration of the loop, and continue with the next:

Do not print banana:

The range() Function

The range() function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and ends at a specified number.

Using the range() function:

Note that range(6) is not the values of 0 to 6, but the values 0 to 5.

The range() function defaults to 0 as a starting value, however it is possible to specify the starting value by adding a parameter: range(2, 6) , which means values from 2 to 6 (but not including 6):

Using the start parameter:

The range() function defaults to increment the sequence by 1, however it is possible to specify the increment value by adding a third parameter: range(2, 30, 3 ) :

Increment the sequence with 3 (default is 1):

Else in For Loop

The else keyword in a for loop specifies a block of code to be executed when the loop is finished:

Print all numbers from 0 to 5, and print a message when the loop has ended:

Note: The else block will NOT be executed if the loop is stopped by a break statement.

Break the loop when x is 3, and see what happens with the else block:

Nested Loops

A nested loop is a loop inside a loop.

The "inner loop" will be executed one time for each iteration of the "outer loop":

Print each adjective for every fruit:

The pass Statement

for loops cannot be empty, but if you for some reason have a for loop with no content, put in the pass statement to avoid getting an error.

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Loop through the items in the fruits list.

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In Python, a for loop is used to iterate over sequences such as lists , strings , tuples , etc.

In the above example, we have created a list called languages . As the list has 3 elements, the loop iterates 3 times.

The value of i is

Swift in the first iteration.
Python in the second iteration.
Go in the third iteration.
for loop Syntax

Here, val accesses each item of the sequence on each iteration. The loop continues until we reach the last item in the sequence.

Flowchart of Python for Loop

Example: Loop Through a String

Here, we have printed each character of the string language using a for loop.

for Loop with Python range()

In Python, the range() function returns a sequence of numbers. For example,

Here, range(4) returns a sequence of 0 , 1 , 2 ,and 3 .

Since the range() function returns a sequence of numbers, we can iterate over it using a for loop. For example,

Here, we used the for loop to iterate over a range from 0 to 3 .

This is how the above program works.

Video: Python for Loop

Sorry about that.

How to Use For Loops in Python: Step by Step

By the end of this tutorial, you will be able to write and use for loops in various scenarios.

[Featured image] Two coders work on using for loop in python on a monitor and a laptop.

How to write a for loop in python, try it yourself, how do for loops work in python, how to break out of a for loop in python, 2. continue, ways to use a for loop in python, looping through a string to print individual characters, iterating over a list or tuple, nesting for loops, using else block with python for loop, key takeaways, keep improving your python skills with coursera..

Materials Required: Latest version of Python ( Python 3 ), an integrated development environment (IDE) of your choice (or terminal), stable internet connection

Prerequisites/helpful expertise: Basic knowledge of Python and programming concepts

For loops are control flow tools. They are used to iterate over objects or sequences—like lists, strings, and tuples. You may use a for loop whenever you have a block of code you want to execute repeatedly.

First, let’s examine the basic structure of a for loop in Python:

for and in are both Python keywords, but you can name your iterator variable and iterable whatever you'd like. Remember to include a colon after your for loop statement, and indent code included in the body of the loop.

Now that we know the syntax, let’s write one.

Tell Python you want to create a for loop by starting the statement with for .

Write the iterator variable (or loop variable). The iterator takes on each value in an iterable (for example a list, tuple, or range) in a for loop one at a time during each iteration of the loop.

Example: Suppose you have a list called box_of_kittens [😾😺😼😽] as your iterable. You could name your iterator variable anything you want, but you might choose to call it 'kitten' to reference that you'll be looping through each individual kitten [😺] in box_of_kittens.

Use the keyword in .

Add the iterable followed by a colon. The iterable (or sequence variable) is the object you will iterate through. In the example above where the loop variable is a kitten, the sequence variable is box_of_kittens because it represents the grouping the single variable is chosen from.

Write your loop statements in an indented block. The indentation lets Python know which statements are inside the loop and which statements are outside the loop.

Solution 1 Expand to view solution

PYTHON Windows numbers = [1,2,3,4,5,6,7,8,9,10] for num in numbers: print(num) 1 2 3 4 numbers = [1,2,3,4,5,6,7,8,9,10] for num in numbers: print(num)

Solution 2 Expand to view solution

PYTHON Windows for num in range(1,11): print(num) 1 2 for num in range(1,11): print(num)

There are three main ways to break out of a for loop in Python:

The break keyword is used to exit a loop early when a certain condition is met. It terminates the loop that contains it and redirects the program flow to the next statement outside the loop.

Does break work for nested loops?

Learn more: How to Use Python Break

You can use the keyword continue to end the current iteration of a for loop. The control flow will continue on to the next iteration.

The pass statement in Python intentionally does nothing. It can be used as a placeholder for future code or when a statement is required by syntax but you don’t want anything to happen.

In the context of a for loop, you can use a pass statement to disregard a conditional statement. The program will continue to run as if there were no conditional statement at all.

A for loop is a general, flexible method of iterating through an iterable object. Any object that can return one member of its group at a time is an iterable in Python. The sections below outline a few examples of for loop use cases.

In this type of for loop, the character is the iterator variable and the string is the sequence variable.

Solution Expand to view solution

In this example, a for loop iterates through each character in the string string from beginning to end. For each character, it concatenates that character to the beginning of the variable reversed_string. By the end of the loop, reversed_string will contain the original string in reverse order.

When iterating over a list or tuple, the basic syntax of a for loop remains the same. However, instead of using a character and a string, you’d use your iterator variable to represent a single element and an iterable variable to represent the list or tuple it belongs to.

You can define your list and a tuple like this:

Then build your for loop:

PYTHON Windows for number in numbers: sum += number 1 2 for number in numbers: sum += number

A nested for loop is a loop inside of a loop. The innermost loop will execute once for each outer loop iteration. Here’s the structure of a nested for loop:

PYTHON Windows for sublist in main_list: for animal in sublist: print(animal) 1 2 3 for sublist in main_list: for animal in sublist: print(animal)

Learn more: How to Use Python If-Else Statements

For loops are used to iterate over objects or sequences.
Any object that can return one member of its group at a time is an iterable in Python.
There are three control statements you can use to break out of a for loop or skip an iteration in Python: break, continue, and pass.
Indentation tells Python which statements are inside or outside of the loop.
Being a Python Developer: What They Can Do, Earn, and More
Python control flow tools documentation
How to Use Python Break
Python Loops Cheat Sheet

You can practice working with for loops with a Guided Project like Concepts in Python: Loops, Functions, and Returns . Or, take the next step in mastering the Python language and earn a certificate from the University of Michigan in Python 3 programming .

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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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 !

< CHAPTER 5. Iteration | Contents | 5.2 While Loops >

For-Loops ¶

A for-loop is a set of instructions that is repeated, or iterated, for every value in a sequence. Sometimes for-loops are referred to as definite loops because they have a predefined begin and end as bounded by the sequence.

The general syntax of a for-loop block is as follows.

CONSTRUCTION : For-loop

A for-loop assigns the looping variable to the first element of the sequence. It executes everything in the code block. Then it assigns the looping variable to the next element of the sequence and executes the code block again. It continues until there are no more elements in the sequence to assign.

TRY IT! What is the sum of every integer from 1 to 3?

WHAT IS HAPPENING?

First, the function range(1, 4) is generating a list of numbers beginning at 1 and ending at 3. Check the description of the function range and get familiar with how to use it. In a very simple form, it is range(start, stop, step) , and the step is optional with 1 as the default.

The variable n is assigned the value 0.

The variable i is assigned the value 1.

The variable n is assigned the value n + i ( \(0 + 1 = 1\) ).

The variable i is assigned the value 2.

The variable n is assigned the value n + i ( \(1 + 2 = 3\) ).

The variable i is assigned the value 3.

The variable n is assigned the value n + i ( \(3 + 3 = 6\) ).

With no more values to assign in the list, the for-loop is terminated with n = 6 .

We present several more examples to give you a sense of how for-loops work. Other examples of sequences that we can iterate over include the elements of a tuple, the characters in a string, and other sequential data types.

EXAMPLE: Print all the characters in the string "banana" .

Alternatively, you could use the index to get each character. But it is not as concise as the previous example. Recall that the length of a string could be determined by using the len function. And we could ignore the start by only giving one number as the stop.

EXAMPLE : Given a list of integers, a , add all the elements of a .

The Python function sum has already been written to handle the previous example. However, assume you wish to add only the even numbers. What would you change to the previous for-loop block to handle this restriction?

NOTE! We use step as 2 in the range function to get the even indexes for list a . Also, a Python shortcut that is commonly used is the operator += . In Python and many other programming languages, a statement like i += 1 is equivalent to i = i + 1 and same is for other operators as -= , *= , /= .

Example Define a dictionary and loop through all the keys and values.

In the above example, we first get all the keys using the method keys , and then use the key to get access the value. Alternatively, we could use the item method in a dictionary, and get the key and value at the same time as show in the following example.

Note that, we could assign two different looping variables at the same time. There are other cases that we could do things similarly. For example, if we have two lists with same length, and we want to loop through them, we could do as the following example using the zip function:

EXAMPLE: Let the function have_digits has the input as a string. The output out should take the value 1 if the string contains digits, and 0 otherwise. You could use the isdigit method of the string to check if the character is a digit.

The first step in the function have_digits assumes that there are no digits in the string s (i.e., the output is 0 or False).

Notice the new keyword break . If executed, the break keyword immediately stops the most immediate for-loop that contains it; that is, if it is contained in a nested for-loop, then it will only stop the innermost for-loop. In this particular case, the break command is executed if we ever find a digit in the string. The code will still function properly without this statement, but since the task is to find out if there are any digit in s , we do not have to keep looking if we find one. Similarly, if a human was given the same task for a long string of characters, that person would not continue looking for digits if he or she already found one. Break statements are used when anything happens in a for-loop that would make you want it to stop early. A less intrusive command is the keyword continue , which skips the remaining code in the current iteration of the for-loop, and continues on to the next element of the looping array. See the following example, that we use the keyword continue to skip the print function to print 2:

EXAMPLE: Let the function my_dist_2_points(xy_points, xy) , where the input argument xy_points is a list of x-y coordinates of a point in Euclidean space, xy is a list that contain an x-y coordinate, and the output d is a list containing the distances from xy to the points contained in each row of xy_points .

Just like if-statements, for-loops can be nested.

EXAMPLE: Let x be a two-dimensional array, [5 6;7 8]. Use a nested for-loop to sum all the elements in x .

s , representing the running total sum, is set to 0.

The outer for-loop begins with looping variable, i, set to 0.

Inner for-loop begins with looping variable, j, set to 0.

s is incremented by x[i,j] = x[0,0] = 5. So s = 5.

Inner for-loop sets j = 1.

s is incremented by x[i,j] = x[0,1] = 6. So s = 11.

Inner for-loop terminates.

Outer for-loop sets i = 1.

s is incremented by x[i,j] = x[1,0] = 7. So s = 18.

s is incremented by x[i,j] = x[1,1] = 8. So s = 26.

Outer for-loop terminates with s = 26.

WARNING! Although possible, do not try to change the looping variable inside of the for-loop. It will make your code very complicated and will likely result in errors.

21 Python for Loop Exercises and Examples

In Python programming, we use for loops to repeat some code a certain number of times. It allows us to execute a statement or a group of statements multiple times by reducing the burden of writing several lines of code.

To get a clear idea about how a for loop works, I have provided 21 examples of using for loop in Python. You can go through these examples and understand the working of for loops in different scenarios.

Let’s dive right in.

1. Python for loop to iterate through the letters in a word

2. Python for loop using the range() function

3. Python for loop to iterate through a list

4. Python for loop to iterate through a dictionary

5. Python for loop using the zip() function for parallel iteration

6. Using else statement inside a for loop in Python

7. Nested for loops in Python (one loop inside another loop)

8. Using break statement inside a for loop in Python

9. Using continue statement inside a for loop in Python

10. Python for loop to count the number of elements in a list

11. Python for loop to find the sum of all numbers in a list

12. Python for loop to find the multiples of 5 in a list

13. Python for loop to print a triangle of stars

14. Python for loop to copy elements from one list to another

15. Python for loop to find the maximum element in a list

16. Python for loop to find the minimum element in a list

17. Python for loop to sort the numbers in a list in ascending order

18. Python for loop to sort the numbers in a list in descending order

19. Python for loop to print the multiples of 3 using range() function

20. Python for loop to print the multiples of 5 using range() function

21. Python for loop to print the numbers in reverse order using range() function

I hope this article was helpful. Check out my post on 18 Python while Loop Examples .

I'm the face behind Pythonista Planet. I learned my first programming language back in 2015. Ever since then, I've been learning programming and immersing myself in technology. On this site, I share everything that I've learned about computer programming.

3 thoughts on “ 21 Python for Loop Exercises and Examples ”

we need some more difficult problems. please give some different questions.

These are some examples of implementing for loop in Python. If you want more coding problems, check out this post: https://pythonistaplanet.com/python-programming-exercises-and-solutions/

myfamily = { “child1” : { “name” : “Emil”, “year” : 2004 }, “child2” : { “name” : “Tobias”, “year” : 2007 }, “child3” : { “name” : “Linus”, “year” : 2011 } }

Find the child the year greater than 2007. Output = child3

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Flow Diagram

For Loop with Range
For Loop with List
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break For Loop
continue For Loop
For Loop with Else Block
. Nested For Loop

Python For Loop can be used to iterate a set of statements once for each item of a sequence or collection.

The sequence or collection could be Range, List, Tuple, Dictionary, Set or a String.

In this tutorial, we will learn how to implement for loop for each of the above said collections. Going forward, we have detailed example programs with Python For Loop .

The syntax of For Loop in Python is

You can access the item variable inside for block. iterable could be a sequence or collection.

The flow chart of Python For Loop is

When program execution enters For loop for the first time, it checks if there is an item from iterable.
Execute the statement(s) inside the For loop block.
Go to step 2 .
The For loop is marked completed, and the execution continues with the next statements in the program.

1. For Loop with Range

In this example, we will use a For loop to iterate over a range of numbers.

Python Program

The range is from 25 until 29. So, the range has items: 25, 26, 27 and 28. The statements inside the For loop are executed for each of these elements.

For these examples, the body of For loop has only one print statement. But, you may write as many statements as required by following indentation .

2. For Loop with List

In this example, we will take a list and iterate over the items of list using For loop.

Please note that, during each iteration, we are able to access the item for which the loop is running. In this case, we are able to access the item using variable x .

3. For Loop with Tuple

In this example, we will take a tuple and iterate over the items of tuple.

The same explanation holds here as well. We could access this item of tuple in For loop using variable x .

4. For Loop with Dictionary

In this example, we will take a dictionary and iterate over the key:value pairs of the dictionary using For loop.

my_dictionary variable returns an iterable to the keys of the dictionary. Therefore, we could get only key into x . But using x as key we are accessing the value from dictionary inside for block.

5. For Loop with Set

In this example, we will take a set of strings, and iterate over each of the items in the set using For loop.

As set is an unordered collection, the order in which For loop accesses the next item from the set is not obvious.

6. For Loop with String

String is a collection of characters. As long as we can get an iterable for the object, we can use For loop.

In this example, we will take a string and iterate over the characters using For loop.

7. break For Loop

From the syntax and flow diagram, we know that For loop will be over only after executing statement(s) for all the elements in the iterable. But, we can break the For loop and end it before it has actually run for all the elements in the iterable using break statement.

In the following program, we shall break the For loop, when the value of element is 7. To realize this, we will use a if statement and execute break statement when the condition evaluates to True.

If there had not been the break statement, For loop would have executed until x is 10.

8. continue For Loop

We can skip the execution of further statements in the For loop body, during that iteration, using continue statement. When continue statement is executed, the For loop continues with the execution of next element in the iterable, rather than completing all the statements in the For loop body.

In the following example program, we shall continue the For loop, when the value of element is 7 .

When x is 7 , continue statement is executed. The print statement after the continue statement in the For loop has been skipped and continued with the next element in the loop, which is 8 .

9. For Loop with Else Block

Just like in an if-else statement, you can use an else block with For loop. This else block is executed after the For loop is completed with all the elements in the iterable.

In the following example, we shall write an else block with For loop.

Complete tutorial: Python for else

10. Nested For Loop

Python For Loop is just like another Python command or statement. So, we can write a For loop inside another For loop and this is called nesting. For loop inside another For loop is called Nested For Loop.

In the following program, we shall write a nested For loop, to print a pattern of numbers to the console.

In this tutorial, we learned to use Python For Loop on different collections, and with statements like break, continue, else block, etc., using well detailed examples.

Frequently Asked Questions

For Loop is used to execute a block of code for each item in an iterable.

Consider that we are given a list of string items. And our requirement is to print the length of each string in the given list. We can use a For Loop to iterate over the string items in the list, and for each item, we can execute the code that prints the length of the string.

The syntax of For loop in Python is

The steps in a Python For Loop are:

Check if there is an item from iterable.

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

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Assignment Operators in Programming

Assignment operators in programming are symbols used to assign values to variables. They offer shorthand notations for performing arithmetic operations and updating variable values in a single step. These operators are fundamental in most programming languages and help streamline code while improving readability.

Table of Content

What are Assignment Operators?

Types of Assignment Operators
Assignment Operators in C
Assignment Operators in C++
Assignment Operators in Java
Assignment Operators in Python
Assignment Operators in C#
Assignment Operators in Javascript
Application of Assignment Operators

Assignment operators are used in programming to assign values to variables. We use an assignment operator to store and update data within a program. They enable programmers to store data in variables and manipulate that data. The most common assignment operator is the equals sign ( = ), which assigns the value on the right side of the operator to the variable on the left side.

Types of Assignment Operators:

Simple Assignment Operator ( = )
Addition Assignment Operator ( += )
Subtraction Assignment Operator ( -= )
Multiplication Assignment Operator ( *= )
Division Assignment Operator ( /= )
Modulus Assignment Operator ( %= )

Below is a table summarizing common assignment operators along with their symbols, description, and examples:

Assignment Operators in C:

Here are the implementation of Assignment Operator in C language:

Assignment Operators in C++:

Here are the implementation of Assignment Operator in C++ language:

Assignment Operators in Java:

Here are the implementation of Assignment Operator in java language:

Assignment Operators in Python:

Here are the implementation of Assignment Operator in python language:

Assignment Operators in C#:

Here are the implementation of Assignment Operator in C# language:

Assignment Operators in Javascript:

Here are the implementation of Assignment Operator in javascript language:

Application of Assignment Operators:

Variable Initialization : Setting initial values to variables during declaration.
Mathematical Operations : Combining arithmetic operations with assignment to update variable values.
Loop Control : Updating loop variables to control loop iterations.
Conditional Statements : Assigning different values based on conditions in conditional statements.
Function Return Values : Storing the return values of functions in variables.
Data Manipulation : Assigning values received from user input or retrieved from databases to variables.

Conclusion:

In conclusion, assignment operators in programming are essential tools for assigning values to variables and performing operations in a concise and efficient manner. They allow programmers to manipulate data and control the flow of their programs effectively. Understanding and using assignment operators correctly is fundamental to writing clear, efficient, and maintainable code in various programming languages.

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Newton method assignment

Hi I have this assignment for class

We are solving (approximately) the equation f(x) = 0, where f is a nice function.

Start by guessing the solution: x0
Locally replace the function f(x) with a straight line and find where this line intersects the x-axis. We denote this point as x1.
Iterate to your heart’s content. We want to make a more precise (n + 1)-estimate xn+1 of the root r of the equation f(x) = 0 from the n-th estimate xn.
In the vicinity of xn, replace f with a straight line (Taylor polynomial of the 1st degree, lol).
The equation of the line (y - yk) = f’0(xk)(x - xk).
We want xk+1 such that y = 0. Also, yk = f(xk).
-f(xk) = f’(xk)(xk+1 - xk) → xk+1 = xk - f(xk) / f’(xk). Just choose the initial estimate x0 and figure out when to stop iterating. Newton’s tangent method Create a function newton(fun, x0, tol = 6) that, using Newton’s tangent method, finds (and returns) an approximate solution to the equation fun = 0 with an initial guess of x0 and with precision 10-tol. Your function must not use any external libraries (especially math). Submit your solution as a single .py file containing the function newton(fun, x0, tol = 6) and any auxiliary functions (e.g., for differentiation).

I wrote this code for it

def derivative(f, x, tol = 6): h = 0.1 df = (f(x+h) - f(x)) / h est = 0 while abs((est - df)) >= 10 ** (-tol): h /= 2 est = df df = (f(x+h) - f(x)) / h return df

import math

def myFun(x): return math.cos(x)

def newton(fun, x0, tol=6): x = x0 - (fun(x0) / derivative(fun, x0)) while abs(x - x0) > 10 ** -tol: x0 = x if derivative(fun, x0) == 0: x = x0 - (fun(x0) / 10 ** -323) else: x = x0 - (fun(x0) / derivative(fun, x0)) return x

print(newton(myFun, 0))

is it correct?

Please fead this About the Python Help category and fix ypur post so that the codes indention is clear to read.

When you run the code does it give the right answers? If not what does it output and what did you expect. What have you done to investigate the problem? Do you suggestions on how to debug your code?

It looks okish. For these algorithms there are always many choices that can be made. You can have all sorts of stopping conditions.

The condition of comparing the derivative with zero is better to replace it with being close to zero. When that condition happen it could be good to check if the value of the function is already small enough. One could be near or at a multiple root of the function.

Likewise, the stopping condition could check if the value of the function is already small enough. You could have a function that is very flat and close or equal to zero, the values of x and x0 still not close together, but the value of the function is already sufficiently close to zero or zero.

The stopping condition could also involve the number of iterations. Sometimes Newton can enter an infinite loop, or too many iterations.

Perhaps the stopping conditions can be kept simple and sweep any problem under the “is a nice function” assumption.

You could pass the derivative as an argument. That way, if they have an explicit way to compute the derivative, then they can use it (without having to re-define your approximate derivative ): newton(x0, fun, dfun=derivative, tol=6)

In the approximate derivative, the stopping condition could also consider how small h is already. Suppose you have a function that changes a lot. Then it would take many iterations before est is close to df . The h could even become zero before this happens.