python assignment in for loop

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Assigning variables through a for loop

I'm trying to use a for loop to assign values to variables - in this case the variables are all called: 'die1', 'die2', 'die3', etc.

What i'm trying to achieve is ending up with 5 variables assigned with a random number. I've tried a few different ways but end up with either 'die' variable not been assigned or that I'm not able to use 'i' or another variable that I count up.

4 Answers 4

For your interpreter, die1 , die2 , etc. are unrelated entities. If they should be grouped as in your case, consider a list :

Now you can access die[0] , die[1] , etc. If you really need independent variables, you can use:

This, however, will raise an error if the number of variables on the left does not match the length of the list / tuple on the right. For flexibility reasons, I would recommend using a list.

Is there any possible way to not use a list, and just store them as seperate variable? I have a list later that looks like this: dice = [die1, die2, die3, die4, die5] And later functions are called as such: dice[i] With 'i' being controlled through a for loop – Tym Apr 9, 2016 at 13:01
This is the elegant solution. – totoro Apr 9, 2016 at 13:12
@Tym You could just create your dice list as indicated here. There should be no justifiable need to first create independent variables if you then stuff them into a list anyway. – user2390182 Apr 9, 2016 at 13:21
Thanks for the update :)! The reason I'm using them is just because I'm not very confident with lists... Poor excuse, I know... – Tym Apr 9, 2016 at 13:54
@Tym: Well you should get confident by practicing using lists! :) Write lots of tiny programs that use lists until you're comfortable with how they behave. Lists are very important, especially in Python, which has several powerful features for handling lists. When code has a bunch of numbered variables like die1, die2, die3, die4, die5 it almost always should be using a list or tuple (or possibly a dict) instead. Using a bunch of numbered variables makes the code bigger and more confusing than it needs to be, making it harder to develop & maintain. – PM 2Ring Apr 9, 2016 at 14:33

I'd suggest making a fixed size list that you assign to so it doesn't grow each time you call append .

Now you can call roll and the die list is updated.

Why is it bad for the list to grow? Isn't it worse to fill up memory with zeros unnecessarily? – zondo Apr 9, 2016 at 13:16
@zondo I am assuming he wants a list of 5 numbers. If using append each call to roll will append 5 new numbers to the list thereby growing it. The list of five numbers (initially zero) is updated with new values each call to roll. So there is no memory fill and no new list is created every time. – totoro Apr 9, 2016 at 13:20
No new list is created anyway. If you don't like the idea of a growing list, use a list comprehension: die = [random.randint(1, 6) for _ in range(5)] – zondo Apr 9, 2016 at 13:24
2 @ThomasChristensen Actually, append does not create a new list, at least not every time ;) . Appending is O(1): wiki.python.org/moin/TimeComplexity – user2390182 Apr 9, 2016 at 13:25
@zondo list comprehensions creates a new list each time it is evaluated right? – totoro Apr 9, 2016 at 13:28

Assuming the goal is to fill a list named die , then you can define it and append the integers at each iteration:

If you mean to different variables, you can define them dynamically using vars :

The line defining the local variables is not mandatory though it'll be easier to work with in an IDE.

If you mean to the first part, or to the second part with few variables, I think that schwobaseggl's solution is more elegant.

Thanks for the response! Mind me asking why you used '_' in the first for look for the looping variable, but 'i' for the second? Also - what's the importance of including vars() in the second part? – Tym Apr 9, 2016 at 13:52
In the first loop, i is not used - a common convention in python is to name this kind of variables with _ . – Elisha Apr 9, 2016 at 14:03

If the number of variables are large it might be not possible to mention all variable names at the beginning. Instead they could be created at each iteration of the for-loop and stored in a dictionary as keys while the random numbers associated with them can be stored as their values in the dictionary.

You could use string joining for creating variable names eg 'die'+'1' = 'die1' . If you call 'die1' as 'n' then you add a new key to the dictionary as Dict[n]=randint(1,6) where randint(1,6) is the value corresponding to 'die1'. This way you can easily use these variables for mathematical operations.

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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 for loops.

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.

Test Yourself With Exercises

Loop through the items in the fruits list.

Start the Exercise

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Python "for" Loops (Definite Iteration)

Table of Contents

Numeric Range Loop

Three-expression loop, collection-based or iterator-based loop, the guts of the python for loop, iterating through a dictionary, the range() function, the break and continue statements, the else clause.

Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: For Loops in Python (Definite Iteration)

This tutorial will show you how to perform definite iteration with a Python for loop.

In the previous tutorial in this introductory series, you learned the following:

Repetitive execution of the same block of code over and over is referred to as iteration .
Definite iteration, in which the number of repetitions is specified explicitly in advance
Indefinite iteration, in which the code block executes until some condition is met
In Python, indefinite iteration is performed with a while loop.

Here’s what you’ll cover in this tutorial:

You’ll start with a comparison of some different paradigms used by programming languages to implement definite iteration.

Then you will learn about iterables and iterators , two concepts that form the basis of definite iteration in Python.

Finally, you’ll tie it all together and learn about Python’s for loops.

Free Download: Get a sample chapter from Python Tricks: The Book that shows you Python’s best practices with simple examples you can apply instantly to write more beautiful + Pythonic code.

Take the Quiz: Test your knowledge with our interactive “The Python for Loop” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

In this quiz, you'll test your understanding of Python's `for` loop and the concepts of definite iteration, iterables, and iterators. With this knowledge, you'll be able to perform repetitive tasks in Python more efficiently.

A Survey of Definite Iteration in Programming

Definite iteration loops are frequently referred to as for loops because for is the keyword that is used to introduce them in nearly all programming languages, including Python.

Historically, programming languages have offered a few assorted flavors of for loop. These are briefly described in the following sections.

The most basic for loop is a simple numeric range statement with start and end values. The exact format varies depending on the language but typically looks something like this:

Here, the body of the loop is executed ten times. The variable i assumes the value 1 on the first iteration, 2 on the second, and so on. This sort of for loop is used in the languages BASIC, Algol, and Pascal.

Another form of for loop popularized by the C programming language contains three parts:

An initialization
An expression specifying an ending condition
An action to be performed at the end of each iteration.

This type of loop has the following form:

Technical Note: In the C programming language, i++ increments the variable i . It is roughly equivalent to i += 1 in Python.

This loop is interpreted as follows:

Initialize i to 1 .
Continue looping as long as i <= 10 .
Increment i by 1 after each loop iteration.

Three-expression for loops are popular because the expressions specified for the three parts can be nearly anything, so this has quite a bit more flexibility than the simpler numeric range form shown above. These for loops are also featured in the C++ , Java , PHP, and Perl languages.

This type of loop iterates over a collection of objects, rather than specifying numeric values or conditions:

Each time through the loop, the variable i takes on the value of the next object in <collection> . This type of for loop is arguably the most generalized and abstract. Perl and PHP also support this type of loop, but it is introduced by the keyword foreach instead of for .

Further Reading: See the For loop Wikipedia page for an in-depth look at the implementation of definite iteration across programming languages.

The Python for Loop

Of the loop types listed above, Python only implements the last: collection-based iteration. At first blush, that may seem like a raw deal, but rest assured that Python’s implementation of definite iteration is so versatile that you won’t end up feeling cheated!

Shortly, you’ll dig into the guts of Python’s for loop in detail. But for now, let’s start with a quick prototype and example, just to get acquainted.

Python’s for loop looks like this:

<iterable> is a collection of objects—for example, a list or tuple. The <statement(s)> in the loop body are denoted by indentation, as with all Python control structures, and are executed once for each item in <iterable> . The loop variable <var> takes on the value of the next element in <iterable> each time through the loop.

Here is a representative example:

In this example, <iterable> is the list a , and <var> is the variable i . Each time through the loop, i takes on a successive item in a , so print() displays the values 'foo' , 'bar' , and 'baz' , respectively. A for loop like this is the Pythonic way to process the items in an iterable.

But what exactly is an iterable? Before examining for loops further, it will be beneficial to delve more deeply into what iterables are in Python.

In Python, iterable means an object can be used in iteration. The term is used as:

An adjective: An object may be described as iterable.
A noun: An object may be characterized as an iterable.

If an object is iterable, it can be passed to the built-in Python function iter() , which returns something called an iterator . Yes, the terminology gets a bit repetitive. Hang in there. It all works out in the end.

Each of the objects in the following example is an iterable and returns some type of iterator when passed to iter() :

These object types, on the other hand, aren’t iterable:

All the data types you have encountered so far that are collection or container types are iterable. These include the string , list , tuple , dict , set , and frozenset types.

But these are by no means the only types that you can iterate over. Many objects that are built into Python or defined in modules are designed to be iterable. For example, open files in Python are iterable. As you will see soon in the tutorial on file I/O, iterating over an open file object reads data from the file.

In fact, almost any object in Python can be made iterable. Even user-defined objects can be designed in such a way that they can be iterated over. (You will find out how that is done in the upcoming article on object-oriented programming.)

Okay, now you know what it means for an object to be iterable, and you know how to use iter() to obtain an iterator from it. Once you’ve got an iterator, what can you do with it?

An iterator is essentially a value producer that yields successive values from its associated iterable object. The built-in function next() is used to obtain the next value from in iterator.

Here is an example using the same list as above:

In this example, a is an iterable list and itr is the associated iterator, obtained with iter() . Each next(itr) call obtains the next value from itr .

Notice how an iterator retains its state internally. It knows which values have been obtained already, so when you call next() , it knows what value to return next.

What happens when the iterator runs out of values? Let’s make one more next() call on the iterator above:

If all the values from an iterator have been returned already, a subsequent next() call raises a StopIteration exception. Any further attempts to obtain values from the iterator will fail.

You can only obtain values from an iterator in one direction. You can’t go backward. There is no prev() function. But you can define two independent iterators on the same iterable object:

Even when iterator itr1 is already at the end of the list, itr2 is still at the beginning. Each iterator maintains its own internal state, independent of the other.

If you want to grab all the values from an iterator at once, you can use the built-in list() function. Among other possible uses, list() takes an iterator as its argument, and returns a list consisting of all the values that the iterator yielded:

Similarly, the built-in tuple() and set() functions return a tuple and a set, respectively, from all the values an iterator yields:

It isn’t necessarily advised to make a habit of this. Part of the elegance of iterators is that they are “lazy.” That means that when you create an iterator, it doesn’t generate all the items it can yield just then. It waits until you ask for them with next() . Items are not created until they are requested.

When you use list() , tuple() , or the like, you are forcing the iterator to generate all its values at once, so they can all be returned. If the total number of objects the iterator returns is very large, that may take a long time.

In fact, it is possible to create an iterator in Python that returns an endless series of objects using generator functions and itertools . If you try to grab all the values at once from an endless iterator, the program will hang .

You now have been introduced to all the concepts you need to fully understand how Python’s for loop works. Before proceeding, let’s review the relevant terms:

Term	Meaning
	The process of looping through the objects or items in a collection
	An object (or the adjective used to describe an object) that can be iterated over
	The object that produces successive items or values from its associated iterable
	The built-in function used to obtain an iterator from an iterable

Now, consider again the simple for loop presented at the start of this tutorial:

This loop can be described entirely in terms of the concepts you have just learned about. To carry out the iteration this for loop describes, Python does the following:

Calls iter() to obtain an iterator for a
Calls next() repeatedly to obtain each item from the iterator in turn
Terminates the loop when next() raises the StopIteration exception

The loop body is executed once for each item next() returns, with loop variable i set to the given item for each iteration.

This sequence of events is summarized in the following diagram:

Perhaps this seems like a lot of unnecessary monkey business, but the benefit is substantial. Python treats looping over all iterables in exactly this way, and in Python, iterables and iterators abound:

Many built-in and library objects are iterable.

There is a Standard Library module called itertools containing many functions that return iterables.

User-defined objects created with Python’s object-oriented capability can be made to be iterable.

Python features a construct called a generator that allows you to create your own iterator in a simple, straightforward way.

You will discover more about all the above throughout this series. They can all be the target of a for loop, and the syntax is the same across the board. It’s elegant in its simplicity and eminently versatile.

You saw earlier that an iterator can be obtained from a dictionary with iter() , so you know dictionaries must be iterable. What happens when you loop through a dictionary? Let’s see:

As you can see, when a for loop iterates through a dictionary , the loop variable is assigned to the dictionary’s keys.

To access the dictionary values within the loop, you can make a dictionary reference using the key as usual:

You can also iterate through a dictionary’s values directly by using .values() :

In fact, you can iterate through both the keys and values of a dictionary simultaneously. That is because the loop variable of a for loop isn’t limited to just a single variable. It can also be a tuple, in which case the assignments are made from the items in the iterable using packing and unpacking, just as with an assignment statement:

As noted in the tutorial on Python dictionaries , the dictionary method .items() effectively returns a list of key/value pairs as tuples:

Thus, the Pythonic way to iterate through a dictionary accessing both the keys and values looks like this:

In the first section of this tutorial, you saw a type of for loop called a numeric range loop , in which starting and ending numeric values are specified. Although this form of for loop isn’t directly built into Python, it is easily arrived at.

For example, if you wanted to iterate through the values from 0 to 4 , you could simply do this:

This solution isn’t too bad when there are just a few numbers. But if the number range were much larger, it would become tedious pretty quickly.

Happily, Python provides a better option—the built-in range() function, which returns an iterable that yields a sequence of integers.

range(<end>) returns an iterable that yields integers starting with 0 , up to but not including <end> :

Note that range() returns an object of class range , not a list or tuple of the values. Because a range object is an iterable, you can obtain the values by iterating over them with a for loop:

You could also snag all the values at once with list() or tuple() . In a REPL session, that can be a convenient way to quickly display what the values are:

However, when range() is used in code that is part of a larger application, it is typically considered poor practice to use list() or tuple() in this way. Like iterators, range objects are lazy—the values in the specified range are not generated until they are requested. Using list() or tuple() on a range object forces all the values to be returned at once. This is rarely necessary, and if the list is long, it can waste time and memory.

range(<begin>, <end>, <stride>) returns an iterable that yields integers starting with <begin> , up to but not including <end> . If specified, <stride> indicates an amount to skip between values (analogous to the stride value used for string and list slicing):

If <stride> is omitted, it defaults to 1 :

All the parameters specified to range() must be integers, but any of them can be negative. Naturally, if <begin> is greater than <end> , <stride> must be negative (if you want any results):

Technical Note: Strictly speaking, range() isn’t exactly a built-in function. It is implemented as a callable class that creates an immutable sequence type. But for practical purposes, it behaves like a built-in function.

For more information on range() , see the Real Python article Python’s range() Function (Guide) .

Altering for Loop Behavior

You saw in the previous tutorial in this introductory series how execution of a while loop can be interrupted with break and continue statements and modified with an else clause . These capabilities are available with the for loop as well.

break and continue work the same way with for loops as with while loops. break terminates the loop completely and proceeds to the first statement following the loop:

continue terminates the current iteration and proceeds to the next iteration:

A for loop can have an else clause as well. The interpretation is analogous to that of a while loop. The else clause will be executed if the loop terminates through exhaustion of the iterable:

The else clause won’t be executed if the list is broken out of with a break statement:

This tutorial presented the for loop, the workhorse of definite iteration in Python.

You also learned about the inner workings of iterables and iterators , two important object types that underlie definite iteration, but also figure prominently in a wide variety of other Python code.

In the next two tutorials in this introductory series, you will shift gears a little and explore how Python programs can interact with the user via input from the keyboard and output to the console.

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Loops in Python – For, While and Nested Loops

Python programming language provides two types of Python loopshecking time. In this article, we will look at Python loops and understand their working with the help of examp – For loop and While loop to handle looping requirements. Loops in Python provides three ways for executing the loops.

While all the ways provide similar basic functionality, they differ in their syntax and condition-checking time. In this article, we will look at Python loops and understand their working with the help of examples.

While Loop in Python

In Python , a while loop is used to execute a block of statements repeatedly until a given condition is satisfied. When the condition becomes false, the line immediately after the loop in the program is executed.

Python While Loop Syntax:

All the statements indented by the same number of character spaces after a programming construct are considered to be part of a single block of code. Python uses indentation as its method of grouping statements.

Let’s learn how to use a while loop in Python with Examples:

Example of Python While Loop

Let’s see a simple example of a while loop in Python. The given Python code uses a ‘ while' loop to print “Hello Geek” three times by incrementing a variable called ‘ count' from 1 to 3.

Using else statement with While Loop in Python

The else clause is only executed when your while condition becomes false. If you break out of the loop, or if an exception is raised, it won’t be executed.

Syntax of While Loop with else statement:

Examples of While Loop with else statement:

Here is an example of while loop with else statement in Python:

The code prints “Hello Geek” three times using a ‘ while' loop and then, after the loop, it prints “In Else Block” because there is an “else” block associated with the ‘ while' loop.

Infinite While Loop in Python

If we want a block of code to execute infinite number of time, we can use the while loop in Python to do so.

The code uses a ‘ while' loop with the condition (count == 0) . This loop will only run as long as count is equal to 0. Since count is initially set to 0, the loop will execute indefinitely because the condition is always true.

Note : It is suggested not to use this type of loop as it is a never-ending infinite loop where the condition is always true and you have to forcefully terminate the compiler.

For Loop in Python

For loops are used for sequential traversal. For example: traversing a list or string or array etc. In Python, there is “for in” loop which is similar to foreach loop in other languages. Let us learn how to use for loops in Python for sequential traversals with examples.

For Loop Syntax:

It can be used to iterate over a range and iterators.

The code uses a Python for loop that iterates over the values from 0 to 3 (not including 4), as specified by the range(0, n) construct. It will print the values of ‘ i' in each iteration of the loop.

Example with List, Tuple, String, and Dictionary Iteration Using for Loops in Python

We can use for loop to iterate lists, tuples , strings and dictionaries in Python.

The code showcases different ways to iterate through various data structures in Python. It demonstrates iteration over lists , tuples , strings , dictionaries , and sets , printing their elements or key-value pairs.

The output displays the contents of each data structure as it is iterated.

Iterating by the Index of Sequences

We can also use the index of elements in the sequence to iterate. The key idea is to first calculate the length of the list and in iterate over the sequence within the range of this length. See the below

Example: This code uses a ‘ for' loop to iterate over a list and print each element. It iterates through the list based on the index of each element, obtained using ‘ range(len(list))' . The result is that it prints each item in the list on separate lines.

Using else Statement with for Loop in Python

We can also combine else statement with for loop like in while loop. But as there is no condition in for loop based on which the execution will terminate so the else block will be executed immediately after for block finishes execution.

In this code, the ‘ for' loop iterates over a list and prints each element, just like in the previous example. However, after the loop is finished, the “else” block is executed. So, in this case, it will print “Inside Else Block” once the loop completes.

Nested Loops in Python

Python programming language allows to use one loop inside another loop which is called nested loop . Following section shows few examples to illustrate the concept.

Nested Loops Syntax:

The syntax for a nested while loop statement in the Python programming language is as follows:

A final note on loop nesting is that we can put any type of loop inside of any other type of loops in Python. For example, a for loop can be inside a while loop or vice versa.

Example: This Python code uses nested ‘ for' loops to create a triangular pattern of numbers. It iterates from 1 to 4 and, in each iteration, prints the current number multiple times based on the iteration number. The result is a pyramid-like pattern of numbers.

Loop Control Statements

Loop control statements change execution from their normal sequence. When execution leaves a scope, all automatic objects that were created in that scope are destroyed. Python supports the following control statements.

Continue Statement

The continue statement in Python returns the control to the beginning of the loop.

Example: This Python code iterates through the characters of the string ‘geeksforgeeks’ . When it encounters the characters ‘e’ or ‘s’ , it uses the continue statement to skip the current iteration and continue with the next character. For all other characters, it prints “Current Letter :” followed by the character. So, the output will display all characters except ‘e’ and ‘s’ , each on a separate line.

Break Statement

The break statement in Python brings control out of the loop.

Example: In this Python code, it iterates through the characters of the string ‘geeksforgeeks’. When it encounters the characters ‘e’ or ‘s’ , it uses the break statement to exit the loop. After the loop is terminated, it prints “Current Letter :” followed by the last character encountered in the loop (either ‘e’ or ‘s’ ). So, the output will display “Current Letter :” followed by the first occurrence of ‘e’ or ‘s’ in the string.

Pass Statement

We use pass statemen t in Python to write empty loops. Pass is also used for empty control statements, functions and classes.

Example: This Python code iterates through the characters of the string ‘geeksforgeeks’ using a ‘ for' loop. However, it doesn’t perform any specific action within the loop, and the ‘ pass' statement is used. After the loop, it prints “Last Letter :” followed by the last character in the string, which is ‘s’ .

How for loop in Python works internally?

Before proceeding to this section, you should have a prior understanding of Python Iterators.

Firstly, lets see how a simple for loops in Python looks like.

Example: This Python code iterates through a list called fruits , containing “apple”, “orange” and “kiwi.” It prints each fruit name on a separate line, displaying them in the order they appear in the list.

Here we can see the for loops in Python that iterates over iterable object fruit which is a list. Lists, sets, dictionaries are few iterable objects while an integer object is not an iterable object. For loops can iterate over any of these iterable objects.

This Python code manually iterates through a list of fruits using an iterator. It prints each fruit’s name one by one and stops when there are no more items in the list.

We can see that under the hood we are calling iter() and next() method.

We have covered Python Loops in this article. We also saw how to use for loop, while loop and nested loop in Python. This article provides different use-case scenarios and examples to demonstrate working of loops and give clear understanding.

Learn More on Loops:

Difference between for loop and while loop in Python
Looping Techniques in Python
Python loop-programs Archives

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Introducing For Loops in Python

Create efficiencies by performing repetitive tasks in a loop.

Within ArcGIS Pro, Python emerges as a powerful tool for handling large datasets. By writing efficient code, we can streamline manual processes. One fundamental concept that every Python programmer should grasp is the for loop .

Understanding For Loops

A for loop allows me to iterate over items in a list. It directs the flow of my program by repeatedly executing a task for every item in that list.

A for loop is just one way to indicate a repeated block of code in Python. The syntax of a for loop can be interpreted something like, “for each item in the list, execute the following block of indented code.”

The for loop introduces the list data type in Python. Just like how we think of lists in the real world, lists in Python contain multiple items within a single variable. The equal sign = is for the variable name assignment and square brackets [ ] go around the complete. Items within the list are separated by a coma.

For example:

Each item in a list has an index position. Python is a zero-indexed program meaning it starts counting from zero. In other words, 0 represents the first position. Likewise, 1 represents the second position, and so forth.

Lists are mutable meaning items can be changed and iterable meaning items can be accessed individually. Lists can contain a mix of data types, like strings, floats, integer tuples, and dictionaries. Lists can be easily joined or concatenated. I’ll have more on lists in another article. Back to for loops.

Here’s an example of a simple for loop iterating over a list where the program is asked to print each item in the list:

The range() Function

The script above might run into issues if I want to include a large number of items in my list, say 50, 100, or even a million iterations. I wouldn’t want to write a list with every number up to 1,000,000.

Python offers us the built-in range() function. The parameter between the parentheses includes the number of items to iterate over up to, but not including that number.

Place Holder Variable

The syntax for a for loop requires a variable name to reference the items in the list. I could create a variable assignment if I need to use that variable throughout my program. Or Python allows me to use an underscore _ as a placeholder variable for the for loop to execute.

Loops are important in ArcGIS Pro for automating repetitive tasks.

A for loop repeats a task for each item in a list.

Lists are a data type in Python that have the properties of being mutable and iterable.

The range() function is a shortcut to create a list of any length.

An underscore _ can be used as a placeholder variable in a for loop if a variable name isn’t needed anywhere else in the program.

Thanks for reading Geospatial Think ! Subscribe to receive new posts and support my work.

About the Author

Stephanie Scavelli is a tech writer passionate about sharing the joy of geospatial thinking in this ever-advancing technological world. She is based in the Mid-Hudson Valley in New York (USA).

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Python For Loop and While Loop

We learned how we can change the flow of our program with the conditional statements if and else. Another way to control the flow is by using a Python for-loop or a Python while-loop. Loops, in essence, allow you to repeat a piece of code.

Table of Contents

1 Python For-loop
2 Python for-loops and lists
3 Python While-loop
4 Infinite loops
5 More types of loops

Python For-loop

There are two ways to create a loop in Python. Let’s first look at Python’s for-loop. A for-loop iterates over the individual elements of the object you feed it. If that sounds difficult, an example will hopefully clarify this:

We stumbled upon two concepts here that need an explanation: iterability and objects.

As you can see in the example code, a string of text is iterable. Most of Python’s data types are iterable in some way or another. If you want to know all the nitty-gritty details, head over to the page about iterators . If you’re new to programming, I suggest you keep reading here and save iterators for later.

The next thing we need to tackle: objects. This is a big subject, and it has its own chapter in this tutorial: Python classes and objects . You don’t need to learn about it now to understand Python for-loops. It’s enough to know that everything in Python is an object, and objects have a certain type and certain properties. Being iterable is one of those properties.

So we know that a for-loop can loop over iterable objects. By returning its members one by one, making it available in a variable (in the above example it’s the variable letter ), you can loop over each element of an iterable with a for-statement.

The general template for a for-loop in Python is:

On each iteration, an element from iterable is assigned to variable . This variable exists and can be used only inside the loop. Once there is nothing more left, the loop stops and the program continues with the next lines of code.

Python for-loops and lists

This is the ideal time to look at a new data type: lists . A Python list can contain zero or more objects. It’s a frequently used data type in Python programming. In other programming languages, there often is no such thing as a list. Most languages do offer arrays, but arrays can only contain one type of data.

Python has arrays too , but we won’t discuss them in this course. In short: arrays can only contain one type of data (like numbers) but they are more efficient at storing that data. Lists, on the other hand, can store a mix of all types of data and are extremely flexible, at the cost of some performance. For a thorough explanation of lists, you can read my article on Python lists . If you’re a beginner, I recommend to keep on reading here though.

Lists, just like strings, are iterable; hence they work very well with a for-loop:

Let’s try that again, but this time in an interactive crumb so you can play around with it:

A couple of things to note:

we create lists with block quotes.
Its contents are objects of whatever type you like, separated by commas, and they don’t need to be of the same type.
We can access the individual elements of a list manually too.

A list can contain all the types we’ve seen so far: numbers, strings, booleans, and even other lists. Indeed, you can create a list of lists if you want! As you can see in the interactive example, we can also access individual elements of a list. Remember that in computer science, we start counting from 0. So mylist[0] gives us the first element, and mylist[1] the second, etcetera.

Here are a couple of things you can try in a REPL or in the interactive code example above:

From the last example, you can see how to access nested lists. An extensive tutorial on Python lists can be found later on in this Python tutorial.

Python While-loop

While the for-loop in Python is a bit hard to understand, because of new concepts like iterability and objects , the while loop is actually much simpler! Its template looks like this:

You should read this as: “while this expression evaluates to True , keep doing the stuff below”.

Let’s take a look at an actual example:

We see an expression that follows the while statement: i <= 4 . As long as this expression evaluates to True , the block inside of the while-loop executes repeatedly.

In the example above, we start with i = 1 . In the first iteration of the loop, we print i and increase it by one. This keeps happening as long as i is smaller than or equal to 4. The output of the print statement confirms that this loop runs four times.

Here’s an interactive example to experiment with yourself:

Infinite loops

Sometimes you want your software to keep running. In such cases, an infinite loop can be of help. Let’s create such an infinite loop in Python:

Why is this an infinite loop? Remember that while takes an expression, and keeps repeating the code as long as that expression evaluates to True . Since the very simple expression ‘ True ‘ is always True , this loop never stops.

Getting out of an Infinite loop

With while loops, it’s easy to make a mistake and find yourself caught in an infinite while-loop. This means the expression never evaluates to False for some reason. It happens to the best of us. You can get out of this situation by pressing control + c at the same time. Since we know how to intentionally create infinite loops, let’s artificially create such a situation. Hit control + c to stop the following loop:

The output will look like this:

If you look closely, you see the characters ^C right before the error, meaning control + c was pressed at that point. This key combination will get you out of most situations where your program runs indefinitely, so it’s good to remember it!

Infinite loops are less common in for-loops because most iterable objects will at some point run out of elements to iterate over. However, if you ever find yourself in an infinite for-loop, you can use this same trick to get out of it.

More types of loops

Later on in the tutorial, you will also learn about Python list comprehensions . A list comprehension is a powerful construct in Python that we can use to create a list based on an existing list. If you’re new to programming, you should learn about other essentials first, though and I suggest you continue with the following section.

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Overview Teaching: 10 min Exercises: 15 min Questions How can I make a program do many things? Objectives Explain what for loops are normally used for. Trace the execution of a simple (unnested) loop and correctly state the values of variables in each iteration. Write for loops that use the Accumulator pattern to aggregate values.

A for loop executes commands once for each value in a collection.

Doing calculations on the values in a list one by one is as painful as working with pressure_001 , pressure_002 , etc.
A for loop tells Python to execute some statements once for each value in a list, a character string, or some other collection.
“for each thing in this group, do these operations”
This for loop is equivalent to:
And the for loop’s output is:

The first line of the for loop must end with a colon, and the body must be indented.

The colon at the end of the first line signals the start of a block of statements.
Any consistent indentation is legal, but almost everyone uses four spaces.
Indentation is always meaningful in Python.
This error can be fixed by removing the extra spaces at the beginning of the second line.

A for loop is made up of a collection, a loop variable, and a body.

The collection, [2, 3, 5] , is what the loop is being run on.
The body, print(number) , specifies what to do for each value in the collection.
The “current thing”.

Loop variables can be called anything.

Created on demand.
Meaningless: their names can be anything at all.

The body of a loop can contain many statements.

But no loop should be more than a few lines long.
Hard for human beings to keep larger chunks of code in mind.

Use range to iterate over a sequence of numbers.

Not a list: the numbers are produced on demand to make looping over large ranges more efficient.
Exactly the legal indices of a list or character string of length N

The Accumulator pattern turns many values into one.

Initialize an accumulator variable to zero, the empty string, or the empty list.
Update the variable with values from a collection.
Add 1 to the current value of the loop variable number .
Add that to the current value of the accumulator variable total .
Assign that to total , replacing the current value.
We have to add number + 1 because range produces 0..9, not 1..10.

Classifying Errors Is an indentation error a syntax error or a runtime error? Solution An IndentationError is a syntax error. Programs with syntax errors cannot be started. A program with a runtime error will start but an error will be thrown under certain conditions.

Tracing Execution Create a table showing the numbers of the lines that are executed when this program runs, and the values of the variables after each line is executed. total = 0 for char in "tin": total = total + 1 Solution Line no Variables 1 total = 0 2 total = 0 char = ‘t’ 3 total = 1 char = ‘t’ 2 total = 1 char = ‘i’ 3 total = 2 char = ‘i’ 2 total = 2 char = ‘n’ 3 total = 3 char = ‘n’

Reversing a String Fill in the blanks in the program below so that it prints “nit” (the reverse of the original character string “tin”). original = "tin" result = ____ for char in original: result = ____ print(result) Solution original = "tin" result = "" for char in original: result = char + result print(result)

Practice Accumulating Fill in the blanks in each of the programs below to produce the indicated result. # Total length of the strings in the list: ["red", "green", "blue"] => 12 total = 0 for word in ["red", "green", "blue"]: ____ = ____ + len(word) print(total) Solution total = 0 for word in ["red", "green", "blue"]: total = total + len(word) print(total)

Solution lengths = [] for word in ["red", "green", "blue"]: lengths.append(len(word)) print(lengths)

Solution words = ["red", "green", "blue"] result = "" for word in words: result = result + word print(result)

Solution acronym = "" for word in ["red", "green", "blue"]: acronym = acronym + word[0].upper() print(acronym)

Cumulative Sum Reorder and properly indent the lines of code below so that they print an array with the cumulative sum of data. The result should be [1, 3, 5, 10] . cumulative += [sum] for number in data: cumulative = [] sum += number sum = 0 print(cumulative) data = [1,2,2,5] Solution sum = 0 data = [1,2,2,5] cumulative = [] for number in data: sum += number cumulative.append(sum) print(cumulative)

Identifying Variable Name Errors Read the code below and try to identify what the errors are without running it. Run the code and read the error message. What type of NameError do you think this is? Is it a string with no quotes, a misspelled variable, or a variable that should have been defined but was not? Fix the error. Repeat steps 2 and 3, until you have fixed all the errors. for number in range(10): # use a if the number is a multiple of 3, otherwise use b if (Number % 3) == 0: message = message + a else: message = message + "b" print(message) Solution message = "" for number in range(10): # use a if the number is a multiple of 3, otherwise use b if (number % 3) == 0: message = message + "a" else: message = message + "b" print(message)

Identifying Item Errors Read the code below and try to identify what the errors are without running it. Run the code, and read the error message. What type of error is it? Fix the error. seasons = ['Spring', 'Summer', 'Fall', 'Winter'] print('My favorite season is ', seasons[4]) Solution seasons = ['Spring', 'Summer', 'Fall', 'Winter'] print('My favorite season is ', seasons[3])

Key Points A for loop executes commands once for each value in a collection. The first line of the for loop must end with a colon, and the body must be indented. Indentation is always meaningful in Python. A for loop is made up of a collection, a loop variable, and a body. Loop variables can be called anything (but it is strongly advised to have a meaningful name to the looping variable). The body of a loop can contain many statements. Use range to iterate over a sequence of numbers. The Accumulator pattern turns many values into one.

Learn Python practically and Get Certified .

Python Fundamentals

Python Variables and Literals
Python Type Conversion
Python Basic Input and Output
Python Operators

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 and Mathematics
Python List
Python Tuple
Python String
Python Sets
Python Dictionary
Python Functions
Python Function Arguments
Python Variable Scope
Python Global Keyword
Python Recursion
Python Modules
Python Package
Python Main function

Python Files

Python Directory and Files Management
Python CSV: Read and Write CSV files
Reading CSV files in Python
Writing CSV files in Python
Python Exception Handling
Python Exceptions
Python Custom Exceptions

Python Object & Class

Python Objects and Classes
Python Inheritance
Python Multiple Inheritance
Polymorphism in Python
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

Precedence and Associativity of Operators in Python
Python Keywords and Identifiers
Python Asserts
Python Json
Python *args and **kwargs

Python Tutorials

Python range() Function

Python enumerate()

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.

Iteration		Last item in sequence?
1st	Prints	No
2nd	Prints	No
3rd	Prints	No
4th	Prints	Yes The loop terminates.

Video: Python for Loop

Sorry about that.

4. More Control Flow Tools ¶

As well as the while statement just introduced, Python uses a few more that we will encounter in this chapter.

4.1. if Statements ¶

Perhaps the most well-known statement type is the if statement. For example:

There can be zero or more elif parts, and the else part is optional. The keyword ‘ elif ’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages.

If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements .

4.2. for Statements ¶

The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):

Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection:

4.3. The range() Function ¶

If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:

The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):

To iterate over the indices of a sequence, you can combine range() and len() as follows:

In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques .

A strange thing happens if you just print a range:

In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.

We say such an object is iterable , that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum() :

Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures , we will discuss in more detail about list() .

4.4. break and continue Statements, and else Clauses on Loops ¶

The break statement breaks out of the innermost enclosing for or while loop.

A for or while loop can include an else clause.

In a for loop, the else clause is executed after the loop reaches its final iteration.

In a while loop, it’s executed after the loop’s condition becomes false.

In either kind of loop, the else clause is not executed if the loop was terminated by a break .

This is exemplified in the following for loop, which searches for prime numbers:

(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)

When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions .

The continue statement, also borrowed from C, continues with the next iteration of the loop:

4.5. pass Statements ¶

The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:

This is commonly used for creating minimal classes:

Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:

4.6. match Statements ¶

A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals:

Note the last block: the “variable name” _ acts as a wildcard and never fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using | (“or”):

Patterns can look like unpacking assignments, and can be used to bind variables:

Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject ( point ). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point .

If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables:

You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable):

A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar ), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to.

Patterns can be arbitrarily nested. For example, if we have a short list of Points, with __match_args__ added, we could match it like this:

We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:

Several other key features of this statement:

Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings.

Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, *rest) work similar to unpacking assignments. The name after * may also be _ , so (x, y, *_) matches a sequence of at least two items without binding the remaining items.

Mapping patterns: {"bandwidth": b, "latency": l} captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.)

Subpatterns may be captured using the as keyword:

will capture the second element of the input as p2 (as long as the input is a sequence of two points)

Most literals are compared by equality, however the singletons True , False and None are compared by identity.

Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable:

For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format.

4.7. Defining Functions ¶

We can create a function that writes the Fibonacci series to an arbitrary boundary:

The keyword def introduces a function definition . It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.

The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring . (More about docstrings can be found in the section Documentation Strings .) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.

The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference , not the value of the object). [ 1 ] When a function calls another function, or calls itself recursively, a new local symbol table is created for that call.

A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be used to access the function:

Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print() :

It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:

This example, as usual, demonstrates some new Python features:

The return statement returns with a value from a function. return without an expression argument returns None . Falling off the end of a function also returns None .

The statement result.append(a) calls a method of the list object result . A method is a function that ‘belongs’ to an object and is named obj.methodname , where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes , see Classes ) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a] , but more efficient.

4.8. More on Defining Functions ¶

It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.

4.8.1. Default Argument Values ¶

The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:

This function can be called in several ways:

giving only the mandatory argument: ask_ok('Do you really want to quit?')

giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2)

or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!')

This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.

The default values are evaluated at the point of function definition in the defining scope, so that

will print 5 .

Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls:

This will print

If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:

4.8.2. Keyword Arguments ¶

Functions can also be called using keyword arguments of the form kwarg=value . For instance, the following function:

accepts one required argument ( voltage ) and three optional arguments ( state , action , and type ). This function can be called in any of the following ways:

but all the following calls would be invalid:

In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:

When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict ) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. ( *name must occur before **name .) For example, if we define a function like this:

It could be called like this:

and of course it would print:

Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call.

4.8.3. Special parameters ¶

By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword.

A function definition may look like:

where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters.

4.8.3.1. Positional-or-Keyword Arguments ¶

If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword.

4.8.3.2. Positional-Only Parameters ¶

Looking at this in a bit more detail, it is possible to mark certain parameters as positional-only . If positional-only , the parameters’ order matters, and the parameters cannot be passed by keyword. Positional-only parameters are placed before a / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters.

Parameters following the / may be positional-or-keyword or keyword-only .

4.8.3.3. Keyword-Only Arguments ¶

To mark parameters as keyword-only , indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter.

4.8.3.4. Function Examples ¶

Consider the following example function definitions paying close attention to the markers / and * :

The first function definition, standard_arg , the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword:

The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition:

The third function kwd_only_args only allows keyword arguments as indicated by a * in the function definition:

And the last uses all three calling conventions in the same function definition:

Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key:

There is no possible call that will make it return True as the keyword 'name' will always bind to the first parameter. For example:

But using / (positional only arguments), it is possible since it allows name as a positional argument and 'name' as a key in the keyword arguments:

In other words, the names of positional-only parameters can be used in **kwds without ambiguity.

4.8.3.5. Recap ¶

The use case will determine which parameters to use in the function definition:

As guidance:

Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords.

Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed.

For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future.

4.8.4. Arbitrary Argument Lists ¶

Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences ). Before the variable number of arguments, zero or more normal arguments may occur.

Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments.

4.8.5. Unpacking Argument Lists ¶

The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the * -operator to unpack the arguments out of a list or tuple:

In the same fashion, dictionaries can deliver keyword arguments with the ** -operator:

4.8.6. Lambda Expressions ¶

Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b . Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:

The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:

4.8.7. Documentation Strings ¶

Here are some conventions about the content and formatting of documentation strings.

The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.

If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).

Here is an example of a multi-line docstring:

4.8.8. Function Annotations ¶

Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information).

Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal -> , followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated:

4.9. Intermezzo: Coding Style ¶

Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style . Most languages can be written (or more concise, formatted ) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.

For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:

Use 4-space indentation, and no tabs.

4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.

Wrap lines so that they don’t exceed 79 characters.

This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.

Use blank lines to separate functions and classes, and larger blocks of code inside functions.

When possible, put comments on a line of their own.

Use docstrings.

Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4) .

Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).

Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case.

Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.

4.1. if Statements
4.2. for Statements
4.3. The range() Function
4.4. break and continue Statements, and else Clauses on Loops
4.5. pass Statements
4.6. match Statements
4.7. Defining Functions
4.8.1. Default Argument Values
4.8.2. Keyword Arguments
4.8.3.1. Positional-or-Keyword Arguments
4.8.3.2. Positional-Only Parameters
4.8.3.3. Keyword-Only Arguments
4.8.3.4. Function Examples
4.8.3.5. Recap
4.8.4. Arbitrary Argument Lists
4.8.5. Unpacking Argument Lists
4.8.6. Lambda Expressions
4.8.7. Documentation Strings
4.8.8. Function Annotations
4.9. Intermezzo: Coding Style

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Python Variable assignment in a for loop - Stack Overflow
Let's have a look what names you access in your code. The locals function helps us: It shows the names in the local scope (and their value). Here's your code, with some debugging output: a = [1, 2, 3] # a is bound. print(locals()) for x in a: # a is read, and for each iteration x is bound.
python - Assigning variables through a for loop - Stack Overflow
Assigning variables through a for loop. Asked 8 years, 1 month ago. Modified 8 years, 1 month ago. Viewed 945 times. 0. I'm trying to use a for loop to assign values to variables - in this case the variables are all called: 'die1', 'die2', 'die3', etc. import random. for i in range(5): die[i] = random.randint(1,6)
Python For Loops - W3Schools
Python For Loops. 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.
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In this introductory tutorial, you'll learn all about how to perform definite iteration with Python for loops. You’ll see how other programming languages implement definite iteration, learn about iterables and iterators, and tie it all together to learn about Python’s for loop.
Loops in Python - For, While and Nested Loops - GeeksforGeeks
In Python, there is no construct defined for do while loop. Python loops only include for loop and while loop but we can modify the while loop to work as do while as in any other languages such as C++ and Java. In Python, we can simulate the behavior of a do-while loop using a while loop with a condition that is initially True and then break out of
Introducing For Loops in Python - by Stephanie Scavelli
The for loop introduces the list data type in Python. Just like how we think of lists in the real world, lists in Python contain multiple items within a single variable. The equal sign = is for the variable name assignment and square brackets [ ] go around the complete. Items within the list are separated by a coma.
Python For Loop and While Loop • Python Land Tutorial
Python While-loop. While the for-loop in Python is a bit hard to understand, because of new concepts like iterability and objects, the while loop is actually much simpler! Its template looks like this: while <expression evaluates to True>: do something. You should read this as: “while this expression evaluates to.
Programming in Python: For Loops - GitHub Pages
A for loop tells Python to execute some statements once for each value in a list, a character string, or some other collection. “for each thing in this group, do these operations” for number in [2, 3, 5]: print(number) This for loop is equivalent to: print(2) print(3) print(5) And the for loop’s output is: 2. 3. 5.
Python for Loop (With Examples) - Programiz">Python for Loop (With Examples) - Programiz
In Python, we use a for loop to iterate over various sequences, such as lists, tuples, sets, strings, or dictionaries. The for loop allows you to iterate through each element of a sequence and perform certain operations on it.
Python 3.12.4 documentation">4. More Control Flow Tools — Python 3.12.4 documentation
break and continue Statements, and else Clauses on Loops¶ The break statement breaks out of the innermost enclosing for or while loop. A for or while loop can include an else clause. In a for loop, the else clause is executed after the loop reaches its final iteration. In a while loop, it’s executed after the loop’s condition becomes false.

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A for loop executes commands once for each value in a collection.

The first line of the for loop must end with a colon, and the body must be indented.

A for loop is made up of a collection, a loop variable, and a body.

Loop variables can be called anything.

The body of a loop can contain many statements.

Use range to iterate over a sequence of numbers.

The Accumulator pattern turns many values into one.

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