python assignment from list

The Walrus Operator: Python 3.8 Assignment Expressions

Table of Contents

Hello, Walrus!

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

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

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

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

In this tutorial, you’ll learn how to:

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

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

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Walrus Operator Fundamentals

Let’s start with some different terms that programmers use to refer to this new syntax. You’ve already seen a few in this tutorial.

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

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

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

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

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

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

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

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

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

Note: You need at least Python 3.8 to try out the examples in this tutorial. If you don’t already have Python 3.8 installed and you have Docker available, a quick way to start working with Python 3.8 is to run one of the official Docker images :

This will download and run the latest stable version of Python 3.8. For more information, see Run Python Versions in Docker: How to Try the Latest Python Release .

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

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

This PEP was originally written by Chris Angelico in February 2018. Following some heated discussion, PEP 572 was accepted by Guido van Rossum in July 2018. Since then, Guido announced that he was stepping down from his role as benevolent dictator for life (BDFL) . Starting in early 2019, Python has been governed by an elected steering council instead.

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

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

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

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

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

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

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

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

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

Writing a traditional assignment statement with = is not allowed inside such parentheses. This helps you catch potential bugs.

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

Walrus Operator Use Cases

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

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

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

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

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

As you can see, the distance from Oslo to Vancouver is just under 7200 kilometers.

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

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

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

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

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

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

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

The variables num_length and num_sum are only used to optimize the calculations inside the dictionary. By using the walrus operator, this role can be made more clear:

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

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

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

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

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

• Line 6 loops over each filename provided by the user. sys.argv is a list containing each argument given on the command line, starting with the name of your script. For more information about sys.argv , you can check out Python Command Line Arguments .
• Line 7 translates each filename string to a pathlib.Path object . Storing a filename in a Path object allows you to conveniently read the text file in the next lines.
• Lines 8 to 12 construct a tuple of counts to represent the number of lines, words, and characters in one text file.
• Line 9 reads a text file and calculates the number of lines by counting newlines.
• Line 10 reads a text file and calculates the number of words by splitting on whitespace.
• Line 11 reads a text file and calculates the number of characters by finding the length of the string.
• Line 13 prints all three counts together with the filename to the console. The *counts syntax unpacks the counts tuple. In this case, the print() statement is equivalent to print(counts[0], counts[1], counts[2], path) .

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

In other words, the wc.py file consists of 13 lines, 34 words, and 316 characters.

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

The contents of the file are assigned to text , which is reused in the next two calculations. The program still functions the same:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Does any city name start with "H" ?

Yes, because "Houston" starts with "H" .

Do all city names start with "H" ?

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

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

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

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

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

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

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

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

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

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

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

One of the main reasons assignments were not expressions in Python from the beginning is that the visual likeness of the assignment operator ( = ) and the equality comparison operator ( == ) could potentially lead to bugs. When introducing assignment expressions, a lot of thought was put into how to avoid similar bugs with the walrus operator. As mentioned earlier , one important feature is that the := operator is never allowed as a direct replacement for the = operator, and vice versa.

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

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

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

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

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

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

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

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

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

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

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

Let’s look at some other situations where assignment expressions are illegal:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

If you need to support older versions of Python, you can’t ship code that uses assignment expressions. There are some projects, like walrus , that can automatically translate walrus operators into code that is compatible with older versions of Python. This allows you to take advantage of assignment expressions when writing your code and still distribute code that is compatible with more Python versions.

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

There are many times it’s possible for you to use the walrus operator, but where it won’t necessarily improve the readability or efficiency of your code. In those cases, you’re better off writing your code in a more traditional manner.

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

In this tutorial, you learned how to:

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

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Python Assignment Operators

Assignment operators in python.

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The Python Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, and bitwise computations. The value the operator operates on is known as the Operand. Here, we will cover Different Assignment operators in Python .

Here are the Assignment Operators in Python with examples.

Assignment Operator

Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand.

Addition Assignment Operator

The Addition Assignment Operator is used to add the right-hand side operand with the left-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the addition assignment operator which will first perform the addition operation and then assign the result to the variable on the left-hand side.

S ubtraction Assignment Operator

The Subtraction Assignment Operator is used to subtract the right-hand side operand from the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the subtraction assignment operator which will first perform the subtraction operation and then assign the result to the variable on the left-hand side.

M ultiplication Assignment Operator

The Multiplication Assignment Operator is used to multiply the right-hand side operand with the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the multiplication assignment operator which will first perform the multiplication operation and then assign the result to the variable on the left-hand side.

D ivision Assignment Operator

The Division Assignment Operator is used to divide the left-hand side operand with the right-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the division assignment operator which will first perform the division operation and then assign the result to the variable on the left-hand side.

M odulus Assignment Operator

The Modulus Assignment Operator is used to take the modulus, that is, it first divides the operands and then takes the remainder and assigns it to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the modulus assignment operator which will first perform the modulus operation and then assign the result to the variable on the left-hand side.

F loor Division Assignment Operator

The Floor Division Assignment Operator is used to divide the left operand with the right operand and then assigs the result(floor value) to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the floor division assignment operator which will first perform the floor division operation and then assign the result to the variable on the left-hand side.

Exponentiation Assignment Operator

The Exponentiation Assignment Operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the exponentiation assignment operator which will first perform exponent operation and then assign the result to the variable on the left-hand side.

Bitwise AND Assignment Operator

The Bitwise AND Assignment Operator is used to perform Bitwise AND operation on both operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise AND assignment operator which will first perform Bitwise AND operation and then assign the result to the variable on the left-hand side.

Bitwise OR Assignment Operator

The Bitwise OR Assignment Operator is used to perform Bitwise OR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise OR assignment operator which will first perform bitwise OR operation and then assign the result to the variable on the left-hand side.

Bitwise XOR Assignment Operator 

The Bitwise XOR Assignment Operator is used to perform Bitwise XOR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise XOR assignment operator which will first perform bitwise XOR operation and then assign the result to the variable on the left-hand side.

Bitwise Right Shift Assignment Operator

The Bitwise Right Shift Assignment Operator is used to perform Bitwise Right Shift Operation on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise right shift assignment operator which will first perform bitwise right shift operation and then assign the result to the variable on the left-hand side.

Bitwise Left Shift Assignment Operator

The Bitwise Left Shift Assignment Operator is used to perform Bitwise Left Shift Opertator on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise left shift assignment operator which will first perform bitwise left shift operation and then assign the result to the variable on the left-hand side.

Walrus Operator

The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression.

Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop . The operator will solve the expression on the right-hand side and assign the value to the left-hand side operand ‘x’ and then execute the remaining code.

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Python List: How To Create, Sort, Append, Remove, And More

The Python list is one of the most used data structures, together with dictionaries . The Python list is not just a list, but can also be used as a stack and even a queue. In this article, I’ll explain everything you might possibly want to know about Python lists:

• how to create lists,
• modify them,
• how to sort lists,
• loop over elements of a list with a for-loop or a list comprehension ,
• how to slice a list,
• append to Python lists,
• … and more!

I’ve included lots of working code examples to demonstrate.

Table of Contents

• 1 How to create a Python list
• 2 Accessing Python list elements
• 3 Adding and removing elements
• 4 How to get List length in Python
• 5 Counting element occurrence in a list
• 6 Check if an item is in a list
• 7 Find the index of an item in a list
• 8 Loop over list elements
• 9 Python list to string
• 10 Sorting Python lists
• 12 Reversing Python lists
• 13 Learn more about Python lists

How to create a Python list

Let’s start by creating a list:

Lists contain regular Python objects, separated by commas and surrounded by brackets. The elements in a list can have any data type , and they can be mixed. You can even create a list of lists. The following lists are all valid:

Using the list() function

Python lists, like all Python data types, are objects. The class of a list is called ‘list’, with a lower case L. If you want to convert another Python object to a list, you can use the list() function, which is actually the constructor of the list class itself. This function takes one argument: an object that is iterable.

So you can convert anything that is iterable into a list. E.g., you can materialize the range function into a list of actual values, or convert a Python set or tuple into a list:

Accessing Python list elements

To access an individual list element, you need to know the position of that element. Since computers start counting at 0, the first element is in position 0, the second element is in position 1, etcetera.

Here are a few examples:

As you can see, you can’t access elements that don’t exist. In this case, Python throws an IndexError exception , with the explanation ‘list index out of range’. In my article on exceptions and the try and except statements , I’ve written about this subject more in-depth in the section on best practices. I recommend you to read it.

Get the last element of a list

If you want to get elements from the end of the list, you can supply a negative value. In this case, we start counting at -1 instead of 0. E.g., to get the last element of a list, you can do this:

Accessing nested list elements

Accessing nested list elements is not that much different. When you access an element that is a list, that list is returned. So to request an element in that list, you need to again use a couple of brackets:

Adding and removing elements

Let’s see how we can add and remove data. There are several ways to remove data from a list. What you use, depends on the situation you’re in. I’ll describe and demonstrate them all in this section.

Append to a Python list

List objects have a number of useful built-in methods, one of which is the append method. When calling append on a list, we append an object to the end of the list:

Combine or merge two lists

Another way of adding elements is adding all the elements from one list to the other. There are two ways to combine lists:

• ‘Add’ them together with the + operator.
• Add all elements of one list to the other with the extend method.

Here’s how you can add two lists together. The result is a new, third list:

The original lists are kept intact. The alternative is to extend one list with another, using the extend method:

While l1 got extended with the elements of l2, l2 stayed the same. Extend appended all values from l2 to l1.

Pop items from a list

The pop() method removes and returns the last item by default unless you give it an index argument.

Here are a couple of examples that demonstrate both the default behavior and the behavior when given an index:

If you’re familiar with the concept of a stack , you can now build one using only the append and pop methods on a list!

Using del() to delete items

There are multiple ways to delete or remove items from a list. While pop returns the item that is deleted from the list, del removes it without returning anything. In fact, you can delete any object, including the entire list, using del:

Remove specific values from a Python list

If you want to remove a specific value instead, you use the remove method. E.g. if you want to remove the first occurrence of the number 2 in a list, you can do so as follows:

As you can see, repeated calls to remove will remove additional twos, until there are none left, in which case Python throws a ValueError exception .

Remove or clear all items from a Python list

To remove all items from a list, use the clear() method:

Remove duplicates from a list

There is no special function or method to remove duplicates from a list, but there are multiple tricks that we can use to do so anyway. The simplest, in my opinion, is using a Python set . Sets are collections of objects, like lists, but can only contain one of each element. More formally, sets are unordered collections of distinct objects.

By converting the list to a set and then back to a list again, we’ve effectively removed all duplicates:

In your own code, you probably want to use this more compact version:

Since sets are very similar to lists, you may not even have to convert them back into a list. If the set offers what you need, use it instead to prevent a double conversion, making your program a little bit faster and more efficient.

Replace items in a list

To replace list items, we assign a new value to a given list index, like so:

How to get List length in Python

In Python, we use the len function to get the length of objects. This is true for lists as well:

If you’re familiar with other programming languages, like Java, you might wonder why Python has a function for this. After all, it could have been one of the built-in methods of a list too, like my_list.len() . This is because, in other languages, this often results in various ways to get an object’s length. E.g., some will call this function len , others will call it length, and yet someone else won’t even implement the function but simply offer a public member variable. And this is exactly the reason why Python chose to standardize the naming of such a common operation!

Counting element occurrence in a list

Don’t confuse the count function with getting the list length, it’s totally different. The built-in count function counts occurrences of a particular value inside that list. Here’s an example:

Since the number 1 occurs three times in the list, my_list.count(1) returned 3.

Check if an item is in a list

To check if an item is in a list, use the following syntax:

Find the index of an item in a list

We can find where an item is inside a list with the index method. For example, in the following list the 4 is located at position 3 (remember that we start counting at zero):

The index method takes two optional parameters: start and stop. With these, we can continue looking for more of the same values. We don’t need to supply an end value if we supply a start value. Now, let’s find both 4’s in the list below:

If you want to do more advanced filtering of lists, you should read my article on list comprehensions .

Loop over list elements

A list is iterable , so we can use a for-loop over the elements of a list just like we can with any other iterable with the ‘for <element> in <iterable>’ syntax:

Python list to string

In Python, you can convert most objects to a string with the str function:

If you’re interested, str is actually the Python string ‘s base class and calling str() constructs a new str object by calling the constructor of the str class. This constructor inspects the provided object and looks for a special method (also called dunder method) called __str__ . If it is present, this method is called. There’s nothing more to it.

If you create your own classes and objects , you can implement the __str__ function yourself to offer a printable version of your objects.

Sorting Python lists

To sort a Python list, we have two options:

• Use the built-in sort method of the list itself.
• Use Python’s built-in sorted() function.

Option one, the built-in method, offers an in-place sort, which means the list itself is modified. In other words, this function does not return anything. Instead, it modifies the list itself.

Option two returns a new list, leaving the original intact. Which one you use depends on the situation you’re in.

In-place list sort in ascending order

Let’s start with the simplest use-case: sorting in ascending order:

In-place list sort in descending order

We can call the sort method with a reverse parameter. If this is set to True, sort reverses the order:

Using sorted()

The following example demonstrates how to sort lists in ascending order, returning a new list with the result:

As you can see from the last statement, the original list is unchanged. Let’s do that again, but now in descending order:

Unsortable lists

We can not sort all lists, since Python can not compare all types with each other. For example, we can sort a list of numbers, like integers and floats, because they have a defined order. We can sort a list of strings as well since Python is able to compare strings too.

However, lists can contain any type of object, and we can’t compare completely different objects, like numbers and strings, to each other. In such cases, Python throws a TypeError :

Although the error might look a bit cryptic, it’s only logical when you know what’s going on. To sort a list, Python needs to compare the objects to each other. So in its sorting algorithm, at some point, it checks if ‘a’ < 1, hence the error: '<' not supported between instances of 'str' and 'int' .

Sometimes you need to get parts of a list. Python has a powerful syntax to do so, called slicing, and it makes working with lists a lot easier compared to some other programming languages. Slicing works on Python lists, but it also works on any other sequence type like strings , tuples , and ranges .

The slicing syntax is as follows:

my_list[start:stop:step]

A couple of notes:

• start is the first element position to include
• stop is exclusive, meaning that the element at position stop won’t be included.
• step is the step size. more on this later.
• start , stop , and step are all optional.
• Negative values can be used too.

To explain how slicing works, it’s best to just look at examples, and try for yourself, so that’s what we’ll do:

The step value

The step value is 1 by default. If you increase it, you can step over elements. Let’s try this:

Going backward

Like with list indexing, we can also supply negative numbers with slicing. Here’s a little ASCII art to show you how counting backward in a list works:

Just remember that you need to set a negative step size in order to go backward:

Reversing Python lists

There are actually three methods you can use to reverse a list in Python:

• An in-place reverse, using the built-in reverse method that every list has natively
• Using list slicing with a negative step size results in a new list
• Create a reverse iterator , with the reversed() function

In the following code crumb, I demonstrate all three. They are explained in detail in the following sections:

Using the built-in reverse method

The list.reverse() method does an in-place reverse, meaning it reorders the list. In other words, the method does not return a new list object, with a reversed order. Here’s how to use reverse() :

Reverse a list with list slicing

Although you can reverse a list with the list.reverse() method that every list has, you can do it with list slicing too, using a negative step size of -1. The difference here is that list slicing results in a new, second list. It keeps the original list intact:

Creating a reverse iterator

Finally, you can use the reversed() built-in function, which creates an iterator that returns all elements of the given iterable (our list) in reverse. This method is quite cheap in terms of CPU and memory usage. All it needs to do is walk backward over the iterable object. It’s doesn’t need to move around data and it doesn’t need to reserve extra memory for a second list. So if you need to iterate over a (large) list in reverse, this should be your choice.

Here’s how you can use this function. Keep in mind, that you can only use the iterator once, but that it’s cheap to make a new one:

Learn more about Python lists

Because there’s a lot to tell about list comprehensions, I created a dedicated article for the topic. A Python list comprehension is a language construct that we use to create a list based on an existing list, without creating for-loops .

Some other resources you might like:

• The official Python docs about lists.
• If you are interested in the internals: lists are often implemented internally as a linked list .
• Python also has arrays , which are very similar and the term will be familiar to people coming from other programming languages. They are more efficient at storing data, but they can only store one type of data.

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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

12 Beginner-Level Python List Exercises with Solutions

• python programming

Need to improve your Python programming skills? In this article, we give you 12 Python list exercises so you can master working with this common data structure.

A data structure is a way of organizing and storing data. Python has several built-in data structures, including lists, arrays, tuples, and dictionaries. They all have different properties, making them useful in different situations. See our article Array vs. List in Python - What’s the Difference? for some specific details. Lists are particularly useful since they’re very flexible. Therefore, becoming proficient in working with lists in Python is an important step towards becoming a good programmer.

In this article, we’ll teach you how Python lists work. We’ll go over 12 exercises designed to teach you different aspects of storing and manipulating data in lists. And don’t worry; detailed solutions are provided. We’ll start with beginner-friendly basics and work our way up to more advanced concepts.

Some of these exercises are taken directly from two of our courses, Python Basics Part 2 and Python Data Structures in Practice . These courses include 74 and 118 exercises, respectively. Other exercises in this article are inspired by the content available in the courses. They should give you a feel for the type and quality of the learning material available on LearnPython.com.

Before We Get Started...

To make sure we’re all on the same page before we start solving the exercises, let’s cover the basics of Python lists. An empty list can be created with square brackets ( [] ) or with Python’s  built-in list() function. We’ll use the first method to create an empty list called data:

We can organize and store data in a list. Let’s create the list again, but this time with some integers separated by a comma:

A list can store many different data types. Let’s define a list with four strings:

When a list is created, each element is assigned a unique integer index. The first element has the index 0, the second has the index 1, and so on.

Python lists have many methods associated with them, some of which we’ll be using in these exercises. For some background reading, check out An Overview of Python List Methods . That’s about all you need to know at this point. Let’s get started with some exercises.

Exercise 1: Accessing List Elements... or Not

Here are a series of mini-exercises to get you warmed up. Using the colors list defined above, print the:

• First element.
• Second element.
• Last element.
• Second-to-last element.
• Second and third elements.
• Element at index 4.
• print(colors[0]) 'red'
• print(colors[1]) 'blue'
• print(colors[-1]) 'yellow'
• print(colors[-2]) 'green'
• print(colors[1:3]) ['blue', 'green']
• print(colors[4]) IndexError: list index out of range

This exercise is very fundamental to understanding how lists work. The key to solving this problem is to remember that indexing in Python starts from zero. That is, the first element has the index 0, the second has the index 1, and so on. Also, the last element has the index -1, the second to last has the index -2, and so on.

Finally, trying to access an element using an index which isn’t present gives an IndexError . You’ll have to become friends with this error, because you’ll meet it all the time in your programming career. For more details on this exercise, check out the article How to Fix the “List index out of range” Error in Python .

In this exercise, we use the print() function to display the results. Since you’ll be using this built-in function often, check out Printing a List in Python – 10 Tips and Tricks for more details about how to use this function.

Exercise 2: Assigning New Values to List Elements

Below is a list with seven integer values representing the daily water level (in cm) in an imaginary lake. However, there is a mistake in the data. The third day’s water level should be 693. Correct the mistake and print the changed list.

This exercise builds off the previous one. You need to access the third element, which has the index 2, and simply reassign the value to 693. Then you print the modified list to see if the change was done correctly.

Exercise 3: Adding New Elements to the List

Add the data for the eighth day to the list from above. The water level was 772 cm on that day. Print the list contents afterwards.

The list.append() method is one of the most important list methods. It’s used to add a new element to the end of the list. After doing so, the length of the list will increase by one.

Exercise 4: Adding Multiple New Elements to the List

Still using the same list, add three consecutive days using a single instruction. The water levels on the 9 th through 11 th days were 772 cm, 770 cm, and 745 cm. Add these values and then print the whole list.

We first have to define a new list, which we call new_data . This new list contains the three consecutive values we want to add to the water_level list. Then we use the addition operator ( + ) to concatenate the two lists. Using the assignment operator ( = ), we assign the results back to the water_level list for the changes to be permanent. Finally, we print the modified list to check the results.

Exercise 5: Deleting a Value from the List

There are two ways to delete data from a list: by using the index or by using the value. Start with the original water_level list we defined in the second exercise and delete the first element using its index. Then define the list again and delete the first element using its value.

Start by defining the list. To delete the first element using its index, use the del operator. Note: Make sure you define the correct element to delete. If you just execute del water_level , you’ll delete the whole list.

For the second method, start again by defining the list. To delete the first element based on its value, use the list.remove() method. It takes one argument: the value to delete. If the value occurs multiple times in the list, only the first one will be deleted.

Exercise 6: Conditional Statements

You are given a list of 12 values in a variable named tourist_arrivals . The values represent the number of tourists (in millions) that visited France in each month of 2016.

Write a program that will ask the user for a number and will check if there is any month with a value that exactly matches the input number. If there is such a month, print: ‘Value found’. Otherwise, print ‘Value not found’.

We start with our list, which contains floats. Using the input() built-in function, we can accept user input from the keyboard and assign the input to the variable number. Then, using an if-else statement, we test if number is in our list. If so, we print ‘Value found’; if not, we print ‘Value not found’.

Exercise 7: Iterating Over List Elements by Value

Below is a list containing Martha’s monthly spending. Her monthly spending falls into three categories:

• Low: Less than0
• Medium: Between 2000.0 and 3000.0
• High: Greater than0

Analyze monthly_spending , create low , medium and high variables, and print the following sentence:

We start by creating three new variables: low , medium , and high , all initialized to zero to store the number of months with spending in the respective categories. Then we loop through the values of the list and use an if-elif-else block to check which category that amount falls into. We use the += operator to increase the counters by one, and in the final line we print the results.

Exercise 8: Iterating Over List Elements by Index

Martha is interested in knowing her average expenses for each half of the year. Compute her average expenses for the first half of the year (January to June) and for the second half of the year (July to December). Display the information as follows:

• Average expense Jan-June: {1st half-year average}
• Average expense July-Dec: {2nd half-year average}

We start with our monthly_spending list. To store the results, we create a list with two elements, both zero. Then we loop through the indexes of the list. The index tells us the month (0 = January, 1 = February, …, 6 = July, … 11 = December). We test if the expense is from earlier than July by testing if the index is less than 6. If so, we use the index to access the value from the monthly_spending list and add the expense to the first element of our sum_expenses list. If not, we add the expense to the second element of our sum_expenses list.

In the Python Data Structures in Practice course, we show you a more concise way to solve the same problem that takes advantage of the enumerate() built-in function.

Exercise 9: Creating New Lists Based on Existing Ones

Martha’s expenses are currently expressed in USD. Create a new list named monthly_spending_eur that contains the same expenditures converted into euros. Use the exchange rate of 1 USD = 0.88 EUR. Round the EUR value to two decimal places using round(value, 2) . Then, print monthly_spending_eur list.

The key to solving this exercise is to initialize an empty list outside the loop. This list will store our expenses in euros. Then we loop through the expenses and append the converted amounts to the new list with the list.append() method. Finally, we print the new list.

Exercise 10: Comparing Two Lists of Different Sizes

In 2017, Martha had two credit cards: one that she used during the whole year, and another that she only used for the first six months. Below you have the monthly spending values from both cards. Your task is to create a new list containing total monthly spending for the entirety of 2017. Round the sums to integers and print the list.

We first need to figure out how long the longest list is. This is what the first if-else statement does. Using the built-in function len() , the length is saved as the variable longer_len . We then loop through all integers from 0 to l onger_len-1 using the built-in range() function. These integers represent the index of our lists.

At the top of the loop, we define another variable monthly_total , which is zero. We have another if statement to check if our integer is less than the length of the respective lists. If so, we add the amount from either or both the spending_card1 or spending_card2 lists to the monthly_total variable. This variable then gets appended to our total_spending list.

Working with more than one list at a time is common. Our article How to Loop Over Multiple Lists in Python contains more examples that show you how to handle multiple Python lists.

Exercise 11: Working with Lists Inside Functions

Write a function named get_long_words(input_list, min_len) . The function accepts a list of strings and returns a new list containing only words at least as long as the minimum length provided as the second argument ( min_len ). Use the list below to test your function:

Exercise 12: Modifying Lists in a Function

Create a function called get_absolute_values() that takes a list and modifies it so that all numbers are given as absolute values (i.e., get rid of the minus sign for negative numbers). do not return anything. test the function using the following list:.

Here we loop through the input list using the index of the elements. Using the index, we test if the element is less than zero. If so, we change the sign of the element in the original list. Test the function by running:

Since the function doesn’t return anything, you’ll need to call the absolutes list again to check how it has been modified. In the course, we also showed you how to do the same by not modifying the original list. It’s important to understand the differences between these two cases.

Do You Need More Python List Exercises?

The course material presented here exposes you to many programming concepts and the exercises are structured to build on top of each other. In this way, you progressively develop new skills. The material in the Python Basics Part 2 and Python Data Structures in Practice courses also shows you how not to solve problems and why certain approaches don’t work. For example, if you want to delete several elements from your list in a loop, you can’t just use the list.remove() method. In the courses, we explain why and show you the correct way.

The courses also cover many more aspects of working with Python lists than we can show in this article, such as sorting lists. For an overview of this topic,  read the articles How to Sort a List of Tuples in Python and How to Sort a List Alphabetically in Python . Happy learning, and we’ll see you in the next article.

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Python Lists

Python has a great built-in list type named "list". List literals are written within square brackets [ ]. Lists work similarly to strings -- use the len() function and square brackets [ ] to access data, with the first element at index 0. (See the official python.org list docs .)

Assignment with an = on lists does not make a copy. Instead, assignment makes the two variables point to the one list in memory.

The "empty list" is just an empty pair of brackets [ ]. The '+' works to append two lists, so [1, 2] + [3, 4] yields [1, 2, 3, 4] (this is just like + with strings).

Python's *for* and *in* constructs are extremely useful, and the first use of them we'll see is with lists. The *for* construct -- for var in list -- is an easy way to look at each element in a list (or other collection). Do not add or remove from the list during iteration.

If you know what sort of thing is in the list, use a variable name in the loop that captures that information such as "num", or "name", or "url". Since Python code does not have other syntax to remind you of types, your variable names are a key way for you to keep straight what is going on. (This is a little misleading. As you gain more exposure to python, you'll see references to type hints which allow you to add typing information to your function definitions. Python doesn't use these type hints when it runs your programs. They are used by other programs such as IDEs (integrated development environments) and static analysis tools like linters/type checkers to validate if your functions are called with compatible arguments.)

The *in* construct on its own is an easy way to test if an element appears in a list (or other collection) -- value in collection -- tests if the value is in the collection, returning True/False.

The for/in constructs are very commonly used in Python code and work on data types other than list, so you should just memorize their syntax. You may have habits from other languages where you start manually iterating over a collection, where in Python you should just use for/in.

You can also use for/in to work on a string. The string acts like a list of its chars, so for ch in s: print(ch) prints all the chars in a string.

The range(n) function yields the numbers 0, 1, ... n-1, and range(a, b) returns a, a+1, ... b-1 -- up to but not including the last number. The combination of the for-loop and the range() function allow you to build a traditional numeric for loop:

There is a variant xrange() which avoids the cost of building the whole list for performance sensitive cases (in Python 3, range() will have the good performance behavior and you can forget about xrange()).

Python also has the standard while-loop, and the *break* and *continue* statements work as in C++ and Java, altering the course of the innermost loop. The above for/in loops solves the common case of iterating over every element in a list, but the while loop gives you total control over the index numbers. Here's a while loop which accesses every 3rd element in a list:

List Methods

Here are some other common list methods.

• list.append(elem) -- adds a single element to the end of the list. Common error: does not return the new list, just modifies the original.
• list.insert(index, elem) -- inserts the element at the given index, shifting elements to the right.
• list.extend(list2) adds the elements in list2 to the end of the list. Using + or += on a list is similar to using extend().
• list.index(elem) -- searches for the given element from the start of the list and returns its index. Throws a ValueError if the element does not appear (use "in" to check without a ValueError).
• list.remove(elem) -- searches for the first instance of the given element and removes it (throws ValueError if not present)
• list.sort() -- sorts the list in place (does not return it). (The sorted() function shown later is preferred.)
• list.reverse() -- reverses the list in place (does not return it)
• list.pop(index) -- removes and returns the element at the given index. Returns the rightmost element if index is omitted (roughly the opposite of append()).

Notice that these are *methods* on a list object, while len() is a function that takes the list (or string or whatever) as an argument.

Common error: note that the above methods do not *return* the modified list, they just modify the original list.

List Build Up

One common pattern is to start a list as the empty list [], then use append() or extend() to add elements to it:

List Slices

Slices work on lists just as with strings, and can also be used to change sub-parts of the list.

Exercise: list1.py

To practice the material in this section, try the problems in list1.py that do not use sorting (in the Basic Exercises ).

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2024-07-23 UTC.

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 lists.

Lists are used to store multiple items in a single variable.

Lists are one of 4 built-in data types in Python used to store collections of data, the other 3 are Tuple , Set , and Dictionary , all with different qualities and usage.

Lists are created using square brackets:

Create a List:

List items are ordered, changeable, and allow duplicate values.

List items are indexed, the first item has index [0] , the second item has index [1] etc.

When we say that lists are ordered, it means that the items have a defined order, and that order will not change.

If you add new items to a list, the new items will be placed at the end of the list.

Note: There are some list methods that will change the order, but in general: the order of the items will not change.

The list is changeable, meaning that we can change, add, and remove items in a list after it has been created.

Allow Duplicates

Since lists are indexed, lists can have items with the same value:

Lists allow duplicate values:

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List Length

To determine how many items a list has, use the len() function:

Print the number of items in the list:

List Items - Data Types

List items can be of any data type:

String, int and boolean data types:

A list can contain different data types:

A list with strings, integers and boolean values:

From Python's perspective, lists are defined as objects with the data type 'list':

What is the data type of a list?

The list() Constructor

It is also possible to use the list() constructor when creating a new list.

Using the list() constructor to make a List:

Python Collections (Arrays)

There are four collection data types in the Python programming language:

• List is a collection which is ordered and changeable. Allows duplicate members.
• Tuple is a collection which is ordered and unchangeable. Allows duplicate members.
• Set is a collection which is unordered, unchangeable*, and unindexed. No duplicate members.
• Dictionary is a collection which is ordered** and changeable. No duplicate members.

*Set items are unchangeable, but you can remove and/or add items whenever you like.

**As of Python version 3.7, dictionaries are ordered . In Python 3.6 and earlier, dictionaries are unordered .

When choosing a collection type, it is useful to understand the properties of that type. Choosing the right type for a particular data set could mean retention of meaning, and, it could mean an increase in efficiency or security.

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Python Programming

Practice Python Exercises and Challenges with Solutions

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

What included in these Python Exercises?

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

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

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

Select the exercise you want to solve .

Basic Exercise for Beginners

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

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

Python Input and Output Exercise

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

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

Python Loop Exercise

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

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

Python Functions Exercise

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

Topics : Functions arguments, built-in functions.

Python String Exercise

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

Python Data Structure Exercise

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

Python List Exercise

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

Python Dictionary Exercise

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

Python Set Exercise

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

Python Tuple Exercise

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

Python Date and Time Exercise

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

Topics : Date, time, DateTime, Calendar.

Python OOP Exercise

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

Topics : Object, Classes, Inheritance

Python JSON Exercise

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

Python NumPy Exercise

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

Python Pandas Exercise

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

Python Matplotlib Exercise

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

Random Data Generation Exercise

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

Topics : random module, secrets module, UUID module

Python Database Exercise

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

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

Exercises for Intermediate developers

The following practice questions are for intermediate Python developers.

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

Exercise 1: Reverse each word of a string

Expected Output

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

Steps to solve this question :

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

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

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

Expected Output :

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

Steps to solve this question : -

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

Exercise 3: Remove items from a list while iterating

Description :

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

Remove numbers greater than 50

Expected Output : -

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

Solution 1: Using while loop

Solution 2: Using for loop and range()

Exercise 4: Reverse Dictionary mapping

Exercise 5: display all duplicate items from a list.

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

Solution 1 : - Using collections.Counter()

Solution 2 : -

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

Exercise 7: print the following number pattern.

Refer to Print patterns in Python to solve this question.

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

Exercise 8: Create an inner function

Question description : -

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

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

Change the element 35 to 3500

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

Under Exercises: -

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

Updated on:  December 8, 2021 | 51 Comments

Python Date and Time Exercise with Solutions

Updated on:  December 8, 2021 | 10 Comments

Python Dictionary Exercise with Solutions

Updated on:  May 6, 2023 | 56 Comments

Python Tuple Exercise with Solutions

Updated on:  December 8, 2021 | 96 Comments

Python Set Exercise with Solutions

Updated on:  October 20, 2022 | 27 Comments

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

Updated on:  July 6, 2024 | 295 Comments

Updated on:  August 2, 2022 | 154 Comments

Updated on:  September 6, 2021 | 109 Comments

Python List Exercise with Solutions

Updated on:  December 8, 2021 | 200 Comments

Updated on:  December 8, 2021 | 7 Comments

Python Data Structure Exercise for Beginners

Updated on:  December 8, 2021 | 116 Comments

Python String Exercise with Solutions

Updated on:  October 6, 2021 | 221 Comments

Updated on:  March 9, 2021 | 23 Comments

Updated on:  March 9, 2021 | 51 Comments

Updated on:  July 20, 2021 | 29 Comments

Python Basic Exercise for Beginners

Updated on:  August 31, 2023 | 493 Comments

Useful Python Tips and Tricks Every Programmer Should Know

Updated on:  May 17, 2021 | 23 Comments

Python random Data generation Exercise

Updated on:  December 8, 2021 | 13 Comments

Python Database Programming Exercise

Updated on:  March 9, 2021 | 17 Comments

• Online Python Code Editor

Updated on:  June 1, 2022 |

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MLB Trade Rumors

Rangers Designate Andrew Knizner For Assignment

By Darragh McDonald | August 6, 2024 at 4:30pm CDT

The Rangers announced that right-hander Tyler Mahle has been reinstated from the 60-day injured list. In corresponding moves, the club optioned left-hander Walter Pennington and designated catcher Andrew Knizner for assignment.

Knizner, 29, signed with the Rangers in the offseason after being non-tendered by the Cardinals. Texas gave him a one-year deal with a $1.825MM salary, knowing that he could be retained via arbitration beyond this season as well.

Unfortunately, the club has been struggling to get much production from the catcher position this year. Jonah Heim hit .258/.317/.438 last year for a 103 wRC+ but he has dropped to a line of .232/.277/.346 and a 73 wRC+ this year. The falloff from Knizner has been even more drastic as he slashed .241/.288/.424 with the Cards last year for a 92 wRC+ but he is hitting .167/.183/.211 this year for a wRC+ of 4.

Perhaps some of that can be attributed to a .206 batting average on balls in play but Knizner has also drawn walks at a paltry 1.1% clip and hit just one home run in his 93 plate appearances, compared to the ten he hit in 241 trips to the plate last year. The Rangers fortified their catching corps by acquiring Carson Kelly from the Tigers prior to the deadline and then optioned Knizner to Triple-A, though he has now been bumped off the 40-man roster altogether.

With the trade deadline now passed, the Rangers will have to put Knizner on waivers in the coming days. Despite his rough season, he could perhaps garner interest based on his past performance and contract status.

He has one option left and therefore a claiming club wouldn’t need to give him an active roster spot right away, though he would be out of options next year in that scenario. He has not yet spent 20 days on optional assignment this year, so it’s possible he could retain that option next year if he either doesn’t get claimed or is kept in the majors by some other club. He also came into this season with four years and 21 days of service time, putting him just shy of the five-year mark at present. If any club felt especially bullish about Knizner’s future, they could claim him, keep him on optional assignment for the rest of the year and then control him via arbitration for two more years.

As for Mahle, he will be taking the mound for the first time in over a year. He required Tommy John surgery in May of last year, just a few months from free agency. The Rangers signed him to a two-year, $22MM deal with the knowledge that they would have to wait for his arrival.

With Mahle and Jacob deGrom both on their way back from surgeries last year, the club felt good enough about their rotation depth to deal Michael Lorenzen to the Royals prior to the deadline. But both Jon Gray and Max Scherzer recently landed on the IL, thinning the group out further. As of right now, the group consists of Mahle, Nathan Eovaldi , Andrew Heaney , José Ureña and Cody Bradford , with Dane Dunning in a long-relief role in the bullpen.

4 hours ago

I thought he would a great depth signing before the season, but he’s been really bad. To be fair most of the offense has been off this year, but he has looked lost, not just off, for long stretches.

3 hours ago

Ironically Knizner was part of the reason the Cardinals were willing to give up Carson Kelly in the Goldschmidt trade.

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Python 3.13.0rc1

Release Date: Aug. 1, 2024

This is the first release candidate of Python 3.13.0

This release, 3.13.0rc1 , is the penultimate release preview. Entering the release candidate phase, only reviewed code changes which are clear bug fixes are allowed between this release candidate and the final release. The second candidate (and the last planned release preview) is scheduled for Tuesday, 2024-09-03, while the official release of 3.13.0 is scheduled for Tuesday, 2024-10-01.

There will be no ABI changes from this point forward in the 3.13 series, and the goal is that there will be as few code changes as possible.

Call to action

We strongly encourage maintainers of third-party Python projects to prepare their projects for 3.13 compatibilities during this phase, and where necessary publish Python 3.13 wheels on PyPI to be ready for the final release of 3.13.0. Any binary wheels built against Python 3.13.0rc1 will work with future versions of Python 3.13. As always, report any issues to the Python bug tracker .

Please keep in mind that this is a preview release and while it's as close to the final release as we can get it, its use is not recommended for production environments.

Core developers: time to work on documentation now

• Are all your changes properly documented?
• Are they mentioned in What's New ?
• Did you notice other changes you know of to have insufficient documentation?

Major new features of the 3.13 series, compared to 3.12

Some of the new major new features and changes in Python 3.13 are:

New features

• A new and improved interactive interpreter , based on PyPy 's, featuring multi-line editing and color support, as well as colorized exception tracebacks .
• An experimental free-threaded build mode , which disables the Global Interpreter Lock, allowing threads to run more concurrently. The build mode is available as an experimental feature in the Windows and macOS installers as well.
• A preliminary, experimental JIT , providing the ground work for significant performance improvements.
• The locals() builtin function (and its C equivalent) now has well-defined semantics when mutating the returned mapping , which allows debuggers to operate more consistently.
• The (cyclic) garbage collector is now incremental , which should mean shorter pauses for collection in programs with a lot of objects.
• A modified version of mimalloc is now included, optional but enabled by default if supported by the platform, and required for the free-threaded build mode.
• Docstrings now have their leading indentation stripped , reducing memory use and the size of .pyc files. (Most tools handling docstrings already strip leading indentation.)
• The dbm module has a new dbm.sqlite3 backend that is used by default when creating new files.
• The minimum supported macOS version was changed from 10.9 to 10.13 (High Sierra) . Older macOS versions will not be supported going forward.
• WASI is now a Tier 2 supported platform . Emscripten is no longer an officially supported platform (but Pyodide continues to support Emscripten).
• iOS is now a Tier 3 supported platform , with Android on the way as well .
• Support for type defaults in type parameters .
• A new type narrowing annotation , typing.TypeIs .
• A new annotation for read-only items in TypeDicts .
• A new annotation for marking deprecations in the type system .

Removals and new deprecations

• PEP 594 (Removing dead batteries from the standard library) scheduled removals of many deprecated modules: aifc , audioop , chunk , cgi , cgitb , crypt , imghdr , mailcap , msilib , nis , nntplib , ossaudiodev , pipes , sndhdr , spwd , sunau , telnetlib , uu , xdrlib , lib2to3 .
• Many other removals of deprecated classes, functions and methods in various standard library modules.
• C API removals and deprecations . (Some removals present in alpha 1 were reverted in alpha 2, as the removals were deemed too disruptive at this time.)
• New deprecations , most of which are scheduled for removal from Python 3.15 or 3.16.

(Hey, fellow core developer, if a feature you find important is missing from this list, let Thomas know .)

For more details on the changes to Python 3.13, see What's new in Python 3.13 . The next pre-release of Python 3.13 will be 3.13.0rc2, the final release candidate , currently scheduled for 2024-09-03.

More resources

• Online Documentation
• PEP 719 , 3.13 Release Schedule
• Report bugs at https://github.com/python/cpython/issues .
• Help fund Python directly (or via GitHub Sponsors ), and support the Python community .

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Introducing the Anaconda Code add-in for Microsoft Excel

Emilie Lewis

Timothy Hewitt

Excel and Python users can now run their Python-powered projects in Excel locally with the Anaconda Code add-in

“I wish there was a way for me to run my Python in Excel locally, without having to run my calculations through Microsoft Cloud.” 

Since Python in Excel was introduced in August 2023, a clear piece of feedback we’ve heard from the community was the desire to run Python locally rather than through Microsoft Cloud, which is what happens when you use Python in Excel. 

Today we are launching the public beta of Anaconda Code, which allows users to run Python code locally in Excel. The technology behind Anaconda Code is powered by PyScript, an Anaconda supported open source project that runs Python locally without install and setup. If you want to learn more about PyScript, visit pyscript.net . 

By bridging the gap between traditional spreadsheet use and advanced coding practices, this solution grants users access to a wider Python ecosystem, enhancing data analysis capabilities while maintaining Excel’s core strengths.

Users can start using Anaconda Code today by visiting App Source , Microsoft’s add-in marketplace, and downloading the Anaconda Toolbox.  

What are the Key Differences Between Python in Excel and Anaconda Code? 

For users looking for more control over their environments in Microsoft Excel or faster performance, Anaconda Code is a great option for running Python code while in Excel. 

Some other key differences include: 

Cells Run Independently

In addition to running Python code cells in “row major order” – meaning any cells with Python code will re-run anytime any Python cell has a change – Anaconda Code can also run cells independently, so the cells containing Python are only re-run if the cell itself is modified. Users can’t reference variables defined in other cells, though they can reference the value returned from a cell, resulting in a much faster run. There is the option to run your Python code in row-major order, but it is not the default setting. 

Range to Multiple Types

When you’re working with a table of data in Python, a pandas dataframe may be just the thing for you. But sometimes you want a NumPy array, or maybe all you need is a list. With Anaconda Code, you have the freedom to choose.

Editable Initialization

Currently, Microsoft Excel’s init.py file is static and cannot be edited. With Anaconda Code, users have the ability to access and edit imports and definitions, allowing you to write top-level functions and classes and reuse them wherever you need. 

A Customizable Environment 

Anaconda Code allows users to customize their environment to their specific needs. Users can simply select which packages they want to use from PyPI (WASM limitations do apply) and get started. Once an environment is created, it will be pinned so when users share workbooks, the spreadsheet will retain the exact environment for all other users. 

Start Using Anaconda Code Today

Anaconda Code is currently in public beta and can be accessed through the Anaconda Toolbox add-in available in AppSource , Microsoft’s add-in marketplace. Users will need to have or create an Anaconda.cloud account to gain full access to Anaconda Toolbox and Anaconda Code. 

Users can leave feedback and ask questions by visiting the Anaconda Code topic page on community.anaconda.cloud.

You may also be interested in:

Anaconda recognized in insidebigdata’s impact 50 list.

Announcing Python in Excel: Next-Level Data Analysis for All

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Python list of lists assignment

One thing first: No, this is not a question about why

gives [1,1,1,1,1,1,1,1,1,1].

My question is a bit more tricky: I do the folling:

Now I assign a value:

And now another one:

It gives me an IndexError: list assignment index out of range.

How can I make this work? As you can see, I want an empty list that contains 3 other lists to which i can append values.

Thanks in advance!

• 1 Did you look at what x[0] = 1 actually does? It replaces one of your nested lists with the number 1. If you want to append values, you should use append , not item assignment. –  BrenBarn Commented Apr 29, 2014 at 8:19
• yes you are right. but this is only an example. in my implementation, i do use append ;) –  PKlumpp Commented Apr 29, 2014 at 8:31

4 Answers 4

I want an empty list that contains 3 other lists to which i can append values.

Your problem correctly stated is: "I want a list of three empty lists" (something cannot be empty and contain things at the same time).

If you want that, why are you doing it in such a convoluted way? What's wrong with the straightforward approach:

If you have such a list, when you do empty_list[0] = 1 , what you end up with is:

Instead, you should do empty_list[0].append(1) , which will give you:

If you want to create an arbitrary number of nested lists:

• good question! the answer is simple: my 3 lists in one list were only an example. originally, i wanted to put 15 lists into a list. of course, your way is working, but it looks somewhat weird if i implement it that way. i just wanted to learn if there is an advanced way of doing this. but thanks ;) –  PKlumpp Commented Apr 29, 2014 at 8:29

gives [1,1,1,1,1,1,1,1,1,1] ? It's an index error (you can't assign at a nonexisting index), because actually []*10 == [] . Your code does exactly the same (I mean an index error). This line

is short (or not) for

so x[1] is an index error.

• sry, you are right, i forgot brackets there. Yes i know the index does not exist, that is what the error message already told me. but how do i solve my problem? –  PKlumpp Commented Apr 29, 2014 at 8:27
• @ZerO I want an empty list that contains 3 other lists I don't understand - it can be empty and contain something at the same time. If you want to append something to a list, then use .append . See Burhan's answer. –  freakish Commented Apr 29, 2014 at 9:04

Maybe something like this could work.

The output would be:

If you would like to access/add elements, you could:

• yes, this does work. but i need a multi-assignment ;) –  PKlumpp Commented Apr 29, 2014 at 11:35

Comparing the output of:

may help answer your finding.

• are you kidding me? Did you even read my question? I think i clearly showed that this is NOT the problem -.- –  PKlumpp Commented Apr 30, 2014 at 6:56
• @ZerO: Did you even try the above code? I see no bearing on your question apart from the subtle but significant difference shown by the above. You have a 10 in your first example, and a 3 in your second. Why? If you already knew the difference between multiplication of lists versus generators, then print i and print i[0] and print i[1] as well and see why you are getting an IndexError . I am on SO thinking I like to learn and help, and I may be wrong in my answer. –  vapace Commented Apr 30, 2014 at 16:32

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Diamondbacks Designate Miguel Castro For Assignment, Cristian Mena to IL

Alex d'agostino | jul 30, 2024.

• Arizona Diamondbacks

According to the team, the Arizona Diamondbacks are designating right-handed reliever Miguel Castro for assignment. The DFA comes as a corresponding move, freeing a roster space for newly-acquired reliever Dylan Floro .

Castro rode a tumultuous caree r with the D-backs. Arizona had signed the hard-throwing righty to a deal ahead of the 2023 season, complete with a vesting option for 2024.

Castro began the 2023 season to great success. Through June 2nd, he posted a brilliant 2.13 ERA and was pitching extremely well in a high-leverage role, including part-time closing duties.

A disastrous blown save against the Atlanta Braves in his next outing was the beginning of Castro's decline. He finished the 2023 season with a 4.31 ERA, identically matched with a 4.31 FIP. His strikeouts were down, and his home runs spiked.

Part of his overall drop in numbers might have come as a result of extreme use, as the reliever pitched 64 2/3 regular season innings in 2023, before pitching another eight in the postseason.

In 2024, Castro only appeared in eight games to begin the season, pitching to a 5.19 ERA over 8 2/3 innings. He was then placed on the 15-day IL on April 23rd, dealing with right shoulder inflammation.

He wouldn't make his next appearance until July 13th. He made three appearances, allowing eight hits, two walks and two home runs, leading to four earned runs over five innings. He only threw 13 2/3 innings for Arizona this season.

His sinker, which profiled as a high-powered pitch, complete with both velocity and movement, had dropped significantly in velocity. Although averaging 96.8 MPH on the pitch in 2023 , Monday night's appearance saw the sinker top out at 93.8 MPH.

With Dylan Floro already in Phoenix and able to join the club right away, Castro will be the odd man out, and will, in all likelihood, end his tenure with the D-backs' organization.

Cristian Mena Placed on 7-day IL

In addition to Castro's DFA, the D-backs will make a roster move in their minor league system.

Right-handed starting pitcher Cristian Mena, who made his MLB debut in early July, was placed on the 7-day injured list by the Triple-A Reno Aces. Baseball America lists him as Arizona's No.12 prospect.

D-backs Farm Director Shaun Larkin confirmed to Sports Illustrated it was a forearm strain, and the righty won't throw for eight weeks, likely ending his season.

Mena, 21, started one game for the D-backs, going just three-plus innings against the Los Angeles Dodgers. He allowed three walks, four hits and two home runs on 70 pitches, and was charged with four earned runs.

Since coming over from the Chicago White Sox in a deal for outfielder Dominic Fletcher, Mena has had a mixed bag season in the minors. He began the year strong, but his ERA spiked, currently sitting at 4.61, albeit in the very hitter-friendly Pacific Coast League.

His last outing saw him go six strong innings, allowing just one unearned run on four hits whole striking out six, but it appears unlikely he'll have the chance to continue an upward trend in 2024.

ALEX D'AGOSTINO

Born and raised in the desert, Alex is a lifelong follower of Arizona sports. Alex also writes for Sports Illustrated/FanNation's All Cardinals, and previously covered the Diamondbacks for FanSided's VenomStrikes. Follow Alex on Twitter @AlexDagAZ