if with assignment python

Python Conditional Assignment

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

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

1. Using Ternary Operator

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

It goes like this:

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

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

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

2. Using if-else statement

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

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

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

3. Using Logical Short Circuit Evaluation

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

The format of logical short circuit evaluation is:

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

Let's see an example:

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

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

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

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

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

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

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

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

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

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

Happy coding! 😊

Python One Line Conditional Assignment

Problem : How to perform one-line if conditional assignments in Python?

Example : Say, you start with the following code.

You want to set the value of x to 42 if boo is True , and do nothing otherwise.

Let’s dive into the different ways to accomplish this in Python. We start with an overview:

Exercise : Run the code. Are all outputs the same?

Next, you’ll dive into each of those methods and boost your one-liner superpower !

Method 1: Ternary Operator

The most basic ternary operator x if c else y returns expression x if the Boolean expression c evaluates to True . Otherwise, if the expression c evaluates to False , the ternary operator returns the alternative expression y .

Let’s go back to our example problem! You want to set the value of x to 42 if boo is True , and do nothing otherwise. Here’s how to do this in a single line:

While using the ternary operator works, you may wonder whether it’s possible to avoid the ...else x part for clarity of the code? In the next method, you’ll learn how!

If you need to improve your understanding of the ternary operator, watch the following video:

The Python Ternary Operator -- And a Surprising One-Liner Hack

You can also read the related article:

Python One Line Ternary

Method 2: Single-Line If Statement

Like in the previous method, you want to set the value of x to 42 if boo is True , and do nothing otherwise. But you don’t want to have a redundant else branch. How to do this in Python?

The solution to skip the else part of the ternary operator is surprisingly simple— use a standard if statement without else branch and write it into a single line of code :

To learn more about what you can pack into a single line, watch my tutorial video “If-Then-Else in One Line Python” :

Method 3: Ternary Tuple Syntax Hack

A shorthand form of the ternary operator is the following tuple syntax .

Syntax : You can use the tuple syntax (x, y)[c] consisting of a tuple (x, y) and a condition c enclosed in a square bracket. Here’s a more intuitive way to represent this tuple syntax.

In fact, the order of the <OnFalse> and <OnTrue> operands is just flipped when compared to the basic ternary operator. First, you have the branch that’s returned if the condition does NOT hold. Second, you run the branch that’s returned if the condition holds.

Clever! The condition boo holds so the return value passed into the x variable is the <OnTrue> branch 42 .

Don’t worry if this confuses you—you’re not alone. You can clarify the tuple syntax once and for all by studying my detailed blog article.

Related Article : Python Ternary — Tuple Syntax Hack

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Module 2: The Essentials of Python »
Conditional Statements
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Conditional Statements 

There are reading-comprehension exercises included throughout the text. These are meant to help you put your reading to practice. Solutions for the exercises are included at the bottom of this page.

In this section, we will be introduced to the if , else , and elif statements. These allow you to specify that blocks of code are to be executed only if specified conditions are found to be true, or perhaps alternative code if the condition is found to be false. For example, the following code will square x if it is a negative number, and will cube x if it is a positive number:

Please refer to the “Basic Python Object Types” subsection to recall the basics of the “boolean” type, which represents True and False values. We will extend that discussion by introducing comparison operations and membership-checking, and then expanding on the utility of the built-in bool type.

Comparison Operations 

Comparison statements will evaluate explicitly to either of the boolean-objects: True or False . There are eight comparison operations in Python:

The first six of these operators are familiar from mathematics:

Note that = and == have very different meanings. The former is the assignment operator, and the latter is the equality operator:

Python allows you to chain comparison operators to create “compound” comparisons:

Whereas == checks to see if two objects have the same value, the is operator checks to see if two objects are actually the same object. For example, creating two lists with the same contents produces two distinct lists, that have the same “value”:

Thus the is operator is most commonly used to check if a variable references the None object, or either of the boolean objects:

Use is not to check if two objects are distinct:

bool and Truth Values of Non-Boolean Objects 

Recall that the two boolean objects True and False formally belong to the int type in addition to bool , and are associated with the values 1 and 0 , respectively:

Likewise Python ascribes boolean values to non-boolean objects. For example,the number 0 is associated with False and non-zero numbers are associated with True . The boolean values of built-in objects can be evaluated with the built-in Python command bool :

and non-zero Python integers are associated with True :

The following built-in Python objects evaluate to False via bool :

Zero of any numeric type: 0 , 0.0 , 0j

Any empty sequence, such as an empty string or list: '' , tuple() , [] , numpy.array([])

Empty dictionaries and sets

Thus non-zero numbers and non-empty sequences/collections evaluate to True via bool .

The bool function allows you to evaluate the boolean values ascribed to various non-boolean objects. For instance, bool([]) returns False wherease bool([1, 2]) returns True .

if , else , and elif 

We now introduce the simple, but powerful if , else , and elif conditional statements. This will allow us to create simple branches in our code. For instance, suppose you are writing code for a video game, and you want to update a character’s status based on her/his number of health-points (an integer). The following code is representative of this:

Each if , elif , and else statement must end in a colon character, and the body of each of these statements is delimited by whitespace .

The following pseudo-code demonstrates the general template for conditional statements:

In practice this can look like:

In its simplest form, a conditional statement requires only an if clause. else and elif clauses can only follow an if clause.

Similarly, conditional statements can have an if and an else without an elif :

Conditional statements can also have an if and an elif without an else :

Note that only one code block within a single if-elif-else statement can be executed: either the “if-block” is executed, or an “elif-block” is executed, or the “else-block” is executed. Consecutive if-statements, however, are completely independent of one another, and thus their code blocks can be executed in sequence, if their respective conditional statements resolve to True .

Reading Comprehension: Conditional statements

Assume my_list is a list. Given the following code:

What will happen if my_list is [] ? Will IndexError be raised? What will first_item be?

Assume variable my_file is a string storing a filename, where a period denotes the end of the filename and the beginning of the file-type. Write code that extracts only the filename.

my_file will have at most one period in it. Accommodate cases where my_file does not include a file-type.

"code.py" \(\rightarrow\) "code"

"doc2.pdf" \(\rightarrow\) "doc2"

"hello_world" \(\rightarrow\) "hello_world"

Inline if-else statements 

Python supports a syntax for writing a restricted version of if-else statements in a single line. The following code:

can be written in a single line as:

This is suggestive of the general underlying syntax for inline if-else statements:

The inline if-else statement :

The expression A if <condition> else B returns A if bool(<condition>) evaluates to True , otherwise this expression will return B .

This syntax is highly restricted compared to the full “if-elif-else” expressions - no “elif” statement is permitted by this inline syntax, nor are multi-line code blocks within the if/else clauses.

Inline if-else statements can be used anywhere, not just on the right side of an assignment statement, and can be quite convenient:

We will see this syntax shine when we learn about comprehension statements. That being said, this syntax should be used judiciously. For example, inline if-else statements ought not be used in arithmetic expressions, for therein lies madness:

Short-Circuiting Logical Expressions 

Armed with our newfound understanding of conditional statements, we briefly return to our discussion of Python’s logic expressions to discuss “short-circuiting”. In Python, a logical expression is evaluated from left to right and will return its boolean value as soon as it is unambiguously determined, leaving any remaining portions of the expression unevaluated . That is, the expression may be short-circuited .

For example, consider the fact that an and operation will only return True if both of its arguments evaluate to True . Thus the expression False and <anything> is guaranteed to return False ; furthermore, when executed, this expression will return False without having evaluated bool(<anything>) .

To demonstrate this behavior, consider the following example:

According to our discussion, the pattern False and short-circuits this expression without it ever evaluating bool(1/0) . Reversing the ordering of the arguments makes this clear.

In practice, short-circuiting can be leveraged in order to condense one’s code. Suppose a section of our code is processing a variable x , which may be either a number or a string . Suppose further that we want to process x in a special way if it is an all-uppercased string. The code

is problematic because isupper can only be called once we are sure that x is a string; this code will raise an error if x is a number. We could instead write

but the more elegant and concise way of handling the nestled checking is to leverage our ability to short-circuit logic expressions.

See, that if x is not a string, that isinstance(x, str) will return False ; thus isinstance(x, str) and x.isupper() will short-circuit and return False without ever evaluating bool(x.isupper()) . This is the preferable way to handle this sort of checking. This code is more concise and readable than the equivalent nested if-statements.

Reading Comprehension: short-circuited expressions

Consider the preceding example of short-circuiting, where we want to catch the case where x is an uppercased string. What is the “bug” in the following code? Why does this fail to utilize short-circuiting correctly?

Links to Official Documentation 

Truth testing

Boolean operations

Comparisons

‘if’ statements

Reading Comprehension Exercise Solutions: 

Conditional statements

If my_list is [] , then bool(my_list) will return False , and the code block will be skipped. Thus first_item will be None .

First, check to see if . is even contained in my_file . If it is, find its index-position, and slice the string up to that index. Otherwise, my_file is already the file name.

Short-circuited expressions

fails to account for the fact that expressions are always evaluated from left to right. That is, bool(x.isupper()) will always be evaluated first in this instance and will raise an error if x is not a string. Thus the following isinstance(x, str) statement is useless.

How to Use Conditional Statements in Python – Examples of if, else, and elif

Conditional statements are an essential part of programming in Python. They allow you to make decisions based on the values of variables or the result of comparisons.

In this article, we'll explore how to use if, else, and elif statements in Python, along with some examples of how to use them in practice.

How to Use the if Statement in Python

The if statement allows you to execute a block of code if a certain condition is true. Here's the basic syntax:

The condition can be any expression that evaluates to a Boolean value (True or False). If the condition is True, the code block indented below the if statement will be executed. If the condition is False, the code block will be skipped.

Here's an example of how to use an if statement to check if a number is positive:

In this example, we use the > operator to compare the value of num to 0. If num is greater than 0, the code block indented below the if statement will be executed, and the message "The number is positive." will be printed.

How to Use the else Statement in Python

The else statement allows you to execute a different block of code if the if condition is False. Here's the basic syntax:

If the condition is True, the code block indented below the if statement will be executed, and the code block indented below the else statement will be skipped.

If the condition is False, the code block indented below the else statement will be executed, and the code block indented below the if statement will be skipped.

Here's an example of how to use an if-else statement to check if a number is positive or negative:

In this example, we use an if-else statement to check if num is greater than 0. If it is, the message "The number is positive." is printed. If it is not (that is, num is negative or zero), the message "The number is negative." is printed.

How to Use the elif Statement in Python

The elif statement allows you to check multiple conditions in sequence, and execute different code blocks depending on which condition is true. Here's the basic syntax:

The elif statement is short for "else if", and can be used multiple times to check additional conditions.

Here's an example of how to use an if-elif-else statement to check if a number is positive, negative, or zero:

Use Cases For Conditional Statements

Example 1: checking if a number is even or odd..

In this example, we use the modulus operator (%) to check if num is evenly divisible by 2.

If the remainder of num divided by 2 is 0, the condition num % 2 == 0 is True, and the code block indented below the if statement will be executed. It will print the message "The number is even."

If the remainder is not 0, the condition is False, and the code block indented below the else statement will be executed, printing the message "The number is odd."

Example 2: Assigning a letter grade based on a numerical score

In this example, we use an if-elif-else statement to assign a letter grade based on a numerical score.

The if statement checks if the score is greater than or equal to 90. If it is, the grade is set to "A". If not, the first elif statement checks if the score is greater than or equal to 80. If it is, the grade is set to "B". If not, the second elif statement checks if the score is greater than or equal to 70, and so on. If none of the conditions are met, the else statement assigns the grade "F".

Example 3: Checking if a year is a leap year

In this example, we use nested if statements to check if a year is a leap year. A year is a leap year if it is divisible by 4, except for years that are divisible by 100 but not divisible by 400.

The outer if statement checks if year is divisible by 4. If it is, the inner if statement checks if it is also divisible by 100. If it is, the innermost if statement checks if it is divisible by 400. If it is, the code block indented below that statement will be executed, printing the message "is a leap year."

If it is not, the code block indented below the else statement inside the inner if statement will be executed, printing the message "is not a leap year.".

If the year is not divisible by 4, the code block indented below the else statement of the outer if statement will be executed, printing the message "is not a leap year."

Example 4: Checking if a string contains a certain character

In this example, we use the in operator to check if the character char is present in the string string. If it is, the condition char in string is True, and the code block indented below the if statement will be executed, printing the message "The string contains the character" followed by the character itself.

If char is not present in string, the condition is False, and the code block indented below the else statement will be executed, printing the message "The string does not contain the character" followed by the character itself.

Conditional statements (if, else, and elif) are fundamental programming constructs that allow you to control the flow of your program based on conditions that you specify. They provide a way to make decisions in your program and execute different code based on those decisions.

In this article, we have seen several examples of how to use these statements in Python, including checking if a number is even or odd, assigning a letter grade based on a numerical score, checking if a year is a leap year, and checking if a string contains a certain character.

By mastering these statements, you can create more powerful and versatile programs that can handle a wider range of tasks and scenarios.

It is important to keep in mind that proper indentation is crucial when using conditional statements in Python, as it determines which code block is executed based on the condition.

With practice, you will become proficient in using these statements to create more complex and effective Python programs.

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Inline If in Python: The Ternary Operator in Python

September 16, 2021 December 20, 2022

In this tutorial, you’ll learn how to create inline if statements in Python. This is often known as the Python ternary operator, which allows you to execute conditional if statements in a single line, allowing statements to take up less space and often be written in my easy-to-understand syntax! Let’s take a look at what you’ll learn.

The Quick Answer: Use the Python Ternary Operator

Table of Contents

What is the Python Ternary Operator?

A ternary operator is an inline statement that evaluates a condition and returns one of two outputs. It’s an operator that’s often used in many programming languages, including Python, as well as math. The Python ternary operator has been around since Python 2.5, despite being delayed multiple times.

The syntax of the Python ternary operator is a little different than that of other languages. Let’s take a look at what it looks like:

Now let’s take a look at how you can actually write an inline if statement in Python.

How Do you Write an Inline If Statement in Python?

Before we dive into writing an inline if statement in Python, let’s take a look at how if statements actually work in Python. With an if statement you must include an if , but you can also choose to include an else statement, as well as one more of else-ifs, which in Python are written as elif .

The traditional Python if statement looks like this:

This can be a little cumbersome to write, especially if you conditions are very simple. Because of this, inline if statements in Python can be really helpful to help you write your code faster.

Let’s take a look at how we can accomplish this in Python:

This is significantly easier to write. Let’s break this down a little bit:

We assign a value to x , which will be evaluated
We declare a variable, y , which we assign to the value of 10, if x is True. Otherwise, we assign it a value of 20.

We can see how this is written out in a much more plain language than a for-loop that may require multiple lines, thereby wasting space.

Tip! This is quite similar to how you’d written a list comprehension. If you want to learn more about Python List Comprehensions, check out my in-depth tutorial here . If you want to learn more about Python for-loops, check out my in-depth guide here .

Now that you know how to write a basic inline if statement in Python, let’s see how you can simplify it even further by omitting the else statement.

How To Write an Inline If Statement Without an Else Statement

Now that you know how to write an inline if statement in Python with an else clause, let’s take a look at how we can do this in Python.

Before we do this, let’s see how we can do this with a traditional if statement in Python

You can see that this still requires you to write two lines. But we know better – we can easily cut this down to a single line. Let’s get started!

We can see here that really what this accomplishes is remove the line break between the if line and the code it executes.

Now let’s take a look at how we can even include an elif clause in our inline if statements in Python!

Check out some other Python tutorials on datagy.io, including our complete guide to styling Pandas and our comprehensive overview of Pivot Tables in Pandas !

How to Write an Inline If Statement With an Elif Statement

Including an else-if, or elif , in your Python inline if statement is a little less intuitive. But it’s definitely doable! So let’s get started. Let’s imagine we want to write this if-statement:

Let’s see how we can easily turn this into an inline if statement in Python:

This is a bit different than what we’ve seen so far, so let’s break it down a bit:

First, we evaluate is x == 1. If that’s true, the conditions end and y = 10.
Otherwise, we create another condition in brackets
First we check if x == 20, and if that’s true, then y = 20. Note that we did not repeated y= here.
Finally, if neither of the other decisions are true, we assign 30 to y

This is definitely a bit more complex to read, so you may be better off creating a traditional if statement.

In this post, you learned how to create inline if statement in Python! You learned about the Python ternary operator and how it works. You also learned how to create inline if statements with else statements, without else statements, as well as with else if statements.

To learn more about Python ternary operators, check out the official documentation here .

Nik Piepenbreier

Nik is the author of datagy.io and has over a decade of experience working with data analytics, data science, and Python. He specializes in teaching developers how to use Python for data science using hands-on tutorials. View Author posts

How to use python if else in one line with examples

December 31, 2023

Python Ternary Operator , Python

How do I write a simple python if else in one line? What are ternary operator in Python? Can we use one liner for complex if and else statements?

In this tutorial I will share different examples to help you understand and learn about usage of ternary operator in one liner if and else condition with Python. Conditional expressions (sometimes called a “ ternary operator ”) have the lowest priority of all Python operations. Programmers coming to Python from C, C++, or Perl sometimes miss the so-called ternary operator ?:. It’s most often used for avoiding a few lines of code and a temporary variable for simple decisions.

I will not go into details of generic ternary operator as this is used across Python for loops and control flow statements. Here we will concentrate on learning python if else in one line using ternary operator

Python if else in one line

The general syntax of single if and else statement in Python is:

Now if we wish to write this in one line using ternary operator, the syntax would be:

In this syntax, first of all the else condition is evaluated.

If condition returns True then value_when_true is returned
If condition returns False then value_when_false is returned

Similarly if you had a variable assigned in the general if else block based on the condition

The same can be written in single line:

Here as well, first of all the condition is evaluated.

if condition returns True then true-expr is assigned to value object
if condition returns False then false-expr is assigned to value object

For simple cases like this, I find it very nice to be able to express that logic in one line instead of four. Remember, as a coder, you spend much more time reading code than writing it, so Python's conciseness is invaluable.

Some important points to remember:

You can use a ternary expression in Python, but only for expressions , not for statements
You cannot use Python if..elif..else block in one line.
The name " ternary " means there are just 3 parts to the operator: condition , then , and else .
Although there are hacks to modify if..elif..else block into if..else block and then use it in single line but that can be complex depending upon conditions and should be avoided
With if-else blocks , only one of the expressions will be executed.
While it may be tempting to always use ternary expressions to condense your code, realise that you may sacrifice readability if the condition as well as the true and false expressions are very complex.

Python Script Example

This is a simple script where we use comparison operator in our if condition

First collect user input in the form of integer and store this value into b
If b is greater than or equal to 0 then return " positive " which will be True condition
If b returns False i.e. above condition was not success then return " negative "
The final returned value i.e. either " positive " or " negative " is stored in object a
Lastly print the value of value a

The multi-line form of this code would be:

Python if..elif..else in one line

Now as I told this earlier, it is not possible to use if..elif..else block in one line using ternary expressions. Although we can hack our way into this but make sure the maximum allowed length of a line in Python is 79 as per PEP-8 Guidelines

We have this if..elif..else block where we return expression based on the condition check:

We can write this if..elif..else block in one-line using this syntax:

In this syntax,

First of all condition2 is evaluated, if return True then expr2 is returned
If condition2 returns False then condition1 is evaluated, if return True then expr1 is returned
If condition1 also returns False then else is executed and expr is returned

As you see, it was easier if we read this in multi-line if..elif..else block while the same becomes hard to understand for beginners.

We can add multiple if else block in this syntax, but we must also adhere to PEP-8 guidelines

Python Script Example-1

In this sample script we collect an integer value from end user and store it in " b ". The order of execution would be:

If the value of b is less than 0 then " neg " is returned
If the value of b is greater than 0 then " pos " is returned.
If both the condition return False , then " zero " is returned

The multi-line form of the code would be:

Output(when if condition is True )

Output(when if condition is False and elif condition is True )

Output(when both if and elif condition are False )

Python script Example-2

We will add some more else blocks in this sample script, the order of the check would be in below sequence :

Collect user input for value b which will be converted to integer type
If value of b is equal to 100 then return " equal to 100 ", If this returns False then next if else condition would be executed
If value of b is equal to 50 then return " equal to 50 ", If this returns False then next if else condition would be executed
If value of b is equal to 40 then return " equal to 40 ", If this returns False then next if else condition would be executed
If value of b is greater than 100 then return " greater than 100 ", If this returns False then next go to else block
Lastly if all the condition return False then return " less than hundred "

The multi-line form of this example would be:

Python nested if..else in one line

We can also use ternary expression to define nested if..else block on one line with Python.

If you have a multi-line code using nested if else block , something like this:

The one line syntax to use this nested if else block in Python would be:

Here, we have added nested if..elif..else inside the else block using ternary expression. The sequence of the check in the following order

If condition1 returns True then expr1 is returned, if it returns False then next condition is checked
If condition-m returns True then expr-m is returned, if it returns False then else block with nested if..elif..else is checked
If condition3 returns True then expr3 is returned, if it returns False then next condition inside the nested block is returned
If condition-n returns True then expr-n is returned, if it returns False then expr5 is returned from the else condition

In this example I am using nested if else inside the else block of our one liner. The order of execution will be in the provided sequence:

First of all collect integer value of b from the end user
If the value of b is equal to 100 then the if condition returns True and " equal to 100 " is returned
If the value of b is equal to 50 then the elif condition returns True and " equal to 50 " is returned
If both if and elif condition returns False then the else block is executed where we have nested if and else condition
Inside the else block , if b is greater than 100 then it returns " greater than 100 " and if it returns False then " less than 100 " is returned

In this tutorial we learned about usage of ternary operator in if else statement to be able to use it in one line. Although Python does not allow if..elif..else statement in one line but we can still break it into if else and then use it in single line form. Similarly we can also use nested if with ternary operator in single line. I shared multiple examples to help you understand the concept of ternary operator with if and else statement of Python programming language

Lastly I hope this tutorial guide on python if else one line was helpful. So, let me know your suggestions and feedback using the comment section.

Deepak Prasad

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Python Fundamentals

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In computer programming, the if statement is a conditional statement. It is used to execute a block of code only when a specific condition is met. For example,

Suppose we need to assign different grades to students based on their scores.

If a student scores above 90 , assign grade A
If a student scores above 75 , assign grade B
If a student scores above 65 , assign grade C

These conditional tasks can be achieved using the if statement.

Python if Statement

An if statement executes a block of code only if the specified condition is met.

Here, if the condition of the if statement is:

True - the body of the if statement executes.
False - the body of the if statement is skipped from execution.

Let's look at an example.

Note: Be mindful of the indentation while writing the if statements. Indentation is the whitespace at the beginning of the code.

Here, the spaces before the print() statement denote that it's the body of the if statement.

Example: Python if Statement

Sample Output 1

In the above example, we have created a variable named number . Notice the test condition ,

As the number is greater than 0 , the condition evaluates True . Hence, the body of the if statement executes.

Sample Output 2

Now, let's change the value of the number to a negative integer, say -5 .

Now, when we run the program, the output will be:

This is because the value of the number is less than 0 . Hence, the condition evaluates to False . And, the body of the if statement is skipped.

An if statement can have an optional else clause. The else statement executes if the condition in the if statement evaluates to False .

Here, if the condition inside the if statement evaluates to

True - the body of if executes, and the body of else is skipped.
False - the body of else executes, and the body of if is skipped

Example: Python if…else Statement

In the above example, we have created a variable named number .

Since the value of the number is 10 , the condition evaluates to True . Hence, code inside the body of if is executed.

If we change the value of the variable to a negative integer, let's say -5 , our output will be:

Here, the test condition evaluates to False . Hence code inside the body of else is executed.

Python if…elif…else Statement

The if...else statement is used to execute a block of code among two alternatives.

However, if we need to make a choice between more than two alternatives, we use the if...elif...else statement.

if condition1 - This checks if condition1 is True . If it is, the program executes code block 1 .
elif condition2 - If condition1 is not True , the program checks condition2 . If condition2 is True , it executes code block 2 .
else - If neither condition1 nor condition2 is True , the program defaults to executing code block 3 .

Example: Python if…elif…else Statement

Since the value of the number is 0 , both the test conditions evaluate to False .

Hence, the statement inside the body of else is executed.

Python Nested if Statements

It is possible to include an if statement inside another if statement. For example,

Here's how this program works.

Video: Python if...else Statement

Sorry about that.

Python Enhancement Proposals

Python »
PEP Index »

PEP 572 – Assignment Expressions

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

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

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

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

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

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

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

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

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

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

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

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

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

they would write:

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

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

Syntax and semantics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Examples from the Python standard library

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

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

Avoid nested if and remove one indentation level.

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

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

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

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

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

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

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

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

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

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

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

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

An example from the low-level UNIX world:

Rejected alternative proposals

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

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

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

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

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

Additional reasons to prefer := over this spelling include:

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

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

NAME = EXPR
if NAME := EXPR

reinforces the visual recognition of assignment expressions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Frequently Raised Objections

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

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

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

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

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

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

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

Appendix A: Tim Peters’s findings

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

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

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

is a vast improvement over the briefer:

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

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

as the briefer:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Let’s add a simple assignment expression.

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

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

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

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

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

Finally, let’s nest two comprehensions.

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

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

This document has been placed in the public domain.

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

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

10 if-else Practice Problems in Python

learn python
python programming

If you're trying to learn Python, you need to practice! These ten if-else Python practice problems provide you some hands-on experience. And don’t worry – we’ve provided full code solutions and detailed explanations!

Python is particularly good for beginners to learn. Its clear syntax can be read almost as clearly as a normal sentence. The if-else statement is a good example of this; it lets you build logic into your programs. This article will walk you through ten i f-else practice exercises in Python. Each one is specifically designed for beginners, helping you hone your understanding of if-else statements.

The exercises in this article are taken directly from our courses, including Python Basics: Part 1 . This course helps absolute beginners understand computer programming with Python; if you’ve never written a line of code, this course is a great place to start.

As the saying goes, practice makes perfect. Learning Python is no exception. As we discuss in Different Ways to Practice Python , doing online courses is a great way to practice a variety of topics. The more you practice writing code, the more comfortable and confident you'll become in your abilities. So – whether you aim to build your own software, delve into data science, or automate mundane tasks – dedicating time to practice is key.

The Python if-else Statement

Before we dive into the exercises, let's briefly discuss the workings of if-else statements in Python. An if-else statement allows your program to make decisions based on certain conditions. The syntax is straightforward:

You provide a condition to evaluate within the if
If that condition is true, the corresponding block of code is executed.
If the condition is false, the code within the optional else block is executed instead.

Here's a simple example:

In this example, if the value of x is greater than 5, the program will print "x is greater than 5"; otherwise, it will print "x is not greater than 5".

This concept forms the backbone of decision-making in Python programs.

To get the most benefit from this article, attempt the problems before reading the solutions. Some of these exercises will have several possible solutions, so also try to think about alternative ways to solve the same problem. Let’s get started.

10 if-else Python Practice Exercises

Exercise 1: are you tall.

Exercise: Write a program that will ask the user for their height in centimeters. Use the input() built-in function to do this. If the height is more than 185 centimeters, print the following line of code:

Explanation: This is a similar syntax to the above template example. The height variable stores the user input as an integer and tests if it’s greater than 185 cm. If so, the message is printed. Notice there is no else; this means if the height is less than or equal to 185, nothing happens.

Exercise 2: More Options

Exercise: Write a program that will ask the user the following question:

If the user answers y , print: Wrong! Canberra is the capital!
If the user answers n , print: Correct!
If the user answers anything else, print: I do not understand your answer!

Explanation: The if statement can be extended with an elif (i.e. else if) to check any answer that returns False to the first condition. If the elif statement also returns False , the indented block under the else is executed.

Exercise 3: Check If a Character Is in a String

Exercise: Write a program to ask the user to do the following:

Provide the name of a country that does not contain any lowercase a or e letters. (Use the prompt: The country is : )
If the user provides a correct string (i.e. one with no a or e inside it), print: You won... unless you made this name up!
Otherwise, print: You lost!

(By the way, possible answers include Hong Kong, Cyprus, and Togo.)

Explanation: This code prompts the user to input a country name using the built-in input() function. Then, it checks if the letters 'a' or 'e' is present in the inputted country name using the in operator. If either letter is found, it prints "You lost!". Otherwise, it prints "You won... unless you made this name up!" using the else statement.

Exercise 4: Count Letter Frequency in a String

Exercise: The letter e is said to be the most frequent letter in the English language. Count and print how many times this letter appears in the poem below:

John Knox was a man of wondrous might, And his words ran high and shrill, For bold and stout was his spirit bright, And strong was his stalwart will. Kings sought in vain his mind to chain, And that giant brain to control, But naught on plain or stormy main Could daunt that mighty soul. John would sit and sigh till morning cold Its shining lamps put out, For thoughts untold on his mind lay hold, And brought but pain and doubt. But light at last on his soul was cast, Away sank pain and sorrow, His soul is gay, in a fair to-day, And looks for a bright to-morrow. (Source: "Unidentified," in Current Opinion, July 1888)

Explanation: The solution starts by initializing a counter with the value of 0. Then a for loop is used to loop through every letter in the string variable poem. The if statement tests if the current letter is an e; if it is, the counter is incremented by 1. Finally, the result of the counter is printed. Note that there is no else needed in this solution, since nothing should happen if the letter is not an e.

Exercise 5: Format Recipes

Exercise: Anne loves cooking and she tries out various recipes from the Internet. Sometimes, though, she finds recipes that are written as a single paragraph of text. For instance:

When you're in the middle of cooking, such a paragraph is difficult to read. Anne would prefer an ordered list, like this:

Cut a slit into the chicken breast.
Stuff it with mustard, mozzarella and cheddar.
Secure the whole thing with rashers of bacon.
Roast for 20 minutes at 200C.

Write a Python script that accepts a recipe string from the user ('Paste your recipe: ') and prints an ordered list of sentences, just like in the example above.

Each sentence of both the input and output should end with a single period.

Explanation: The solution takes a recipe input from the user with input() , then iterates through each character in the recipe. Whenever it encounters a period ( '.' ), it increments a counter, adds a new line character ( '\n' ) and the incremented counter followed by a period to an ordered list. If it encounters any other character, it simply adds that character to the ordered list. Finally, it calculates the length of the counter, removes the last part of the ordered list to exclude the unnecessary counter, and prints the modified ordered list, which displays the recipe with numbered steps.

Exercise 6: Nested if Statements

Exercise: A leap year is a year that consists of 366 (not 365) days. It occurs roughly every four years. More specifically, a year is considered leap if it is either divisible by 4 but not by 100 or it is divisible by 400.

Write a program that asks the user for a year and replies with either leap year or not a leap year.

Explanation: This program takes a year from the user as input. It checks if the year is divisible by 4. If it is, it then checks if the year is divisible by 100. If it is, it further checks if the year is divisible by 400. If it is, it prints 'leap year' because it satisfies all the conditions for a leap year. This is all achieved with nesting.

If it's divisible by 4, but not by 100, or if it’s not divisible by 400, the program prints 'not a leap year'. If the year is not divisible by 100, it directly prints 'leap year' because it satisfies the basic condition for a leap year (being divisible by 4). If the year is not divisible by 4, it prints 'not a leap year'.

Exercise 7: Scrabble

Exercise: Create a function called can_build_word(letters_list, word) that takes a list of letters and a word. The function should return True if the word uses only the letters from the list.

Like in real Scrabble, you cannot use one tile twice in the same word. For example, if a list of letters contains only one 'a', ‘banana’ returns False. The function should also return False if the word contains any letters not in the list.

For example …

… should return True. On the other hand …

… should return False because there is only one 'f'.

One thing to keep in mind: Neither the provided letter list nor the word should be changed by your function. It may be tempting to remove letters from the letters list to remove the used tiles. This should not be the case. Instead, you should copy the passed list. Simply use the list() function and pass it another list:

This way we can safely operate on a copy of the list without changing the contents of the original list.

Explanation: The function starts by making a copy of the list of letters to avoid modifying the original. Then, it iterates through each letter in the word. For each letter, it checks if that letter exists in the list copy. If it does, it removes that letter from the copy, which ensures each letter is used only once.

If the letter doesn't exist in the copy of the letters list, it means the word cannot be formed using the available letters; it returns False. If all the letters in the word can be found and removed from the list copy, it means the word can indeed be formed; it returns True .

If you are looking for additional material to help you learn how to work with Python lists, our article 12 Beginner-Level Python List Exercises with Solutions has got you covered.

Exercise 8: for Loops and if Statements

Exercise: Write a program that asks for a positive integer and prints all of its divisors, one by one, on separate lines in ascending order.

A divisor is a number that divides a particular number with no remainder. For example, the divisors of 4 are 1, 2, and 4.

To check if a is a divisor of b , you can use the modulo operator %. a % b returns the remainder of dividing a by b . For example, 9 % 2 is 1 because 9 = (4 * 2) + 1, and 9 % 3 is 0 because 9 divides into 3 with no remainder. If a % b == 0 , then b is a divisor of a .

Explanation: This solution takes an input number from the user and then iterates through numbers from 1 to that input number (inclusive). For each number i in this range, it checks if the input number is divisible by i with no remainder ( number % i == 0 ). If the condition is satisfied, it means that i is a factor of the input number.

Looping is another important skill in Python. This problem could also be solved with a while loop. Take a look at 10 Python Loop Exercises with Solutions to learn more about for and while loops.

Exercise 9: if Statements and Data Structures

Exercise: Write a function named get_character_frequencies() that takes a string as input and returns a dictionary of lowercase characters as keys and the number of their occurrences as values. Ignore the case of a character.

Explanation: This function initializes an empty dictionary ( freq_dict ) to store the frequencies of characters. It iterates through each character in the input word using a for loop. Within the loop, it converts each character to lowercase using the lower() method; this ensures case-insensitive counting.

The program checks if the lowercase character is already a key in the dictionary. If it's not, it adds the character as a key and sets its frequency to 1. If it is already a key, it increments the frequency count for that character by 1 and returns the dictionary.

If you’re not familiar with how Python dictionaries work, Top 10 Python Dictionary Exercises for Beginners will give you experience working with this important data structure.

Exercise 10: if Statements and Multiple Returns

Exercise: Write a function named return_bigger(a, b) that takes two numbers and returns the bigger one. If the numbers are equal, return 0.

Explanation: When using if statements in a function, you can define a return value in the indented block of code under the if-else statement. This makes your code simpler and more readable – another great example of the clear syntax which makes Python such an attractive tool for all programmers.

Do You Want More Python Practice Exercises?

Learning anything new is a challenge. Relying on a variety of resources is a good way to get a broad background. Read some books and blogs to get a theoretical understanding, watch videos online, take advantage of the active Python community on platforms like Stack Overflow . And most importantly, do some hands-on practice. We discuss these points in detail in What’s the Best Way to Practice Python?

The Python practice exercises and solutions shown here were taken directly from several of our interactive online courses, including Python Basics Practice , Python Practice: Word Games , and Working with Strings in Python . The courses build up your skills and knowledge, giving you exposure to a variety of topics.

And if you’re already motivated to take your skills to the next level, have a go at another 10 Python Practice Exercises for Beginners with Solutions . Happy coding!

Python Operators

Precedence and associativity of operators in python.

Python Arithmetic Operators
Difference between / vs. // operator in Python
Python - Star or Asterisk operator ( * )
What does the Double Star operator mean in Python?
Division Operators in Python
Modulo operator (%) in Python
Python Logical Operators
Python OR Operator
Difference between 'and' and '&' in Python
not Operator in Python | Boolean Logic

Ternary Operator in Python

Python Bitwise Operators

Python Assignment Operators

Assignment operators in python.

Walrus Operator in Python 3.8
Increment += and Decrement -= Assignment Operators in Python
Merging and Updating Dictionary Operators in Python 3.9
New '=' Operator in Python3.8 f-string

Python Relational Operators

Comparison Operators in Python
Python NOT EQUAL operator
Difference between == and is operator in Python
Chaining comparison operators in Python
Python Membership and Identity Operators
Difference between != and is not operator in Python

In Python programming, Operators in general are used to perform operations on values and variables. These are standard symbols used for logical and arithmetic operations. In this article, we will look into different types of Python operators.

OPERATORS: These are the special symbols. Eg- + , * , /, etc.
OPERAND: It is the value on which the operator is applied.

Types of Operators in Python

Arithmetic Operators
Comparison Operators
Logical Operators
Bitwise Operators
Assignment Operators
Identity Operators and Membership Operators

Arithmetic Operators in Python

Python Arithmetic operators are used to perform basic mathematical operations like addition, subtraction, multiplication , and division .

In Python 3.x the result of division is a floating-point while in Python 2.x division of 2 integers was an integer. To obtain an integer result in Python 3.x floored (// integer) is used.

Example of Arithmetic Operators in Python

Division operators.

In Python programming language Division Operators allow you to divide two numbers and return a quotient, i.e., the first number or number at the left is divided by the second number or number at the right and returns the quotient.

There are two types of division operators:

Float division

Floor division

The quotient returned by this operator is always a float number, no matter if two numbers are integers. For example:

Example: The code performs division operations and prints the results. It demonstrates that both integer and floating-point divisions return accurate results. For example, ’10/2′ results in ‘5.0’ , and ‘-10/2’ results in ‘-5.0’ .

Integer division( Floor division)

The quotient returned by this operator is dependent on the argument being passed. If any of the numbers is float, it returns output in float. It is also known as Floor division because, if any number is negative, then the output will be floored. For example:

Example: The code demonstrates integer (floor) division operations using the // in Python operators . It provides results as follows: ’10//3′ equals ‘3’ , ‘-5//2’ equals ‘-3’ , ‘ 5.0//2′ equals ‘2.0’ , and ‘-5.0//2’ equals ‘-3.0’ . Integer division returns the largest integer less than or equal to the division result.

Precedence of Arithmetic Operators in Python

The precedence of Arithmetic Operators in Python is as follows:

P – Parentheses
E – Exponentiation
M – Multiplication (Multiplication and division have the same precedence)
D – Division
A – Addition (Addition and subtraction have the same precedence)
S – Subtraction

The modulus of Python operators helps us extract the last digit/s of a number. For example:

x % 10 -> yields the last digit
x % 100 -> yield last two digits

Arithmetic Operators With Addition, Subtraction, Multiplication, Modulo and Power

Here is an example showing how different Arithmetic Operators in Python work:

Example: The code performs basic arithmetic operations with the values of ‘a’ and ‘b’ . It adds (‘+’) , subtracts (‘-‘) , multiplies (‘*’) , computes the remainder (‘%’) , and raises a to the power of ‘b (**)’ . The results of these operations are printed.

Note: Refer to Differences between / and // for some interesting facts about these two Python operators.

Comparison of Python Operators

In Python Comparison of Relational operators compares the values. It either returns True or False according to the condition.

= is an assignment operator and == comparison operator.

Precedence of Comparison Operators in Python

In Python, the comparison operators have lower precedence than the arithmetic operators. All the operators within comparison operators have the same precedence order.

Example of Comparison Operators in Python

Let’s see an example of Comparison Operators in Python.

Example: The code compares the values of ‘a’ and ‘b’ using various comparison Python operators and prints the results. It checks if ‘a’ is greater than, less than, equal to, not equal to, greater than, or equal to, and less than or equal to ‘b’ .

Logical Operators in Python

Python Logical operators perform Logical AND , Logical OR , and Logical NOT operations. It is used to combine conditional statements.

Precedence of Logical Operators in Python

The precedence of Logical Operators in Python is as follows:

Logical not
logical and

Example of Logical Operators in Python

The following code shows how to implement Logical Operators in Python:

Example: The code performs logical operations with Boolean values. It checks if both ‘a’ and ‘b’ are true ( ‘and’ ), if at least one of them is true ( ‘or’ ), and negates the value of ‘a’ using ‘not’ . The results are printed accordingly.

Bitwise Operators in Python

Python Bitwise operators act on bits and perform bit-by-bit operations. These are used to operate on binary numbers.

Precedence of Bitwise Operators in Python

The precedence of Bitwise Operators in Python is as follows:

Bitwise NOT
Bitwise Shift
Bitwise AND
Bitwise XOR

Here is an example showing how Bitwise Operators in Python work:

Example: The code demonstrates various bitwise operations with the values of ‘a’ and ‘b’ . It performs bitwise AND (&) , OR (|) , NOT (~) , XOR (^) , right shift (>>) , and left shift (<<) operations and prints the results. These operations manipulate the binary representations of the numbers.

Python Assignment operators are used to assign values to the variables.

Let’s see an example of Assignment Operators in Python.

Example: The code starts with ‘a’ and ‘b’ both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on ‘b’ . The results of each operation are printed, showing the impact of these operations on the value of ‘b’ .

Identity Operators in Python

In Python, is and is not are the identity operators both are used to check if two values are located on the same part of the memory. Two variables that are equal do not imply that they are identical.

Example Identity Operators in Python

Let’s see an example of Identity Operators in Python.

Example: The code uses identity operators to compare variables in Python. It checks if ‘a’ is not the same object as ‘b’ (which is true because they have different values) and if ‘a’ is the same object as ‘c’ (which is true because ‘c’ was assigned the value of ‘a’ ).

Membership Operators in Python

In Python, in and not in are the membership operators that are used to test whether a value or variable is in a sequence.

Examples of Membership Operators in Python

The following code shows how to implement Membership Operators in Python:

Example: The code checks for the presence of values ‘x’ and ‘y’ in the list. It prints whether or not each value is present in the list. ‘x’ is not in the list, and ‘y’ is present, as indicated by the printed messages. The code uses the ‘in’ and ‘not in’ Python operators to perform these checks.

in Python, Ternary operators also known as conditional expressions are operators that evaluate something based on a condition being true or false. It was added to Python in version 2.5.

It simply allows testing a condition in a single line replacing the multiline if-else making the code compact.

Syntax : [on_true] if [expression] else [on_false]

Examples of Ternary Operator in Python

The code assigns values to variables ‘a’ and ‘b’ (10 and 20, respectively). It then uses a conditional assignment to determine the smaller of the two values and assigns it to the variable ‘min’ . Finally, it prints the value of ‘min’ , which is 10 in this case.

In Python, Operator precedence and associativity determine the priorities of the operator.

Operator Precedence in Python

This is used in an expression with more than one operator with different precedence to determine which operation to perform first.

Let’s see an example of how Operator Precedence in Python works:

Example: The code first calculates and prints the value of the expression 10 + 20 * 30 , which is 610. Then, it checks a condition based on the values of the ‘name’ and ‘age’ variables. Since the name is “ Alex” and the condition is satisfied using the or operator, it prints “Hello! Welcome.”

Operator Associativity in Python

If an expression contains two or more operators with the same precedence then Operator Associativity is used to determine. It can either be Left to Right or from Right to Left.

The following code shows how Operator Associativity in Python works:

Example: The code showcases various mathematical operations. It calculates and prints the results of division and multiplication, addition and subtraction, subtraction within parentheses, and exponentiation. The code illustrates different mathematical calculations and their outcomes.

To try your knowledge of Python Operators, you can take out the quiz on Operators in Python .

Python Operator Exercise Questions

Below are two Exercise Questions on Python Operators. We have covered arithmetic operators and comparison operators in these exercise questions. For more exercises on Python Operators visit the page mentioned below.

Q1. Code to implement basic arithmetic operations on integers

Q2. Code to implement Comparison operations on integers

Explore more Exercises: Practice Exercise on Operators in Python

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Data at Depth

How GPT-4 Easily Solved My Python Assignment In Record Time

A re-design in computer science curriculum and evaluation is desperately needed.

As quickly as you can say “GPT-4”, LLMs have completely changed the way in which Computer Science professors can deliver and evaluate quality computer science curriculum.

I am one of these unfortunate souls who must change — I have created and delivered a university-level computer programming courses for the past 25 years.

Data at Depth is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

This past year, more than any other year, is requiring a re-think in the way that I structure and design computer science courses.

This is true for most faculty - particularly if one is responsible for delivering an introductory comp sci curriculum.

Why? GPT-4 can handle this curriculum with no problem.

GPT-4 has dismantled one of my clever, well-thought-out assignments that I give to first-year Python students .

Here’s how.

The Assignment: A Data Analysis and Visualization Project

My well-thought-out lab assignment goes like this:

Go and find data for the past 3 Canadian Federal Elections and combine this data with current polling data to visualize trends in the Canadian political landscape. Using Python and the Plotly library, display the results of your findings using a grouped bar chart.

A fun assignment — students get to go dig around on the Internet to find some data. Then they need to go through a number of steps to create a nice visual display.

My expectation is that the data would look something like this:

Once we have the data organized in a spreadsheet, we create the Python code to extract the data (i.e. from an XLSX file or a CSV file), store it in a data frame, and display using a grouped bar chart similar to this:

Here is an overview of the main steps required for students to complete this assignment successfully:

Find a reliable source of data on the internet.

Download and clean the data and save it into an accessible format (CSV)

Write the Python code to access the CSV file, open it, and to read the CSV data

Write the Python code to save the CSV data into a pandas data frame

Research the appropriate Python libraries to display grouped bar chart data.

Using an appropriate Python library, write the code to display the grouped bar chart.

There are quite a few steps required so it is a good test of a student's ability to go and find some information, organize it, and write some cool Python code to display it.

A Few GPT-4 Prompts And The Assignment is Complete

Can GPT-4 handle this type of assignment? Absolutely.

While GPT-4 doesn’t officially allow for the “scraping” and organizing of data sets, it is fairly easy to get around this. So, where do we start? Let’s ask GPT-4 to help us find the best resources for this data:

My Prompt to GPT-4: Can you list the 5 best resources for specific datasets related to the past 3 Canada Federal elections as well as current polling results for political parties in Canada.

GPT-4 Response: Certainly! says GPT-4 cheerfully. And it lists the five best resources, which gives me the opportunity to ask it my next question (based on the previous question):

My Prompt to GPT-4: OK, ChatGPT, using the 5 best polling resources above, can you provide a dataset that is representative of the past 3 Canada Federal elections and the current polling results for Canadian political parties?

GPT-4 Response: Absolutely, here are the results:

Well, that’s cool — the results and the current polling data (a quick check verifies that this data is indeed correct).

Now, how about putting this into CSV format?

My Prompt to GPT-4: Can you please organize the table above into CSV format?

GPT-4 Response: Yes, of course I can! Here it is:

So we can Copy/Paste/Save this CSV data in a file called “ election_results.csv ”. Next, this sort of data is best shown using a data visualization tool.

Python and Dash work just great for this sort of chart, so let’s just ask ChatGPT to create the code to display this in a web browser:

Data Visualization: Chart Display with Python

This is where an immense amount of time, effort, and brain power can be saved by a student.

My Prompt to GPT-4: Referring to the data in the previous response, can you create code in Python and dash to display the results to a web page. Assume the file is called “election_results.csv” and that it is in the same directory as the code file.

GPT-4 Response: Certainly! Here’s the code:

The data frame ( df ) is set from the CSV file, and the data is laid out and displayed using the Python Plotly dash library:

Copy/paste the full code into your favorite editor ( PyCharm for me) and click Run.

By default, the Python dash app will run in your default browser at port 8050.

So find the instance of your default browser that is running and you should see the app up and running at localhost:8050 .

Boom-shak-a-laka, we have a colour-coded and labeled bar chart that gives us the results of the past 3 elections and the current polling data.

Needs some cleaning up — doesn’t quite fit data-visualization best practices.

Again, this is where GPT-4 absolutely shines. All we need is another one-sentence prompt to completely re-align our chart.

For example, we can create horizontal bar chart to better organize the party names (easier to read), and remove the grid lines and background.

Prompt to GPT-4: Make it a horizontal bar chart, remove the grid lines and the background color

GPT-4 responds with another set of complete and fully working code.

Our new results:

OK, that’s great. GPT-4 can generate the Both of these examples cover the basics — it shows the rise (and fall) of each political party over time. It’s definitely worth a decent grade.

OR - we can even ask GPT-4 to generate the code to organize the results by year

Prompt to GPT-4: OK, let’s go back to the original vertical bar chart, but this time, please group the bar chart by year.

Again, GPT-4 responds with another set of code that you can Copy/Paste/Run without any modification. The results:

Bottom line, it took GPT-4 less than 10 minutes to accomplish a first-year Python course assignment that would normally take students a number of hours (and lots of steps) to complete. And it provided 3 possible solutions in that period of time.

An assignment like this may be worth approximately 5–10% of an “Introduction to Programming” course grade (for example, as part of hands-on lab assignments)

With GPT-4, it required me to write (or even manipulate) exactly 0 lines of Python code.

I can’t give students assignments like this anymore - at least not for grades. LLMs can do the work for the students - with ease.

In Summary…

So the big question here is how good are your students (or you if you are one) copy/paste skills?

Probably pretty darn good. If you are planning on giving these types of assignments to students, you will need to consider that they will use GPT-4 as a tool to solve the problem.

SO — one approach is the “head in the sand” approach — ban GPT-4 at your institution, forbid students from using it, and then use all your time and resources to fight against it. I know institutions that are still doing this.

And I say good luck with that.

OR — a second approach is one where you start to think about how GPT-4 has changed the programming landscape, and look at how you can design your assignments and evaluations with the assumption that this tool will be used to solve the problem.

The first approach seems regressive — and the second approach progressive.

I would rather be (and now AM) working with the students on how to use an exciting new tool to solve a problem rather than fighting against the students using a tool that is only going to become more sophisticated, useful, and ubiquitous.

As it is absolutely is, almost daily.

Just my .02…

What is your experience using GPT-4 data science analysis tools?

I would love to hear your comments!

If you want to learn more about how to prompt engineer GPT-4, sign up for this free 5-day email course on Prompting GPT-4 for data visuals. Let me know what you think:

https://stats-and-stories.ck.page/prompting_gpt4

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The best new features and fixes in Python 3.13

A new jit compiler, the first no-gil edition of python, better errors and typing enhancement, and the removal of dead batteries are all part of python 3.13..

Senior Writer, InfoWorld |

The best new features and fixes in Python 3.13

The first beta of Python 3.13 has just been released . This article presents a rundown of the most significant new features in Python 3.13 and what they mean for Python developers. Things may change between now and the first production release of 3.13, but the first beta means all the major feature additions and changes are now frozen.

New features in the Python 3.13 first beta

Here's a first look at these new features in the Python 3.13 beta release:

The experimental JIT

The no-GIL build of Python

Improved error messages

Enhancements to python types.

No more "dead batteries"

Python 3.11 introduced the Specializing Adaptive Interpreter . When the interpreter detects that some operations predictably involve the same types, those operations are "specialized." The generic bytecode used for that code is swapped with bytecode specific to working with those types, which delivers speed boosts of anywhere from 10% to 25% for those regions of the code.

Python 3.12 brought more specializations and other refinements to the interpreter. Now, Python 3.13 adds new elements to the JIT that generate actual machine code at runtime, instead of just specialized bytecode. The resulting speedup isn't much just yet—maybe 5%—but it paves the way for future optimizations that weren't previously possible.

Right now, the JIT is considered experimental—it's not enabled by default, and can only be enabled by compiling CPython from source with certain flags. If in time it yields a significant performance boost (5% or more), and doesn't impose a large management burden on the CPython team or Python's users as a whole, it'll become a fully supported build option. Whether or not it will be enabled for official releases will still be up to the managers for a given platform's CPython builds.

The no-GIL 'free-threaded' build of Python

The official term for possible future versions of CPython with no Global Interpreter Lock (or GIL) is "free-threaded CPython." This CPython build allows threads to run fully in parallel, without mediation from the GIL. To that end, CPU-bound work that once only benefited from being run in multiple processes can run in multiple threads .

Free-threaded CPython is also experimental. It's not enabled by default in the shipped builds, so it needs to be enabled at compile time. If future work with the free-threaded builds shows it can improve multithreaded performance without impacting single-threaded performance, it'll be promoted to a fully supported option. In time, the free-threaded build of CPython may become the default.

The REPL, or interactive interpreter, launches when you run Python from the command line without executing a program. Python 3.13's REPL has enhancements to make it less stodgy and more like an actual editor:

Output to the console now has color enabled by default. This enhancement provides richer error messages, for instance.
You can open the interactive pydoc help browser by pressing F1.
You can browse the command-line history with F2.
You can paste large blocks of code more easily by pressing F3 to enable a special block-paste mode.
You can just type exit or quit , instead of exit() or quit() , to leave the REPL.

Note that these improvements currently are only available on Linux and macOS. They are not available on Microsoft Windows, not even when using the new Windows Terminal console host.

Error traces in Python have become more precise and detailed over the last two releases. Python 3.13 continues on that trajectory.

If you attempt to import something that has the same name as the module currently in context, Python will provide a detailed error to that effect, and encourage you to rename the current module. This is a very frequent source of bugs—and not only for beginners. It's a common mistake to name a module after something in the standard library.
If you pass a function an incorrect keyword argument, the error will suggest some possible correct arguments, based on what's available in the function being called.

Python's type hinting system has expanded in functionality and utility with each new version. Version 3.13 adds three big new changes.

Type parameters support defaults

typing.TypeVar , typing.ParamSpec , and typing.TypeVarTuple all let you define defaults to be used if no type is explicitly specified. For instance:

In cases where T is not explicitly defined when used, str is assumed to be the default.

typing.TypeIs for type narrowing

In Python generally, we can use isinstance() to make decisions based on whether or not something is a given type. typing.TypeIs lets us do the same thing in Python's type hinting mechanisms. This way, functions used to validate whether or not something is a given type can be annotated to show they perform that narrowing behavior, rather than just a return type. This is useful as a way to add precise type checker coverage to those functions.

typing.ReadOnly for read-only annotation

The typing.TypedDict type was created to annotate dictionaries with fixed types for the values associated with certain keys. typing.Readonly lets you annotate specific values in a TypedDict as read-only . An example is a list that you can only append to or pop from, not replace with a string or other type.

No more 'dead batteries'

Python 3.11 identified a slew of Python standard library modules that were obsolete and no longer being maintained . The plan was to mark them as deprecated for 3.11 and 3.12, and then remove them entirely in Python 3.13. As of now, those "dead batteries" (as they've been called) are now permanently removed. Many of the removed modules can be replaced with third-party modules, or their functionality can be emulated using other standard library components.

Users can expect more deprecations to come over the next three versions of Python, as well. Most are methods for various standard library components that are rarely used or undocumented.

Next read this:

Why companies are leaving the cloud
5 easy ways to run an LLM locally
Coding with AI: Tips and best practices from developers
Meet Zig: The modern alternative to C
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Serdar Yegulalp is a senior writer at InfoWorld, focused on machine learning, containerization, devops, the Python ecosystem, and periodic reviews.

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COMMENTS

python
If one line code is definitely going to happen for you, Python 3.8 introduces assignment expressions affectionately known as "the walrus operator". someBoolValue and (num := 20) The 20 will be assigned to num if the first boolean expression is True .
Assign within if statement Python
For Python 3.8+, PEP 572 introduces Assignment Expressions This allows assigning to variables within an expression using the notation NAME := expr . It can be used within if statements, for example:
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The one liner doesn't work because, in Python, assignment (fruit = isBig(y)) is a statement, not an expression.In C, C++, Perl, and countless other languages it is an expression, and you can put it in an if or a while or whatever you like, but not in Python, because the creators of Python thought that this was too easily misused (or abused) to write "clever" code (like you're trying to).
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Python's use of indentation is clean, concise, and consistent. ... A common use of the conditional expression is to select variable assignment. For example, suppose you want to find the larger of two numbers. Of course, there is a built-in function, max(), that does just this (and more) that you could use. But suppose you want to write your ...
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Many programming languages have a ternary operator, which defines a conditional expression. The most common usage is to make a terse, simple dependent assignment statement. In other words, it offers a one-line code to evaluate the first expression if the condition is true; otherwise, it considers the second expression.
Python Conditional Assignment (in 3 Ways)
Example 1. While working with lists, we often need to check if a list is empty or not, and if it is empty then we need to assign some default value to it. Let's see how we can do it using conditional assignment. my_list = [] # assigning default value to my_list if it is empty. my_list = my_list or [1, 2, 3] print(my_list) # output: [1, 2, 3 ...
How to Write the Python if Statement in one Line
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Python's Assignment Operator: Write Robust Assignments
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Python One Line Conditional Assignment
First, you have the branch that's returned if the condition does NOT hold. Second, you run the branch that's returned if the condition holds. x = (x, 42) [boo] Clever! The condition boo holds so the return value passed into the x variable is the <OnTrue> branch 42. Don't worry if this confuses you—you're not alone.
Assignment Expressions: The Walrus Operator
In this lesson, you'll learn about the biggest change in Python 3.8: the introduction of assignment expressions.Assignment expression are written with a new notation (:=).This operator is often called the walrus operator as it resembles the eyes and tusks of a walrus on its side.. Assignment expressions allow you to assign and return a value in the same expression.
Conditional Statements
In its simplest form, a conditional statement requires only an if clause. else and elif clauses can only follow an if clause. # A conditional statement consisting of # an "if"-clause, only. x = -1 if x < 0: x = x ** 2 # x is now 1. Similarly, conditional statements can have an if and an else without an elif:
How To Use Assignment Expressions in Python
Python 3.8, released in October 2019, adds assignment expressions to Python via the := syntax. The assignment expression syntax is also sometimes called "the walrus operator" because := vaguely resembles a walrus with tusks. Assignment expressions allow variable assignments to occur inside of larger expressions.
How to Use Conditional Statements in Python
In this article, we have seen several examples of how to use these statements in Python, including checking if a number is even or odd, assigning a letter grade based on a numerical score, checking if a year is a leap year, and checking if a string contains a certain character.
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Output: x is equal to y. Python first checks if the condition x < y is met. It isn't, so it goes on to the second condition, which in Python, we write as elif, which is short for else if. If the first condition isn't met, check the second condition, and if it's met, execute the expression. Else, do something else.
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The Python ternary Expression determines if a condition is true or false and then returns the appropriate value in accordance with the result. The ternary Expression is useful in cases where we need to assign a value to a variable based on a simple condition, and we want to keep our code more concise — all in just one line of code.
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A ternary operator is an inline statement that evaluates a condition and returns one of two outputs. It's an operator that's often used in many programming languages, including Python, as well as math. The Python ternary operator has been around since Python 2.5, despite being delayed multiple times.
How to use python if else in one line with examples
Python nested if..else in one line. We can also use ternary expression to define nested if..else block on one line with Python.. Syntax. If you have a multi-line code using nested if else block, something like this:. if condition1: expr1 elif condition-m: expr-m else: if condition3: expr3 elif condition-n: expr-n else: expr5. The one line syntax to use this nested if else block in Python would be:
Python if, if...else Statement (With Examples)
Python if Statement. An if statement executes a block of code only if the specified condition is met.. Syntax. if condition: # body of if statement. Here, if the condition of the if statement is: . True - the body of the if statement executes.; False - the body of the if statement is skipped from execution.; Let's look at an example. Working of if Statement
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PEP 572 seeks to add assignment expressions (or "inline assignments") to the language, but it has seen a prolonged discussion over multiple huge threads on the python-dev mailing list—even after multiple rounds on python-ideas. Those threads were often contentious and were clearly voluminous to the point where many probably just tuned them out.
PEP 572
Unparenthesized assignment expressions are prohibited for the value of a keyword argument in a call. Example: foo(x = y := f(x)) # INVALID foo(x=(y := f(x))) # Valid, though probably confusing. This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.
Assignment Operators in Python
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. Python. # Assigning values using # Assignment Operator a = 3 b = 5 c = a + b # Output print(c) Output. 8.
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Here's a simple example: x = 10. if x > 5: print("x is greater than 5") else: print("x is not greater than 5") In this example, if the value of x is greater than 5, the program will print "x is greater than 5"; otherwise, it will print "x is not greater than 5". This concept forms the backbone of decision-making in Python programs.
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Assignment Operators in Python. Let's see an example of Assignment Operators in Python. Example: The code starts with 'a' and 'b' both having the value 10. It then performs a series of operations: addition, subtraction, multiplication, and a left shift operation on 'b'.
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Using conditions in variable assignments in Python. 4. Assign within if statement Python. 0. Python3.x: Variable Assignment for Different Cases. 2. How to simplify variable assignment by way of IF statement. Hot Network Questions Decode a Caesar ciphertext with high probability
The Walrus Operator: Python 3.8 Assignment Expressions
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.
How GPT-4 Easily Solved My Python Assignment In Record Time
An assignment like this may be worth approximately 5-10% of an "Introduction to Programming" course grade (for example, as part of hands-on lab assignments) With GPT-4, it required me to write (or even manipulate) exactly 0 lines of Python code.
The best new features and fixes in Python 3.13
A new JIT compiler, the first no-GIL edition of Python, better errors and typing enhancement, and the removal of dead batteries are all part of Python 3.13. The first beta of Python 3.13 has just ...
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One line if assignment in python
3. following this topic One line if-condition-assignment. Is there a way to shorten the suggested statement there: num1 = (20 if intvalue else 10) in case that the assigned value is the same one in the condition? this is how it looks now: num1 = (intvalue if intvalue else 10) intvalue appears twice. Is there a way to use intvalue just once and ...