python assignment in conditional

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's Assignment Operator: Write Robust Assignments

Table of Contents

The Assignment Statement Syntax

The assignment operator, assignments and variables, other assignment syntax, initializing and updating variables, making multiple variables refer to the same object, updating lists through indices and slices, adding and updating dictionary keys, doing parallel assignments, unpacking iterables, providing default argument values, augmented mathematical assignment operators, augmented assignments for concatenation and repetition, augmented bitwise assignment operators, annotated assignment statements, assignment expressions with the walrus operator, managed attribute assignments, define or call a function, work with classes, import modules and objects, use a decorator, access the control variable in a for loop or a comprehension, use the as keyword, access the _ special variable in an interactive session, built-in objects, named constants.

Python’s assignment operators allow you to define assignment statements . This type of statement lets you create, initialize, and update variables throughout your code. Variables are a fundamental cornerstone in every piece of code, and assignment statements give you complete control over variable creation and mutation.

Learning about the Python assignment operator and its use for writing assignment statements will arm you with powerful tools for writing better and more robust Python code.

In this tutorial, you’ll:

• Use Python’s assignment operator to write assignment statements
• Take advantage of augmented assignments in Python
• Explore assignment variants, like assignment expressions and managed attributes
• Become aware of illegal and dangerous assignments in Python

You’ll dive deep into Python’s assignment statements. To get the most out of this tutorial, you should be comfortable with several basic topics, including variables , built-in data types , comprehensions , functions , and Python keywords . Before diving into some of the later sections, you should also be familiar with intermediate topics, such as object-oriented programming , constants , imports , type hints , properties , descriptors , and decorators .

Free Source Code: Click here to download the free assignment operator source code that you’ll use to write assignment statements that allow you to create, initialize, and update variables in your code.

Assignment Statements and the Assignment Operator

One of the most powerful programming language features is the ability to create, access, and mutate variables . In Python, a variable is a name that refers to a concrete value or object, allowing you to reuse that value or object throughout your code.

To create a new variable or to update the value of an existing one in Python, you’ll use an assignment statement . This statement has the following three components:

• A left operand, which must be a variable
• The assignment operator ( = )
• A right operand, which can be a concrete value , an object , or an expression

Here’s how an assignment statement will generally look in Python:

Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal —or an expression that evaluates to a value.

To execute an assignment statement like the above, Python runs the following steps:

• Evaluate the right-hand expression to produce a concrete value or object . This value will live at a specific memory address in your computer.
• Store the object’s memory address in the left-hand variable . This step creates a new variable if the current one doesn’t already exist or updates the value of an existing variable.

The second step shows that variables work differently in Python than in other programming languages. In Python, variables aren’t containers for objects. Python variables point to a value or object through its memory address. They store memory addresses rather than objects.

This behavior difference directly impacts how data moves around in Python, which is always by reference . In most cases, this difference is irrelevant in your day-to-day coding, but it’s still good to know.

The central component of an assignment statement is the assignment operator . This operator is represented by the = symbol, which separates two operands:

• A value or an expression that evaluates to a concrete value

Operators are special symbols that perform mathematical , logical , and bitwise operations in a programming language. The objects (or object) on which an operator operates are called operands .

Unary operators, like the not Boolean operator, operate on a single object or operand, while binary operators act on two. That means the assignment operator is a binary operator.

Note: Like C , Python uses == for equality comparisons and = for assignments. Unlike C, Python doesn’t allow you to accidentally use the assignment operator ( = ) in an equality comparison.

Equality is a symmetrical relationship, and assignment is not. For example, the expression a == 42 is equivalent to 42 == a . In contrast, the statement a = 42 is correct and legal, while 42 = a isn’t allowed. You’ll learn more about illegal assignments later on.

The right-hand operand in an assignment statement can be any Python object, such as a number , list , string , dictionary , or even a user-defined object. It can also be an expression. In the end, expressions always evaluate to concrete objects, which is their return value.

Here are a few examples of assignments in Python:

The first two sample assignments in this code snippet use concrete values, also known as literals , to create and initialize number and greeting . The third example assigns the result of a math expression to the total variable, while the last example uses a Boolean expression.

Note: You can use the built-in id() function to inspect the memory address stored in a given variable.

Here’s a short example of how this function works:

The number in your output represents the memory address stored in number . Through this address, Python can access the content of number , which is the integer 42 in this example.

If you run this code on your computer, then you’ll get a different memory address because this value varies from execution to execution and computer to computer.

Unlike expressions, assignment statements don’t have a return value because their purpose is to make the association between the variable and its value. That’s why the Python interpreter doesn’t issue any output in the above examples.

Now that you know the basics of how to write an assignment statement, it’s time to tackle why you would want to use one.

The assignment statement is the explicit way for you to associate a name with an object in Python. You can use this statement for two main purposes:

• Creating and initializing new variables
• Updating the values of existing variables

When you use a variable name as the left operand in an assignment statement for the first time, you’re creating a new variable. At the same time, you’re initializing the variable to point to the value of the right operand.

On the other hand, when you use an existing variable in a new assignment, you’re updating or mutating the variable’s value. Strictly speaking, every new assignment will make the variable refer to a new value and stop referring to the old one. Python will garbage-collect all the values that are no longer referenced by any existing variable.

Assignment statements not only assign a value to a variable but also determine the data type of the variable at hand. This additional behavior is another important detail to consider in this kind of statement.

Because Python is a dynamically typed language, successive assignments to a given variable can change the variable’s data type. Changing the data type of a variable during a program’s execution is considered bad practice and highly discouraged. It can lead to subtle bugs that can be difficult to track down.

Unlike in math equations, in Python assignments, the left operand must be a variable rather than an expression or a value. For example, the following construct is illegal, and Python flags it as invalid syntax:

In this example, you have expressions on both sides of the = sign, and this isn’t allowed in Python code. The error message suggests that you may be confusing the equality operator with the assignment one, but that’s not the case. You’re really running an invalid assignment.

To correct this construct and convert it into a valid assignment, you’ll have to do something like the following:

In this code snippet, you first import the sqrt() function from the math module. Then you isolate the hypotenuse variable in the original equation by using the sqrt() function. Now your code works correctly.

Now you know what kind of syntax is invalid. But don’t get the idea that assignment statements are rigid and inflexible. In fact, they offer lots of room for customization, as you’ll learn next.

Python’s assignment statements are pretty flexible and versatile. You can write them in several ways, depending on your specific needs and preferences. Here’s a quick summary of the main ways to write assignments in Python:

Up to this point, you’ve mostly learned about the base assignment syntax in the above code snippet. In the following sections, you’ll learn about multiple, parallel, and augmented assignments. You’ll also learn about assignments with iterable unpacking.

Read on to see the assignment statements in action!

Assignment Statements in Action

You’ll find and use assignment statements everywhere in your Python code. They’re a fundamental part of the language, providing an explicit way to create, initialize, and mutate variables.

You can use assignment statements with plain names, like number or counter . You can also use assignments in more complicated scenarios, such as with:

• Qualified attribute names , like user.name
• Indices and slices of mutable sequences, like a_list[i] and a_list[i:j]
• Dictionary keys , like a_dict[key]

This list isn’t exhaustive. However, it gives you some idea of how flexible these statements are. You can even assign multiple values to an equal number of variables in a single line, commonly known as parallel assignment . Additionally, you can simultaneously assign the values in an iterable to a comma-separated group of variables in what’s known as an iterable unpacking operation.

In the following sections, you’ll dive deeper into all these topics and a few other exciting things that you can do with assignment statements in Python.

The most elementary use case of an assignment statement is to create a new variable and initialize it using a particular value or expression:

All these statements create new variables, assigning them initial values or expressions. For an initial value, you should always use the most sensible and least surprising value that you can think of. For example, initializing a counter to something different from 0 may be confusing and unexpected because counters almost always start having counted no objects.

Updating a variable’s current value or state is another common use case of assignment statements. In Python, assigning a new value to an existing variable doesn’t modify the variable’s current value. Instead, it causes the variable to refer to a different value. The previous value will be garbage-collected if no other variable refers to it.

Consider the following examples:

These examples run two consecutive assignments on the same variable. The first one assigns the string "Hello, World!" to a new variable named greeting .

The second assignment updates the value of greeting by reassigning it the "Hi, Pythonistas!" string. In this example, the original value of greeting —the "Hello, World!" string— is lost and garbage-collected. From this point on, you can’t access the old "Hello, World!" string.

Even though running multiple assignments on the same variable during a program’s execution is common practice, you should use this feature with caution. Changing the value of a variable can make your code difficult to read, understand, and debug. To comprehend the code fully, you’ll have to remember all the places where the variable was changed and the sequential order of those changes.

Because assignments also define the data type of their target variables, it’s also possible for your code to accidentally change the type of a given variable at runtime. A change like this can lead to breaking errors, like AttributeError exceptions. Remember that strings don’t have the same methods and attributes as lists or dictionaries, for example.

In Python, you can make several variables reference the same object in a multiple-assignment line. This can be useful when you want to initialize several similar variables using the same initial value:

In this example, you chain two assignment operators in a single line. This way, your two variables refer to the same initial value of 0 . Note how both variables hold the same memory address, so they point to the same instance of 0 .

When it comes to integer variables, Python exhibits a curious behavior. It provides a numeric interval where multiple assignments behave the same as independent assignments. Consider the following examples:

To create n and m , you use independent assignments. Therefore, they should point to different instances of the number 42 . However, both variables hold the same object, which you confirm by comparing their corresponding memory addresses.

Now check what happens when you use a greater initial value:

Now n and m hold different memory addresses, which means they point to different instances of the integer number 300 . In contrast, when you use multiple assignments, both variables refer to the same object. This tiny difference can save you small bits of memory if you frequently initialize integer variables in your code.

The implicit behavior of making independent assignments point to the same integer number is actually an optimization called interning . It consists of globally caching the most commonly used integer values in day-to-day programming.

Under the hood, Python defines a numeric interval in which interning takes place. That’s the interning interval for integer numbers. You can determine this interval using a small script like the following:

This script helps you determine the interning interval by comparing integer numbers from -10 to 500 . If you run the script from your command line, then you’ll get an output like the following:

This output means that if you use a single number between -5 and 256 to initialize several variables in independent statements, then all these variables will point to the same object, which will help you save small bits of memory in your code.

In contrast, if you use a number that falls outside of the interning interval, then your variables will point to different objects instead. Each of these objects will occupy a different memory spot.

You can use the assignment operator to mutate the value stored at a given index in a Python list. The operator also works with list slices . The syntax to write these types of assignment statements is the following:

In the first construct, expression can return any Python object, including another list. In the second construct, expression must return a series of values as a list, tuple, or any other sequence. You’ll get a TypeError if expression returns a single value.

Note: When creating slice objects, you can use up to three arguments. These arguments are start , stop , and step . They define the number that starts the slice, the number at which the slicing must stop retrieving values, and the step between values.

Here’s an example of updating an individual value in a list:

In this example, you update the value at index 2 using an assignment statement. The original number at that index was 7 , and after the assignment, the number is 3 .

Note: Using indices and the assignment operator to update a value in a tuple or a character in a string isn’t possible because tuples and strings are immutable data types in Python.

Their immutability means that you can’t change their items in place :

You can’t use the assignment operator to change individual items in tuples or strings. These data types are immutable and don’t support item assignments.

It’s important to note that you can’t add new values to a list by using indices that don’t exist in the target list:

In this example, you try to add a new value to the end of numbers by using an index that doesn’t exist. This assignment isn’t allowed because there’s no way to guarantee that new indices will be consecutive. If you ever want to add a single value to the end of a list, then use the .append() method.

If you want to update several consecutive values in a list, then you can use slicing and an assignment statement:

In the first example, you update the letters between indices 1 and 3 without including the letter at 3 . The second example updates the letters from index 3 until the end of the list. Note that this slicing appends a new value to the list because the target slice is shorter than the assigned values.

Also note that the new values were provided through a tuple, which means that this type of assignment allows you to use other types of sequences to update your target list.

The third example updates a single value using a slice where both indices are equal. In this example, the assignment inserts a new item into your target list.

In the final example, you use a step of 2 to replace alternating letters with their lowercase counterparts. This slicing starts at index 1 and runs through the whole list, stepping by two items each time.

Updating the value of an existing key or adding new key-value pairs to a dictionary is another common use case of assignment statements. To do these operations, you can use the following syntax:

The first construct helps you update the current value of an existing key, while the second construct allows you to add a new key-value pair to the dictionary.

For example, to update an existing key, you can do something like this:

In this example, you update the current inventory of oranges in your store using an assignment. The left operand is the existing dictionary key, and the right operand is the desired new value.

While you can’t add new values to a list by assignment, dictionaries do allow you to add new key-value pairs using the assignment operator. In the example below, you add a lemon key to inventory :

In this example, you successfully add a new key-value pair to your inventory with 100 units. This addition is possible because dictionaries don’t have consecutive indices but unique keys, which are safe to add by assignment.

The assignment statement does more than assign the result of a single expression to a single variable. It can also cope nicely with assigning multiple values to multiple variables simultaneously in what’s known as a parallel assignment .

Here’s the general syntax for parallel assignments in Python:

Note that the left side of the statement can be either a tuple or a list of variables. Remember that to create a tuple, you just need a series of comma-separated elements. In this case, these elements must be variables.

The right side of the statement must be a sequence or iterable of values or expressions. In any case, the number of elements in the right operand must match the number of variables on the left. Otherwise, you’ll get a ValueError exception.

In the following example, you compute the two solutions of a quadratic equation using a parallel assignment:

In this example, you first import sqrt() from the math module. Then you initialize the equation’s coefficients in a parallel assignment.

The equation’s solution is computed in another parallel assignment. The left operand contains a tuple of two variables, x1 and x2 . The right operand consists of a tuple of expressions that compute the solutions for the equation. Note how each result is assigned to each variable by position.

A classical use case of parallel assignment is to swap values between variables:

The highlighted line does the magic and swaps the values of previous_value and next_value at the same time. Note that in a programming language that doesn’t support this kind of assignment, you’d have to use a temporary variable to produce the same effect:

In this example, instead of using parallel assignment to swap values between variables, you use a new variable to temporarily store the value of previous_value to avoid losing its reference.

For a concrete example of when you’d need to swap values between variables, say you’re learning how to implement the bubble sort algorithm , and you come up with the following function:

In the highlighted line, you use a parallel assignment to swap values in place if the current value is less than the next value in the input list. To dive deeper into the bubble sort algorithm and into sorting algorithms in general, check out Sorting Algorithms in Python .

You can use assignment statements for iterable unpacking in Python. Unpacking an iterable means assigning its values to a series of variables one by one. The iterable must be the right operand in the assignment, while the variables must be the left operand.

Like in parallel assignments, the variables must come as a tuple or list. The number of variables must match the number of values in the iterable. Alternatively, you can use the unpacking operator ( * ) to grab several values in a variable if the number of variables doesn’t match the iterable length.

Here’s the general syntax for iterable unpacking in Python:

Iterable unpacking is a powerful feature that you can use all around your code. It can help you write more readable and concise code. For example, you may find yourself doing something like this:

Whenever you do something like this in your code, go ahead and replace it with a more readable iterable unpacking using a single and elegant assignment, like in the following code snippet:

The numbers list on the right side contains four values. The assignment operator unpacks these values into the four variables on the left side of the statement. The values in numbers get assigned to variables in the same order that they appear in the iterable. The assignment is done by position.

Note: Because Python sets are also iterables, you can use them in an iterable unpacking operation. However, it won’t be clear which value goes to which variable because sets are unordered data structures.

The above example shows the most common form of iterable unpacking in Python. The main condition for the example to work is that the number of variables matches the number of values in the iterable.

What if you don’t know the iterable length upfront? Will the unpacking work? It’ll work if you use the * operator to pack several values into one of your target variables.

For example, say that you want to unpack the first and second values in numbers into two different variables. Additionally, you would like to pack the rest of the values in a single variable conveniently called rest . In this case, you can use the unpacking operator like in the following code:

In this example, first and second hold the first and second values in numbers , respectively. These values are assigned by position. The * operator packs all the remaining values in the input iterable into rest .

The unpacking operator ( * ) can appear at any position in your series of target variables. However, you can only use one instance of the operator:

The iterable unpacking operator works in any position in your list of variables. Note that you can only use one unpacking operator per assignment. Using more than one unpacking operator isn’t allowed and raises a SyntaxError .

Dropping away unwanted values from the iterable is a common use case for the iterable unpacking operator. Consider the following example:

In Python, if you want to signal that a variable won’t be used, then you use an underscore ( _ ) as the variable’s name. In this example, useful holds the only value that you need to use from the input iterable. The _ variable is a placeholder that guarantees that the unpacking works correctly. You won’t use the values that end up in this disposable variable.

Note: In the example above, if your target iterable is a sequence data type, such as a list or tuple, then it’s best to access its last item directly.

To do this, you can use the -1 index:

Using -1 gives you access to the last item of any sequence data type. In contrast, if you’re dealing with iterators , then you won’t be able to use indices. That’s when the *_ syntax comes to your rescue.

The pattern used in the above example comes in handy when you have a function that returns multiple values, and you only need a few of these values in your code. The os.walk() function may provide a good example of this situation.

This function allows you to iterate over the content of a directory recursively. The function returns a generator object that yields three-item tuples. Each tuple contains the following items:

• The path to the current directory as a string
• The names of all the immediate subdirectories as a list of strings
• The names of all the files in the current directory as a list of strings

Now say that you want to iterate over your home directory and list only the files. You can do something like this:

This code will issue a long output depending on the current content of your home directory. Note that you need to provide a string with the path to your user folder for the example to work. The _ placeholder variable will hold the unwanted data.

In contrast, the filenames variable will hold the list of files in the current directory, which is the data that you need. The code will print the list of filenames. Go ahead and give it a try!

The assignment operator also comes in handy when you need to provide default argument values in your functions and methods. Default argument values allow you to define functions that take arguments with sensible defaults. These defaults allow you to call the function with specific values or to simply rely on the defaults.

As an example, consider the following function:

This function takes one argument, called name . This argument has a sensible default value that’ll be used when you call the function without arguments. To provide this sensible default value, you use an assignment.

Note: According to PEP 8 , the style guide for Python code, you shouldn’t use spaces around the assignment operator when providing default argument values in function definitions.

Here’s how the function works:

If you don’t provide a name during the call to greet() , then the function uses the default value provided in the definition. If you provide a name, then the function uses it instead of the default one.

Up to this point, you’ve learned a lot about the Python assignment operator and how to use it for writing different types of assignment statements. In the following sections, you’ll dive into a great feature of assignment statements in Python. You’ll learn about augmented assignments .

Augmented Assignment Operators in Python

Python supports what are known as augmented assignments . An augmented assignment combines the assignment operator with another operator to make the statement more concise. Most Python math and bitwise operators have an augmented assignment variation that looks something like this:

Note that $ isn’t a valid Python operator. In this example, it’s a placeholder for a generic operator. This statement works as follows:

• Evaluate expression to produce a value.
• Run the operation defined by the operator that prefixes the = sign, using the previous value of variable and the return value of expression as operands.
• Assign the resulting value back to variable .

In practice, an augmented assignment like the above is equivalent to the following statement:

As you can conclude, augmented assignments are syntactic sugar . They provide a shorthand notation for a specific and popular kind of assignment.

For example, say that you need to define a counter variable to count some stuff in your code. You can use the += operator to increment counter by 1 using the following code:

In this example, the += operator, known as augmented addition , adds 1 to the previous value in counter each time you run the statement counter += 1 .

It’s important to note that unlike regular assignments, augmented assignments don’t create new variables. They only allow you to update existing variables. If you use an augmented assignment with an undefined variable, then you get a NameError :

Python evaluates the right side of the statement before assigning the resulting value back to the target variable. In this specific example, when Python tries to compute x + 1 , it finds that x isn’t defined.

Great! You now know that an augmented assignment consists of combining the assignment operator with another operator, like a math or bitwise operator. To continue this discussion, you’ll learn which math operators have an augmented variation in Python.

An equation like x = x + b doesn’t make sense in math. But in programming, a statement like x = x + b is perfectly valid and can be extremely useful. It adds b to x and reassigns the result back to x .

As you already learned, Python provides an operator to shorten x = x + b . Yes, the += operator allows you to write x += b instead. Python also offers augmented assignment operators for most math operators. Here’s a summary:

The Example column provides generic examples of how to use the operators in actual code. Note that x must be previously defined for the operators to work correctly. On the other hand, y can be either a concrete value or an expression that returns a value.

Note: The matrix multiplication operator ( @ ) doesn’t support augmented assignments yet.

Consider the following example of matrix multiplication using NumPy arrays:

Note that the exception traceback indicates that the operation isn’t supported yet.

To illustrate how augmented assignment operators work, say that you need to create a function that takes an iterable of numeric values and returns their sum. You can write this function like in the code below:

In this function, you first initialize total to 0 . In each iteration, the loop adds a new number to total using the augmented addition operator ( += ). When the loop terminates, total holds the sum of all the input numbers. Variables like total are known as accumulators . The += operator is typically used to update accumulators.

Note: Computing the sum of a series of numeric values is a common operation in programming. Python provides the built-in sum() function for this specific computation.

Another interesting example of using an augmented assignment is when you need to implement a countdown while loop to reverse an iterable. In this case, you can use the -= operator:

In this example, custom_reversed() is a generator function because it uses yield . Calling the function creates an iterator that yields items from the input iterable in reverse order. To decrement the control variable, index , you use an augmented subtraction statement that subtracts 1 from the variable in every iteration.

Note: Similar to summing the values in an iterable, reversing an iterable is also a common requirement. Python provides the built-in reversed() function for this specific computation, so you don’t have to implement your own. The above example only intends to show the -= operator in action.

Finally, counters are a special type of accumulators that allow you to count objects. Here’s an example of a letter counter:

To create this counter, you use a Python dictionary. The keys store the letters. The values store the counts. Again, to increment the counter, you use an augmented addition.

Counters are so common in programming that Python provides a tool specially designed to facilitate the task of counting. Check out Python’s Counter: The Pythonic Way to Count Objects for a complete guide on how to use this tool.

The += and *= augmented assignment operators also work with sequences , such as lists, tuples, and strings. The += operator performs augmented concatenations , while the *= operator performs augmented repetition .

These operators behave differently with mutable and immutable data types:

Note that the augmented concatenation operator operates on two sequences, while the augmented repetition operator works on a sequence and an integer number.

Consider the following examples and pay attention to the result of calling the id() function:

Mutable sequences like lists support the += augmented assignment operator through the .__iadd__() method, which performs an in-place addition. This method mutates the underlying list, appending new values to its end.

Note: If the left operand is mutable, then x += y may not be completely equivalent to x = x + y . For example, if you do list_1 = list_1 + list_2 instead of list_1 += list_2 above, then you’ll create a new list instead of mutating the existing one. This may be important if other variables refer to the same list.

Immutable sequences, such as tuples and strings, don’t provide an .__iadd__() method. Therefore, augmented concatenations fall back to the .__add__() method, which doesn’t modify the sequence in place but returns a new sequence.

There’s another difference between mutable and immutable sequences when you use them in an augmented concatenation. Consider the following examples:

With mutable sequences, the data to be concatenated can come as a list, tuple, string, or any other iterable. In contrast, with immutable sequences, the data can only come as objects of the same type. You can concatenate tuples to tuples and strings to strings, for example.

Again, the augmented repetition operator works with a sequence on the left side of the operator and an integer on the right side. This integer value represents the number of repetitions to get in the resulting sequence:

When the *= operator operates on a mutable sequence, it falls back to the .__imul__() method, which performs the operation in place, modifying the underlying sequence. In contrast, if *= operates on an immutable sequence, then .__mul__() is called, returning a new sequence of the same type.

Note: Values of n less than 0 are treated as 0 , which returns an empty sequence of the same data type as the target sequence on the left side of the *= operand.

Note that a_list[0] is a_list[3] returns True . This is because the *= operator doesn’t make a copy of the repeated data. It only reflects the data. This behavior can be a source of issues when you use the operator with mutable values.

For example, say that you want to create a list of lists to represent a matrix, and you need to initialize the list with n empty lists, like in the following code:

In this example, you use the *= operator to populate matrix with three empty lists. Now check out what happens when you try to populate the first sublist in matrix :

The appended values are reflected in the three sublists. This happens because the *= operator doesn’t make copies of the data that you want to repeat. It only reflects the data. Therefore, every sublist in matrix points to the same object and memory address.

If you ever need to initialize a list with a bunch of empty sublists, then use a list comprehension :

This time, when you populate the first sublist of matrix , your changes aren’t propagated to the other sublists. This is because all the sublists are different objects that live in different memory addresses.

Bitwise operators also have their augmented versions. The logic behind them is similar to that of the math operators. The following table summarizes the augmented bitwise operators that Python provides:

The augmented bitwise assignment operators perform the intended operation by taking the current value of the left operand as a starting point for the computation. Consider the following example, which uses the & and &= operators:

Programmers who work with high-level languages like Python rarely use bitwise operations in day-to-day coding. However, these types of operations can be useful in some situations.

For example, say that you’re implementing a Unix-style permission system for your users to access a given resource. In this case, you can use the characters "r" for reading, "w" for writing, and "x" for execution permissions, respectively. However, using bit-based permissions could be more memory efficient:

You can assign permissions to your users with the OR bitwise operator or the augmented OR bitwise operator. Finally, you can use the bitwise AND operator to check if a user has a certain permission, as you did in the final two examples.

You’ve learned a lot about augmented assignment operators and statements in this and the previous sections. These operators apply to math, concatenation, repetition, and bitwise operations. Now you’re ready to look at other assignment variants that you can use in your code or find in other developers’ code.

Other Assignment Variants

So far, you’ve learned that Python’s assignment statements and the assignment operator are present in many different scenarios and use cases. Those use cases include variable creation and initialization, parallel assignments, iterable unpacking, augmented assignments, and more.

In the following sections, you’ll learn about a few variants of assignment statements that can be useful in your future coding. You can also find these assignment variants in other developers’ code. So, you should be aware of them and know how they work in practice.

In short, you’ll learn about:

• Annotated assignment statements with type hints
• Assignment expressions with the walrus operator
• Managed attribute assignments with properties and descriptors
• Implicit assignments in Python

These topics will take you through several interesting and useful examples that showcase the power of Python’s assignment statements.

PEP 526 introduced a dedicated syntax for variable annotation back in Python 3.6 . The syntax consists of the variable name followed by a colon ( : ) and the variable type:

Even though these statements declare three variables with their corresponding data types, the variables aren’t actually created or initialized. So, for example, you can’t use any of these variables in an augmented assignment statement:

If you try to use one of the previously declared variables in an augmented assignment, then you get a NameError because the annotation syntax doesn’t define the variable. To actually define it, you need to use an assignment.

The good news is that you can use the variable annotation syntax in an assignment statement with the = operator:

The first statement in this example is what you can call an annotated assignment statement in Python. You may ask yourself why you should use type annotations in this type of assignment if everybody can see that counter holds an integer number. You’re right. In this example, the variable type is unambiguous.

However, imagine what would happen if you found a variable initialization like the following:

What would be the data type of each user in users ? If the initialization of users is far away from the definition of the User class, then there’s no quick way to answer this question. To clarify this ambiguity, you can provide the appropriate type hint for users :

Now you’re clearly communicating that users will hold a list of User instances. Using type hints in assignment statements that initialize variables to empty collection data types—such as lists, tuples, or dictionaries—allows you to provide more context about how your code works. This practice will make your code more explicit and less error-prone.

Up to this point, you’ve learned that regular assignment statements with the = operator don’t have a return value. They just create or update variables. Therefore, you can’t use a regular assignment to assign a value to a variable within the context of an expression.

Python 3.8 changed this by introducing a new type of assignment statement through PEP 572 . This new statement is known as an assignment expression or named expression .

Note: Expressions are a special type of statement in Python. Their distinguishing characteristic is that expressions always have a return value, which isn’t the case with all types of statements.

Unlike regular assignments, assignment expressions have a return value, which is why they’re called expressions in the first place. This return value is automatically assigned to a variable. To write an assignment expression, you must use the walrus operator ( := ), which was named for its resemblance to the eyes and tusks of a walrus lying on its side.

The general syntax of an assignment statement is as follows:

This expression looks like a regular assignment. However, instead of using the assignment operator ( = ), it uses the walrus operator ( := ). For the expression to work correctly, the enclosing parentheses are required in most use cases. However, there are certain situations in which these parentheses are superfluous. Either way, they won’t hurt you.

Assignment expressions come in handy when you want to reuse the result of an expression or part of an expression without using a dedicated assignment to grab this value beforehand.

Note: Assignment expressions with the walrus operator have several practical use cases. They also have a few restrictions. For example, they’re illegal in certain contexts, such as lambda functions, parallel assignments, and augmented assignments.

For a deep dive into this special type of assignment, check out The Walrus Operator: Python’s Assignment Expressions .

A particularly handy use case for assignment expressions is when you need to grab the result of an expression used in the context of a conditional statement. For example, say that you need to write a function to compute the mean of a sample of numeric values. Without the walrus operator, you could do something like this:

In this example, the sample size ( n ) is a value that you need to reuse in two different computations. First, you need to check whether the sample has data points or not. Then you need to use the sample size to compute the mean. To be able to reuse n , you wrote a dedicated assignment statement at the beginning of your function to grab the sample size.

You can avoid this extra step by combining it with the first use of the target value, len(sample) , using an assignment expression like the following:

The assignment expression introduced in the conditional computes the sample size and assigns it to n . This way, you guarantee that you have a reference to the sample size to use in further computations.

Because the assignment expression returns the sample size anyway, the conditional can check whether that size equals 0 or not and then take a certain course of action depending on the result of this check. The return statement computes the sample’s mean and sends the result back to the function caller.

Python provides a few tools that allow you to fine-tune the operations behind the assignment of attributes. The attributes that run implicit operations on assignments are commonly referred to as managed attributes .

Properties are the most commonly used tool for providing managed attributes in your classes. However, you can also use descriptors and, in some cases, the .__setitem__() special method.

To understand what fine-tuning the operation behind an assignment means, say that you need a Point class that only allows numeric values for its coordinates, x and y . To write this class, you must set up a validation mechanism to reject non-numeric values. You can use properties to attach the validation functionality on top of x and y .

Here’s how you can write your class:

In Point , you use properties for the .x and .y coordinates. Each property has a getter and a setter method . The getter method returns the attribute at hand. The setter method runs the input validation using a try … except block and the built-in float() function. Then the method assigns the result to the actual attribute.

Here’s how your class works in practice:

When you use a property-based attribute as the left operand in an assignment statement, Python automatically calls the property’s setter method, running any computation from it.

Because both .x and .y are properties, the input validation runs whenever you assign a value to either attribute. In the first example, the input values are valid numbers and the validation passes. In the final example, "one" isn’t a valid numeric value, so the validation fails.

If you look at your Point class, you’ll note that it follows a repetitive pattern, with the getter and setter methods looking quite similar. To avoid this repetition, you can use a descriptor instead of a property.

A descriptor is a class that implements the descriptor protocol , which consists of four special methods :

• .__get__() runs when you access the attribute represented by the descriptor.
• .__set__() runs when you use the attribute in an assignment statement.
• .__delete__() runs when you use the attribute in a del statement.
• .__set_name__() sets the attribute’s name, creating a name-aware attribute.

Here’s how your code may look if you use a descriptor to represent the coordinates of your Point class:

You’ve removed repetitive code by defining Coordinate as a descriptor that manages the input validation in a single place. Go ahead and run the following code to try out the new implementation of Point :

Great! The class works as expected. Thanks to the Coordinate descriptor, you now have a more concise and non-repetitive version of your original code.

Another way to fine-tune the operations behind an assignment statement is to provide a custom implementation of .__setitem__() in your class. You’ll use this method in classes representing mutable data collections, such as custom list-like or dictionary-like classes.

As an example, say that you need to create a dictionary-like class that stores its keys in lowercase letters:

In this example, you create a dictionary-like class by subclassing UserDict from collections . Your class implements a .__setitem__() method, which takes key and value as arguments. The method uses str.lower() to convert key into lowercase letters before storing it in the underlying dictionary.

Python implicitly calls .__setitem__() every time you use a key as the left operand in an assignment statement. This behavior allows you to tweak how you process the assignment of keys in your custom dictionary.

Implicit Assignments in Python

Python implicitly runs assignments in many different contexts. In most cases, these implicit assignments are part of the language syntax. In other cases, they support specific behaviors.

Whenever you complete an action in the following list, Python runs an implicit assignment for you:

• Define or call a function
• Define or instantiate a class
• Use the current instance , self
• Import modules and objects
• Use a decorator
• Use the control variable in a for loop or a comprehension
• Use the as qualifier in with statements , imports, and try … except blocks
• Access the _ special variable in an interactive session

Behind the scenes, Python performs an assignment in every one of the above situations. In the following subsections, you’ll take a tour of all these situations.

When you define a function, the def keyword implicitly assigns a function object to your function’s name. Here’s an example:

From this point on, the name greet refers to a function object that lives at a given memory address in your computer. You can call the function using its name and a pair of parentheses with appropriate arguments. This way, you can reuse greet() wherever you need it.

If you call your greet() function with fellow as an argument, then Python implicitly assigns the input argument value to the name parameter on the function’s definition. The parameter will hold a reference to the input arguments.

When you define a class with the class keyword, you’re assigning a specific name to a class object . You can later use this name to create instances of that class. Consider the following example:

In this example, the name User holds a reference to a class object, which was defined in __main__.User . Like with a function, when you call the class’s constructor with the appropriate arguments to create an instance, Python assigns the arguments to the parameters defined in the class initializer .

Another example of implicit assignments is the current instance of a class, which in Python is called self by convention. This name implicitly gets a reference to the current object whenever you instantiate a class. Thanks to this implicit assignment, you can access .name and .job from within the class without getting a NameError in your code.

Import statements are another variant of implicit assignments in Python. Through an import statement, you assign a name to a module object, class, function, or any other imported object. This name is then created in your current namespace so that you can access it later in your code:

In this example, you import the sys module object from the standard library and assign it to the sys name, which is now available in your namespace, as you can conclude from the second call to the built-in dir() function.

You also run an implicit assignment when you use a decorator in your code. The decorator syntax is just a shortcut for a formal assignment like the following:

Here, you call decorator() with a function object as an argument. This call will typically add functionality on top of the existing function, func() , and return a function object, which is then reassigned to the func name.

The decorator syntax is syntactic sugar for replacing the previous assignment, which you can now write as follows:

Even though this new code looks pretty different from the above assignment, the code implicitly runs the same steps.

Another situation in which Python automatically runs an implicit assignment is when you use a for loop or a comprehension. In both cases, you can have one or more control variables that you then use in the loop or comprehension body:

The memory address of control_variable changes on each iteration of the loop. This is because Python internally reassigns a new value from the loop iterable to the loop control variable on each cycle.

The same behavior appears in comprehensions:

In the end, comprehensions work like for loops but use a more concise syntax. This comprehension creates a new list of strings that mimic the output from the previous example.

The as keyword in with statements, except clauses, and import statements is another example of an implicit assignment in Python. This time, the assignment isn’t completely implicit because the as keyword provides an explicit way to define the target variable.

In a with statement, the target variable that follows the as keyword will hold a reference to the context manager that you’re working with. As an example, say that you have a hello.txt file with the following content:

You want to open this file and print each of its lines on your screen. In this case, you can use the with statement to open the file using the built-in open() function.

In the example below, you accomplish this. You also add some calls to print() that display information about the target variable defined by the as keyword:

This with statement uses the open() function to open hello.txt . The open() function is a context manager that returns a text file object represented by an io.TextIOWrapper instance.

Since you’ve defined a hello target variable with the as keyword, now that variable holds a reference to the file object itself. You confirm this by printing the object and its memory address. Finally, the for loop iterates over the lines and prints this content to the screen.

When it comes to using the as keyword in the context of an except clause, the target variable will contain an exception object if any exception occurs:

In this example, you run a division that raises a ZeroDivisionError . The as keyword assigns the raised exception to error . Note that when you print the exception object, you get only the message because exceptions have a custom .__str__() method that supports this behavior.

There’s a final detail to remember when using the as specifier in a try … except block like the one in the above example. Once you leave the except block, the target variable goes out of scope , and you can’t use it anymore.

Finally, Python’s import statements also support the as keyword. In this context, you can use as to import objects with a different name:

In these examples, you use the as keyword to import the numpy package with the np name and pandas with the name pd . If you call dir() , then you’ll realize that np and pd are now in your namespace. However, the numpy and pandas names are not.

Using the as keyword in your imports comes in handy when you want to use shorter names for your objects or when you need to use different objects that originally had the same name in your code. It’s also useful when you want to make your imported names non-public using a leading underscore, like in import sys as _sys .

The final implicit assignment that you’ll learn about in this tutorial only occurs when you’re using Python in an interactive session. Every time you run a statement that returns a value, the interpreter stores the result in a special variable denoted by a single underscore character ( _ ).

You can access this special variable as you’d access any other variable:

These examples cover several situations in which Python internally uses the _ variable. The first two examples evaluate expressions. Expressions always have a return value, which is automatically assigned to the _ variable every time.

When it comes to function calls, note that if your function returns a fruitful value, then _ will hold it. In contrast, if your function returns None , then the _ variable will remain untouched.

The next example consists of a regular assignment statement. As you already know, regular assignments don’t return any value, so the _ variable isn’t updated after these statements run. Finally, note that accessing a variable in an interactive session returns the value stored in the target variable. This value is then assigned to the _ variable.

Note that since _ is a regular variable, you can use it in other expressions:

In this example, you first create a list of values. Then you call len() to get the number of values in the list. Python automatically stores this value in the _ variable. Finally, you use _ to compute the mean of your list of values.

Now that you’ve learned about some of the implicit assignments that Python runs under the hood, it’s time to dig into a final assignment-related topic. In the following few sections, you’ll learn about some illegal and dangerous assignments that you should be aware of and avoid in your code.

Illegal and Dangerous Assignments in Python

In Python, you’ll find a few situations in which using assignments is either forbidden or dangerous. You must be aware of these special situations and try to avoid them in your code.

In the following sections, you’ll learn when using assignment statements isn’t allowed in Python. You’ll also learn about some situations in which using assignments should be avoided if you want to keep your code consistent and robust.

You can’t use Python keywords as variable names in assignment statements. This kind of assignment is explicitly forbidden. If you try to use a keyword as a variable name in an assignment, then you get a SyntaxError :

Whenever you try to use a keyword as the left operand in an assignment statement, you get a SyntaxError . Keywords are an intrinsic part of the language and can’t be overridden.

If you ever feel the need to name one of your variables using a Python keyword, then you can append an underscore to the name of your variable:

In this example, you’re using the desired name for your variables. Because you added a final underscore to the names, Python doesn’t recognize them as keywords, so it doesn’t raise an error.

Note: Even though adding an underscore at the end of a name is an officially recommended practice , it can be confusing sometimes. Therefore, try to find an alternative name or use a synonym whenever you find yourself using this convention.

For example, you can write something like this:

In this example, using the name booking_class for your variable is way clearer and more descriptive than using class_ .

You’ll also find that you can use only a few keywords as part of the right operand in an assignment statement. Those keywords will generally define simple statements that return a value or object. These include lambda , and , or , not , True , False , None , in , and is . You can also use the for keyword when it’s part of a comprehension and the if keyword when it’s used as part of a ternary operator .

In an assignment, you can never use a compound statement as the right operand. Compound statements are those that require an indented block, such as for and while loops, conditionals, with statements, try … except blocks, and class or function definitions.

Sometimes, you need to name variables, but the desired or ideal name is already taken and used as a built-in name. If this is your case, think harder and find another name. Don’t shadow the built-in.

Shadowing built-in names can cause hard-to-identify problems in your code. A common example of this issue is using list or dict to name user-defined variables. In this case, you override the corresponding built-in names, which won’t work as expected if you use them later in your code.

Consider the following example:

The exception in this example may sound surprising. How come you can’t use list() to build a list from a call to map() that returns a generator of square numbers?

By using the name list to identify your list of numbers, you shadowed the built-in list name. Now that name points to a list object rather than the built-in class. List objects aren’t callable, so your code no longer works.

In Python, you’ll have nothing that warns against using built-in, standard-library, or even relevant third-party names to identify your own variables. Therefore, you should keep an eye out for this practice. It can be a source of hard-to-debug errors.

In programming, a constant refers to a name associated with a value that never changes during a program’s execution. Unlike other programming languages, Python doesn’t have a dedicated syntax for defining constants. This fact implies that Python doesn’t have constants in the strict sense of the word.

Python only has variables. If you need a constant in Python, then you’ll have to define a variable and guarantee that it won’t change during your code’s execution. To do that, you must avoid using that variable as the left operand in an assignment statement.

To tell other Python programmers that a given variable should be treated as a constant, you must write your variable’s name in capital letters with underscores separating the words. This naming convention has been adopted by the Python community and is a recommendation that you’ll find in the Constants section of PEP 8 .

In the following examples, you define some constants in Python:

The problem with these constants is that they’re actually variables. Nothing prevents you from changing their value during your code’s execution. So, at any time, you can do something like the following:

These assignments modify the value of two of your original constants. Python doesn’t complain about these changes, which can cause issues later in your code. As a Python developer, you must guarantee that named constants in your code remain constant.

The only way to do that is never to use named constants in an assignment statement other than the constant definition.

You’ve learned a lot about Python’s assignment operators and how to use them for writing assignment statements . With this type of statement, you can create, initialize, and update variables according to your needs. Now you have the required skills to fully manage the creation and mutation of variables in your Python code.

In this tutorial, you’ve learned how to:

• Write assignment statements using Python’s assignment operators
• Work with augmented assignments in Python
• Explore assignment variants, like assignment expression and managed attributes
• Identify illegal and dangerous assignments in Python

Learning about the Python assignment operator and how to use it in assignment statements is a fundamental skill in Python. It empowers you to write reliable and effective Python code.

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Python's Assignment Operator: Write Robust Assignments (Source Code)

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How to Write the Python if Statement in one Line

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Have you ever heard of writing a Python if statement in a single line? Here, we explore multiple ways to do exactly that, including using conditional expressions in Python.

The if statement is one of the most fundamental statements in Python. In this article, we learn how to write the Python if in one line.

The if is a key piece in writing Python code. It allows developers to control the flow and logic of their code based on information received at runtime. However, many Python developers do not know they may reduce the length and complexity of their if statements by writing them in a single line.

For this article, we assume you’re somewhat familiar with Python conditions and comparisons. If not, don’t worry! Our Python Basics Course will get you up to speed in no time. This course is included in the Python Basics Track , a full-fledged Python learning track designed for complete beginners.

We start with a recap on how Python if statements work. Then, we explore some examples of how to write if statements in a single line. Let’s get started!

How the if Statement Works in Python

Let’s start with the basics. An if statement in Python is used to determine whether a condition is True or False . This information can then be used to perform specific actions in the code, essentially controlling its logic during execution.

The structure of the basic if statement is as follows:

The <expression> is the code that evaluates to either True or False . If this code evaluates to True, then the code below (represented by <perform_action> ) executes.

Python uses whitespaces to indicate which lines are controlled by the if statement. The if statement controls all indented lines below it. Typically, the indentation is set to four spaces (read this post if you’re having trouble with the indentation ).

As a simple example, the code below prints a message if and only if the current weather is sunny:

The if statement in Python has two optional components: the elif statement, which executes only if the preceding if/elif statements are False ; and the else statement, which executes only if all of the preceding if/elif statements are False. While we may have as many elif statements as we want, we may only have a single else statement at the very end of the code block.

Here’s the basic structure:

Here’s how our previous example looks after adding elif and else statements. Change the value of the weather variable to see a different message printed:

How to Write a Python if in one Line

Writing an if statement in Python (along with the optional elif and else statements) uses a lot of whitespaces. Some people may find it confusing or tiresome to follow each statement and its corresponding indented lines.

To overcome this, there is a trick many Python developers often overlook: write an if statement in a single line !

Though not the standard, Python does allow us to write an if statement and its associated action in the same line. Here’s the basic structure:

As you can see, not much has changed. We simply need to “pull” the indented line <perform_action> up to the right of the colon character ( : ). It’s that simple!

Let’s check it with a real example. The code below works as it did previously despite the if statement being in a single line. Test it out and see for yourself:

Writing a Python if Statement With Multiple Actions in one Line

That’s all well and good, but what if my if statement has multiple actions under its control? When using the standard indentation, we separate different actions in multiple indented lines as the structure below shows:

Can we do this in a single line? The surprising answer is yes! We use semicolons to separate each action in the same line as if placed in different lines.

Here’s how the structure looks:

And an example of this functionality:

Have you noticed how each call to the print() function appears in its own line? This indicates we have successfully executed multiple actions from a single line. Nice!

By the way, interested in learning more about the print() function? We have an article on the ins and outs of the print() function .

Writing a Full Python if/elif/else Block Using Single Lines

You may have seen this coming, but we can even write elif and else statements each in a single line. To do so, we use the same syntax as writing an if statement in a single line.

Here’s the general structure:

Looks simple, right? Depending on the content of your expressions and actions, you may find this structure easier to read and understand compared to the indented blocks.

Here’s our previous example of a full if/elif/else block, rewritten as single lines:

Using Python Conditional Expressions to Write an if/else Block in one Line

There’s still a final trick to writing a Python if in one line. Conditional expressions in Python (also known as Python ternary operators) can run an if/else block in a single line.

A conditional expression is even more compact! Remember it took at least two lines to write a block containing both if and else statements in our last example.

In contrast, here’s how a conditional expression is structured:

The syntax is somewhat harder to follow at first, but the basic idea is that <expression> is a test. If the test evaluates to True , then <value_if_true> is the result. Otherwise, the expression results in <value_if_false> .

As you can see, conditional expressions always evaluate to a single value in the end. They are not complete replacements for an if/elif/else block. In fact, we cannot have elif statements in them at all. However, they’re most helpful when determining a single value depending on a single condition.

Take a look at the code below, which determines the value of is_baby depending on whether or not the age is below five:

This is the exact use case for a conditional expression! Here’s how we rewrite this if/else block in a single line:

Much simpler!

Go Even Further With Python!

We hope you now know many ways to write a Python if in one line. We’ve reached the end of the article, but don’t stop practicing now!

If you do not know where to go next, read this post on how to get beyond the basics in Python . If you’d rather get technical, we have a post on the best code editors and IDEs for Python . Remember to keep improving!

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Basic Statements in Python

Table of contents, what is a statement in python, statement set, multi-line statements, simple statements, expression statements, the assert statement, the try statement.

In Python, statements are instructions or commands that you write to perform specific actions or tasks. They are the building blocks of a Python program.

A statement is a line of code that performs a specific action. It is the smallest unit of code that can be executed by the Python interpreter.

Assignment Statement

In this example, the value 10 is assigned to the variable x using the assignment statement.

Conditional Statement

In this example, the if-else statement is used to check the value of x and print a corresponding message.

By using statements, programmers can instruct the computer to perform a variety of tasks, from simple arithmetic operations to complex decision-making processes. Proper use of statements is crucial to writing efficient and effective Python code.

Here's a table summarizing various types of statements in Python:

Please note that this table provides a brief overview of each statement type, and there may be additional details and variations for each statement.

Multi-line statements are a convenient way to write long code in Python without making it cluttered. They allow you to write several lines of code as a single statement, making it easier for developers to read and understand the code. Here are two examples of multi-line statements in Python:

• Using backslash:
• Using parentheses:

Simple statements are the smallest unit of execution in Python programming language and they do not contain any logical or conditional expressions. They are usually composed of a single line of code and can perform basic operations such as assigning values to variables , printing out values, or calling functions .

Examples of simple statements in Python:

Simple statements are essential to programming in Python and are often used in combination with more complex statements to create robust programs and applications.

Expression statements in Python are lines of code that evaluate and produce a value. They are used to assign values to variables, call functions, and perform other operations that produce a result.

In this example, we assign the value 5 to the variable x , then add 3 to x and assign the result ( 8 ) to the variable y . Finally, we print the value of y .

In this example, we define a function square that takes one argument ( x ) and returns its square. We then call the function with the argument 5 and assign the result ( 25 ) to the variable result . Finally, we print the value of result .

Overall, expression statements are an essential part of Python programming and allow for the execution of mathematical and computational operations.

The assert statement in Python is used to test conditions and trigger an error if the condition is not met. It is often used for debugging and testing purposes.

Where condition is the expression that is tested, and message is the optional error message that is displayed when the condition is not met.

In this example, the assert statement tests whether x is equal to 5 . If the condition is met, the statement has no effect. If the condition is not met, an error will be raised with the message x should be 5 .

In this example, the assert statement tests whether y is not equal to 0 before performing the division. If the condition is met, the division proceeds as normal. If the condition is not met, an error will be raised with the message Cannot divide by zero .

Overall, assert statements are a useful tool in Python for debugging and testing, as they can help catch errors early on. They are also easily disabled in production code to avoid any unnecessary overhead.

The try statement in Python is used to catch exceptions that may occur during the execution of a block of code. It ensures that even when an error occurs, the code does not stop running.

Examples of Error Processing

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• Match Statements
• Operators in Python Statements
• The IF Statement

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Python Conditional Assignment Operator in Python 3

Python is a versatile programming language that offers a wide range of features and functionalities. One such feature is the conditional assignment operator, which allows programmers to assign a value to a variable based on a condition. This operator, also known as the “ternary operator,” provides a concise and elegant way to write conditional statements in Python.

The conditional assignment operator in Python follows the syntax: value_if_true if condition else value_if_false . It evaluates the condition and returns the value_if_true if the condition is true, otherwise it returns the value_if_false.

This operator can be particularly useful when you want to assign a value to a variable based on a simple condition without the need for an if-else statement. It allows you to write more compact and readable code, reducing the number of lines and improving code efficiency.

Let’s consider a simple example to understand the usage of the conditional assignment operator:

In this example, we assign the value “Adult” to the variable “status” if the age is greater than or equal to 18. Otherwise, we assign the value “Minor”. The output of this code will be “Adult” since the age is 25.

The conditional assignment operator can also be used in more complex scenarios. For instance, consider the following example:

In this example, we assign the sum of x and y to the variable “result” if x is greater than y. Otherwise, we assign the difference between x and y. The output of this code will be 15 since x is greater than y.

Related Evidence

The conditional assignment operator is a widely used feature in Python programming. It provides a concise and efficient way to assign values based on conditions. Many Python developers appreciate its simplicity and readability.

Furthermore, the conditional assignment operator is supported in Python 3 and later versions. It is an integral part of the language’s syntax and is widely documented in Python’s official documentation and various programming resources.

Overall, the conditional assignment operator in Python is a powerful tool that allows programmers to write more concise and efficient code. Its usage can greatly enhance code readability and reduce the complexity of conditional statements.

The Python Conditional Assignment Operator, also known as the “walrus operator,” was introduced in Python 3.8. It allows you to assign a value to a variable based on a condition in a single line of code. This operator is denoted by “:=” and can be used in various scenarios to simplify your code and make it more readable.

In the first example, we use the conditional assignment operator to assign the value True to the variable is_adult if the age is greater than or equal to 18. Otherwise, it assigns False. This simplifies the code and makes it more concise.

In the second example, we assign a default value “John Doe” to the variable name if it is None. This is achieved using the conditional assignment operator in combination with the logical OR operator. If the variable name is None, the expression evaluates to True, and the default_name value is assigned to name.

The third example demonstrates how the conditional assignment operator can be used to assign a value from a function call if the current value of the variable is None. This can be useful when you want to assign a default value from a function only if it hasn’t been set previously.

Overall, the Python Conditional Assignment Operator is a powerful tool that allows you to write more concise and readable code. It simplifies assignments based on conditions and reduces the need for multiple lines of code. However, it should be used judiciously to maintain code clarity and avoid excessive complexity.

Reference Links:

• Python 3.8 Documentation – Assignment Expressions
• GeeksforGeeks – Assignment Expressions in Python
• Real Python – Assignment Expressions (The Walrus Operator)

In conclusion, the Python Conditional Assignment Operator is a valuable addition to the language that simplifies assignments based on conditions. It allows you to write more concise and readable code by reducing the need for multiple lines of code. However, it is important to use it judiciously and maintain code clarity to avoid excessive complexity. The operator has been well-received by the Python community and has become a popular feature in Python 3.8 and later versions.

Related Posts

Converting Unicode String Dictionary to Dictionary in Python 3

Creating a Single Row DataFrame in Python using Pandas

Using the ‘break’ Statement Outside a Loop in Python 3

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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

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• Python »
• 3.12.6 Documentation »
• The Python Language Reference »
• 6. Expressions
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6. Expressions ¶

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

and no semantics are given, the semantics of this form of name are the same as for othername .

6.1. Arithmetic conversions ¶

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

If either argument is a complex number, the other is converted to complex;

otherwise, if either argument is a floating-point number, the other is converted to floating point;

otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

6.2. Atoms ¶

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

6.2.1. Identifiers (Names) ¶

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

6.2.1.1. Private name mangling ¶

When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class.

The class specifications .

More precisely, private names are transformed to a longer form before code is generated for them. If the transformed name is longer than 255 characters, implementation-defined truncation may happen.

The transformation is independent of the syntactical context in which the identifier is used but only the following private identifiers are mangled:

Any name used as the name of a variable that is assigned or read or any name of an attribute being accessed.

The __name__ attribute of nested functions, classes, and type aliases is however not mangled.

The name of imported modules, e.g., __spam in import __spam . If the module is part of a package (i.e., its name contains a dot), the name is not mangled, e.g., the __foo in import __foo.bar is not mangled.

The name of an imported member, e.g., __f in from spam import __f .

The transformation rule is defined as follows:

The class name, with leading underscores removed and a single leading underscore inserted, is inserted in front of the identifier, e.g., the identifier __spam occurring in a class named Foo , _Foo or __Foo is transformed to _Foo__spam .

If the class name consists only of underscores, the transformation is the identity, e.g., the identifier __spam occurring in a class named _ or __ is left as is.

6.2.2. Literals ¶

Python supports string and bytes literals and various numeric literals:

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating-point number, complex number) with the given value. The value may be approximated in the case of floating-point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

6.2.3. Parenthesized forms ¶

A parenthesized form is an optional expression list enclosed in parentheses:

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

6.2.4. Displays for lists, sets and dictionaries ¶

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

either the container contents are listed explicitly, or

they are computed via a set of looping and filtering instructions, called a comprehension .

Common syntax elements for comprehensions are:

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)] .

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator . A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions.

If a comprehension contains async for clauses, or if it contains await expressions or other asynchronous comprehensions anywhere except the iterable expression in the leftmost for clause, it is called an asynchronous comprehension . An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530 .

Added in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

6.2.5. List displays ¶

A list display is a possibly empty series of expressions enclosed in square brackets:

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

6.2.6. Set displays ¶

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with {} ; this literal constructs an empty dictionary.

6.2.7. Dictionary displays ¶

A dictionary display is a possibly empty series of dict items (key/value pairs) enclosed in curly braces:

A dictionary display yields a new dictionary object.

If a comma-separated sequence of dict items is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding value. This means that you can specify the same key multiple times in the dict item list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking . Its operand must be a mapping . Each mapping item is added to the new dictionary. Later values replace values already set by earlier dict items and earlier dictionary unpackings.

Added in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448 .

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy . (To summarize, the key type should be hashable , which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last value (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572 .

6.2.8. Generator expressions ¶

A generator expression is a compact generator notation in parentheses:

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)) .

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression . An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators ).

Added in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

6.2.9. Yield expressions ¶

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions .

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None . Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError , while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration , or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

The proposal for adding generators and the yield statement to Python.

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

6.2.9.1. Generator-iterator methods ¶

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None . The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__() ’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

6.2.9.2. Examples ¶

Here is a simple example that demonstrates the behavior of generators and generator functions:

For examples using yield from , see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

6.2.9.3. Asynchronous generator functions ¶

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None . Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break , the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks() . When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py .

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

6.2.9.4. Asynchronous generator-iterator methods ¶

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration , or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

6.3. Primaries ¶

Primaries represent the most tightly bound operations of the language. Their syntax is:

6.3.1. Attribute references ¶

An attribute reference is a primary followed by a period and a name:

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. The type and value produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

This production can be customized by overriding the __getattribute__() method or the __getattr__() method. The __getattribute__() method is called first and either returns a value or raises AttributeError if the attribute is not available.

If an AttributeError is raised and the object has a __getattr__() method, that method is called as a fallback.

6.3.2. Subscriptions ¶

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__() . When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__ , see __class_getitem__ versus __getitem__ .

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__() :

Mappings. If the primary is a mapping , the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.

Sequences. If the primary is a sequence , the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str , list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences . However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x . The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters . A character is not a separate data type but a string of exactly one character.

6.3.3. Slicings ¶

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy ) whose start , stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

6.3.4. Calls ¶

A call calls a callable object (e.g., a function ) with a possibly empty series of arguments :

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None , it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable . Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4) , if y evaluates to a sequence y1 , …, yM , this is equivalent to a call with M+4 positional arguments x1 , x2 , y1 , …, yM , x3 , x4 .

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping , the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings ( * ), and keyword arguments may follow dictionary unpackings ( ** ). Originally proposed by PEP 448 .

A call always returns some value, possibly None , unless it raises an exception. How this value is computed depends on the type of the callable object.

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions . When the code block executes a return statement, this specifies the return value of the function call.

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

A new instance of that class is returned.

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

The class must define a __call__() method; the effect is then the same as if that method was called.

6.4. Await expression ¶

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function .

Added in version 3.5.

6.5. The power operator ¶

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1 .

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100 , but 10**-2 returns 0.01 .

Raising 0.0 to a negative power results in a ZeroDivisionError . Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError .)

This operation can be customized using the special __pow__() and __rpow__() methods.

6.6. Unary arithmetic and bitwise operations ¶

All unary arithmetic and bitwise operations have the same priority:

The unary - (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

The unary ~ (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1) . It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

6.7. Binary arithmetic operations ¶

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

This operation can be customized using the special __matmul__() and __rmatmul__() methods.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

The division operation can be customized using the special __truediv__() and __rtruediv__() methods. The floor division operation can be customized using the special __floordiv__() and __rfloordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating-point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34 .) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand [ 1 ] .

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y) . Floor division and modulo are also connected with the built-in function divmod() : divmod(x, y) == (x//y, x%y) . [ 2 ] .

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting .

The modulo operation can be customized using the special __mod__() and __rmod__() methods.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating-point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The - (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() and __rsub__() methods.

6.8. Shifting operations ¶

The shifting operations have lower priority than the arithmetic operations:

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

The left shift operation can be customized using the special __lshift__() and __rlshift__() methods. The right shift operation can be customized using the special __rshift__() and __rrshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n) . A left shift by n bits is defined as multiplication with pow(2,n) .

6.9. Binary bitwise operations ¶

Each of the three bitwise operations has a different priority level:

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

6.10. Comparisons ¶

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

Comparisons yield boolean values: True or False . Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a , b , c , …, y , z are expressions and op1 , op2 , …, opN are comparison operators, then a op1 b op2 c ... y opN z is equivalent to a op1 b and b op2 c and ... y opN z , except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c , so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

6.10.1. Value comparisons ¶

The operators < , > , == , >= , <= , and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object , they inherit the default comparison behavior from object . Types can customize their comparison behavior by implementing rich comparison methods like __lt__() , described in Basic customization .

The default behavior for equality comparison ( == and != ) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y ).

A default order comparison ( < , > , <= , and >= ) is not provided; an attempt raises TypeError . A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

Numbers of built-in numeric types ( Numeric Types — int, float, complex ) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

The not-a-number values float('NaN') and decimal.Decimal('NaN') are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float('NaN') , 3 < x , x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not , never the equality operators.

Binary sequences (instances of bytes or bytearray ) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.

Strings (instances of str ) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord() ) of their characters. [ 3 ]

Strings and binary sequences cannot be directly compared.

Sequences (instances of tuple , list , or range ) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError .

Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

Lexicographical comparison between built-in collections works as follows:

For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).

Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y ). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).

Mappings (instances of dict ) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

Order comparisons ( < , > , <= , and >= ) raise TypeError .

Sets (instances of set or frozenset ) can be compared within and across their types.

They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets {1,2} and {2,3} are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min() , max() , and sorted() produce undefined results given a list of sets as inputs).

Comparison of sets enforces reflexivity of its elements.

Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

Equality comparison should be reflexive. In other words, identical objects should compare equal:

x is y implies x == y

Comparison should be symmetric. In other words, the following expressions should have the same result:

x == y and y == x x != y and y != x x < y and y > x x <= y and y >= x

Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

x > y and y > z implies x > z x < y and y <= z implies x < z

Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

x == y and not x != y x < y and not x >= y (for total ordering) x > y and not x <= y (for total ordering)

The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

6.10.2. Membership test operations ¶

The operators in and not in test for membership. x in s evaluates to True if x is a member of s , and False otherwise. x not in s returns the negation of x in s . All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y) .

For the string and bytes types, x in y is True if and only if x is a substring of y . An equivalent test is y.find(x) != -1 . Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True .

For user-defined classes which define the __contains__() method, x in y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__() , x in y is True if some value z , for which the expression x is z or x == z is true, is produced while iterating over y . If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__() , x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i] , and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in .

6.10.3. Identity comparisons ¶

The operators is and is not test for an object’s identity: x is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. [ 4 ]

6.11. Boolean operations ¶

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False , None , numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x ; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x ; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True , but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or 'foo' yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not 'foo' produces False rather than '' .)

6.12. Assignment expressions ¶

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier , while also returning the value of the expression .

One common use case is when handling matched regular expressions:

Or, when processing a file stream in chunks:

Assignment expressions must be surrounded by parentheses when used as expression statements and when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert , with , and assignment statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

Added in version 3.8: See PEP 572 for more details about assignment expressions.

6.13. Conditional expressions ¶

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x . If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

6.14. Lambdas ¶

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

6.15. Expression lists ¶

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking . Its operand must be an iterable . The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

Added in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448 .

A trailing comma is required only to create a one-item tuple, such as 1, ; it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: () .)

6.16. Evaluation order ¶

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

6.17. Operator precedence ¶

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

Table of Contents

• 6.1. Arithmetic conversions
• 6.2.1.1. Private name mangling
• 6.2.2. Literals
• 6.2.3. Parenthesized forms
• 6.2.4. Displays for lists, sets and dictionaries
• 6.2.5. List displays
• 6.2.6. Set displays
• 6.2.7. Dictionary displays
• 6.2.8. Generator expressions
• 6.2.9.1. Generator-iterator methods
• 6.2.9.2. Examples
• 6.2.9.3. Asynchronous generator functions
• 6.2.9.4. Asynchronous generator-iterator methods
• 6.3.1. Attribute references
• 6.3.2. Subscriptions
• 6.3.3. Slicings
• 6.3.4. Calls
• 6.4. Await expression
• 6.5. The power operator
• 6.6. Unary arithmetic and bitwise operations
• 6.7. Binary arithmetic operations
• 6.8. Shifting operations
• 6.9. Binary bitwise operations
• 6.10.1. Value comparisons
• 6.10.2. Membership test operations
• 6.10.3. Identity comparisons
• 6.11. Boolean operations
• 6.12. Assignment expressions
• 6.13. Conditional expressions
• 6.14. Lambdas
• 6.15. Expression lists
• 6.16. Evaluation order
• 6.17. Operator precedence

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Data to Fish

5 ways to apply an IF condition in Pandas DataFrame

In this guide, you’ll see 5 different ways to apply an IF condition in Pandas DataFrame.

Specifically, you’ll see how to apply an IF condition for:

• Set of numbers
• Set of numbers and lambda
• Strings and lambda
• OR condition

Applying an IF condition in Pandas DataFrame

Let’s now review the following 5 cases:

(1) IF condition – Set of numbers

Suppose that you created a DataFrame in Python that has 10 numbers (from 1 to 10). You then want to apply the following IF conditions:

• If the number is equal or lower than 4, then assign the value of ‘ Yes ‘
• Otherwise, if the number is greater than 4, then assign the value of ‘ No ‘

This is the general structure that you may use to create the IF condition:

For our example, the Python code would look like this:

Here is the result that you’ll get in Python:

(2) IF condition – set of numbers and  lambda

You’ll now see how to get the same results as in case 1 by using lambda, where the conditions are:

Here is the generic structure that you may apply in Python:

For our example:

This is the result that you’ll get, which matches with case 1:

(3) IF condition – strings

Now, let’s create a DataFrame that contains only strings/text with 4  names : Jon, Bill, Maria and Emma.

The conditions are:

• If the name is equal to ‘Bill,’ then assign the value of ‘ Match ‘
• Otherwise, if the name is not   ‘Bill,’ then assign the value of ‘ Mismatch ‘

Once you run the above Python code, you’ll see:

(4) IF condition – strings and lambda 

You’ll get the same results as in case 3 by using lambda:

And here is the output from Python:

(5) IF condition with OR

Now let’s apply these conditions:

• If the name is ‘Bill’  or ‘Emma,’ then assign the value of ‘ Match ‘
• Otherwise, if the name is neither ‘Bill’ nor ‘Emma,’ then assign the value of ‘ Mismatch ‘

Run the Python code, and you’ll get the following result:

Applying an IF condition under an existing DataFrame column

So far you have seen how to apply an IF condition by creating a new column.

Alternatively, you may store the results under an existing DataFrame column.

For example, let’s say that you created a DataFrame that has 12 numbers, where the last two numbers are zeros :

‘set_of_numbers’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0 , 0 ]

You may then apply the following IF conditions, and then store the results under the existing ‘ set_of_numbers ‘ column:

• If the number is equal to 0 , then change the value to 999
• If the number is equal to 5 , then change the value to 555

Here are the before and after results, where the ‘5’ became ‘555’ and the 0’s became ‘999’ under the existing ‘set_of_numbers’ column:

On another instance, you may have a DataFrame that contains NaN values . You can then apply an IF condition to replace those values with zeros , as in the example below:

Before you’ll see the NaN values, and after you’ll see the zero values:

You just saw how to apply an IF condition in Pandas DataFrame . There are indeed multiple ways to apply such a condition in Python. You can achieve the same results by using either lambda, or just by sticking with Pandas.

At the end, it boils down to working with the method that is best suited to your needs.

Finally, you may want to check the following external source for additional information about Pandas DataFrame .

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Python supports the usual logical conditions from mathematics:

• Equals: a == b
• Not Equals: a != b
• Less than: a < b
• Less than or equal to: a <= b
• Greater than: a > b
• Greater than or equal to: a >= b

These conditions can be used in several ways, most commonly in "if statements" and loops.

An "if statement" is written by using the if keyword.

If statement:

In this example we use two variables, a and b , which are used as part of the if statement to test whether b is greater than a . As a is 33 , and b is 200 , we know that 200 is greater than 33, and so we print to screen that "b is greater than a".

Indentation

If statement, without indentation (will raise an error):

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The elif keyword is Python's way of saying "if the previous conditions were not true, then try this condition".

In this example a is equal to b , so the first condition is not true, but the elif condition is true, so we print to screen that "a and b are equal".

The else keyword catches anything which isn't caught by the preceding conditions.

In this example a is greater than b , so the first condition is not true, also the elif condition is not true, so we go to the else condition and print to screen that "a is greater than b".

You can also have an else without the elif :

Short Hand If

If you have only one statement to execute, you can put it on the same line as the if statement.

One line if statement:

Short Hand If ... Else

If you have only one statement to execute, one for if, and one for else, you can put it all on the same line:

One line if else statement:

This technique is known as Ternary Operators , or Conditional Expressions .

You can also have multiple else statements on the same line:

One line if else statement, with 3 conditions:

The and keyword is a logical operator, and is used to combine conditional statements:

Test if a is greater than b , AND if c is greater than a :

The or keyword is a logical operator, and is used to combine conditional statements:

Test if a is greater than b , OR if a is greater than c :

The not keyword is a logical operator, and is used to reverse the result of the conditional statement:

Test if a is NOT greater than b :

You can have if statements inside if statements, this is called nested if statements.

The pass Statement

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

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Important Questions and Notes

70+ Python if else Statement Important Practice Questions

Table of Content

Python if else statement practice – test 1.

• Python if else Statement Practice – Test 2
• Python if else Statement Practice – Test 3
• Python if else Statement Practice – Test 4
• Python if else Statement Practice – Test 5
• Python if else Statement Practice – Test 6
• Python if else Statement Practice – Test 7

Python if else Statement Pract ice Test 2

Python if else Statement Practice Test 3

Q1. Write a program to check whether a number entered is three digit number or not.

Q2. Write a program to check whether a person is eligible for voting or not.(voting age >=18)

Q3. Write a program to check whether a person is senior citizen or not.

Q4. Write a program to find the lowest number out of two numbers excepted from user.

Q5. Write a program to find the largest number out of two numbers excepted from user.

Q6. Write a program to check whether a number (accepted from user) is positive or negative.

Q7. Write a program to check whether a number is even or odd.

Q8. Write a program to display the spell of a digit accepted from user (like user input 0 and display ZERO and so on)

Q8. Write a program to whether a number (accepted from user) is divisible by 2 and 3 both.

Q9. Write a program to find the largest number out of three numbers excepted from user.

Python if else Statement Practice Test 4

Q1. Accept the temperature in degree Celsius of water and check whether it is boiling or not (boiling point of water in 100  o C.

Q2. _________ is a graphical representation of steps (algorithm/flow chart)

Q3. Python has _________ statement as empty statement (Pass/Fail)

Q4. In python, a block is a group of _______statement having same indentation level.(consecutive/alternate)

Q5. Out of “elif” and “else if”, which is the correct statement in python?

Q6. Accept the age of 4 people and display the youngest one ?

Q7. What is the purpose of else in if elif ladder?

Q8. Accept the age of 4 people and display the oldest one.

Q9. Write a program to check whether a number  is prime or not.

Q10. Write a program to check a character is vowel or not.

Python if else Statement Practice Test 5

Q1. Accept the following from the user and calculate the percentage of class attended:

a.     Total number of working days

b.     Total number of days for absent

    After calculating percentage show that, If the percentage is less than 75, than student will not be able to sit in exam.

Q2. Accept the percentage from the user and display the  grade according to the following criteria:

•      Below 25 —- D
•     25 to 45 —- C
•     45 to 50 —- B
•     50 to 60 –– B+
•     60 to 80 — A
•     Above 80 –- A+

Q3. A company decided to give bonus to employee according to following criteria:

    Time period of Service                Bonus

    More than 10 years             10%

    >=6 and <=10                   8%

    Less than 6 years              5%

    Ask user for their salary and years of service and print the net bonus amount.

Q4. Accept the marked price from the user and  calculate the Net amount as(Marked Price –    Discount) to pay according to following criteria:

Q5. Write a program to accept percentage and display the Category according to the  following criteria :

Q6. Accept three sides of a triangle and check whether it is an equilateral, isosceles or scalene triangle.

An equilateral triangle is a triangle in which all three sides are equal.

A scalene triangle is a triangle that has three unequal sides.

An isosceles triangle is a triangle with (at least) two equal sides.

Q7. Write a program to accept two numbers and mathematical operators and perform operation accordingly.

Enter First Number: 7

Enter Second Number : 9

Enter operator : +

Your Answer is : 16

Q8. Accept the age, sex (‘M’, ‘F’), number of days and display the wages accordingly

If age does not fall in any range then display the following message: “Enter appropriate age”

Python if else Statement Practice Test 6

Q1. Accept three numbers from the user and display the second largest number.

Q2. Accept three sides of triangle and check whether the triangle is possible or not.

(triangle is possible only when sum of any two sides is greater than 3 rd  side)

Q3. Consider the following code

What will the above code print if the variables i, j, and k have the following values?

(a)    i = 3, j = 5, k = 7

(b)    i = -2, j = -5, k = 9

(c)    i = 8, j = 15, k = 12

(d)    i = 13, j = 15, k = 13

(e)    i = 3, j = 5, k = 17

(f)    i = 25, j = 15, k = 17

Q4. Accept the electric units from user and calculate the bill according to the following rates.

First 100 Units     :  Free

Next 200 Units      :  Rs 2 per day.

Above 300 Units    :  Rs 5 per day.

if number of unit is 500 then total bill = 0 +400 + 1000 = 1400

Q5. Accept the number of days from the user and calculate the charge for library according to following :

Till five days : Rs 2/day.

Six to ten days  : Rs 3/day.

11 to 15 days  : Rs 4/day

After 15 days    : Rs 5/day

Q6. Accept the kilometers covered and calculate the bill according to the following criteria:

First 10 Km              Rs11/km

Next 90Km               Rs 10/km

After that               Rs9/km

Q7. Accept the marks of English, Math and Science, Social Studies Subject and display the stream allotted according to following

All Subjects more than 80 marks —       Science Stream

English >80 and Math, Science above 50 –Commerce Stream

English > 80 and Social studies > 80    —   Humanities

Python if else Statement Practice Test 7

Q1. Evaluate the following statements:

• d=True        

The idea of giving above assignment of python if else is to give students a lot of practice of python if else concept and he/she would be able to do all types of question related to “python if else”

Disclaimer : I tried to give correct code of all the answers of above python if else assignments. The code is not copied from any other site or any other assignment of python if else. Please share feedback of above python if else assignment to [email protected] so that i can improve and give better content to you.

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32 thoughts on “70+ Python if else Statement Important Practice Questions”

This is really use full. Please can you provide the answer for this one.

# Accept the marks of English, Math and Science, Social Studies Subject and display the stream allotted according to following

# All Subjects more than 80 marks — Science Stream # English >80 and Math, Science above 50 –Commerce Stream # English > 80 and Social studies > 80 — Humanities

Thankyou Ram babu.

eng = int(input(“ENGLISH : “)) sst = int(input(“SOCIAL STUDIES : “)) mth = int(input(“MATHS : “)) sci = int(input(“SCIENCE : “))

if eng > 80 and mth > 80 and sci > 80 and sst > 80: print(“Science Stream”) elif eng > 80 and (80 > mth and sci > 50): print(“Commerce Stream”) elif (eng and sst > 80) and (mth and sci < 80): print("Humanities")

if mar > 80: print(english,social,) print(science stream,humanities) elif mar >50 and mar <80: print(math , science) print("commerce stream")

m,e,s,so=map(int,input().split()) if m>80 and e>80: if s>80 and so>80: print(“alloted science stream”) if e>80: if m>50 and s>50: print(“alloted commers”) elif so>80: print(“alloted humanites”)

Sir it is so easy question

eng = int(input(“Enter your English marks : “)) math = int(input(“Enter your Math marks : “)) science = int(input(“Enter your Science marks : “)) ss = int(input(“Enter your Social Studies : “))

if eng >= 80 and math >= 80 and science >= 80 and ss >= 80: print (“Science Stream”) elif eng >= 80 and (50>=math and science >= 50): print (“Commerce Stream”) elif eng >= 80 and (ss >=80): print (“Humanities”)

a=int(input(“Please enter your English marks: “)) b=int(input(“Please enter your Maths marks: “)) c=int(input(“Please enter your Social Science marks: “)) d=int(input(“Please enter your Science marks: “)) if a>80 and b>80 and c>80 and d>80: print(“The stream alloted to you is Science.”) elif(a>80 and b>50 and d>50): print(“The stream alloted to you is Commerce.”) elif(a>80 and c>80): print(“The stream alloted to you is Humanities.”) else: print(“Sorry! But no stream is alloted to you.”)

Hello, can you please tell me why the we need to have 500 for calculations nu>200 in Q8 Test1 ?

Q8. Write a program to calculate the electricity bill […] if nu>200: amt=500+(nu-200)*10 print(“Amount to pay :”,amt)

I don’t get it!

Hi Actually first 100 units are free and next 100 units are charged @ 5/unit so for next 100 unit amount is 5*100=500

of course!!! thank you so much

Write a program to calculate the electricity bill (accept number of unit from user) according to the following criteria : Unit Price First 100 units no charge Next 100 units Rs 5 per unit After 200 units Rs 10 per unit (For example if input unit is 350 than total bill amount is Rs2000)

if any one help on this.

I have a problem is Test 6, Ques no 3.??? Can anyone solve this by explaining in detail??

Firstly, the given code is written in 11 lines. a) i=3, j=5, k=7 (consider these values for i, j and k)

As first line is true as it satisfies the condition for (i<j) i.e., 3<5, it ignores else block which is at line 6 and enters into the "if" block which is at line 1. After entering inside the first "if" block which is at line 1. As again the condition is true at line 2, it enters into 3rd line thereafter "i" gets the value of "j" which means "i" value is 5 now. And ignores the "else" block which is at line 4. So, it it directly goes to line 11 and prints the values for i,j and k…here i value becomes 5. 'J' and 'K' values don't change as they are not being affected like i.

unit= int(input(“enter unit”)) if unit<=100: print("their is no charges") elif unit in range(101,201): price=100*0+(unit-100)*5 print("your electricity bill is",price) else: price=100*0+500+(unit-200)*10 print(price)

amount=0 n=int(input(“Enter the units”) if n>=100: amount=0 if n>100 and n200 amount= 500 +(n-200)*10 // 100 units is rs 5 per unit (100*5=500) print(“amount to be paid:”,amount)

x=int(input(“Enter the number of units: “)) amt=0 if x100 and x200: amt=500+(x-200)*5 print(amt)

km = int(input(‘Enter kms = ‘))

if (km 10 and km <= 100): charge = 110 + ((km- 10) * 10)

else: charge = 1010 + (km – 100) * 9 print('total bill amount is ',charge)

units = float(input(“Enter electricity unit charges: “)) if units <= 50: bill = units * 0.50 elif units <= 150: bill = 50 * 0.50 + (units – 50) * 0.75 elif units <=250: bill = 50 * 0.50 + 100 * 0.75 + (units – 100) * 1.20 else: bill = 50 * 0.50 + 100 * 0.75 + 100 * 1.20 + (units – 100) * 1.50 surcharge = bill * 0.20 total_bill = bill + surcharge print("Electricity bill: ", bill) print("Surcharge: ", surcharge) print("Total bill: ", total_bill)

For example Enter unit is 450 100 = free 450-100= 350 350 __ 100 Ra 5 =500 250 ___ 250*10=2500 2500+500= 3000

bro in the question it said that for first 100 units its for free , then next 100 units you have to pay 5 ruppes per unit that means 100*5=500 ruppes and after 200 unit u have to pay 10 ruppes per unit that means previous 100*5=500 and after 200 units the formula is (num-200)*10 ruppes and also u have to pay the prevoius unit i.e. 500 thats why it written amt = 500+(num-200)*10

That’s amazing and please tell me that it’s enough for learn and practice the code in condition programs?

ME= int(input(‘Enter English Marks out of 100: ‘)) MM= int(input(‘Enter Maths Marks out of 100: ‘)) MS= int(input(‘Enter Science Marks out of 100: ‘)) MSS= int(input(‘Enter SST Marks out of 100: ‘)) SF = float(ME+MM+MS+MSS) CF = float(ME+MM+MS+MSS) Hm = float(MS+MSS) if SF>=320: print (‘You are eligible for Science Faculty’) elif CF>=260 and CF=50 and Hm <200: print ('You are eligible for Humanities') (Its a junior and just a beginner Python programmer from Pakistan. I just tried this whole code on my own. Sb may find it a bit pathetic. Mee too. But it somehow ran. But not with accurate demanded Output. But to some extent. Need your guidance dear all. Thanks

science=int(input(“Enter Your Science Marks:”)) english=int(input(“Enter Your english marks:”)) math=int(input(“Enter your maths marks:”)) socialstu=int(input(“Enter Your marks Socialstudies:”)) allsubject = (science+english+math+socialstu)/400*100 if allsubject>80: print(“You go for Science Stream”) elif english>=80 and math>50 and science>50: print(“You go for commerce stream”) elif english>=80 and socialstu>80: print(“You go for Humanities”) else: print(“You entered wrong subject”)

# last digit print

num=int(input(‘enter the any number—>’)) b=num%10 print(b) if b: print(b) else: print(“0”)

Wowwwwwwww!!!! really helped me alot… studied it before my practical.

e= int(input(“Enter marks of English: “)) m= int(input(“Enter marks of Maths: “)) s= int(input(“Enter marks of Science: “)) ss= int(input(“Enter marks of Social Science: “)) if e>80 and m>80 and s>80 and ss>80: print(“Science Stream allotted”) elif e>80 and m>50 and s>50: print(“Commerce Stream allotted”) elif e>80 and ss>80: print(“Humanities Stream allotted”)

english_marks = int(input(“Enter marks in English: “)) math_marks = int(input(“Enter marks in Mathematics: “)) science_marks = int(input(“Enter marks in Science: “)) computers_marks = int(input(“Enter marks in Computers: “))

average_percentage = (english_marks + math_marks + science_marks + computers_marks) / 4

if average_percentage > 80: print(‘You got Science Stream’) elif 60 <= average_percentage <= 80: print('You got Commerce Stream') elif 50 <= average_percentage < 60: print('You got Arts Stream') else: print('Sorry! No stream is allotted to you')

a=int(input(“enter the umit:”)) if a<=100: print("no charge") elif 100<a<=200: print("bonus is",5*a) elif 200<a<350: print("bonus is",10*a) else: print("bonus: if unit is 350 is",2000)

How do I get to while-loop?

eng_marks=int(input(“Enter the marks in eng subject: “)) math_marks=int(input(“Enter the marks in math subject: “)) science_marks=int(input(“Enter the marks in science subject: “)) social_marks=int(input(“Enter the marks in social subject: “)) total_marks=eng_marks+math_marks+science_marks+social_marks if eng_marks>80 and math_marks > 80 and science_marks > 80 and social_marks > 80: print(“You are eligible for Science Stream”) elif (eng_marks >80 and math_marks>80 )and science_marks >50 : print(“You are eligible for the Commerce Stream”) elif eng_marks > 80 and social_marks > 80: print(“You are eligible for the Humanities”)

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Assignment Operators in Python

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

Here are the Assignment Operators in Python with examples.

Assignment Operator

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

Addition Assignment Operator

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

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

S ubtraction Assignment Operator

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

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

M ultiplication Assignment Operator

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

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

D ivision Assignment Operator

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

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

M odulus Assignment Operator

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

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

F loor Division Assignment Operator

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

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

Exponentiation Assignment Operator

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

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

Bitwise AND Assignment Operator

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

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

Bitwise OR Assignment Operator

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

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

Bitwise XOR Assignment Operator 

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

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

Bitwise Right Shift Assignment Operator

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

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

Bitwise Left Shift Assignment Operator

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

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

Walrus Operator

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

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

Assignment Operators in Python – FAQs

What are assignment operators in python.

Assignment operators in Python are used to assign values to variables. These operators can also perform additional operations during the assignment. The basic assignment operator is = , which simply assigns the value of the right-hand operand to the left-hand operand. Other common assignment operators include += , -= , *= , /= , %= , and more, which perform an operation on the variable and then assign the result back to the variable.

What is the := Operator in Python?

The := operator, introduced in Python 3.8, is known as the “walrus operator”. It is an assignment expression, which means that it assigns values to variables as part of a larger expression. Its main benefit is that it allows you to assign values to variables within expressions, including within conditions of loops and if statements, thereby reducing the need for additional lines of code. Here’s an example: # Example of using the walrus operator in a while loop while (n := int(input("Enter a number (0 to stop): "))) != 0: print(f"You entered: {n}") This loop continues to prompt the user for input and immediately uses that input in both the condition check and the loop body.

What is the Assignment Operator in Structure?

In programming languages that use structures (like C or C++), the assignment operator = is used to copy values from one structure variable to another. Each member of the structure is copied from the source structure to the destination structure. Python, however, does not have a built-in concept of ‘structures’ as in C or C++; instead, similar functionality is achieved through classes or dictionaries.

What is the Assignment Operator in Python Dictionary?

In Python dictionaries, the assignment operator = is used to assign a new key-value pair to the dictionary or update the value of an existing key. Here’s how you might use it: my_dict = {} # Create an empty dictionary my_dict['key1'] = 'value1' # Assign a new key-value pair my_dict['key1'] = 'updated value' # Update the value of an existing key print(my_dict) # Output: {'key1': 'updated value'}

What is += and -= in Python?

The += and -= operators in Python are compound assignment operators. += adds the right-hand operand to the left-hand operand and assigns the result to the left-hand operand. Conversely, -= subtracts the right-hand operand from the left-hand operand and assigns the result to the left-hand operand. Here are examples of both: # Example of using += a = 5 a += 3 # Equivalent to a = a + 3 print(a) # Output: 8 # Example of using -= b = 10 b -= 4 # Equivalent to b = b - 4 print(b) # Output: 6 These operators make code more concise and are commonly used in loops and iterative data processing.

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Assign variable in while loop condition in Python?

I just came across this piece of code

and thought, there must be a better way to do this, than using an infinite loop with break .

So I tried:

and, obviously, got an error.

Is there any way to avoid using a break in that situation?

Ideally, you'd want to avoid saying readline twice... IMHO, repeating is even worse than just a break , especially if the statement is complex.

• variable-assignment

• 3 While this is a good question and I think the for line in data solution is a good fit for this specific problem, I don't think there's anything wrong with the while True: ... break idiom. Don't be afraid of it. :-) –  Kirk Strauser Commented Jul 8, 2011 at 22:53
• 4 These answers provide alternatives to assignment in the conditional of the while-loop, but really don't answer the question: is there a way to do assignment in the while-loop? I'm running into this same problem, trying to do while (character = string[i]): I know that a for-loop is a better way to iterate over a string, but my conditional is actually much more complex than this, and I want to do this assignment as the right-hand side of an "or" within the conditional. –  user2760926 Commented Sep 9, 2013 at 9:30
• 1 @KirkStrauser The problem with the break construction is, that it is using four lines to express something, which other languages can do in just one line. However it does the right thing. None of the answers given so far has provided a better general purpose solution. They either only work with iterators or duplicate the assignment, which is worse than three extra lines of code for the break version. –  kasperd Commented Nov 21, 2014 at 3:18

10 Answers 10

Starting Python 3.8 , and the introduction of assignment expressions (PEP 572) ( := operator), it's now possible to capture the condition value ( data.readline() ) of the while loop as a variable ( line ) in order to re-use it within the body of the loop:

• 5 As a side note, an explicit condition may be written as while (line := data.readline()) is not None: –  Hi-Angel Commented May 18, 2023 at 15:00

Try this one, works for files opened with open('filename')

• 3 +1 for being exemplified in the python core documentation: docs.python.org/2/library/functions.html#iter –  ThorSummoner Commented Oct 15, 2014 at 2:59

If you aren't doing anything fancier with data, like reading more lines later on, there's always:

• I was trying to play Stump The Sushi Eater by thinking of a type of object data might be that would support .readline() but not __iter__(). I'm drawing a blank. Do you know of any offhand? –  Kirk Strauser Commented Jul 8, 2011 at 22:32
• Doesn't this require reading the entire file into memory first? That doesn't seem applicable for large files. (Especially if the file is larger than your ram can hold!) –  ThorSummoner Commented Oct 15, 2014 at 2:48
• 1 If data is a file object (which is an odd name, but that's the way the OP used it), then the entire file will not be read into memory. for line in data will iterate over lines, reading them as needed. –  Ned Batchelder Commented Oct 15, 2014 at 11:37
• 1 @NedBatchelder: according to the docs at docs.python.org/2/library/stdtypes.html#file.next - and my unfortunate experience - the filepointer is not where you'd expect it to be (e.g. for a data.tell() ) with for line in data and might even be at the end of the file even before the last line is read. So, it doesn't quite "read them as needed" if you're counting on python/os to do the accounting of where you are in the file. –  mpag Commented Jan 5, 2017 at 18:25
• 2 @mpag There's definitely no guarantee (and I didn't mean to imply there was) that each line is read precisely as needed. I was countering the notion that the entire file would be read into memory. If you are iterating by lines, you can't make any assumptions about where the file pointer is. –  Ned Batchelder Commented Jan 5, 2017 at 22:23

This isn't much better, but this is the way I usually do it. Python doesn't return the value upon variable assignment like other languages (e.g., Java).

• 7 I'm not a big fan of that, especially if ... do stuff ... is sizable as it requires you to keep the flow of the entire loop in mind as you hack around on it. For example, if you add something like if line.startswith('foo'): continue later without realizing that line is only updated at the very end, then you've accidentally created an infinite loop. –  Kirk Strauser Commented Jul 8, 2011 at 22:22
• 1 @Kirk - In part, I agree ,but the alternatives aren't much better. Ideally, the class you're using implements a generator and you can just use a for loop, but there are certain cases where you need a while loop ( e.g., 'while cur_time>expected_time:'). I don't know if the OPs post is much better, but I suppose its a matter of opinion :) –  dfb Commented Jul 8, 2011 at 22:33
• A classic while loop, and understandable for any quality of programmer. Probably the best choice for future maintenance purposes. –  Kim Commented Jan 24, 2019 at 17:09
• 2 @Kirk Strauser One could argue if ... do stuff ... is so long you lost track of what's going on in your loop then you're probably doing it wrong. –  arkan Commented Apr 26, 2019 at 6:11

? It large depends on the semantics of the data object's readline semantics. If data is a file object, that'll work.

Will iterate over each line in the file , rather than using a while . It is a much more common idiom for the task of reading a file in my experience (in Python).

In fact, data does not have to be a file but merely provide an iterator.

According to the FAQ from Python's documentation, iterating over the input with for construct or running an infinite while True loop and using break statement to terminate it, are preferred and idiomatic ways of iteration.

If data is a file, as stated in other answers, using for line in file will work fine. If data is not a file, and a random data reading object, then you should implement it as an iterator, implementing __iter__ and next methods.

The next method should to the reading, check if there is more data, and if not, raise StopIteration . If you do this, you can continue using the for line in data idiom.

You could do:

• 8 That will execute the body of the loop one more time than it is supposed to. –  kasperd Commented Nov 21, 2014 at 3:15

If data has a function that returns an iterator instead of readline (say data.iterate ), you could simply do:

• 1 Don't do that unless you know data is tiny (and really not even then) as .readlines() slurps the entire contents into RAM, but it doesn't really buy you anything in return. –  Kirk Strauser Commented Jul 8, 2011 at 22:30
• It should work fine if the function returns an iterator instead of the entire list, correct? –  TorelTwiddler Commented Jul 8, 2011 at 22:38
• Yes, but I haven't seen .readlines() implemented that way. The docs for file.readlines() say that it will "[r]ead until EOF using readline() and return a list containing the lines thus read." –  Kirk Strauser Commented Jul 8, 2011 at 22:49
• I like that answer better. :-) However, the usual name for iterate is __iter__ , and then you can re-write the loop as for line in data . –  Kirk Strauser Commented Jul 8, 2011 at 22:55
• True, but I'm going to leave it like this, since there are already 4 other answers that have for line in data . =D –  TorelTwiddler Commented Jul 8, 2011 at 23:02

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