In Python, operators are special symbols, combinations of symbols, or keywords that designate some type of computation. You can combine objects and operators to build expressions that perform the actual computation. So, operators are the building blocks of expressions, which you can use to manipulate your data. Therefore, understanding how operators work in Python is essential for you as a programmer.
In this tutorial, you’ll learn about the operators that Python currently supports. You’ll also learn the basics of how to use these operators to build expressions.
In this tutorial, you’ll:
 Get to know Python’s arithmetic operators and use them to build arithmetic expressions
 Explore Python’s comparison, Boolean, identity, and membership operators
 Build expressions with comparison, Boolean, identity, and membership operators
 Learn about Python’s bitwise operators and how to use them
 Combine and repeat sequences using the concatenation and repetition operators
 Understand the augmented assignment operators and how they work
To get the most out of this tutorial, you should have a basic understanding of Python programming concepts, such as variables, assignments, and builtin data types.
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Getting Started With Operators and Expressions
In programming, an operator is usually a symbol or combination of symbols that allows you to perform a specific operation. This operation can act on one or more operands. If the operation involves a single operand, then the operator is unary. If the operator involves two operands, then the operator is binary.
For example, in Python, you can use the minus sign (
) as a unary operator to declare a negative number. You can also use it to subtract two numbers:
>>> 273.15
273.15
>>> 5  2
3
In this code snippet, the minus sign (
) in the first example is a unary operator, and the number 273.15
is the operand. In the second example, the same symbol is a binary operator, and the numbers 5
and 2
are its left and right operands.
Programming languages typically have operators built in as part of their syntax. In many languages, including Python, you can also create your own operator or modify the behavior of existing ones, which is a powerful and advanced feature to have.
In practice, operators provide a quick shortcut for you to manipulate data, perform mathematical calculations, compare values, run Boolean tests, assign values to variables, and more. In Python, an operator may be a symbol, a combination of symbols, or a keyword, depending on the type of operator that you’re dealing with.
For example, you’ve already seen the subtraction operator, which is represented with a single minus sign (
). The equality operator is a double equal sign (==
). So, it’s a combination of symbols:
>>> 42 == 42
True
In this example, you use the Python equality operator (==
) to compare two numbers. As a result, you get True
, which is one of Python’s Boolean values.
Speaking of Boolean values, the Boolean or logical operators in Python are keywords rather than signs, as you’ll learn in the section about Boolean operators and expressions. So, instead of the odd signs like 
, &&
, and !
that many other programming languages use, Python uses or
, and
, and not
.
Using keywords instead of odd signs is a really cool design decision that’s consistent with the fact that Python loves and encourages code’s readability.
You’ll find several categories or groups of operators in Python. Here’s a quick list of those categories:
 Assignment operators
 Arithmetic operators
 Comparison operators
 Boolean or logical operators
 Identity operators
 Membership operators
 Concatenation and repetition operators
 Bitwise operators
All these types of operators take care of specific types of computations and dataprocessing tasks. You’ll learn more about these categories throughout this tutorial. However, before jumping into more practical discussions, you need to know that the most elementary goal of an operator is to be part of an expression. Operators by themselves don’t do much:
>>> 
File "<input>", line 1

^
SyntaxError: incomplete input
>>> ==
File "<input>", line 1
==
^^
SyntaxError: incomplete input
>>> or
File "<input>", line 1
or
^^
SyntaxError: incomplete input
As you can see in this code snippet, if you use an operator without the required operands, then you’ll get a syntax error. So, operators must be part of expressions, which you can build using Python objects as operands.
So, what is an expression anyway? Python has simple and compound statements. A simple statement is a construct that occupies a single logical line, like an assignment statement. A compound statement is a construct that occupies multiple logical lines, such as a for
loop or a conditional statement. An expression is a simple statement that produces and returns a value.
You’ll find operators in many expressions. Here are a few examples:
>>> 7 + 5
12
>>> 42 / 2
21.0
>>> 5 == 5
True
In the first two examples, you use the addition and division operators to construct two arithmetic expressions whose operands are integer numbers. In the last example, you use the equality operator to create a comparison expression. In all cases, you get a specific value after executing the expression.
Note that not all expressions use operators. For example, a bare function call is an expression that doesn’t require any operator:
>>> abs(7)
7
>>> pow(2, 8)
256
>>> print("Hello, World!")
Hello, World!
In the first example, you call the builtin abs()
function to get the absolute value of 7
. Then, you compute 2
to the power of 8
using the builtin pow()
function. These function calls occupy a single logical line and return a value. So, they’re expressions.
Finally, the call to the builtin print()
function is another expression. This time, the function doesn’t return a fruitful value, but it still returns None
, which is the Python null type. So, the call is technically an expression.
Note: All Python functions have a return value, either explicit or implicit. If you don’t provide an explicit return
statement when defining a function, then Python will automatically make the function return None
.
Even though all expressions are statements, not all statements are expressions. For example, pure assignment statements don’t return any value, as you’ll learn in a moment. Therefore, they’re not expressions. The assignment operator is a special operator that doesn’t create an expression but a statement.
Note: Since version 3.8, Python also has what it calls assignment expressions. These are special types of assignments that do return a value. You’ll learn more about this topic in the section The Walrus Operator and Assignment Expressions.
Okay! That was a quick introduction to operators and expressions in Python. Now it’s time to dive deeper into the topic. To kick things off, you’ll start with the assignment operator and statements.
The Assignment Operator and Statements
The assignment operator is one of the most frequently used operators in Python. The operator consists of a single equal sign (=
), and it operates on two operands. The lefthand operand is typically a variable, while the righthand operand is an expression.
Note: As you already learned, the assignment operator doesn’t create an expression. Instead, it creates a statement that doesn’t return any value.
The assignment operator allows you to assign values to variables. Strictly speaking, in Python, this operator makes variables or names refer to specific objects in your computer’s memory. In other words, an assignment creates a reference to a concrete object and attaches that reference to the target variable.
Note: To dive deeper into using the assignment operator, check out Python’s Assignment Operator: Write Robust Assignments.
For example, all the statements below create new variables that hold references to specific objects:
>>> number = 42
>>> day = "Friday"
>>> digits = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
>>> letters = ["a", "b", "c"]
In the first statement, you create the number
variable, which holds a reference to the number 42
in your computer’s memory. You can also say that the name number
points to 42
, which is a concrete object.
In the rest of the examples, you create other variables that point to other types of objects, such as a string, tuple, and list, respectively.
You’ll use the assignment operator in many of the examples that you’ll write throughout this tutorial. More importantly, you’ll use this operator many times in your own code. It’ll be your forever friend. Now you can dive into other Python operators!
Arithmetic Operators and Expressions in Python
Arithmetic operators are those operators that allow you to perform arithmetic operations on numeric values. Yes, they come from math, and in most cases, you’ll represent them with the usual math signs. The following table lists the arithmetic operators that Python currently supports:
Operator  Type  Operation  Sample Expression  Result 

+ 
Unary  Positive  +a 
a without any transformation since this is simply a complement to negation 
+ 
Binary  Addition  a + b 
The arithmetic sum of a and b 
 
Unary  Negation  a 
The value of a but with the opposite sign 
 
Binary  Subtraction  a  b 
b subtracted from a 
* 
Binary  Multiplication  a * b 
The product of a and b 
/ 
Binary  Division  a / b 
The quotient of a divided by b , expressed as a float 
% 
Binary  Modulo  a % b 
The remainder of a divided by b 
// 
Binary  Floor division or integer division  a // b 
The quotient of a divided by b , rounded to the next smallest whole number 
** 
Binary  Exponentiation  a**b 
a raised to the power of b 
Note that a
and b
in the Sample Expression column represent numeric values, such as integer, floatingpoint, complex, rational, and decimal numbers.
Here are some examples of these operators in use:
>>> a = 5
>>> b = 2
>>> +a
5
>>> b
2
>>> a + b
7
>>> a  b
3
>>> a * b
10
>>> a / b
2.5
>>> a % b
1
>>> a // b
2
>>> a**b
25
In this code snippet, you first create two new variables, a
and b
, holding 5
and 2
, respectively. Then you use these variables to create different arithmetic expressions using a specific operator in each expression.
Note: The Python REPL will display the return value of an expression as a way to provide immediate feedback to you. So, when you’re in an interactive session, you don’t need to use the print()
function to check the result of an expression. You can just type in the expression and press Enter to get the result.
Again, the standard division operator (/
) always returns a floatingpoint number, even if the dividend is evenly divisible by the divisor:
>>> 10 / 5
2.0
>>> 10.0 / 5
2.0
In the first example, 10
is evenly divisible by 5
. Therefore, this operation could return the integer 2
. However, it returns the floatingpoint number 2.0
. In the second example, 10.0
is a floatingpoint number, and 5
is an integer. In this case, Python internally promotes 5
to 5.0
and runs the division. The result is a floatingpoint number too.
Note: With complex numbers, the division operator doesn’t return a floatingpoint number but a complex one:
>>> 10 / 5j
2j
Here, you run a division between an integer and a complex number. In this case, the standard division operator returns a complex number.
Finally, consider the following examples of using the floor division (//
) operator:
>>> 10 // 4
2
>>> 10 // 4
2
>>> 10 // 4
3
>>> 10 // 4
3
Floor division always rounds down. This means that the result is the greatest integer that’s smaller than or equal to the quotient. For positive numbers, it’s as though the fractional portion is truncated, leaving only the integer portion.
Comparison Operators and Expressions in Python
The Python comparison operators allow you to compare numerical values and any other objects that support them. The table below lists all the currently available comparison operators in Python:
Operator  Operation  Sample Expression  Result 

== 
Equal to  a == b 
• True if the value of a is equal to the value of b • False otherwise 
!= 
Not equal to  a != b 
• True if a isn’t equal to b • False otherwise 
< 
Less than  a < b 
• True if a is less than b • False otherwise 
<= 
Less than or equal to  a <= b 
• True if a is less than or equal to b • False otherwise 
> 
Greater than  a > b 
• True if a is greater than b • False otherwise 
>= 
Greater than or equal to  a >= b 
• True if a is greater than or equal to b • False otherwise 
The comparison operators are all binary. This means that they require left and right operands. These operators always return a Boolean value (True
or False
) that depends on the truth value of the comparison at hand.
Note that comparisons between objects of different data types often don’t make sense and sometimes aren’t allowed in Python. For example, you can compare a number and a string for equality with the ==
operator. However, you’ll get False
as a result:
>>> 2 == "2"
False
The integer 2
isn’t equal to the string "2"
. Therefore, you get False
as a result. You can also use the !=
operator in the above expression, in which case you’ll get True
as a result.
Nonequality comparisons between operands of different data types raise a TypeError
exception:
>>> 5 < "7"
Traceback (most recent call last):
...
TypeError: '<' not supported between instances of 'int' and 'str'
In this example, Python raises a TypeError
exception because a less than comparison (<
) doesn’t make sense between an integer and a string. So, the operation isn’t allowed.
It’s important to note that in the context of comparisons, integer and floatingpoint values are compatible, and you can compare them.
You’ll typically use and find comparison operators in Boolean contexts like conditional statements and while
loops. They allow you to make decisions and define a program’s control flow.
The comparison operators work on several types of operands, such as numbers, strings, tuples, and lists. In the following sections, you’ll explore the differences.
Comparison of Integer Values
Probably, the more straightforward comparisons in Python and in math are those involving integer numbers. They allow you to count real objects, which is a familiar daytoday task. In fact, the nonnegative integers are also called natural numbers. So, comparing this type of number is probably pretty intuitive, and doing so in Python is no exception.
Consider the following examples that compare integer numbers:
>>> a = 10
>>> b = 20
>>> a == b
False
>>> a != b
True
>>> a < b
True
>>> a <= b
True
>>> a > b
False
>>> a >= b
False
>>> x = 30
>>> y = 30
>>> x == y
True
>>> x != y
False
>>> x < y
False
>>> x <= y
True
>>> x > y
False
>>> x >= y
True
In the first set of examples, you define two variables, a
and b
, to run a few comparisons between them. The value of a
is less than the value of b
. So, every comparison expression returns the expected Boolean value. The second set of examples uses two values that are equal, and again, you get the expected results.
Comparison of FloatingPoint Values
Comparing floatingpoint numbers is a bit more complicated than comparing integers. The value stored in a float
object may not be precisely what you’d think it would be. For that reason, it’s bad practice to compare floatingpoint values for exact equality using the ==
operator.
Consider the example below:
>>> x = 1.1 + 2.2
>>> x == 3.3
False
>>> 1.1 + 2.2
3.3000000000000003
Yikes! The internal representation of this addition isn’t exactly equal to 3.3
, as you can see in the final example. So, comparing x
to 3.3
with the equality operator returns False
.
To compare floatingpoint numbers for equality, you need to use a different approach. The preferred way to determine whether two floatingpoint values are equal is to determine whether they’re close to one another, given some tolerance.
The math
module from the standard library provides a function conveniently called isclose()
that will help you with float
comparison. The function takes two numbers and tests them for approximate equality:
>>> from math import isclose
>>> x = 1.1 + 2.2
>>> isclose(x, 3.3)
True
In this example, you use the isclose()
function to compare x
and 3.3
for approximate equality. This time, you get True
as a result because both numbers are close enough to be considered equal.
For further details on using isclose()
, check out the Find the Closeness of Numbers With Python isclose()
section in The Python math
Module: Everything You Need to Know.
Comparison of Strings
You can also use the comparison operators to compare Python strings in your code. In this context, you need to be aware of how Python internally compares string objects. In practice, Python compares strings character by character using each character’s Unicode code point. Unicode is Python’s default character set.
You can use the builtin ord()
function to learn the Unicode code point of any character in Python. Consider the following examples:
>>> ord("A")
65
>>> ord("a")
97
>>> "A" == "a"
False
>>> "A" > "a"
False
>>> "A" < "a"
True
The uppercase "A"
has a lower Unicode point than the lowercase "a"
. So, "A"
is less than "a"
. In the end, Python compares characters using integer numbers. So, the same rules that Python uses to compare integers apply to string comparison.
When it comes to strings with several characters, Python runs the comparison character by character in a loop.
The comparison uses lexicographical ordering, which means that Python compares the first item from each string. If their Unicode code points are different, this difference determines the comparison result. If the Unicode code points are equal, then Python compares the next two characters, and so on, until either string is exhausted:
>>> "Hello" > "HellO"
True
>>> ord("o")
111
>>> ord("O")
79
In this example, Python compares both operands character by character. When it reaches the end of the string, it compares "o"
and "O"
. Because the lowercase letter has a greater Unicode code point, the first version of the string is greater than the second.
You can also compare strings of different lengths:
>>> "Hello" > "Hello, World!"
False
In this example, Python runs a characterbycharacter comparison as usual. If it runs out of characters, then the shorter string is less than the longer one. This also means that the empty string is the smallest possible string.
Comparison of Lists and Tuples
In your Python journey, you can also face the need to compare lists with other lists and tuples with other tuples. These data types also support the standard comparison operators. Like with strings, when you use a comparison operator to compare two lists or two tuples, Python runs an itembyitem comparison.
Note that Python applies specific rules depending on the type of the contained items. Here are some examples that compare lists and tuples of integer values:
>>> [2, 3] == [2, 3]
True
>>> (2, 3) == (2, 3)
True
>>> [5, 6, 7] < [7, 5, 6]
True
>>> (5, 6, 7) < (7, 5, 6)
True
>>> [4, 3, 2] < [4, 3, 2]
False
>>> (4, 3, 2) < (4, 3, 2)
False
In these examples, you compare lists and tuples of numbers using the standard comparison operators. When comparing these data types, Python runs an itembyitem comparison.
For example, in the first expression above, Python compares the 2
in the left operand and the 2
in the right operand. Because they’re equal, Python continues comparing 3
and 3
to conclude that both lists are equal. The same thing happens in the second example, where you compare tuples containing the same data.
It’s important to note that you can actually compare lists to tuples using the ==
and !=
operators. However, you can’t compare lists and tuples using the <
, >
, <=
, and >=
operators:
>>> [2, 3] == (2, 3)
False
>>> [2, 3] != (2, 3)
True
>>> [2, 3] > (2, 3)
Traceback (most recent call last):
...
TypeError: '>' not supported between instances of 'list' and 'tuple'
>>> [2, 3] <= (2, 3)
Traceback (most recent call last):
...
TypeError: '<=' not supported between instances of 'list' and 'tuple'
Python supports equality comparison between lists and tuples. However, it doesn’t support the rest of the comparison operators, as you can conclude from the final two examples. If you try to use them, then you get a TypeError
telling you that the operation isn’t supported.
You can also compare lists and tuples of different lengths:
>>> [5, 6, 7] < [8]
True
>>> (5, 6, 7) < (8,)
True
>>> [5, 6, 7] == [5]
False
>>> (5, 6, 7) == (5,)
False
>>> [5, 6, 7] > [5]
True
>>> (5, 6, 7) > (5,)
True
In the first two examples, you get True
as a result because 5
is less than 8
. That fact is sufficient for Python to solve the comparison. In the second pair of examples, you get False
. This result makes sense because the compared sequences don’t have the same length, so they can’t be equal.
In the final pair of examples, Python compares 5
with 5
. They’re equal, so the comparison continues. Because there are no more values to compare in the righthand operands, Python concludes that the lefthand operands are greater.
As you can see, comparing lists and tuples can be tricky. It’s also an expensive operation that, in the worst case, requires traversing two entire sequences. Things get more complex and expensive when the contained items are also sequences. In those situations, Python will also have to compare items in a valuebyvalue manner, which adds cost to the operation.
Boolean Operators and Expressions in Python
Python has three Boolean or logical operators: and
, or
, and not
. They define a set of operations denoted by the generic operators AND
, OR
, and NOT
. With these operators, you can create compound conditions.
In the following sections, you’ll learn how the Python Boolean operators work. Especially, you’ll learn that some of them behave differently when you use them with Boolean values or with regular objects as operands.
Boolean Expressions Involving Boolean Operands
You’ll find many objects and expressions that are of Boolean type or bool
, as Python calls this type. In other words, many objects evaluate to True
or False
, which are the Python Boolean values.
For example, when you evaluate an expression using a comparison operator, the result of that expression is always of bool
type:
>>> age = 20
>>> is_adult = age > 18
>>> is_adult
True
>>> type(is_adult)
<class 'bool'>
In this example, the expression age > 18
returns a Boolean value, which you store in the is_adult
variable. Now is_adult
is of bool
type, as you can see after calling the builtin type()
function.
You can also find Python builtin and custom functions that return a Boolean value. This type of function is known as a predicate function. The builtin all()
, any()
, callable()
, and isinstance()
functions are all good examples of this practice.
Consider the following examples:
>>> number = 42
>>> validation_conditions = (
... isinstance(number, int),
... number % 2 == 0,
... )
>>> all(validation_conditions)
True
>>> callable(number)
False
>>> callable(print)
True
In this code snippet, you first define a variable called number
using your old friend the assignment operator. Then you create another variable called validation_conditions
. This variable holds a tuple of expressions. The first expression uses isinstance()
to check whether number
is an integer value.
The second is a compound expression that combines the modulo (%
) and equality (==
) operators to create a condition that checks whether the input value is an even number. In this condition, the modulo operator returns the remainder of dividing number
by 2
, and the equality operator compares the result with 0
, returning True
or False
as the comparison’s result.
Then you use the all()
function to determine if all the conditions are true. In this example, because number = 42
, the conditions are true, and all()
returns True
. You can play with the value of number
if you’d like to experiment a bit.
In the final two examples, you use the callable()
function. As its name suggests, this function allows you to determine whether an object is callable. Being callable means that you can call the object with a pair of parentheses and appropriate arguments, as you’d call any Python function.
The number
variable isn’t callable, and the function returns False
, accordingly. In contrast, the print()
function is callable, so callable()
returns True
.
All the previous discussion is the basis for understanding how the Python logical operators work with Boolean operands.
Logical expressions involving and
, or
, and not
are straightforward when the operands are Boolean. Here’s a summary. Note that x
and y
represent Boolean operands:
Operator  Sample Expression  Result 

and 
x and y 
• True if both x and y are True • False otherwise 
or 
x or y 
• True if either x or y is True • False otherwise 
not 
not x 
• True if x is False • False if x is True 
This table summarizes the truth value of expressions that you can create using the logical operators with Boolean operands. There’s something to note in this summary. Unlike and
and or
, which are binary operators, the not
operator is unary, meaning that it operates on one operand. This operand must always be on the right side.
Now it’s time to take a look at how the operators work in practice. Here are a few examples of using the and
operator with Boolean operands:
>>> 5 < 7 and 3 == 3
True
>>> 5 < 7 and 3 != 3
False
>>> 5 > 7 and 3 == 3
False
>>> 5 > 7 and 3 != 3
False
In the first example, both operands return True
. Therefore, the and
expression returns True
as a result. In the second example, the lefthand operand is True
, but the righthand operand is False
. Because of this, the and
operator returns False
.
In the third example, the lefthand operand is False
. In this case, the and
operator immediately returns False
and never evaluates the 3 == 3
condition. This behavior is called shortcircuit evaluation. You’ll learn more about it in a moment.
Note: Shortcircuit evaluation is also called McCarthy evaluation in honor of computer scientist John McCarthy.
In the final example, both conditions return False
. Again, and
returns False
as a result. However, because of the shortcircuit evaluation, the righthand expression isn’t evaluated.
What about the or
operator? Here are a few examples that demonstrate how it works:
>>> 5 < 7 or 3 == 3
True
>>> 5 < 7 or 3 != 3
True
>>> 5 > 7 or 3 == 3
True
>>> 5 > 7 or 3 != 3
False
In the first three examples, at least one of the conditions returns True
. In all cases, the or
operator returns True
. Note that if the lefthand operand is True
, then or
applies shortcircuit evaluation and doesn’t evaluate the righthand operand. This makes sense. If the lefthand operand is True
, then or
already knows the final result. Why would it need to continue the evaluation if the result won’t change?
In the final example, both operands are False
, and this is the only situation where or
returns False
. It’s important to note that if the lefthand operand is False
, then or
has to evaluate the righthand operand to arrive at a final conclusion.
Finally, you have the not
operator, which negates the current truth value of an object or expression:
>>> 5 < 7
True
>>> not 5 < 7
False
If you place not
before an expression, then you get the inverse truth value. When the expression returns True
, you get False
. When the expression evaluates to False
, you get True
.
There is a fundamental behavior distinction between not
and the other two Boolean operators. In a not
expression, you always get a Boolean value as a result. That’s not always the rule that governs and
and or
expressions, as you’ll learn in the Boolean Expressions Involving Other Types of Operands section.
Evaluation of Regular Objects in a Boolean Context
In practice, most Python objects and expressions aren’t Boolean. In other words, most objects and expressions don’t have a True
or False
value but a different type of value. However, you can use any Python object in a Boolean context, such as a conditional statement or a while
loop.
In Python, all objects have a specific truth value. So, you can use the logical operators with all types of operands.
Python has wellestablished rules to determine the truth value of an object when you use that object in a Boolean context or as an operand in an expression built with logical operators. Here’s what the documentation says about this topic:
By default, an object is considered true unless its class defines either a
__bool__()
method that returnsFalse
or a__len__()
method that returns zero, when called with the object. Here are most of the builtin objects considered false:
 constants defined to be false:
None
andFalse
. zero of any numeric type:
0
,0.0
,0j
,Decimal(0)
,Fraction(0, 1)
 empty sequences and collections:
''
,()
,[]
,{}
,set()
,range(0)
(Source)
You can determine the truth value of an object by calling the builtin bool()
function with that object as an argument. If bool()
returns True
, then the object is truthy. If bool()
returns False
, then it’s falsy.
For numeric values, you have that a zero value is falsy, while a nonzero value is truthy:
>>> bool(0), bool(0.0), bool(0.0+0j)
(False, False, False)
>>> bool(3), bool(3.14159), bool(1.0+1j)
(True, True, True)
Python considers the zero value of all numeric types falsy. All the other values are truthy, regardless of how close to zero they are.
Note: Instead of a function, bool()
is a class. However, because Python developers typically use this class as a function, you’ll find that most people refer to it as a function rather than as a class. Additionally, the documentation lists this class on the builtin functions page. This is one of those cases where practicality beats purity.
When it comes to evaluating strings, you have that an empty string is always falsy, while a nonempty string is truthy:
>>> bool("")
False
>>> bool(" ")
True
>>> bool("Hello")
True
Note that strings containing white spaces are also truthy in Python’s eyes. So, don’t confuse empty strings with whitespace strings.
Finally, builtin container data types, such as lists, tuples, sets, and dictionaries, are falsy when they’re empty. Otherwise, Python considers them truthy objects:
>>> bool([])
False
>>> bool([1, 2, 3])
True
>>> bool(())
False
>>> bool(("John", 25, "Python Dev"))
True
>>> bool(set())
False
>>> bool({"square", "circle", "triangle"})
True
>>> bool({})
False
>>> bool({"name": "John", "age": 25, "job": "Python Dev"})
True
To determine the truth value of container data types, Python relies on the .__len__()
special method. This method provides support for the builtin len()
function, which you can use to determine the number of items in a given container.
In general, if .__len__()
returns 0
, then Python considers the container a falsy object, which is consistent with the general rules you’ve just learned before.
All the discussion about the truth value of Python objects in this section is key to understanding how the logical operators behave when they take arbitrary objects as operands.
Boolean Expressions Involving Other Types of Operands
You can also use any objects, such as numbers or strings, as operands to and
, or
, and not
. You can even use combinations of a Boolean object and a regular one. In these situations, the result depends on the truth value of the operands.
Note: Boolean expressions that combine two Boolean operands are a special case of a more general rule that allows you to use the logical operators with all kinds of operands. In every case, you’ll get one of the operands as a result.
You’ve already learned how Python determines the truth value of objects. So, you’re ready to dive into creating expressions with logic operators and regular objects.
To start off, below is a table that summarizes what you get when you use two objects, x
and y
, in an and
expression:
If x is 
x and y returns 

Truthy  y 
Falsy  x 
It’s important to emphasize a subtle detail in the above table. When you use and
in an expression, you don’t always get True
or False
as a result. Instead, you get one of the operands. You only get True
or False
if the returned operand has either of these values.
Here are some code examples that use integer values. Remember that in Python, the zero value of numeric types is falsy. The rest of the values are truthy:
>>> 3 and 4
4
>>> 0 and 4
0
>>> 3 and 0
0
In the first expression, the lefthand operand (3
) is truthy. So, you get the righthand operand (4
) as a result.
In the second example, the lefthand operand (0
) is falsy, and you get it as a result. In this case, Python applies the shortcircuit evaluation technique. It already knows that the whole expression is false because 0
is falsy, so Python returns 0
immediately without evaluating the righthand operand.
In the final expression, the lefthand operand (3
) is truthy. Therefore Python needs to evaluate the righthand operand to make a conclusion. As a result, you get the righthand operand, no matter what its truth value is.
Note: To dive deeper into the and
operator, check out Using the “and” Boolean Operator in Python.
When it comes to using the or
operator, you also get one of the operands as a result. This is what happens for two arbitrary objects, x
and y
:
If x is 
x or y returns 

Truthy  x 
Falsy  y 
Again, the expression x or y
doesn’t evaluate to either True
or False
. Instead, it returns one of its operands, x
or y
.
As you can conclude from the above table, if the lefthand operand is truthy, then you get it as a result. Otherwise, you get the second operand. Here are some examples that demonstrate this behavior:
>>> 3 or 4
3
>>> 0 or 4
4
>>> 3 or 0
3
In the first example, the lefthand operand is truthy, and or
immediately returns it. In this case, Python doesn’t evaluate the second operand because it already knows the final result. In the second example, the lefthand operand is falsy, and Python has to evaluate the righthand one to determine the result.
In the last example, the lefthand operand is truthy, and that fact defines the result of the expression. There’s no need to evaluate the righthand operand.
An expression like x or y
is truthy if either x
or y
is truthy, and falsy if both x
and y
are falsy. This type of expression returns the first truthy operand that it finds. If both operands are falsy, then the expression returns the righthand operand. To see this latter behavior in action, consider the following example:
>>> 0 or []
[]
In this specific expression, both operands are falsy. So, the or
operator returns the righthand operand, and the whole expression is falsy as a result.
Note: To learn more about the or
operator, check out Using the “or” Boolean Operator in Python.
Finally, you have the not
operator. You can also use this one with any object as an operand. Here’s what happens:
If x is 
not x returns 

Truthy  False 
Falsy  True 
The not
operator has a uniform behavior. It always returns a Boolean value. This behavior differs from its sibling operators, and
and or
.
Here are some code examples:
>>> not 3
False
>>> not 0
True
In the first example, the operand, 3
, is truthy from Python’s point of view. So, the operator returns False
. In the second example, the operand is falsy, and not
returns True
.
Note: To better understand the not
operator, check out Using the “not” Boolean Operator in Python.
In summary, the Python not
operator negates the truth value of an object and always returns a Boolean value. This latter behavior differs from the behavior of its sibling operators and
and or
, which return operands rather than Boolean values.
Compound Logical Expressions and ShortCircuit Evaluation
So far, you’ve seen expressions with only a single or
or and
operator and two operands. However, you can also create compound logical expressions with multiple logical operators and operands.
To illustrate how to create a compound expression using or
, consider the following toy example:
x1 or x2 or x3 or ... or xn
This expression returns the first truthy value. If all the preceding x
variables are falsy, then the expression returns the last value, xn
.
Note: In an expression like the one above, Python uses shortcircuit evaluation. The operands are evaluated in order from left to right. As soon as one is found to be true, the entire expression is known to be true. At that point, Python stops evaluating operands. The value of the entire expression is that of the x
that terminates the evaluation.
To help demonstrate shortcircuit evaluation, suppose that you have an identity function, f()
, that behaves as follows:
 Takes a single argument
 Displays the function and its argument on the screen
 Returns the argument as its return value
Here’s the code to define this function and also a few examples of how it works:
>>> def f(arg):
... print(f"> f({arg}) = {arg}")
... return arg
...
>>> f(0)
> f(0) = 0
0
>>> f(False)
> f(False) = False
False
>>> f(1.5)
> f(1.5) = 1.5
1.5
The f()
function displays its argument, which visually confirms whether you called the function. It also returns the argument as you passed it in the call. Because of this behavior, you can make the expression f(arg)
be truthy or falsy by specifying a value for arg
that’s truthy or falsy, respectively.
Now, consider the following compound logical expression:
>>> f(0) or f(False) or f(1) or f(2) or f(3)
> f(0) = 0
> f(False) = False
> f(1) = 1
1
In this example, Python first evaluates f(0)
, which returns 0
. This value is falsy. The expression isn’t true yet, so the evaluation continues from left to right. The next operand, f(False)
, returns False
. That value is also falsy, so the evaluation continues.
Next up is f(1)
. That evaluates to 1
, which is truthy. At that point, Python stops the evaluation because it already knows that the entire expression is truthy. Consequently, Python returns 1
as the value of the expression and never evaluates the remaining operands, f(2)
and f(3)
. You can confirm from the output that the f(2)
and f(3)
calls don’t occur.
A similar behavior appears in an expression with multiple and
operators like the following one:
x1 and x2 and x3 and ... and xn
This expression is truthy if all the operands are truthy. If at least one operand is falsy, then the expression is also falsy.
In this example, shortcircuit evaluation dictates that Python stops evaluating as soon as an operand happens to be falsy. At that point, the entire expression is known to be false. Once that’s the case, Python stops evaluating operands and returns the falsy operand that terminated the evaluation.
Here are two examples that confirm the shortcircuiting behavior:
>>> f(1) and f(False) and f(2) and f(3)
> f(1) = 1
> f(False) = False
False
>>> f(1) and f(0.0) and f(2) and f(3)
> f(1) = 1
> f(0.0) = 0.0
0.0
In both examples, the evaluation stops at the first falsy term—f(False)
in the first case, f(0.0)
in the second case—and neither the f(2)
nor the f(3)
call occurs. In the end, the expressions return False
and 0.0
, respectively.
If all the operands are truthy, then Python evaluates them all and returns the last (rightmost) one as the value of the expression:
>>> f(1) and f(2.2) and f("Hello")
> f(1) = 1
> f(2.2) = 2.2
> f(Hello) = Hello
'Hello'
>>> f(1) and f(2.2) and f(0)
> f(1) = 1
> f(2.2) = 2.2
> f(0) = 0
0
In the first example, all the operands are truthy. The expression is also truthy and returns the last operand. In the second example, all the operands are truthy except for the last one. The expression is falsy and returns the last operand.
Idioms That Exploit ShortCircuit Evaluation
As you dig into Python, you’ll find that there are some common idiomatic patterns that exploit shortcircuit evaluation for conciseness of expression, performance, and safety. For example, you can take advantage of this type of evaluation for:
 Avoiding an exception
 Providing a default value
 Skipping a costly operation
To illustrate the first point, suppose you have two variables, a
and b
, and you want to know whether the division of b
by a
results in a number greater than 0
. In this case, you can run the following expression or condition:
>>> a = 3
>>> b = 1
>>> (b / a) > 0
True
This code works. However, you need to account for the possibility that a
might be 0
, in which case you’ll get an exception:
>>> a = 0
>>> b = 1
>>> (b / a) > 0
Traceback (most recent call last):
...
ZeroDivisionError: division by zero
In this example, the divisor is 0
, which makes Python raise a ZeroDivisionError
exception. This exception breaks your code. You can skip this error with an expression like the following:
>>> a = 0
>>> b = 1
>>> a != 0 and (b / a) > 0
False
When a
is 0
, a != 0
is false. Python’s shortcircuit evaluation ensures that the evaluation stops at that point, which means that (b / a)
never runs, and the error never occurs.
Using this technique, you can implement a function to determine whether an integer is divisible by another integer:
def is_divisible(a, b):
return b != 0 and a % b == 0
In this function, if b
is 0
, then a / b
isn’t defined. So, the numbers aren’t divisible. If b
is different from 0
, then the result will depend on the remainder of the division.
Selecting a default value when a specified value is falsy is another idiom that takes advantage of the shortcircuit evaluation feature of Python’s logical operators.
For example, say that you have a variable that’s supposed to contain a country’s name. At some point, this variable can end up holding an empty string. If that’s the case, then you’d like the variable to hold a default county name. You can also do this with the or
operator:
>>> country = "Canada"
>>> default_country = "United States"
>>> country or default_country
'Canada'
>>> country = ""
>>> country or default_country
'United States'
If country
is nonempty, then it’s truthy. In this scenario, the expression will return the first truthy value, which is country
in the first or
expression. The evaluation stops, and you get "Canada"
as a result.
On the other hand, if country
is an empty string, then it’s falsy. The evaluation continues to the next operand, default_country
, which is truthy. Finally, you get the default country as a result.
Another interesting use case for shortcircuit evaluation is to avoid costly operations while creating compound logical expressions. For example, if you have a costly operation that should only run if a given condition is false, then you can use or
like in the following snippet:
data_is_clean or clean_data(data)
In this construct, your clean_data()
function represents a costly operation. Because of shortcircuit evaluation, this function will only run when data_is_clean
is false, which means that your data isn’t clean.
Another variation of this technique is when you want to run a costly operation if a given condition is true. In this case, you can use the and
operator:
data_is_updated and process_data(data)
In this example, the and
operator evaluates data_is_updated
. If this variable is true, then the evaluation continues, and the process_data()
function runs. Otherwise, the evaluation stops, and process_data()
never runs.
Compound vs Chained Expressions
Sometimes you have a compound expression that uses the and
operator to join comparison expressions. For example, say that you want to determine if a number is in a given interval. You can solve this problem with a compound expression like the following:
>>> number = 5
>>> number >= 0 and number <= 10
True
>>> number = 42
>>> number >= 0 and number <= 10
False
In this example, you use the and
operator to join two comparison expressions that allow you to find out if number
is in the interval from 0
to 10
, both included.
In Python, you can make this compound expression more concise by chaining the comparison operators together. For example, the following chained expression is equivalent to the previous compound one:
>>> number = 5
>>> 0 <= number <= 10
True
This expression is more concise and readable than the original expression. You can quickly realize that this code is checking if the number is between 0
and 10
. Note that in most programming languages, this chained expression doesn’t make sense. In Python, it works like a charm.
In other programming languages, this expression would probably start by evaluating 0 <= number
, which is true. This true value would then be compared with 10
, which doesn’t make much sense, so the expression fails.
Python internally processes this type of expression as an equivalent and
expression, such as 0 <= number and number <= 10
. That’s why you get the correct result in the example above.
Conditional Expressions or the Ternary Operator
Python has what it calls conditional expressions. These kinds of expressions are inspired by the ternary operator that looks like a ? b : c
and is used in other programming languages. This construct evaluates to b
if the value of a
is true, and otherwise evaluates to c
. Because of this, sometimes the equivalent Python syntax is also known as the ternary operator.
However, in Python, the expression looks more readable:
variable = expression_1 if condition else expression_2
This expression returns expression_1
if the condition is true and expression_2
otherwise. Note that this expression is equivalent to a regular conditional like the following:
if condition:
variable = expression_1
else:
variable = expression_2
So, why does Python need this syntax? PEP 308 introduced conditional expressions as an effort to avoid the prevalence of errorprone attempts to achieve the same effect of a traditional ternary operator using the and
and or
operators in an expression like the following:
variable = condition and expression_1 or expression_2
However, this expression doesn’t work as expected, returning expression_2
when expression_1
is falsy.
Some Python developers would avoid the syntax of conditional expressions in favor of a regular conditional statement. In any case, this syntax can be handy in some situations because it provides a concise tool for writing twoway conditionals.
Here’s an example of how to use the conditional expression syntax in your code:
>>> day = "Sunday"
>>> open_time = "11AM" if day == "Sunday" else "9AM"
>>> open_time
'11AM'
>>> day = "Monday"
>>> open_time = "11AM" if day == "Sunday" else "9AM"
>>> open_time
'9AM'
When day
is equal to "Sunday"
, the condition is true and you get the first expression, "11AM"
, as a result. If the condition is false, then you get the second expression, "9AM"
. Note that similarly to the and
and or
operators, the conditional expression returns the value of one of its expressions rather than a Boolean value.
Identity Operators and Expressions in Python
Python provides two operators, is
and is not
, that allow you to determine whether two operands have the same identity. In other words, they let you check if the operands refer to the same object. Note that identity isn’t the same thing as equality. The latter aims to check whether two operands contain the same data.
Note: To learn more about the difference between identity and equality, check out Python ‘!=’ Is Not ‘is not’: Comparing Objects in Python.
Here’s a summary of Python’s identity operators. Note that x
and y
are variables that point to objects:
Operator  Sample Expression  Result 

is 
x is y 
• True if x and y hold a reference to the same inmemory object• False otherwise 
is not 
x is not y 
• True if x points to an object different from the object that y points to• False otherwise 
These two Python operators are keywords instead of odd symbols. This is part of Python’s goal of favoring readability in its syntax.
Here’s an example of two variables, x
and y
, that refer to objects that are equal but not identical:
>>> x = 1001
>>> y = 1001
>>> x == y
True
>>> x is y
False
In this example, x
and y
refer to objects whose value is 1001
. So, they’re equal. However, they don’t reference the same object. That’s why the is
operator returns False
. You can check an object’s identity using the builtin id()
function:
>>> id(x)
4417772080
>>> id(y)
4417766416
As you can conclude from the id()
output, x
and y
don’t have the same identity. So, they’re different objects, and because of that, the expression x is y
returns False
. In other words, you get False
because you have two different instances of 1001
stored in your computer’s memory.
When you make an assignment like y = x
, Python creates a second reference to the same object. Again, you can confirm that with the id()
function or the is
operator:
>>> a = "Hello, Pythonista!"
>>> b = a
>>> id(a)
4417651936
>>> id(b)
4417651936
>>> a is b
True
In this example, a
and b
hold references to the same object, the string "Hello, Pythonista!"
. Therefore, the id()
function returns the same identity when you call it with a
and b
. Similarly, the is
operator returns True
.
Note: You should note that, on your computer, you’ll get a different identity number when you call id()
in the example above. The key detail is that the identity number will be the same for a
and b
.
Finally, the is not
operator is the opposite of is
. So, you can use is not
to determine if two names don’t refer to the same object:
>>> x = 1001
>>> y = 1001
>>> x is not y
True
>>> a = "Hello, Pythonista!"
>>> b = a
>>> a is not b
False
In the first example, because x
and y
point to different objects in your computer’s memory, the is not
operator returns True
. In the second example, because a
and b
are references to the same object, the is not
operator returns False
.
Note: The syntax not x is y
also works the same as x is not y
. However, the former syntax looks odd and is difficult to read. That’s why Python recognizes is not
as an operator and encourages its use for readability.
Again, the is not
operator highlights Python’s readability goals. In general, both identity operators allow you to write checks that read as plain English.
Membership Operators and Expressions in Python
Sometimes you need to determine whether a value is present in a container data type, such as a list, tuple, or set. In other words, you may need to check if a given value is or is not a member of a collection of values. Python calls this kind of check a membership test.
Note: For a deep dive into how Python’s membership tests work, check out Python’s “in” and “not in” Operators: Check for Membership.
Membership tests are quite common and useful in programming. As with many other common operations, Python has dedicated operators for membership tests. The table below lists the membership operators in Python:
Operator  Sample Expression  Result 

in 
value in collection 
• True if value is present in collection • False otherwise 
not in 
value not in collection 
• True if value is not present in collection of values• False otherwise 
As usual, Python favors readability by using English words as operators instead of potentially confusing symbols or combinations of symbols.
Note: The syntax not value in collection
also works in Python. However, this syntax looks odd and is difficult to read. So, to keep your code clean and readable, you should use value not in collection
, which almost reads as plain English.
The Python in
and not in
operators are binary. This means that you can create membership expressions by connecting two operands with either operator. However, the operands in a membership expression have particular characteristics:
 Left operand: The value that you want to look for in a collection of values
 Right operand: The collection of values where the target value may be found
To better understand the in
operator, below you have two demonstrative examples consisting of determining whether a value is in a list:
>>> 5 in [2, 3, 5, 9, 7]
True
>>> 8 in [2, 3, 5, 9, 7]
False
The first expression returns True
because 5
is in the list of numbers. The second expression returns False
because 8
isn’t in the list.
The not in
membership operator runs the opposite test as the in
operator. It allows you to check whether an integer value is not in a collection of values:
>>> 5 not in [2, 3, 5, 9, 7]
False
>>> 8 not in [2, 3, 5, 9, 7]
True
In the first example, you get False
because 5
is in the target list. In the second example, you get True
because 8
isn’t in the list of values. This may sound like a tongue twister because of the negative logic. To avoid confusion, remember that you’re trying to determine if the value is not part of a given collection of values.
Concatenation and Repetition Operators and Expressions
There are two operators in Python that acquire a slightly different meaning when you use them with sequence data types, such as lists, tuples, and strings. With these types of operands, the +
operator defines a concatenation operator, and the *
operator represents the repetition operator:
Operator  Operation  Sample Expression  Result 

+ 
Concatenation  seq_1 + seq_2 
A new sequence containing all the items from both operands 
* 
Repetition  seq * n 
A new sequence containing the items of seq repeated n times 
Both operators are binary. The concatenation operator takes two sequences as operands and returns a new sequence of the same type. The repetition operator takes a sequence and an integer number as operands. Like in regular multiplication, the order of the operands doesn’t alter the repetition’s result.
Note: To learn more about concatenating string objects, check out Efficient String Concatenation in Python.
Here are some examples of how the concatenation operator works in practice:
>>> "Hello, " + "World!"
'Hello, World!'
>>> ("A", "B", "C") + ("D", "E", "F")
('A', 'B', 'C', 'D', 'E', 'F')
>>> [0, 1, 2, 3] + [4, 5, 6]
[0, 1, 2, 3, 4, 5, 6]
In the first example, you use the concatenation operator (+
) to join two strings together. The operator returns a completely new string object that combines the two original strings.
In the second example, you concatenate two tuples of letters together. Again, the operator returns a new tuple object containing all the items from the original operands. In the final example, you do something similar but this time with two lists.
Note: To learn more about concatenating lists, check out the Concatenating Lists section in the tutorial Python’s list
Data Type: A Deep Dive With Examples.
When it comes to the repetition operator, the idea is to repeat the content of a given sequence a certain number of times. Here are a few examples:
>>> "Hello" * 3
'HelloHelloHello'
>>> 3 * "World!"
'World!World!World!'
>>> ("A", "B", "C") * 3
('A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C')
>>> 3 * [1, 2, 3]
[1, 2, 3, 1, 2, 3, 1, 2, 3]
In the first example, you use the repetition operator (*
) to repeat the "Hello"
string three times. In the second example, you change the order of the operands by placing the integer number on the left and the target string on the right. This example shows that the order of the operands doesn’t affect the result.
The next examples use the repetition operators with a tuple and a list, respectively. In both cases, you get a new object of the same type containing the items in the original sequence repeated three times.
The Walrus Operator and Assignment Expressions
Regular assignment statements with the =
operator don’t have a return value, as you already learned. Instead, the assignment operator creates or updates variables. Because of this, the operator can’t be part of an expression.
Since Python 3.8, you have access to a new operator that allows for a new type of assignment. This new assignment is called assignment expression or named expression. The new operator is called the walrus operator, and it’s the combination of a colon and an equal sign (:=
).
Note: The name walrus comes from the fact that this operator resembles the eyes and tusks of a walrus lying on its side. For a deep dive into how this operator works, check out The Walrus Operator: Python 3.8 Assignment Expressions.
Unlike regular assignments, assignment expressions do have a return value, which is why they’re expressions. So, the operator accomplishes two tasks:
 Returns the expression’s result
 Assigns the result to a variable
The walrus operator is also a binary operator. Its lefthand operand must be a variable name, and its righthand operand can be any Python expression. The operator will evaluate the expression, assign its value to the target variable, and return the value.
The general syntax of an assignment expression is as follows:
(variable := expression)
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, in certain situations, you won’t need them. Either way, they won’t hurt you, so it’s safe to use them.
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. It’s particularly useful in the context of a conditional statement. To illustrate, the example below shows a toy function that checks the length of a string object:
>>> def validate_length(string):
... if (n := len(string)) < 8:
... print(f"Length {n} is too short, needs at least 8")
... else:
... print(f"Length {n} is okay!")
...
>>> validate_length("Pythonista")
Length 10 is okay!
>>> validate_length("Python")
Length 6 is too short, needs at least 8
In this example, you use a conditional statement to check whether the input string has fewer than 8
characters.
The assignment expression, (n := len(string))
, computes the string length and assigns it to n
. Then it returns the value that results from calling len()
, which finally gets compared with 8
. This way, you guarantee that you have a reference to the string length to use in further operations.
Bitwise Operators and Expressions in Python
Bitwise operators treat operands as sequences of binary digits and operate on them bit by bit. Currently, Python supports the following bitwise operators:
Operator  Operation  Sample Expression  Result 

& 
Bitwise AND  a & b 
• Each bit position in the result is the logical AND of the bits in the corresponding position of the operands. • 1 if both bits are 1 , otherwise 0 . 
 
Bitwise OR  a  b 
• Each bit position in the result is the logical OR of the bits in the corresponding position of the operands. • 1 if either bit is 1 , otherwise 0 . 
~ 
Bitwise NOT  ~a 
• Each bit position in the result is the logical negation of the bit in the corresponding position of the operand. • 1 if the bit is 0 and 0 if the bit is 1 . 
^ 
Bitwise XOR (exclusive OR)  a ^ b 
• Each bit position in the result is the logical XOR of the bits in the corresponding position of the operands. • 1 if the bits in the operands are different, 0 if they’re equal. 
>> 
Bitwise right shift  a >> n 
Each bit is shifted right n places. 
<< 
Bitwise left shift  a << n 
Each bit is shifted left n places. 
As you can see in this table, most bitwise operators are binary, which means that they expect two operands. The bitwise NOT operator (~
) is the only unary operator because it expects a single operand, which should always appear at the right side of the expression.
You can use Python’s bitwise operators to manipulate your data at its most granular level, the bits. These operators are commonly useful when you want to write lowlevel algorithms, such as compression, encryption, and others.
Note: For a deep dive into the bitwise operators, check out Bitwise Operators in Python. You can also check out Build a Maze Solver in Python Using Graphs for an example of using bitwise operators to construct a binary file format.
Here are some examples that illustrate how some of the bitwise operators work in practice:
>>> # Bitwise AND
>>> # 0b1100 12
>>> # & 0b1010 10
>>> # 
>>> # = 0b1000 8
>>> bin(0b1100 & 0b1010)
'0b1000'
>>> 12 & 10
8
>>> # Bitwise OR
>>> # 0b1100 12
>>> #  0b1010 10
>>> # 
>>> # = 0b1110 14
>>> bin(0b1100  0b1010)
'0b1110'
>>> 12  10
14
In the first example, you use the bitwise AND operator. The commented lines begin with #
and provide a visual representation of what happens at the bit level. Note how each bit in the result is the logical AND of the bits in the corresponding position of the operands.
The second example shows how the bitwise OR operator works. In this case, the resulting bits are the logical OR test of the corresponding bits in the operands.
In all the examples, you’ve used the builtin bin()
function to display the result as a binary object. If you don’t wrap the expression in a call to bin()
, then you’ll get the integer representation of the output.
Operator Precedence in Python
Up to this point, you’ve coded sample expressions that mostly use one or two different types of operators. However, what if you need to create compound expressions that use several different types of operators, such as comparison, arithmetic, Boolean, and others? How does Python decide which operation runs first?
Consider the following math expression:
>>> 20 + 4 * 10
60
There might be ambiguity in this expression. Should Python perform the addition 20 + 4
first and then multiply the result by 10
? Should Python run the multiplication 4 * 10
first, and the addition second?
Because the result is 60
, you can conclude that Python has chosen the latter approach. If it had chosen the former, then the result would be 240
. This follows a standard algebraic rule that you’ll find in virtually all programming languages.
All operators that Python supports have a precedence compared to other operators. This precedence defines the order in which Python runs the operators in a compound expression.
In an expression, Python runs the operators of highest precedence first. After obtaining those results, Python runs the operators of the next highest precedence. This process continues until the expression is fully evaluated. Any operators of equal precedence are performed in lefttoright order.
Here’s the order of precedence of the Python operators that you’ve seen so far, from highest to lowest:
Operators  Description 

** 
Exponentiation 
+x , x , ~x 
Unary positive, unary negation, bitwise negation 
* , / , // , % 
Multiplication, division, floor division, modulo 
+ ,  
Addition, subtraction 
<< , >> 
Bitwise shifts 
& 
Bitwise AND 
^ 
Bitwise XOR 
 
Bitwise OR 
== , != , < , <= , > , >= , is , is not , in , not in 
Comparisons, identity, and membership 
not 
Boolean NOT 
and 
Boolean AND 
or 
Boolean OR 
:= 
Walrus 
Operators at the top of the table have the highest precedence, and those at the bottom of the table have the lowest precedence. Any operators in the same row of the table have equal precedence.
Getting back to your initial example, Python runs the multiplication because the multiplication operator has a higher precedence than the addition one.
Here’s another illustrative example:
>>> 2 * 3 ** 4 * 5
810
In the example above, Python first raises 3
to the power of 4
, which equals 81
. Then, it carries out the multiplications in order from left to right: 2 * 81 = 162
and 162 * 5 = 810
.
You can override the default operator precedence using parentheses to group terms as you do in math. The subexpressions in parentheses will run before expressions that aren’t in parentheses.
Here are some examples that show how a pair of parentheses can affect the result of an expression:
>>> (20 + 4) * 10
240
>>> 2 * 3 ** (4 * 5)
6973568802
In the first example, Python computes the expression 20 + 4
first because it’s wrapped in parentheses. Then Python multiplies the result by 10
, and the expression returns 240
. This result is completely different from what you got at the beginning of this section.
In the second example, Python evaluates 4 * 5
first. Then it raises 3
to the power of the resulting value. Finally, Python multiplies the result by 2
, returning 6973568802
.
There’s nothing wrong with making liberal use of parentheses, even when they aren’t necessary to change the order of evaluation. Sometimes it’s a good practice to use parentheses because they can improve your code’s readability and relieve the reader from having to recall operator precedence from memory.
Consider the following example:
(a < 10) and (b > 30)
Here the parentheses are unnecessary, as the comparison operators have higher precedence than and
. However, some might find the parenthesized version clearer than the version without parentheses:
a < 10 and b > 30
On the other hand, some developers might prefer this latter version of the expression. It’s a matter of personal preference. The point is that you can always use parentheses if you feel that they make your code more readable, even if they aren’t necessary to change the order of evaluation.
Augmented Assignment Operators and Expressions
So far, you’ve learned that a single equal sign (=
) represents the assignment operator and allows you to assign a value to a variable. Having a righthand operand that contains other variables is perfectly valid, as you’ve also learned. In particular, the expression to the right of the assignment operator can include the same variable that’s on the left of the operand.
That last sentence may sound confusing, so here’s an example that clarifies the point:
>>> total = 10
>>> total = total + 5
>>> total
15
In this example, total
is an accumulator variable that you use to accumulate successive values. You should read this example as total
is equal to the current value of total
plus 5
. This expression effectively increases the value of total
, which is now 15
.
Note that this type of assignment only makes sense if the variable in question already has a value. If you try the assignment with an undefined variable, then you get an error:
>>> count = count + 1
Traceback (most recent call last):
...
NameError: name 'count' is not defined. Did you mean: 'round'?
In this example, the count
variable isn’t defined before the assignment, so it doesn’t have a current value. In consequence, Python raises a NameError
exception to let you know about the issue.
This type of assignment helps you create accumulators and counter variables, for example. Therefore, it’s quite a common task in programming. As in many similar cases, Python offers a more convenient solution. It supports a shorthand syntax called augmented assignment:
>>> total = 10
>>> total += 5
>>> total
15
In the highlighted line, you use the augmented addition operator (+=
). With this operator, you create an assignment that’s fully equivalent to total = total + 5
.
Python supports many augmented assignment operators. In general, the syntax for this type of assignment looks something like this:
variable $= expression
Note that the dollar sign ($
) isn’t a valid Python operator. In this example, it’s a placeholder for a generic operator. The above statement works as follows:
 Evaluate
expression
to produce a value.  Run the operation defined by the operator that prefixes the assignment operator (
=
), using the current value ofvariable
and the return value ofexpression
as operands.  Assign the resulting value back to
variable
.
The table below shows a summary of the augmented operators for arithmetic operations:
Operator  Description  Sample Expression  Equivalent Expression 

+= 
Adds the right operand to the left operand and stores the result in the left operand  x += y 
x = x + y 
= 
Subtracts the right operand from the left operand and stores the result in the left operand  x = y 
x = x  y 
*= 
Multiplies the right operand with the left operand and stores the result in the left operand  x *= y 
x = x * y 
/= 
Divides the left operand by the right operand and stores the result in the left operand  x /= y 
x = x / y 
//= 
Performs floor division of the left operand by the right operand and stores the result in the left operand  x //= y 
x = x // y 
%= 
Finds the remainder of dividing the left operand by the right operand and stores the result in the left operand  x %= y 
x = x % y 
**= 
Raises the left operand to the power of the right operand and stores the result in the left operand  x **= y 
x = x**y 
As you can conclude from this table, all the arithmetic operators have an augmented version in Python. You can use these augmented operators as a shortcut when creating accumulators, counters, and similar objects.
Did the augmented arithmetic operators look neat and useful to you? The good news is that there are more. You also have augmented bitwise operators in Python:
Operator  Operation  Example  Equivalent 

&= 
Augmented bitwise AND (conjunction)  x &= y 
x = x & y 
= 
Augmented bitwise OR (disjunction)  x = y 
x = x  y 
^= 
Augmented bitwise XOR (exclusive disjunction)  x ^= y 
x = x ^ y 
>>= 
Augmented bitwise right shift  x >>= y 
x = x >> y 
<<= 
Augmented bitwise left shift  x <<= y 
x = x << y 
Finally, the concatenation and repetition operators have augmented variations too. These variations behave differently with mutable and immutable data types:
Operator  Description  Example 

+= 
• Runs an augmented concatenation operation on the target sequence. • Mutable sequences are updated in place. • If the sequence is immutable, then a new sequence is created and assigned back to the target name. 
seq_1 += seq_2 
*= 
• Adds seq to itself n times.• Mutable sequences are updated in place. • If the sequence is immutable, then a new sequence is created and assigned back to the target name. 
seq *= n 
Note that the augmented concatenation operator works on two sequences, while the augmented repetition operator works on a sequence and an integer number.
Conclusion
Now you know what operators Python supports and how to use them. Operators are symbols, combinations of symbols, or keywords that you can use along with Python objects to build different types of expressions and perform computations in your code.
In this tutorial, you’ve learned:
 What Python’s arithmetic operators are and how to use them in arithmetic expressions
 What Python’s comparison, Boolean, identity, membership operators are
 How to write expressions using comparison, Boolean, identity, and membership operators
 Which bitwise operators Python supports and how to use them
 How to combine and repeat sequences using the concatenation and repetition operators
 What the augmented assignment operators are and how they work
In other words, you’ve covered an awful lot of ground! If you’d like a handy cheat sheet that can jog your memory on all that you’ve learned, then click the link below:
Free Bonus: Click here to download your comprehensive cheat sheet covering the various operators in Python.
With all this knowledge about operators, you’re better prepared as a Python developer. You’ll be able to write better and more robust expressions in your code.
Take the Quiz: Test your knowledge with our interactive “Python Operators and Expressions” quiz. Upon completion you will receive a score so you can track your learning progress over time: