# Python Operator Precedence – Learn how to perform operations in Python

There are many different types of operators.

When evaluating complex expressions like **5+2*4%6-1 and 13 or 3** one might easily get confused about in which order the operations will be performed.

This Python operator precedence article will help you in understanding how these expressions are **evaluated** and the **order of precedence** Python follows.

## Python Operators Precedence Table

Here we have a table that is arranged in the ascending order of precedence of operators.

The new **Assignment expression (:=) operator** from Python **3.8** onwards has the **lowest precedence** while **parentheses()** have the **highest precedence**.

Operator | Description |

:= | Assignment expression (Lowest precedence) |

lambda | Lambda expression |

if-else | Conditional expression |

or | Boolean OR |

and | Boolean AND |

not x | Boolean NOT |

<, <=, >, >=, | Comparison operators |

!=, == | Equality operators |

in, not in, is, is not, | Identity operators, membership operators |

| | Bitwise OR |

^ | Bitwise XOR |

& | Bitwise AND |

<<, >> | Left and right Shifts |

+, – | Addition and subtraction |

*, @, /, //, % | Multiplication, matrix multiplication, division, floor division, remainder |

+x, -x, ~x | Unary plus, Unary minus, bitwise NOT |

** | Exponentiation |

await x | Await expression |

x[index], x[index:index], x(arguments…), x.attribute | Subscription, slicing, call, attribute reference |

(expressions…), [expressions…], {key: value…}, {expressions…} | Binding or parenthesized expression, list display, dictionary display, set display |

() | Parentheses (Highest precedence) |

### What are Expressions in Python?

Before we move further, let’s first understand what are expressions.

An expression is made with **combinations of variables**, **values**, **operators** and **function calls.**

The Python interpreter **evaluates** the valid expression.

Have a look at a very simple expression.

**Code:**

5 - 2

**Output: **

5-2 is an expression that contains a **single operator**.

However, an expression can also contain **multiple operators** and **operands**.

**Code:**

10-5/5

**Output: **

In this expression, it first **divided** the 5/5 and then **subtracted** the result from 10 because, in Python, the division operator has **higher precedence** than subtraction.

Let’s look at this example:

**Code:**

(10-5)/5

**Output: **

Here, with the use of **parentheses**, we force the interpreter to first evaluate the expression **inside** the parentheses and then continue the overall evaluation.

### Python Operators Precedence Rule – PEMDAS

You might have heard about the **BODMAS rule** in your school’s mathematics class.

Python also uses a similar type of rule known as **PEMDAS**.

**P –** Parentheses

**E –** Exponentiation

**M –** Multiplication

**D –** Division

**A –** Addition

**S –** Subtraction

The precedence of operators is listed from **High to low**.

To remember the abbreviations, we have a funny mnemonic **“Please Excuse My Dear Aunt Sally”**.

Now we apply the PEMDAS rule and evaluate the following expression –

**Code:**

((((6+4)*2)-10)//2)-4*2

**Output: **

How did we get -3?

Let’s break down the evaluation:

(6+4) = 10

(10*2) = 20

(20-10) = 10

(10//2) = 5

4*2 = 8

5-8 = -3

### Associativity of Operators in Python

If you observed the precedence table, you may have noticed that many cells had **more than one operator** which means that they have the **same precedence**.

So then, which will be evaluated first is managed by the **associativity of operators**.

#### 1. Associative Operators

The associative operators are **division**, **multiplication**, **remainder**, etc. and the expressions will be evaluated from **left to right**.

Almost all operators except the **exponentiation(**) operator** support left-to-right associativity.

**Example 1:**

Suppose **modulus(%)** and** division(/)** **operators** have the **same precedence**.

So, if both operators are present in an expression, then the **left one** is evaluated **first**.

**Code: **

45 % 10 / 2

**Output: **

First, 45%10 gives 5 and then 5/2 gives us 2.5 as output.

If this was evaluated from right to left, we would get a **different** output.

**Code:**

45% (10/2)

**Output:**

Here, we **forced** the expression to evaluate from right to left.

**Example 2:**

The **exponentiation operator** evaluates from right to left.

**Code:**

2**2**3

**Output:**

If we want to see the output of left to right, we can use **parentheses**.

**Code:**

(2**2)**3

**Output:**

#### 2. Non-Associative Operators

The comparison operator and the assignment operators do not support **associativity** which means that an expression like **10<20<30** doesn’t mean** (10<20)<30** or **10<(20<30 )**.

They both mean the same thing as they are evaluated from **left to right**.

The statement **10<20<30** means** 10<20** and **20<30**.

You can also chain the assignment operators in any order and they will behave the same way.

**a = b = c = d** will be same as** b = a = d = c** or **d = c = b = a**.

### Short-Circuiting in Python Operators Precedence

As we saw how Python mostly evaluates the expression from **left-to-right**.

In expression with **‘and’**, **‘or’** **operators**, Python uses **Short-Circuiting** which means that it will evaluate the right side only when it is **needed**.

You’ll understand this better with examples.

#### 1. Short-circuiting with and/or

The **boolean operation** will stop executing when we arrive at the truth value of the expression.

- x or y: Evaluates y only when x is false.
- x and y: Evaluates y only when x is true.

**Code: **

0 or “Hey” and 1

**Output: **

0 or “Hey” returns “Hey”

“Hey” and 1 returns 1

#### 2. Short-circuiting with all()/ any()

The inbuilt functions **all()** and **any()** also **supports short-circuiting**.

**all()**function checks that all statements should be ‘True’.- So when the first ‘False’ statement occurs, it
**stops**further executing and returns**False**.

**Code:**

def short_ciruit(i): print(“Executing”) return i print(all(short_circuit(i) for i in [1,2,3,0,5,6] ) )

**Output:**

Executing

Executing

Executing

False

**any()**function returns “True” if one of the statements is true.- So, when the first ‘True’ statement occurs, we don’t need to
**execute**any further and simply return**“True”**.

**Code:**

def short_ciruit(i): print(“Executing”) return i print(any(short_circuit(i) for i in [0,0,3,0,5,6] ) )

**Output:**

Executing

Executing

True

#### 3. Short-circuiting with conditional operators

**Conditional operators** also follow short-circuiting.

Let’s see it with an example.

**Code:**

def check(iWatch how this unfurls with conditional operators like > and <. Have a look at Python Bitwise Operator) return i print(5>20>check(50))

**Output:**

The statement stopped executing when the statement becomes false and it was no longer needed to execute it further so the **check(50) method** didn’t run.

## Summary

In this article, we studied the important topic of Python **operators precedence table**.

We understood the rules of operator precedence and how Python evaluates complex expressions.

Some operators are **associative** while some are **non-associative**.

Later on, we saw more on **short-circuiting** that Python stops executing when it is sure of the result and thus it doesn’t need to execute code any further.

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