assignment operator in python program

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Python Assignment Operators

Assignment operators in python.

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

Python Relational Operators

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

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

Here are the Assignment Operators in Python with examples.

Assignment Operator

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

Addition Assignment Operator

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

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

S ubtraction Assignment Operator

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

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

M ultiplication Assignment Operator

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

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

D ivision Assignment Operator

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

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

M odulus Assignment Operator

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

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

F loor Division Assignment Operator

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

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

Exponentiation Assignment Operator

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

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

Bitwise AND Assignment Operator

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

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

Bitwise OR Assignment Operator

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

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

Bitwise XOR Assignment Operator

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

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

Bitwise Right Shift Assignment Operator

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

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

Bitwise Left Shift Assignment Operator

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

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

Walrus Operator

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

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

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Python Assignment Operator

The = (equal to) symbol is defined as assignment operator in Python. The value of Python expression on its right is assigned to a single variable on its left. The = symbol as in programming in general (and Python in particular) should not be confused with its usage in Mathematics, where it states that the expressions on the either side of the symbol are equal.

Example of Assignment Operator in Python

Consider following Python statements −

At the first instance, at least for somebody new to programming but who knows maths, the statement "a=a+b" looks strange. How could a be equal to "a+b"? However, it needs to be reemphasized that the = symbol is an assignment operator here and not used to show the equality of LHS and RHS.

Because it is an assignment, the expression on right evaluates to 15, the value is assigned to a.

In the statement "a+=b", the two operators "+" and "=" can be combined in a "+=" operator. It is called as add and assign operator. In a single statement, it performs addition of two operands "a" and "b", and result is assigned to operand on left, i.e., "a".

Augmented Assignment Operators in Python

In addition to the simple assignment operator, Python provides few more assignment operators for advanced use. They are called cumulative or augmented assignment operators. In this chapter, we shall learn to use augmented assignment operators defined in Python.

Python has the augmented assignment operators for all arithmetic and comparison operators.

Python augmented assignment operators combines addition and assignment in one statement. Since Python supports mixed arithmetic, the two operands may be of different types. However, the type of left operand changes to the operand of on right, if it is wider.

The += operator is an augmented operator. It is also called cumulative addition operator, as it adds "b" in "a" and assigns the result back to a variable.

The following are the augmented assignment operators in Python:

Augmented Addition Operator
Augmented Subtraction Operator
Augmented Multiplication Operator
Augmented Division Operator
Augmented Modulus Operator
Augmented Exponent Operator
Augmented Floor division Operator

Augmented Addition Operator (+=)

Following examples will help in understanding how the "+=" operator works −

It will produce the following output −

Augmented Subtraction Operator (-=)

Use -= symbol to perform subtract and assign operations in a single statement. The "a-=b" statement performs "a=a-b" assignment. Operands may be of any number type. Python performs implicit type casting on the object which is narrower in size.

Augmented Multiplication Operator (*=)

The "*=" operator works on similar principle. "a*=b" performs multiply and assign operations, and is equivalent to "a=a*b". In case of augmented multiplication of two complex numbers, the rule of multiplication as discussed in the previous chapter is applicable.

Augmented Division Operator (/=)

The combination symbol "/=" acts as divide and assignment operator, hence "a/=b" is equivalent to "a=a/b". The division operation of int or float operands is float. Division of two complex numbers returns a complex number. Given below are examples of augmented division operator.

Augmented Modulus Operator (%=)

To perform modulus and assignment operation in a single statement, use the %= operator. Like the mod operator, its augmented version also is not supported for complex number.

Augmented Exponent Operator (**=)

The "**=" operator results in computation of "a" raised to "b", and assigning the value back to "a". Given below are some examples −

Augmented Floor division Operator (//=)

For performing floor division and assignment in a single statement, use the "//=" operator. "a//=b" is equivalent to "a=a//b". This operator cannot be used with complex numbers.

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Assignment Operators

Add and assign, subtract and assign, multiply and assign, divide and assign, floor divide and assign, exponent and assign, modulo and assign.

For demonstration purposes, let’s use a single variable, num . Initially, we set num to 6. We can apply all of these operators to num and update it accordingly.

Assigning the value of 6 to num results in num being 6.

Expression: num = 6

Adding 3 to num and assigning the result back to num would result in 9.

Expression: num += 3

Subtracting 3 from num and assigning the result back to num would result in 6.

Expression: num -= 3

Multiplying num by 3 and assigning the result back to num would result in 18.

Expression: num *= 3

Dividing num by 3 and assigning the result back to num would result in 6.0 (always a float).

Expression: num /= 3

Performing floor division on num by 3 and assigning the result back to num would result in 2.

Expression: num //= 3

Raising num to the power of 3 and assigning the result back to num would result in 216.

Expression: num **= 3

Calculating the remainder when num is divided by 3 and assigning the result back to num would result in 2.

Expression: num %= 3

We can effectively put this into Python code, and you can experiment with the code yourself! Click the “Run” button to see the output.

The above code is useful when we want to update the same number. We can also use two different numbers and use the assignment operators to apply them on two different values.

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Assignment operator in python.

Last Updated on June 8, 2023 by Prepbytes

To fully comprehend the assignment operators in Python, it is important to have a basic understanding of what operators are. Operators are utilized to carry out a variety of operations, including mathematical, bitwise, and logical operations, among others, by connecting operands. Operands are the values that are acted upon by operators. In Python, the assignment operator is used to assign a value to a variable. The assignment operator is represented by the equals sign (=), and it is the most commonly used operator in Python. In this article, we will explore the assignment operator in Python, how it works, and its different types.

What is an Assignment Operator in Python?

The assignment operator in Python is used to assign a value to a variable. The assignment operator is represented by the equals sign (=), and it is used to assign a value to a variable. When an assignment operator is used, the value on the right-hand side is assigned to the variable on the left-hand side. This is a fundamental operation in programming, as it allows developers to store data in variables that can be used throughout their code.

For example, consider the following line of code:

Explanation: In this case, the value 10 is assigned to the variable a using the assignment operator. The variable a now holds the value 10, and this value can be used in other parts of the code. This simple example illustrates the basic usage and importance of assignment operators in Python programming.

Types of Assignment Operator in Python

There are several types of assignment operator in Python that are used to perform different operations. Let’s explore each type of assignment operator in Python in detail with the help of some code examples.

1. Simple Assignment Operator (=)

The simple assignment operator is the most commonly used operator in Python. It is used to assign a value to a variable. The syntax for the simple assignment operator is:

Here, the value on the right-hand side of the equals sign is assigned to the variable on the left-hand side. For example

Explanation: In this case, the value 25 is assigned to the variable a using the simple assignment operator. The variable a now holds the value 25.

2. Addition Assignment Operator (+=)

The addition assignment operator is used to add a value to a variable and store the result in the same variable. The syntax for the addition assignment operator is:

Here, the value on the right-hand side is added to the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is incremented by 5 using the addition assignment operator. The result, 15, is then printed to the console.

3. Subtraction Assignment Operator (-=)

The subtraction assignment operator is used to subtract a value from a variable and store the result in the same variable. The syntax for the subtraction assignment operator is

Here, the value on the right-hand side is subtracted from the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is decremented by 5 using the subtraction assignment operator. The result, 5, is then printed to the console.

4. Multiplication Assignment Operator (*=)

The multiplication assignment operator is used to multiply a variable by a value and store the result in the same variable. The syntax for the multiplication assignment operator is:

Here, the value on the right-hand side is multiplied by the variable on the left-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is multiplied by 5 using the multiplication assignment operator. The result, 50, is then printed to the console.

5. Division Assignment Operator (/=)

The division assignment operator is used to divide a variable by a value and store the result in the same variable. The syntax for the division assignment operator is:

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 5 using the division assignment operator. The result, 2.0, is then printed to the console.

6. Modulus Assignment Operator (%=)

The modulus assignment operator is used to find the remainder of the division of a variable by a value and store the result in the same variable. The syntax for the modulus assignment operator is

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the remainder is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 3 using the modulus assignment operator. The remainder, 1, is then printed to the console.

7. Floor Division Assignment Operator (//=)

The floor division assignment operator is used to divide a variable by a value and round the result down to the nearest integer, and store the result in the same variable. The syntax for the floor division assignment operator is:

Here, the variable on the left-hand side is divided by the value on the right-hand side, and the result is rounded down to the nearest integer. The rounded result is then stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is divided by 3 using the floor division assignment operator. The result, 3, is then printed to the console.

8. Exponentiation Assignment Operator (**=)

The exponentiation assignment operator is used to raise a variable to the power of a value and store the result in the same variable. The syntax for the exponentiation assignment operator is:

Here, the variable on the left-hand side is raised to the power of the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example

Explanation: In this case, the value of a is raised to the power of 3 using the exponentiation assignment operator. The result, 8, is then printed to the console.

9. Bitwise AND Assignment Operator (&=)

The bitwise AND assignment operator is used to perform a bitwise AND operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise AND assignment operator is:

Here, the variable on the left-hand side is ANDed with the value on the right-hand side using the bitwise AND operator, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is ANDed with 3 using the bitwise AND assignment operator. The result, 2, is then printed to the console.

10. Bitwise OR Assignment Operator (|=)

The bitwise OR assignment operator is used to perform a bitwise OR operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise OR assignment operator is:

Here, the variable on the left-hand side is ORed with the value on the right-hand side using the bitwise OR operator, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is ORed with 3 using the bitwise OR assignment operator. The result, 7, is then printed to the console.

11. Bitwise XOR Assignment Operator (^=)

The bitwise XOR assignment operator is used to perform a bitwise XOR operation on the binary representation of a variable and a value, and store the result in the same variable. The syntax for the bitwise XOR assignment operator is:

Here, the variable on the left-hand side is XORed with the value on the right-hand side using the bitwise XOR operator, and the result are stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is XORed with 3 using the bitwise XOR assignment operator. The result, 5, is then printed to the console.

12. Bitwise Right Shift Assignment Operator (>>=)

The bitwise right shift assignment operator is used to shift the bits of a variable to the right by a specified number of positions, and store the result in the same variable. The syntax for the bitwise right shift assignment operator is:

Here, the variable on the left-hand side has its bits shifted to the right by the number of positions specified by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example,

Explanation: In this case, the value of a is shifted 2 positions to the right using the bitwise right shift assignment operator. The result, 2, is then printed to the console.

13. Bitwise Left Shift Assignment Operator (<<=)

The bitwise left shift assignment operator is used to shift the bits of a variable to the left by a specified number of positions, and store the result in the same variable. The syntax for the bitwise left shift assignment operator is:

Here, the variable on the left-hand side has its bits shifted to the left by the number of positions specified by the value on the right-hand side, and the result is stored back in the variable on the left-hand side. For example,

Conclusion Assignment operator in Python is used to assign values to variables, and it comes in different types. The simple assignment operator (=) assigns a value to a variable. The augmented assignment operators (+=, -=, *=, /=, %=, &=, |=, ^=, >>=, <<=) perform a specified operation and assign the result to the same variable in one step. The modulus assignment operator (%) calculates the remainder of a division operation and assigns the result to the same variable. The bitwise assignment operators (&=, |=, ^=, >>=, <<=) perform bitwise operations and assign the result to the same variable. The bitwise right shift assignment operator (>>=) shifts the bits of a variable to the right by a specified number of positions and stores the result in the same variable. The bitwise left shift assignment operator (<<=) shifts the bits of a variable to the left by a specified number of positions and stores the result in the same variable. These operators are useful in simplifying and shortening code that involves assigning and manipulating values in a single step.

Here are some Frequently Asked Questions on Assignment Operator in Python:

Q1 – Can I use the assignment operator to assign multiple values to multiple variables at once? Ans – Yes, you can use the assignment operator to assign multiple values to multiple variables at once, separated by commas. For example, "x, y, z = 1, 2, 3" would assign the value 1 to x, 2 to y, and 3 to z.

Q2 – Is it possible to chain assignment operators in Python? Ans – Yes, you can chain assignment operators in Python to perform multiple operations in one line of code. For example, "x = y = z = 1" would assign the value 1 to all three variables.

Q3 – How do I perform a conditional assignment in Python? Ans – To perform a conditional assignment in Python, you can use the ternary operator. For example, "x = a (if a > b) else b" would assign the value of a to x if a is greater than b, otherwise it would assign the value of b to x.

Q4 – What happens if I use an undefined variable in an assignment operation in Python? Ans – If you use an undefined variable in an assignment operation in Python, you will get a NameError. Make sure you have defined the variable before trying to assign a value to it.

Q5 – Can I use assignment operators with non-numeric data types in Python? Ans – Yes, you can use assignment operators with non-numeric data types in Python, such as strings or lists. For example, "my_list += [4, 5, 6]" would append the values 4, 5, and 6 to the end of the list named my_list.

Python Assignment Operators

Lesson Contents

Python assignment operators are one of the operator types and assign values to variables . We use arithmetic operators here in combination with a variable.

Let’s take a look at some examples.

Operator Assignment (=)

This is the most basic assignment operator and we used it before in the lessons about lists , tuples , and dictionaries . For example, we can assign a value (integer) to a variable:

Operator Addition (+=)

We can add a number to our variable like this:

Using the above operator is the same as doing this:

The += operator is shorter to write but the end result is the same.

Operator Subtraction (-=)

We can also subtract a value. For example:

Using this operator is the same as doing this:

Operator Multiplication (*=)

We can also use multiplication. We’ll multiply our variable by 4:

Which is similar to:

Operator Division (/=)

Let’s try the divide operator:

This is the same as:

Operator Modulus (%=)

We can also calculate the modulus. How about this:

This is the same as doing it like this:

Operator Exponentiation (**=)

How about exponentiation? Let’s give it a try:

Which is the same as doing it like this:

Operator Floor Division (//=)

The last one, floor division:

You have now learned how to use the Python assignment operators to assign values to variables and how you can use them with arithmetic operators . I hope you enjoyed this lesson. If you have any questions, please leave a comment.

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Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise

Operators are special symbols that perform some operation on operands and returns the result. For example, 5 + 6 is an expression where + is an operator that performs arithmetic add operation on numeric left operand 5 and the right side operand 6 and returns a sum of two operands as a result.

Python includes the operator module that includes underlying methods for each operator. For example, the + operator calls the operator.add(a,b) method.

Above, expression 5 + 6 is equivalent to the expression operator.add(5, 6) and operator.__add__(5, 6) . Many function names are those used for special methods, without the double underscores (dunder methods). For backward compatibility, many of these have functions with the double underscores kept.

Python includes the following categories of operators:

Arithmetic Operators

Assignment operators, comparison operators, logical operators, identity operators, membership test operators, bitwise operators.

Arithmetic operators perform the common mathematical operation on the numeric operands.

The arithmetic operators return the type of result depends on the type of operands, as below.

If either operand is a complex number, the result is converted to complex;
If either operand is a floating point number, the result is converted to floating point;
If both operands are integers, then the result is an integer and no conversion is needed.

The following table lists all the arithmetic operators in Python:

The assignment operators are used to assign values to variables. The following table lists all the arithmetic operators in Python:

The comparison operators compare two operands and return a boolean either True or False. The following table lists comparison operators in Python.

The logical operators are used to combine two boolean expressions. The logical operations are generally applicable to all objects, and support truth tests, identity tests, and boolean operations.

The identity operators check whether the two objects have the same id value e.i. both the objects point to the same memory location.

The membership test operators in and not in test whether the sequence has a given item or not. For the string and bytes types, x in y is True if and only if x is a substring of y .

Bitwise operators perform operations on binary operands.

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Assignment Operators in Python

In this article, we will learn about all the “ Assignment Operators in Python ” with examples of each. Assignment operators in Python are essential tools for manipulating and assigning values to variables. These operators not only let you store data in variables but also perform various operations simultaneously. Understanding these operators is crucial for efficient Python programming, as they streamline code while making it more readable and maintainable. If you encounter any challenges in grasping these operators, don’t hesitate to seek Python help from the experts at CodingZap.

What Are Operators and Operands?

Operators can be understood as the mathematical symbols we have seen since childhood, like the +, -, *, / =. They are used to perform mathematical, logical or bitwise operations in a programming language.

Operands are the values or variables on which operators act. For example, “+” will add the values of two operands, and similarly “*” will multiply the values of the 2 operands.

In the expression “a+b”, “+” is the operator and “a” and “b” are the operands.

What Is Assignment Operators in Python?

Assignment operators are a fundamental concept in Python and most programming languages. They are used to assign values to variables, as well as initialize and update them. They assign the value on the right-hand side to the variable on the left.

In Python, the primary assignment operator is the “Simple assignment operator (=)”, which as the name suggests, assigns values to variables. However, Python offers a rich set of assignment operators that provide more functionality than simple assignments. These operators not only assign values but also perform specific operations at the same time, such as addition, subtraction, multiplication, division, bitwise operations, and more.

What Are the Basics of Assignments?

First, we’ll clarify some basic rules about the assignments in Python. For this, we’ll use the simple assignment operator “=”.

A sample program using “=” is as follows:

In the above example, the value on the right-hand side is assigned to the variable on the left. We check the value assigned by printing the variable and get the expected result, i.e. 5.

In the example above, the statement a = 5 is called an assignment statement, which assigns values to the variables.

The syntax for an assignment statement is as follows:

As we can see, it is composed of three components:

The left operand of an assignment statement must always and only be a variable.
Then comes the assignment operator itself.
The right operand can be a value, an expression or an object.

Now that we have this rule clear let us look at the assignment operators in Python. The assignment operators allow us to create, initialise and update the variables.

The simple assignment operator “=”

It is the primary assignment operator

Let’s look at some examples to understand the uses of the simple assignment operator:

As we can see in the above code, the simple assignment operator is used to create every variable type in Python, be it a primitive integer, string, or complex data structure. Strings are also an interesting concept in Python, if you’re interested to know how to compare strings in Python then you can check out our article.

Updating values

The assignment operators are also used to update variable values in Python. An example is as follows:

In the above example, we initialized our integer and list with initial values and printed them for reference. Then, we modified these values and printed them again to verify the changes.

Multiple and parallel assignments

Python provides additional functionality to perform multiple and parallel assignments to variables in a single line. This reduces lines of code and complexity. Let’s understand them with an example:

In the above code, we demonstrate multiple and parallel assignments.

We can assign the same literal value to multiple variables in multiple assignments. In the above code, the integer value 45 is assigned to all the variables a,b,c. The data type of all the variables in a multiple-assignment statement must be the same.

In parallel assignments, we can assign different values to different variables using comma-separated variables and values on either side of the assignment operator. The values are stored in the order of writing the pairs. The variable types need not be the same in parallel assignments.

Augmented assignment operators in Python

The simple assignment operator is used to assign values to variables in Python. However, Python also supports complex assignments using which we can calculate various values and assign them to the variable in a single line.

The basic syntax for augmented assignment operators is as follows:

The ‘$’ in the above syntax can be replaced by various operators to perform operations on operands before assigning the final value to the variable.

Python equates the above syntax to the following expression:

We’ll understand these operators better if we directly look at their examples.

Let’s have a look at all the augmented assignment operators in Python one at a time:

Add assignment operator “+=”

This operator adds the left and the right operands and assigns the resulting value to the variable on the left.

An example would be

Which will equate to

It is important to note that only the value of “a” will change, whereas the value of “b” will remain the same. This is because the sum of “a” and “b” is finally stored in the variable “a”.

Let’s look at the code and its output:

In the above code, we initialized 2 variables and added the value of the second variable to the first one using the add assignment operator. Note that the value of “a” changes, but the value of “b” remains the same.

This operator can also be used on some data structures like lists and tuples, using which we can append values at the end of the list, as can be seen in the code above.

Subtract assignment operator

This operator is used to subtract the right operand from the left and assign the resulting value to the variable on the left.

A sample code is as follows:

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. Then, we subtracted the value of “b” from “a” using the subtract assignment operator. The final expression equated to “a=a-b”. At last, we printed the final values of “a” and “b”.

Multiplication assignment operator

The multiplication assignment operator is used to multiply the right operand with the left and assign the resulting value to the variable on the left.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. Then, we multiplied the values of “a” and “b” using the multiplication assignment operator. The final expression equated to “a=a*b”. At last, we printed the final values of “a” and “b” to check the resulting values.

Division assignment operator

The division assignment operator divides the left operand from the right and assigns the resulting value to the operand on the left.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. Then, we divided “a” by “b” using the division assignment operator. The final expression equated to “a=a/b”. At last, we printed the final values of “a” and “b”.

Floor assignment operator

The floor assignment operator divides the operand on the left from the right and rounds the value to the greatest integer less than or equal to the resultant value. This value is then stored in the operand on the left.

Sample code is as follows:

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. Then, we divided “a” from “b” using the floor assignment operator. This operator also divides the variables in the same way as the division assignment operator but rounds off the answer to the greatest integer less than or equal to the answer. In this case, the value “16.66” was rounded off to “16”.

At last, we printed the final values of “a” and “b”.

Modulus assignment operator

The modulus assignment operator is used to extract the remainder after dividing the operand on the left from the right. The remainder is then stored in the operand on the left.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the modulus assignment operator to get the remainder of the division “a/b”, and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

Exponentiation assignment operator

This operator is used to calculate the exponent of the left-side operand raised to the power of the right-side operand, which is then stored in the operand on the left.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the exponentiation assignment operator to get the value of “a” raised to the power of “b”, and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

Bitwise AND assignment operator

This operator calculates the Bitwise AND value of the operands on the left and right and stores the value in the left operand.

Sample code:

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the bitwise AND assignment operator to get the bitwise AND of “a” and “b”, and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

Bitwise OR assignment operator

This operator calculates the Bitwise OR value of the operands on the left and right and stores the value in the left operand.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the bitwise OR assignment operator to get the bitwise

OR of “a” and “b”, and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

Bitwise XOR assignment operator

This operator calculates the XOR of the 2 operands and stores the value in the left operand.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the bitwise XOR assignment operator to get the bitwise XOR of “a” and “b” and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

Bitwise Left Shift assignment operator

This operator left-shifts the bits of the operand on the left by the units as declared by the operand on the right and stores the value in the left operand.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the bitwise left shift assignment operator to left shift the bits of “a” by “b” units and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

Bitwise Right Shift assignment operator

This operator right-shifts the bits of the operand on the left by the units as declared by the operand on the right, and stores the value in the left operand.

In the above code, we initialized the 2 variables “a” and “b” and printed their values for reference. We then used the bitwise right shift assignment operator to right shift the bits of “a” by “b” units, and store the resulting value in “a”. At last, we printed the final values of “a” and “b”.

In this article, we learned about the assignment operators in Python. The primary assignment operator is the simple assignment operator (“=”).

We then learned that Python supports more complex assignment operators that can be used to calculate values in place.

The assignment operators can be used to calculate mathematical, logical, and bitwise operations.

We also saw a consistent property of all the assignment operators, in which they calculated the expression on the right and assigned the resulting value to the variable on the left.

There are many reasons why students look for assignment help online like difficulty in understanding the assignment, time limitation, etc. So, if you’re also looking then you can always hire CodinngZap experts.

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Python Assignment Operator

Introduction

Chained Assignment

Shorthand assignment, shorthand assignment operators, playground: assignment operator practice, assignment methods.

All assignment operators are used to assign values to variables. Wait, is there more than one assignment operator? Yes, but they're all quite similar to the ones you've seen. You've used the most common assignment operator, and its symbol is a single equals sign ( = ).

For example, to assign x the value of 10 you type the following:

Different Assignment Methods

You have used this assignment statement before to assign values to variables. Apart from this very common way of using it, a few other situations use the same symbol for slightly different assignments.

You can assign the same value to multiple variables in one swoop by using an assignment chain:

This construct assigns 10 to x , y , and z . Using the chained assignment statement in Python is rare, but if you see it around, now you know what that's about.

Shorthand assignments, on the other hand, are a common occurrence in Python code. This is where the other assignment operators come into play. Shorthand assignments make writing code more efficient and can improve readability---at least once you know about them!

For example, think of a situation where you have a variable x and you want to add 1 to that variable:

This works well and is perfectly fine Python code. However, there is a more concise way of writing the same code using shorthand assignment :

Check out how the second line in these two code snippets is different. You don't need to write the name of the variable x a second time using the shorthand operator += like in the example above.

Both code examples shown achieve the exact same result and are equivalent. The shorthand assignment allows you to use less code to complete the task.

Python comes with a couple of shorthand assignment operators. Some of the most common ones include the following:

These operators are combinations of familiar arithmetic operators with the assignment operator ( = ). You have already used some of Python's arithmetic operators, and you'll learn more about them in the upcoming lesson.

Play around and combine different operators you can think of with the assignment operator below.

Which ones work and do what you expect them to?
Which ones don't?

Summary: Python Assignment Operator

Assignment operators are used to assign values to variables
Shorthand assignment is the most commonly used in Python
The table summarizing the assignment operators is provided in the lesson
Chain Assignment : A method used to assign multiple variables at one
Shorthand Assignment : A series of short forms for manipulating data

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If that is about as clear as mud don't worry we're going to walk through a number of examples. And one very nice thing about the syntax for assignment operators is that it is nearly identical to a standard type of operator. So if you memorize the list of all the python operators then you're going to be able to use each one of these assignment operators quite easily.

The very first thing I'm going to do is let's first make sure that we can print out the total. So right here we have a total and it's an integer that equals 100. Now if we wanted to add say 10 to 100 how would we go about doing that? We could reassign the value total and we could say total and then just add 10. So let's see if this works right here. I'm going to run it and you can see we have a hundred and ten. So that works.

However, whenever you find yourself performing this type of calculation what you can do is use an assignment operator. And so the syntax for that is going to get rid of everything here in the middle and say plus equals and then whatever value. In this case I want to add onto it.

So you can see we have our operator and then right afterward you have an equal sign. And this is going to do is exactly like what we had before. So if I run this again you can see total is a hundred and ten

I'm going to just so you have a reference in the show notes I'm going to say that total equals total plus 10. This is exactly the same as what we're doing right here we're simply using assignment in order to do it.

I'm going to quickly go through each one of the other elements that you can use assignment for. And if you go back and you reference the show notes or your own notes for whenever you kept track of all of the different operators you're going to notice a trend. And that is because they're all exactly the same. So here if I want to subtract 10 from the total I can simply use the subtraction operator here run it again. And now you can see we have 90. Now don't be confused because we only temporarily change the value to 1 10. So when I commented this out and I ran it from scratch it took the total and it subtracted 10 from that total and that's what got printed out.

I'm going to copy this and the next one down the line is going to be multiplication. So in this case I'm going to say multiply with the asterisk the total and I'm just going to say times two just so we can see exactly what the value is going to be. And now we can see that's 200 which makes sense.

So we've taken total we have multiplied it by two and we have piped the entire thing into the total variable. So far so good. As you may have guessed next when we're going to do is division. So now I'm going to say total and then we're going to perform this division assignment and we're going to say divide this by 10 run it and you can see it gives us the value and it converts it to a float of ten point zero.

Now if this is starting to get a little bit much. Let's take a quick pause and see exactly what this is doing. Remember that all we're doing here is it's a shortcut. You could still perform it the same way we have in number 3 I could say total is equal to the total divided by 10. And if I run this you'll see we get ten point zero. And let's see what this warning is it says redefinition of total type from int to float. So we don't have to worry about this and this for if you're building Python programs you're very rarely ever going to see the syntax and it's because we have this assignment operator right here. So that is for division. And we also have the ability to use floor division as well. So if I run this you're going to see it's 10. But one thing you may notice is it's 10 it's not ten point zero. So remember that our floor division returns an integer it doesn't return a floating-point number. So if that is what you want then you can perform that task just like we did there.

Next one on the list is our exponents. I'm going to say the total and we're going to say we're going to assign that to the total squared. So going to run this and we get ten thousand. Just like you'd expect. And we have one more which is the modulus operator. So here remember it is the percent equals 2. And this is going to return zero because 100 is even if we changed 100 to be 101. This is going to return one because remember the typical purpose of the modulus operator is to let you know if you're working with an event or an odd value.

Now with all this being said, I wanted to show you every different option that you could use the assignment operator on. But I want to say that the most common way that you're going to use this or the most common one is going to be this one right here where we're adding or subtracting. So those are going to be the two most common. And what usually you're going to use that for is when you're incrementing or decrementing values so a very common way to do this would actually be like we have our total right here. So we have a total of 100 and you could imagine it being a shopping cart and it's 100 dollars and you could say product 2 and set this equal to 120. And then if I say product 3 and set this equal to 10. And so what I could do here is I could say total plus equals product to and then we could take the value and say product 3 and now if I run this you can see the value is 230.

So that's a very common way whenever you want to generate a sum you can use this type of syntax which is much faster and it's also going to be a more pythonic way it's going to be the way you're going to see in standard Python programs whenever you're wanting to generate a sum and then reset and reassign the value.

So in review, that is how you can use assignment operators in Python.

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Operators are used to perform operations on variables and values.

In the example below, we use the + operator to add together two values:

Python divides the operators in the following groups:

Arithmetic operators
Assignment operators
Comparison operators
Logical operators
Identity operators
Membership operators
Bitwise operators

Python Arithmetic Operators

Arithmetic operators are used with numeric values to perform common mathematical operations:

Python Assignment Operators

Assignment operators are used to assign values to variables:

Python Comparison Operators

Comparison operators are used to compare two values:

Python Logical Operators

Logical operators are used to combine conditional statements:

Python Identity Operators

Identity operators are used to compare the objects, not if they are equal, but if they are actually the same object, with the same memory location:

Python Membership Operators

Membership operators are used to test if a sequence is presented in an object:

Python Bitwise Operators

Bitwise operators are used to compare (binary) numbers:

Operator Precedence

Operator precedence describes the order in which operations are performed.

Parentheses has the highest precedence, meaning that expressions inside parentheses must be evaluated first:

Multiplication * has higher precedence than addition + , and therefor multiplications are evaluated before additions:

The precedence order is described in the table below, starting with the highest precedence at the top:

If two operators have the same precedence, the expression is evaluated from left to right.

Addition + and subtraction - has the same precedence, and therefor we evaluate the expression from left to right:

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Precedence of Python Operators

The combination of values, variables , operators , and function calls is termed as an expression. The Python interpreter can evaluate a valid expression.

For example:

Here 5 - 7 is an expression. There can be more than one operator in an expression.

To evaluate these types of expressions there is a rule of precedence in Python. It guides the order in which these operations are carried out.

For example, multiplication has higher precedence than subtraction.

But we can change this order using parentheses () as it has higher precedence than multiplication.

The operator precedence in Python is listed in the following table. It is in descending order (upper group has higher precedence than the lower ones).

Let's look at some examples:

Suppose we're constructing an if...else block which runs if when lunch is either fruit or sandwich and only if money is more than or equal to 2 .

This program runs if block even when money is 0 . It does not give us the desired output since the precedence of and is higher than or .

We can get the desired output by using parenthesis () in the following way:

Associativity of Python Operators

We can see in the above table that more than one operator exists in the same group. These operators have the same precedence.

When two operators have the same precedence, associativity helps to determine the order of operations.

Associativity is the order in which an expression is evaluated that has multiple operators of the same precedence. Almost all the operators have left-to-right associativity.

For example, multiplication and floor division have the same precedence. Hence, if both of them are present in an expression, the left one is evaluated first.

Note : Exponent operator ** has right-to-left associativity in Python.

We can see that 2 ** 3 ** 2 is equivalent to 2 ** (3 ** 2) .

Non associative operators

Some operators like assignment operators and comparison operators do not have associativity in Python. There are separate rules for sequences of this kind of operator and cannot be expressed as associativity.

For example, x < y < z neither means (x < y) < z nor x < (y < z) . x < y < z is equivalent to x < y and y < z , and is evaluated from left-to-right.

Furthermore, while chaining of assignments like x = y = z = 1 is perfectly valid, x = y = z+= 2 will result in error.

Sorry about that.

3. Data model ¶

3.1. objects, values and types ¶.

Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.)

Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The is operator compares the identity of two objects; the id() function returns an integer representing its identity.

CPython implementation detail: For CPython, id(x) is the memory address where x is stored.

An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The type() function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable. [ 1 ]

The value of some objects can change. Objects whose value can change are said to be mutable ; objects whose value is unchangeable once they are created are called immutable . (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.

Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable.

CPython implementation detail: CPython currently uses a reference-counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the gc module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (so you should always close files explicitly).

Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a try … except statement may keep objects alive.

Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a close() method. Programs are strongly recommended to explicitly close such objects. The try … finally statement and the with statement provide convenient ways to do this.

Some objects contain references to other objects; these are called containers . Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.

Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1 , a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = [] , c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d .)

3.2. The standard type hierarchy ¶

Below is a list of the types that are built into Python. Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.

Some of the type descriptions below contain a paragraph listing ‘special attributes.’ These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future.

3.2.1. None ¶

This type has a single value. There is a single object with this value. This object is accessed through the built-in name None . It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don’t explicitly return anything. Its truth value is false.

3.2.2. NotImplemented ¶

This type has a single value. There is a single object with this value. This object is accessed through the built-in name NotImplemented . Numeric methods and rich comparison methods should return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) It should not be evaluated in a boolean context.

See Implementing the arithmetic operations for more details.

Changed in version 3.9: Evaluating NotImplemented in a boolean context is deprecated. While it currently evaluates as true, it will emit a DeprecationWarning . It will raise a TypeError in a future version of Python.

Changed in version 3.14: Evaluating NotImplemented in a boolean context now raises a TypeError .

3.2.3. Ellipsis ¶

This type has a single value. There is a single object with this value. This object is accessed through the literal ... or the built-in name Ellipsis . Its truth value is true.

3.2.4. numbers.Number ¶

These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers.

The string representations of the numeric classes, computed by __repr__() and __str__() , have the following properties:

They are valid numeric literals which, when passed to their class constructor, produce an object having the value of the original numeric.

The representation is in base 10, when possible.

Leading zeros, possibly excepting a single zero before a decimal point, are not shown.

Trailing zeros, possibly excepting a single zero after a decimal point, are not shown.

A sign is shown only when the number is negative.

Python distinguishes between integers, floating point numbers, and complex numbers:

3.2.4.1. numbers.Integral ¶

These represent elements from the mathematical set of integers (positive and negative).

The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers.

There are two types of integers:

These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2’s complement which gives the illusion of an infinite string of sign bits extending to the left.

These represent the truth values False and True. The two objects representing the values False and True are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings "False" or "True" are returned, respectively.

3.2.4.2. numbers.Real ( float ) ¶

These represent machine-level double precision floating point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in processor and memory usage that are usually the reason for using these are dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating point numbers.

3.2.4.3. numbers.Complex ( complex ) ¶

These represent complex numbers as a pair of machine-level double precision floating point numbers. The same caveats apply as for floating point numbers. The real and imaginary parts of a complex number z can be retrieved through the read-only attributes z.real and z.imag .

3.2.5. Sequences ¶

These represent finite ordered sets indexed by non-negative numbers. The built-in function len() returns the number of items of a sequence. When the length of a sequence is n , the index set contains the numbers 0, 1, …, n -1. Item i of sequence a is selected by a[i] . Some sequences, including built-in sequences, interpret negative subscripts by adding the sequence length. For example, a[-2] equals a[n-2] , the second to last item of sequence a with length n .

Sequences also support slicing: a[i:j] selects all items with index k such that i <= k < j . When used as an expression, a slice is a sequence of the same type. The comment above about negative indexes also applies to negative slice positions.

Some sequences also support “extended slicing” with a third “step” parameter: a[i:j:k] selects all items of a with index x where x = i + n*k , n >= 0 and i <= x < j .

Sequences are distinguished according to their mutability:

3.2.5.1. Immutable sequences ¶

An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.)

The following types are immutable sequences:

A string is a sequence of values that represent Unicode code points. All the code points in the range U+0000 - U+10FFFF can be represented in a string. Python doesn’t have a char type; instead, every code point in the string is represented as a string object with length 1 . The built-in function ord() converts a code point from its string form to an integer in the range 0 - 10FFFF ; chr() converts an integer in the range 0 - 10FFFF to the corresponding length 1 string object. str.encode() can be used to convert a str to bytes using the given text encoding, and bytes.decode() can be used to achieve the opposite.

The items of a tuple are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a ‘singleton’) can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses.

A bytes object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (like b'abc' ) and the built-in bytes() constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via the decode() method.

3.2.5.2. Mutable sequences ¶

Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and del (delete) statements.

The collections and array module provide additional examples of mutable sequence types.

There are currently two intrinsic mutable sequence types:

The items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.)

A bytearray object is a mutable array. They are created by the built-in bytearray() constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutable bytes objects.

3.2.6. Set types ¶

These represent unordered, finite sets of unique, immutable objects. As such, they cannot be indexed by any subscript. However, they can be iterated over, and the built-in function len() returns the number of items in a set. Common uses for sets are fast membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.

For set elements, the same immutability rules apply as for dictionary keys. Note that numeric types obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0 ), only one of them can be contained in a set.

There are currently two intrinsic set types:

These represent a mutable set. They are created by the built-in set() constructor and can be modified afterwards by several methods, such as add() .

These represent an immutable set. They are created by the built-in frozenset() constructor. As a frozenset is immutable and hashable , it can be used again as an element of another set, or as a dictionary key.

3.2.7. Mappings ¶

These represent finite sets of objects indexed by arbitrary index sets. The subscript notation a[k] selects the item indexed by k from the mapping a ; this can be used in expressions and as the target of assignments or del statements. The built-in function len() returns the number of items in a mapping.

There is currently a single intrinsic mapping type:

3.2.7.1. Dictionaries ¶

These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key’s hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0 ) then they can be used interchangeably to index the same dictionary entry.

Dictionaries preserve insertion order, meaning that keys will be produced in the same order they were added sequentially over the dictionary. Replacing an existing key does not change the order, however removing a key and re-inserting it will add it to the end instead of keeping its old place.

Dictionaries are mutable; they can be created by the {...} notation (see section Dictionary displays ).

The extension modules dbm.ndbm and dbm.gnu provide additional examples of mapping types, as does the collections module.

Changed in version 3.7: Dictionaries did not preserve insertion order in versions of Python before 3.6. In CPython 3.6, insertion order was preserved, but it was considered an implementation detail at that time rather than a language guarantee.

3.2.8. Callable types ¶

These are the types to which the function call operation (see section Calls ) can be applied:

3.2.8.1. User-defined functions ¶

A user-defined function object is created by a function definition (see section Function definitions ). It should be called with an argument list containing the same number of items as the function’s formal parameter list.

3.2.8.1.1. Special read-only attributes ¶

3.2.8.1.2. special writable attributes ¶.

Most of these attributes check the type of the assigned value:

Function objects also support getting and setting arbitrary attributes, which can be used, for example, to attach metadata to functions. Regular attribute dot-notation is used to get and set such attributes.

CPython implementation detail: CPython’s current implementation only supports function attributes on user-defined functions. Function attributes on built-in functions may be supported in the future.

Additional information about a function’s definition can be retrieved from its code object (accessible via the __code__ attribute).

3.2.8.2. Instance methods ¶

An instance method object combines a class, a class instance and any callable object (normally a user-defined function).

Special read-only attributes:

Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object .

User-defined method objects may be created when getting an attribute of a class (perhaps via an instance of that class), if that attribute is a user-defined function object or a classmethod object.

When an instance method object is created by retrieving a user-defined function object from a class via one of its instances, its __self__ attribute is the instance, and the method object is said to be bound . The new method’s __func__ attribute is the original function object.

When an instance method object is created by retrieving a classmethod object from a class or instance, its __self__ attribute is the class itself, and its __func__ attribute is the function object underlying the class method.

When an instance method object is called, the underlying function ( __func__ ) is called, inserting the class instance ( __self__ ) in front of the argument list. For instance, when C is a class which contains a definition for a function f() , and x is an instance of C , calling x.f(1) is equivalent to calling C.f(x, 1) .

When an instance method object is derived from a classmethod object, the “class instance” stored in __self__ will actually be the class itself, so that calling either x.f(1) or C.f(1) is equivalent to calling f(C,1) where f is the underlying function.

Note that the transformation from function object to instance method object happens each time the attribute is retrieved from the instance. In some cases, a fruitful optimization is to assign the attribute to a local variable and call that local variable. Also notice that this transformation only happens for user-defined functions; other callable objects (and all non-callable objects) are retrieved without transformation. It is also important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this only happens when the function is an attribute of the class.

3.2.8.3. Generator functions ¶

A function or method which uses the yield statement (see section The yield statement ) is called a generator function . Such a function, when called, always returns an iterator object which can be used to execute the body of the function: calling the iterator’s iterator.__next__() method will cause the function to execute until it provides a value using the yield statement. When the function executes a return statement or falls off the end, a StopIteration exception is raised and the iterator will have reached the end of the set of values to be returned.

3.2.8.4. Coroutine functions ¶

A function or method which is defined using async def is called a coroutine function . Such a function, when called, returns a coroutine object. It may contain await expressions, as well as async with and async for statements. See also the Coroutine Objects section.

3.2.8.5. Asynchronous generator functions ¶

A function or method which is defined using async def and which uses the yield statement is called a asynchronous generator function . Such a function, when called, returns an asynchronous iterator object which can be used in an async for statement to execute the body of the function.

Calling the asynchronous iterator’s aiterator.__anext__ method will return an awaitable which when awaited will execute until it provides a value using the yield expression. When the function executes an empty return statement or falls off the end, a StopAsyncIteration exception is raised and the asynchronous iterator will have reached the end of the set of values to be yielded.

3.2.8.6. Built-in functions ¶

A built-in function object is a wrapper around a C function. Examples of built-in functions are len() and math.sin() ( math is a standard built-in module). The number and type of the arguments are determined by the C function. Special read-only attributes:

__doc__ is the function’s documentation string, or None if unavailable. See function.__doc__ .

__name__ is the function’s name. See function.__name__ .

__self__ is set to None (but see the next item).

__module__ is the name of the module the function was defined in or None if unavailable. See function.__module__ .

3.2.8.7. Built-in methods ¶

This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is alist.append() , assuming alist is a list object. In this case, the special read-only attribute __self__ is set to the object denoted by alist . (The attribute has the same semantics as it does with other instance methods .)

3.2.8.8. Classes ¶

Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override __new__() . The arguments of the call are passed to __new__() and, in the typical case, to __init__() to initialize the new instance.

3.2.8.9. Class Instances ¶

Instances of arbitrary classes can be made callable by defining a __call__() method in their class.

3.2.9. Modules ¶

Modules are a basic organizational unit of Python code, and are created by the import system as invoked either by the import statement, or by calling functions such as importlib.import_module() and built-in __import__() . A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the __globals__ attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., m.x is equivalent to m.__dict__["x"] . A module object does not contain the code object used to initialize the module (since it isn’t needed once the initialization is done).

Attribute assignment updates the module’s namespace dictionary, e.g., m.x = 1 is equivalent to m.__dict__["x"] = 1 .

Predefined (writable) attributes:

__name__ The module’s name. __doc__ The module’s documentation string, or None if unavailable. __file__ The pathname of the file from which the module was loaded, if it was loaded from a file. The __file__ attribute may be missing for certain types of modules, such as C modules that are statically linked into the interpreter. For extension modules loaded dynamically from a shared library, it’s the pathname of the shared library file. __annotations__ A dictionary containing variable annotations collected during module body execution. For best practices on working with __annotations__ , please see Annotations Best Practices .

Special read-only attribute: __dict__ is the module’s namespace as a dictionary object.

CPython implementation detail: Because of the way CPython clears module dictionaries, the module dictionary will be cleared when the module falls out of scope even if the dictionary still has live references. To avoid this, copy the dictionary or keep the module around while using its dictionary directly.

3.2.10. Custom classes ¶

Custom class types are typically created by class definitions (see section Class definitions ). A class has a namespace implemented by a dictionary object. Class attribute references are translated to lookups in this dictionary, e.g., C.x is translated to C.__dict__["x"] (although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor. Additional details on the C3 MRO used by Python can be found at The Python 2.3 Method Resolution Order .

When a class attribute reference (for class C , say) would yield a class method object, it is transformed into an instance method object whose __self__ attribute is C . When it would yield a staticmethod object, it is transformed into the object wrapped by the static method object. See section Implementing Descriptors for another way in which attributes retrieved from a class may differ from those actually contained in its __dict__ .

Class attribute assignments update the class’s dictionary, never the dictionary of a base class.

A class object can be called (see above) to yield a class instance (see below).

Special attributes:

__name__ The class name. __module__ The name of the module in which the class was defined. __dict__ The dictionary containing the class’s namespace. __bases__ A tuple containing the base classes, in the order of their occurrence in the base class list. __doc__ The class’s documentation string, or None if undefined. __annotations__ A dictionary containing variable annotations collected during class body execution. For best practices on working with __annotations__ , please see Annotations Best Practices . __type_params__ A tuple containing the type parameters of a generic class . __static_attributes__ A tuple containing names of attributes of this class which are accessed through self.X from any function in its body. __firstlineno__ The line number of the first line of the class definition, including decorators.

3.2.11. Class instances ¶

A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance’s class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose __self__ attribute is the instance. Static method and class method objects are also transformed; see above under “Classes”. See section Implementing Descriptors for another way in which attributes of a class retrieved via its instances may differ from the objects actually stored in the class’s __dict__ . If no class attribute is found, and the object’s class has a __getattr__() method, that is called to satisfy the lookup.

Attribute assignments and deletions update the instance’s dictionary, never a class’s dictionary. If the class has a __setattr__() or __delattr__() method, this is called instead of updating the instance dictionary directly.

Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section Special method names .

Special attributes: __dict__ is the attribute dictionary; __class__ is the instance’s class.

3.2.12. I/O objects (also known as file objects) ¶

A file object represents an open file. Various shortcuts are available to create file objects: the open() built-in function, and also os.popen() , os.fdopen() , and the makefile() method of socket objects (and perhaps by other functions or methods provided by extension modules).

The objects sys.stdin , sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter’s standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the io.TextIOBase abstract class.

3.2.13. Internal types ¶

A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness.

3.2.13.1. Code objects ¶

Code objects represent byte-compiled executable Python code, or bytecode . The difference between a code object and a function object is that the function object contains an explicit reference to the function’s globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects.

3.2.13.1.1. Special read-only attributes ¶

The following flag bits are defined for co_flags : bit 0x04 is set if the function uses the *arguments syntax to accept an arbitrary number of positional arguments; bit 0x08 is set if the function uses the **keywords syntax to accept arbitrary keyword arguments; bit 0x20 is set if the function is a generator. See Code Objects Bit Flags for details on the semantics of each flags that might be present.

Future feature declarations ( from __future__ import division ) also use bits in co_flags to indicate whether a code object was compiled with a particular feature enabled: bit 0x2000 is set if the function was compiled with future division enabled; bits 0x10 and 0x1000 were used in earlier versions of Python.

Other bits in co_flags are reserved for internal use.

If a code object represents a function, the first item in co_consts is the documentation string of the function, or None if undefined.

3.2.13.1.2. Methods on code objects ¶

Returns an iterable over the source code positions of each bytecode instruction in the code object.

The iterator returns tuple s containing the (start_line, end_line, start_column, end_column) . The i-th tuple corresponds to the position of the source code that compiled to the i-th instruction. Column information is 0-indexed utf-8 byte offsets on the given source line.

This positional information can be missing. A non-exhaustive lists of cases where this may happen:

Running the interpreter with -X no_debug_ranges .

Loading a pyc file compiled while using -X no_debug_ranges .

Position tuples corresponding to artificial instructions.

Line and column numbers that can’t be represented due to implementation specific limitations.

When this occurs, some or all of the tuple elements can be None .

Added in version 3.11.

This feature requires storing column positions in code objects which may result in a small increase of disk usage of compiled Python files or interpreter memory usage. To avoid storing the extra information and/or deactivate printing the extra traceback information, the -X no_debug_ranges command line flag or the PYTHONNODEBUGRANGES environment variable can be used.

Returns an iterator that yields information about successive ranges of bytecode s. Each item yielded is a (start, end, lineno) tuple :

start (an int ) represents the offset (inclusive) of the start of the bytecode range

end (an int ) represents the offset (exclusive) of the end of the bytecode range

lineno is an int representing the line number of the bytecode range, or None if the bytecodes in the given range have no line number

The items yielded will have the following properties:

The first range yielded will have a start of 0.

The (start, end) ranges will be non-decreasing and consecutive. That is, for any pair of tuple s, the start of the second will be equal to the end of the first.

No range will be backwards: end >= start for all triples.

The last tuple yielded will have end equal to the size of the bytecode .

Zero-width ranges, where start == end , are allowed. Zero-width ranges are used for lines that are present in the source code, but have been eliminated by the bytecode compiler.

Added in version 3.10.

The PEP that introduced the co_lines() method.

Return a copy of the code object with new values for the specified fields.

Code objects are also supported by the generic function copy.replace() .

Added in version 3.8.

3.2.13.2. Frame objects ¶

Frame objects represent execution frames. They may occur in traceback objects , and are also passed to registered trace functions.

3.2.13.2.1. Special read-only attributes ¶

3.2.13.2.2. special writable attributes ¶, 3.2.13.2.3. frame object methods ¶.

Frame objects support one method:

This method clears all references to local variables held by the frame. Also, if the frame belonged to a generator , the generator is finalized. This helps break reference cycles involving frame objects (for example when catching an exception and storing its traceback for later use).

RuntimeError is raised if the frame is currently executing or suspended.

Added in version 3.4.

Changed in version 3.13: Attempting to clear a suspended frame raises RuntimeError (as has always been the case for executing frames).

3.2.13.3. Traceback objects ¶

Traceback objects represent the stack trace of an exception . A traceback object is implicitly created when an exception occurs, and may also be explicitly created by calling types.TracebackType .

Changed in version 3.7: Traceback objects can now be explicitly instantiated from Python code.

For implicitly created tracebacks, when the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section The try statement .) It is accessible as the third item of the tuple returned by sys.exc_info() , and as the __traceback__ attribute of the caught exception.

When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as sys.last_traceback .

For explicitly created tracebacks, it is up to the creator of the traceback to determine how the tb_next attributes should be linked to form a full stack trace.

The line number and last instruction in the traceback may differ from the line number of its frame object if the exception occurred in a try statement with no matching except clause or with a finally clause.

The special writable attribute tb_next is the next level in the stack trace (towards the frame where the exception occurred), or None if there is no next level.

Changed in version 3.7: This attribute is now writable

3.2.13.4. Slice objects ¶

Slice objects are used to represent slices for __getitem__() methods. They are also created by the built-in slice() function.

Special read-only attributes: start is the lower bound; stop is the upper bound; step is the step value; each is None if omitted. These attributes can have any type.

Slice objects support one method:

This method takes a single integer argument length and computes information about the slice that the slice object would describe if applied to a sequence of length items. It returns a tuple of three integers; respectively these are the start and stop indices and the step or stride length of the slice. Missing or out-of-bounds indices are handled in a manner consistent with regular slices.

3.2.13.5. Static method objects ¶

Static method objects provide a way of defeating the transformation of function objects to method objects described above. A static method object is a wrapper around any other object, usually a user-defined method object. When a static method object is retrieved from a class or a class instance, the object actually returned is the wrapped object, which is not subject to any further transformation. Static method objects are also callable. Static method objects are created by the built-in staticmethod() constructor.

3.2.13.6. Class method objects ¶

A class method object, like a static method object, is a wrapper around another object that alters the way in which that object is retrieved from classes and class instances. The behaviour of class method objects upon such retrieval is described above, under “instance methods” . Class method objects are created by the built-in classmethod() constructor.

3.3. Special method names ¶

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading , allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named __getitem__() , and x is an instance of this class, then x[i] is roughly equivalent to type(x).__getitem__(x, i) . Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically AttributeError or TypeError ).

Setting a special method to None indicates that the corresponding operation is not available. For example, if a class sets __iter__() to None , the class is not iterable, so calling iter() on its instances will raise a TypeError (without falling back to __getitem__() ). [ 2 ]

When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the NodeList interface in the W3C’s Document Object Model.)

3.3.1. Basic customization ¶

Called to create a new instance of class cls . __new__() is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of __new__() should be the new object instance (usually an instance of cls ).

Typical implementations create a new instance of the class by invoking the superclass’s __new__() method using super().__new__(cls[, ...]) with appropriate arguments and then modifying the newly created instance as necessary before returning it.

If __new__() is invoked during object construction and it returns an instance of cls , then the new instance’s __init__() method will be invoked like __init__(self[, ...]) , where self is the new instance and the remaining arguments are the same as were passed to the object constructor.

If __new__() does not return an instance of cls , then the new instance’s __init__() method will not be invoked.

__new__() is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation.

Called after the instance has been created (by __new__() ), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an __init__() method, the derived class’s __init__() method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: super().__init__([args...]) .

Because __new__() and __init__() work together in constructing objects ( __new__() to create it, and __init__() to customize it), no non- None value may be returned by __init__() ; doing so will cause a TypeError to be raised at runtime.

Called when the instance is about to be destroyed. This is also called a finalizer or (improperly) a destructor. If a base class has a __del__() method, the derived class’s __del__() method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance.

It is possible (though not recommended!) for the __del__() method to postpone destruction of the instance by creating a new reference to it. This is called object resurrection . It is implementation-dependent whether __del__() is called a second time when a resurrected object is about to be destroyed; the current CPython implementation only calls it once.

It is not guaranteed that __del__() methods are called for objects that still exist when the interpreter exits.

del x doesn’t directly call x.__del__() — the former decrements the reference count for x by one, and the latter is only called when x ’s reference count reaches zero.

CPython implementation detail: It is possible for a reference cycle to prevent the reference count of an object from going to zero. In this case, the cycle will be later detected and deleted by the cyclic garbage collector . A common cause of reference cycles is when an exception has been caught in a local variable. The frame’s locals then reference the exception, which references its own traceback, which references the locals of all frames caught in the traceback.

Documentation for the gc module.

Due to the precarious circumstances under which __del__() methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to sys.stderr instead. In particular:

__del__() can be invoked when arbitrary code is being executed, including from any arbitrary thread. If __del__() needs to take a lock or invoke any other blocking resource, it may deadlock as the resource may already be taken by the code that gets interrupted to execute __del__() .

__del__() can be executed during interpreter shutdown. As a consequence, the global variables it needs to access (including other modules) may already have been deleted or set to None . Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the __del__() method is called.

Called by the repr() built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form <...some useful description...> should be returned. The return value must be a string object. If a class defines __repr__() but not __str__() , then __repr__() is also used when an “informal” string representation of instances of that class is required.

This is typically used for debugging, so it is important that the representation is information-rich and unambiguous.

Called by str(object) and the built-in functions format() and print() to compute the “informal” or nicely printable string representation of an object. The return value must be a string object.

This method differs from object.__repr__() in that there is no expectation that __str__() return a valid Python expression: a more convenient or concise representation can be used.

The default implementation defined by the built-in type object calls object.__repr__() .

Called by bytes to compute a byte-string representation of an object. This should return a bytes object.

Called by the format() built-in function, and by extension, evaluation of formatted string literals and the str.format() method, to produce a “formatted” string representation of an object. The format_spec argument is a string that contains a description of the formatting options desired. The interpretation of the format_spec argument is up to the type implementing __format__() , however most classes will either delegate formatting to one of the built-in types, or use a similar formatting option syntax.

See Format Specification Mini-Language for a description of the standard formatting syntax.

The return value must be a string object.

Changed in version 3.4: The __format__ method of object itself raises a TypeError if passed any non-empty string.

Changed in version 3.7: object.__format__(x, '') is now equivalent to str(x) rather than format(str(x), '') .

These are the so-called “rich comparison” methods. The correspondence between operator symbols and method names is as follows: x<y calls x.__lt__(y) , x<=y calls x.__le__(y) , x==y calls x.__eq__(y) , x!=y calls x.__ne__(y) , x>y calls x.__gt__(y) , and x>=y calls x.__ge__(y) .

A rich comparison method may return the singleton NotImplemented if it does not implement the operation for a given pair of arguments. By convention, False and True are returned for a successful comparison. However, these methods can return any value, so if the comparison operator is used in a Boolean context (e.g., in the condition of an if statement), Python will call bool() on the value to determine if the result is true or false.

By default, object implements __eq__() by using is , returning NotImplemented in the case of a false comparison: True if x is y else NotImplemented . For __ne__() , by default it delegates to __eq__() and inverts the result unless it is NotImplemented . There are no other implied relationships among the comparison operators or default implementations; for example, the truth of (x<y or x==y) does not imply x<=y . To automatically generate ordering operations from a single root operation, see functools.total_ordering() .

See the paragraph on __hash__() for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, __lt__() and __gt__() are each other’s reflection, __le__() and __ge__() are each other’s reflection, and __eq__() and __ne__() are their own reflection. If the operands are of different types, and the right operand’s type is a direct or indirect subclass of the left operand’s type, the reflected method of the right operand has priority, otherwise the left operand’s method has priority. Virtual subclassing is not considered.

When no appropriate method returns any value other than NotImplemented , the == and != operators will fall back to is and is not , respectively.

Called by built-in function hash() and for operations on members of hashed collections including set , frozenset , and dict . The __hash__() method should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example:

hash() truncates the value returned from an object’s custom __hash__() method to the size of a Py_ssize_t . This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object’s __hash__() must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is with python -c "import sys; print(sys.hash_info.width)" .

If a class does not define an __eq__() method it should not define a __hash__() operation either; if it defines __eq__() but not __hash__() , its instances will not be usable as items in hashable collections. If a class defines mutable objects and implements an __eq__() method, it should not implement __hash__() , since the implementation of hashable collections requires that a key’s hash value is immutable (if the object’s hash value changes, it will be in the wrong hash bucket).

User-defined classes have __eq__() and __hash__() methods by default; with them, all objects compare unequal (except with themselves) and x.__hash__() returns an appropriate value such that x == y implies both that x is y and hash(x) == hash(y) .

A class that overrides __eq__() and does not define __hash__() will have its __hash__() implicitly set to None . When the __hash__() method of a class is None , instances of the class will raise an appropriate TypeError when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checking isinstance(obj, collections.abc.Hashable) .

If a class that overrides __eq__() needs to retain the implementation of __hash__() from a parent class, the interpreter must be told this explicitly by setting __hash__ = <ParentClass>.__hash__ .

If a class that does not override __eq__() wishes to suppress hash support, it should include __hash__ = None in the class definition. A class which defines its own __hash__() that explicitly raises a TypeError would be incorrectly identified as hashable by an isinstance(obj, collections.abc.Hashable) call.

By default, the __hash__() values of str and bytes objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.

This is intended to provide protection against a denial-of-service caused by carefully chosen inputs that exploit the worst case performance of a dict insertion, O ( n 2 ) complexity. See http://ocert.org/advisories/ocert-2011-003.html for details.

Changing hash values affects the iteration order of sets. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).

3.3.2. Customizing attribute access ¶

The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of x.name ) for class instances.

Called when the default attribute access fails with an AttributeError (either __getattribute__() raises an AttributeError because name is not an instance attribute or an attribute in the class tree for self ; or __get__() of a name property raises AttributeError ). This method should either return the (computed) attribute value or raise an AttributeError exception.

Note that if the attribute is found through the normal mechanism, __getattr__() is not called. (This is an intentional asymmetry between __getattr__() and __setattr__() .) This is done both for efficiency reasons and because otherwise __getattr__() would have no way to access other attributes of the instance. Note that at least for instance variables, you can take total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the __getattribute__() method below for a way to actually get total control over attribute access.

Called unconditionally to implement attribute accesses for instances of the class. If the class also defines __getattr__() , the latter will not be called unless __getattribute__() either calls it explicitly or raises an AttributeError . This method should return the (computed) attribute value or raise an AttributeError exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, object.__getattribute__(self, name) .

This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions . See Special method lookup .

For certain sensitive attribute accesses, raises an auditing event object.__getattr__ with arguments obj and name .

Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). name is the attribute name, value is the value to be assigned to it.

If __setattr__() wants to assign to an instance attribute, it should call the base class method with the same name, for example, object.__setattr__(self, name, value) .

For certain sensitive attribute assignments, raises an auditing event object.__setattr__ with arguments obj , name , value .

Like __setattr__() but for attribute deletion instead of assignment. This should only be implemented if del obj.name is meaningful for the object.

For certain sensitive attribute deletions, raises an auditing event object.__delattr__ with arguments obj and name .

Called when dir() is called on the object. An iterable must be returned. dir() converts the returned iterable to a list and sorts it.

3.3.2.1. Customizing module attribute access ¶

Special names __getattr__ and __dir__ can be also used to customize access to module attributes. The __getattr__ function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an AttributeError . If an attribute is not found on a module object through the normal lookup, i.e. object.__getattribute__() , then __getattr__ is searched in the module __dict__ before raising an AttributeError . If found, it is called with the attribute name and the result is returned.

The __dir__ function should accept no arguments, and return an iterable of strings that represents the names accessible on module. If present, this function overrides the standard dir() search on a module.

For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the __class__ attribute of a module object to a subclass of types.ModuleType . For example:

Defining module __getattr__ and setting module __class__ only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected.

Changed in version 3.5: __class__ module attribute is now writable.

Added in version 3.7: __getattr__ and __dir__ module attributes.

Describes the __getattr__ and __dir__ functions on modules.

3.3.2.2. Implementing Descriptors ¶

The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’ __dict__ .

Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional owner argument is the owner class, while instance is the instance that the attribute was accessed through, or None when the attribute is accessed through the owner .

This method should return the computed attribute value or raise an AttributeError exception.

PEP 252 specifies that __get__() is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own __getattribute__() implementation always passes in both arguments whether they are required or not.

Called to set the attribute on an instance instance of the owner class to a new value, value .

Note, adding __set__() or __delete__() changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors for more details.

Called to delete the attribute on an instance instance of the owner class.

Instances of descriptors may also have the __objclass__ attribute present:

The attribute __objclass__ is interpreted by the inspect module as specifying the class where this object was defined (setting this appropriately can assist in runtime introspection of dynamic class attributes). For callables, it may indicate that an instance of the given type (or a subclass) is expected or required as the first positional argument (for example, CPython sets this attribute for unbound methods that are implemented in C).

3.3.2.3. Invoking Descriptors ¶

In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: __get__() , __set__() , and __delete__() . If any of those methods are defined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, a.x has a lookup chain starting with a.__dict__['x'] , then type(a).__dict__['x'] , and continuing through the base classes of type(a) excluding metaclasses.

However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called.

The starting point for descriptor invocation is a binding, a.x . How the arguments are assembled depends on a :

The simplest and least common call is when user code directly invokes a descriptor method: x.__get__(a) .

If binding to an object instance, a.x is transformed into the call: type(a).__dict__['x'].__get__(a, type(a)) .

If binding to a class, A.x is transformed into the call: A.__dict__['x'].__get__(None, A) .

A dotted lookup such as super(A, a).x searches a.__class__.__mro__ for a base class B following A and then returns B.__dict__['x'].__get__(a, A) . If not a descriptor, x is returned unchanged.

For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of __get__() , __set__() and __delete__() . If it does not define __get__() , then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines __set__() and/or __delete__() , it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both __get__() and __set__() , while non-data descriptors have just the __get__() method. Data descriptors with __get__() and __set__() (and/or __delete__() ) defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances.

Python methods (including those decorated with @staticmethod and @classmethod ) are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class.

The property() function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property.

3.3.2.4. slots ¶

__slots__ allow us to explicitly declare data members (like properties) and deny the creation of __dict__ and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)

The space saved over using __dict__ can be significant. Attribute lookup speed can be significantly improved as well.

This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance.

Notes on using __slots__ :

When inheriting from a class without __slots__ , the __dict__ and __weakref__ attribute of the instances will always be accessible.

Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises AttributeError . If dynamic assignment of new variables is desired, then add '__dict__' to the sequence of strings in the __slots__ declaration.

Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add '__weakref__' to the sequence of strings in the __slots__ declaration.

__slots__ are implemented at the class level by creating descriptors for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__ ; otherwise, the class attribute would overwrite the descriptor assignment.

The action of a __slots__ declaration is not limited to the class where it is defined. __slots__ declared in parents are available in child classes. However, child subclasses will get a __dict__ and __weakref__ unless they also define __slots__ (which should only contain names of any additional slots).

If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.

TypeError will be raised if nonempty __slots__ are defined for a class derived from a "variable-length" built-in type such as int , bytes , and tuple .

Any non-string iterable may be assigned to __slots__ .

If a dictionary is used to assign __slots__ , the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by inspect.getdoc() and displayed in the output of help() .

__class__ assignment works only if both classes have the same __slots__ .

Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise TypeError .

If an iterator is used for __slots__ then a descriptor is created for each of the iterator’s values. However, the __slots__ attribute will be an empty iterator.

3.3.3. Customizing class creation ¶

Whenever a class inherits from another class, __init_subclass__() is called on the parent class. This way, it is possible to write classes which change the behavior of subclasses. This is closely related to class decorators, but where class decorators only affect the specific class they’re applied to, __init_subclass__ solely applies to future subclasses of the class defining the method.

This method is called whenever the containing class is subclassed. cls is then the new subclass. If defined as a normal instance method, this method is implicitly converted to a class method.

Keyword arguments which are given to a new class are passed to the parent class’s __init_subclass__ . For compatibility with other classes using __init_subclass__ , one should take out the needed keyword arguments and pass the others over to the base class, as in:

The default implementation object.__init_subclass__ does nothing, but raises an error if it is called with any arguments.

The metaclass hint metaclass is consumed by the rest of the type machinery, and is never passed to __init_subclass__ implementations. The actual metaclass (rather than the explicit hint) can be accessed as type(cls) .

Added in version 3.6.

When a class is created, type.__new__() scans the class variables and makes callbacks to those with a __set_name__() hook.

Automatically called at the time the owning class owner is created. The object has been assigned to name in that class:

If the class variable is assigned after the class is created, __set_name__() will not be called automatically. If needed, __set_name__() can be called directly:

See Creating the class object for more details.

3.3.3.1. Metaclasses ¶

By default, classes are constructed using type() . The class body is executed in a new namespace and the class name is bound locally to the result of type(name, bases, namespace) .

The class creation process can be customized by passing the metaclass keyword argument in the class definition line, or by inheriting from an existing class that included such an argument. In the following example, both MyClass and MySubclass are instances of Meta :

Any other keyword arguments that are specified in the class definition are passed through to all metaclass operations described below.

When a class definition is executed, the following steps occur:

MRO entries are resolved;

the appropriate metaclass is determined;

the class namespace is prepared;

the class body is executed;

the class object is created.

3.3.3.2. Resolving MRO entries ¶

If a base that appears in a class definition is not an instance of type , then an __mro_entries__() method is searched on the base. If an __mro_entries__() method is found, the base is substituted with the result of a call to __mro_entries__() when creating the class. The method is called with the original bases tuple passed to the bases parameter, and must return a tuple of classes that will be used instead of the base. The returned tuple may be empty: in these cases, the original base is ignored.

Dynamically resolve bases that are not instances of type .

Retrieve a class’s “original bases” prior to modifications by __mro_entries__() .

Core support for typing module and generic types.

3.3.3.3. Determining the appropriate metaclass ¶

The appropriate metaclass for a class definition is determined as follows:

if no bases and no explicit metaclass are given, then type() is used;

if an explicit metaclass is given and it is not an instance of type() , then it is used directly as the metaclass;

if an instance of type() is given as the explicit metaclass, or bases are defined, then the most derived metaclass is used.

The most derived metaclass is selected from the explicitly specified metaclass (if any) and the metaclasses (i.e. type(cls) ) of all specified base classes. The most derived metaclass is one which is a subtype of all of these candidate metaclasses. If none of the candidate metaclasses meets that criterion, then the class definition will fail with TypeError .

3.3.3.4. Preparing the class namespace ¶

Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a __prepare__ attribute, it is called as namespace = metaclass.__prepare__(name, bases, **kwds) (where the additional keyword arguments, if any, come from the class definition). The __prepare__ method should be implemented as a classmethod . The namespace returned by __prepare__ is passed in to __new__ , but when the final class object is created the namespace is copied into a new dict .

If the metaclass has no __prepare__ attribute, then the class namespace is initialised as an empty ordered mapping.

Introduced the __prepare__ namespace hook

3.3.3.5. Executing the class body ¶

The class body is executed (approximately) as exec(body, globals(), namespace) . The key difference from a normal call to exec() is that lexical scoping allows the class body (including any methods) to reference names from the current and outer scopes when the class definition occurs inside a function.

However, even when the class definition occurs inside the function, methods defined inside the class still cannot see names defined at the class scope. Class variables must be accessed through the first parameter of instance or class methods, or through the implicit lexically scoped __class__ reference described in the next section.

3.3.3.6. Creating the class object ¶

Once the class namespace has been populated by executing the class body, the class object is created by calling metaclass(name, bases, namespace, **kwds) (the additional keywords passed here are the same as those passed to __prepare__ ).

This class object is the one that will be referenced by the zero-argument form of super() . __class__ is an implicit closure reference created by the compiler if any methods in a class body refer to either __class__ or super . This allows the zero argument form of super() to correctly identify the class being defined based on lexical scoping, while the class or instance that was used to make the current call is identified based on the first argument passed to the method.

CPython implementation detail: In CPython 3.6 and later, the __class__ cell is passed to the metaclass as a __classcell__ entry in the class namespace. If present, this must be propagated up to the type.__new__ call in order for the class to be initialised correctly. Failing to do so will result in a RuntimeError in Python 3.8.

When using the default metaclass type , or any metaclass that ultimately calls type.__new__ , the following additional customization steps are invoked after creating the class object:

The type.__new__ method collects all of the attributes in the class namespace that define a __set_name__() method;

Those __set_name__ methods are called with the class being defined and the assigned name of that particular attribute;

The __init_subclass__() hook is called on the immediate parent of the new class in its method resolution order.

After the class object is created, it is passed to the class decorators included in the class definition (if any) and the resulting object is bound in the local namespace as the defined class.

When a new class is created by type.__new__ , the object provided as the namespace parameter is copied to a new ordered mapping and the original object is discarded. The new copy is wrapped in a read-only proxy, which becomes the __dict__ attribute of the class object.

Describes the implicit __class__ closure reference

3.3.3.7. Uses for metaclasses ¶

The potential uses for metaclasses are boundless. Some ideas that have been explored include enum, logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.

3.3.4. Customizing instance and subclass checks ¶

The following methods are used to override the default behavior of the isinstance() and issubclass() built-in functions.

In particular, the metaclass abc.ABCMeta implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs.

Return true if instance should be considered a (direct or indirect) instance of class . If defined, called to implement isinstance(instance, class) .

Return true if subclass should be considered a (direct or indirect) subclass of class . If defined, called to implement issubclass(subclass, class) .

Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class.

Includes the specification for customizing isinstance() and issubclass() behavior through __instancecheck__() and __subclasscheck__() , with motivation for this functionality in the context of adding Abstract Base Classes (see the abc module) to the language.

3.3.5. Emulating generic types ¶

When using type annotations , it is often useful to parameterize a generic type using Python’s square-brackets notation. For example, the annotation list[int] might be used to signify a list in which all the elements are of type int .

Introducing Python’s framework for type annotations

Documentation for objects representing parameterized generic classes

Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers.

A class can generally only be parameterized if it defines the special class method __class_getitem__() .

Return an object representing the specialization of a generic class by type arguments found in key .

When defined on a class, __class_getitem__() is automatically a class method. As such, there is no need for it to be decorated with @classmethod when it is defined.

3.3.5.1. The purpose of __class_getitem__ ¶

The purpose of __class_getitem__() is to allow runtime parameterization of standard-library generic classes in order to more easily apply type hints to these classes.

To implement custom generic classes that can be parameterized at runtime and understood by static type-checkers, users should either inherit from a standard library class that already implements __class_getitem__() , or inherit from typing.Generic , which has its own implementation of __class_getitem__() .

Custom implementations of __class_getitem__() on classes defined outside of the standard library may not be understood by third-party type-checkers such as mypy. Using __class_getitem__() on any class for purposes other than type hinting is discouraged.

3.3.5.2. __class_getitem versus getitem__ ¶

Usually, the subscription of an object using square brackets will call the __getitem__() instance method defined on the object’s class. However, if the object being subscribed is itself a class, the class method __class_getitem__() may be called instead. __class_getitem__() should return a GenericAlias object if it is properly defined.

Presented with the expression obj[x] , the Python interpreter follows something like the following process to decide whether __getitem__() or __class_getitem__() should be called:

In Python, all classes are themselves instances of other classes. The class of a class is known as that class’s metaclass , and most classes have the type class as their metaclass. type does not define __getitem__() , meaning that expressions such as list[int] , dict[str, float] and tuple[str, bytes] all result in __class_getitem__() being called:

However, if a class has a custom metaclass that defines __getitem__() , subscribing the class may result in different behaviour. An example of this can be found in the enum module:

Introducing __class_getitem__() , and outlining when a subscription results in __class_getitem__() being called instead of __getitem__()

3.3.6. Emulating callable objects ¶

Called when the instance is “called” as a function; if this method is defined, x(arg1, arg2, ...) roughly translates to type(x).__call__(x, arg1, ...) .

3.3.7. Emulating container types ¶

The following methods can be defined to implement container objects. Containers usually are sequences (such as lists or tuples ) or mappings (like dictionaries ), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers k for which 0 <= k < N where N is the length of the sequence, or slice objects, which define a range of items. It is also recommended that mappings provide the methods keys() , values() , items() , get() , clear() , setdefault() , pop() , popitem() , copy() , and update() behaving similar to those for Python’s standard dictionary objects. The collections.abc module provides a MutableMapping abstract base class to help create those methods from a base set of __getitem__() , __setitem__() , __delitem__() , and keys() . Mutable sequences should provide methods append() , count() , index() , extend() , insert() , pop() , remove() , reverse() and sort() , like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods __add__() , __radd__() , __iadd__() , __mul__() , __rmul__() and __imul__() described below; they should not define other numerical operators. It is recommended that both mappings and sequences implement the __contains__() method to allow efficient use of the in operator; for mappings, in should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the __iter__() method to allow efficient iteration through the container; for mappings, __iter__() should iterate through the object’s keys; for sequences, it should iterate through the values.

Called to implement the built-in function len() . Should return the length of the object, an integer >= 0. Also, an object that doesn’t define a __bool__() method and whose __len__() method returns zero is considered to be false in a Boolean context.

CPython implementation detail: In CPython, the length is required to be at most sys.maxsize . If the length is larger than sys.maxsize some features (such as len() ) may raise OverflowError . To prevent raising OverflowError by truth value testing, an object must define a __bool__() method.

Called to implement operator.length_hint() . Should return an estimated length for the object (which may be greater or less than the actual length). The length must be an integer >= 0. The return value may also be NotImplemented , which is treated the same as if the __length_hint__ method didn’t exist at all. This method is purely an optimization and is never required for correctness.

Slicing is done exclusively with the following three methods. A call like

is translated to

and so forth. Missing slice items are always filled in with None .

Called to implement evaluation of self[key] . For sequence types, the accepted keys should be integers. Optionally, they may support slice objects as well. Negative index support is also optional. If key is of an inappropriate type, TypeError may be raised; if key is a value outside the set of indexes for the sequence (after any special interpretation of negative values), IndexError should be raised. For mapping types, if key is missing (not in the container), KeyError should be raised.

for loops expect that an IndexError will be raised for illegal indexes to allow proper detection of the end of the sequence.

When subscripting a class , the special class method __class_getitem__() may be called instead of __getitem__() . See __class_getitem__ versus __getitem__ for more details.

Called to implement assignment to self[key] . Same note as for __getitem__() . This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper key values as for the __getitem__() method.

Called to implement deletion of self[key] . Same note as for __getitem__() . This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper key values as for the __getitem__() method.

Called by dict . __getitem__() to implement self[key] for dict subclasses when key is not in the dictionary.

This method is called when an iterator is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container.

Called (if present) by the reversed() built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order.

If the __reversed__() method is not provided, the reversed() built-in will fall back to using the sequence protocol ( __len__() and __getitem__() ). Objects that support the sequence protocol should only provide __reversed__() if they can provide an implementation that is more efficient than the one provided by reversed() .

The membership test operators ( in and not in ) are normally implemented as an iteration through a container. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be iterable.

Called to implement membership test operators. Should return true if item is in self , false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs.

For objects that don’t define __contains__() , the membership test first tries iteration via __iter__() , then the old sequence iteration protocol via __getitem__() , see this section in the language reference .

3.3.8. Emulating numeric types ¶

The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined.

These methods are called to implement the binary arithmetic operations ( + , - , * , @ , / , // , % , divmod() , pow() , ** , << , >> , & , ^ , | ). For instance, to evaluate the expression x + y , where x is an instance of a class that has an __add__() method, type(x).__add__(x, y) is called. The __divmod__() method should be the equivalent to using __floordiv__() and __mod__() ; it should not be related to __truediv__() . Note that __pow__() should be defined to accept an optional third argument if the ternary version of the built-in pow() function is to be supported.

If one of those methods does not support the operation with the supplied arguments, it should return NotImplemented .

These methods are called to implement the binary arithmetic operations ( + , - , * , @ , / , // , % , divmod() , pow() , ** , << , >> , & , ^ , | ) with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation [ 3 ] and the operands are of different types. [ 4 ] For instance, to evaluate the expression x - y , where y is an instance of a class that has an __rsub__() method, type(y).__rsub__(y, x) is called if type(x).__sub__(x, y) returns NotImplemented .

Note that ternary pow() will not try calling __rpow__() (the coercion rules would become too complicated).

If the right operand’s type is a subclass of the left operand’s type and that subclass provides a different implementation of the reflected method for the operation, this method will be called before the left operand’s non-reflected method. This behavior allows subclasses to override their ancestors’ operations.

These methods are called to implement the augmented arithmetic assignments ( += , -= , *= , @= , /= , //= , %= , **= , <<= , >>= , &= , ^= , |= ). These methods should attempt to do the operation in-place (modifying self ) and return the result (which could be, but does not have to be, self ). If a specific method is not defined, or if that method returns NotImplemented , the augmented assignment falls back to the normal methods. For instance, if x is an instance of a class with an __iadd__() method, x += y is equivalent to x = x.__iadd__(y) . If __iadd__() does not exist, or if x.__iadd__(y) returns NotImplemented , x.__add__(y) and y.__radd__(x) are considered, as with the evaluation of x + y . In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += [‘item’] raise an exception when the addition works? ), but this behavior is in fact part of the data model.

Called to implement the unary arithmetic operations ( - , + , abs() and ~ ).

Called to implement the built-in functions complex() , int() and float() . Should return a value of the appropriate type.

Called to implement operator.index() , and whenever Python needs to losslessly convert the numeric object to an integer object (such as in slicing, or in the built-in bin() , hex() and oct() functions). Presence of this method indicates that the numeric object is an integer type. Must return an integer.

If __int__() , __float__() and __complex__() are not defined then corresponding built-in functions int() , float() and complex() fall back to __index__() .

Called to implement the built-in function round() and math functions trunc() , floor() and ceil() . Unless ndigits is passed to __round__() all these methods should return the value of the object truncated to an Integral (typically an int ).

The built-in function int() falls back to __trunc__() if neither __int__() nor __index__() is defined.

Changed in version 3.11: The delegation of int() to __trunc__() is deprecated.

3.3.9. With Statement Context Managers ¶

A context manager is an object that defines the runtime context to be established when executing a with statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the with statement (described in section The with statement ), but can also be used by directly invoking their methods.

Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc.

For more information on context managers, see Context Manager Types .

Enter the runtime context related to this object. The with statement will bind this method’s return value to the target(s) specified in the as clause of the statement, if any.

Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be None .

If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method.

Note that __exit__() methods should not reraise the passed-in exception; this is the caller’s responsibility.

The specification, background, and examples for the Python with statement.

3.3.10. Customizing positional arguments in class pattern matching ¶

When using a class name in a pattern, positional arguments in the pattern are not allowed by default, i.e. case MyClass(x, y) is typically invalid without special support in MyClass . To be able to use that kind of pattern, the class needs to define a __match_args__ attribute.

This class variable can be assigned a tuple of strings. When this class is used in a class pattern with positional arguments, each positional argument will be converted into a keyword argument, using the corresponding value in __match_args__ as the keyword. The absence of this attribute is equivalent to setting it to () .

For example, if MyClass.__match_args__ is ("left", "center", "right") that means that case MyClass(x, y) is equivalent to case MyClass(left=x, center=y) . Note that the number of arguments in the pattern must be smaller than or equal to the number of elements in __match_args__ ; if it is larger, the pattern match attempt will raise a TypeError .

The specification for the Python match statement.

3.3.11. Emulating buffer types ¶

The buffer protocol provides a way for Python objects to expose efficient access to a low-level memory array. This protocol is implemented by builtin types such as bytes and memoryview , and third-party libraries may define additional buffer types.

While buffer types are usually implemented in C, it is also possible to implement the protocol in Python.

Called when a buffer is requested from self (for example, by the memoryview constructor). The flags argument is an integer representing the kind of buffer requested, affecting for example whether the returned buffer is read-only or writable. inspect.BufferFlags provides a convenient way to interpret the flags. The method must return a memoryview object.

Called when a buffer is no longer needed. The buffer argument is a memoryview object that was previously returned by __buffer__() . The method must release any resources associated with the buffer. This method should return None . Buffer objects that do not need to perform any cleanup are not required to implement this method.

Added in version 3.12.

Introduces the Python __buffer__ and __release_buffer__ methods.

ABC for buffer types.

3.3.12. Special method lookup ¶

For custom classes, implicit invocations of special methods are only guaranteed to work correctly if defined on an object’s type, not in the object’s instance dictionary. That behaviour is the reason why the following code raises an exception:

The rationale behind this behaviour lies with a number of special methods such as __hash__() and __repr__() that are implemented by all objects, including type objects. If the implicit lookup of these methods used the conventional lookup process, they would fail when invoked on the type object itself:

Incorrectly attempting to invoke an unbound method of a class in this way is sometimes referred to as ‘metaclass confusion’, and is avoided by bypassing the instance when looking up special methods:

In addition to bypassing any instance attributes in the interest of correctness, implicit special method lookup generally also bypasses the __getattribute__() method even of the object’s metaclass:

Bypassing the __getattribute__() machinery in this fashion provides significant scope for speed optimisations within the interpreter, at the cost of some flexibility in the handling of special methods (the special method must be set on the class object itself in order to be consistently invoked by the interpreter).

3.4. Coroutines ¶

3.4.1. awaitable objects ¶.

An awaitable object generally implements an __await__() method. Coroutine objects returned from async def functions are awaitable.

The generator iterator objects returned from generators decorated with types.coroutine() are also awaitable, but they do not implement __await__() .

Must return an iterator . Should be used to implement awaitable objects. For instance, asyncio.Future implements this method to be compatible with the await expression.

The language doesn’t place any restriction on the type or value of the objects yielded by the iterator returned by __await__ , as this is specific to the implementation of the asynchronous execution framework (e.g. asyncio ) that will be managing the awaitable object.

Added in version 3.5.

PEP 492 for additional information about awaitable objects.

3.4.2. Coroutine Objects ¶

Coroutine objects are awaitable objects. A coroutine’s execution can be controlled by calling __await__() and iterating over the result. When the coroutine has finished executing and returns, the iterator raises StopIteration , and the exception’s value attribute holds the return value. If the coroutine raises an exception, it is propagated by the iterator. Coroutines should not directly raise unhandled StopIteration exceptions.

Coroutines also have the methods listed below, which are analogous to those of generators (see Generator-iterator methods ). However, unlike generators, coroutines do not directly support iteration.

Changed in version 3.5.2: It is a RuntimeError to await on a coroutine more than once.

Starts or resumes execution of the coroutine. If value is None , this is equivalent to advancing the iterator returned by __await__() . If value is not None , this method delegates to the send() method of the iterator that caused the coroutine to suspend. The result (return value, StopIteration , or other exception) is the same as when iterating over the __await__() return value, described above.

Raises the specified exception in the coroutine. This method delegates to the throw() method of the iterator that caused the coroutine to suspend, if it has such a method. Otherwise, the exception is raised at the suspension point. The result (return value, StopIteration , or other exception) is the same as when iterating over the __await__() return value, described above. If the exception is not caught in the coroutine, it propagates back to the caller.

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

Causes the coroutine to clean itself up and exit. If the coroutine is suspended, this method first delegates to the close() method of the iterator that caused the coroutine to suspend, if it has such a method. Then it raises GeneratorExit at the suspension point, causing the coroutine to immediately clean itself up. Finally, the coroutine is marked as having finished executing, even if it was never started.

Coroutine objects are automatically closed using the above process when they are about to be destroyed.

3.4.3. Asynchronous Iterators ¶

An asynchronous iterator can call asynchronous code in its __anext__ method.

Asynchronous iterators can be used in an async for statement.

Must return an asynchronous iterator object.

Must return an awaitable resulting in a next value of the iterator. Should raise a StopAsyncIteration error when the iteration is over.

An example of an asynchronous iterable object:

Changed in version 3.7: Prior to Python 3.7, __aiter__() could return an awaitable that would resolve to an asynchronous iterator .

Starting with Python 3.7, __aiter__() must return an asynchronous iterator object. Returning anything else will result in a TypeError error.

3.4.4. Asynchronous Context Managers ¶

An asynchronous context manager is a context manager that is able to suspend execution in its __aenter__ and __aexit__ methods.

Asynchronous context managers can be used in an async with statement.

Semantically similar to __enter__() , the only difference being that it must return an awaitable .

Semantically similar to __exit__() , the only difference being that it must return an awaitable .

An example of an asynchronous context manager class:

3.1. Objects, values and types
3.2.1. None
3.2.2. NotImplemented
3.2.3. Ellipsis
3.2.4.1. numbers.Integral
3.2.4.2. numbers.Real ( float )
3.2.4.3. numbers.Complex ( complex )
3.2.5.1. Immutable sequences
3.2.5.2. Mutable sequences
3.2.6. Set types
3.2.7.1. Dictionaries
3.2.8.1.1. Special read-only attributes
3.2.8.1.2. Special writable attributes
3.2.8.2. Instance methods
3.2.8.3. Generator functions
3.2.8.4. Coroutine functions
3.2.8.5. Asynchronous generator functions
3.2.8.6. Built-in functions
3.2.8.7. Built-in methods
3.2.8.8. Classes
3.2.8.9. Class Instances
3.2.9. Modules
3.2.10. Custom classes
3.2.11. Class instances
3.2.12. I/O objects (also known as file objects)
3.2.13.1.1. Special read-only attributes
3.2.13.1.2. Methods on code objects
3.2.13.2.1. Special read-only attributes
3.2.13.2.2. Special writable attributes
3.2.13.2.3. Frame object methods
3.2.13.3. Traceback objects
3.2.13.4. Slice objects
3.2.13.5. Static method objects
3.2.13.6. Class method objects
3.3.1. Basic customization
3.3.2.1. Customizing module attribute access
3.3.2.2. Implementing Descriptors
3.3.2.3. Invoking Descriptors
3.3.2.4. __slots__
3.3.3.1. Metaclasses
3.3.3.2. Resolving MRO entries
3.3.3.3. Determining the appropriate metaclass
3.3.3.4. Preparing the class namespace
3.3.3.5. Executing the class body
3.3.3.6. Creating the class object
3.3.3.7. Uses for metaclasses
3.3.4. Customizing instance and subclass checks
3.3.5.1. The purpose of __class_getitem__
3.3.5.2. __class_getitem__ versus __getitem__
3.3.6. Emulating callable objects
3.3.7. Emulating container types
3.3.8. Emulating numeric types
3.3.9. With Statement Context Managers
3.3.10. Customizing positional arguments in class pattern matching
3.3.11. Emulating buffer types
3.3.12. Special method lookup
3.4.1. Awaitable Objects
3.4.2. Coroutine Objects
3.4.3. Asynchronous Iterators
3.4.4. Asynchronous Context Managers

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Python Relational Operators

Assignment Operator

Addition Assignment Operator

S ubtraction Assignment Operator

M ultiplication Assignment Operator

D ivision Assignment Operator

M odulus Assignment Operator

F loor Division Assignment Operator

Exponentiation Assignment Operator

Bitwise AND Assignment Operator

Bitwise OR Assignment Operator

Bitwise XOR Assignment Operator

Bitwise Right Shift Assignment Operator

Bitwise Left Shift Assignment Operator

Walrus Operator

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Python - Assignment Operators

Python Assignment Operator

Example of Assignment Operator in Python

Augmented Assignment Operators in Python

Augmented Addition Operator (+=)

Augmented Subtraction Operator (-=)

Augmented Multiplication Operator (*=)

Augmented Division Operator (/=)

Augmented Modulus Operator (%=)

Augmented Exponent Operator (**=)

Augmented Floor division Operator (//=)

Assignment Operators

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What is an Assignment Operator in Python?

Types of Assignment Operator in Python

1. Simple Assignment Operator (=)

2. Addition Assignment Operator (+=)

3. Subtraction Assignment Operator (-=)

4. Multiplication Assignment Operator (*=)

5. Division Assignment Operator (/=)

6. Modulus Assignment Operator (%=)

7. Floor Division Assignment Operator (//=)

8. Exponentiation Assignment Operator (**=)

9. Bitwise AND Assignment Operator (&=)

10. Bitwise OR Assignment Operator (|=)

11. Bitwise XOR Assignment Operator (^=)

12. Bitwise Right Shift Assignment Operator (>>=)

13. Bitwise Left Shift Assignment Operator (<<=)

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Python Assignment Operators

Operator Assignment (=)

Operator Addition (+=)

Operator Subtraction (-=)

Operator Multiplication (*=)

Operator Division (/=)

Operator Modulus (%=)

Operator Exponentiation (**=)

Operator Floor Division (//=)

Python Operators: Arithmetic, Assignment, Comparison, Logical, Identity, Membership, Bitwise

Arithmetic Operators

Assignment Operators in Python

What Are Operators and Operands?

What Are the Basics of Assignments?

The simple assignment operator “=”

Updating values

Multiple and parallel assignments

Augmented assignment operators in Python

Add assignment operator “+=”

Subtract assignment operator

Multiplication assignment operator

Division assignment operator

Floor assignment operator

Modulus assignment operator

Exponentiation assignment operator

Bitwise AND assignment operator

Bitwise OR assignment operator

Bitwise XOR assignment operator

Bitwise Left Shift assignment operator