Types of operators in Python

To become an expert in Python programming and creating effective Python programs, you must know Python operators. Python has a variety of operators that allow one to perform simple arithmetic operations, complex logical tests, and many other operations in Python.

Every section in this tutorial on Python Operators has real-life examples and use cases for working with such operators, and through them, you will develop confidence in working with operators in Python in real programs and will have cleaner and more efficient codes.

Table of Contents:

• What are Python Operators?
• Types of Operators in Python
  • Arithmetic Operators in Python
  • Comparison Operators in Python
  • Assignment Operators in Python
  • Logical Operators in Python
  • Membership Operators in Python
  • Bitwise Operator in Python
  • Identity Operators in Python
  • Ternary Operators in Python
  • Augmented Assignment Operators
• Python Operator Precedence
• Operator Overloading in Python
• Using the operator Module for Functional Programming
  • Using operator.add with map()
  • Using operator with reduce()
  • Sorting with operator.attrgetter()
• Using the Walrus Operator (:=) for Performance Optimization
• How Python Handles Floating-Point Precision in Arithmetic Operators
• Conclusion

What are Python Operators?

Python operators are the special type of symbols or keywords that generally perform different kinds of operations on variables and values. These operators typically act as instructions that Python interprets in order to perform operations like calculations, comparisons, and logical evaluations.

Operators in Python are one of the most important concepts in Python programming that allows developers to manipulate any data, perform mathematical calculations, and maintain the flow of programs effectively. Whether you are doing any arithmetic operations, making logical decisions, or even assigning values, these Python operators generally streamline code execution and improve performance in your Python development.

Types of Operators in Python

• Arithmetic Operators: Useful in addition, subtraction, multiplication, and division operators.
• Comparison (Relational) Operators: Useful to evaluate values and direct program flow.
• Assignment Operators: Useful to assign and update variable values effectively.
• Logical Operators: Form compound conditions in decision structures.
• Membership and Identity Operators: Alter strings and differentiate references in memory.
• Bitwise Operators: It work with direct binary representations of numbers.

Arithmetic Operators in Python

Arithmetic operators are used to perform various mathematical operations such as addition, subtraction, etc. The following table contains all the arithmetic operations and their descriptions, along with examples.

Example: Arithmetic Operators in Python

Output:

Comparison Operators in Python

Comparison Operators also called relational operators generally compare the values on both sides of the operator and conclude on the relation between the values. After comparison, it returns the Boolean value, i.e., either true or false. The following table contains different types of comparison operators and their descriptions, along with respective examples.

Example: Comparison Operators in Python

Output:

Assignment Operators in Python

Assignment operators are used to assign values to Python Variables. The assignment is sometimes done directly, and sometimes the operator first performs some kind of mathematical operation and then assigns the value to the operand. The following table contains all types of assignment operators with their descriptions and respective examples.

Example: Assignment Operators in Python

Output:

Logical Operators in Python

Logical operators are mainly used for conditional statements. There are three types of logical operators, namely, AND, OR, and NOT. The following table contains all logical operators with their descriptions, as well as their respective examples.

Example: Logical Operators in Python

Output:

Short-Circuit Evaluation in Logical Operators

The ‘and’ & ‘or’ operators in Python generally follow the short circuit evaluation which means that they simply stop checking the expressions as the result is being calculated.

Example:

Output:

This behavior of Python logical operators typically improves the overall performance and also prevents unnecessary function calls.

Membership Operators in Python

Membership operators are used to test if a value is available in a sequence or not. It can be any sequence such as (Python String, Python List, Python Set, Python Tuple, and Python Dictionary). There are two types of membership operators, namely, in and not in. The following table contains the description and the respective examples of both membership operators.

Example: Membership Operators in Python

Output:

Bitwise Operator in Python

It performs bit-by-bit operations. For instance, suppose there are two variables,

 I = 10 and J = 20

Their binary values are:

I = 10 = 0000 1010

J = 20 = 0001 0100

Now, let us see how the bitwise operator in Python works.

Example: Bitwise Operators in Python

Output:

Identity Operators in Python

Identity operators are used to compare the memory addresses of two different objects. The two types of identity operators in Python are ‘is’ and ‘is not’. The following table contains the description of these two operators, along with their respective examples.

Example: Identity Operators in Python

Output:

Ternary Operators in Python

Ternary operators, which can also be called conditional expressions evaluate something based on a particular condition being true or false. It simply allows the programmer to test a condition in a single line replacing the multi-line if-else statements, and hence makes the code compact. They are more concise than a traditional if…else statement.

Example: Ternary Operators in Python

Output:

Augmented Assignment Operators

The augmented assignment operators in Python are the combination of arithmetic operators and assignment operators that are used to improve the overall code efficiency.

Example: Augmented Assignment Operators

Output:

Python Operator Precedence

In some expressions, there is more than one operator. To evaluate such expressions, there is a rule of precedence, known as Python Operator Precedence. It guides the order in which these operations are carried out.

The table in the image given below shows the operator precedence in Python in descending order:

Operator Overloading in Python

Operator Overloading lets you extend the meaning of operators that are predefined. It provides an expanded definition of the operator. The same operator also behaves in different ways with different data types. Take the example of the + operator. It performs addition when done on numbers, but will concatenate two strings and merge two lists.

Example:

Output:

When p1 + p2 is executed, the __add__ method is called, and the result is a new Point object with the sum of the x and y values.

Using the operator Module for Functional Programming

The operator module of Python mostly provides a group of optimized and built-in operators mostly equaling the regular operators. It typically has a very major contribution towards functional programming where the functions are mostly treated with importance and can be conducted in a more readable and concise way.

Python’s operator module works seamlessly with map(), filter(), and reduce() and merely makes the processes optimized and more efficient.

1. Using operator.add with map()

Output:

Here, operator.mul replaces lambda x: x * 2, making the function call more efficient.

2. Using operator with reduce()

Output:

Instead of using lambda a, b: a + b, operator.add makes the code cleaner and faster.

3. Sorting with operator.attrgetter()

Output:

Here, operator.attrgetter(“marks”) makes sorting based on object attributes more elegant.

Using the Walrus Operator (:=) for Performance Optimization

The Walrus operator (:=) of Python, implemented in the 3.8 version of Python, mostly offers the assignment within the expressions that simply boosts the performance of the code by not performing redundant calculations. It’s very useful in a lot of concepts of Python such as loops, list comprehensions, and conditional statements where every value is mostly needed more than once.

Example: Without Walrus Operator

Output:

Here, value ** 2 is calculated twice (once in the if condition and again in append).

Example: With Walrus Operator

Output:

• It will boost the performance gain as the computation happens only once per iteration.
• It also Improves readability by reducing redundant calculations.

How Python Handles Floating-Point Precision in Arithmetic Operators

Python generally uses IEEE 754 double-precision floating-point format that simply can lead to precision errors in arithmetic operations.

Output:

Instead of 0.3, Python returns 0.30000000000000004 due to binary representation limitations.

How to Handle Floating-Point Precision Issues

1. Using the decimal Module for High-precision

Output:

2. Using round() to Limit Precision

Output:

3. Using math.isclose() for Comparisons

Output:

Key Takeaways:

• Python typically stores the float in binary which leads to rounding issues.
• Use a decimal module when exact precision is required.
• Use math.isclose() for reliable floating-point comparisons.

Conclusion

With that, you have completed this Python Operators Tutorial in detail, having acquired in-depth mastery in working with operators for efficient and well-organized Python development. Let’s go over some important points:

Key Takeaways for Python Developers:

• Types of Python Operators: You understand Python arithmetic operators, Python relational operators, Python assignment operators, Python logical operators, Python membership operators, Python bitwise operators, and Python identity operators and when and why they’re utilized in real life.
• Optimum Code Practices: Appropriate use of comparison and assignment operators can make a big impact on codes.
• Hands-on Exercise: Practise examples have aided in creating a sound basis for effective working with operators for you.

Now, in case, you’re interested in knowing about practicing data science techniques with Python, then you can refer to this blog regarding Python Data Science tutorial.

Also, review our Python certification training Course. Besides, go through these free Python Interview Questions prepared by professionals in the field.