How to Set Up Visual Studio Code for Python
Python
05.02.2025
Reading time: 10 min
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Creating and debugging programs in Python is easier when using a specialized Integrated Development Environment (IDE). With an IDE, you can quickly and efficiently develop, test, and debug programs.
Visual Studio Code (VS Code) for Python provides full support for the language and offers a wide range of plugins and extensions. In this article, we will install Visual Studio Code on three operating systems (Windows, macOS, Linux) and set it up for Python programming, including the use of popular plugins.
Prerequisites
To install and set up Visual Studio Code for Python, we will need the following:
-
A personal or work computer with Windows 10/11, macOS, or Ubuntu Linux distribution version 24.04 pre-installed. Alternatively, you can rent a dedicated server or a virtual machine with Windows Server 2016/2019/2022. If using regular versions of Windows, you can download your own ISO image in advance. You can also rent a server with Ubuntu.
Installing the Python Interpreter
Before installing VS Code, we need to install the Python interpreter on all three operating systems — Windows, macOS, and Linux.
On Windows
-
Go to the official Python website and download the installer file. In this case, we will be installing Python version 3.13.1.
- Run the installer file.
- You will have two installation options:
-
-
Install Now — This performs a full installation, including documentation files, the package manager pip, the tcl/tk library for graphical interface support, and standard libraries.
-
Customize Installation — This option allows you to choose which components to install.
-
We will use the full installation. Make sure to check the box next to the option Add python.exe to PATH and click Install Now. The installation process will begin.
-
Once the installation is complete, the program will notify you that it has finished.
On macOS
On macOS, the Python interpreter is pre-installed by default. You can verify this by running the following command in the terminal:
python3 --version
However, the installed version may be outdated. If necessary, you can install a newer version. To do this, we will use the Homebrew package manager. First, if Homebrew is not installed on your system, you can install it by running the following command:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"Next, you need to check which versions of Python are available for installation. Use the following command:
brew search pythonIn our case, several versions of Python are available:
Install the latest available version, Python 3.13, by running the following command:
brew install python@3.13Check the Python version again:
python3 --version
python3.13 --version
As shown in the screenshot above, running the python3 --version still shows the old version (Python 3.9.6). However, the newly installed version (3.13) can be accessed using the command python3.13 --version. If needed, you can change the default Python version to the newly installed one. To do this, first get the full path to the newly installed Python interpreter using the following command:
brew --prefix python@3.13Then, check which shell you are using:
echo $SHELLDepending on the shell used, open the corresponding file for editing:
For bash or sh:
nano ~/.bashrcFor zsh:
nano ~/.zshrcAdd the following line at the end of the file:
export PATH="/opt/homebrew/opt/python@3.13/bin:$PATH"Save the changes and reload the file:
source ~/.zshrcNow, when you check the Python version, it will display the latest installed version:
python3 --version
On Ubuntu
By default, Python is pre-installed on almost all Linux distributions, including Ubuntu. In the latest supported versions of Ubuntu, the current version of Python is installed:
python3 --version
However, if Python is not installed for any reason, you can install it by running the following command:
apt -y install python3Installing Visual Studio Code
You can install Visual Studio Code on your personal computer. You can also rent a dedicated or cloud server with Windows Server or one of the available Linux distributions. If the required distribution is not available in the list of offered images, you can upload your own.
We will cover the installation of Visual Studio Code on three operating systems: Windows, macOS, and Linux (Ubuntu 24.04 distribution).
For Windows
Visual Studio Code supports installation on Windows 10 and Windows 11. It also supports Windows Server distributions, from version 2016 to 2022.
We will install it on Windows 10.
- Go to the official website and download the installer.
- This will download an
.exeinstallation file. Run the installer file. - On the first step, accept the license agreement by selecting the option "I accept the agreement".
- Next, the installer will prompt you to choose an installation location. You can choose the default path suggested by the installer or specify your own.
- If necessary, you can create a shortcut for the program in the Windows menu. If you don’t want to create a shortcut, select the option "Don’t create a Start Menu folder" at the bottom:
- The next step lets you configure additional options by ticking the corresponding checkboxes:
-
-
Create a desktop icon — creates a shortcut on the desktop for quick access to the program.
-
Add “Open with Code” action to Windows Explorer file context menu — adds the "Open with Code" option to the context menu when right-clicking on a file. This option allows you to quickly open any file directly in Visual Studio Code.
-
Add “Open with Code” action to Windows Explorer directory file context menu — similar to the above option but adds the "Open with Code" option to the context menu of directories (folders).
-
Register Code as an editor for supported file types — makes Visual Studio Code the default editor for certain file types (e.g., .c, .cpp, .py, .java, .js, .html files).
-
Add to PATH (require shell restart) — adds Visual Studio Code to the system’s PATH variable so it can be launched from the command line (cmd).
-
- Once all necessary options are set, Visual Studio Code is ready for installation. Click Install.
After the installation is complete, you can launch the program immediately:
For macOS
- Go to the official website and download the installer:
- After downloading, you will have a ZIP archive. Inside the archive, you will find the executable file, which you need to extract to the "Applications" directory.
- On the first launch, the system will notify you that this file was downloaded from the internet and may not have vulnerabilities. Click Open to continue:
For Linux (Ubuntu)
Visual Studio Code supports installation on Linux distributions such as Ubuntu, Debian, Red Hat, Fedora, and SUSE. You need a graphical desktop environment to install Visual Studio Code on Linux (GNOME, KDE, Xfce, etc.).
Let’s consider the installation of Visual Studio Code on Ubuntu 24.04 with the Xfce desktop environment. You can also install Visual Studio Code using Snapcraft.
-
Go to the official website and download the installer for your Linux distribution. In our case, we need the
.debinstaller:
-
Once the file is downloaded, open a terminal (console) and navigate to the directory where the file was downloaded (e.g.,
/root). -
To install, run the following command where
code_1.96.2-1734607745_amd64.debis the name of the downloaded file:
dpkg -i code_1.96.2-1734607745_amd64.deb-
During installation, a message will prompt you to add Microsoft repositories to the system. Select <Yes> and press Enter:
-
Wait for the installation to complete.
-
Once the installation is finished, you can launch the program from the applications menu (for distributions using Xfce, Visual Studio Code is available in the menu: Applications → Development):
Adding Python Interpreter to PATH Variable in Windows
If you haven't checked the Add python.exe to PATH checkbox during the Python installation on Windows, you need to manually add the full path to the interpreter to run Python from the command line.
To do this:
- Press Win+R, type
sysdm.cplin the Run window, and press Enter. - In the window that opens, go to the Advanced tab and click on the Environment Variables button.
- To add a user-level variable, select the Path variable under User variables and click on Edit.
- Double-click on an empty field or click the New button.
- Enter the full path to the Python interpreter file. By default, the Python interpreter is located at the following path:
C:\Users\<Username>\AppData\Local\Programs\Python\Python313For example:
C:\Users\Administrator\AppData\Local\Programs\Python\Python313
- After entering the path, click OK to save the changes.
- To verify, open the command prompt and type
python. If the path to the interpreter is correctly specified, the Python console will open.
Setting Up Python Interpreter in Visual Studio Code
In Windows
Once Python is installed, you need to connect it to Visual Studio Code. To do this:
-
Open Visual Studio Code and click the New File... button on the home page to create a new file.
Alternatively, you can create a Python project in Visual Studio Code by clicking on Open Folder…, where you can select the entire project folder containing the files.
-
Type any name for the file, use the
.pyextension, and press Enter.
-
Save the file in any location. Ensure that the file name ends with the
.pyextension.
- Once the file is saved, the interface in Visual Studio Code will display a prompt at the bottom right, suggesting you install the recommended Python extension.
- To run Python in Visual Studio Code, you first need to select the Python interpreter. A button will appear at the bottom of the panel with a warning: Select Interpreter. Click on it.
- In the menu that appears, select Enter interpreter path… and press Enter.
- Specify the full path to the Python interpreter. By default, it is located at:
C:\Users\Administrator\AppData\Local\Programs\Python\Python313where Administrator is your user account name. Select the file named python and click on Select Interpreter.
To test it, write a simple program that calculates the square root of a number:
import math
num = float(input("Enter a number to find its square root: "))
if num >= 0:
sqrt_result = math.sqrt(num)
print(f"The square root of {num} is {sqrt_result}")
else:
print("Square root of a negative number is not real.")
In Visual Studio Code, to run the Python code, click the Run Python File button at the top-right. If the interpreter is set up correctly, the program will run successfully.
In macOS
On macOS operating systems, the Python interpreter is automatically recognized. Simply create a new .py file as described in the Windows section above and run the program directly.
In Ubuntu
Similarly to macOS, in the Ubuntu distribution, VS Code automatically detects the installed Python interpreter in the system. All you need to do is create a new .py file and run the program directly.
Recommended Extensions for Python in Visual Studio Code
VS Code offers a wide range of Python extensions (plugins) that simplify the development process. Here are some of the most popular ones:
Pylance
Pylance provides code analysis, autocompletion, and IntelliSense support, making Python development more efficient and user-friendly. Key features include fast autocompletion, type checking, and IntelliSense support.
Jupyter
The Jupyter extension is a powerful tool for working with interactive notebooks directly in the editor. It’s especially useful for data analysis, machine learning, visualization, and interactive programming tasks.
autoDocstring — Python Docstring Generator
autoDocstring is a popular extension that helps automatically generate docstrings for Python functions, methods, and classes. Docstrings improve code readability and serve as built-in documentation.
isort
isort is a tool for automatically sorting and organizing imports in Python code. You can configure it in Visual Studio Code to make working with imports easier and to improve code readability.
Conclusion
This article covered the installation and setup of Visual Studio Code for Python development. Visual Studio Code offers full support for Python and provides the ability to extend its functionality through various plugins, making the coding process easier and more efficient.
Python
05.02.2025
Reading time: 10 min
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Python
Python Static Method
A static method in Python is bound to the class itself rather than any instance of that class. So, you can call it without first creating an object and without access to instance data (self).
To create a static method we need to use a decorator, specifically @staticmethod. It will tell Python to call the method on the class rather than an instance.
Static methods are excellent for utility or helper functions that are logically connected to the class but don't need to access any of its properties.
When To Use & Not to Use a Python Static Method
Static methods are frequently used in real-world code for tasks like input validation, data formatting, and calculations—especially when that logic naturally belongs with a class but doesn't need its state.
Here's an example from a User class that checks email format:
class User:
@staticmethod
def is_valid_email(email):
return "@" in email and "." in email
This method doesn't depend on any part of the User instance, but conceptually belongs in the class. It can be used anywhere as User.is_valid_email(email), keeping your code cleaner and more organized.
If the logic requires access to or modification of instance attributes or class-level data, avoid using a static method as it won't help here. For instance, if you are working with user settings or need to monitor object creation, you will require a class method or an instance method instead.
class Counter:
count = 0
@classmethod
def increment(cls):
cls.count += 1
In this example, using a static method would prevent access to cls.count, making it useless for this kind of task.
Python Static Method vs Class Method
Though they look similar, class and static methods in Python have different uses; so, let's now quickly review their differences.
Defined inside a class, a class method is connected to that class rather than an instance. Conventionally called cls, the class itself is the first parameter; so, it can access and change class-level data. Factory patterns, alternate constructors, or any activity applicable to the class as a whole and not individual instances are often implemented via class methods.
Conversely, a static method is defined within a class but does not start with either self or cls parameters. It is just a regular function that “lives” inside a class but doesn’t interact with the class or its instances. For utility tasks that are conceptually related to the class but don’t depend on its state, static methods are perfect.
Here's a quick breakdown of the Python class/static methods differences:
Feature
Class Method
Static Method
Binding
Bound to the class
Not bound to class or instance
First parameter
cls (class itself)
None (no self or cls)
Access to class/instance data
Yes
No
Common use cases
Factory methods, class-level behavior
Utility/helper functions
Decorator
@classmethod
@staticmethod
Python Static Method vs Regular Functions
You might ask: why not just define a function outside the class instead of using a static method? The answer is structure. A static method keeps related logic grouped within the class, even if it doesn't interact with the class or its instances.
# Regular function
def is_even(n):
return n % 2 == 0
# Static method inside a class
class NumberUtils:
@staticmethod
def is_even(n):
return n % 2 == 0
Both functions do the same thing, but placing is_even inside NumberUtils helps keep utility logic organized and easier to find later.
Let’s proceed to the hands-on Python static method examples.
Example #1
Imagine that we have a MathUtils class that contains a static method for calculating the factorial:
class MathUtils:
@staticmethod
def factorial(n):
if n == 0:
return 1
else:
return n * MathUtils.factorial(n-1)
Next, let's enter:
print(MathUtils.factorial(5))120
We get the factorial of 5, which is 120. Here, the factorial static method does not use any attributes of the class instance, only the input argument n. And we called it using the MathUtils.factorial(n) syntax without creating an instance of the MathUtils class.
In Python, static methods apply not only in classes but also in modules and packages. The @staticmethod decorator marks a function you define inside a class if it does not interact with instance-specific data. The function exists on its own; it is related to the class logically but is independent of its internal state.
Managed solution for Backend development
Example #2
Let's say we're working with a StringUtils module with a static method for checking if a string is a palindrome. The code will be:
def is_palindrome(string): return string == string[::-1]
This function doesn't rely on any instance-specific data — it simply performs a check on the input. That makes it a good candidate for a static method. To organize it within a class and signal that it doesn't depend on the class state, we can use the @staticmethod decorator like this:
class StringUtils: @staticmethod def is_palindrome(string): return string == string[::-1]
Let's enter for verification:
print(StringUtils.is_palindrome("deed"))True
print(StringUtils.is_palindrome("deer"))False
That's correct, the first word is a palindrome, so the interpreter outputs True, but the second word is not, and we get False.
So, we can call the is_palindrome method through the StringUtils class using the StringUtils.is_palindrome(string) syntax instead of importing the is_palindrome function and calling it directly.
-
Python static method and class instance also differ in that the static cannot affect the state of an instance. Since they do not have access to the instance, they cannot alter attribute values, which makes sense. Instance methods are how one may modify the instance state of a class.
Example #3
Let's look at another example. Suppose we have a Person class that has an age attribute and a static is_adult method that checks the value against the age of majority:
class Person: def __init__(self, age): self.age = age @staticmethod def is_adult(age): return age >= 21
Next, let's create an age variable with a value of 24, call the is_adult static method from the Person class with this value and store its result in the is_adult variable, like this:
age = 24is_adult = Person.is_adult(age)
Now to test this, let's enter:
print(is_adult)True
Since the age matches the condition specified in the static method, we get True. In the example, the is_adult static method serves as an auxiliary tool—a helper function—accepting the age argument but without access to the age attribute of the Person class instance.
Conclusion
Static methods improve code readability and make it possible to reuse it. They are also more convenient when compared to standard Python functions. Static methods are convenient as, unlike functions, they do not call for a separate import. Therefore, applying Python class static methods can help you streamline and work with your code greatly. And, as you've probably seen from the examples above, they are quite easy to master.
On our app platform you can find Python applications, such as Celery, Django, FastAPI and Flask.
16 April 2025 · 6 min to read
Python
Input in Python
Python provides interactive capabilities through various tools, one of which is the input() function. Its primary purpose is to receive user input. This function makes Python programs meaningful because without user interaction, applications would have limited utility.
How the Python Input Works
This function operates as follows:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
First, the user is asked to enter their name, then their age. Both inputs are captured using a special operator that stores the entered values in the variables user_name and user_age. These values can then be used in the program.
For example, we can create an age-based access condition for a website (by converting the age input to an integer using int()) and display a welcome message using the entered name:
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
So, what happens when int() receives an empty value? If the user presses Enter without entering anything, let's see what happens by extending the program:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
Pressing Enter moves the program to the next line of code. If there is no next line, the program exits. The last line can be written as:
input('Press Enter to exit')
If there are no more lines in the program, it will exit. Here is the complete version of the program:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
input('Press Enter to exit')
input('Press Enter to exit')
If the user enters an acceptable age, they will see the message inside the else block. Otherwise, they will see only the if block message and the final exit prompt. The input() function is used four times in this program, and in the last two cases, it does not store any values but serves to move to the next part of the code or exit the program.
input() in the Python Interpreter
The above example is a complete program, but you can also execute it line by line in the Python interpreter. However, in this case, you must enter data immediately to continue:
>>> user_name = input('Enter your name: ')
Enter your name: Jamie
>>> user_age = int(input('How old are you? '))
How old are you? 18
The code will still execute, and values will be stored in variables. This method allows testing specific code blocks. However, keep in mind that values are retained only until you exit the interactive mode. It is recommended to save your code in a .py file.
Input Conversion Methods: int(), float(), split()
Sometimes, we need to convert user input into a specific data type, such as an integer, a floating-point number, or a list.
Integer conversion (int())
We've already seen this in a previous example:
user_age = int(input('How old are you? '))
The int() function converts input into an integer, allowing Python to process it as a numeric type. By default, numbers entered by users are treated as strings, so Python requires explicit conversion. A more detailed approach would be:
user_age = input('How old are you? ')
user_age = int(user_age)
The first method is shorter and more convenient, but the second method is useful for understanding function behavior.
Floating-point conversion (float())
To convert user input into a floating-point number, use float():
height = float(input('Enter your height (e.g., 1.72): '))
weight = float(input('Enter your weight (e.g., 80.3): '))
Or using a more detailed approach:
height = input('Enter your height (e.g., 1.72): ')
height = float(height)
weight = input('Enter your weight (e.g., 80.3): ')
weight = float(weight)
Now, the program can perform calculations with floating-point numbers.
Converting Input into a List (split())
The split() method converts input text into a list of words:
animals = input('Enter your favorite animals separated by spaces: ').split()
print('Here they are as a list:', animals)
Example output:
Enter your favorite animals separated by spaces: cat dog rabbit fox bear
Here they are as a list: ['cat', 'dog', 'rabbit', 'fox', 'bear']
Handling Input Errors
Users often make mistakes while entering data or may intentionally enter incorrect characters. In such cases, incorrect input can cause the program to crash:
>>> height = float(input('Enter your height (e.g., 1.72): '))
Enter your height (e.g., 1.72): 1m72
Traceback (most recent call last):
File "<pyshell#2>", line 1, in <module>
height = float(input('Enter your height (e.g., 1.72): '))
ValueError: could not convert string to float: '1m72'
The error message indicates that Python cannot convert the string into a float. To prevent such crashes, we use the try-except block:
try:
height = float(input('Enter your height (e.g., 1.72): '))
except ValueError:
height = float(input('Please enter your height in the correct format: '))
We can also modify our initial age-input program to be more robust:
try:
user_age = int(input('How old are you? '))
except ValueError:
user_age = int(input('Please enter a number: '))
However, the program will still crash if the user enters incorrect data again. To make it more resilient, we can use a while loop:
while True:
try:
height = float(input('Enter your height (e.g., 1.72): '))
break
except ValueError:
print('Let’s try again.')
continue
print('Thank you!')
Here, we use a while loop with break and continue. The program works as follows:
If the input is correct, the loop breaks, and the program proceeds to the final message: print('Thank you!').
If the program cannot convert input to a float, it catches an exception (ValueError) and displays the message "Let’s try again."
The continue statement prevents the program from crashing and loops back to request input again.
Now, the user must enter valid data before proceeding.
Here is the complete code for a more resilient program:
user_name = input('Enter your name: ')
while True:
try:
user_age = int(input('How old are you? '))
break
except ValueError:
print('Are you sure?')
continue
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
input('Press Enter to exit')
This program still allows unrealistic inputs (e.g., 3 meters tall or 300 years old). To enforce realistic values, additional range checks would be needed, but that is beyond the scope of this article.
08 April 2025 · 6 min to read
Python
Operators in Python
Python operators are tools used to perform various actions with variables, as well as numerical and other values called operands—objects on which operations are performed. There are several types of Python operators:
Arithmetic
Comparison
Assignment
Identity
Membership
Logical
Bitwise
This article will examine each of them in detail and provide examples.
Arithmetic Operators
For addition, subtraction, multiplication, and division, we use the Python operators +, -, *, and / respectively:
>>> 24 + 28
52
>>> 24 - 28
-4
>>> 24 * 28
672
>>> 24 / 28
0.8571428571428571
For exponentiation, ** is used:
>>> 5 ** 2
25
>>> 5 ** 3
125
>>> 5 ** 4
625
For floor division (integer division without remainder), // is used:
>>> 61 // 12
5
>>> 52 // 22
2
>>> 75 // 3
25
>>> 77 // 3
25
The % operator returns the remainder (modulo division):
>>> 62 % 6
2
>>> 65 % 9
2
>>> 48 % 5
3
>>> 48 % 12
0
Comparison Operators
Python has six comparison operators: >, <, >=, <=, ==, !=.
Note that equality in Python is written as ==, because a single = is used for assignment. The != operator is used for "not equal to." When comparing values, Python will return True or False depending on whether the expressions are true or false.
>>> 26 > 58
False
>>> 26 < 58
True
>>> 26 >= 26
True
>>> 58 <= 57
False
>>> 50 == 50
True
>>> 50 != 50
False
>>> 50 != 51
True
Assignment Operators
A single = is used for assigning values to variables:
>>> b = 5
>>> variants = 20
Python also provides convenient shorthand operators that combine arithmetic operations with assignment: +=, -=, *=, /=, //=, %=. For example:
>>> cars = 5
>>> cars += 7
>>> cars
12
This is equivalent to:
>>> cars = cars + 7
>>> cars
12
The first version is more compact. Other assignment operators work similarly:
>>> train = 11
>>> train -= 2
>>> train
9
>>> moto = 3
>>> moto *= 7
>>> moto
21
>>> plain = 8
>>> plain /= 4
>>> plain
2.0
Notice that in the last case, the result is a floating-point number (float), not an integer (int).
Identity Operators
Python has two identity operators: is and is not. The results are True or False, similar to comparison operators.
>>> 55 is 55
True
>>> 55 is 56
False
>>> 55 is not 55
False
>>> 55 is not 56
True
>>> 55 is '55'
False
>>> '55' is "55"
True
In the last two examples:
55 is '55' returned False because an integer and a string were compared.
'55' is "55" returned True because both operands are strings.
Python does not differentiate between single and double quotes, so the identity check was successful.
Membership Operators
There are only two membership operators in Python: in and not in. They check whether a certain value exists within a sequence. For example:
>>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach')
>>> 'house' in wordlist
False
>>> 'assistant' in wordlist
True
>>> 'assistant' and 'streetcar' in wordlist
True
In the last case, a logical operator (and) was used, which leads us to the next topic.
Logical Operators
Python has three logical operators: and, or, and not.
and returns True only if all operands are true. It can process any number of values. Using an example from the previous section:
>>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach')
>>> 'assistant' and 'streetcar' in wordlist
True
>>> 'fraudster' and 'dancer' and 'heat' and 'blank' in wordlist
True
>>> 'fraudster' and 'dancer' and 'heat' and 'blank' and 'house' in wordlist
False
Since 'house' is not in the sequence, the result is False. These operations also work with numerical values:
>>> numbers = 54 > 55 and 22 > 21
>>> print(numbers)
False
One of the expressions is false, and and requires all conditions to be true.
or works differently: it returns True if at least one operand is true. If we replace and with or in the previous example, we get:
>>> numbers = 54 > 55 or 22 > 21
>>> print(numbers)
True
Here, 22 > 21 is true, so the overall expression evaluates to True, even though 54 > 55 is false.
not reverses logical values:
>>> first = True
>>> second = False
>>> print(not first)
False
>>> print(not second)
True
As seen in the example, not flips True to False and vice versa.
Bitwise Operators
Bitwise operators are used in Python to manipulate objects at the bit level. There are five of them (shift operators belong to the same type, as they differ only in shift direction):
& (AND)
| (OR)
^ (XOR)
~ (NOT)
<< and >> (shift operators)
Bitwise operators are based on Boolean logic principles and work as follows:
& (AND) returns 1 if both operands contain 1; otherwise, it returns 0:
>>> 1 & 1
1
>>> 1 & 0
0
>>> 0 & 1
0
>>> 0 & 0
0
| (OR) returns 1 if at least one operand contains 1, otherwise 0:
>>> 1 | 1
1
>>> 1 | 0
1
>>> 0 | 1
1
>>> 0 | 0
0
^ (XOR) returns 1 if the operands are different and 0 if they are the same:
>>> 1 ^ 1
0
>>> 1 ^ 0
1
>>> 0 ^ 1
1
>>> 0 ^ 0
0
~ (NOT) inverts bits, turning positive values into negative ones with a shift of one:
>>> ~5
-6
>>> ~-5
4
>>> ~7
-8
>>> ~-7
6
>>> ~9
-10
>>> ~-9
8
<< and >> shift bits by a specified number of positions:
>>> 1 << 1
2
>>> 1 >> 1
0
To understand shifts, let’s break down values into bits:
0 = 00
1 = 01
2 = 10
Shifting 1 left by one bit gives 2, while shifting right results in 0. What happens if we shift by two positions?
>>> 1 << 2
4
>>> 1 >> 2
0
1 = 001
2 = 010
4 = 100
Shifting 1 two places to the left results in 4 (100 in binary). Shifting right always results in zero because bits are discarded.
For more details, refer to our article on bitwise operators.
Difference Between Operators and Functions
You may have noticed that we have included no functions in this overview. The confusion between operators and functions arises because both perform similar actions—transforming objects. However:
Functions are broader and can operate on strings, entire blocks of code, and more.
Operators work only with individual values and variables.
In Python, a function can consist of a block of operators, but operators can never contain functions.
08 April 2025 · 6 min to read
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