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How to Set Up Visual Studio Code for Python

How to Set Up Visual Studio Code for Python
Hostman Team
Technical writer
Python
05.02.2025
Reading time: 10 min

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

  1. Go to the official Python website and download the installer file. In this case, we will be installing Python version 3.13.1.

F0a25261 3828 4bc2 923d 786a97e670be (1)

  1. Run the installer file.
  2. 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.

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  1. Once the installation is complete, the program will notify you that it has finished.

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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

D4a7a3e0 283f 4b80 Bb4b 2324887d13ae

However, the installed version may be outdated.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 python

In our case, several versions of Python are available:

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Install the latest available version, Python 3.13, by running the following command:

brew install python@3.13

Check the Python version again:

python3 --version
python3.13 --version

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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@3.13

Then, check which shell you are using:

echo $SHELL

Depending on the shell used, open the corresponding file for editing:

For bash or sh:

nano ~/.bashrc

For zsh:

nano ~/.zshrc

Add 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 ~/.zshrc

Now, when you check the Python version, it will display the latest installed version:

python3 --version

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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

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However, if Python is not installed for any reason, you can install it by running the following command:

apt -y install python3

Installing 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. 

  1. Go to the official website and download the installer.

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  1. This will download an .exe installation file. Run the installer file. 
  2. On the first step, accept the license agreement by selecting the option "I accept the agreement".

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  1. 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.

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  1. 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:

Image3

  1. 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).

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  1. 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:

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For macOS

  1. Go to the official website and download the installer:

Image33

  1. 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.
  2. 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:

Image19

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.

  1. Go to the official website and download the installer for your Linux distribution. In our case, we need the .deb installer:

Image37

  1. Once the file is downloaded, open a terminal (console) and navigate to the directory where the file was downloaded (e.g., /root).

  2. To install, run the following command where code_1.96.2-1734607745_amd64.deb is the name of the downloaded file:

dpkg -i code_1.96.2-1734607745_amd64.deb
  1. During installation, a message will prompt you to add Microsoft repositories to the system. Select <Yes> and press Enter:

Image39

  1. Wait for the installation to complete.

  2. 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: ApplicationsDevelopment):

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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:

  1. Press Win+R, type sysdm.cpl in the Run window, and press Enter.
  2. In the window that opens, go to the Advanced tab and click on the Environment Variables button.

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  1. To add a user-level variable, select the Path variable under User variables and click on Edit.

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  1. Double-click on an empty field or click the New button.
  2. 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\Python313

For example:

C:\Users\Administrator\AppData\Local\Programs\Python\Python313

Image20 (1)

  1. After entering the path, click OK to save the changes.
  2. To verify, open the command prompt and type python. If the path to the interpreter is correctly specified, the Python console will open.

Image33 (1)

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:

  1. Open Visual Studio Code and click the New File... button on the home page to create a new file. 

Image12 (1)

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.

  1. Type any name for the file, use the .py extension, and press Enter.

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  1. Save the file in any location. Ensure that the file name ends with the .py extension.

Image23 (1)

  1. 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.

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  1. 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.

Image11 (1)

  1. In the menu that appears, select Enter interpreter path… and press Enter.

Image24

  1. Specify the full path to the Python interpreter. By default, it is located at:
C:\Users\Administrator\AppData\Local\Programs\Python\Python313

where Administrator is your user account name. Select the file named python and click on Select Interpreter.

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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.

Image8

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.

Image4

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.

Image18 (1)

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.

Image9

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.

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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.

Image25

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.

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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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Command-Line Option and Argument Parsing using argparse in Python

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This description is displayed when the user runs the program via the --help option. Including --timeout: The --timeout option is not obligatory (indicated by the -- prefix). The type=int makes the argument for --timeout an integer. The default=30 is provided so that in case the user does not enter a value, then the timeout would be 30 seconds. The help parameter adds a description to the argument, and it will also appear in the help documentation. Parsing Process: The parse_args() function processes user inputs and makes them accessible as attributes of the args object. In our example, we access args.timeout and print out its value. 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Here, we will specify a --mode option with two default values: basic and advanced. import argparse # Creating argument parser parser = argparse.ArgumentParser(description="Demonstrating the use of choices in argparse.") # Adding the --mode argument with predefined choices parser.add_argument('--mode', choices=['basic', 'advanced'], help="Choose the mode of operation") # Parse the arguments args = parser.parse_args() # Access and display the selected mode if args.mode: print(f"Mode selected: {args.mode}") else: print("No mode selected. Please choose 'basic' or 'advanced'.") Adding --mode: The choices argument indicates that valid options for the --mode are basic and advanced. The application will fail when the user supplies an input other than in choices. Help Text: The help parameter gives valuable information when the --help command is executed. 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21 July 2025 · 10 min to read
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How to Get the Length of a List in Python

Lists in Python are used almost everywhere. In this tutorial we will look at four ways to find the length of a Python list: by using built‑in functions, recursion, and a loop. Knowing the length of a list is most often required to iterate through it and perform various operations on it. len() function len() is a built‑in Python function for finding the length of a list. It takes one argument—the list itself—and returns an integer equal to the list’s length. The same function also works with other iterable objects, such as strings. Country_list = ["The United States of America", "Cyprus", "Netherlands", "Germany"] count = len(Country_list) print("There are", count, "countries") Output: There are 4 countries Finding the Length of a List with a Loop You can determine a list’s length in Python with a for loop. The idea is to traverse the entire list while incrementing a counter by  1 on each iteration. Let’s wrap this in a separate function: def list_length(list): counter = 0 for i in list: counter = counter + 1 return counter Country_list = ["The United States of America", "Cyprus", "Netherlands", "Germany", "Japan"] count = list_length(Country_list) print("There are", count, "countries") Output: There are 5 countries Finding the Length of a List with Recursion The same task can be solved with recursion: def list_length_recursive(list): if not list: return 0 return 1 + list_length_recursive(list[1:]) Country_list = ["The United States of America", "Cyprus", "Netherlands","Germany", "Japan", "Poland"] count = list_length_recursive(Country_list) print("There are", count, "countries") Output: There are 6 countries How it works. The function list_length_recursive() receives a list as input. If the list is empty, it returns 0—the length of an empty list. Otherwise it calls itself recursively with the argument list[1:], a slice of the original list starting from index 1 (i.e., the list without the element at index 0). The result of that call is added to 1. With each recursive step the returned value grows by one while the list shrinks by one element. length_hint() function The length_hint() function lives in the operator module. That module contains functions analogous to Python’s internal operators: addition, subtraction, comparison, and so on. length_hint() returns the length of iterable objects such as strings, tuples, dictionaries, and lists. It works similarly to len(): from operator import length_hint Country_list = ["The United States of America", "Cyprus", "Netherlands","Germany", "Japan", "Poland", "Sweden"] count = length_hint(Country_list) print("There are", count, "countries") Output: There are 7 countries Note that length_hint() must be imported before use. Conclusion In this guide we covered four ways to determine the length of a list in Python. Under equal conditions the most efficient method is len(). The other approaches are justified mainly when you are implementing custom classes similar to list.
17 July 2025 · 3 min to read
Python

Understanding the main() Function in Python

In any complex program, it’s crucial to organize the code properly: define a starting point and separate its logical components. In Python, modules can be executed on their own or imported into other modules, so a well‑designed program must detect the execution context and adjust its behavior accordingly.  Separating run‑time code from import‑time code prevents premature execution, and having a single entry point makes it easier to configure launch parameters, pass command‑line arguments, and set up tests. When all important logic is gathered in one place, adding automated tests and rolling out new features becomes much more convenient.  For exactly these reasons it is common in Python to create a dedicated function that is called only when the script is run directly. Thanks to it, the code stays clean, modular, and controllable. That function, usually named main(), is the focus of this article. All examples were executed with Python 3.10.12 on a Hostman cloud server running Ubuntu 22.04. Each script was placed in a separate .py file (e.g., script.py) and started with: python script.py The scripts are written so they can be run just as easily in any online Python compiler for quick demonstrations. What Is the main() Function in Python The simplest Python code might look like: print("Hello, world!")  # direct execution Or a script might execute statements in sequence at file level: print("Hello, world!")       # action #1 print("How are you, world?") # action #2 print("Good‑bye, world...")  # action #3 That trivial arrangement works only for the simplest scripts. As a program grows, the logic quickly becomes tangled and demands re‑organization: # function containing the program’s main logic (entry point) def main():     print("Hello, world!") # launch the main logic if __name__ == "__main__":     main()                    # call the function with the main logic With more actions the code might look like: def main(): print("Hello, world!") print("How are you, world?") print("Good‑bye, world...") if __name__ == "__main__": main() This implementation has several important aspects, discussed below. The main() Function The core program logic lives inside a separate function. Although the name can be anything, developers usually choose main, mirroring C, C++, Java, and other languages.  Both helper code and the main logic are encapsulated: nothing sits “naked” at file scope. # greeting helper def greet(name): print(f"Hello, {name}!") # program logic def main(): name = input("Enter your name: ") greet(name) # launch the program if __name__ == "__main__": main() Thus main() acts as the entry point just as in many other languages. The if __name__ == "__main__" Check Before calling main() comes the somewhat odd construct if __name__ == "__main__":.  Its purpose is to split running from importing logic: If the script runs directly, the code inside the if block executes. If the script is imported, the block is skipped. Inside that block, you can put any code—not only the main() call: if __name__ == "__main__":     print("Any code can live here, not only main()") __name__ is one of Python’s built‑in “dunder” (double‑underscore) variables, often called magic or special. All dunder objects are defined and used internally by Python, but regular users can read them too. Depending on the context, __name__ holds: "__main__" when the module runs as a standalone script. The module’s own name when it is imported elsewhere. This lets a module discover its execution context. Advantages of Using  main() Organization Helper functions and classes, as well as the main function, are wrapped separately, making them easy to find and read. Global code is minimal—only initialization stays at file scope: def process_data(data): return [d * 2 for d in data] def main(): raw = [1, 2, 3, 4] result = process_data(raw) print("Result:", result) if __name__ == "__main__": main() A consistent style means no data manipulation happens at the file level. Even in a large script you can quickly locate the start of execution and any auxiliary sections. Isolation When code is written directly at the module level, every temporary variable, file handle, or connection lives in the global namespace, which can be painful for debugging and testing. Importing such a module pollutes the importer’s globals: # executes immediately on import values = [2, 4, 6] doubles = [] for v in values: doubles.append(v * 2) print("Doubled values:", doubles) With main() everything is local; when the function returns, its variables vanish: def double_list(items): return [x * 2 for x in items] # create a new list with doubled elements def main(): values = [2, 4, 6] result = double_list(values) print("Doubled values:", result) if __name__ == "__main__": main() That’s invaluable for unit testing, where you might run specific functions (including  main()) without triggering the whole program. Safety Without the __name__ check, top‑level code runs even on import—usually undesirable and potentially harmful. some.py: print("This code will execute even on import!") def useful_function(): return 42 main.py: import some print("The logic of the imported module executed itself...") Console: This code will execute even on import! The logic of the imported module executed itself... The safer some.py: def useful_function():     return 42 def main():     print("This code will not run on import") main() plus the __name__ check guard against accidental execution. Inside main() you can also verify user permissions or environment variables. How to Write main() in Python Remember: main() is not a language construct, just a regular function promoted to “entry point.” To ensure it runs only when the script starts directly: Tools – define helper functions with business logic. Logic – assemble them inside main() in the desired order. Check – add the if __name__ == "__main__" guard.  This template yields structured, import‑safe, test‑friendly code—excellent practice for any sizable Python project. Example Python Program Using main() # import the standard counter from collections import Counter # runs no matter how the program starts print("The text‑analysis program is active") # text‑analysis helper def analyze_text(text): words = text.split() # split text into words total = len(words) # total word count unique = len(set(words)) # unique word count avg_len = sum(len(w) for w in words) / total if total else 0 freq = Counter(words) # build frequency counter top3 = freq.most_common(3) # top three words return { 'total': total, 'unique': unique, 'avg_len': avg_len, 'top3': top3 } # program’s main logic def main(): print("Enter text (multiple lines). Press Enter on an empty line to finish:") lines = [] while True: line = input() if not line: break lines.append(line) text = ' '.join(lines) stats = analyze_text(text) print(f"\nTotal number of words: {stats['total']}") print(f"Unique words: {stats['unique']}") print(f"Average word length: {stats['avg_len']:.2f}") print("Top‑3 most frequent words:") for word, count in stats['top3']: print(f" {word!r}: {count} time(s)") # launch program if __name__ == "__main__": main() Running the script prints a prompt: Enter text (multiple lines). Press Enter on an empty line to finish: Input first line: Star cruiser Orion glided silently through the darkness of intergalactic space. Second line: Signals of unknown life‑forms flashed on the onboard sensors where the nebula glowed with a phosphorescent light. Third line: The cruiser checked the sensors, then the cruiser activated the defense system, and the cruiser returned to its course. Console output: The text‑analysis program is active Total number of words: 47 Unique words: 37 Average word length: 5.68 Top‑3 most frequent words: 'the': 7 time(s) 'cruiser': 4 time(s) 'of': 2 time(s) If you import this program (file program.py) elsewhere: import program         # importing program.py Only the code outside main() runs: The text‑analysis program is active So, a moderately complex text‑analysis utility achieves clear logic separation and context detection. When to Use main() and When Not To Use  main() (almost always appropriate) when: Medium/large scripts – significant code with non‑trivial logic, multiple functions/classes. Libraries or CLI utilities – you want parts of the module importable without side effects. Autotests – you need to test pure logic without extra boilerplate. You can skip main() when: Tiny one‑off scripts – trivial logic for a quick data tweak. Educational snippets – short examples illustrating a few syntax features. In short, if your Python program is a standalone utility or app with multiple processing stages, command‑line arguments, and external resources—introduce  main(). If it’s a small throw‑away script, omitting main() keeps things concise. Conclusion The  main() function in Python serves two critical purposes: Isolates the program’s core logic from the global namespace. Separates standalone‑execution logic from import logic. Thus, a Python file evolves from a straightforward script of sequential actions into a fully‑fledged program with an entry point, encapsulated logic, and the ability to detect its runtime environment.
14 July 2025 · 8 min to read

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