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How to Create a Virtual Environment in Python

How to Create a Virtual Environment in Python
Hostman Team
Technical writer
Python
21.03.2025
Reading time: 6 min

This article will teach you how to create a Python virtual environment. It is useful for Python developers to avoid issues with different versions of libraries.

A simple example: You have two applications that rely on the same library, but each requires a different version.

Another example: You want to ensure that your application runs independently of library updates installed in the global Python storage.

A third example: You do not have access to this global storage.

The solution in all three cases is to create a Python virtual environment. 

The module name venv is short for Virtual Environment. It is a great tool for project isolation, functioning like a sandbox. It allows you to run an application with its dependencies without interfering with other applications that use different versions of the same software. As a result, each application runs in its own virtual environment, isolated from others, increasing the overall stability of all applications.

How to Create a Virtual Environment in Python 3

Good news: You don’t need to install venv separately on Windows—it is part of the standard Python 3 library and comes with the interpreter.

On Linux, however, venv is not always included in the OS package, so you might need to install it. On Ubuntu/Debian, use the following command:

sudo apt install -y python3-venv

Some Python packages require compilation from source code, so you might also need to install the following dependencies:

sudo apt install -y build-essential libssl-dev libffi-dev python3-dev

Now, let's see how to create a Python 3 virtual environment in Windows and Linux using venv.

Step 1: Creating the Virtual Environment

Use the following command for all operating systems:

python -m venv venv

Here, -m tells Python to run the venv module. The second venv specifies the directory (venv/lib/python3.8/site-packages/, the version may vary) where Python will store all libraries and components required for isolated application execution.

Step 2: Activating the Virtual Environment

Activation differs between Windows and Linux.

On Windows, run:

venv\Scripts\activate.bat

On Linux (and MacOS), use:

source venv/bin/activate

If everything is set up correctly, you will see an output like this:

(venv) root@purplegate:/var/test#

Now you can start working on your project within the isolated environment!

Other Tools

Of course, venv is the most modern tool for creating virtual environments. However, it was only introduced in Python 3. So what should those working with older versions of Python do? The answer: try other tools that offer additional useful features—otherwise, we wouldn’t mention them at all. Below is a brief overview of these alternatives, followed by a more detailed look at the most popular one.

  • virtualenv – A simple and user-friendly tool that is widely used when deploying applications. It’s useful to learn, and we’ll provide instructions on how to use it below.
  • pyenv – Allows you to isolate different Python versions. This is helpful if you need to run multiple versions of Python, for example, for testing purposes.
  • virtualenvwrapper – A wrapper for virtualenv that helps manage virtual environments by simplifying tasks like creating, copying, and deleting environments. One of its advantages is that it allows easy switching between environments and supports various plugins for extended functionality.

Creating a Virtual Environment Using virtualenv

Let's go through the process using Linux as an example. However, running Python virtualenv on Windows is almost the same, with differences mainly in file paths and scripts, which we’ll mention separately.

Step 1: Install virtualenv

You can download the source code and install it manually, but the easiest way is to use pip. Just enter the following command in your terminal:

pip install virtualenv

Step 2: Create a Virtual Environment

This step requires just a simple command:

virtualenv myenv

This will create a new directory in the current folder. Instead of myenv, you can use any other name for your environment.

Directory structure of the virtual environment:

  • /myenv/bin – Contains scripts for managing the environment, a copy of the Python interpreter, pip, and some package management utilities. In Windows, this folder is located at /myenv/Scripts.
  • /myenv/lib and /myenv/include – Store the environment’s core libraries. Any newly installed files will go into /myenv/lib/pythonX.X/site-packages/, where X.X represents your Python version.

Step 3: Activate the Virtual Environment

Activation differs slightly between Linux and Windows.

For Linux, use:

source myenv/bin/activate

For Windows, run:

myenv\Scripts\activate.bat

Once activated, you will see the virtual environment’s name in your command line prompt.

If you create the virtual environment with the --system-site-packages flag, it will have access to the system-wide package storage:

virtualenv --system-site-packages myenv

Keep in mind that the system package paths differ:

  • Linux: /usr/lib/python3.8/site-packages
  • Windows: \Python38\Lib\site-packages

Version numbers may vary depending on your installation.

Step 4: Deactivate the Virtual Environment

Once you’re done working in the Python virtual environment, you should exit it properly.

For Linux, run:

deactivate

For Windows, use the batch file:

myenv\Scripts\deactivate.bat

What's New?

In addition to the venv and virtualenv modules we’ve already covered, there are more modern tools that provide greater flexibility in managing Python projects, including virtual environments:

  • Poetry – A package manager that helps manage application dependencies within a virtual environment. It also simplifies testing and deployment by automating many processes.
  • Pipenv – Another package manager that integrates pip and virtualenv, along with several other useful tools. It is designed to make environment and package management easier, as many developers eventually encounter version control issues in their projects.

Each of these tools deserves a deep dive, but for now, let’s focus on the key features of both.

Poetry: The Essentials

Poetry handles all aspects of managing libraries within a virtual environment, including installing, updating, and publishing them. The functionality of pip alone is often insufficient for these tasks.

Additionally, Poetry allows you to create and package applications with a single command (replace myproject with your actual project name):

poetry new myproject

If you want to initialize an existing directory as a Poetry project, use:

poetry init

Poetry can also:

  • Publish projects to private repositories
  • Track and manage dependencies
  • Enforce version control
  • Simplify working on private virtual servers by ensuring reliable project isolation

Pipenv: The Essentials

In simple terms, Pipenv is like pip + virtualenv, but with enhanced features. It eliminates the need for the traditional and sometimes cumbersome requirements.txt file.

Instead, Pipenv uses:

  • Pipfile.lock – Ensures package version consistency, which enhances security.
  • Pipfile – A more advanced replacement for requirements.txt. Unlike its predecessor, Pipfile updates automatically as package versions change, which is particularly useful for teams, reducing dependency conflicts.

Now you have a complete set of tools at your disposal, and managing multiple dependencies with different versions should no longer be a challenge! 

Python
21.03.2025
Reading time: 6 min

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Command-Line Option and Argument Parsing using argparse in Python

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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. 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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. 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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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