How about Levinux Qemu virtual machine running small linux distro with python? Levinux (download ~20 MB) is a tiny virtual Linux server that runs from USB or Dropbox with a double-click (no install or admin rights required) on Macs, Windows or Linux PCs—making it the perfect learning environment, and way to run & keep your code safe for life! Think of it as an introduction to old-skool “short stack” development—more relevant now then ever as Linux/Unix gets embedded into everything. More on reddit.com

Of course you can. You'll have to sign up to use some kind of cloud computing service like AWS, GCP, Azure, Linode, Vultr, DigitalOcean, Heroku, etc. Almost all of these have some kind of free trial or free tier of service too, so you can try it out or do small workloads without incurring fees. Just make sure to turn off (or delete in some cases) your resources after you are done using them or you can rack up charges without realizing. More on reddit.com

There is no "Python Virtual Machine", really. The python executable contains a bytecode compiler and a bytecode interpreter, but those are an implementation detail - you can have a Python implementation that compiles your code ahead of time, like Nuitka, or one using JIT compilation, like PyPy, or one that does not compile anything at all, like MicroPython. More on reddit.com

A Python virtual environment is created using an already installed version of Python which is copied to a new folder of your choosing. See my notes below. Python Setup Setting up a Python environment can be confusing. There are web based offerings that can be used instead, such as replit.com. You might also come across Jupyter Notebook options, which can seem very easy to work but have some issues to be aware of. Pre-installed system Python Some operating system environments come with a version of Python pre-installed. For many years, macOS included an installation of python 2.7. (Now has a more recent version.) This is known as the system version of Python. There may be python code used for utility purposes on your system that depend on this version of python. You can still install your own version. Installing Python There are multiple ways of installing Python using a package manager for your OS, e.g. homebrew (macOS third party), chocolatey (Windows third party) or winget (Windows standard package manager), apt (many linux distributions) or using the Python Software Foundation (PSF) installer from python.org or some kind of app store for your operating system. You could also use docker containers with Python install inside them. PSF offer the reference implementation of Python, known as CPython (written in C and Python). The executable on your system will be called python (python.exe on Windows). Beginners are probably best served using the PSF installer. Terminal / Console For most purposes, terminal is the same as console. It is the text based, rather than graphical based, window / screen you work in. Your operating system will offer a command or terminal environment. Python by default outputs to a terminal and reads user input from a terminal. Libraries / Frameworks / Packages Python comes with many batteries included in the form of libraries of code providing more specialist functionality. Already installed as part of a standard installation of Python (the CPython reference implementation of Python, written in C and Python, from the Python Software Foundation at python.org). These libraries are not automatically loaded into memory every time you start a Python running as that would use a lot of memory up and slow down start up time. Instead, you use, in your code, the command import , e.g. import math print(math.pi) There are thousands of additional packages / libraries / frameworks available that don't come as standard with an installation of Python that you have to install yourself. Quality, support (and safety) varies. That's where the package manager pip comes in. It uses an official repository of such additional code that it searches for a match to what you ask to be installed. For example, using a command / powershell / terminal environment for your operating system, pip install numpy would install the numpy library from the pypi respository. There is a complication here. Typically, on macOS or most linux systems, you would say, pip3 install numpy or alternatively, python3 -m pip install numpy This is because python often refers to the now unsupported older version 2.x of Python and for years we lived with both 2.x and 3.x. On Windows, you will often see py used instead, py -m pip install numpy where py refers to the python launcher which should invoke the most up-to-date version of Python installed on your system regardless of PATH settings. Some Code Editors and IDEs (Integrated Development Environments), such as VS Code and PyCharm, include their own facilities to install packages using pip or some other tool. This just saves you typing the commands. They also often offering their own terminal window(s). Running Python The CPython programme can be invoked for two different purposes: to attempt to execute a simple text file of python code (typically the files have an extension of .py to enter an interactive shell, with a >>> prompt, where you can enter python commands and get instant responses - great for trying things out So, entering the below, as appropriate for your operating system, python python3 py on its own, no file name after it, you will enter an interactive session. Enter exit() to return to the operating system command line IDLE Editor A standard installation from python.org for Windows or macOS includes a programme called IDLE. This is a simple code editor and execution environment. By default, when you first open it, it opens a single window with a Python shell, with the >>> prompt already open. To create a new text file to enter Python code into, you need to use your operating system means of access the standard menu and select File | New. Once you've entered code, press F5 to attempt to run the code (you will be prompted to save the file first). This is really the easiest editor to use to begin with. Virtual Environments Given the thousands of packages (libraries, frameworks, etc) out there, you can see that if you are working on several different projects, you can end up installing a vast range of different packages, only a few of which will be used for any particular project. This is where Python virtual environments come in. Not to be confused with virtual machines. Typically created on a project-by-project basis. Install only the packages required for a project. This helps avoid conflicts between packages, especially version complications. Most popular code editors and IDEs, including Microsoft's VS Code and Jetbrain's PyCharm, offer built-in features to help to start off new projects and create and activate Python virtual environments. You can create a new Python virtual environment from your operating system command line environment using, for Windows, py -m venv venv or, for macOS / linux, python3 -m venv venv which creates a new folder in the current working directory called venv (taken from the last argument, you can use a different name). You then activate using, for Windows, venv\Scripts\activate or, for macOS / linux, source venv/bin/activate the command deactivate for any plaform will deactive the virtual environment and return you to using the base environment. More on reddit.com