The old way to manage virtual environments was with the venv tool. To install it, use the command sudo apt install python3-venv.

To manage virtual environments in a modern way, the pipenv module (https://docs.pipenv.org/) was developed; it automatically creates and manages virtual environments for projects, as well as adding and removing packages from Pipfile when you install/uninstall packages. It can be installed using pip install pipenv.

Pipfile is an alternative to requirements.txt, which is used to specify exact versions of modules to include in a program. Pipfile actually comprises two separate files: Pipfile and (optionally) Pipfile.lock. Pipfile is simply a listing of the source location of imported modules, the module names themselves (defaulting to the most recent version), and any development packages that are required. pipfile.py, below, is an example of a Pipfile from the Pipenv site (https://docs.pipenv.org/basics/#example-pipfile-pipfile-lock):

[[source]]
url = "https://pypi.python.org/simple"
verify_ssl = true
name = "pypi"

[packages]
requests = "*"


[dev-packages]
pytest = "*"

Pipfile.lock takes the Pipfile and sets actual version numbers to all the packages, as well as identifying specific hashes for those files. Hashed values are beneficial to minimize security risks; that is, if a particular module version has a vulnerability, its hash value allows it to be easily identified, rather than having to search by version name or some other method. pipfile_lock.py, below, is an example of a Pipfile.lock file from the Pipenv site (https://docs.pipenv.org/basics/#example-pipfile-pipfile-lock):

{
  "_meta": {
    "hash": {
      "sha256": "8d14434df45e0ef884d6c3f6e8048ba72335637a8631cc44792f52fd20b6f97a"
    },
    "host-environment-markers": {
      "implementation_name": "cpython",
      "implementation_version": "3.6.1",
      "os_name": "posix",
      "platform_machine": "x86_64",
      "platform_python_implementation": "CPython",
      "platform_release": "16.7.0",
      "platform_system": "Darwin",
      "platform_version": "Darwin Kernel Version 16.7.0: Thu Jun 15 17:36:27 PDT 2017; root:xnu-3789.70.16~2/RELEASE_X86_64",
      "python_full_version": "3.6.1",
      "python_version": "3.6",
      "sys_platform": "darwin"
    },
    "pipfile-spec": 5,
    "requires": {},
    "sources": [
      {
        "name": "pypi",
        "url": "https://pypi.python.org/simple",
        "verify_ssl": true
      }
    ]
  },
  "default": {
    "certifi": {
      "hashes": [
        "sha256:54a07c09c586b0e4c619f02a5e94e36619da8e2b053e20f594348c0611803704",
        "sha256:40523d2efb60523e113b44602298f0960e900388cf3bb6043f645cf57ea9e3f5"
      ],
      "version": "==2017.7.27.1"
    },
    "chardet": {
      "hashes": [
         "sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691",
         "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae"
      ],
      "version": "==3.0.4"
    },
***further entries truncated***

      >>> python3 -m venv <dir_name>

      >>> source <dir_name>/bin/activate

      >>> pip install <module>

      >>> cd <project_name>

      >>> pipenv install <module>

The preceding pipenv example shows the developer changing to the desired directory for the project, and then invoking pipenv to simultaneously create the virtual environment, activate it, and install the desired module.

In addition to creating the virtual environment, once you have created your Python program, you can run the program using pipenv as well:

>>> pipenv run python3 <program_name>.py

Doing this ensures all installed packages in the virtual environment are available to your program, thus reducing the likelihood of unexpected errors.

When launching a pipenv shell, a new virtual environment is created, with indications of where the environment is created in the file system. In this case, two environment executables are created, referencing both the Python 3.6 command and the default Python command. (Depending on the systems, these may actually reference different versions of Python. For example, the default Python command may call the Python 2.7 environment instead of Python 3.6.)

On a side note, the -m option indicates that Python is to run the module as a stand-alone script, that is, its contents will be ran within the __main__ namespace. Doing this means you don't have to know the full path to the module, as Python will look for the script in sys.path. In other words, for modules that you would normally import into another Python file can be run directly from the command line.

In the example of running pipenv, the command takes advantage of the fact that Python allows the -m option to run a module directly or allow it to be imported; in this case, pipenv imports venv to create the virtual environment as part of the creation process.