12. Virtual Environments and Packages
*************************************


12.1. Introduction
==================

Python applications will often use packages and modules that don’t
come as part of the standard library.  Applications will sometimes
need a specific version of a library, because the application may
require that a particular bug has been fixed or the application may be
written using an obsolete version of the library’s interface.

This means it may not be possible for one Python installation to meet
the requirements of every application.  If application A needs version
1.0 of a particular module but application B needs version 2.0, then
the requirements are in conflict and installing either version 1.0 or
2.0 will leave one application unable to run.

The solution for this problem is to create a *virtual environment*, a
self-contained directory tree that contains a Python installation for
a particular version of Python, plus a number of additional packages.

Different applications can then use different virtual environments. To
resolve the earlier example of conflicting requirements, application A
can have its own virtual environment with version 1.0 installed while
application B has another virtual environment with version 2.0. If
application B requires a library be upgraded to version 3.0, this will
not affect application A’s environment.


12.2. Creating Virtual Environments
===================================

The module used to create and manage virtual environments is called
"venv".  "venv" will usually install the most recent version of Python
that you have available. If you have multiple versions of Python on
your system, you can select a specific Python version by running
"python3" or whichever version you want.

To create a virtual environment, decide upon a directory where you
want to place it, and run the "venv" module as a script with the
directory path:

   python3 -m venv tutorial-env

This will create the "tutorial-env" directory if it doesn’t exist, and
also create directories inside it containing a copy of the Python
interpreter, the standard library, and various supporting files.

A common directory location for a virtual environment is ".venv". This
name keeps the directory typically hidden in your shell and thus out
of the way while giving it a name that explains why the directory
exists. It also prevents clashing with ".env" environment variable
definition files that some tooling supports.

Once you’ve created a virtual environment, you may activate it.

On Windows, run:

   tutorial-env\Scripts\activate.bat

On Unix or MacOS, run:

   source tutorial-env/bin/activate

(This script is written for the bash shell.  If you use the **csh** or
**fish** shells, there are alternate "activate.csh" and
"activate.fish" scripts you should use instead.)

Activating the virtual environment will change your shell’s prompt to
show what virtual environment you’re using, and modify the environment
so that running "python" will get you that particular version and
installation of Python. For example:

   $ source ~/envs/tutorial-env/bin/activate
   (tutorial-env) $ python
   Python 3.5.1 (default, May  6 2016, 10:59:36)
     ...
   >>> import sys
   >>> sys.path
   ['', '/usr/local/lib/python35.zip', ...,
   '~/envs/tutorial-env/lib/python3.5/site-packages']
   >>>


12.3. Managing Packages with pip
================================

You can install, upgrade, and remove packages using a program called
**pip**.  By default "pip" will install packages from the Python
Package Index, <https://pypi.org>.  You can browse the Python Package
Index by going to it in your web browser, or you can use "pip"’s
limited search feature:

   (tutorial-env) $ pip search astronomy
   skyfield               - Elegant astronomy for Python
   gary                   - Galactic astronomy and gravitational dynamics.
   novas                  - The United States Naval Observatory NOVAS astronomy library
   astroobs               - Provides astronomy ephemeris to plan telescope observations
   PyAstronomy            - A collection of astronomy related tools for Python.
   ...

"pip" has a number of subcommands: “search”, “install”, “uninstall”,
“freeze”, etc.  (Consult the Installing Python Modules guide for
complete documentation for "pip".)

You can install the latest version of a package by specifying a
package’s name:

   (tutorial-env) $ pip install novas
   Collecting novas
     Downloading novas-3.1.1.3.tar.gz (136kB)
   Installing collected packages: novas
     Running setup.py install for novas
   Successfully installed novas-3.1.1.3

You can also install a specific version of a package by giving the
package name  followed by "==" and the version number:

   (tutorial-env) $ pip install requests==2.6.0
   Collecting requests==2.6.0
     Using cached requests-2.6.0-py2.py3-none-any.whl
   Installing collected packages: requests
   Successfully installed requests-2.6.0

If you re-run this command, "pip" will notice that the requested
version is already installed and do nothing.  You can supply a
different version number to get that version, or you can run "pip
install --upgrade" to upgrade the package to the latest version:

   (tutorial-env) $ pip install --upgrade requests
   Collecting requests
   Installing collected packages: requests
     Found existing installation: requests 2.6.0
       Uninstalling requests-2.6.0:
         Successfully uninstalled requests-2.6.0
   Successfully installed requests-2.7.0

"pip uninstall" followed by one or more package names will remove the
packages from the virtual environment.

"pip show" will display information about a particular package:

   (tutorial-env) $ pip show requests
   ---
   Metadata-Version: 2.0
   Name: requests
   Version: 2.7.0
   Summary: Python HTTP for Humans.
   Home-page: http://python-requests.org
   Author: Kenneth Reitz
   Author-email: me@kennethreitz.com
   License: Apache 2.0
   Location: /Users/akuchling/envs/tutorial-env/lib/python3.4/site-packages
   Requires:

"pip list" will display all of the packages installed in the virtual
environment:

   (tutorial-env) $ pip list
   novas (3.1.1.3)
   numpy (1.9.2)
   pip (7.0.3)
   requests (2.7.0)
   setuptools (16.0)

"pip freeze" will produce a similar list of the installed packages,
but the output uses the format that "pip install" expects. A common
convention is to put this list in a "requirements.txt" file:

   (tutorial-env) $ pip freeze > requirements.txt
   (tutorial-env) $ cat requirements.txt
   novas==3.1.1.3
   numpy==1.9.2
   requests==2.7.0

The "requirements.txt" can then be committed to version control and
shipped as part of an application.  Users can then install all the
necessary packages with "install -r":

   (tutorial-env) $ pip install -r requirements.txt
   Collecting novas==3.1.1.3 (from -r requirements.txt (line 1))
     ...
   Collecting numpy==1.9.2 (from -r requirements.txt (line 2))
     ...
   Collecting requests==2.7.0 (from -r requirements.txt (line 3))
     ...
   Installing collected packages: novas, numpy, requests
     Running setup.py install for novas
   Successfully installed novas-3.1.1.3 numpy-1.9.2 requests-2.7.0

"pip" has many more options.  Consult the Installing Python Modules
guide for complete documentation for "pip".  When you’ve written a
package and want to make it available on the Python Package Index,
consult the Distributing Python Modules guide.
