User Guide

Running pip

pip is a command line program. When you install pip, a pip command is added to your system, which can be run from the command prompt as follows:

$ pip <pip arguments>

If you cannot run the pip command directly (possibly because the location where it was installed isn't on your operating system's PATH) then you can run pip via the Python interpreter:

$ python -m pip <pip arguments>

On Windows, the py launcher can be used:

$ py -m pip <pip arguments>

Even though pip is available from your Python installation as an importable module, via import pip, it is not supported to use pip in this way. For more details, see Using pip from your program.

Installing Packages

pip supports installing from PyPI, version control, local projects, and directly from distribution files.

The most common scenario is to install from PyPI using Requirement Specifiers

$ pip install SomePackage            # latest version
$ pip install SomePackage==1.0.4     # specific version
$ pip install 'SomePackage>=1.0.4'     # minimum version

For more information and examples, see the pip install reference.

Requirements Files

"Requirements files" are files containing a list of items to be installed using pip install like so:

pip install -r requirements.txt

Details on the format of the files are here: Requirements File Format.

Logically, a Requirements file is just a list of pip install arguments placed in a file. Note that you should not rely on the items in the file being installed by pip in any particular order.

In practice, there are 4 common uses of Requirements files:

  1. Requirements files are used to hold the result from pip freeze for the purpose of achieving repeatable installations. In this case, your requirement file contains a pinned version of everything that was installed when pip freeze was run.

    pip freeze > requirements.txt
    pip install -r requirements.txt
    
  2. Requirements files are used to force pip to properly resolve dependencies. As it is now, pip doesn't have true dependency resolution, but instead simply uses the first specification it finds for a project. E.g. if pkg1 requires pkg3>=1.0 and pkg2 requires pkg3>=1.0,<=2.0, and if pkg1 is resolved first, pip will only use pkg3>=1.0, and could easily end up installing a version of pkg3 that conflicts with the needs of pkg2. To solve this problem, you can place pkg3>=1.0,<=2.0 (i.e. the correct specification) into your requirements file directly along with the other top level requirements. Like so:

    pkg1
    pkg2
    pkg3>=1.0,<=2.0
    
  3. Requirements files are used to force pip to install an alternate version of a sub-dependency. For example, suppose ProjectA in your requirements file requires ProjectB, but the latest version (v1.3) has a bug, you can force pip to accept earlier versions like so:

    ProjectA
    ProjectB<1.3
    
  4. Requirements files are used to override a dependency with a local patch that lives in version control. For example, suppose a dependency, SomeDependency from PyPI has a bug, and you can't wait for an upstream fix. You could clone/copy the src, make the fix, and place it in VCS with the tag sometag. You'd reference it in your requirements file with a line like so:

    git+https://myvcs.com/some_dependency@sometag#egg=SomeDependency
    

    If SomeDependency was previously a top-level requirement in your requirements file, then replace that line with the new line. If SomeDependency is a sub-dependency, then add the new line.

It's important to be clear that pip determines package dependencies using install_requires metadata, not by discovering requirements.txt files embedded in projects.

See also:

Constraints Files

Constraints files are requirements files that only control which version of a requirement is installed, not whether it is installed or not. Their syntax and contents is nearly identical to Requirements Files. There is one key difference: Including a package in a constraints file does not trigger installation of the package.

Use a constraints file like so:

pip install -c constraints.txt

Constraints files are used for exactly the same reason as requirements files when you don't know exactly what things you want to install. For instance, say that the "helloworld" package doesn't work in your environment, so you have a local patched version. Some things you install depend on "helloworld", and some don't.

One way to ensure that the patched version is used consistently is to manually audit the dependencies of everything you install, and if "helloworld" is present, write a requirements file to use when installing that thing.

Constraints files offer a better way: write a single constraints file for your organisation and use that everywhere. If the thing being installed requires "helloworld" to be installed, your fixed version specified in your constraints file will be used.

Constraints file support was added in pip 7.1.

Installing from Wheels

"Wheel" is a built, archive format that can greatly speed installation compared to building and installing from source archives. For more information, see the Wheel docs , PEP427, and PEP425

Pip prefers Wheels where they are available. To disable this, use the --no-binary flag for pip install.

If no satisfactory wheels are found, pip will default to finding source archives.

To install directly from a wheel archive:

pip install SomePackage-1.0-py2.py3-none-any.whl

For the cases where wheels are not available, pip offers pip wheel as a convenience, to build wheels for all your requirements and dependencies.

pip wheel requires the wheel package to be installed, which provides the "bdist_wheel" setuptools extension that it uses.

To build wheels for your requirements and all their dependencies to a local directory:

pip install wheel
pip wheel --wheel-dir=/local/wheels -r requirements.txt

And then to install those requirements just using your local directory of wheels (and not from PyPI):

pip install --no-index --find-links=/local/wheels -r requirements.txt

Uninstalling Packages

pip is able to uninstall most packages like so:

$ pip uninstall SomePackage

pip also performs an automatic uninstall of an old version of a package before upgrading to a newer version.

For more information and examples, see the pip uninstall reference.

Listing Packages

To list installed packages:

$ pip list
docutils (0.9.1)
Jinja2 (2.6)
Pygments (1.5)
Sphinx (1.1.2)

To list outdated packages, and show the latest version available:

$ pip list --outdated
docutils (Current: 0.9.1 Latest: 0.10)
Sphinx (Current: 1.1.2 Latest: 1.1.3)

To show details about an installed package:

$ pip show sphinx
---
Name: Sphinx
Version: 1.1.3
Location: /my/env/lib/pythonx.x/site-packages
Requires: Pygments, Jinja2, docutils

For more information and examples, see the pip list and pip show reference pages.

Searching for Packages

pip can search PyPI for packages using the pip search command:

$ pip search "query"

The query will be used to search the names and summaries of all packages.

For more information and examples, see the pip search reference.

Configuration

Config file

pip allows you to set all command line option defaults in a standard ini style config file.

The names and locations of the configuration files vary slightly across platforms. You may have per-user, per-virtualenv or site-wide (shared amongst all users) configuration:

Per-user:

  • On Unix the default configuration file is: $HOME/.config/pip/pip.conf which respects the XDG_CONFIG_HOME environment variable.
  • On macOS the configuration file is $HOME/Library/Application Support/pip/pip.conf if directory $HOME/Library/Application Support/pip exists else $HOME/.config/pip/pip.conf.
  • On Windows the configuration file is %APPDATA%\pip\pip.ini.

There are also a legacy per-user configuration file which is also respected, these are located at:

  • On Unix and macOS the configuration file is: $HOME/.pip/pip.conf
  • On Windows the configuration file is: %HOME%\pip\pip.ini

You can set a custom path location for this config file using the environment variable PIP_CONFIG_FILE.

Inside a virtualenv:

  • On Unix and macOS the file is $VIRTUAL_ENV/pip.conf
  • On Windows the file is: %VIRTUAL_ENV%\pip.ini

Site-wide:

  • On Unix the file may be located in /etc/pip.conf. Alternatively it may be in a "pip" subdirectory of any of the paths set in the environment variable XDG_CONFIG_DIRS (if it exists), for example /etc/xdg/pip/pip.conf.
  • On macOS the file is: /Library/Application Support/pip/pip.conf
  • On Windows XP the file is: C:\Documents and Settings\All Users\Application Data\pip\pip.ini
  • On Windows 7 and later the file is hidden, but writeable at C:\ProgramData\pip\pip.ini
  • Site-wide configuration is not supported on Windows Vista

If multiple configuration files are found by pip then they are combined in the following order:

  1. Firstly the site-wide file is read, then
  2. The per-user file is read, and finally
  3. The virtualenv-specific file is read.

Each file read overrides any values read from previous files, so if the global timeout is specified in both the site-wide file and the per-user file then the latter value is the one that will be used.

The names of the settings are derived from the long command line option, e.g. if you want to use a different package index (--index-url) and set the HTTP timeout (--default-timeout) to 60 seconds your config file would look like this:

[global]
timeout = 60
index-url = http://download.zope.org/ppix

Each subcommand can be configured optionally in its own section so that every global setting with the same name will be overridden; e.g. decreasing the timeout to 10 seconds when running the freeze (Freezing Requirements) command and using 60 seconds for all other commands is possible with:

[global]
timeout = 60

[freeze]
timeout = 10

Boolean options like --ignore-installed or --no-dependencies can be set like this:

[install]
ignore-installed = true
no-dependencies = yes

To enable the boolean options --no-compile and --no-cache-dir, falsy values have to be used:

[global]
no-cache-dir = false

[install]
no-compile = no

Appending options like --find-links can be written on multiple lines:

[global]
find-links =
    http://download.example.com

[install]
find-links =
    http://mirror1.example.com
    http://mirror2.example.com

Environment Variables

pip's command line options can be set with environment variables using the format PIP_<UPPER_LONG_NAME> . Dashes (-) have to be replaced with underscores (_).

For example, to set the default timeout:

export PIP_DEFAULT_TIMEOUT=60

This is the same as passing the option to pip directly:

pip --default-timeout=60 [...]

For command line options which can be repeated, use a space to separate multiple values. For example:

export PIP_FIND_LINKS="http://mirror1.example.com http://mirror2.example.com"

is the same as calling:

pip install --find-links=http://mirror1.example.com --find-links=http://mirror2.example.com

Config Precedence

Command line options have precedence over environment variables, which have precedence over the config file.

Within the config file, command specific sections have precedence over the global section.

Examples:

  • --host=foo overrides PIP_HOST=foo
  • PIP_HOST=foo overrides a config file with [global] host = foo
  • A command specific section in the config file [<command>] host = bar overrides the option with same name in the [global] config file section

Command Completion

pip comes with support for command line completion in bash, zsh and fish.

To setup for bash:

$ pip completion --bash >> ~/.profile

To setup for zsh:

$ pip completion --zsh >> ~/.zprofile

To setup for fish:

$ pip completion --fish > ~/.config/fish/completions/pip.fish

Alternatively, you can use the result of the completion command directly with the eval function of your shell, e.g. by adding the following to your startup file:

eval "`pip completion --bash`"

Installing from local packages

In some cases, you may want to install from local packages only, with no traffic to PyPI.

First, download the archives that fulfill your requirements:

$ pip install --download DIR -r requirements.txt

Note that pip install --download will look in your wheel cache first, before trying to download from PyPI. If you've never installed your requirements before, you won't have a wheel cache for those items. In that case, if some of your requirements don't come as wheels from PyPI, and you want wheels, then run this instead:

$ pip wheel --wheel-dir DIR -r requirements.txt

Then, to install from local only, you'll be using --find-links and --no-index like so:

$ pip install --no-index --find-links=DIR -r requirements.txt

"Only if needed" Recursive Upgrade

pip install --upgrade now has a --upgrade-strategy option which controls how pip handles upgrading of dependencies. There are 2 upgrade strategies supported:

  • eager: upgrades all dependencies regardless of whether they still satisfy the new parent requirements
  • only-if-needed: upgrades a dependency only if it does not satisfy the new parent requirements

The default strategy is only-if-needed. This was changed in pip 10.0 due to the breaking nature of eager when upgrading conflicting dependencies.

As an historic note, an earlier "fix" for getting the only-if-needed behaviour was:

pip install --upgrade --no-deps SomePackage
pip install SomePackage

A proposal for an upgrade-all command is being considered as a safer alternative to the behaviour of eager upgrading.

User Installs

With Python 2.6 came the "user scheme" for installation, which means that all Python distributions support an alternative install location that is specific to a user. The default location for each OS is explained in the python documentation for the site.USER_BASE variable. This mode of installation can be turned on by specifying the --user option to pip install.

Moreover, the "user scheme" can be customized by setting the PYTHONUSERBASE environment variable, which updates the value of site.USER_BASE.

To install "SomePackage" into an environment with site.USER_BASE customized to '/myappenv', do the following:

export PYTHONUSERBASE=/myappenv
pip install --user SomePackage

pip install --user follows four rules:

  1. When globally installed packages are on the python path, and they conflict with the installation requirements, they are ignored, and not uninstalled.
  2. When globally installed packages are on the python path, and they satisfy the installation requirements, pip does nothing, and reports that requirement is satisfied (similar to how global packages can satisfy requirements when installing packages in a --system-site-packages virtualenv).
  3. pip will not perform a --user install in a --no-site-packages virtualenv (i.e. the default kind of virtualenv), due to the user site not being on the python path. The installation would be pointless.
  4. In a --system-site-packages virtualenv, pip will not install a package that conflicts with a package in the virtualenv site-packages. The --user installation would lack sys.path precedence and be pointless.

To make the rules clearer, here are some examples:

From within a --no-site-packages virtualenv (i.e. the default kind):

$ pip install --user SomePackage
Can not perform a '--user' install. User site-packages are not visible in this virtualenv.

From within a --system-site-packages virtualenv where SomePackage==0.3 is already installed in the virtualenv:

$ pip install --user SomePackage==0.4
Will not install to the user site because it will lack sys.path precedence

From within a real python, where SomePackage is not installed globally:

$ pip install --user SomePackage
[...]
Successfully installed SomePackage

From within a real python, where SomePackage is installed globally, but is not the latest version:

$ pip install --user SomePackage
[...]
Requirement already satisfied (use --upgrade to upgrade)

$ pip install --user --upgrade SomePackage
[...]
Successfully installed SomePackage

From within a real python, where SomePackage is installed globally, and is the latest version:

$ pip install --user SomePackage
[...]
Requirement already satisfied (use --upgrade to upgrade)

$ pip install --user --upgrade SomePackage
[...]
Requirement already up-to-date: SomePackage

# force the install
$ pip install --user --ignore-installed SomePackage
[...]
Successfully installed SomePackage

Ensuring Repeatability

pip can achieve various levels of repeatability:

Pinned Version Numbers

Pinning the versions of your dependencies in the requirements file protects you from bugs or incompatibilities in newly released versions:

SomePackage == 1.2.3
DependencyOfSomePackage == 4.5.6

Using pip freeze to generate the requirements file will ensure that not only the top-level dependencies are included but their sub-dependencies as well, and so on. Perform the installation using --no-deps for an extra dose of insurance against installing anything not explicitly listed.

This strategy is easy to implement and works across OSes and architectures. However, it trusts PyPI and the certificate authority chain. It also relies on indices and find-links locations not allowing packages to change without a version increase. (PyPI does protect against this.)

Hash-checking Mode

Beyond pinning version numbers, you can add hashes against which to verify downloaded packages:

FooProject == 1.2 --hash=sha256:2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824

This protects against a compromise of PyPI or the HTTPS certificate chain. It also guards against a package changing without its version number changing (on indexes that allow this). This approach is a good fit for automated server deployments.

Hash-checking mode is a labor-saving alternative to running a private index server containing approved packages: it removes the need to upload packages, maintain ACLs, and keep an audit trail (which a VCS gives you on the requirements file for free). It can also substitute for a vendor library, providing easier upgrades and less VCS noise. It does not, of course, provide the availability benefits of a private index or a vendor library.

For more, see pip install's discussion of hash-checking mode.

Installation Bundles

Using pip wheel, you can bundle up all of a project's dependencies, with any compilation done, into a single archive. This allows installation when index servers are unavailable and avoids time-consuming recompilation. Create an archive like this:

$ tempdir=$(mktemp -d /tmp/wheelhouse-XXXXX)
$ pip wheel -r requirements.txt --wheel-dir=$tempdir
$ cwd=`pwd`
$ (cd "$tempdir"; tar -cjvf "$cwd/bundled.tar.bz2" *)

You can then install from the archive like this:

$ tempdir=$(mktemp -d /tmp/wheelhouse-XXXXX)
$ (cd $tempdir; tar -xvf /path/to/bundled.tar.bz2)
$ pip install --force-reinstall --ignore-installed --upgrade --no-index --no-deps $tempdir/*

Note that compiled packages are typically OS- and architecture-specific, so these archives are not necessarily portable across machines.

Hash-checking mode can be used along with this method to ensure that future archives are built with identical packages.

Warning

Finally, beware of the setup_requires keyword arg in setup.py. The (rare) packages that use it will cause those dependencies to be downloaded by setuptools directly, skipping pip's protections. If you need to use such a package, see Controlling setup_requires.

Using pip from your program

As noted previously, pip is a command line program. While it is implemented in Python, and so is available from your Python code via import pip, you must not use pip's internal APIs in this way. There are a number of reasons for this:

  1. The pip code assumes that is in sole control of the global state of the program. Pip manages things like the logging system configuration, or the values of the standard IO streams, without considering the possibility that user code might be affected.
  2. Pip's code is not thread safe. If you were to run pip in a thread, there is no guarantee that either your code or pip's would work as you expect.
  3. Pip assumes that once it has finished its work, the process will terminate. It doesn't need to handle the possibility that other code will continue to run after that point, so (for example) calling pip twice in the same process is likely to have issues.

This does not mean that the pip developers are opposed in principle to the idea that pip could be used as a library - it's just that this isn't how it was written, and it would be a lot of work to redesign the internals for use as a library, handling all of the above issues, and designing a usable, robust and stable API that we could guarantee would remain available across multiple releases of pip. And we simply don't currently have the resources to even consider such a task.

What this means in practice is that everything inside of pip is considered an implementation detail. Even the fact that the import name is pip is subject to change without notice. While we do try not to break things as much as possible, all the internal APIs can change at any time, for any reason. It also means that we generally won't fix issues that are a result of using pip in an unsupported way.

It should also be noted that installing packages into sys.path in a running Python process is something that should only be done with care. The import system caches certain data, and installing new packages while a program is running may not always behave as expected. In practice, there is rarely an issue, but it is something to be aware of.

Having said all of the above, it is worth covering the options available if you decide that you do want to run pip from within your program. The most reliable approach, and the one that is fully supported, is to run pip in a subprocess. This is easily done using the standard subprocess module:

subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'my_package'])

If you want to process the output further, use one of the other APIs in the module:

reqs = subprocess.check_output([sys.executable, '-m', 'pip', 'freeze'])

If you don't want to use pip's command line functionality, but are rather trying to implement code that works with Python packages, their metadata, or PyPI, then you should consider other, supported, packages that offer this type of ability. Some examples that you could consider include:

  • packaging - Utilities to work with standard package metadata (versions, requirements, etc.)
  • setuptools (specifically pkg_resources) - Functions for querying what packages the user has installed on their system.
  • distlib - Packaging and distribution utilities (including functions for interacting with PyPI).