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:
python -m pip <pip arguments>
python -m pip executes pip using the Python interpreter you
specified as python. So
/usr/bin/python3.7 -m pip means
you are executing pip for your interpreter located at
py -m pip <pip arguments>
py -m pip executes pip using the latest Python interpreter you
have installed. For more details, read the Python Windows launcher docs.
pip supports installing from PyPI, version control, local projects, and directly from distribution files.
python -m pip install SomePackage # latest version python -m pip install SomePackage==1.0.4 # specific version python -m pip install 'SomePackage>=1.0.4' # minimum version
py -m pip install SomePackage # latest version py -m pip install SomePackage==1.0.4 # specific version py -m pip install 'SomePackage>=1.0.4' # minimum version
For more information and examples, see the pip install reference.
Basic Authentication Credentials#
This is now covered in Authentication.
This is now covered in Authentication.
This is now covered in Authentication.
Using a Proxy Server#
When installing packages from PyPI, pip requires internet access, which in many corporate environments requires an outbound HTTP proxy server.
pip can be configured to connect through a proxy server in various ways:
--proxycommand-line option to specify a proxy in the form
proxyin a Config file
by setting the standard environment-variables
using the environment variable
PIP_USER_AGENT_USER_DATAto include a JSON-encoded string in the user-agent variable used in pip’s requests.
“Requirements files” are files containing a list of items to be installed using pip install like so:
python -m pip install -r requirements.txt
py -m 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:
Requirements files are used to hold the result from pip freeze for the purpose of achieving Repeatable Installs. In this case, your requirement file contains a pinned version of everything that was installed when
pip freezewas run.
python -m pip freeze > requirements.txt python -m pip install -r requirements.txt
py -m pip freeze > requirements.txt py -m pip install -r requirements.txt
Requirements files are used to force pip to properly resolve dependencies. pip 20.2 and earlier doesn’t have true dependency resolution, but instead simply uses the first specification it finds for a project. E.g. if
pkg3>=1.0,<=2.0, and if
pkg1is resolved first, pip will only use
pkg3>=1.0, and could easily end up installing a version of
pkg3that 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
Requirements files are used to force pip to install an alternate version of a sub-dependency. For example, suppose
ProjectAin your requirements file requires
ProjectB, but the latest version (v1.3) has a bug, you can force pip to accept earlier versions like so:
Requirements files are used to override a dependency with a local patch that lives in version control. For example, suppose a dependency
SomeDependencyfrom 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:
SomeDependencywas previously a top-level requirement in your requirements file, then replace that line with the new line. If
SomeDependencyis a sub-dependency, then add the new line.
It’s important to be clear that pip determines package dependencies using
not by discovering
requirements.txt files embedded in projects.
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 a subset of Requirements Files, with several kinds of syntax not allowed: constraints must have a name, they cannot be editable, and they cannot specify extras. In terms of semantics, 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:
python -m pip install -c constraints.txt
py -m 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. In Changes to the pip dependency resolver in 20.3 (2020) we did a fairly comprehensive overhaul, removing several undocumented and unsupported quirks from the previous implementation, and stripped constraints files down to being purely a way to specify global (version) limits for packages.
Installing from Wheels#
If no satisfactory wheels are found, pip will default to finding source archives.
To install directly from a wheel archive:
python -m pip install SomePackage-1.0-py2.py3-none-any.whl
py -m pip install SomePackage-1.0-py2.py3-none-any.whl
To include optional dependencies provided in the
metadata in the wheel, you must add quotes around the install target
python -m pip install './somepackage-1.0-py2.py3-none-any.whl[my-extras]'
py -m pip install './somepackage-1.0-py2.py3-none-any.whl[my-extras]'
In the future, the
path[extras] syntax may become deprecated. It is
recommended to use PEP 508 syntax wherever possible.
For the cases where wheels are not available, pip offers pip wheel as a convenience, to build wheels for all your requirements and dependencies.
To build wheels for your requirements and all their dependencies to a local directory:
python -m pip install wheel python -m pip wheel --wheel-dir=/local/wheels -r requirements.txt
py -m pip install wheel py -m 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):
python -m pip install --no-index --find-links=/local/wheels -r requirements.txt
py -m pip install --no-index --find-links=/local/wheels -r requirements.txt
pip is able to uninstall most packages like so:
python -m pip uninstall SomePackage
py -m 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.
To list installed packages:
$ python -m pip list docutils (0.9.1) Jinja2 (2.6) Pygments (1.5) Sphinx (1.1.2)
C:\> py -m 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:
$ python -m pip list --outdated docutils (Current: 0.9.1 Latest: 0.10) Sphinx (Current: 1.1.2 Latest: 1.1.3)
C:\> py -m 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:
$ python -m pip show sphinx --- Name: Sphinx Version: 1.1.3 Location: /my/env/lib/pythonx.x/site-packages Requires: Pygments, Jinja2, docutils
C:\> py -m pip show sphinx --- Name: Sphinx Version: 1.1.3 Location: /my/env/lib/pythonx.x/site-packages Requires: Pygments, Jinja2, docutils
Searching for Packages#
pip can search PyPI for packages using the
python -m pip search "query"
py -m 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.
This is now covered in Configuration.
This is now covered in Configuration.
This is now covered in Configuration.
This is now covered in Configuration.
pip comes with support for command line completion in bash, zsh and fish.
To setup for bash:
python -m pip completion --bash >> ~/.profile
To setup for zsh:
python -m pip completion --zsh >> ~/.zprofile
To setup for fish:
python -m pip completion --fish > ~/.config/fish/completions/pip.fish
To setup for powershell:
python -m pip completion --powershell | Out-File -Encoding default -Append $PROFILE
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
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:
python -m pip download --destination-directory DIR -r requirements.txt
py -m pip download --destination-directory DIR -r requirements.txt
pip 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
python -m pip wheel --wheel-dir DIR -r requirements.txt
py -m pip wheel --wheel-dir DIR -r requirements.txt
python -m pip install --no-index --find-links=DIR -r requirements.txt
py -m 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
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.
It is important to note that
--upgrade affects direct requirements (e.g.
those specified on the command-line or via a requirements file) while
--upgrade-strategy affects indirect requirements (dependencies of direct
As an example, say
SomePackage has a dependency,
both of them are already installed but are not the latest available versions:
pip install SomePackage: will not upgrade the existing
pip install --upgrade SomePackage: will upgrade
SomePackage, but not
SomeDependency(unless a minimum requirement is not met).
pip install --upgrade SomePackage --upgrade-strategy=eager: upgrades both
As an historic note, an earlier “fix” for getting the
python -m pip install --upgrade --no-deps SomePackage python -m pip install SomePackage
py -m pip install --upgrade --no-deps SomePackage py -m pip install SomePackage
A proposal for an
upgrade-all command is being considered as a safer
alternative to the behaviour of eager upgrading.
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
Moreover, the “user scheme” can be customized by setting the
PYTHONUSERBASE environment variable, which updates the value of
To install “SomePackage” into an environment with
site.USER_BASE customized to
‘/myappenv’, do the following:
export PYTHONUSERBASE=/myappenv python -m pip install --user SomePackage
set PYTHONUSERBASE=c:/myappenv py -m pip install --user SomePackage
pip install --user follows four rules:
When globally installed packages are on the python path, and they conflict with the installation requirements, they are ignored, and not uninstalled.
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
pip will not perform a
--userinstall in a
--no-site-packagesvirtualenv (i.e. the default kind of virtualenv), due to the user site not being on the python path. The installation would be pointless.
--system-site-packagesvirtualenv, 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):
$ python -m pip install --user SomePackage Can not perform a '--user' install. User site-packages are not visible in this virtualenv.
C:\> py -m 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
is already installed in the virtualenv:
$ python -m pip install --user SomePackage==0.4 Will not install to the user site because it will lack sys.path precedence
C:\> py -m 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:
$ python -m pip install --user SomePackage [...] Successfully installed SomePackage
C:\> py -m pip install --user SomePackage [...] Successfully installed SomePackage
From within a real python, where
SomePackage is installed globally, but
is not the latest version:
$ python -m pip install --user SomePackage [...] Requirement already satisfied (use --upgrade to upgrade) $ python -m pip install --user --upgrade SomePackage [...] Successfully installed SomePackage
C:\> py -m pip install --user SomePackage [...] Requirement already satisfied (use --upgrade to upgrade) C:\> py -m pip install --user --upgrade SomePackage [...] Successfully installed SomePackage
From within a real python, where
SomePackage is installed globally, and
is the latest version:
$ python -m pip install --user SomePackage [...] Requirement already satisfied (use --upgrade to upgrade) $ python -m pip install --user --upgrade SomePackage [...] Requirement already up-to-date: SomePackage # force the install $ python -m pip install --user --ignore-installed SomePackage [...] Successfully installed SomePackage
C:\> py -m pip install --user SomePackage [...] Requirement already satisfied (use --upgrade to upgrade) C:\> py -m pip install --user --upgrade SomePackage [...] Requirement already up-to-date: SomePackage # force the install C:\> py -m pip install --user --ignore-installed SomePackage [...] Successfully installed SomePackage
This is now covered in Repeatable Installs.
Fixing conflicting dependencies#
This is now covered in Dependency Resolution.
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:
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.
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.
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
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.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. We are using freeze here which outputs installed packages in requirements format.:
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.)
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).
Changes to the pip dependency resolver in 20.3 (2020)#
pip 20.3 has a new dependency resolver, on by default for Python 3 users. (pip 20.1 and 20.2 included pre-release versions of the new dependency resolver, hidden behind optional user flags.) Read below for a migration guide, how to invoke the legacy resolver, and the deprecation timeline. We also made a two-minute video explanation you can watch.
We will continue to improve the pip dependency resolver in response to testers’ feedback. Please give us feedback through the resolver testing survey.
Watch out for#
The big change in this release is to the pip dependency resolver within pip.
Computers need to know the right order to install pieces of software
x, you need to install
y first”). So, when Python
programmers share software as packages, they have to precisely describe
those installation prerequisites, and pip needs to navigate tricky
situations where it’s getting conflicting instructions. This new
dependency resolver will make pip better at handling that tricky
logic, and make pip easier for you to use and troubleshoot.
The most significant changes to the resolver are:
It will reduce inconsistency: it will no longer install a combination of packages that is mutually inconsistent. In older versions of pip, it is possible for pip to install a package which does not satisfy the declared requirements of another installed package. For example, in pip 20.0,
pip install "six<1.12" "virtualenv==20.0.2"does the wrong thing, “successfully” installing
six==1.11, even though
six>=1.12.0,<2(defined here). The new resolver, instead, outright rejects installing anything if it gets that input.
It will be stricter - if you ask pip to install two packages with incompatible requirements, it will refuse (rather than installing a broken combination, like it did in previous versions).
So, if you have been using workarounds to force pip to deal with incompatible or inconsistent requirements combinations, now’s a good time to fix the underlying problem in the packages, because pip will be stricter from here on out.
This also means that, when you run a
pip install command, pip only
considers the packages you are installing in that command, and may
break already-installed packages. It will not guarantee that your
environment will be consistent all the time. If you
pip install x
pip install y, it’s possible that the version of
you get will be different than it would be if you had run
install x y in a single command. We are considering changing this
behavior (per #7744) and would like your thoughts on what
pip’s behavior should be; please answer our survey on upgrades that
We are also changing our support for Constraints Files, editable installs, and related functionality. We did a fairly comprehensive overhaul and stripped constraints files down to being purely a way to specify global (version) limits for packages, and so some combinations that used to be allowed will now cause errors. Specifically:
Constraints don’t override the existing requirements; they simply constrain what versions are visible as input to the resolver (see #9020)
Providing an editable requirement (
-e .) does not cause pip to ignore version specifiers or constraints (see #8076), and if you have a conflict between a pinned requirement and a local directory then pip will indicate that it cannot find a version satisfying both (see #8307)
If necessary to satisfy constraints, pip will happily reinstall packages, upgrading or downgrading, without needing any additional command-line options (see #8115 and Options that control the installation process)
Links are not allowed as constraints (see #8253)
Constraints cannot have extras (see #6628)
Per our Python 2 Support policy, pip 20.3 users who are using Python 2 will use the legacy resolver by default. Python 2 users should upgrade to Python 3 as soon as possible, since in pip 21.0 in January 2021, pip dropped support for Python 2 altogether.
How to upgrade and migrate#
Install pip 20.3 with
python -m pip install --upgrade pip.
Validate your current environment by running
pip check. This will report if you have any inconsistencies in your set of installed packages. Having a clean installation will make it much less likely that you will hit issues with the new resolver (and may address hidden problems in your current environment!). If you run
pip checkand run into stuff you can’t figure out, please ask for help in our issue tracker or chat.
Test the new version of pip.
While we have tried to make sure that pip’s test suite covers as many cases as we can, we are very aware that there are people using pip with many different workflows and build processes, and we will not be able to cover all of those without your help.
If you use pip to install your software, try out the new resolver and let us know if it works for you with
pip install. Try:
installing several packages simultaneously
re-creating an environment using a
pip install --force-reinstallto check whether it does what you think it should
using constraints files
the “Setups to test with special attention” and “Examples to try” below
If you have a build pipeline that depends on pip installing your dependencies for you, check that the new resolver does what you need.
Run your project’s CI (test suite, build process, etc.) using the new resolver, and let us know of any issues.
If you have encountered resolver issues with pip in the past, check whether the new resolver fixes them, and read Fixing conflicting dependencies. Also, let us know if the new resolver has issues with any workarounds you put in to address the current resolver’s limitations. We’ll need to ensure that people can transition off such workarounds smoothly.
If you develop or support a tool that wraps pip or uses it to deliver part of your functionality, please test your integration with pip 20.3.
Troubleshoot and try these workarounds if necessary.
If pip is taking longer to install packages, read Dependency resolution backtracking for ways to reduce the time pip spends backtracking due to dependency conflicts.
If you don’t want pip to actually resolve dependencies, use the
--no-depsoption. This is useful when you have a set of package versions that work together in reality, even though their metadata says that they conflict. For guidance on a long-term fix, read Fixing conflicting dependencies.
If you run into resolution errors and need a workaround while you’re fixing their root causes, you can choose the old resolver behavior using the flag
--use-deprecated=legacy-resolver. This will work until we release pip 21.0 (see Deprecation timeline).
Please report bugs through the resolver testing survey.
Setups to test with special attention#
Requirements files with 100+ packages
Installation workflows that involve multiple requirements files
Requirements files that include hashes (Hash-checking Mode) or pinned dependencies (perhaps as output from
Using Constraints Files
Continuous integration/continuous deployment setups
Installing from any kind of version control systems (i.e., Git, Subversion, Mercurial, or CVS), per VCS Support
Installing from source code held in local directories
Examples to try#
pip install flake8-import-order==0.17.1 flake8==3.5.0 --use-feature=2020-resolver
pip install tornado==5.0 sprockets.http==1.5.0 --use-feature=2020-resolver
Tell us about#
Specific things we’d love to get feedback on:
Cases where the new resolver produces the wrong result, obviously. We hope there won’t be too many of these, but we’d like to trap such bugs before we remove the legacy resolver.
Cases where the resolver produced an error when you believe it should have been able to work out what to do.
Cases where the resolver gives an error because there’s a problem with your requirements, but you need better information to work out what’s wrong.
If you have workarounds to address issues with the current resolver, does the new resolver let you remove those workarounds? Tell us!
Please let us know through the resolver testing survey.
pip 20.1: an alpha version of the new resolver was available, opt-in, using the optional flag
--unstable-feature=resolver. pip defaulted to legacy behavior.
pip 20.2: a beta of the new resolver was available, opt-in, using the flag
--use-feature=2020-resolver. pip defaulted to legacy behavior. Users of pip 20.2 who want pip to default to using the new resolver can run
pip config set global.use-feature 2020-resolver(for more on that and the alternate
PIP_USE_FEATUREenvironment variable option, see issue 8661).
pip 20.3: pip defaults to the new resolver in Python 3 environments, but a user can opt-out and choose the old resolver behavior, using the flag
--use-deprecated=legacy-resolver. In Python 2 environments, pip defaults to the old resolver, and the new one is available using the flag
pip 21.0: pip uses new resolver by default, and the old resolver is no longer supported. It will be removed after a currently undecided amount of time, as the removal is dependent on pip’s volunteer maintainers’ availability. Python 2 support is removed per our Python 2 Support policy.
Since this work will not change user-visible behavior described in the pip documentation, this change is not covered by the Deprecation Policy.
Context and followup#
As discussed in our announcement on the PSF blog, the pip team are in the process of developing a new “dependency resolver” (the part of pip that works out what to install based on your requirements).