pip installs packages. Python packages. An easy_install replacement
pip is a replacement for easy_install. It uses mostly the same techniques for finding packages, so packages that were made easy_installable should be pip-installable as well.
pip is meant to improve on easy_install. Some of the improvements:
- All packages are downloaded before installation. Partially-completed installation doesn’t occur as a result.
- Care is taken to present useful output on the console.
- The reasons for actions are kept track of. For instance, if a package is being installed, pip keeps track of why that package was required.
- Error messages should be useful.
- The code is relatively concise and cohesive, making it easier to use programmatically.
- Packages don’t have to be installed as egg archives, they can be installed flat (while keeping the egg metadata).
- Native support for other version control systems (Git, Mercurial and Bazaar)
- Uninstallation of packages.
- Simple to define fixed sets of requirements and reliably reproduce a set of packages.
pip is complementary with virtualenv, and it is encouraged that you use virtualenv to isolate your installation.
Differences From easy_install
pip cannot install some packages. Specifically:
- It cannot install from eggs. It only installs from source. (In the future it would be good if it could install binaries from Windows .exe or .msi – binary install on other platforms is not a priority.)
- It doesn’t understand Setuptools extras (like package[test]). This should be added eventually.
- It is incompatible with some packages that customize distutils or setuptools in their setup.py files.
- Maybe it doesn’t work on Windows. At least, the author doesn’t test on Windows often.
- It also has some extra features. Extra features the author thinks are great.
pip is able to uninstall most installed packages with pip uninstall package-name.
Known exceptions include pure-distutils packages installed with python setup.py install (such packages leave behind no metadata allowing determination of what files were installed), and script wrappers installed by develop-installs (python setup.py develop).
pip also performs an automatic uninstall of an old version of a package before upgrading to a newer version, so outdated files (and egg-info data) from conflicting versions aren’t left hanging around to cause trouble. The old version of the package is automatically restored if the new version fails to download or install.
When installing software, and Python packages in particular, it’s common that you get a lot of libraries installed. You just did easy_install MyPackage and you get a dozen packages. Each of these packages has its own version.
Maybe you ran that installation and it works. Great! Will it keep working? Did you have to provide special options to get it to find everything? Did you have to install a bunch of other optional pieces? Most of all, will you be able to do it again?
If you’ve ever tried to setup an application on a new system, or with slightly updated pieces, and had it fail, pip requirements are for you. If you haven’t had this problem then you will eventually, so pip requirements are for you too – requirements make explicit, repeatable installation of packages.
So what are requirements files? They are very simple: lists of packages to install. Instead of running something like pip MyApp and getting whatever libraries come along, you can create a requirements file something like:
MyApp Framework==0.9.4 Library>=0.2
Then, regardless of what MyApp lists in setup.py, you’ll get a specific version of Framework and at least the 0.2 version of Library. (You might think you could list these specific versions in setup.py – try it and you’ll quickly see why that doesn’t work.) You can add optional libraries and support tools that MyApp doesn’t strictly require.
You can also include “editable” packages – packages that are checked out from Subversion, Git, Mercurial and Bazaar. These are just like using the -e option to pip. They look like:
You have to start the URL with svn+ (git+, hg+ or bzr+), and you have to include #egg=Package so pip knows what to expect at that URL. You can also include @rev in the URL, e.g., @275 to check out revision 275.
So you have a working set of packages, and you want to be able to install them elsewhere. Requirements files let you install exact versions, but it won’t tell you what all the exact versions are.
To create a new requirements file from a known working environment, use:
$ pip freeze > stable-req.txt
This will write a listing of all installed libraries to stable-req.txt with exact versions for every library. You may want to edit the file down after generating (e.g., to eliminate unnecessary libraries), but it’ll give you a stable starting point for constructing your requirements file.
You can also give it an existing requirements file, and it will use that as a sort of template for the new file. So if you do:
$ pip freeze -r devel-req.txt > stable-req.txt
it will keep the packages listed in devel-req.txt in order and preserve comments.
Another way to distribute a set of libraries is a bundle format (specific to pip). This format is not stable at this time (there simply hasn’t been any feedback, nor a great deal of thought). A bundle file contains all the source for your package, and you can have pip install them all together. Once you have the bundle file further network access won’t be necessary. To build a bundle file, do:
$ pip bundle MyApp.pybundle MyApp
(Using a requirements file would be wise.) Then someone else can get the file MyApp.pybundle and run:
$ pip install MyApp.pybundle
This is not a binary format. This only packages source. If you have binary packages, then the person who installs the files will have to have a compiler, any necessary headers installed, etc. Binary packages are hard, this is relatively easy.
Using pip with virtualenv
pip is most nutritious when used with virtualenv. One of the reasons pip doesn’t install “multi-version” eggs is that virtualenv removes much of the need for it.
pip does not have to be installed to use it, you can run python path/to/pip.py and it will work. This is intended to avoid the bootstrapping problem of installation. You can also run pip inside any virtualenv environment, like:
$ virtualenv new-env/ ... creates new-env/ ... $ pip install -E new-env/ MyPackage
This is exactly equivalent to:
$ ./new-env/bin/python path/to/pip.py install MyPackage
Except, if you have virtualenv installed and the path new-env/ doesn’t exist, then a new virtualenv will be created.
pip also has two advanced features for working with virtualenvs – both of which activated by defining a variable in your environment.
To tell pip to only run if there is a virtualenv currently activated, and to bail if not, use:
To tell pip to automatically use the currently active virtualenv:
Providing an environment with -E will be ignored.
Using pip with virtualenvwrapper
If you are using virtualenvwrapper, you might want pip to automatically create its virtualenvs in your $WORKON_HOME.
You can tell pip to do so by defining PIP_VIRTUALENV_BASE in your environment and setting it to the same value as that of $WORKON_HOME.
Do so by adding the line:
in your .bashrc under the line starting with export WORKON_HOME.
Using pip with buildout
Command line completion
pip comes with support for command line completion in bash and zsh and allows you tab complete commands and options. To enable it you simply need copy the required shell script to the your shell startup file (e.g. .profile or .zprofile) by running the special completion command, e.g. for bash:
$ pip completion --bash >> ~/.profile
And for zsh:
$ pip completion --zsh >> ~/.zprofile
Alternatively, you can use the result of the completion command directly with the eval function of you shell, e.g. by adding:
eval `pip completion --bash`
to your startup file.
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