Develop Reference

pip installing into .local with compiled binaries for several platforms?

I am using some compiled modules on a cluster where my home directory is shared across a few different architectures. By manually copying files I can have these two version peacefully co-existing:
/home/wright/.local/lib/python3.8/site-packages/ImageD11/_cImageD11.cpython-38-powerpc64le-linux-gnu.so
/home/wright/.local/lib/python3.8/site-packages/ImageD11/_cImageD11.cpython-38-x86_64-linux-gnu.so
Is there a way to get pip to leave the previously compiled version(s) in place when installing for another architecture?

This just needed the -I or --ignore-installed for pip. It then ignores the installed version(s) and writes over the top. Just re-run it for each architecture, platform and python version you need.
Other useful options may be --force-reinstall and almost always you'll need --no-deps as well as remembering python -m pip install pip --upgrade before starting.
Like this: you can run a code together with the system python, or any of the other venv's you find. Obviously, that code needs to be version tolerant with respect to any dependencies.

Cannot uninstall 'numpy'

I've been trying to install astropy and at the end of the installation I get this message:
Cannot uninstall 'numpy'. It is a distutils installed project and thus
we cannot accurately determine which files belong to it which would
lead to only a partial uninstall.
I have tried: pip uninstall numpy and then I get the same message.
I have Python 2.7.10 in a macOS High Sierra version 13.10.5

This doesn't directly answer your question, but that's because you're asking the wrong question.
Astropy requires Python 3.5 or 3.6. Trying to get it working with Apple's pre-installed Python 2.7 is a waste of time. You might be able to get an old version working this way, but not by using the installation instructions on astropy.org, and it won't be supported even if you do.
The easy solution is to just Install the latest Anaconda 5.x with Python 3.6, because it comes with Astropy built in.
The almost-as-easy solution is to install Python 3.6 from either a python.org binary installer, or Homebrew, and then use pip3 or, better, python3 -m pip to install everything, as explained on the Astropy install page.
Either way, before doing anything else, you want to get back to a clean slate. In particular, you do not want pip, or any other scripts, attached to Apple's Python 2.7; they will only cause confusion. Assuming you can't reinstall macOS from scratch, the best way to do this is:
Look in /Library/Python/2.7/site-packages and delete everything there except for README and Extras.pth.
Look in /usr/local/bin for symlinks to anything in that site-packages. (If you don't know much about using Unix, try this command: ls -l /usr/local/bin | grep 2.7.) You'll probably have pip and pip2.7 here, and probably nothing else. But whatever you have here, delete it.
Now, when you install Python 3.6, the only thing named pip anywhere will be that Python 3.6's pip. You still want to use pip3 or python3 -m pip, but if you screw up and type pip by accident, it won't break anything.
Also, you should strongly consider using a virtual environment. See the Python Packaging Authority's User Guide (or the Anaconda docs, if you went that way) for more on this.

One simple solution I found:
sudo -H pip install astropy --ignore-installed numpy

I also had this issue and couldn't get around it in a clean way, but this is what I did:
Inside the Lib folder search "numpy" for an egg_info file (eg. numpy-1.11.0.dev0_2329eae.egg-info).
In my case, this is what Pip was looking at to determine if a current version already exists. I deleted it, then ran normal
pip install numpy
and installed the newest version.
I don't recommend this because I don't understand what it's doing under the hood and it doesn't properly uninstall the old version which could be a recipe for trouble down the line, but if you're desperate like I was then maybe this is a solution for you.

pip uninstall fails with "owned by OS" - even under sudo

I'm working on a DevOps project for a client who's using Python. Though I never used it professionally, I know a few things, such as using virtualenv and pip - though not in great detail.
When I looked at the staging box, which I am trying to prepare for running a functional test suite, I saw chaos. Tons of packages installed globally, and those installed inside a virtualenv not matching the requirements.txt of the project. OK, thought I, there's a lot of cleaning up. Starting with global packages.
However, I ran into a problem at once:
➜ ~ pip uninstall PyYAML
Not uninstalling PyYAML at /usr/lib/python2.7/dist-packages, owned by OS
OK, someone must've done a 'sudo pip install PyYAML'. I think I know how to fix it:
➜ ~ sudo pip uninstall PyYAML
Not uninstalling PyYAML at /usr/lib/python2.7/dist-packages, owned by OS
Uh, apparently I don't.
A search revealed some similar conflicts caused by users installing packages bypassing pip, but I'm not convinced - why would pip even know about them, if that was the case? Unless the "other" way is placing them in the same location pip would use - but if that's the case, why would it fail to uninstall under sudo?

Pip denies to uninstall these packages because Debian developers patched it to behave so. This allows you to use both pip and apt simultaneously. The "original" pip program doesn't have such functionality
Update: my answer is relevant only to old versions of Pip. For the latest versions, Pip is configured to modify only the files which reside only in its "home directory" - that is /usr/local/lib/python3.* for Debian. For the latest tools, you will get these errors when you try to delete the package, installed by apt:
For pip 9.0.1-2.3~ubuntu1 (installed from Ubuntu repository):
Not uninstalling pyyaml at /usr/lib/python3/dist-packages, outside environment /usr
For pip 10.0.1 (original, installed from pypi.org):
Cannot uninstall 'PyYAML'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.

The point is not that pip cannot install the package because you don't have enough permissions, but because it is not a package installed through pip, so it doesn't want to uninstall it.
dist-packages is where packages installed by the OS package manager reside; as they are handled by another package manager (e.g. apt on Ubuntu/Debian, pacman on Arch, rpm/yum on CentOS, ... ) pip won't touch them (but still has to know about them as they are installed packages, so they can be used to satisfy dependencies of pip-installed packages).
You should also probably avoid to touch them unless you use the correct package manager, and even so, they may have been installed automatically to satisfy the dependencies of some program, so you may not remove them without breaking it. This can usually be checked quite easily, although the exact way depends from the precise Linux distribution you are using.

Why isn't pip updating my numpy and scipy?

My problem is that pip won't update my Python Packages, even though there are no errors.
It is similar to this one, but I am still now sure what to do. Basically, ALL my packages for python appear to be ridiculously outdated, even after updating everything via pip. Here are the details:
I am using pip, version 1.5.6.
I am using Python, version 2.7.5
I am on a Mac OSX, verion 10.9.5.
Using that, I have:
My numpy version is 1.6.2.
My scipy version is 0.11.0.
My matplotlib version is 1.1.1.
Even after I try:
sudo pip uninstall numpy
Followed by:
sudo pip install numpy
They both complete successfully, but when I go into python and check the version of numpy, it is still the old one. (As are all the other packages).
Not sure what is going on here?... How can this be fixed? P.S. I am new to this, so I might need explicit instructions. Thanks. Also, if anyone wants, I can provide a screenshot of pip as it is installing numpy.
EDIT:
Commands I ran as per the comments:
$which -a pip
/usr/local/bin/pip
$ head -1 $(which pip)
#!/usr/bin/python
$ which -a python
/usr/bin/python

In OS X 10.9, Apple's Python comes with a bunch of pre-installed extra packages, in a directory named /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python. Including numpy.
And the way they're installed (as if by using easy_install with an ancient pre-0.7 version of setuptools, but not into either of the normal easy_install destinations), pip doesn't know anything about them.
So, what happens is that sudo pip install numpy installs a separate copy of numpy into '/Library/Python/2.7/site-packages'—but in your sys.path, the Extras directory comes before the site-packages directory, so import numpy still finds Apple's copy. I'm not sure why that is, but it's probably not something you want to monkey with.
So, how do you fix this?
The two best solutions are:
Use virtualenv, and install your numpy and friends into a virtual environment, instead of system-wide. This has the downside that you have to learn how to use virtualenv—but that's definitely worth doing at some point, and if you have the time to learn it now, go for it.
Upgrade to Python 3.x, either from a python.org installer or via Homebrew. Python 3.4 or later comes with pip, and doesn't come with any pip-unfriendly pre-installed packages. And, unlike installing a separate 2.7, it doesn't interfere with Apple's Python at all; python3 and python, pip3 and pip, etc., will all be separate programs, and you don't have to learn anything about how PATH works or any of that. This has the downside that you have to learn Python 3.x, which has some major changes, so again, a bit of a learning curve, but again, definitely worth doing at some point.
Assuming neither of those is possible, I think the simplest option is to use easy_install instead of pip, for the packages you want to install newer versions of any of Apple's "extras". You can get a full list of those by looking at what's in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python. When you upgrade numpy, you probably also want to upgrade scipy and matplotlib; I think everything else there is unrelated. (You can of course upgrade PyObjC or dateutil or anything else you care about there, but you don't have to.)
This isn't an ideal solution; there are a lot of reasons easy_install is inferior to pip (e.g., not having an uninstaller, so you're going to have to remember where that /Library/blah/blah path is (or find it again by printout out sys.path from inside Python). I wouldn't normally suggest easy_install for anything except readline and pip itself (and then only with Apple's Python). But in this case, I think it's simpler than the other alternatives.

Old question, but I found it when trying to solve this issue, will post my solution.
I found #abarnert's diagnosis to be correct and helpful, but I don't like any of the solutions: I really want to upgrade the default version of numpy. The challenge is that the directory these guys are in (which #abarnert mentioned) cannot be touched even by sudo, as they are in this "wheel" group. In fact, if you go there and do sudo rm -rf blah, it will give you a permission denied error.
To get around this, we have to take drastic action:
Reboot the computer in recovery mode
Find the terminal and type csrutil disable
Reboot normally, then upgrade numpy with pip2 install --user --upgrade numpy (and same for any other packages that have this problem)
Repeat steps a and b, this time changing "disable" to "enable"
Note: "csrutil disable" is serious business that can destabilize your machine, I would use it only when absolutely necessary and re-enable it ASAP. But AFAIK it's the only way to upgrade Python packages in a wheel directory.

Rename the numpy and scipy versions installed by Apple in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/ so it starts using the newer versions installed by Pip.

Why use pip over easy_install?

A tweet reads:
Don't use easy_install, unless you
like stabbing yourself in the face.
Use pip.
Why use pip over easy_install? Doesn't the fault lie with PyPI and package authors mostly? If an author uploads crap source tarball (eg: missing files, no setup.py) to PyPI, then both pip and easy_install will fail. Other than cosmetic differences, why do Python people (like in the above tweet) seem to strongly favor pip over easy_install?
(Let's assume that we're talking about easy_install from the Distribute package, that is maintained by the community)

From Ian Bicking's own introduction to pip:
pip was originally written to improve on easy_install in the following ways
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.

Many of the answers here are out of date for 2015 (although the initially accepted one from Daniel Roseman is not). Here's the current state of things:
Binary packages are now distributed as wheels (.whl files)—not just on PyPI, but in third-party repositories like Christoph Gohlke's Extension Packages for Windows. pip can handle wheels; easy_install cannot.
Virtual environments (which come built-in with 3.4, or can be added to 2.6+/3.1+ with virtualenv) have become a very important and prominent tool (and recommended in the official docs); they include pip out of the box, but don't even work properly with easy_install.
The distribute package that included easy_install is no longer maintained. Its improvements over setuptools got merged back into setuptools. Trying to install distribute will just install setuptools instead.
easy_install itself is only quasi-maintained.
All of the cases where pip used to be inferior to easy_install—installing from an unpacked source tree, from a DVCS repo, etc.—are long-gone; you can pip install ., pip install git+https://.
pip comes with the official Python 2.7 and 3.4+ packages from python.org, and a pip bootstrap is included by default if you build from source.
The various incomplete bits of documentation on installing, using, and building packages have been replaced by the Python Packaging User Guide. Python's own documentation on Installing Python Modules now defers to this user guide, and explicitly calls out pip as "the preferred installer program".
Other new features have been added to pip over the years that will never be in easy_install. For example, pip makes it easy to clone your site-packages by building a requirements file and then installing it with a single command on each side. Or to convert your requirements file to a local repo to use for in-house development. And so on.
The only good reason that I know of to use easy_install in 2015 is the special case of using Apple's pre-installed Python versions with OS X 10.5-10.8. Since 10.5, Apple has included easy_install, but as of 10.10 they still don't include pip. With 10.9+, you should still just use get-pip.py, but for 10.5-10.8, this has some problems, so it's easier to sudo easy_install pip. (In general, easy_install pip is a bad idea; it's only for OS X 10.5-10.8 that you want to do this.) Also, 10.5-10.8 include readline in a way that easy_install knows how to kludge around but pip doesn't, so you also want to sudo easy_install readline if you want to upgrade that.

Another—as of yet unmentioned—reason for favoring pip is because it is the new hotness and will continue to be used in the future.
The infographic below—from the Current State of Packaging section in the The Hitchhiker's Guide to Packaging v1.0—shows that setuptools/easy_install will go away in the future.
Here's another infographic from distribute's documentation showing that Setuptools and easy_install will be replaced by the new hotness—distribute and pip. While pip is still the new hotness, Distribute merged with Setuptools in 2013 with the release of Setuptools v0.7.

Two reasons, there may be more:
pip provides an uninstall command
if an installation fails in the middle, pip will leave you in a clean state.

REQUIREMENTS files.
Seriously, I use this in conjunction with virtualenv every day.
QUICK DEPENDENCY MANAGEMENT TUTORIAL, FOLKS
Requirements files allow you to create a snapshot of all packages that have been installed through pip. By encapsulating those packages in a virtualenvironment, you can have your codebase work off a very specific set of packages and share that codebase with others.
From Heroku's documentation https://devcenter.heroku.com/articles/python
You create a virtual environment, and set your shell to use it. (bash/*nix instructions)
virtualenv env
source env/bin/activate
Now all python scripts run with this shell will use this environment's packages and configuration. Now you can install a package locally to this environment without needing to install it globally on your machine.
pip install flask
Now you can dump the info about which packages are installed with
pip freeze > requirements.txt
If you checked that file into version control, when someone else gets your code, they can setup their own virtual environment and install all the dependencies with:
pip install -r requirements.txt
Any time you can automate tedium like this is awesome.

pip won't install binary packages and isn't well tested on Windows.
As Windows doesn't come with a compiler by default pip often can't be used there. easy_install can install binary packages for Windows.

UPDATE: setuptools has absorbed distribute as opposed to the other way around, as some thought. setuptools is up-to-date with the latest distutils changes and the wheel format. Hence, easy_install and pip are more or less on equal footing now.
Source: http://pythonhosted.org/setuptools/merge-faq.html#why-setuptools-and-not-distribute-or-another-name

As an addition to fuzzyman's reply:
pip won't install binary packages and isn't well tested on Windows.
As Windows doesn't come with a compiler by default pip often can't be
used there. easy_install can install binary packages for Windows.
Here is a trick on Windows:
you can use easy_install <package> to install binary packages to avoid building a binary
you can use pip uninstall <package> even if you used easy_install.
This is just a work-around that works for me on windows.
Actually I always use pip if no binaries are involved.
See the current pip doku: http://www.pip-installer.org/en/latest/other-tools.html#pip-compared-to-easy-install
I will ask on the mailing list what is planned for that.
Here is the latest update:
The new supported way to install binaries is going to be wheel!
It is not yet in the standard, but almost. Current version is still an alpha: 1.0.0a1
https://pypi.python.org/pypi/wheel
http://wheel.readthedocs.org/en/latest/
I will test wheel by creating an OS X installer for PySide using wheel instead of eggs. Will get back and report about this.
cheers - Chris
A quick update:
The transition to wheel is almost over. Most packages are supporting wheel.
I promised to build wheels for PySide, and I did that last summer. Works great!
HINT:
A few developers failed so far to support the wheel format, simply because they forget to
replace distutils by setuptools.
Often, it is easy to convert such packages by replacing this single word in setup.py.

Just met one special case that I had to use easy_install instead of pip, or I have to pull the source codes directly.
For the package GitPython, the version in pip is too old, which is 0.1.7, while the one from easy_install is the latest which is 0.3.2.rc1.
I'm using Python 2.7.8. I'm not sure about the underlay mechanism of easy_install and pip, but at least the versions of some packages may be different from each other, and sometimes easy_install is the one with newer version.
easy_install GitPython