The output of pip freeze on my machine has included the following odd line:
command-not-found==0.2.44
When trying to install requirements on a different machine, I got the obvious No distributions at all found for command-not-found==0.2.44. Is this a pip bug? Or is there any real python package of that name, one which does not exist in pypi?
Indeed, as mentioned in the follow up comments, Ubuntu has a python package, installed via dpkg/apt that is called "python-commandnotfound"
$apt-cache search command-not-found
command-not-found - Suggest installation of packages in interactive bash sessions
command-not-found-data - Set of data files for command-not-found.
python-commandnotfound - Python 2 bindings for command-not-found.
python3-commandnotfound - Python 3 bindings for command-not-found.
As this is provided via apt, and not available in the pypi repo, you won't be able to install it via pip, but pip will see that it is installed. For the purposes of showing installed packages, pip doesn't care if a package is installed via apt, easy_install, pip, manually, etc.
In short, if you actually need it on another host (which I assume you don't) you'll need to apt-get install python-commandnotfound.
Related
It's a great hassle when installing some packages in a VE and conda or pip downloads them again even when I already have it in my base environment. Since I have limited internet bandwidth and I'm assuming I'll work with many different VE's, it will take a lot of time to download basic packages such as OpenCV/Tensorflow.
By default, pip caches anything it downloads, and will used the cached version whenever possible. This cache is shared between your base environment and all virtual environments. So unless you pass the --no-cache-dir option, pip downloading a package means it has not previously downloaded a compatible version of that package. If you already have that package installed in your base environment or another virtual environment and it downloads it anyway, this probably means one or more of the following is true:
You installed your existing version with a method other than pip.
There is a newer version available, and you didn't specify, for example, pip install pandas=1.1.5 (if that's the version you already have elsewhere). Pip will install the newest compatible version for your environment, unless you tell it otherwise.
The VE you're installing to is a different Python version (e.g. created with Pyenv), and needs a different build.
I'm less familiar with the specifics of conda, and I can't seem to find anything in its online docs that focuses on the default caching behavior. However, a how-to for modifying the cache location seems to assume that the default behavior is similar to how pip works. Perhaps someone else with more Anaconda experience can chime in as well.
So except for the caveats above, as long as you're installing a package with the same method you did last time, you shouldn't have to download anything.
If you want to simplify the process of installing all the same packages (that were installed via pip) in a new VE that you already have in another environment, pip can automate that too. Run pip freeze > requirements.txt in the first environment, and copy the resulting file to your newly created VE. There, run pip install -r requirements.txt and pip will install all the packages that were installed (via pip) in the first environment. (Note that pip freeze records version numbers as well, so this won't install newer versions that may be available -- whether this is a good or bad thing depends on your needs.)
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.
I am managing several modules on an HPC, and want to install some requirements for a tool using pip.
I won't use virtualenv because they don't work well with our module system. I want to install module-local versions of packages and will set PYTHONPATH correctly when the module is loaded, and this has worked just fine when the packages I am installing are not also installed in the default python environment.
What I do not want to do is uninstall the default python's versions of packages while I am installing module-local versions.
For example, one package requires numpy==1.6, and the default version installed with the python I am using is 1.8.0. When I
pip install --install-option="--prefix=$RE_PYTHON" numpy==1.6
where RE_PYTHON points to the top of the module-local site-packages directory, numpy==1.6 installs fine, then pip goes ahead and starts uninstalling 1.8.0 from the tree of the python I am using (why it wants to uninstall a newer version is beyond me but I want to avoid this even when I am doing a local install of e.g. numpy==1.10.1).
How can I prevent pip from doing that? It is really annoying and I have not been able to find a solution that doesn't involve virtualenv.
You have to explicitly tell pip to ignore the current installed package by specifying the -I option (or --ignore-installed). So you should use:
PYTHONUSERBASE=$RE_PYTHON pip install -I --user numpy==1.6
This is mentioned in this answer by Ian Bicking.
Until today, I have been using the macports version of python27 and installing python packages through macports. Today, I needed some packages which were not available through macports; I learned about pip and found them there. After installing these packages through pip, however, I realized that neither pip nor macports could see what had been installed by the other. So, for consistency, I decided to uninstall all macports packages, install python27 and py27-pip through macports and then proceed to install all of my python packages through pip.
This worked fine, but since macports does not know about my pip-installed python packages, I ran into trouble when installing something else which depends on python (e.g., inkscape): macports tried to install its own version of, e.g. py27-numpy (already installed by pip) and then failed installation because it "already exists and does not belong to a registered port."
Is there a consistent way to use pip and to get macports to recognize that the python packages it might need for something else are already installed?
The solution is: dont use Macports for installing Python's packages.
Macports is a general package manager and it registers installed packages in its database.
Pip is a package manager for Python so if you want to install Python package, use appropriate package management tool. Pip doesnt have it's own database to keep evidence about installed stuff - it just checks Python's path to see if the package is there (and that's what you want).
Sooner or later you'll use Virtualenv anyway and you'll need pip to install packages in there too so it's better to use it everywhere.
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