I tried installing pygments on Debian 9 using both apt-get install python3-pygments and pip install Pygments but no method makes it work on the command line.
My suspicion is that my PATH doesn't contain the path of the installations. I added /usr/lib/python3/dist-packages/ to my PATH since the pygments package is installed there but of course this is not a solution since there's not a single bin or executable there. There are scripts there but none with the 'x' permission.
What dir should I add to my PATH variable then? apt-file list python3-pygments only shows the previously added dir (/usr/lib/python3/dist-packages/).
Maybe the problem is unrelated to my PATH. I don't know. But I need to make this work in order to use the minted latex package.
There's this quote on the pygments website which makes me think I need to install something else:
You can use Pygments from the shell, provided you installed the pygmentize script.
I wish they could write more about how to install said script since I can't find anything on their website or the internet. For what I see online, most people just install pygments normally and then they are able to use minted so I'm skeptical of this "script".
Finally I found it! I uninstalled and re-installed and this time I got the useful warning:
Installing collected packages: Pygments
WARNING: The script pygmentize.exe is installed in 'C:\Users\CarmanBr\AppData\Local\Programs\Python\Python310\Scripts' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Updated my path and now it's finally working!
I am using requirement.txt to specify the package dependencies that are used in my python application. And everything seems to work fine for packages of which either there are no internal dependencies or for the one using the package dependencies which are not already installed.
The issue occurs when i try to install a package which has a nested dependency on some other package and an older version of this package is already installed.
I know i can avoid this while installing a package manually bu using pip install -U --no-deps <package_name>. I want to understand how to do this using the requirement.txt as the deployment and requirement installation is an automated process.
Note:
The already installed package is not something i am directly using in my project but is part of a different project on the same server.
Thanks in advance.
Dependency resolution is a fairly complicated problem. A requirements.txt just specifies your dependencies with optional version ranges. If you want to "lock" your transitive dependencies (dependencies of dependencies) in place you would have to produce a requirements.txt that contains exact versions of every package you install with something like pip freeze. This doesn't solve the problem but it would at least point out to you on an install which dependencies conflict so that you can manually pick the right versions.
That being said the new (as of writing) officially supported tool for managing application dependencies is Pipenv. This tool will both manage the exact versions of transitive dependencies for you (so you won't have to maintain a "requirements.txt" manually) and it will isolate the packages that your code requires from the rest of the system. (It does this using the virtualenv tool under the hood). This isolation should fix your problems with breaking a colocated project since your project can have different versions of libraries than the rest of the system.
(TL;DR Try using Pipenv and see if your problem just disappears)
I've been trying to install IMGAUG package for an ML project. But the installation gets stuck when it tries to install scikit-image
My input: pip install scikit-image
output:
Collecting imgaug
Using cached
https://files.pythonhosted.org/...
Requirement already satisfied: scipy in
c:\users\*<username>*\appdata\local\programs\python\python37\lib\site-
packages (from imgaug) (1.1.0)
Collecting scikit-image>=0.11.0 (from imgaug)
Using cached https://files.pythonhosted.org/packages/...
Complete output from command python setup.py egg_info:
----------------------------------------
Command "python setup.py egg_info" failed with error code 3221225477 in
C:\Users\<name>~1.<name2>\AppData\Local\Temp\pip-install-qmdp6ysz\scikit-image\
Note: I've already tried installing other versions of it, upgrading setuptools and pip. Error persists.
PS: Now it's showing up on everything I try to install.
(Scroll down to a horizontal line to skip explanation and go straight to the suggested solution if you wish)
3221225477 is 0xC0000005 which is NTSTATUS STATUS_ACCESS_VIOLATION; the corresponsing error message is The instruction at 0x%08lx referenced memory at 0x%08lx. The memory could not be %s..
In Windows, a process usually quits with this exit code if it tries to access an invalid memory address and Windows terminates it as a result. If you install Visual Studio, you'll be able to pinpoint the exact module at fault as shown on the link.
Now, this error means a bug in or an incompatibility between some of your installed extension modules (or in Python engine itself, but this is very unlikely in comparison).
The easiest way to fix is to clean up any problems with the involved modules' installation and (if that isn't enough) update them to the latest versions, hoping that whatever is causing that is fixed in them.
In particular, scipy in c:\users\*<username>*\appdata\local\programs\python\python37\lib\site-packages looks suspicious: you aren't using --user in your pip command
which suggests that your didn't pay attention to this flag when using pip before (it's official that it CAN lead to version conflicts), and some of your installed packages are installed both into %ProgramFiles%\Python37\Lib\site-packages and %APPDATA%\Python\Python37\ib\site-packages, with different versions in these two locations.
I hereby suggest you to:
decide where you want your 3rd-party modules to be
%ProgramFiles% is system-wide and requires elevation to manage, %APPDATA% is per-user and doesn't require elevation
Unless you don't have administrative rights at your machine (you do, judging by the command you gave) or have special needs, keep everything in the system-wide location for simplicity
uninstall all the modules in the other location (pip uninstall <name(s)> with or without --user)
reinstall them to the desired location, updating existing versions (-U pip flag)
if that wasn't enough to solve the problem (very unlikely), update all packages to the latest versions
This happened to me also. However I resolved it by uninstalling the package (pip uninstall ), then installing it using conda rather than pip (conda install ).
Why will it not install? I thought I followed the correct procedures
Just use bounding quotes for the full path of .whl file.
pip install "C:\...full path with spaces\pywin32-...-win_amd64 (1).whl"
Of course make sure pip install wheels was run first.
Alternative way is using easy_install (not necessary to download the installer manually):
easy_install.exe https://github.com/jmdaweb/TWBlue_deps_windows/raw/master/x64/pywin32-220.win-amd64-py2.7.exe
But the second way may cause problems with py2exe if you have plans to use it. Maybe pip install pypiwin32 is OK for you (it will install pyWin32 build 219, should work just fine for most cases).
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