python vs - What is the difference between pip and conda?




virtualenv install (9)

For WINDOWS users

"standard" packaging tools situation is improving recently:

  • on pypi itself, there are now 48% of wheel packages as of sept. 11th 2015 (up from 38% in may 2015 , 24% in sept. 2014),

  • the wheel format is now supported out-of-the-box per latest python 2.7.9,

"standard"+"tweaks" packaging tools situation is improving also:

  • you can find nearly all scientific packages on wheel format at http://www.lfd.uci.edu/~gohlke/pythonlibs,

  • the mingwpy project may bring one day a 'compilation' package to windows users, allowing to install everything from source when needed.

"Conda" packaging remains better for the market it serves, and highlights areas where the "standard" should improve.

(also, the dependency specification multiple-effort, in standard wheel system and in conda system, or buildout, is not very pythonic, it would be nice if all these packaging 'core' techniques could converge, via a sort of PEP)

I know pip is a package manager for python packages. However, I saw the installation on IPython's website use conda to install IPython.

Can I use pip to install IPython? Why should I use conda as another python package manager when I already have pip?

What is the difference between pip and conda?


Here is a short rundown:

pip

  • Python packages only.
  • Compiles everything from source. EDIT: pip now installs binary wheels, if they are available.
  • Blessed by the core Python community (i.e., Python 3.4+ includes code that automatically boostraps pip).

conda

  • Python agnostic. The main focus of existing packages are for Python, and indeed conda itself is written in Python, but you can also have conda packages for C libraries, or R packages, or really anything.
  • Installs binaries. There is a tool called conda build that builds packages from source, but conda install itself installs things from already built conda packages.
  • External. Conda is the package manager of Anaconda, the Python distribution provided by Continuum Analytics, but it can be used outside of Anaconda too. You can use it with an existing Python installation by pip installing it (though this is not recommended unless you have a good reason to use an existing installation).

In both cases:

  • Written in Python
  • Open source (conda is BSD and pip is MIT)

The first two bullet points of conda are really what make it advantageous over pip for many packages. Since pip installs from source, it can be painful to install things with it if you are unable to compile the source code (this is especially true on Windows, but it can even be true on Linux if the packages have some difficult C or FORTRAN library dependencies). Conda installs from binary, meaning that someone (e.g., Continuum) has already done the hard work of compiling the package, and so the installation is easy.

There are also some differences if you are interested in building your own packages. For instance, pip is built on top of setuptools, whereas conda uses its own format, which has some advantages (like being static, and again, Python agnostic).


Quote from Conda for Data Science article onto continuum website:

Conda vs pip

Python programmers are probably familiar with pip to download packages from PyPI and manage their requirements. Although, both conda and pip are package managers, they are very different:

  • Pip is specific for Python packages and conda is language-agnostic, which means we can use conda to manage packages from any language Pip compiles from source and conda installs binaries, removing the burden of compilation
  • Conda creates language-agnostic environments natively whereas pip relies on virtualenv to manage only Python environments Though it is recommended to always use conda packages, conda also includes pip, so you don’t have to choose between the two. For example, to install a python package that does not have a conda package, but is available through pip, just run, for example:
conda install pip
pip install gensim

Quoting from Conda: Myths and Misconceptions (a comprehensive description):

...

Myth #3: Conda and pip are direct competitors

Reality: Conda and pip serve different purposes, and only directly compete in a small subset of tasks: namely installing Python packages in isolated environments.

Pip, which stands for Pip Installs Packages, is Python's officially-sanctioned package manager, and is most commonly used to install packages published on the Python Package Index (PyPI). Both pip and PyPI are governed and supported by the Python Packaging Authority (PyPA).

In short, pip is a general-purpose manager for Python packages; conda is a language-agnostic cross-platform environment manager. For the user, the most salient distinction is probably this: pip installs python packages within any environment; conda installs any package within conda environments. If all you are doing is installing Python packages within an isolated environment, conda and pip+virtualenv are mostly interchangeable, modulo some difference in dependency handling and package availability. By isolated environment I mean a conda-env or virtualenv, in which you can install packages without modifying your system Python installation.

Even setting aside Myth #2, if we focus on just installation of Python packages, conda and pip serve different audiences and different purposes. If you want to, say, manage Python packages within an existing system Python installation, conda can't help you: by design, it can only install packages within conda environments. If you want to, say, work with the many Python packages which rely on external dependencies (NumPy, SciPy, and Matplotlib are common examples), while tracking those dependencies in a meaningful way, pip can't help you: by design, it manages Python packages and only Python packages.

Conda and pip are not competitors, but rather tools focused on different groups of users and patterns of use.


Quoting from the Conda blog:

Having been involved in the python world for so long, we are all aware of pip, easy_install, and virtualenv, but these tools did not meet all of our specific requirements. The main problem is that they are focused around Python, neglecting non-Python library dependencies, such as HDF5, MKL, LLVM, etc., which do not have a setup.py in their source code and also do not install files into Python’s site-packages directory.

So Conda is a packaging tool and installer that aims to do more than what pip does; handle library dependencies outside of the Python packages as well as the Python packages themselves. Conda also creates a virtual environment, like virtualenv does.

As such, Conda should be compared to Buildout perhaps, another tool that lets you handle both Python and non-Python installation tasks.

Because Conda introduces a new packaging format, you cannot use pip and Conda interchangeably; pip cannot install the Conda package format. You can use the two tools side by side (by installing pip with conda install pip) but they do not interoperate either.


pip is for Python only

conda is only for Anaconda + other scientific packages like R dependencies etc. NOT everyone needs Anaconda that already comes with Python. Anaconda is mostly for those who do Machine learning/deep learning etc. Casual Python dev won't run Anaconda on his laptop.


The other answers give a fair description of the details, but I want to highlight some high-level points.

pip is a package manager that facilitates installation, upgrade, and uninstallation of python packages. It also works with virtual python environments.

conda is a package manager for any software (installation, upgrade and uninstallation). It also works with virtual system environments.

One of the goals with the design of conda is to facilitate package management for the entire software stack required by users, of which one or more python versions may only be a small part. This includes low-level libraries, such as linear algebra, compilers, such as mingw on Windows, editors, version control tools like Hg and Git, or whatever else requires distribution and management.

For version management, pip allows you to switch between and manage multiple python environments.

Conda allows you to switch between and manage multiple general purpose environments across which multiple other things can vary in version number, like C-libraries, or compilers, or test-suites, or database engines and so on.

Conda is not Windows-centric, but on Windows it is by far the superior solution currently available when complex scientific packages requiring compilation are required to be installed and managed.

I want to weep when I think of how much time I have lost trying to compile many of these packages via pip on Windows, or debug failed pip install sessions when compilation was required.

As a final point, Continuum Analytics also hosts (free) binstar.org (now called repo.continuum.io) to allow regular package developers to create their own custom (built!) software stacks that their package-users will be able to conda install from.


Can I use pip to install iPython?

Sure, both (first approach on page)

pip install ipython

and (third approach, second is conda)

You can manually download IPython from GitHub or PyPI. To install one of these versions, unpack it and run the following from the top-level source directory using the Terminal:

pip install .

are officially recommended ways to install.

Why should I use conda as another python package manager when I already have pip?

As said here:

If you need a specific package, maybe only for one project, or if you need to share the project with someone else, conda seems more appropriate.

Conda surpasses pip in (YMMV)

  • projects that use non-python tools
  • sharing with colleagues
  • switching between versions
  • switching between projects with different library versions

What is the difference between pip and conda?

That is extensively answered by everyone else.


A little compilation

@staticmethod A way to write a method inside a class without reference to the object it is being called on. So no need to pass implicit argument like self or cls. It is written exactly the same how written outside the class, but it is not of no use in python because if you need to encapsulate a method inside a class since this method needs to be the part of that class @staticmethod is comes handy in that case.

@classmethod It is important when you want to write a factory method and by this custom attribute(s) can be attached in a class. This attribute(s) can be overridden in the inherited class.

A comparison between these two methods can be as below





python pip ipython package-managers conda