What is the difference between pip and conda?

Disclaimer: This answer describes the state of things as it was a decade ago, at that time pip did not support binary packages. Conda was specifically created to better support building and distributing binary packages, in particular data science libraries with C extensions. For reference, pip only gained widespread support for portable binary packages with wheels (pip 1.4 in 2013) and the manylinux1 specification (pip 8.1 in March 2016). See the more recent answer for more history.

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 bootstraps 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 an environment and package manager. It is included in the Anaconda Python distribution provided by Continuum Analytics (now called Anaconda, Inc.).

conda is independently developed and supported outside of Anaconda. The conda-forge infrastructure is completely independent of Anaconda, Inc. Anaconda, Inc. is one of a large team of maintainers in the conda package open source community.

conda is an environment manager written in Python and is language-agnostic. conda environment management functions cover the functionality provided by venv, virtualenv, pipenv, pyenv, and other Python-specific package managers. You could use conda within an existing Python installation by pip installing it (though this is not recommended unless you have a good reason to use an existing installation). As of 2022, conda and pip are not fully aware of one another package management activities within a virtual environment, not are they interoperable for Python package management.

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).

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 anaconda.org) to allow regular package developers to create their own custom (built!) software stacks that their package-users will be able to conda install from.

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.

Since writing this answer, Anaconda has published a new page on Understanding Conda and Pip, which echoes this as well:

This highlights a key difference between conda and pip. Pip installs Python packages whereas conda installs packages which may contain software written in any language. For example, before using pip, a Python interpreter must be installed via a system package manager or by downloading and running an installer. Conda on the other hand can install Python packages as well as the Python interpreter directly.

and further on

Occasionally a package is needed which is not available as a conda package but is available on PyPI and can be installed with pip. In these cases, it makes sense to try to use both conda and pip.