免责声明:这个答案描述的是十年前的情况，当时pip还不支持二进制包。Conda是专门为更好地支持构建和分发二进制包而创建的，特别是带有C扩展的数据科学库。作为参考，pip只获得了带轮子的便携式二进制包的广泛支持(2013年的pip 1.4)和manylinux1规范(2016年3月的pip 8.1)。查看最近的答案了解更多历史。

以下是一个简短的概述:

pip

只支持Python包。 从源代码编译所有内容。编辑:pip现在安装二进制车轮，如果他们是可用的。 受到核心Python社区的支持(即Python 3.4+包含自动引导pip的代码)。

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 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.

在这两种情况下:

用Python编写 开源(conda是BSD, pip是MIT) 警告:虽然conda本身是开源的，但包存储库由Anaconda Inc托管，并且在商业使用方面有限制。

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.

如果您对构建自己的包感兴趣，也有一些不同之处。例如，pip构建在setuptools之上，而conda使用自己的格式，这有一些优点(比如是静态的，并且与Python无关)。

I may have found one further difference of a minor nature. I have my python environments under /usr rather than /home or whatever. In order to install to it, I would have to use sudo install pip. For me, the undesired side effect of sudo install pip was slightly different than what are widely reported elsewhere: after doing so, I had to run python with sudo in order to import any of the sudo-installed packages. I gave up on that and eventually found I could use sudo conda to install packages to an environment under /usr which then imported normally without needing sudo permission for python. I even used sudo conda to fix a broken pip rather than using sudo pip uninstall pip or sudo pip --upgrade install pip.

免责声明:这个答案描述的是十年前的情况，当时pip还不支持二进制包。Conda是专门为更好地支持构建和分发二进制包而创建的，特别是带有C扩展的数据科学库。作为参考，pip只获得了带轮子的便携式二进制包的广泛支持(2013年的pip 1.4)和manylinux1规范(2016年3月的pip 8.1)。查看最近的答案了解更多历史。

以下是一个简短的概述:

pip

只支持Python包。 从源代码编译所有内容。编辑:pip现在安装二进制车轮，如果他们是可用的。 受到核心Python社区的支持(即Python 3.4+包含自动引导pip的代码)。

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 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.

在这两种情况下:

用Python编写 开源(conda是BSD, pip是MIT) 警告:虽然conda本身是开源的，但包存储库由Anaconda Inc托管，并且在商业使用方面有限制。

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.

如果您对构建自己的包感兴趣，也有一些不同之处。例如，pip构建在setuptools之上，而conda使用自己的格式，这有一些优点(比如是静态的，并且与Python无关)。

引用Conda: Myths and misconcepts(一个全面的描述):

...

误解3:Conda和pip是直接竞争对手

事实:Conda和pip服务于不同的目的，并且只在一小部分任务上直接竞争:即在孤立的环境中安装Python包。

Pip是Pip安装包的缩写，是Python官方认可的包管理器，最常用于安装发布在Python包索引(PyPI)上的包。pip和PyPI都由Python打包管理局(PyPA)管理和支持。

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和pip不是竞争对手，而是专注于不同用户组和使用模式的工具。

我可以用pip安装iPython吗?

当然，两者都有(第一个方法)

pip install ipython

(第三种方法，第二种是conda)

您可以从GitHub或PyPI手动下载IPython。安装一个 对于这些版本，解压它并从顶层运行以下命令 源目录使用终端: PIP安装。

都是官方推荐的安装方式。

当我已经有pip时，为什么我要使用conda作为另一个python包管理器?

正如这里所说:

如果您需要一个特定的包(可能只用于一个项目)，或者需要与其他人共享该项目，那么conda似乎更合适。

Conda在(YMMV)中超过pip

使用非python工具的项目 与同事分享 版本切换 在具有不同库版本的项目之间切换

pip和conda的区别是什么?

每个人都广泛地回答了这个问题。

WINDOWS用户

“标准”包装工具的情况最近有所改善:

截至2015年9月11日，pypi本身的车轮包装数量为48%(2015年5月为38%，2014年9月为24%)， 最新的python 2.7.9版本现在支持开箱即用的wheel格式，

“标准”+“微调”包装工具的情况也在改善:

你可以在http://www.lfd.uci.edu/~gohlke/pythonlibs上找到几乎所有的科学软件包， mingwpy项目可能有一天会给Windows用户带来一个“编译”包，允许他们在需要的时候从源代码安装所有东西。

“Conda”包装对于它所服务的市场来说仍然更好，并强调了“标准”应该改进的地方。

(此外，在标准wheel系统和conda系统或buildout中的依赖规范multiple-effort不是很python化，如果所有这些打包“核心”技术可以通过某种PEP聚合在一起就好了)