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.

(2021更新)

使用pip，它是Python 3以来的官方包管理器。

pip

basics pip is the default package manager for python pip is built-in as of Python 3.0 Usage: python3 -m venv myenv; source myenv/bin/activate; python3 -m pip install requests Packages are downloaded from pypi.org, the official public python repository It can install precompiled binaries (wheels) when available, or source (tar/zip archive). Compiled binaries are important because many packages are mixed Python/C/other with third-party dependencies and complex build chains. They MUST be distributed as binaries to be ready-to-use. advanced pip can actually install from any archive, wheel, or git/svn repo... ...that can be located on disk, or on a HTTP URL, or a personal pypi server. pip install git+https://github.com/psf/requests.git@v2.25.0 for example (it can be useful for testing patches on a branch). pip install https://download.pytorch.org/whl/cpu/torch-1.9.0%2Bcpu-cp39-cp39-linux_x86_64.whl (that wheel is Python 3.9 on Linux). when installing from source, pip will automatically build the package. (it's not always possible, try building TensorFlow without the google build system :D) binary wheels can be python-version specific and OS specific, see manylinux specification to maximize portability.

conda

You are NOT permitted to use Anaconda or packages from Anaconda repositories for commercial use, unless you acquire a license. Conda is a third party package manager from conda. It's popularized by anaconda, a Python distribution including most common data science libraries ready-to-use. You will use conda when you use anaconda. Packages are downloaded from the anaconda repo. It only installs precompiled packages. Conda has its own format of packages. It doesn't use wheels. conda install to install a package. conda build to build a package. conda can build the python interpreter (and other C packages it depends on). That's how an interpreter is built and bundled for anaconda. conda allows to install and upgrade the Python interpreter (pip does not). advanced Historically, the selling point of conda was to support building and installing binary packages, because pip did not support binary packages very well (until wheels and manylinux2010 spec). Emphasis on building packages. Conda has extensive build settings and it stores extensive metadata, to work with dependencies and build chains. Some projects use conda to initiate complex build systems and generate a wheel, that is published to pypi.org for pip.

easy_install/鸡蛋

For historical reference only. DO NOT USE egg is an abandoned format of package, it was used up to mid 2010s and completely replaced by wheels. an egg is a zip archive, it contains python source files and/or compiled libraries. eggs are used with easy_install and the first releases of pip. easy_install was yet another package manager, that preceded pip and conda. It was removed in setuptools v58.3 (year 2021). it too caused a lot of confusion, just like pip vs conda :D egg files are slow to load, poorly specified, and OS specific. Each egg was setup in a separate directory, an import mypackage would have to look for mypackage.py in potentially hundreds of directories (how many libraries were installed?). That was slow and not friendly to the filesystem cache.

从历史上看，上述三个工具都是开源的，并且是用Python编写的。 然而，conda背后的公司在2020年更新了他们的服务条款，禁止商业使用，小心!

有趣的事实:构建Python解释器唯一严格要求的依赖项是zlib(一个zip库)，因为压缩是加载更多包所必需的。鸡蛋和轮子包是zip文件。

为什么有这么多选择?

问得好。

让我们深入研究Python和计算机的历史。= D

纯python包总是能很好地与这些打包器一起工作。问题不仅在于python包。

世界上大多数的代码都依赖于C，这对于Python解释器来说是如此，它是用C编写的。对于许多Python包来说也是如此，这些包是围绕C库的Python包装器或混合了Python /C/ c++代码的项目。

任何涉及SSL、压缩、GUI (X11和Windows子系统)、数学库、GPU、CUDA等的东西……通常与一些C代码相结合。

这给打包和分发Python库带来了麻烦，因为不仅仅是Python代码可以在任何地方运行。库必须编译，编译需要编译器、系统库和第三方库，然后一旦编译，生成的二进制代码只适用于特定的系统和python版本。

最初，python可以很好地分发纯python库，但是很少支持分发二进制库。在2010年前后，您尝试使用numpy或cassandra时会遇到很多错误。它下载了源代码，但是由于缺少依赖项而无法编译。或者它下载了一个预构建的包(当时可能是一个egg)，在使用时崩溃并发生SEGFAULT，因为它是为另一个系统构建的。这简直是一场噩梦。

从2012年开始，pip和wheels解决了这个问题。然后等待许多年，让人们采用这些工具，并让这些工具传播到稳定的Linux发行版(许多开发人员依赖/usr/bin/python)。二进制包的问题一直延续到2010年代末。

作为参考，这就是为什么要运行的第一个命令是python3 -m venv myvenv && source myvenv/bin/activate && pip install -upgrade pip setuptools在旧系统上，因为操作系统自带的是5年前的旧python+pip，它有bug，不能识别当前的包格式。

Conda并行地研究他们自己的解决方案。Anaconda专门用于使数据科学库易于开箱即用(数据科学= C和c++无处不在)，因此他们必须提出一个专门用于构建和分发二进制包的包管理器conda。

如果你现在安装任何带有pip install xxx的软件包，它都可以工作。这是推荐的安装包的方式，并且在当前版本的Python中是内置的。

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。

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.

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。

其他答案对细节进行了合理的描述，但我想强调一些高级点。

PIP是一个包管理器，可以方便地安装、升级和卸载python包。它也适用于虚拟python环境。

Conda是任何软件(安装、升级和卸载)的包管理器。它还适用于虚拟系统环境。

conda设计的目标之一是方便用户所需的整个软件堆栈的包管理，其中一个或多个python版本可能只是一小部分。这包括低级库，如线性代数，编译器，如Windows上的mingw，编辑器，版本控制工具，如Hg和Git，或任何其他需要分发和管理的工具。

对于版本管理，pip允许您在多个python环境之间切换和管理。

Conda允许您在多个通用环境之间切换和管理，在这些环境中，许多其他东西的版本号可能不同，比如c库、编译器、测试套件或数据库引擎等等。

Conda不是以Windows为中心的，但在Windows上，当需要安装和管理需要编译的复杂科学包时，它是目前可用的最好的解决方案。

当我想到在Windows上通过pip编译许多这些包，或者在需要编译时调试失败的pip安装会话时，我浪费了多少时间，我想哭。

最后一点，Continuum Analytics还提供(免费的)binstar.org(现在叫anaconda.org)，允许常规的软件包开发人员创建他们自己的自定义(构建的!)软件堆栈，他们的软件包用户可以从中安装conda。