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)

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。

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。

引用Conda for Data Science在Continuum网站上的文章:

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

PIP是一个包管理器。

Conda既是包管理器，也是环境管理器。

细节:

依赖项检查

Pip and conda also differ in how dependency relationships within an environment are fulfilled. When installing packages, pip installs dependencies in a recursive, serial loop. No effort is made to ensure that the dependencies of all packages are fulfilled simultaneously. This can lead to environments that are broken in subtle ways, if packages installed earlier in the order have incompatible dependency versions relative to packages installed later in the order. In contrast, conda uses a satisfiability (SAT) solver to verify that all requirements of all packages installed in an environment are met. This check can take extra time but helps prevent the creation of broken environments. As long as package metadata about dependencies is correct, conda will predictably produce working environments.

参考文献

理解康达和皮普

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)