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博客:

由于在python世界中工作了很长时间，我们都知道pip、easy_install和virtualenv，但是这些工具并不能满足我们所有的特定需求。主要的问题是它们关注Python，忽略了非Python库的依赖关系，如HDF5、MKL、LLVM等，这些库的源代码中没有setup.py，也没有将文件安装到Python的site-packages目录中。

因此，Conda是一个打包工具和安装程序，它的目标是比pip做的更多;处理Python包之外的库依赖项以及Python包本身。Conda还创建了一个虚拟环境，就像virtualenv一样。

因此，也许可以将Conda与Buildout进行比较，后者是另一个可以同时处理Python和非Python安装任务的工具。

因为Conda引入了一种新的打包格式，所以pip和Conda不能互换使用;pip无法安装Conda包格式。您可以同时使用这两个工具(通过使用conda install pip安装pip)，但它们也不能互操作。

在写下这个答案后，Anaconda发表了一个关于理解Conda和Pip的新页面，这也与此相呼应:

这突出了conda和pip之间的一个关键区别。Pip安装Python包，而conda安装的包可能包含用任何语言编写的软件。例如，在使用pip之前，必须通过系统包管理器或下载并运行安装程序来安装Python解释器。另一方面，Conda可以直接安装Python包以及Python解释器。

再进一步

有时需要一个包，这个包不能作为conda包使用，但可以在PyPI上使用，并且可以用pip安装。在这些情况下，尝试同时使用conda和pip是有意义的。

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.

这个答案来自于上面@user5994461的答案

您可以使用pip进行包管理。自Python 3以来，Pip是Python.org的官方内置包管理器。

PIP不是虚拟环境管理器。

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

conda is an open source environment manager AND package manager maintained by the open source community. It is separate from Anaconda, Inc. and does not require a commercial license to use. conda is also bundled into Anaconda Navigator, a popular commercial Python distribution from Anaconda, Inc. Anaconda) that includes most common data science and Python developer libraries ready-to-use. You will use conda when you use Anaconda Navigator GUI. Packages may be downloaded from conda-forge, anaconda repo4, and other public and private conda package "channels" (aka repos). It only installs precompiled packages. conda has its own package format. 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 Navigator. conda allows to install and upgrade the Python interpreter (pip does not). advanced Historically, one 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. conda emphasizes building and managing virtual environments. conda is by design a programming language-agnostic virtual environment manager. conda can install and manage other package managers such as npm, pip, and other language package managers. Can I use Anaconda Navigator packages for commercial use? The new language states that use by individual hobbyists, students, universities, non-profit organizations, or businesses with less than 200 employees is allowed, and all other usage is considered commercial and thus requires a business relationship with Anaconda. (as of Oct 28, 2020) IF you are a large developer organization, i.e., greater than 200 employees, you are NOT permitted to use Anaconda or packages from Anaconda repository for commercial use, unless you acquire a license. Pulling and using (properly open-sourced) packages from conda-forge repository do not require commercial licenses from Anaconda, Inc. Developers are free to build their own conda packages using the packaging tools provided in the conda-forge infrastructure.

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.

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。

Continuum Analytics(后来更名为Anaconda, Inc.)并行开发他们自己的解决方案(发布为Anaconda Navigator)。Anaconda Navigator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)，因此他们提出了一个专门用于构建和分发二进制包的包管理器，并将其构建到环境管理器conda中。

如果你现在安装任何带有pip install xxx的软件包，它通常都能正常工作。pip是安装内置到当前版本Python中的包的推荐方法。

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仅用于Python

conda只适用于Anaconda +其他科学包，如R依赖等。并不是每个人都需要蟒蛇，因为它已经和Python一起出现了。Anaconda主要是为那些做机器学习/深度学习等的人准备的。普通的Python开发人员不会在他的笔记本电脑上运行Anaconda。