水蟒vs.迷你康达

问题:水蟒vs.迷你康达

Anaconda存储库中,有两种安装程序:

Anaconda安装程序 ”和“ Miniconda安装程序 ”。

他们有什么区别?

此外,对于安装程序文件,Anaconda2-4.4.0.1-Linux-ppc64le.sh,这是什么2-4.4.0.1立场?

In the Anaconda repository, there are two types of installers:

Anaconda installers” and “Miniconda installers“.

What are their differences?

Besides, for an installer file, Anaconda2-4.4.0.1-Linux-ppc64le.sh, what does 2-4.4.0.1 stand for?


回答 0

区别在于miniconda只是在交付存储库管理系统。因此,当您安装它时,只有管理系统没有软件包。而对于Anaconda,它就像带有某些内置软件包的发行版。

与任何Linux发行版一样,有些发行版捆绑了所包含软件包的许多更新。这就是为什么版本编号有所不同的原因。如果您仅决定升级Anaconda,那么您将更新整个系统。

The difference is that miniconda is just shipping the repository management system. So when you install it there is just the management system without packages. Whereas with Anaconda, it is like a distribution with some built in packages.

Like with any Linux distribution, there are some releases which bundles lots of updates for the included packages. That is why there is a difference in version numbering. If you only decide to upgrade Anaconda, you are updating a whole system.


回答 1

根据原始文档(链接现在不可用):

如果您选择Anaconda,则:

  • 是conda或Python的新手
  • 就像一次安装Python和150多个科学软件包的便利一样
  • 有时间和磁盘空间(几分钟和3 GB),和/或
  • 不想安装要单独使用的每个软件包。

如果您选择Miniconda,则:

  • 不要介意安装要单独使用的每个软件包。
  • 没有时间或磁盘空间来一次安装150个以上的软件包,和/或
  • 只想快速访问Python和conda命令,并希望以后整理其他程序。

我自己使用Miniconda。Pythonis肿。许多软件包从未使用过,并且在需要时仍可以轻松安装。

请注意,Conda是软件包管理器(例如,conda list显示环境中所有已安装的软件包),而Anaconda和Miniconda是发行版。软件分发是可以在系统上安装和使用的,预先构建和预先配置的软件包的集合。程序包管理器是一种工具,可自动执行安装,更新和删除程序包的过程。

Anaconda是PyData生态系统中中央软件的完整发行版,并且包含Python本身以及用于数百个第三方开源项目的二进制文件。Miniconda本质上是一个空conda环境的安装程序,仅包含Conda,其依赖项和Python。 来源

一旦安装了Conda,您就可以从头开始安装所需的任何软件包以及任何所需的Python版本。

2-4.4.0.1是Anaconda安装包的版本号。奇怪的是,它没有列在其“ 旧软件包列表”中

在2016年4月,Anaconda版本从2.5跃升至4.0,以避免与Python版本2和3混淆。版本4.0包含Anaconda Navigator。

可以在此处找到后续版本的发行说明。

Per the original docs (link is now dead):

Choose Anaconda if you:

  • Are new to conda or Python
  • Like the convenience of having Python and over 150 scientific packages automatically installed at once
  • Have the time and disk space (a few minutes and 3 GB), and/or
  • Don’t want to install each of the packages you want to use individually.

Choose Miniconda if you:

  • Do not mind installing each of the packages you want to use individually.
  • Do not have time or disk space to install over 150 packages at once, and/or
  • Just want fast access to Python and the conda commands, and wish to sort out the other programs later.

I use Miniconda myself. Anaconda is bloated. Many of the packages are never used and could still be easily installed if and when needed.

Note that Conda is the package manager (e.g. conda list displays all installed packages in the environment), whereas Anaconda and Miniconda are distributions. A software distribution is a collection of packages, pre-built and pre-configured, that can be installed and used on a system. A package manager is a tool that automates the process of installing, updating, and removing packages.

Anaconda is a full distribution of the central software in the PyData ecosystem, and includes Python itself along with the binaries for several hundred third-party open-source projects. Miniconda is essentially an installer for an empty conda environment, containing only Conda, its dependencies, and Python. Source.

Once Conda is installed, you can then install whatever package you need from scratch along with any desired version of Python.

2-4.4.0.1 is the version number for your Anaconda installation package. Strangely, it is not listed in their Old Package Lists.

In April 2016, the Anaconda versioning jumped from 2.5 to 4.0 in order to avoid confusion with Python versions 2 & 3. Version 4.0 included the Anaconda Navigator.

Release notes for subsequent versions can be found here.


回答 2

简要

conda 既是命令行工具,又是python包。

Miniconda安装程序= Python + conda

Anaconda安装程序= Python conda++ meta包 anaconda

meta Python pkg anaconda=数据科学中日常使用的约160 Python pkg

Anaconda安装程序= Miniconda安装程序+ conda install anaconda

详情

  1. conda 是python管理器和环境管理器,这使得

    • 安装软件包 conda install flake8
    • 使用任何版本的Python创建环境 conda create -n myenv python=3.6
  2. Miniconda安装程序= Python + conda

    conda,软件包管理器和环境管理器,是一个Python软件包。这样就安装了Python。原因畅达分发Python解释器与它自己的库/依存关系,但不是你的操作系统上现有的其他最小的相关喜欢opensslncursessqlite,等还安装了。

    基本上,Miniconda是公正的,conda并且它具有最小的依赖关系conda安装的环境是“基础”环境,以前称为“根”环境。

  3. Anaconda安装程序= Python conda++ meta包anaconda

  4. meta Python程序包anaconda=约160 pkg pkg,用于数据科学中的日常使用

    元软件包是不包含实际软件的软件包,仅依赖于要安装的其他软件包。

    anacondaAnaconda Cloud下载一个meta包并从中提取内容。实际列出了要安装的160多个软件包info/recipe/meta.yaml

    package:
        name: anaconda
        version: '2019.07'
    build:
        ignore_run_exports:
            - '*'
        number: '0'
        pin_depends: strict
        string: py36_0
    requirements:
        build:
            - python 3.6.8 haf84260_0
        is_meta_pkg:
            - true
        run:
            - alabaster 0.7.12 py36_0
            - anaconda-client 1.7.2 py36_0
            - anaconda-project 0.8.3 py_0
            # ...
            - beautifulsoup4 4.7.1 py36_1
            # ...
            - curl 7.65.2 ha441bb4_0
            # ...
            - hdf5 1.10.4 hfa1e0ec_0
            # ...
            - ipykernel 5.1.1 py36h39e3cac_0
            - ipython 7.6.1 py36h39e3cac_0
            - ipython_genutils 0.2.0 py36h241746c_0
            - ipywidgets 7.5.0 py_0
            # ...
            - jupyter 1.0.0 py36_7
            - jupyter_client 5.3.1 py_0
            - jupyter_console 6.0.0 py36_0
            - jupyter_core 4.5.0 py_0
            - jupyterlab 1.0.2 py36hf63ae98_0
            - jupyterlab_server 1.0.0 py_0
            # ...
            - matplotlib 3.1.0 py36h54f8f79_0
            # ...
            - mkl 2019.4 233
            - mkl-service 2.0.2 py36h1de35cc_0
            - mkl_fft 1.0.12 py36h5e564d8_0
            - mkl_random 1.0.2 py36h27c97d8_0
            # ...
            - nltk 3.4.4 py36_0
            # ...
            - numpy 1.16.4 py36hacdab7b_0
            - numpy-base 1.16.4 py36h6575580_0
            - numpydoc 0.9.1 py_0
            # ...
            - pandas 0.24.2 py36h0a44026_0
            - pandoc 2.2.3.2 0
            # ...
            - pillow 6.1.0 py36hb68e598_0
            # ...
            - pyqt 5.9.2 py36h655552a_2
            # ...
            - qt 5.9.7 h468cd18_1
            - qtawesome 0.5.7 py36_1
            - qtconsole 4.5.1 py_0
            - qtpy 1.8.0 py_0
            # ...
            - requests 2.22.0 py36_0
            # ...
            - sphinx 2.1.2 py_0
            - sphinxcontrib 1.0 py36_1
            - sphinxcontrib-applehelp 1.0.1 py_0
            - sphinxcontrib-devhelp 1.0.1 py_0
            - sphinxcontrib-htmlhelp 1.0.2 py_0
            - sphinxcontrib-jsmath 1.0.1 py_0
            - sphinxcontrib-qthelp 1.0.2 py_0
            - sphinxcontrib-serializinghtml 1.1.3 py_0
            - sphinxcontrib-websupport 1.1.2 py_0
            - spyder 3.3.6 py36_0
            - spyder-kernels 0.5.1 py36_0
            # ...
    

    来自meta pkg的预安装软件包anaconda主要用于Web抓取和数据科学。像requestsbeautifulsoupnumpynltk,等。

    如果您安装了Miniconda,conda install anaconda则将使其与Anaconda安装相同,但安装文件夹名称不同。

  5. Miniconda2和Miniconda。Anaconda2和Anaconda。

    2表示conda在“基本”环境中捆绑的Python解释器是Python 2,而不是Python 3。

Brief

conda is both a command line tool, and a python package.

Miniconda installer = Python + conda

Anaconda installer = Python + conda + meta package anaconda

meta Python pkg anaconda = about 160 Python pkgs for daily use in data science

Anaconda installer = Miniconda installer + conda install anaconda

Detail

  1. conda is a python manager and an environment manager, which makes it possible to

    • install package with conda install flake8
    • create an environment with any version of Python with conda create -n myenv python=3.6
  2. Miniconda installer = Python + conda

    conda, the package manager and environment manager, is a Python package. So Python is installed. Cause conda distribute Python interpreter with its own libraries/dependencies but not the existing ones on your operating system, other minimal dependencies like openssl, ncurses, sqlite, etc are installed as well.

    Basically, Miniconda is just conda and its minimal dependencies. And the environment where conda is installed is the “base” environment, which is previously called “root” environment.

  3. Anaconda installer = Python + conda + meta package anaconda

  4. meta Python package anaconda = about 160 Python pkgs for daily use in data science

    Meta packages, are packages that do NOT contain actual softwares and simply depend on other packages to be installed.

    Download an anaconda meta package from Anaconda Cloud and extract the content from it. The actual 160+ packages to be installed are listed in info/recipe/meta.yaml.

    package:
        name: anaconda
        version: '2019.07'
    build:
        ignore_run_exports:
            - '*'
        number: '0'
        pin_depends: strict
        string: py36_0
    requirements:
        build:
            - python 3.6.8 haf84260_0
        is_meta_pkg:
            - true
        run:
            - alabaster 0.7.12 py36_0
            - anaconda-client 1.7.2 py36_0
            - anaconda-project 0.8.3 py_0
            # ...
            - beautifulsoup4 4.7.1 py36_1
            # ...
            - curl 7.65.2 ha441bb4_0
            # ...
            - hdf5 1.10.4 hfa1e0ec_0
            # ...
            - ipykernel 5.1.1 py36h39e3cac_0
            - ipython 7.6.1 py36h39e3cac_0
            - ipython_genutils 0.2.0 py36h241746c_0
            - ipywidgets 7.5.0 py_0
            # ...
            - jupyter 1.0.0 py36_7
            - jupyter_client 5.3.1 py_0
            - jupyter_console 6.0.0 py36_0
            - jupyter_core 4.5.0 py_0
            - jupyterlab 1.0.2 py36hf63ae98_0
            - jupyterlab_server 1.0.0 py_0
            # ...
            - matplotlib 3.1.0 py36h54f8f79_0
            # ...
            - mkl 2019.4 233
            - mkl-service 2.0.2 py36h1de35cc_0
            - mkl_fft 1.0.12 py36h5e564d8_0
            - mkl_random 1.0.2 py36h27c97d8_0
            # ...
            - nltk 3.4.4 py36_0
            # ...
            - numpy 1.16.4 py36hacdab7b_0
            - numpy-base 1.16.4 py36h6575580_0
            - numpydoc 0.9.1 py_0
            # ...
            - pandas 0.24.2 py36h0a44026_0
            - pandoc 2.2.3.2 0
            # ...
            - pillow 6.1.0 py36hb68e598_0
            # ...
            - pyqt 5.9.2 py36h655552a_2
            # ...
            - qt 5.9.7 h468cd18_1
            - qtawesome 0.5.7 py36_1
            - qtconsole 4.5.1 py_0
            - qtpy 1.8.0 py_0
            # ...
            - requests 2.22.0 py36_0
            # ...
            - sphinx 2.1.2 py_0
            - sphinxcontrib 1.0 py36_1
            - sphinxcontrib-applehelp 1.0.1 py_0
            - sphinxcontrib-devhelp 1.0.1 py_0
            - sphinxcontrib-htmlhelp 1.0.2 py_0
            - sphinxcontrib-jsmath 1.0.1 py_0
            - sphinxcontrib-qthelp 1.0.2 py_0
            - sphinxcontrib-serializinghtml 1.1.3 py_0
            - sphinxcontrib-websupport 1.1.2 py_0
            - spyder 3.3.6 py36_0
            - spyder-kernels 0.5.1 py36_0
            # ...
    

    The pre-installed packages from meta pkg anaconda are mainly for web scraping and data science. Like requests, beautifulsoup, numpy, nltk, etc.

    If you have a Miniconda installed, conda install anaconda will make it same as an Anaconda installation, except that the installation folder names are different.

  5. Miniconda2 v.s. Miniconda. Anaconda2 v.s. Anaconda.

    2 means the bundled Python interpreter for conda in the “base” environment is Python 2, but not Python 3.


回答 3

Miniconda可以为您提供Python解释器本身,以及一个名为conda的命令行工具,该工具可以作为跨平台的软件包管理器运行,专门针对Python软件包,其实质类似于Linux用户可能熟悉的apt或yum工具。

Anaconda既包含Python又包含conda,并捆绑了一系列其他针对科学计算的预安装软件包。由于此捆绑包的大小,预计安装会占用几GB的磁盘空间。

资料来源:Jake VanderPlas的《Python数据科学手册》

Miniconda gives you the Python interpreter itself, along with a command-line tool called conda which operates as a cross-platform package manager geared toward Python packages, similar in spirit to the apt or yum tools that Linux users might be familiar with.

Anaconda includes both Python and conda, and additionally bundles a suite of other pre-installed packages geared toward scientific computing. Because of the size of this bundle, expect the installation to consume several gigabytes of disk space.

Source: Jake VanderPlas’s Python Data Science Handbook


回答 4

2Anaconda2,这意味着Python的主要版本将2.x的,而不是安装在3.x Anaconda3。当前版本具有Python 2.7.13。

4.4.0.1是Anaconda的版本号。当前发布的版本是4.4.0,我假设该版本是.1次要版本或用于其他类似用途。我使用的Windows版本仅4.4.0在文件名中说。

现在其他人已经解释了Anaconda和Miniconda之间的区别,因此我将跳过。

The 2 in Anaconda2 means that the main version of Python will be 2.x rather than the 3.x installed in Anaconda3. The current release has Python 2.7.13.

The 4.4.0.1 is the version number of Anaconda. The current advertised version is 4.4.0 and I assume the .1 is a minor release or for other similar use. The Windows releases, which I use, just say 4.4.0 in the file name.

Others have now explained the difference between Anaconda and Miniconda, so I’ll skip that.


回答 5

Anaconda是一个非常大的安装〜2 GB,对于那些不熟悉与其他软件包管理器一起安装模块或软件包的用户而言,最为有用。

Anaconda似乎正在提升自己作为Jupyter的官方包装经理。不是。Anaconda将Jupyter,R,python和许多软件包捆绑在一起安装。

Anaconda不需要安装Jupyter Lab或R内核。有关在其他地方安装Jupyter Lab或Notebook的大量信息。其他地方,还有许多有关安装R Studio的信息。下面显示了如何直接从R Studio安装R内核:

要在没有Anaconda的情况下安装R内核,请启动R Studio。在R终端窗口中,输入以下三个命令:

install.packages("devtools")
devtools::install_github("IRkernel/IRkernel")
IRkernel::installspec()

做完了 下次打开Jupyter时,R内核将可用。

Anaconda is a very large installation ~ 2 GB and is most useful for those users who are not familiar with installing modules or packages with other package managers.

Anaconda seems to be promoting itself as the official package manager of Jupyter. It’s not. Anaconda bundles Jupyter, R, python, and many packages with its installation.

Anaconda is not necessary for installing Jupyter Lab or the R kernel. There is plenty of information available elsewhere for installing Jupyter Lab or Notebooks. There is also plenty of information elsewhere for installing R studio. The following shows how to install the R kernel directly from R Studio:

To install the R kernel, without Anaconda, start R Studio. In the R terminal window enter these three commands:

install.packages("devtools")
devtools::install_github("IRkernel/IRkernel")
IRkernel::installspec()

Done. The next time Jupyter is opened, the R kernel will be available and available.


回答 6

无论Python和miniconda使用畅达包管理器。Anacondaminiconda之间的主要区别在于

Anaconda发行版预装了所有软件包,而miniconda发行版只是管理系统,没有任何预装软件包。如果使用miniconda,则必须分别下载单个软件包和库。

我个人使用Anaconda发行版,因为我真的不必担心单个软件包的安装。

miniconda的一个缺点是,安装每个单个包可利用长量时间。与安装和使用Anaconda相比,花费的时间要少得多。

但是,anaconda中有一些我从未使用过的软件包(QtConsole,Glueviz,Orange3)。我什至不知道他们的目的。因此,Python的一个缺点是它占用了比所需更多的空间。

Both Anaconda and miniconda use the conda package manager. The chief differece between between Anaconda and miniconda,however,is that

The Anaconda distribution comes pre-loaded with all the packages while the miniconda distribution is just the management system without any pre-loaded packages. If one uses miniconda, one has to download individual packages and libraries separately.

I personally use Anaconda distribution as I dont really have to worry much about individual package installations.

A disadvantage of miniconda is that installing each individual package can take a long amount of time. Compared to that installing and using Anaconda takes a lot less time.

However, there are some packages in anaconda (QtConsole, Glueviz,Orange3) that I have never had to use. I dont even know their purpose. So a disadvantage of anaconda is that it occupies more space than needed.