It is an open source, easy-to-install high performance Python and R distribution, with the conda
package and environment manager and collection of 1,000+ open source packages with free community
support.
Installing Python in a terminal is no joy. Many scientific packages require a specific version of Python
to run, and it’s difficult to keep them from interacting with each other. It is even harder to keep them
updated. Anaconda Distribution makes getting and maintaining these packages quick and easy.
It’s Anaconda Distribution without the collection of 1,000+ open source packages.
With Miniconda you install only the packages you want with the conda command,
conda is the package management tool for Anaconda Python installations. Anaconda Python is a
distribution from Continuum Analytics specifically aimed at the scientific community, and in particular
on Windows where the installation of binary extensions is often difficult.
Conda is a completely separate tool to pip, virtualenv and wheel, but provides many of their combined
features in terms of package management, virtual environment management and deployment of binary
extensions.
Conda does not install packages from PyPI and can install only from the official Continuum
repositories, or anaconda.org (a place for user-contributed conda packages), or a local (e.g. intranet)
package server. However, note that pip can be installed into, and work side-by-side with conda for
managing distributions from PyPI.
另外，下面是我在搜寻这个问题的过程中查看过的文档，下面列出来和大家分享
anaconda文档
最省心的Python版本和第三方库管理——初探Anaconda
Conda：误解与迷思
关于conda和anaconda不可不知的误解和事实
Conda: Myths and Misconceptions
anaconda,conda,pip的关系
对conda和pip的一点认识
If you quit from the Python interpreter and enter it again, the definitions you have made (functions
and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off
using a text editor to prepare the input for the interpreter and running it with that file as input instead.
This is known as creating a script. As your program gets longer, you may want to split it into several
files for easier maintenance. You may also want to use a handy function that you’ve written in several
programs without copying its definition into each program.
To support this, Python has a way to put definitions in a file and use them in a script or in an
interactive instance of the interpreter. Such a file is called a module; definitions from a module can
be imported into other modules or into the main module (the collection of variables that you have
access to in a script executed at the top level and in calculator mode).
Packages are a way of structuring Python’s module namespace by using “dotted module names”.
For example, the module name A.B designates a submodule named B in a package named A. Just like
the use of modules saves the authors of different modules from having to worry about each other’s
global variable names, the use of dotted module names saves the authors of multi-module packages
like NumPy or the Python Imaging Library from having to worry about each other’s module names.
更多关于包和模块的概念可以阅读下面两篇文章：
