【原创】算法基础之Anaconda（1）简介、安装、使用

Anaconda 2

官方：https://www.anaconda.com/

一 简介

The Most Popular Python Data Science Platform

Anaconda® is a package manager, an environment manager, a Python/R data science distribution, and a collection of over 1,500+ open source packages. Anaconda is free and easy to install, and it offers free community support.

anaconda是一个package管理器，一个环境管理器，一个python/r语言的数据科学发布包，包含1500+开源包；anaconda是免费的并且很容易安装，提供免费社区支持；

Packages available in Anaconda

• Over 200 packages are automatically installed with Anaconda.
• Over 2000 additional open source packages (including R) can be individually installed from the Anaconda repository with the conda install command.
• Thousands of other packages are available from Anaconda Cloud.
• You can download other packages using the pip install command that is installed with Anaconda. Pip packages provide many of the features of conda packages and in some cases they can work together. However, the preference should be to install the conda package if it is available.
• You can also make your own custom packages using the conda build command, and you can share them with others by uploading them to Anaconda Cloud, PyPi or other repositories.

二 安装

# wget https://repo.continuum.io/archive/Anaconda2-2018.12-Linux-x86_64.sh
# chmod u+x Anaconda2-2018.12-Linux-x86_64.sh
# ./Anaconda2-2018.12-Linux-x86_64.sh

三 使用

常用的numpy、pandas、scikit-learn、scipy、matlotlib等包都已安装好，另外还可以下载tensorflow等，

$ python
# scipy
import scipy
print('scipy: %s' % scipy.__version__)
# numpy
import numpy
print('numpy: %s' % numpy.__version__)
# matplotlib
import matplotlib
print('matplotlib: %s' % matplotlib.__version__)
# pandas
import pandas
print('pandas: %s' % pandas.__version__)
# statsmodels
import statsmodels
print('statsmodels: %s' % statsmodels.__version__)
# scikit-learn
import sklearn
print('sklearn: %s' % sklearn.__version__)

单独测试一下numpy

# python
Python 2.7.5 (default, Oct 30 2018, 23:45:53)
[GCC 4.8.5 20150623 (Red Hat 4.8.5-36)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from numpy import *
>>> a = arange(15).reshape(3, 5)
>>> a.shape
(3, 5)
>>> a.ndim
2
>>> a.dtype.name
'int64'
>>> a.itemsize
8
>>> a.size
15
>>> type(a)
<type 'numpy.ndarray'>
>>>