Anaconda安装使用教程 python 科学计算 发行版

1.原生python的不方便

作为一个数据与算法工作者，Python的使用频率很高。现阶段python做科学计算的标配是numpy+scipy+matplotlib+sklearn+pandas。可惜的是，原生的python是不带这些包的。于是，每次遇到一个新机器，需要安装这些包。更可气的是，昨晚本博主为了在新机器上安装sklearn，足足花了两小时，中间踩了无数之前没遇到过的天坑加上天朝坑爹的网络。。。作为一个搭建了无数次科学计算环境的老司机还遇到这种情况，估计新手们就更无比郁闷了。于是老司机就想，有没有一个东西把所有常用的科学计算工具都集成好，这样就省了每次搭环境的天坑。。。google一把，发现了今天文章的主角：Anaconda。

2.先看看Anaconda是个什么鬼

Anaconda：蟒蛇，估计来源就是python logo里那条可爱的小蟒蛇吧。 
mac版下载地址：https://www.continuum.io/downloads#_macosx 
看看官网首页是怎么介绍的： 
Anaconda is the leading open data science platform powered by Python. The open source version of Anaconda is a high performance distribution of Python and R and includes over 100 of the most popular Python, R and Scala packages for data science. Additionally, you’ll have access to over 720 packages that can easily be installed with conda, our renowned package, dependency and environment manager, that is included in Anaconda. Anaconda is BSD licensed which gives you permission to use Anaconda commercially and for redistribution. See the packages included with Anaconda and the Anaconda changelog.

通过上面这段牛逼闪闪的介绍，我们知道Anaconda是一个基于python的科学计算平台，这个平台里包含有python,r,scala等绝大部分主流的用于科学计算的包。

接下来自然就是开始下载了。因为集成有很多牛逼科学计算包的缘故，所以安装包自然也小不了，比如我下载的mac版就有360M。那就慢慢下着吧。还好网络虽然不是很快，好歹还是稳定的，能到一两百k，一个小时左右能下完。这段时间就先干点别的吧。

3.安装配置

下载完成以后，跟mac里安装普通软件一样，双击安装即可。 
安装完以后，开始进行相应的配置。因为我平时使用eclipse开发，正好官网都贴心地给出了在IDE里怎么配置使用，里面就有eclipse，前提是eclipse已经安装了pydev插件。

以下eclipse配置方法来自官网： 
After you have Eclipse, PyDev, and Anaconda installed, follow these steps to set Anaconda Python as your default by adding it as a new interpreter, and then selecting that new interpreter:

Open the Eclipse Preferences window: 
 
Go to PyDev -> Interpreters -> Python Interpreter. 
Click the New button: 
 
In the “Interpreter Name” box, type “Anaconda Python”. 
Browse to ~/anaconda/bin/python or wherever your Anaconda Python is installed. 
Click the OK button. 
 
In the next window, select all the folders and click the OK button again to select the folders to add to the SYSTEM python path. 
 
The Python Interpreters window will now display Anaconda Python. Click OK. 
 
You are now ready to use Anaconda Python with your Eclipse and PyDev installation.

如果是其他IDE，可以上官网查看其他配置方法。具体地址： 
https://docs.continuum.io/anaconda/ide_integration#id8

4.查看Anaconda的基本用法

配置完成以后，查看一下此时系统的python：

lei.wang ~ $ which python/Users/lei.wang/anaconda/bin/pythonlei.wang ~ $ python --versionPython 2.7.12 :: Anaconda 4.1.1 (x86_64)
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此时，系统默认的python已经变成了Anaconda的版本！ 
为什么会这样呢？原来是安装过程中，偷偷给我们在home目录下生成了一个.bashrc_profile文件，并在里面加入了PATH：

# added by Anaconda2 4.1.1 installerexport PATH="/Users/wanglei/anaconda/bin:$PATH"
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所以这个时候我们的bash里使用python的话，已经指向了anaconda里的python解释器。 
如果使用的不是mac的标准bash，而是zsh，不用着急，将上面一行配置复制粘贴到.zshrc文件中，然后source一下.zshrc文件即可！

执行一下conda命令：

lei.wang ~ $ condausage: conda [-h] [-V] [--debug] command ...conda is a tool for managing and deploying applications, environments and packages.Options:positional arguments:  command    info         Display information about current conda install.    help         Displays a list of available conda commands and their help                 strings.    list         List linked packages in a conda environment.    search       Search for packages and display their information. The input                 is a Python regular expression. To perform a search with a                 search string that starts with a -, separate the search from                 the options with --, like 'conda search -- -h'. A * in the                 results means that package is installed in the current                 environment. A . means that package is not installed but is                 cached in the pkgs directory.    create       Create a new conda environment from a list of specified                 packages.    install      Installs a list of packages into a specified conda                 environment.    update       Updates conda packages to the latest compatible version. This                 command accepts a list of package names and updates them to                 the latest versions that are compatible with all other                 packages in the environment. Conda attempts to install the                 newest versions of the requested packages. To accomplish                 this, it may update some packages that are already installed,                 or install additional packages. To prevent existing packages                 from updating, use the --no-update-deps option. This may                 force conda to install older versions of the requested                 packages, and it does not prevent additional dependency                 packages from being installed. If you wish to skip dependency                 checking altogether, use the '--force' option. This may                 result in an environment with incompatible packages, so this                 option must be used with great caution.    upgrade      Alias for conda update. See conda update --help.    remove       Remove a list of packages from a specified conda environment.    uninstall    Alias for conda remove. See conda remove --help.    config       Modify configuration values in .condarc. This is modeled                 after the git config command. Writes to the user .condarc                 file (/Users/lei.wang/.condarc) by default.    init         Initialize conda into a regular environment (when conda was                 installed as a Python package, e.g. using pip). (DEPRECATED)    clean        Remove unused packages and caches.    package      Low-level conda package utility. (EXPERIMENTAL)    bundle       Create or extract a "bundle package" (EXPERIMENTAL)...
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信息太长了，后面的部分就不列举了。不过看到前面这部分选项，就已经足够让我们兴奋了：基本的list，search，install，upgrade，uninstall等功能都包含，说明我们可以向apt-get一样方便管理python的各种依赖了。。。

先list一下，查看里面都带了哪些牛逼闪闪的科学计算包：

ei.wang ~ $ conda list# packages in environment at /Users/lei.wang/anaconda:#_nb_ext_conf              0.2.0                    py27_0alabaster                 0.7.8                    py27_0anaconda                  4.1.1               np111py27_0anaconda-client           1.4.0                    py27_0anaconda-navigator        1.2.1                    py27_0appnope                   0.1.0                    py27_0appscript                 1.0.1                    py27_0argcomplete               1.0.0                    py27_1astropy                   1.2.1               np111py27_0babel                     2.3.3                    py27_0backports                 1.0                      py27_0backports_abc             0.4                      py27_0beautifulsoup4            4.4.1                    py27_0bitarray                  0.8.1                    py27_0blaze                     0.10.1                   py27_0bokeh                     0.12.0                   py27_0boto                      2.40.0                   py27_0bottleneck                1.1.0               np111py27_0cdecimal                  2.3                      py27_2cffi                      1.6.0                    py27_0chest                     0.2.3                    py27_0click                     6.6                      py27_0cloudpickle               0.2.1                    py27_0clyent                    1.2.2                    py27_0colorama                  0.3.7                    py27_0conda                     4.1.6                    py27_0conda-build               1.21.3                   py27_0conda-env                 2.5.1                    py27_0configobj                 5.0.6                    py27_0configparser              3.5.0b2                  py27_1contextlib2               0.5.3                    py27_0cryptography              1.4                      py27_0curl                      7.49.0                        0cycler                    0.10.0                   py27_0cython                    0.24                     py27_0cytoolz                   0.8.0                    py27_0dask                      0.10.0                   py27_0datashape                 0.5.2                    py27_0decorator                 4.0.10                   py27_0dill                      0.2.5                    py27_0docutils                  0.12                     py27_2dynd-python               0.7.2                    py27_0entrypoints               0.2.2                    py27_0enum34                    1.1.6                    py27_0et_xmlfile                1.0.1                    py27_0fastcache                 1.0.2                    py27_1flask                     0.11.1                   py27_0flask-cors                2.1.2                    py27_0freetype                  2.5.5                         1funcsigs                  1.0.2                    py27_0functools32               3.2.3.2                  py27_0futures                   3.0.5                    py27_0get_terminal_size         1.0.0                    py27_0gevent                    1.1.1                    py27_0greenlet                  0.4.10                   py27_0grin                      1.2.1                    py27_3h5py                      2.6.0               np111py27_1hdf5                      1.8.16                        0heapdict                  1.0.0                    py27_1idna                      2.1                      py27_0imagesize                 0.7.1                    py27_0ipaddress                 1.0.16                   py27_0ipykernel                 4.3.1                    py27_0ipython                   4.2.0                    py27_1ipython_genutils          0.1.0                    py27_0ipywidgets                4.1.1                    py27_0itsdangerous              0.24                     py27_0jbig                      2.1                           0jdcal                     1.2                      py27_1jedi                      0.9.0                    py27_1jinja2                    2.8                      py27_1jpeg                      8d                            1jsonschema                2.5.1                    py27_0jupyter                   1.0.0                    py27_3jupyter_client            4.3.0                    py27_0jupyter_console           4.1.1                    py27_0jupyter_core              4.1.0                    py27_0libdynd                   0.7.2                         0libpng                    1.6.22                        0libtiff                   4.0.6                         2libxml2                   2.9.2                         0libxslt                   1.1.28                        2llvmlite                  0.11.0                   py27_0locket                    0.2.0                    py27_1lxml                      3.6.0                    py27_0markupsafe                0.23                     py27_2matplotlib                1.5.1               np111py27_0mistune                   0.7.2                    py27_1mkl                       11.3.3                        0mkl-service               1.1.2                    py27_2mpmath                    0.19                     py27_1multipledispatch          0.4.8                    py27_0nb_anacondacloud          1.1.0                    py27_0nb_conda                  1.1.0                    py27_0nb_conda_kernels          1.0.3                    py27_0nbconvert                 4.2.0                    py27_0nbformat                  4.0.1                    py27_0nbpresent                 3.0.2                    py27_0networkx                  1.11                     py27_0nltk                      3.2.1                    py27_0nose                      1.3.7                    py27_1notebook                  4.2.1                    py27_0numba                     0.26.0              np111py27_0numexpr                   2.6.0               np111py27_0numpy                     1.11.1                   py27_0odo                       0.5.0                    py27_1openpyxl                  2.3.2                    py27_0openssl                   1.0.2h                        1pandas                    0.18.1              np111py27_0partd                     0.3.4                    py27_0path.py                   8.2.1                    py27_0pathlib2                  2.1.0                    py27_0patsy                     0.4.1                    py27_0pep8                      1.7.0                    py27_0pexpect                   4.0.1                    py27_0pickleshare               0.7.2                    py27_0pillow                    3.2.0                    py27_1pip                       8.1.2                    py27_0ply                       3.8                      py27_0psutil                    4.3.0                    py27_0ptyprocess                0.5.1                    py27_0py                        1.4.31                   py27_0pyasn1                    0.1.9                    py27_0pyaudio                   0.2.7                    py27_0pycosat                   0.6.1                    py27_1pycparser                 2.14                     py27_1pycrypto                  2.6.1                    py27_4pycurl                    7.43.0                   py27_0pyflakes                  1.2.3                    py27_0pygments                  2.1.3                    py27_0pyopenssl                 0.16.0                   py27_0pyparsing                 2.1.4                    py27_0pyqt                      4.11.4                   py27_3pytables                  3.2.2               np111py27_4pytest                    2.9.2                    py27_0python                    2.7.12                        1python-dateutil           2.5.3                    py27_0python.app                1.2                      py27_4pytz                      2016.4                   py27_0pyyaml                    3.11                     py27_4pyzmq                     15.2.0                   py27_1qt                        4.8.7                         3qtconsole                 4.2.1                    py27_0qtpy                      1.0.2                    py27_0readline                  6.2                           2redis                     3.2.0                         0redis-py                  2.10.5                   py27_0requests                  2.10.0                   py27_0rope                      0.9.4                    py27_1ruamel_yaml               0.11.7                   py27_0scikit-image              0.12.3              np111py27_1scikit-learn              0.17.1              np111py27_2scipy                     0.17.1              np111py27_1setuptools                23.0.0                   py27_0simplegeneric             0.8.1                    py27_1singledispatch            3.4.0.3                  py27_0sip                       4.16.9                   py27_0six                       1.10.0                   py27_0snowballstemmer           1.2.1                    py27_0sockjs-tornado            1.0.3                    py27_0sphinx                    1.4.1                    py27_0sphinx_rtd_theme          0.1.9                    py27_0spyder                    2.3.9                    py27_0sqlalchemy                1.0.13                   py27_0sqlite                    3.13.0                        0ssl_match_hostname        3.4.0.2                  py27_1statsmodels               0.6.1               np111py27_1sympy                     1.0                      py27_0terminado                 0.6                      py27_0tk                        8.5.18                        0toolz                     0.8.0                    py27_0tornado                   4.3                      py27_1traitlets                 4.2.1                    py27_0unicodecsv                0.14.1                   py27_0werkzeug                  0.11.10                  py27_0wheel                     0.29.0                   py27_0xlrd                      1.0.0                    py27_0xlsxwriter                0.9.2                    py27_0xlwings                   0.7.2                    py27_0xlwt                      1.1.2                    py27_0xz                        5.2.2                         0yaml                      0.1.6                         0zlib                      1.2.8                         3
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好吧，至少我常用的都已经在这了。太方便了。

5.写个demo测试一下sklearn

为了测试一下是不是真像传说中那么好用，从网络上现找了部分简单的测试代码：

#!/usr/bin/env python#coding:utf-8'''Created on 2016年7月15日@author: lei.wang'''import numpy as npimport urllibfrom sklearn import preprocessingfrom sklearn import metricsfrom sklearn.ensemble import ExtraTreesClassifierfrom sklearn.linear_model import LogisticRegressiondef t1():    url = "http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"    raw_data = urllib.urlopen(url)    dataset = np.loadtxt(raw_data,delimiter=",")    X = dataset[:,0:7]    y = dataset[:,8]    # normalize the data attributes    normalized_X = preprocessing.normalize(X)    # standardize the data attributes    standardized_X = preprocessing.scale(X)    model = ExtraTreesClassifier()    model.fit(X, y)    # display the relative importance of each attribute    print model.feature_importances_    model = LogisticRegression()    model.fit(X, y)    print(model)    # make predictions    expected = y    predicted = model.predict(X)    # summarize the fit of the model    print(metrics.classification_report(expected, predicted))    print(metrics.confusion_matrix(expected, predicted))t1()
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让代码run起来，得到如下结果：

[ 0.13697671  0.26771573  0.11139943  0.08658428  0.079841    0.16862413  0.1488587 ]LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,          verbose=0, warm_start=False)             precision    recall  f1-score   support        0.0       0.79      0.89      0.84       500        1.0       0.74      0.55      0.63       268avg / total       0.77      0.77      0.77       768[[447  53] [120 148]]
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好吧，sklearn表现正常，能正常输出预期结果。看来，Anaconda确实是为搞算法与数据的同志们提供了一个非常好的工具，省去了我们各种搭环境找依赖包的烦恼！向开发了这么好用工具的程序猿们致敬！